INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75260, 2010, 'bio', 'Bayesian rule learning for biomedical data mining', 'Gopalakrishnan, Vanathi|Lustgarten, Jonathan L|Visweswaran, Shyam|Cooper, Gregory F', 'Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M-183, Pittsburgh, PA 15260, USA. vanathi@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M-183, Pittsburgh, PA 15260, USA. vanathi@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M-183, Pittsburgh, PA 15260, USA. vanathi@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Avenue Suite M-183, Pittsburgh, PA 15260, USA. vanathi@pitt.edu', 'United States|United States|United States|United States', 0, 'Bayes Theorem|Biomarkers|Data Mining|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq005', '20080512', 'Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially lead to the discovery of better, more discriminative markers. Data mining methods are routinely applied to such analyses of biomedical datasets generated from high-throughput omic technologies applied to clinical samples from tissues or bodily fluids. Past work has demonstrated that rule models can be successfully applied to this problem, since they can produce understandable models that facilitate review of discriminative biomarkers by biomedical scientists. While many rule-based methods produce rules that make predictions under uncertainty, they typically do not quantify the uncertainty in the validity of the rule itself. This article describes an approach that uses a Bayesian score to evaluate rule models.'),
(75261, 2010, 'bio', 'PSiFR: an integrated resource for prediction of protein structure and function', 'Pandit, Shashi B|Brylinski, Michal|Zhou, Hongyi|Gao, Mu|Arakaki, Adrian K|Skolnick, Jeffrey', 'Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 9, 'Binding Sites|Databases, Protein|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq006', '20080513', 'In the post-genomic era, the annotation of protein function facilitates the understanding of various biological processes. To extend the range of function annotation methods to the twilight zone of sequence identity, we have developed approaches that exploit both protein tertiary structure and/or protein sequence evolutionary relationships. To serve the scientific community, we have integrated the structure prediction tools, TASSER, TASSER-Lite and METATASSER, and the functional inference tools, FINDSITE, a structure-based algorithm for binding site prediction, Gene Ontology molecular function inference and ligand screening, EFICAz(2), a sequence-based approach to enzyme function inference and DBD-hunter, an algorithm for predicting DNA-binding proteins and associated DNA-binding residues, into a unified web resource, Protein Structure and Function prediction Resource (PSiFR).'),
(75262, 2010, 'bio', 'Random distance dependent attachment as a model for neural network generation in the Caenorhabditis elegans', 'Itzhack, Royi|Louzoun, Yoram', 'Math Department and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 52900, Israel.|Math Department and Gonda Brain Research Center, Bar Ilan University, Ramat Gan 52900, Israel.', 'Israel|Israel', 0, 'Algorithms|Animals|Caenorhabditis elegans|Models, Neurological|Nerve Net|Neurons', 'Journal Article', '10.1093/bioinformatics/btq015', '20081220', 'The topology of the network induced by the neurons connectivitys in the Caenorhabditis elegans differs from most common random networks. The neurons positions of the C.elegans have been previously explained as being optimal to induce the required network wiring. We here propose a complementary explanation that the network wiring is the direct result of a local stochastic synapse formation process.'),
(75263, 2010, 'bio', 'Next-generation bioinformatics: using many-core processor architecture to develop a web service for sequence alignment', 'Gálvez, Sergio|Díaz, David|Hernández, Pilar|Esteban, Francisco J|Caballero, Juan A|Dorado, Gabriel', 'Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es|Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es|Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es|Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es|Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es|Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es', 'Spain|Spain|Spain|Spain|Spain|Spain', 25, 'Algorithms|Computational Biology|Internet|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btq017', '20081221', 'Bioinformatics algorithms and computing power are the main bottlenecks for analyzing huge amount of data generated by the current technologies, such as the next-generation sequencing methodologies. At the same time, most powerful microprocessors are based on many-core chips, yet most applications cannot exploit such power, requiring parallelized algorithms. As an example of next-generation bioinformatics, we have developed from scratch a new parallelization of the Needleman-Wunsch (NW) sequence alignment algorithm for the 64-core Tile64 microprocessor. The unprecedented performance it offers for a standalone personal computer (PC) is discussed, optimally aligning sequences up to 20 times faster than the non-parallelized version, thus saving valuable time.'),
(75264, 2010, 'bio', 'Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS', 'Greene, Casey S|Sinnott-Armstrong, Nicholas A|Himmelstein, Daniel S|Park, Paul J|Moore, Jason H|Harris, Brent T', 'Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.|Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.|Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.|Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.|Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.|Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA.', 'Lebanon|Lebanon|Lebanon|Lebanon|Lebanon|Lebanon', 0, 'Amyotrophic Lateral Sclerosis|Databases, Genetic|Epistasis, Genetic|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq009', '20081222', 'Epistasis, the presence of gene-gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis.'),
(75265, 2010, 'bio', 'Protein secondary structure appears to be robust under in silico evolution while protein disorder appears not to be', 'Schaefer, Christian|Schlessinger, Avner|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics (C2B2), Columbia University, 1130 St Nicholas Ave., New York, NY 10032, USA. schaefer@rostlab.org|Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics (C2B2), Columbia University, 1130 St Nicholas Ave., New York, NY 10032, USA. schaefer@rostlab.org|Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics (C2B2), Columbia University, 1130 St Nicholas Ave., New York, NY 10032, USA. schaefer@rostlab.org', 'United States|United States|United States', 38, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Protein Folding|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1093/bioinformatics/btq012', '20081223', 'The mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder.'),
(75266, 2010, 'bio', 'rMAT--an R/Bioconductor package for analyzing ChIP-chip experiments', 'Droit, Arnaud|Cheung, Charles|Gottardo, Raphael', 'Institut de recherches cliniques de Montreal, 110, avenue des Pins Ouest, Montreal, QC H2W 1R7, Canada. arnaud.droit@ircm.qc.ca|Institut de recherches cliniques de Montreal, 110, avenue des Pins Ouest, Montreal, QC H2W 1R7, Canada. arnaud.droit@ircm.qc.ca|Institut de recherches cliniques de Montreal, 110, avenue des Pins Ouest, Montreal, QC H2W 1R7, Canada. arnaud.droit@ircm.qc.ca', 'Canada|Canada|Canada', 0, 'Binding Sites|Chromatin Immunoprecipitation|Databases, Genetic|Genome|Oligonucleotide Array Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq023', '20089513', 'Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) has evolved as a popular technique to study DNA-protein binding or post-translational chromatin/histone modifications at the genomic level. However, the raw microarray intensities generate a massive amount of data, creating a need for efficient analysis algorithms and statistical methods to identify enriched regions.'),
(75267, 2010, 'bio', 'iDBPs: a web server for the identification of DNA binding proteins', 'Nimrod, Guy|Schushan, Maya|Szilágyi, András|Leslie, Christina|Ben-Tal, Nir', 'Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.|Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.|Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.|Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.|Department of Biochemistry, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Binding Sites|DNA-Binding Proteins|Databases, Protein|Internet|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btq019', '20089514', 'The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile, the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies.'),
(75268, 2010, 'bio', 'ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes', 'Otto, Thomas Dan|Catanho, Marcos|Tristão, Cristian|Bezerra, Márcia|Fernandes, Renan Mathias|Elias, Guilherme Steinberger|Scaglia, Alexandre Capeletto|Bovermann, Bill|Berstis, Viktors|Lifschitz, Sergio|de Miranda, Antonio Basílio|Degrave, Wim', 'Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 1, 'Algorithms|Databases, Protein|Genome|Genomics|Phylogeny|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq011', '20089515', 'Many analyses in modern biological research are based on comparisons between biological sequences, resulting in functional, evolutionary and structural inferences. When large numbers of sequences are compared, heuristics are often used resulting in a certain lack of accuracy. In order to improve and validate results of such comparisons, we have performed radical all-against-all comparisons of 4 million protein sequences belonging to the RefSeq database, using an implementation of the Smith-Waterman algorithm. This extremely intensive computational approach was made possible with the help of World Community Grid, through the Genome Comparison Project. The resulting database, ProteinWorldDB, which contains coordinates of pairwise protein alignments and their respective scores, is now made available. Users can download, compare and analyze the results, filtered by genomes, protein functions or clusters. ProteinWorldDB is integrated with annotations derived from Swiss-Prot, Pfam, KEGG, NCBI Taxonomy database and gene ontology. The database is a unique and valuable asset, representing a major effort to create a reliable and consistent dataset of cross-comparisons of the whole protein content encoded in hundreds of completely sequenced genomes using a rigorous dynamic programming approach.'),
(75269, 2010, 'bio', 'Computational quantification of metabolic fluxes from a single isotope snapshot: application to an animal biopsy', 'Binsl, Thomas W|Alders, David J C|Heringa, Jaap|Groeneveld, A B Johan|van Beek, Johannes H G M', 'Centre for Integrative Bioinformatics, VU University Amsterdam, 1081HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics, VU University Amsterdam, 1081HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics, VU University Amsterdam, 1081HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics, VU University Amsterdam, 1081HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics, VU University Amsterdam, 1081HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Animals|Biopsy|Carbon Isotopes|Citric Acid Cycle|Computer Simulation|Isotope Labeling|Magnetic Resonance Spectroscopy|Oxygen Consumption', 'Journal Article', '10.1093/bioinformatics/btq018', '20097912', 'Quantitative determination of metabolic fluxes in single tissue biopsies is difficult. We report a novel analysis approach and software package for in vivo flux quantification using stable isotope labeling.'),
(75270, 2010, 'bio', 'Correcting population stratification in genetic association studies using a phylogenetic approach', 'Li, Mingyao|Reilly, Muredach P|Rader, Daniel J|Wang, Li-San', 'Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. mingyao@upenn.edu|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. mingyao@upenn.edu|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. mingyao@upenn.edu|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA. mingyao@upenn.edu', 'United States|United States|United States|United States', 0, 'Databases, Genetic|Genetics, Population|Genome, Human|Genomics|Humans|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btq025', '20097913', 'The rapid development of genotyping technology and extensive cataloguing of single nucleotide polymorphisms (SNPs) across the human genome have made genetic association studies the mainstream for gene mapping of complex human diseases. For many diseases, the most practical approach is the population-based design with unrelated individuals. Although having the advantages of easier sample collection and greater power than family-based designs, unrecognized population stratification in the study samples can lead to both false-positive and false-negative findings and might obscure the true association signals if not appropriately corrected.'),
(75271, 2010, 'bio', 'Exploring classification strategies with the CoEPrA 2006 contest', 'Demir-Kavuk, Ozgur|Riedesel, Henning|Knapp, Ernst-Walter', 'Institute of Chemistry and Biochemistry, Free University of Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.|Institute of Chemistry and Biochemistry, Free University of Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.|Institute of Chemistry and Biochemistry, Free University of Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.', 'Germany|Germany|Germany', 6, 'Algorithms|Binding Sites|Databases, Factual|Drug Design|Ligands|Peptides|Quantitative Structure-Activity Relationship|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btq021', '20097914', 'In silico methods to classify compounds as potential drugs that bind to a specific target become increasingly important for drug design. To build classification devices training sets of drugs with known activities are needed. For many such classification problems, not only qualitative but also quantitative information of a specific property (e.g. binding affinity) is available. The latter can be used to build a regression scheme to predict this property for new compounds. Predicting a compound property explicitly is generally more difficult than classifying that the property lies below or above a given threshold value. Hence, an indirect classification that is based on regression may lead to poorer results than a direct classification scheme. In fact, initially researchers are only interested to classify compounds as potential drugs. The activities of these compounds are subsequently measured in wet lab.'),
(75272, 2010, 'bio', 'WellReader: a MATLAB program for the analysis of fluorescence and luminescence reporter gene data', 'Boyer, Frédéric|Besson, Bruno|Baptist, Guillaume|Izard, Jérôme|Pinel, Corinne|Ropers, Delphine|Geiselmann, Johannes|de Jong, Hidde', 'INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.|INRIA Grenoble-Rhône-Alpes, Saint Ismier Cedex, France.', 'France|France|France|France|France|France|France|France', 0, 'Algorithms|Computational Biology|Computer Graphics|Fluorescent Dyes|Gene Expression Profiling|Gene Expression Regulation|Genes, Reporter|Luminescence|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btq016', '20097915', 'Fluorescent and luminescent reporter gene systems in combination with automated microplate readers allow real-time monitoring of gene expression on the population level at high precision and sampling density. This generates large amounts of data for the analysis of which computer tools are missing to date.'),
(75273, 2010, 'bio', 'iMotifs: an integrated sequence motif visualization and analysis environment', 'Piipari, Matias|Down, Thomas A|Saini, Harpreet|Enright, Anton|Hubbard, Tim J P', 'Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 3, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq026', '20106815', 'Short sequence motifs are an important class of models in molecular biology, used most commonly for describing transcription factor binding site specificity patterns. High-throughput methods have been recently developed for detecting regulatory factor binding sites in vivo and in vitro and consequently high-quality binding site motif data are becoming available for increasing number of organisms and regulatory factors. Development of intuitive tools for the study of sequence motifs is therefore important. iMotifs is a graphical motif analysis environment that allows visualization of annotated sequence motifs and scored motif hits in sequences. It also offers motif inference with the sensitive NestedMICA algorithm, as well as overrepresentation and pairwise motif matching capabilities. All of the analysis functionality is provided without the need to convert between file formats or learn different command line interfaces. The application includes a bundled and graphically integrated version of the NestedMICA motif inference suite that has no outside dependencies. Problems associated with local deployment of software are therefore avoided.'),
(75274, 2010, 'bio', 'FineStr: a web server for single-base-resolution nucleosome positioning', 'Gabdank, Idan|Barash, Danny|Trifonov, Edward N', 'Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel. gabdank@cs.bgu.ac.il|Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel. gabdank@cs.bgu.ac.il|Department of Computer Science, Ben Gurion University of the Negev, Beer Sheva 84105, Israel. gabdank@cs.bgu.ac.il', 'Israel|Israel|Israel', 0, 'Animals|Caenorhabditis elegans|Chromatin|Internet|Nucleosomes|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq030', '20106816', 'The DNA in eukaryotic cells is packed into the chromatin that is composed of nucleosomes. Positioning of the nucleosome core particles on the sequence is a problem of great interest because of the role nucleosomes play in different cellular processes including gene regulation. Using the sequence structure of 10.4 base DNA repeat presented in our previous works and nucleosome core DNA sequences database, we have derived the complete nucleosome DNA bendability matrix of Caenorhabditis elegans. We have developed a web server named FineStr that allows users to upload genomic sequences in FASTA format and to perform a single-base-resolution nucleosome mapping on them.'),
(75275, 2010, 'bio', 'XDIA: improving on the label-free data-independent analysis', 'Carvalho, Paulo C|Han, Xuemei|Xu, Tao|Cociorva, Daniel|Carvalho, Maria da Gloria|Barbosa, Valmir C|Yates, John R', 'Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro, Brazil. paulo@pcarvalho.com', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Databases, Factual|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btq031', '20106817', 'XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation, it significantly increases identified spectra (approximately 250%) and unique peptides (approximately 30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively.'),
(75276, 2010, 'bio', 'Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar', 'Mottaz, Anaïs|David, Fabrice P A|Veuthey, Anne-Lise|Yip, Yum L', 'Swiss Institute of Bioinformatics , Centre Médical Universitaire, 1211 Geneva 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss Institute of Bioinformatics , Centre Médical Universitaire, 1211 Geneva 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss Institute of Bioinformatics , Centre Médical Universitaire, 1211 Geneva 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss Institute of Bioinformatics , Centre Médical Universitaire, 1211 Geneva 4, Switzerland. anais.mottaz@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 69, 'Amino Acid Sequence|Amino Acids|Databases, Protein|Phenotype|Polymorphism, Single Nucleotide|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq028', '20106818', 'The SwissVar portal provides access to a comprehensive collection of single amino acid polymorphisms and diseases in the UniProtKB/Swiss-Prot database via a unique search engine. In particular, it gives direct access to the newly improved Swiss-Prot variant pages. The key strength of this portal is that it provides a possibility to query for similar diseases, as well as the underlying protein products and the molecular details of each variant. In the context of the recently proposed molecular view on diseases, the SwissVar portal should be in a unique position to provide valuable information for researchers and to advance research in this area.'),
(75277, 2010, 'bio', 'invertFREGENE: software for simulating inversions in population genetic data', 'OReilly, Paul F|Coin, Lachlan J M|Hoggart, Clive J', 'Department of Epidemiology and Biostatistics, Imperial College.|Department of Epidemiology and Biostatistics, Imperial College.|Department of Epidemiology and Biostatistics, Imperial College.', '-|-|-', 0, 'Databases, Genetic|Genetics, Population|Genomics|Polymorphism, Single Nucleotide|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btq029', '20106819', 'Inversions are a common form of structural variation, which may have a marked effect on the genome and methods to infer quantities of interest such as those relating to population structure and natural selection. However, due to the challenge in detecting inversions, little is presently known about their impact. Software to simulate inversions could be used to provide a better understanding of how to detect and account for them, but while there are several software packages for simulating population genetic data, none incorporate inversion polymorphisms. Here, we describe a software package, modified from the forward-in-time simulator FREGENE, which simulates the evolution of an inversion polymorphism, of specified length, location, frequency and age, in a population of sequences. We describe previously unreported signatures of inversions in SNP data observed in invertFREGENE results and a known inversion in humans.'),
(75278, 2010, 'bio', 'Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach', 'Wicker, Jörg|Fenner, Kathrin|Ellis, Lynda|Wackett, Larry|Kramer, Stefan', 'Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.|Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.|Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.|Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.|Institut für Informatik/I12, Technische Universität München, D-85748 Garching b. München, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Artificial Intelligence|Biodegradation, Environmental|Biotransformation|Computational Biology|Databases, Factual', 'Journal Article', '10.1093/bioinformatics/btq024', '20106820', 'Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice.'),
(75279, 2010, 'bio', 'A hidden Ising model for ChIP-chip data analysis', 'Mo, Qianxing|Liang, Faming', 'Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. moq@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA. moq@mskcc.org', 'United States|United States', 6, 'Chromatin Immunoprecipitation|Databases, Genetic|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq032', '20110277', 'Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis.'),
(75280, 2010, 'bio', 'BEDTools: a flexible suite of utilities for comparing genomic features', 'Quinlan, Aaron R|Hall, Ira M', 'Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. aaronquinlan@gmail.com|Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA. aaronquinlan@gmail.com', 'United States|United States', 7014, 'Genome|Genomics|Internet|Software', 'Comparative Study', '10.1093/bioinformatics/btq033', '20110278', 'Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.'),
(75281, 2010, 'bio', 'A fast and automated solution for accurately resolving protein domain architectures', 'Yeats, Corin|Redfern, Oliver C|Orengo, Christine', 'Department of Structural and Molecular Biology, UCL, London WC1E 6BT, UK. yeats@biochem.ucl.ac.uk|Department of Structural and Molecular Biology, UCL, London WC1E 6BT, UK. yeats@biochem.ucl.ac.uk|Department of Structural and Molecular Biology, UCL, London WC1E 6BT, UK. yeats@biochem.ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Protein|Genomics|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btq034', '20118117', 'Accurate prediction of the domain content and arrangement in multi-domain proteins (which make up >65% of the large-scale protein databases) provides a valuable tool for function prediction, comparative genomics and studies of molecular evolution. However, scanning a multi-domain protein against a database of domain sequence profiles can often produce conflicting and overlapping matches. We have developed a novel method that employs heaviest weighted clique-finding (HCF), which we show significantly outperforms standard published approaches based on successively assigning the best non-overlapping match (Best Match Cascade, BMC).'),
(75282, 2010, 'bio', 'Gene selection in microarray survival studies under possibly non-proportional hazards', 'Dunkler, Daniela|Schemper, Michael|Heinze, Georg', 'Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.|Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.|Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, 1090 Vienna, Austria.', 'Austria|Austria|Austria', 18, 'Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Regression Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq035', '20118118', 'Univariate Cox regression (COX) is often used to select genes possibly linked to survival. With non-proportional hazards (NPH), COX could lead to under- or over-estimation of effects. The effect size measure c=P(T(1)<T(0)), i.e. the probability that a person randomly chosen from group G(1) dies earlier than a person from G(0), is independent of the proportional hazards (PH) assumption. Here we consider its generalization to continuous data c and investigate the suitability of c for gene selection.'),
(75283, 2010, 'bio', 'The Locus Lookup tool at MaizeGDB: identification of genomic regions in maize by integrating sequence information with physical and genetic maps', 'Andorf, Carson M|Lawrence, Carolyn J|Harper, Lisa C|Schaeffer, Mary L|Campbell, Darwin A|Sen, Taner Z', 'US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.|US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.|US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.|US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.|US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.|US Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States|United States|United States', 62, 'Computational Biology|Databases, Genetic|Genome, Plant|Internet|Sequence Analysis, DNA|Software|User-Computer Interface|Zea mays', 'Journal Article', '10.1093/bioinformatics/btp556', '20124413', 'Methods to automatically integrate sequence information with physical and genetic maps are scarce. The Locus Lookup tool enables researchers to define windows of genomic sequence likely to contain loci of interest where only genetic or physical mapping associations are reported. Using the Locus Lookup tool, researchers will be able to locate specific genes more efficiently that will ultimately help them develop a better maize plant. With the availability of the well-documented source code, the tool can be easily adapted to other biological systems.'),
(75284, 2010, 'bio', 'Visualizing SNP statistics in the context of linkage disequilibrium using LD-Plus', 'Bush, William S|Dudek, Scott M|Ritchie, Marylyn D', 'Department of Molecular Physiology, Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Molecular Physiology, Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA.|Department of Molecular Physiology, Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, TN, USA.', 'United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Genotype|Haplotypes|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp678', '20130027', 'Often in human genetic analysis, multiple tables of single nucleotide polymorphism (SNP) statistics are shown alongside a Haploview style correlation plot. Readers are then asked to make inferences that incorporate knowledge across these multiple sets of results. To better facilitate a collective understanding of all available data, we developed a Ruby-based web application, LD-Plus, to generate figures that simultaneously display physical location of SNPs, binary SNP attributes (such as coding/non-coding or presence on genotyping platforms), common haplotypes and their frequencies and continuously scaled values (such as F(st), minor allele frequency, genotyping efficiency or P-values), all in the context of the D and r(2) linkage disequilibrium structures. Combining these results into one comprehensive figure reduces dereferencing between figures and tables, and can provide unique insights into genetic features that are not clearly seen when results are partitioned across multiple figures and tables.'),
(75285, 2010, 'bio', 'A visual framework for sequence analysis using n-grams and spectral rearrangement', 'Maetschke, Stefan R|Kassahn, Karin S|Dunn, Jasmyn A|Han, Siew-Ping|Curley, Eva Z|Stacey, Katryn J|Ragan, Mark A', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 12, 'Computer Graphics|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq042', '20130028', 'Protein sequences are often composed of regions that have distinct evolutionary histories as a consequence of domain shuffling, recombination or gene conversion. New approaches are required to discover, visualize and analyze these sequence regions and thus enable a better understanding of protein evolution.'),
(75286, 2010, 'bio', 'Small-sample precision of ROC-related estimates', 'Hanczar, Blaise|Hua, Jianping|Sima, Chao|Weinstein, John|Bittner, Michael|Dougherty, Edward R', 'LIPADE, University Paris Descartes, Paris, France.|LIPADE, University Paris Descartes, Paris, France.|LIPADE, University Paris Descartes, Paris, France.|LIPADE, University Paris Descartes, Paris, France.|LIPADE, University Paris Descartes, Paris, France.|LIPADE, University Paris Descartes, Paris, France.', 'France|France|France|France|France|France', 0, 'Algorithms|False Positive Reactions|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btq037', '20130029', 'The receiver operator characteristic (ROC) curves are commonly used in biomedical applications to judge the performance of a discriminant across varying decision thresholds. The estimated ROC curve depends on the true positive rate (TPR) and false positive rate (FPR), with the key metric being the area under the curve (AUC). With small samples these rates need to be estimated from the training data, so a natural question arises: How well do the estimates of the AUC, TPR and FPR compare with the true metrics?'),
(75287, 2010, 'bio', 'Identifying biologically relevant differences between metagenomic communities', 'Parks, Donovan H|Beiko, Robert G', 'Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1W5.|Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1W5.', 'Canada|Canada', 572, 'Base Sequence|Computational Biology|Databases, Genetic|Genome, Bacterial|Metagenome|Metagenomics|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq041', '20130030', 'Metagenomics is the study of genetic material recovered directly from environmental samples. Taxonomic and functional differences between metagenomic samples can highlight the influence of ecological factors on patterns of microbial life in a wide range of habitats. Statistical hypothesis tests can help us distinguish ecological influences from sampling artifacts, but knowledge of only the P-value from a statistical hypothesis test is insufficient to make inferences about biological relevance. Current reporting practices for pairwise comparative metagenomics are inadequate, and better tools are needed for comparative metagenomic analysis.'),
(75288, 2010, 'bio', 'Six Rossmannoid folds, including the Class I aminoacyl-tRNA synthetases, share a partial core with the anti-codon-binding domain of a Class II aminoacyl-tRNA synthetase', 'Cammer, Stephen|Carter, Charles W', 'Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, VA 24061, USA. cammer@vbi.vt.edu|Virginia Bioinformatics Institute at Virginia Tech, Blacksburg, VA 24061, USA. cammer@vbi.vt.edu', 'United States|United States', 27, 'Amino Acyl-tRNA Synthetases|Binding Sites|Codon|Crystallography, X-Ray|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq039', '20130031', 'Similarities in core residue packing provide evidence for divergence or convergence not reported using other methods.'),
(75289, 2010, 'bio', 'Maximal conditional chi-square importance in random forests', 'Wang, Minghui|Chen, Xiang|Zhang, Heping', 'Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA.', 'United States|United States|United States', 0, 'Chi-Square Distribution|Genome|Genome-Wide Association Study|Genomics|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq038', '20130032', 'High-dimensional data are frequently generated in genome-wide association studies (GWAS) and other studies. It is important to identify features such as single nucleotide polymorphisms (SNPs) in GWAS that are associated with a disease. Random forests represent a very useful approach for this purpose, using a variable importance score. This importance score has several shortcomings. We propose an alternative importance measure to overcome those shortcomings.'),
(75290, 2010, 'bio', 'Cascleave: towards more accurate prediction of caspase substrate cleavage sites', 'Song, Jiangning|Tan, Hao|Shen, Hongbin|Mahmood, Khalid|Boyd, Sarah E|Webb, Geoffrey I|Akutsu, Tatsuya|Whisstock, James C', 'Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au|Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. jiangning.song@med.monash.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Caspases|Databases, Protein|Proteomics|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btq043', '20130033', 'The caspase family of cysteine proteases play essential roles in key biological processes such as programmed cell death, differentiation, proliferation, necrosis and inflammation. The complete repertoire of caspase substrates remains to be fully characterized. Accordingly, systematic computational screening studies of caspase substrate cleavage sites may provide insight into the substrate specificity of caspases and further facilitating the discovery of putative novel substrates.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75291, 2010, 'bio', 'Improving protein secondary structure prediction using a simple k-mer model', 'Madera, Martin|Calmus, Ryan|Thiltgen, Grant|Karplus, Kevin|Gough, Julian', 'Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.|Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.|Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.|Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.|Department of Computer Science, University of Bristol, Woodland Road, Bristol BS8 1UB, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq020', '20130034', 'Some first order methods for protein sequence analysis inherently treat each position as independent. We develop a general framework for introducing longer range interactions. We then demonstrate the power of our approach by applying it to secondary structure prediction, under the independence assumption, sequences produced by existing methods can produce features that are not protein like, an extreme example being a helix of length 1. Our goal was to make the predictions from state of the art methods more realistic, without loss of performance by other measures.'),
(75292, 2010, 'bio', 'SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors', 'Goya, Rodrigo|Sun, Mark G F|Morin, Ryan D|Leung, Gillian|Ha, Gavin|Wiegand, Kimberley C|Senz, Janine|Crisan, Anamaria|Marra, Marco A|Hirst, Martin|Huntsman, David|Murphy, Kevin P|Aparicio, Sam|Shah, Sohrab P', 'Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.|Department of Molecular Oncology Breast Cancer Research Program, British Columbia Cancer Research Centre, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Base Sequence|Databases, Genetic|Gene Expression Profiling|Genetic Variation|Genome, Human|Humans|Molecular Sequence Data|Neoplasms|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq040', '20130035', 'Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery.'),
(75293, 2010, 'bio', 'Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning', 'Wu, Chia-Chin|Asgharzadeh, Shahab|Triche, Timothy J|DArgenio, David Z', 'Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.|Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.|Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.|Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.', 'United States|United States|United States|United States', 17, 'Databases, Factual|Genomics|Humans|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btq044', '20134029', 'Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.'),
(75294, 2010, 'bio', 'Non-linear classification for on-the-fly fractional mass filtering and targeted precursor fragmentation in mass spectrometry experiments', 'Kirchner, Marc|Timm, Wiebke|Fong, Peying|Wangemann, Philine|Steen, Hanno', 'Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.', 'United States|United States|United States|United States|United States', 13, 'Mass Spectrometry|Proteins|Proteome|Proteomics|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btq036', '20134030', 'Mass spectrometry (MS) has become the method of choice for protein/peptide sequence and modification analysis. The technology employs a two-step approach: ionized peptide precursor masses are detected, selected for fragmentation, and the fragment mass spectra are collected for computational analysis. Current precursor selection schemes are based on data- or information-dependent acquisition (DDA/IDA), where fragmentation mass candidates are selected by intensity and are subsequently included in a dynamic exclusion list to avoid constant refragmentation of highly abundant species. DDA/IDA methods do not exploit valuable information that is contained in the fractional mass of high-accuracy precursor mass measurements delivered by current instrumentation.'),
(75295, 2010, 'bio', 'Modeling macro-molecular interfaces with Intervor', 'Loriot, Sébastien|Cazals, Frédéric', 'INRIA Sophia-Antipolis-Méditerranée, Algorithms-Biology-Structure, Sophia-Antipolis, France.|INRIA Sophia-Antipolis-Méditerranée, Algorithms-Biology-Structure, Sophia-Antipolis, France.', 'France|France', 0, 'Databases, Factual|Internet|Macromolecular Substances|Models, Theoretical|Software|Surface Properties|Water', 'Journal Article', '10.1093/bioinformatics/btq052', '20139468', 'Intervor is a software computing a parameter-free representation of macro-molecular interfaces, based on the alpha-complex of the atoms. Given two interacting partners, possibly with water molecules squeezed in-between them, Intervor computes an interface model which has the following characteristics: (i) it identifies the atoms of the partners which are in direct contact and those whose interaction is water mediated, (ii) it defines a geometric complex separating the partners, the Voronoi interface, whose geometric and topological descriptions are straightforward (surface area, number of patches, curvature), (iii) it allows the definition of the depth of atoms at the interface, thus going beyond the traditional dissection of an interface into a core and a rim. These features can be used to investigate correlations between structural parameters and key properties such as the conservation of residues, their polarity, the water dynamics at the interface, mutagenesis data, etc.'),
(75296, 2010, 'bio', 'Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks', 'Gao, Jing|Tarcea, V Glenn|Karnovsky, Alla|Mirel, Barbara R|Weymouth, Terry E|Beecher, Christopher W|Cavalcoli, James D|Athey, Brian D|Omenn, Gilbert S|Burant, Charles F|Jagadish, H V', 'National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu|National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. jinggao@umich.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 113, 'Databases, Factual|Humans|Metabolic Networks and Pathways|Metabolomics|Software', 'Journal Article', '10.1093/bioinformatics/btq048', '20139469', 'Metscape is a plug-in for Cytoscape, used to visualize and interpret metabolomic data in the context of human metabolic networks. We have developed a metabolite database by extracting and integrating information from several public sources. By querying this database, Metscape allows users to trace the connections between metabolites and genes, visualize compound networks and display compound structures as well as information for reactions, enzymes, genes and pathways. Applying the pathway filter, users can create subnetworks that consist of compounds and reactions from a given pathway. Metscape allows users to upload experimental data, and visualize and explore compound networks over time, or experimental conditions. Color and size of the nodes are used to visualize these dynamic changes. Metscape can display the entire metabolic network or any of the pathway-specific networks that exist in the database.'),
(75297, 2010, 'bio', 'Snoopy--a unifying Petri net framework to investigate biomolecular networks', 'Rohr, Christian|Marwan, Wolfgang|Heiner, Monika', 'Magdeburg Centre for Systems Biology (MaCS), Otto von Guericke University, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. snoopy@informatik.tu-cottbus.de|Magdeburg Centre for Systems Biology (MaCS), Otto von Guericke University, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. snoopy@informatik.tu-cottbus.de|Magdeburg Centre for Systems Biology (MaCS), Otto von Guericke University, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. snoopy@informatik.tu-cottbus.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq050', '20139470', 'To investigate biomolecular networks, Snoopy provides a unifying Petri net framework comprising a family of related Petri net classes. Models can be hierarchically structured, allowing for the mastering of larger networks. To move easily between the qualitative, stochastic and continuous modelling paradigms, models can be converted into each other. We get models sharing structure, but specialized by their kinetic information. The analysis and iterative reverse engineering of biomolecular networks is supported by the simultaneous use of several Petri net classes, while the graphical user interface adapts dynamically to the active one. Built-in animation and simulation are complemented by exports to various analysis tools. Snoopy facilitates the addition of new Petri net classes thanks to its generic design.'),
(75298, 2010, 'bio', 'Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data', 'Reverter, Antonio|Hudson, Nicholas J|Nagaraj, Shivashankar H|Pérez-Enciso, Miguel|Dalrymple, Brian P', 'Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au|Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au|Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au|Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au|Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, St. Lucia, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au', 'Australia|Australia|Australia|Australia|Australia', 92, 'Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Promoter Regions, Genetic|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq051', '20144946', 'Although transcription factors (TF) play a central regulatory role, their detection from expression data is limited due to their low, and often sparse, expression. In order to fill this gap, we propose a regulatory impact factor (RIF) metric to identify critical TF from gene expression data.'),
(75299, 2010, 'bio', 'Microindel detection in short-read sequence data', 'Krawitz, Peter|Rödelsperger, Christian|Jäger, Marten|Jostins, Luke|Bauer, Sebastian|Robinson, Peter N', 'Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com|Institute for Medical Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin. peter.krawitz@googlemail.com', '-|-|-|-|-|-', 70, 'Algorithms|Base Sequence|Genomics|INDEL Mutation|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq027', '20144947', 'Several recent studies have demonstrated the effectiveness of resequencing and single nucleotide variant (SNV) detection by deep short-read sequencing platforms. While several reliable algorithms are available for automated SNV detection, the automated detection of microindels in deep short-read data presents a new bioinformatics challenge.'),
(75300, 2010, 'bio', 'Fast and SNP-tolerant detection of complex variants and splicing in short reads', 'Wu, Thomas D|Nacu, Serban', 'Department of Bioinformatics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, USA. twu@gene.com|Department of Bioinformatics, Genentech, Inc., 1 DNA Way, South San Francisco, CA, USA. twu@gene.com', 'United States|United States', 0, 'Base Sequence|DNA, Recombinant|Genetic Variation|Genomics|Polymorphism, Single Nucleotide|RNA Splicing', 'Journal Article', '10.1093/bioinformatics/btq057', '20147302', 'Next-generation sequencing captures sequence differences in reads relative to a reference genome or transcriptome, including splicing events and complex variants involving multiple mismatches and long indels. We present computational methods for fast detection of complex variants and splicing in short reads, based on a successively constrained search process of merging and filtering position lists from a genomic index. Our methods are implemented in GSNAP (Genomic Short-read Nucleotide Alignment Program), which can align both single- and paired-end reads as short as 14 nt and of arbitrarily long length. It can detect short- and long-distance splicing, including interchromosomal splicing, in individual reads, using probabilistic models or a database of known splice sites. Our program also permits SNP-tolerant alignment to a reference space of all possible combinations of major and minor alleles, and can align reads from bisulfite-treated DNA for the study of methylation state.'),
(75301, 2010, 'bio', 'SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements', 'Munshaw, Supriya|Kepler, Thomas B', 'Center for Computational Immunology, Computational Biology and Bioinformatics Program, Duke University, Durham, NC 27705, USA.|Center for Computational Immunology, Computational Biology and Bioinformatics Program, Duke University, Durham, NC 27705, USA.', 'United States|United States', 0, 'Amino Acid Sequence|B-Lymphocytes|Base Sequence|Gene Rearrangement, B-Lymphocyte|Genes, Immunoglobulin|Immunoglobulins|Markov Chains|Molecular Sequence Data|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq056', '20147303', 'The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements.'),
(75302, 2010, 'bio', 'A link between H3K27me3 mark and exon length in the gene promoters of pluripotent and differentiated cells', 'Chen, Liang', 'Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA. liang.chen@usc.edu', 'United States', 2, 'Animals|Cell Differentiation|CpG Islands|Exons|Gene Expression Profiling|Histones|Humans|Pluripotent Stem Cells|Promoter Regions, Genetic', 'Journal Article', '10.1093/bioinformatics/btq047', '20147304', 'We conducted a reanalysis of genome-wide histone H3 tail methylation data in mammalian pluripotent and differentiated cells. We show that the promoters marked with histone H3 lysine 27 trimethylation (H3K27me3) tend to have more exonic positions in the promoter regions. However, this is not due to any preferential marking on exons over introns by H3K27me3. The relationship is also independent the status of histone H3 lysine 4 trimethylation (H3K4me3) mark, CpG content and the platforms used in the high-throughput profiling of histone modifications. It provides evidence for the link between histone modifications and transcribed exons in promoter regions.'),
(75303, 2010, 'bio', 'Estimating replicate time shifts using Gaussian process regression', 'Liu, Qiang|Lin, Kevin K|Andersen, Bogi|Smyth, Padhraic|Ihler, Alexander', 'Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA.', 'United States|United States|United States|United States|United States', 11, 'Animals|Computational Biology|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq022', '20147305', 'Time-course gene expression datasets provide important insights into dynamic aspects of biological processes, such as circadian rhythms, cell cycle and organ development. In a typical microarray time-course experiment, measurements are obtained at each time point from multiple replicate samples. Accurately recovering the gene expression patterns from experimental observations is made challenging by both measurement noise and variation among replicates rates of development. Prior work on this topic has focused on inference of expression patterns assuming that the replicate times are synchronized. We develop a statistical approach that simultaneously infers both (i) the underlying (hidden) expression profile for each gene, as well as (ii) the biological time for each individual replicate. Our approach is based on Gaussian process regression (GPR) combined with a probabilistic model that accounts for uncertainty about the biological development time of each replicate.'),
(75304, 2010, 'bio', 'Skyline: an open source document editor for creating and analyzing targeted proteomics experiments', 'MacLean, Brendan|Tomazela, Daniela M|Shulman, Nicholas|Chambers, Matthew|Finney, Gregory L|Frewen, Barbara|Kern, Randall|Tabb, David L|Liebler, Daniel C|MacCoss, Michael J', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. brendanx@u.washington.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Protein|Mass Spectrometry|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq054', '20147306', 'Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools.'),
(75305, 2010, 'bio', 'Assigning roles to DNA regulatory motifs using comparative genomics', 'Buske, Fabian A|Bodén, Mikael|Bauer, Denis C|Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD 4072, Australia.', 'Australia|Australia|Australia|Australia', 77, 'Algorithms|Base Sequence|Binding Sites|DNA|Genomics|Sequence Analysis, DNA|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btq049', '20147307', 'Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network.'),
(75306, 2010, 'bio', 'MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8', 'Wang, Zheng|Eickholt, Jesse|Cheng, Jianlin', 'Department of Computer Science, Informatics Institute and C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, Informatics Institute and C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, Informatics Institute and C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 68, 'Computational Biology|Databases, Protein|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq058', '20150411', 'Protein structure prediction is one of the most important problems in structural bioinformatics. Here we describe MULTICOM, a multi-level combination approach to improve the various steps in protein structure prediction. In contrast to those methods which look for the best templates, alignments and models, our approach tries to combine complementary and alternative templates, alignments and models to achieve on average better accuracy.'),
(75307, 2010, 'bio', 'Gene function prediction from synthetic lethality networks via ranking on demand', 'Lippert, Christoph|Ghahramani, Zoubin|Borgwardt, Karsten M', 'Machine Learning & Computational Biology Research Group, Max Planck Institutes, Tübingen, Germany. christoph.lippert@tuebingen.mpg.de|Machine Learning & Computational Biology Research Group, Max Planck Institutes, Tübingen, Germany. christoph.lippert@tuebingen.mpg.de|Machine Learning & Computational Biology Research Group, Max Planck Institutes, Tübingen, Germany. christoph.lippert@tuebingen.mpg.de', 'Germany|Germany|Germany', 0, 'Algorithms|Gene Regulatory Networks|Genes, Lethal|Genome, Fungal|Genomics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btq053', '20154010', 'Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks.'),
(75308, 2010, 'bio', 'Phybase: an R package for species tree analysis', 'Liu, Liang|Yu, Lili', 'Department of Agriculture and Natural resources, Delaware State University, Dover, DE 19901, USA. lliu@desu.edu|Department of Agriculture and Natural resources, Delaware State University, Dover, DE 19901, USA. lliu@desu.edu', 'United States|United States', 0, 'Computational Biology|Phylogeny|Population Density|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btq062', '20156990', 'Phybase is an R package for phylogenetic analysis using species trees. It provides functions to read, write, manipulate, simulate, estimate, summarize and plot species trees, which contain not only the topology and branch lengths but also population sizes.'),
(75309, 2010, 'bio', 'iPHACE: integrative navigation in pharmacological space', 'Garcia-Serna, Ricard|Ursu, Oleg|Oprea, Tudor I|Mestres, Jordi', 'Chemogenomics Laboratory, Research Unit on Biomedical Informatics (GRIB), Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.|Chemogenomics Laboratory, Research Unit on Biomedical Informatics (GRIB), Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.|Chemogenomics Laboratory, Research Unit on Biomedical Informatics (GRIB), Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.|Chemogenomics Laboratory, Research Unit on Biomedical Informatics (GRIB), Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Binding Sites|Databases, Protein|Drug Design|Internet|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq061', '20156991', 'The increasing availability of experimentally determined binding affinities for drugs on multiple protein targets requires the design of specific mining and visualization tools that graphically integrate chemical and biological data in an efficient environment. With this aim, we developed iPHACE, an integrative web-based tool to navigate in the pharmacological space defined by small molecule drugs contained in the IUPHAR-DB, with additional interactions present in PDSP. Extending beyond traditional querying and filtering tools, iPHACE offers a means to extract knowledge from the target profile of drugs as well as from the drug profile of protein targets.'),
(75310, 2010, 'bio', 'Standard virtual biological parts: a repository of modular modeling components for synthetic biology', 'Cooling, M T|Rouilly, V|Misirli, G|Lawson, J|Yu, T|Hallinan, J|Wipat, A', 'Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz|Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. m.cooling@auckland.ac.nz', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand', 65, 'Computational Biology|Databases, Factual|Models, Biological|Promoter Regions, Genetic|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btq063', '20160009', 'Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design.'),
(75311, 2010, 'bio', 'How significant is a protein structure similarity with TM-score = 05?', 'Xu, Jinrui|Zhang, Yang', 'Department of Medical School, Center for Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.|Department of Medical School, Center for Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.', 'United States|United States', 343, 'Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btq066', '20164152', 'Protein structure similarity is often measured by root mean squared deviation, global distance test score and template modeling score (TM-score). However, the scores themselves cannot provide information on how significant the structural similarity is. Also, it lacks a quantitative relation between the scores and conventional fold classifications. This article aims to answer two questions: (i) what is the statistical significance of TM-score? (ii) What is the probability of two proteins having the same fold given a specific TM-score?'),
(75312, 2010, 'bio', 'SLiM on Diet: finding short linear motifs on domain interaction interfaces in Protein Data Bank', 'Hugo, Willy|Song, Fushan|Aung, Zeyar|Ng, See-Kiong|Sung, Wing-Kin', 'Department of Computer Science, National University of Singapore, 13 Computing Drive S(117417), Singapore.|Department of Computer Science, National University of Singapore, 13 Computing Drive S(117417), Singapore.|Department of Computer Science, National University of Singapore, 13 Computing Drive S(117417), Singapore.|Department of Computer Science, National University of Singapore, 13 Computing Drive S(117417), Singapore.|Department of Computer Science, National University of Singapore, 13 Computing Drive S(117417), Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Amino Acid Motifs|Databases, Protein|Genomics|Protein Interaction Domains and Motifs|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btq065', '20167627', 'An important class of protein interactions involves the binding of a proteins domain to a short linear motif (SLiM) on its interacting partner. Extracting such motifs, either experimentally or computationally, is challenging because of their weak binding and high degree of degeneracy. Recent rapid increase of available protein structures provides an excellent opportunity to study SLiMs directly from their 3D structures.'),
(75313, 2010, 'bio', 'Treephyler: fast taxonomic profiling of metagenomes', 'Schreiber, Fabian|Gumrich, Peter|Daniel, Rolf|Meinicke, Peter', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. fschrei@gwdg.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. fschrei@gwdg.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. fschrei@gwdg.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. fschrei@gwdg.de', 'Germany|Germany|Germany|Germany', 39, 'Databases, Factual|Genomics|Metagenome|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btq070', '20172941', 'Assessment of phylogenetic diversity is a key element to the analysis of microbial communities. Tools are needed to handle next-generation sequencing data and to cope with the computational complexity of large-scale studies. Here, we present Treephyler, a tool for fast taxonomic profiling of metagenomes. Treephyler was evaluated on real metagenome to assess its performance in comparison to previous approaches for taxonomic profiling. Results indicate that Treephyler is in terms of speed and accuracy prepared for next-generation sequencing techniques and large-scale analysis.'),
(75314, 2010, 'bio', 'CandiSNPer: a web tool for the identification of candidate SNPs for causal variants', 'Schmitt, Armin O|Assmus, Jens|Bortfeldt, Ralf H|Brockmann, Gudrun A', 'Department for Crop and Animal Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. armin.schmitt@agrar.hu-berlin.de|Department for Crop and Animal Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. armin.schmitt@agrar.hu-berlin.de|Department for Crop and Animal Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. armin.schmitt@agrar.hu-berlin.de|Department for Crop and Animal Sciences, Humboldt-Universität zu Berlin, Invalidenstrasse 42, 10115 Berlin, Germany. armin.schmitt@agrar.hu-berlin.de', 'Germany|Germany|Germany|Germany', 14, 'Genetic Variation|Genome-Wide Association Study|Genomics|Genotype|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btq068', '20172942', 'Human single nucleotide polymorphism (SNP) chips which are used in genome-wide association studies (GWAS) permit the genotyping of up to 4 million SNPs simultaneously. To date, about 1000 human SNPs have been identified as statistically significantly associated with a disease or another trait of interest. The identified SNP is not necessarily the causal variant, but it is rather in linkage disequilibrium (LD) with it. CandiSNPer is a software tool that determines the LD region around a significant SNP from a GWAS. It provides a list with functional annotation and LD values for the SNPs found in the LD region. This list contains not only the SNPs for which genotyping data are available, but all SNPs with rs-IDs, thus increasing the likelihood to include the causal variant. Furthermore, plots showing the LD values are generated. CandiSNPer facilitates the preselection of candidate SNPs for causal variants.'),
(75315, 2010, 'bio', 'A meta-analysis of two-dimensional electrophoresis pattern of the Parkinsons disease-related protein DJ-1', 'Natale, Massimo|Bonino, Dario|Consoli, Paolo|Alberio, Tiziana|Ravid, Rivka G|Fasano, Mauro|Bucci, Enrico M', 'BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.|BioDigitalValley S.r.l., Via Carlo Viola 78, 11026 Pont Saint Martin (AO), Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 21, 'Electrophoresis, Gel, Two-Dimensional|Humans|Intracellular Signaling Peptides and Proteins|Oncogene Proteins|Parkinson Disease|Protein Deglycase DJ-1|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq073', '20172943', 'The two-dimensional electrophoresis (2-DE) pattern of proteins is thought to be specifically related to the physiological or pathological condition at the moment of sample preparation. On this ground, most proteomic studies move to identify specific hallmarks for a number of different conditions. However, the information arising from these investigations is often incomplete due to inherent limitations of the technique, to extensive protein post-translational modifications and sometimes to the paucity of available samples. The meta-analysis of proteomic data can provide valuable information pertinent to various biological processes that otherwise remains hidden.'),
(75316, 2010, 'bio', 'A principal skeleton algorithm for standardizing confocal images of fruit fly nervous systems', 'Qu, Lei|Peng, Hanchuan', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.', 'United States|United States', 0, 'Algorithms|Animals|Cell Line|Drosophila melanogaster|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Confocal|Nervous System', 'Journal Article', '10.1093/bioinformatics/btq072', '20172944', 'The fruit fly (Drosophila melanogaster) is a commonly used model organism in biology. We are currently building a 3D digital atlas of the fruit fly larval nervous system (LNS) based on a large collection of fly larva GAL4 lines, each of which targets a subset of neurons. To achieve such a goal, we need to automatically align a number of high-resolution confocal image stacks of these GAL4 lines. One commonly employed strategy in image pattern registration is to first globally align images using an affine transform, followed by local non-linear warping. Unfortunately, the spatially articulated and often twisted LNS makes it difficult to globally align the images directly using the affine method. In a parallel project to build a 3D digital map of the adult fly ventral nerve cord (VNC), we are confronted with a similar problem.'),
(75317, 2010, 'bio', 'DASS-GUI: a user interface for identification and analysis of significant patterns in non-sequential data', 'Hollunder, Jens|Friedel, Maik|Kuiper, Martin|Wilhelm, Thomas', 'Department of Plant Systems Biology, VIB, Department of Molecular Genetics, Ghent University, Technologiepark 927, B-9052 Gent, Belgium. jehol@psb.vib-ugent.be|Department of Plant Systems Biology, VIB, Department of Molecular Genetics, Ghent University, Technologiepark 927, B-9052 Gent, Belgium. jehol@psb.vib-ugent.be|Department of Plant Systems Biology, VIB, Department of Molecular Genetics, Ghent University, Technologiepark 927, B-9052 Gent, Belgium. jehol@psb.vib-ugent.be|Department of Plant Systems Biology, VIB, Department of Molecular Genetics, Ghent University, Technologiepark 927, B-9052 Gent, Belgium. jehol@psb.vib-ugent.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Computer Graphics|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq071', '20172945', 'Many large omics datasets have been published and many more are expected in the near future. New analysis methods are needed for best exploitation. We have developed a graphical user interface (GUI) for easy data analysis. Our discovery of all significant substructures (DASS) approach elucidates the underlying modularity, a typical feature of complex biological data. It is related to biclustering and other data mining approaches. Importantly, DASS-GUI also allows handling of multi-sets and calculation of statistical significances. DASS-GUI contains tools for further analysis of the identified patterns: analysis of the pattern hierarchy, enrichment analysis, module validation, analysis of additional numerical data, easy handling of synonymous names, clustering, filtering and merging. Different export options allow easy usage of additional tools such as Cytoscape.'),
(75318, 2010, 'bio', 'Viewing cancer genes from co-evolving gene modules', 'Zhu, Jing|Xiao, Hui|Shen, Xiaopei|Wang, Jing|Zou, Jinfeng|Zhang, Lin|Yang, Da|Ma, Wencai|Yao, Chen|Gong, Xue|Zhang, Min|Zhang, Yang|Guo, Zheng', 'Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.', 'China|China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Animals|Gene Regulatory Networks|Genes, Neoplasm|Genomics|Humans|Neoplasm Proteins|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btq055', '20176579', 'Studying the evolutionary conservation of cancer genes can improve our understanding of the genetic basis of human cancers. Functionally related proteins encoded by genes tend to interact with each other in a modular fashion, which may affect both the mode and tempo of their evolution.'),
(75319, 2010, 'bio', 'An optimal experimental design approach to model discrimination in dynamic biochemical systems', 'Skanda, Dominik|Lebiedz, Dirk', 'Center for Analysis of Biological Systems (ZBSA), University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany.|Center for Analysis of Biological Systems (ZBSA), University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany.', 'Germany|Germany', 32, 'Algorithms|Computational Biology|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq074', '20176580', 'Finding suitable models of dynamic biochemical systems is an important task in systems biology approaches to the biosciences. On the one hand, a correct model helps to understand the underlying mechanisms and on the other hand, one can use the model to predict the behavior of a biological system under various circumstances. Typically, before the correct model of a biochemical system is found, different hypothetical models might be reasonable and consistent with previous knowledge and available data. The main goal now is to find the best suited model out of different hypotheses. The process of falsifying inappropriate candidate models is called model discrimination.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75320, 2010, 'bio', 'GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach', 'Zhang, Song|Cao, Jing|Kong, Y Megan|Scheuermann, Richard H', 'Department of Clinical Sciences, U.T. Southwestern Medical Center, 5323 Harry Hines Boulevard Dallas, TX 75390-9072, USA. song.zhang@utsouthwestern.edu|Department of Clinical Sciences, U.T. Southwestern Medical Center, 5323 Harry Hines Boulevard Dallas, TX 75390-9072, USA. song.zhang@utsouthwestern.edu|Department of Clinical Sciences, U.T. Southwestern Medical Center, 5323 Harry Hines Boulevard Dallas, TX 75390-9072, USA. song.zhang@utsouthwestern.edu|Department of Clinical Sciences, U.T. Southwestern Medical Center, 5323 Harry Hines Boulevard Dallas, TX 75390-9072, USA. song.zhang@utsouthwestern.edu', 'United States|United States|United States|United States', 0, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq059', '20176581', 'A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy.'),
(75321, 2010, 'bio', 'SBRML: a markup language for associating systems biology data with models', 'Dada, Joseph O|Spasixc4x87, Irena|Paton, Norman W|Mendes, Pedro', 'Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.|Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.|Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.|Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 41, 'Computational Biology|Databases, Factual|Oligonucleotide Array Sequence Analysis|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq069', '20176582', 'Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations.'),
(75322, 2010, 'bio', 'Domain adaptation for semantic role labeling in the biomedical domain', 'Dahlmeier, Daniel|Ng, Hwee Tou', 'NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore.|NUS Graduate School for Integrative Sciences and Engineering, Singapore 117456, Singapore.', 'Singapore|Singapore', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Natural Language Processing|Semantics', 'Journal Article', '10.1093/bioinformatics/btq075', '20179074', 'Semantic role labeling (SRL) is a natural language processing (NLP) task that extracts a shallow meaning representation from free text sentences. Several efforts to create SRL systems for the biomedical domain have been made during the last few years. However, state-of-the-art SRL relies on manually annotated training instances, which are rare and expensive to prepare. In this article, we address SRL for the biomedical domain as a domain adaptation problem to leverage existing SRL resources from the newswire domain.'),
(75323, 2010, 'bio', 'Accelerated similarity searching and clustering of large compound sets by geometric embedding and locality sensitive hashing', 'Cao, Yiqun|Jiang, Tao|Girke, Thomas', 'Department of Computer Science & Engineering and Department of Botany & Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science & Engineering and Department of Botany & Plant Sciences, University of California, Riverside, CA 92521, USA.|Department of Computer Science & Engineering and Department of Botany & Plant Sciences, University of California, Riverside, CA 92521, USA.', 'United States|United States|United States', 26, 'Cluster Analysis|Computational Biology|Information Storage and Retrieval|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btq067', '20179075', 'Similarity searching and clustering of chemical compounds by structural similarities are important computational approaches for identifying drug-like small molecules. Most algorithms available for these tasks are limited by their speed and scalability, and cannot handle todays large compound databases with several million entries.'),
(75324, 2010, 'bio', 'GOSemSim: an R package for measuring semantic similarity among GO terms and gene products', 'Yu, Guangchuang|Li, Fei|Qin, Yide|Bo, Xiaochen|Wu, Yibo|Wang, Shengqi', 'Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Animals|Computational Biology|Genes|Humans|Natural Language Processing|Proteins|Semantics|Software|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btq064', '20179076', 'The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. Four information content (IC)- and a graph-based methods are implemented in the GOSemSim package, multiple species including human, rat, mouse, fly and yeast are also supported. The functions provided by the GOSemSim offer flexibility for applications, and can be easily integrated into high-throughput analysis pipelines.'),
(75325, 2010, 'bio', 'The power of protein interaction networks for associating genes with diseases', 'Navlakha, Saket|Kingsford, Carl', 'Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland College Park, College Park, MD 20742, USA.|Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer Science, University of Maryland College Park, College Park, MD 20742, USA.', 'United States|United States', 0, 'Algorithms|Databases, Genetic|Disease|Genes|Humans|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btq076', '20185403', 'Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.'),
(75326, 2010, 'bio', 'Genomicus: a database and a browser to study gene synteny in modern and ancestral genomes', 'Muffato, Matthieu|Louis, Alexandra|Poisnel, Charles-Edouard|Roest Crollius, Hugues', 'Dyogen Group, Institut de Biologie de lEcole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, 75005 Paris, France.|Dyogen Group, Institut de Biologie de lEcole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, 75005 Paris, France.|Dyogen Group, Institut de Biologie de lEcole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, 75005 Paris, France.|Dyogen Group, Institut de Biologie de lEcole Normale Supérieure (IBENS), Centre National de la Recherche Scientifique UMR8197, Institut National de la Santé et de la Recherche Médicale U1024, 75005 Paris, France.', 'France|France|France|France', 152, 'Databases, Genetic|Evolution, Molecular|Genes|Genome|Genomics|Internet|Software|Synteny', 'Journal Article', '10.1093/bioinformatics/btq079', '20185404', 'Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data.'),
(75327, 2010, 'bio', 'SPICi: a fast clustering algorithm for large biological networks', 'Jiang, Peng|Singh, Mona', 'Lewis-Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.|Lewis-Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.', 'United States|United States', 145, 'Algorithms|Cluster Analysis|Databases, Protein|Metabolic Networks and Pathways|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq078', '20185405', 'Clustering algorithms play an important role in the analysis of biological networks, and can be used to uncover functional modules and obtain hints about cellular organization. While most available clustering algorithms work well on biological networks of moderate size, such as the yeast protein physical interaction network, they either fail or are too slow in practice for larger networks, such as functional networks for higher eukaryotes. Since an increasing number of larger biological networks are being determined, the limitations of current clustering approaches curtail the types of biological network analyses that can be performed.'),
(75328, 2010, 'bio', 'Biased hosting of intronic microRNA genes', 'Golan, David|Levy, Carmit|Friedman, Brad|Shomron, Noam', 'Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel', 0, 'Algorithms|Computer Simulation|Evolution, Molecular|Introns|MicroRNAs|RNA Precursors|RNA Splicing', 'Journal Article', '10.1093/bioinformatics/btq077', '20185406', 'MicroRNAs (miRNAs) are involved in an abundant class of post-transcriptional regulation activated through binding to the 3() -untranslated region (UTR) of mRNAs. The current wealth of mammalian miRNA genes results mostly from genomic duplication events. Many of these events are located within introns of transcriptional units. In order to better understand the genomic expansion of miRNA genes, we investigated the distribution of intronic miRNAs.'),
(75329, 2010, 'bio', 'ESBTL: efficient PDB parser and data structure for the structural and geometric analysis of biological macromolecules', 'Loriot, Sébastien|Cazals, Frédéric|Bernauer, Julie', 'ABS, INRIA Sophia Antipolis, 2004 route des Lucioles, 06902 Sophia-Antipolis, France.|ABS, INRIA Sophia Antipolis, 2004 route des Lucioles, 06902 Sophia-Antipolis, France.|ABS, INRIA Sophia Antipolis, 2004 route des Lucioles, 06902 Sophia-Antipolis, France.', 'France|France|France', 5, 'Computational Biology|Databases, Protein|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq083', '20185407', 'The ever increasing number of structural biological data calls for robust and efficient software for analysis. Easy Structural Biology Template Library (ESBTL) is a lightweight C++ library that allows the handling of PDB data and provides a data structure suitable for geometric constructions and analyses. The parser and data model provided by this ready-to-use include-only library allows adequate treatment of usually discarded information (insertion code, atom occupancy, etc.) while still being able to detect badly formatted files. The template-based structure allows rapid design of new computational structural biology applications and is fully compatible with the new remediated PDB archive format. It also allows the code to be easy-to-use while being versatile enough to allow advanced user developments.'),
(75330, 2010, 'bio', 'Exploiting sequence similarity to validate the sensitivity of SNP arrays in detecting fine-scaled copy number variations', 'Wong, Gerard|Leckie, Christopher|Gorringe, Kylie L|Haviv, Izhak|Campbell, Ian G|Kowalczyk, Adam', 'Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au|Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au|Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au|Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au|Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au|Victoria Research Laboratory, National ICT Australia, Department of Computer Science and Software Engineering, University of Melbourne, Australia. gwong@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|DNA Copy Number Variations|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq088', '20189937', 'High-density single nucleotide polymorphism (SNP) genotyping arrays are efficient and cost effective platforms for the detection of copy number variation (CNV). To ensure accuracy in probe synthesis and to minimize production costs, short oligonucleotide probe sequences are used. The use of short probe sequences limits the specificity of binding targets in the human genome. The specificity of these short probeset sequences has yet to be fully analysed against a normal reference human genome. Sequence similarity can artificially elevate or suppress copy number measurements, and hence reduce the reliability of affected probe readings. For the purpose of detecting narrow CNVs reliably down to the width of a single probeset, sequence similarity is an important issue that needs to be addressed.'),
(75331, 2010, 'bio', 'Post hoc power estimation in large-scale multiple testing problems', 'Zehetmayer, Sonja|Posch, Martin', 'Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.|Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.', 'Austria|Austria', 6, 'Algorithms|Biomarkers, Tumor|Gene Expression Profiling|Genomics|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq085', '20189938', 'The statistical power or multiple Type II error rate in large-scale multiple testing problems as, for example, in gene expression microarray experiments, depends on typically unknown parameters and is therefore difficult to assess a priori. However, it has been suggested to estimate the multiple Type II error rate post hoc, based on the observed data.'),
(75332, 2010, 'bio', 'BioNet: an R-Package for the functional analysis of biological networks', 'Beisser, Daniela|Klau, Gunnar W|Dandekar, Thomas|Müller, Tobias|Dittrich, Marcus T', 'Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 145, 'Databases, Factual|Genomics|Protein Interaction Mapping|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/btq089', '20189939', 'Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules.'),
(75333, 2010, 'bio', 'A min-cut algorithm for the consistency problem in multiple sequence alignment', 'Corel, Eduardo|Pitschi, Florian|Morgenstern, Burkhard', 'Georg-August-Universität, Institut für Mikrobiologie und Genetik, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ecorel@gwdg.de|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ecorel@gwdg.de|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ecorel@gwdg.de', 'Germany|Germany|Germany', 21, 'Algorithms|Databases, Genetic|Genomics|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq082', '20189940', 'Multiple sequence alignments can be constructed on the basis of pairwise local sequence similarities. This approach is rather flexible and can combine the advantages of global and local alignment methods. The restriction to pairwise alignments as building blocks, however, can lead to misalignments since weak homologies may be missed if only pairs of sequences are compared.'),
(75334, 2010, 'bio', 'CPFP: a central proteomics facilities pipeline', 'Trudgian, David C|Thomas, Benjamin|McGowan, Simon J|Kessler, Benedikt M|Salek, Mogjiborahman|Acuto, Oreste', 'Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk|Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK. dctrud@ccmp.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 71, 'Databases, Protein|Mass Spectrometry|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btq081', '20189941', 'The central proteomics facilities pipeline (CPFP) provides identification, validation, and quantitation of peptides and proteins from LC-MS/MS datasets through an easy to use web interface. It is the first analysis pipeline targeted specifically at the needs of proteomics core facilities, reducing the data analysis load on staff, and allowing facility clients to easily access and work with their data. Identification of peptides is performed using multiple search engines, their output combined and validated using state-of-the-art techniques for improved results. Cluster execution of jobs allows analysis capacity to be increased easily as demand grows.'),
(75335, 2010, 'bio', 'High quality SNP calling using Illumina data at shallow coverage', 'Malhis, Nawar|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Agency, Vancouver BC, Canada. nmalhis@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Vancouver BC, Canada. nmalhis@bcgsc.ca', 'Canada|Canada', 37, 'Algorithms|Databases, Genetic|Genome|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq092', '20190250', 'Detection of single nucleotide polymorphisms (SNPs) has been a major application in processing second generation sequencing (SGS) data. In principle, SNPs are called on single base differences between a reference genome and a sequence generated from SGS short reads of a sample genome. However, this exercise is far from trivial, several parameters related to sequencing quality, and/or reference genome properties, play essential effect on the accuracy of called SNPs especially at shallow coverage data. In this work, we present Slider II, an alignment and SNP calling approach that demonstrates improved algorithmic approaches enabling larger number of called SNPs with lower false positive rate. In addition to the regular alignment and SNP calling, as an optional feature, Slider II is capable of utilizing information about known SNPs of a target genome, as priors, in the alignment and SNPs calling to enhance its capability of detecting these known SNPs and novel SNPs and mutations in their vicinity.'),
(75336, 2010, 'bio', 'Feature-incorporated alignment based ligand-binding residue prediction for carbohydrate-binding modules', 'Chou, Wei-Yao|Chou, Wei-I|Pai, Tun-Wen|Lin, Shu-Chuan|Jiang, Ting-Ying|Tang, Chuan-Yi|Chang, Margaret Dah-Tsyr', 'Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.|Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 300, Republic of China.', 'China|China|China|China|China|China|China', 6, 'Arabidopsis|Binding Sites|Carbohydrates|Ligands|Models, Molecular|Protein Conformation|Sequence Alignment|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btq084', '20190251', 'Carbohydrate-binding modules (CBMs) share similar secondary and tertiary topology, but their primary sequence identity is low. Computational identification of ligand-binding residues allows biologists to better understand the protein-carbohydrate binding mechanism. In general, functional characterization can be alternatively solved by alignment-based manners. As alignment accuracy based on conventional methods is often sensitive to sequence identity, low sequence identity among query sequences makes it difficult to precisely locate small portions of relevant features. Therefore, we propose a feature-incorporated alignment (FIA) to flexibly align conserved signatures in CBMs. Then, an FIA-based target-template prediction model was further implemented to identify functional ligand-binding residues.'),
(75337, 2010, 'bio', 'SDOP-DB: a comparative standardized-protocol database for mouse phenotypic analyses', 'Tanaka, Nobuhiko|Waki, Kazunori|Kaneda, Hideki|Suzuki, Tomohiro|Yamada, Ikuko|Furuse, Tamio|Kobayashi, Kimio|Motegi, Hiromi|Toki, Hideaki|Inoue, Maki|Minowa, Osamu|Noda, Tetsuo|Takao, Keizo|Miyakawa, Tsuyoshi|Takahashi, Aki|Koide, Tsuyoshi|Wakana, Shigeharu|Masuya, Hiroshi', 'Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.|Technology and Development Unit for Knowledge Base of Mouse Phenotype, RIKEN BioResource Center, Tsukuba 305-0074, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japa', 2, 'Animals|Databases, Factual|Genomics|Internet|Mice|Phenotype|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btq095', '20194625', 'This article reports the development of SDOP-DB, which can provide definite, detailed and easy comparison of experimental protocols used in mouse phenotypic analyses among institutes or laboratories. Because SDOP-DB is fully compliant with international standards, it can act as a practical foundation for international sharing and integration of mouse phenotypic information.'),
(75338, 2010, 'bio', 'An Ergatis-based prokaryotic genome annotation web server', 'Hemmerich, Chris|Buechlein, Aaron|Podicheti, Ram|Revanna, Kashi V|Dong, Qunfeng', 'Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.|Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47405, USA.', 'India|India|India|India|India', 86, 'Databases, Genetic|Genome|Genomics|Internet|Prokaryotic Cells|Software', 'Journal Article', '10.1093/bioinformatics/btq090', '20194626', 'Ergatis is a flexible workflow management system for designing and executing complex bioinformatics pipelines. However, its complexity restricts its usage to only highly skilled bioinformaticians. We have developed a web-based prokaryotic genome annotation server, Integrative Services for Genomics Analysis (ISGA), which builds upon the Ergatis workflow system, integrates other dynamic analysis tools and provides intuitive web interfaces for biologists to customize and execute their own annotation pipelines. ISGA is designed to be installed at genomics core facilities and be used directly by biologists.'),
(75339, 2010, 'bio', 'ScripTree: scripting phylogenetic graphics', 'Chevenet, François|Croce, Olivier|Hebrard, Maxime|Christen, Richard|Berry, Vincent', 'GEMI, IRD/CNRS UMR 2724, 911 avenue Agropolis, 34394 Montpellier, France. chevenet@ird.fr|GEMI, IRD/CNRS UMR 2724, 911 avenue Agropolis, 34394 Montpellier, France. chevenet@ird.fr|GEMI, IRD/CNRS UMR 2724, 911 avenue Agropolis, 34394 Montpellier, France. chevenet@ird.fr|GEMI, IRD/CNRS UMR 2724, 911 avenue Agropolis, 34394 Montpellier, France. chevenet@ird.fr|GEMI, IRD/CNRS UMR 2724, 911 avenue Agropolis, 34394 Montpellier, France. chevenet@ird.fr', 'France|France|France|France|France', 16, 'Databases, Genetic|Genomics|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq086', '20194627', 'There is a large amount of tools for interactive display of phylogenetic trees. However, there is a shortage of tools for the automation of tree rendering. Scripting phylogenetic graphics would enable the saving of graphical analyses involving numerous and complex tree handling operations and would allow the automation of repetitive tasks. ScripTree is a tool intended to fill this gap. It is an interpreter to be used in batch mode. Phylogenetic graphics instructions, related to tree rendering as well as tree annotation, are stored in a text file and processed in a sequential way.'),
(75340, 2010, 'bio', 'Inferring dynamic gene networks under varying conditions for transcriptomic network comparison', 'Shimamura, Teppei|Imoto, Seiya|Yamaguchi, Rui|Nagasaki, Masao|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. shima@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. shima@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. shima@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. shima@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. shima@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 10, 'Cell Line, Tumor|Gene Expression Profiling|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq080', '20197286', 'Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series.'),
(75341, 2010, 'bio', 'Modeling sample variables with an Experimental Factor Ontology', 'Malone, James|Holloway, Ele|Adamusiak, Tomasz|Kapushesky, Misha|Zheng, Jie|Kolesnikov, Nikolay|Zhukova, Anna|Brazma, Alvis|Parkinson, Helen', 'Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk|Microarray Informatics Team, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK. malone@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 238, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btq099', '20200009', 'Describing biological sample variables with ontologies is complex due to the cross-domain nature of experiments. Ontologies provide annotation solutions, however, for cross-domain investigations, multiple ontologies are needed to represent the data. These are subject to rapid change, are often not interoperable and present complexities that are a barrier to biological resource users.'),
(75342, 2010, 'bio', 'PaperMaker: validation of biomedical scientific publications', 'Rebholz-Schuhmann, D|Kavaliauskas, S|Pezik, P', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rebholz@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rebholz@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rebholz@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 2, 'Databases, Factual|Information Storage and Retrieval|Internet|MEDLINE|PubMed|Publications|Software|United States', 'Journal Article', '10.1093/bioinformatics/btq060', '20200010', 'The automatic analysis of scientific literature can support authors in writing their manuscripts.'),
(75343, 2010, 'bio', 'Cross-species common regulatory network inference without requirement for prior gene affiliation', 'Gholami, Amin Moghaddas|Fellenberg, Kurt', 'Chair of Proteomics and Bioanalytics, Center for Integrated Protein Sciences Munich (CIPSM), Technische Universität München, Emil Erlenmeyer Forum 5, 85354 Freising, Germany.|Chair of Proteomics and Bioanalytics, Center for Integrated Protein Sciences Munich (CIPSM), Technische Universität München, Emil Erlenmeyer Forum 5, 85354 Freising, Germany.', 'Germany|Germany', 13, 'Algorithms|Bayes Theorem|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq096', '20200011', 'Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity.'),
(75344, 2010, 'bio', 'Statistical expression deconvolution from mixed tissue samples', 'Clarke, Jennifer|Seo, Pearl|Clarke, Bertrand', 'Department of Medicine, University of Miami, 1120 NW 14th St, Suite 611, Miami, FL 33136, USA. jclarke@med.miami.edu|Department of Medicine, University of Miami, 1120 NW 14th St, Suite 611, Miami, FL 33136, USA. jclarke@med.miami.edu|Department of Medicine, University of Miami, 1120 NW 14th St, Suite 611, Miami, FL 33136, USA. jclarke@med.miami.edu', 'United States|United States|United States', 47, 'Cell Line, Tumor|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btq097', '20202973', 'Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied.'),
(75345, 2010, 'bio', 'An extended IUPAC nomenclature code for polymorphic nucleic acids', 'Johnson, Andrew D', 'Department of Medicine, University of Miami, 1120 NW 14th St, Suite 611, Miami, FL 33136, USA. jclarke@med.miami.edu', 'United States', 16, 'Base Sequence|Computer Simulation|Nucleic Acids|Terminology as Topic', 'Letter', '10.1093/bioinformatics/btq098', '20202974', 'The International Union of Pure and Applied Chemistry (IUPAC) code specified nearly 25 years ago provides a nomenclature for incompletely specified nucleic acids. However, no system currently exists that allows for the informatics representation of the relative abundance at polymorphic nucleic acids (e.g. single nucleotide polymorphisms) in a single specified character, or a string of characters. Here, I propose such an information code as a natural extension to the IUPAC nomenclature code, and present some potential uses and limitations to such a code. The primary anticipated use of this extended nomenclature code is to assist in the representation of the rapidly growing space of information in human genetic variation.'),
(75346, 2010, 'bio', 'Filtering error from SOLiD Output', 'Sasson, Ariella|Michael, Todd P', 'Waksman Institute of Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08554, USA.|Waksman Institute of Microbiology, Rutgers, The State University of New Jersey, Piscataway, NJ 08554, USA.', 'Jersey|Jersey', 22, 'Databases, Genetic|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq045', '20207696', 'Here, we report the development of a filtering framework designed for efficient identification of both polyclonal and independent errors within SOLiD sequence data. The filtering utilizes the quality values reported by SOLiDs primary analysis for the identification of the two different types of errors. The filtering framework facilitates the passage of high-quality data into a variety of functional genomics applications, including de novo assemblers and sequence matching programs for SNP calling, improving the output quality and reducing resources necessary for analysis.'),
(75347, 2010, 'bio', 'Comparing genome-wide chromatin profiles using ChIP-chip or ChIP-seq', 'Johannes, Frank|Wardenaar, René|Colomé-Tatché, Maria|Mousson, Florence|de Graaf, Petra|Mokry, Michal|Guryev, Victor|Timmers, H Th Marc|Cuppen, Edwin|Jansen, Ritsert C', 'Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl|Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, Biologisch Centrum, 9751 NN Haren, The Netherlands. f.johannes@rug.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 25, 'Chromatin|Chromatin Immunoprecipitation|Epigenesis, Genetic|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btq087', '20208068', 'ChIP-chip and ChIP-seq technologies provide genome-wide measurements of various types of chromatin marks at an unprecedented resolution. With ChIP samples collected from different tissue types and/or individuals, we can now begin to characterize stochastic or systematic changes in epigenetic patterns during development (intra-individual) or at the population level (inter-individual). This requires statistical methods that permit a simultaneous comparison of multiple ChIP samples on a global as well as locus-specific scale. Current analytical approaches are mainly geared toward single sample investigations, and therefore have limited applicability in this comparative setting. This shortcoming presents a bottleneck in biological interpretations of multiple sample data.'),
(75348, 2010, 'bio', 'Genome-wide synteny through highly sensitive sequence alignment: Satsuma', 'Grabherr, Manfred G|Russell, Pamela|Meyer, Miriah|Mauceli, Evan|Alföldi, Jessica|Di Palma, Federica|Lindblad-Toh, Kerstin', 'Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org|Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA. grabherr@broadinstitute.org', 'United States|United States|United States|United States|United States|United States|United States', 86, 'Algorithms|Animals|Computational Biology|Fourier Analysis|Genome|Genomics|Humans|Models, Statistical|Oryza|Probability|Programming Languages|Sequence Alignment|Software|Sorghum|Tetraodontiformes', 'Journal Article', '10.1093/bioinformatics/btq102', '20208069', 'Comparative genomics heavily relies on alignments of large and often complex DNA sequences. From an engineering perspective, the problem here is to provide maximum sensitivity (to find all there is to find), specificity (to only find real homology) and speed (to accommodate the billions of base pairs of vertebrate genomes).'),
(75349, 2010, 'bio', 'Reducing the algorithmic variability in transcriptome-based inference', 'Tuna, Salih|Niranjan, Mahesan', 'School of Electronics and Computer Science, University of Southampton, Southampton, UK.|School of Electronics and Computer Science, University of Southampton, Southampton, UK.', 'United Kingdom|United Kingdom', 4, 'Algorithms|Cell Line, Tumor|Computational Biology|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Male|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|RNA, Messenger|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btq104', '20212019', 'High-throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies.'),
(75350, 2010, 'bio', 'Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network', 'Li, Yongjin|Patra, Jagdish C', 'School of Computer Engineering, Nanyang Technological University, Singapore. yongjin.li@gmail.com|School of Computer Engineering, Nanyang Technological University, Singapore. yongjin.li@gmail.com', 'Singapore|Singapore', 236, 'Algorithms|Alzheimer Disease|Computational Biology|Gene Regulatory Networks|Genome-Wide Association Study|Genomics|Genotype|Humans|Models, Biological|Models, Genetic|Models, Statistical|Mutation|Phenotype', 'Journal Article', '10.1093/bioinformatics/btq108', '20215462', 'Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene-phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases.'),
(75351, 2010, 'bio', 'PanCGHweb: a web tool for genotype calling in pangenome CGH data', 'Bayjanov, Jumamurat R|Siezen, Roland J|van Hijum, Sacha A F T', 'Radboud University Medical Centre, Nijmegen Center for Molecular Life Sciences, Nijmegen, The Netherlands.|Radboud University Medical Centre, Nijmegen Center for Molecular Life Sciences, Nijmegen, The Netherlands.|Radboud University Medical Centre, Nijmegen Center for Molecular Life Sciences, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 11, 'Algorithms|Computational Biology|Genetic Variation|Genome|Genomics|Genotype|Internet|Lactococcus lactis|Models, Genetic|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Open Reading Frames|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq103', '20219865', 'A pangenome is the total of genes present in strains of the same species. Pangenome microarrays allow determining the genomic content of bacterial strains more accurately than conventional comparative genome hybridization microarrays. PanCGHweb is the first tool that effectively calls genotype based on pangenome microarray data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75352, 2010, 'bio', 'Integrative mixture of experts to combine clinical factors and gene markers', 'Lê Cao, Kim-Anh|Meugnier, Emmanuelle|McLachlan, Geoffrey J', 'ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia. k.lecao@uq.edu.au|ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia. k.lecao@uq.edu.au|ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia. k.lecao@uq.edu.au', 'Australia|Australia|Australia', 19, 'Algorithms|Bayes Theorem|Biomarkers, Tumor|Computational Biology|Gene Expression Profiling|Genetic Markers|Humans|Male|Medical Oncology|Models, Biological|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btq107', '20223834', 'Microarrays are being increasingly used in cancer research to better characterize and classify tumors by selecting marker genes. However, as very few of these genes have been validated as predictive biomarkers so far, it is mostly conventional clinical and pathological factors that are being used as prognostic indicators of clinical course. Combining clinical data with gene expression data may add valuable information, but it is a challenging task due to their categorical versus continuous characteristics. We have further developed the mixture of experts (ME) methodology, a promising approach to tackle complex non-linear problems. Several variants are proposed in integrative ME as well as the inclusion of various gene selection methods to select a hybrid signature.'),
(75353, 2010, 'bio', 'Localized motif discovery in gene regulatory sequences', 'Narang, Vipin|Mittal, Ankush|Sung, Wing-Kin', 'Department of Computer Science, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, Singapore.|Department of Computer Science, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore', 13, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Animals|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|Gene Regulatory Networks|Humans|Mice|Models, Statistical|Molecular Sequence Data|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btq106', '20223835', 'Discovery of nucleotide motifs that are localized with respect to a certain biological landmark is important in several appli-cations, such as in regulatory sequences flanking the transcription start site, in the neighborhood of known transcription factor binding sites, and in transcription factor binding regions discovered by massively parallel sequencing (ChIP-Seq).'),
(75354, 2010, 'bio', 'TiSGeD: a database for tissue-specific genes', 'Xiao, Sheng-Jian|Zhang, Chi|Zou, Quan|Ji, Zhi-Liang', 'Key Laboratory for Cell Biology and Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Fujian, P R China.|Key Laboratory for Cell Biology and Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Fujian, P R China.|Key Laboratory for Cell Biology and Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Fujian, P R China.|Key Laboratory for Cell Biology and Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Fujian, P R China.', 'China|China|China|China', 85, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Gene Expression Profiling|Humans|Mice|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Rats', 'Journal Article', '10.1093/bioinformatics/btq109', '20223836', 'The tissue-specific genes are a group of genes whose function and expression are preferred in one or several tissues/cell types. Identification of these genes helps better understanding of tissue-gene relationship, etiology and discovery of novel tissue-specific drug targets. In this study, a statistical method is introduced to detect tissue-specific genes from more than 123 125 gene expression profiles over 107 human tissues, 67 mouse tissues and 30 rat tissues. As a result, a novel subject-specialized repository, namely the tissue-specific genes database (TiSGeD), is developed to represent the analyzed results. Auxiliary information of tissue-specific genes was also collected from biomedical literatures.'),
(75355, 2010, 'bio', 'Inferring cluster-based networks from differently stimulated multiple time-course gene expression data', 'Shiraishi, Yuichi|Kimura, Shuhei|Okada, Mariko', 'RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan. yshira@riken.jp|RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan. yshira@riken.jp|RIKEN Research Center for Allergy and Immunology, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan. yshira@riken.jp', 'Japan|Japan|Japan', 0, 'Cell Line, Tumor|Cluster Analysis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq094', '20223837', 'Clustering and gene network inference often help to predict the biological functions of gene subsets. Recently, researchers have accumulated a large amount of time-course transcriptome data collected under different treatment conditions to understand the physiological states of cells in response to extracellular stimuli and to identify drug-responsive genes. Although a variety of statistical methods for clustering and inferring gene networks from expression profiles have been proposed, most of these are not tailored to simultaneously treat expression data collected under multiple stimulation conditions.'),
(75356, 2010, 'bio', 'Pathway discovery in metabolic networks by subgraph extraction', 'Faust, Karoline|Dupont, Pierre|Callut, Jérôme|van Helden, Jacques', 'Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Bruxelles, Belgium. kfaust@ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Bruxelles, Belgium. kfaust@ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Bruxelles, Belgium. kfaust@ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Bruxelles, Belgium. kfaust@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Adenosine Triphosphate|Algorithms|Computational Biology|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation|Metabolic Networks and Pathways|Models, Biological|Models, Theoretical|NADP|Phylogeny|Probability|Reproducibility of Results|Saccharomyces cerevisiae|Water', 'Journal Article', '10.1093/bioinformatics/btq105', '20228128', 'Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well.'),
(75357, 2010, 'bio', 'DensiTree: making sense of sets of phylogenetic trees', 'Bouckaert, Remco R', 'Department of Computer Science, Auckland University. remco@cs.auckland.ac.nz', '-', 284, 'Bayes Theorem|Computational Biology|Evolution, Molecular|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btq110', '20228129', 'Bayesian analysis through programs like BEAST (Drummond and Rumbaut, 2007) and MrBayes (Huelsenbeck et al., 2001) provides a powerful method for reconstruction of evolutionary relationships. One of the benefits of Bayesian methods is that well-founded estimates of uncertainty in models can be made available. So, for example, not only the mean time of a most recent common ancestor (tMRCA) is estimated, but also the spread. This distribution over model space is represented by a set of trees, which can be rather large and difficult to interpret. DensiTree is a tool that helps navigating these sets of trees.'),
(75358, 2010, 'bio', 'Composition bias and the origin of ORFan genes', 'Yomtovian, Inbal|Teerakulkittipong, Nuttinee|Lee, Byungkook|Moult, John|Unger, Ron', 'Department of Computer Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.|Department of Computer Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.|Department of Computer Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.|Department of Computer Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.|Department of Computer Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Evolution, Molecular|Genome|Genomics|Open Reading Frames|Prokaryotic Cells', 'Journal Article', '10.1093/bioinformatics/btq093', '20231229', 'Intriguingly, sequence analysis of genomes reveals that a large number of genes are unique to each organism. The origin of these genes, termed ORFans, is not known. Here, we explore the origin of ORFan genes by defining a simple measure called composition bias, based on the deviation of the amino acid composition of a given sequence from the average composition of all proteins of a given genome.'),
(75359, 2010, 'bio', 'Mutual information is critically dependent on prior assumptions: would the correct estimate of mutual information please identify itself?', 'Fernandes, Andrew D|Gloor, Gregory B', 'Department of Biochemistry, The University of Western Ontario, London, ON, Canada. andrew@fernandes.org|Department of Biochemistry, The University of Western Ontario, London, ON, Canada. andrew@fernandes.org', 'Canada|Canada', 17, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Protein|Escherichia coli|Models, Statistical|Models, Theoretical|Probability|Software', 'Journal Article', '10.1093/bioinformatics/btq111', '20236946', 'Mutual information (MI) is a quantity that measures the dependence between two arbitrary random variables and has been repeatedly used to solve a wide variety of bioinformatic problems. Recently, when attempting to quantify the effects of sampling variance on computed values of MI in proteins, we encountered striking differences among various novel estimates of MI. These differences revealed that estimating the true value of MI is not a straightforward procedure, and minor variations of assumptions yielded remarkably different estimates.'),
(75360, 2010, 'bio', 'A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking', 'Ballester, Pedro J|Mitchell, John B O', 'Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. pedro.ballester@ebi.ac.uk|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. pedro.ballester@ebi.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Drug Design|Ligands|Models, Statistical|Protein Binding|Protein Conformation|Proteins|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btq112', '20236947', 'Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions.'),
(75361, 2010, 'bio', 'SeSAW: balancing sequence and structural information in protein functional mapping', 'Standley, Daron M|Yamashita, Reiko|Kinjo, Akira R|Toh, Hiroyuki|Nakamura, Haruki', 'WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan. standley@ifrec.osaka-u.ac.jp|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan. standley@ifrec.osaka-u.ac.jp|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan. standley@ifrec.osaka-u.ac.jp|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan. standley@ifrec.osaka-u.ac.jp|WPI Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan. standley@ifrec.osaka-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Amino Acid Motifs|Animals|Biochemistry|Computational Biology|Computer Graphics|Genomics|Internet|Mice|Models, Statistical|Sequence Alignment|Sequence Analysis, Protein|Software|Thermus thermophilus', 'Journal Article', '10.1093/bioinformatics/btq116', '20299324', 'Functional similarity between proteins is evident at both the sequence and structure levels. SeSAW is a web-based program for identifying functionally or evolutionarily conserved motifs in protein structures by locating sequence and structural similarities, and quantifying these at the level of individual residues. Results can be visualized in 2D, as annotated alignments, or in 3D, as structural superpositions. An example is given for both an experimentally determined query structure and a homology model.'),
(75362, 2010, 'bio', 'Going from where to why--interpretable prediction of protein subcellular localization', 'Briesemeister, Sebastian|Rahnenführer, Jörg|Kohlbacher, Oliver', 'Division for Simulation of Biological Systems, Universität Tübingen, Tübingen, Germany. briese@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, Universität Tübingen, Tübingen, Germany. briese@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, Universität Tübingen, Tübingen, Germany. briese@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 104, 'Algorithms|Animals|Artificial Intelligence|Bayes Theorem|Computational Biology|Gene Expression Profiling|Humans|Models, Biological|Models, Statistical|Mutation|Probability|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btq115', '20299325', 'Protein subcellular localization is pivotal in understanding a proteins function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations.'),
(75363, 2010, 'bio', 'HALO--a Java framework for precise transcript half-life determination', 'Friedel, Caroline C|Kaufmann, Stefanie|Dölken, Lars|Zimmer, Ralf', 'Institut für Informatik, Ludwig-Maximilians-Universität München, München, Germany. caroline.friedel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, München, Germany. caroline.friedel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, München, Germany. caroline.friedel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, München, Germany. caroline.friedel@bio.ifi.lmu.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Computer Graphics|Gene Expression Profiling|Genome|Oligonucleotide Array Sequence Analysis|Programming Languages|RNA|RNA Stability|RNA, Messenger|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq117', '20299326', 'Recent improvements in experimental technologies now allow measurements of de novo transcription and/or RNA decay at whole transcriptome level and determination of precise transcript half-lives. Such transcript half-lives provide important insights into the regulation of biological processes and the relative contributions of RNA decay and de novo transcription to differential gene expression. In this article, we present HALO (Half-life Organizer), the first software for the precise determination of transcript half-lives from measurements of RNA de novo transcription or decay determined with microarrays or RNA-seq. In addition, methods for quality control, filtering and normalization are supplied. HALO provides a graphical user interface, command-line tools and a well-documented Java application programming interface (API). Thus, it can be used both by biologists to determine transcript half-lives fast and reliably with the provided user interfaces as well as software developers integrating transcript half-life analysis into other gene expression profiling pipelines.'),
(75364, 2010, 'bio', 'ArchSchema: a tool for interactive graphing of related Pfam domain architectures', 'Tamuri, Asif U|Laskowski, Roman A', 'Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, London, UK.|Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, London, UK.', 'United Kingdom|United Kingdom', 20, 'Algorithms|Computational Biology|Computer Graphics|Databases, Protein|Humans|Markov Chains|Protein Structure, Tertiary|Proto-Oncogene Proteins c-cbl|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq119', '20299327', 'ArchSchema is a Java Web Start application that generates a dynamic 2D network of related Pfam domain architectures. Each node corresponds to a different architecture (shown as a sequence of coloured boxes) and indicates whether any 3D structural information is available in the PDB. Satellite nodes can show either the UniProt codes or the PDB codes of proteins having the given architecture. Search options allow search by UniProt code or Pfam domain identifier, and results can be filtered by domain, organism, or by selecting only proteins in the PDB.'),
(75365, 2010, 'bio', 'Identification of genetic network dynamics with unate structure', 'Porreca, Riccardo|Cinquemani, Eugenio|Lygeros, John|Ferrari-Trecate, Giancarlo', 'Institut für Automatik, ETH Zürich, Zürich, Switzerland.|Institut für Automatik, ETH Zürich, Zürich, Switzerland.|Institut für Automatik, ETH Zürich, Zürich, Switzerland.|Institut für Automatik, ETH Zürich, Zürich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 26, 'Algorithms|Computational Biology|Computer Simulation|Gene Regulatory Networks|Kinetics|Models, Genetic|Models, Statistical|Models, Theoretical|Normal Distribution|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btq120', '20305266', 'Modern experimental techniques for time course measurement of gene expression enable the identification of dynamical models of genetic regulatory networks. In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structures is clearly prohibitive. Modelling and identification methods for the a priori selection of network structures compatible with biological knowledge and experimental data are necessary to make the identification problem tractable.'),
(75366, 2010, 'bio', 'LOTUS, a new domain associated with small RNA pathways in the germline', 'Callebaut, Isabelle|Mornon, Jean-Paul', 'IMPMC-UMR7590, Université Pierre et Marie Curie-Paris 6, Paris, France. isabelle.callebaut@impmc.upmc.fr|IMPMC-UMR7590, Université Pierre et Marie Curie-Paris 6, Paris, France. isabelle.callebaut@impmc.upmc.fr', 'France|France', 0, 'Amino Acid Sequence|Animals|Cluster Analysis|Computational Biology|Drosophila|Drosophila Proteins|Germ Cells|Humans|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|RNA, Small Interfering|Ribonucleoproteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btq122', '20305267', 'We describe here LOTUS, a hitherto uncharacterized small globular domain, which was identified using sensitive sequence profile analysis. The LOTUS domain is found in germline-specific proteins that are present in the nuage/polar granules of germ cells. TDRD5 and TDRD7, two mammalian members of the germline Tudor group, possess three copies of the LOTUS domain in their extreme N-termini. The Tudor domains of these proteins bind symmetric dimethyl arginines present on the germ cell-specific Piwi proteins, which form a particular clade of Argonaute proteins. Piwi proteins interact with a specific class of non-coding RNAs [piwi-interacting RNAs (piRNAs)] and play a key role in the repression (silencing) of transposons and possibly other germline-specific functions. A LOTUS domain is also present in the Oskar protein, a critical component of the pole plasm in the Drosophila oocyte, which is required for germ cell formation. LOTUS domains are found in various proteins from metazoans and plants, are often associated with RNA-specific modules and are likely to adopt a winged helix fold. This suggests a germline-specific role in the mRNA localization and/or translation or a specific function toward piRNAs.'),
(75367, 2010, 'bio', 'ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment', 'Konc, Janez|Janezic, Dusanka', 'National Institute of Chemistry, Ljubljana, Slovenia.|National Institute of Chemistry, Ljubljana, Slovenia.', 'Slovenia|Slovenia', 0, 'Algorithms|Binding Sites|Computational Biology|Crystallography, X-Ray|DNA|Databases, Protein|Ligands|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Alignment|Structural Homology, Protein|Surface Properties', 'Journal Article', '10.1093/bioinformatics/btq100', '20305268', 'Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation.'),
(75368, 2010, 'bio', 'CoP: a database for characterizing co-expressed gene modules with biological information in plants', 'Ogata, Yoshiyuki|Suzuki, Hideyuki|Sakurai, Nozomu|Shibata, Daisuke', 'Department of Biotechnology Research, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.|Department of Biotechnology Research, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.|Department of Biotechnology Research, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.|Department of Biotechnology Research, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.', 'Japan|Japan|Japan|Japan', 58, 'Algorithms|Arabidopsis|Computational Biology|Databases, Genetic|Gene Expression Regulation, Plant|Gene Regulatory Networks|Genes, Plant|Genome, Plant|Models, Genetic|Oligonucleotide Array Sequence Analysis|Plants', 'Journal Article', '10.1093/bioinformatics/btq121', '20305269', 'Using a large dataset (10 022 assays) obtained from public plant microarray databases, we developed the CoP database for associating co-expressed gene modules with biological information such as gene ontology terms and, if available, metabolic pathway names. The Confeito algorithm developed previously in our laboratory, which is suitable to calculate the interconnectivity between genes in co-expressed gene network, was applied to extract co-expressed gene modules. The database includes the gene modules for Arabidopsis thaliana (thale cress) and seven crops, Glycine max (soybean), Hordeum vulgare (barley), Oryza sativa (rice), Populus trichocarpa (poplar), Triticum aestivum (wheat), Vitis vinifera (grape) and Zea mays (maize).'),
(75369, 2010, 'bio', 'Curators of the world unite: the International Society of Biocuration', 'Bateman, Alex', 'Department of Biotechnology Research, Kazusa DNA Research Institute, Kisarazu, Chiba, Japan.', 'Japan', 26, 'Computational Biology|Data Collection|Databases, Factual|Internet|Societies, Scientific', 'Editorial', '10.1093/bioinformatics/btq101', '20305270', '-'),
(75370, 2010, 'bio', 'varLD: a program for quantifying variation in linkage disequilibrium patterns between populations', 'Ong, Rick Twee-Hee|Teo, Yik-Ying', 'NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.|NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore.', 'Singapore|Singapore', 0, 'Blood Coagulation|Chromosome Mapping|Computational Biology|Genetic Variation|Genetics, Population|Genome-Wide Association Study|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide|Programming Languages|Software|Warfarin', 'Journal Article', '10.1093/bioinformatics/btq125', '20308177', 'Linkage disequilibrium (LD) differences between populations can potentially result in failure to replicate primary signals of trait association in independent genome-wide association studies (GWAS). However, such inter-population LD differences can be leveraged to narrow the search for common causal variants responsible for the association signals observed. The ability to assess and quantify LD variations among populations is thus expected to contribute to both replication and fine-mapping stages of GWAS.'),
(75371, 2010, 'bio', 'PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways', 'Schramm, Gunnar|Wiesberg, Stefan|Diessl, Nicolle|Kranz, Anna-Lena|Sagulenko, Vitalia|Oswald, Marcus|Reinelt, Gerhard|Westermann, Frank|Eils, Roland|König, Rainer', 'Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.|Department of Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Algorithms|Cell Line, Tumor|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genome|Glutamic Acid|Humans|Metabolic Networks and Pathways|Models, Genetic|Neuroblastoma|Purines|Pyrimidines|Software', 'Journal Article', '10.1093/bioinformatics/btq113', '20335275', 'Gene expression profiling by microarrays or transcript sequencing enables observing the pathogenic function of tumors on a mesoscopic level.'),
(75372, 2010, 'bio', 'PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations', 'Denny, Joshua C|Ritchie, Marylyn D|Basford, Melissa A|Pulley, Jill M|Bastarache, Lisa|Brown-Gentry, Kristin|Wang, Deede|Masys, Dan R|Roden, Dan M|Crawford, Dana C', 'Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. josh.denny@vanderbilt.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Arthritis, Rheumatoid|Atrial Fibrillation|Carotid Stenosis|Computational Biology|Coronary Artery Disease|Crohn Disease|Europe|Genotype|Humans|Lupus Erythematosus, Systemic|Multiple Sclerosis|Polymorphism, Single Nucleotide|Risk Factors|Software', 'Journal Article', '10.1093/bioinformatics/btq126', '20335276', 'Emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association scans (PheWAS) for disease-gene associations. We propose a novel method to scan phenomic data for genetic associations using International Classification of Disease (ICD9) billing codes, which are available in most EMR systems. We have developed a code translation table to automatically define 776 different disease populations and their controls using prevalent ICD9 codes derived from EMR data. As a proof of concept of this algorithm, we genotyped the first 6005 European-Americans accrued into BioVU, Vanderbilts DNA biobank, at five single nucleotide polymorphisms (SNPs) with previously reported disease associations: atrial fibrillation, Crohns disease, carotid artery stenosis, coronary artery disease, multiple sclerosis, systemic lupus erythematosus and rheumatoid arthritis. The PheWAS software generated cases and control populations across all ICD9 code groups for each of these five SNPs, and disease-SNP associations were analyzed. The primary outcome of this study was replication of seven previously known SNP-disease associations for these SNPs.'),
(75373, 2010, 'bio', 'TopoGSA: network topological gene set analysis', 'Glaab, Enrico|Baudot, Anaïs|Krasnogor, Natalio|Valencia, Alfonso', 'School of Computer Science, Nottingham University, Nottingham, UK.|School of Computer Science, Nottingham University, Nottingham, UK.|School of Computer Science, Nottingham University, Nottingham, UK.|School of Computer Science, Nottingham University, Nottingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Arabidopsis|Caenorhabditis elegans|Computational Biology|Drosophila melanogaster|Gene Expression Regulation|Gene Regulatory Networks|Humans|Models, Genetic|Mutation|Neoplasms|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btq131', '20335277', 'TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular pathways and processes.'),
(75374, 2010, 'bio', 'EBImage--an R package for image processing with applications to cellular phenotypes', 'Pau, Grégoire|Fuchs, Florian|Sklyar, Oleg|Boutros, Michael|Huber, Wolfgang', 'EMBL-European Bioinformatics Institute, Cambridge, UK. gregoire.pau@ebi.ac.uk|EMBL-European Bioinformatics Institute, Cambridge, UK. gregoire.pau@ebi.ac.uk|EMBL-European Bioinformatics Institute, Cambridge, UK. gregoire.pau@ebi.ac.uk|EMBL-European Bioinformatics Institute, Cambridge, UK. gregoire.pau@ebi.ac.uk|EMBL-European Bioinformatics Institute, Cambridge, UK. gregoire.pau@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 265, 'Computer Graphics|Databases, Factual|Image Processing, Computer-Assisted|Information Storage and Retrieval|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq046', '20338898', 'EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization.'),
(75375, 2010, 'bio', 'Multi-threaded vectorized distance matrix computation on the CELL/BE and x86/SSE2 architectures', 'Wirawan, Adrianto|Kwoh, Chee Keong|Schmidt, Bertil', 'School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg', 'Singapore|Singapore|Singapore', 5, 'Computational Biology|Pattern Recognition, Automated|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq135', '20348545', 'Multiple sequence alignment is an important tool in bioinformatics. Although efficient heuristic algorithms exist for this problem, the exponential growth of biological data demands an even higher throughput. The recent emergence of multi-core technologies has made it possible to achieve a highly improved execution time for many bioinformatics applications. In this article, we introduce an implementation that accelerates the distance matrix computation on x86 and Cell Broadband Engine, a homogeneous and heterogeneous multi-core system, respectively. By taking advantage of multiple processors as well as Single Instruction Multiple Data vectorization, we were able to achieve speed-ups of two orders of magnitude compared to the publicly available implementation utilized in ClustalW.'),
(75376, 2010, 'bio', 'SICAGO: Semi-supervised cluster analysis using semantic distance between gene pairs in Gene Ontology', 'Kang, Bo-Yeong|Ko, Song|Kim, Dae-Won', 'School of Mechanical Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea.|School of Mechanical Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea.|School of Mechanical Engineering, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu 702-701, Korea.', '-|-|-', 10, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq133', '20348546', 'Despite the importance of using the semantic distance to improve the performance of conventional expression-based clustering, there are few freely available software that provides a clustering algorithm using the ontology-based semantic distances as prior knowledge. Here, we present the SICAGO (SemI-supervised Cluster Analysis using semantic distance between gene pairs in Gene Ontology) system that helps to discover the groups of genes more effectively using prior knowledge extracted from Gene Ontology.'),
(75377, 2010, 'bio', 'DistanceScan: a tool for promoter modeling', 'Shelest, Vladimir|Albrecht, Daniela|Shelest, Ekaterina', 'Research group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Beutenbergstr. 11a, 07745 Jena, Germany.|Research group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Beutenbergstr. 11a, 07745 Jena, Germany.|Research group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Beutenbergstr. 11a, 07745 Jena, Germany.', 'Germany|Germany|Germany', 4, 'Binding Sites|Databases, Genetic|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq132', '20360058', 'The state of the art in promoter modeling for higher eukaryotes is predicting not single transcription factor binding sites (TFBSs), but their combinations. The new tool utilizes a previously developed method of distance distributions of TFBS pairs. We model the random distribution of distances and compare it with the distribution observed in the query sequences. Comparison of the profiles allows filtering out the noise and retaining the potentially functional combinations. This approach has proved its usefulness as a filtering technique for the selection of TFBS pairs for promoter modeling and is now implemented as a tool in R. As an input, it can use the outputs of three different TFBS- and motif-predictive tools (Gibbs Sampler for motifs, Match and MEME/FIMO for PWM-based search). The output is a list of predicted pairs on overrepresented distances with assigned scores, P-values and plots showing the distribution of pairs in the input sequences.'),
(75378, 2010, 'bio', 'Building a high-quality sense inventory for improved abbreviation disambiguation', 'Okazaki, Naoaki|Ananiadou, Sophia|Tsujii, Junichi', 'Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan. okazaki@chokkan.org|Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan. okazaki@chokkan.org|Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan. okazaki@chokkan.org', 'Japan|Japan|Japan', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Bibliographic|Dictionaries as Topic|MEDLINE|Models, Statistical|Natural Language Processing|Polymerase Chain Reaction|Reproducibility of Results|Software|Tomography, X-Ray Computed', 'Journal Article', '10.1093/bioinformatics/btq129', '20360059', 'The ultimate goal of abbreviation management is to disambiguate every occurrence of an abbreviation into its expanded form (concept or sense). To collect expanded forms for abbreviations, previous studies have recognized abbreviations and their expanded forms in parenthetical expressions of bio-medical texts. However, expanded forms extracted by abbreviation recognition are mixtures of concepts/senses and their term variations. Consequently, a list of expanded forms should be structured into a sense inventory, which provides possible concepts or senses for abbreviation disambiguation.'),
(75379, 2010, 'bio', 'Supervised normalization of microarrays', 'Mecham, Brigham H|Nelson, Peter S|Storey, John D', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States', 65, 'Algorithms|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq118', '20363728', 'A major challenge in utilizing microarray technologies to measure nucleic acid abundances is normalization, the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.'),
(75380, 2010, 'bio', 'An integer programming formulation to identify the sparse network architecture governing differentiation of embryonic stem cells', 'Banerjee, Ipsita|Maiti, Spandan|Parashurama, Natesh|Yarmush, Martin', 'Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, 51 Blossom Street, Boston, MA-02114, USA. ipb1@pitt.edu|Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, 51 Blossom Street, Boston, MA-02114, USA. ipb1@pitt.edu|Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, 51 Blossom Street, Boston, MA-02114, USA. ipb1@pitt.edu|Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, 51 Blossom Street, Boston, MA-02114, USA. ipb1@pitt.edu', 'United States|United States|United States|United States', 8, 'Algorithms|Cell Differentiation|Computational Biology|Embryonic Stem Cells|Gene Expression Profiling|Gene Regulatory Networks|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq139', '20363729', 'Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.'),
(75381, 2010, 'bio', 'ACCUSA--accurate SNP calling on draft genomes', 'Fröhler, Sebastian|Dieterich, Christoph', 'Bioinformatics in Quantitative Biology, The Berlin Institute for Medical Systems Biology at Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin-Buch, Germany.|Bioinformatics in Quantitative Biology, The Berlin Institute for Medical Systems Biology at Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin-Buch, Germany.', 'Germany|Germany', 0, 'Genome|Genomics|Polymorphism, Single Nucleotide|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btq138', '20363730', 'Next generation sequencing technologies facilitate genome-wide analysis of several biological processes. We are interested in whole-genome genotyping. To our knowledge, none of the existing single nucleotide polymorphism (SNP) callers consider the quality of the reference genome, which is not necessary for high-quality assemblies of well-studied model organisms. However, most genome projects will remain in draft status with little to no genome assembly improvement due to time and financial constraints. Here, we present a simple yet elegant solution (ACCUSA) that considers both the read qualities as well as the reference genomes quality using a Bayesian framework. We demonstrate that ACCUSA is as good as the current SNP calling software in detecting true SNPs. More importantly, ACCUSA does not call spurious SNPs, which originate from a poor reference sequence.'),
(75382, 2010, 'bio', 'Studying the co-evolution of protein families with the Mirrortree web server', 'Ochoa, David|Pazos, Florencio', 'National Centre for Biotechnology, Computational Systems Biology Group (CNB-CSIC), c/ Darwin, 3. Cantoblanco, 28049 Madrid, Spain.|National Centre for Biotechnology, Computational Systems Biology Group (CNB-CSIC), c/ Darwin, 3. Cantoblanco, 28049 Madrid, Spain.', 'Spain|Spain', 40, 'Databases, Protein|Evolution, Molecular|Internet|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq137', '20363731', 'The Mirrortree server allows to graphically and interactively study the co-evolution of two protein families, and investigate their possible interactions and functional relationships in a taxonomic context. The server includes the possibility of starting from single sequences and hence it can be used by non-expert users.'),
(75383, 2010, 'bio', 'Streamlining the construction of large-scale dynamic models using generic kinetic equations', 'Adiamah, Delali A|Handl, Julia|Schwartz, Jean-Marc', 'Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK.|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK.|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Glycolysis|Kinetics|Metabolic Networks and Pathways|Proteome|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btq136', '20363732', 'Studying biological systems, not just at an individual component level but at a system-wide level, gives us great potential to understand fundamental functions and essential biological properties. Despite considerable advances in the topological analysis of metabolic networks, inadequate knowledge of the enzyme kinetic rate laws and their associated parameter values still hampers large-scale kinetic modelling. Furthermore, the integration of gene expression and protein levels into kinetic models is not straightforward.'),
(75384, 2010, 'bio', 'Modular analysis of gene expression data with R', 'Csárdi, Gábor|Kutalik, Zoltán|Bergmann, Sven', 'Department of Medical Genetics, University of Lausanne, Rue de Bugnon 27, CH-1005 Lausanne, Switzerland.|Department of Medical Genetics, University of Lausanne, Rue de Bugnon 27, CH-1005 Lausanne, Switzerland.|Department of Medical Genetics, University of Lausanne, Rue de Bugnon 27, CH-1005 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Databases, Genetic|Gene Expression|Gene Expression Profiling|Pattern Recognition, Automated|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq130', '20371495', 'Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called modules. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different resolutions of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data.'),
(75385, 2010, 'bio', 'A signal-noise model for significance analysis of ChIP-seq with negative control', 'Xu, Han|Handoko, Lusy|Wei, Xueliang|Ye, Chaopeng|Sheng, Jianpeng|Wei, Chia-Lin|Lin, Feng|Sung, Wing-Kin', 'Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.|Genome Institute of Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 103, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|Gene Expression Regulation|Genome|Histones|Models, Statistical|Poisson Distribution|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btq128', '20371496', 'ChIP-seq is becoming the main approach to the genome-wide study of protein-DNA interactions and histone modifications. Existing informatics tools perform well to extract strong ChIP-enriched sites. However, two questions remain to be answered: (i) to which extent is a ChIP-seq experiment able to reveal the weak ChIP-enriched sites? (ii) are the weak sites biologically meaningful? To answer these questions, it is necessary to identify the weak ChIP signals from background noise.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75386, 2010, 'bio', 'Payao: a community platform for SBML pathway model curation', 'Matsuoka, Yukiko|Ghosh, Samik|Kikuchi, Norihiro|Kitano, Hiroaki', 'The Systems Biology Institute, Tokyo, Japan.|The Systems Biology Institute, Tokyo, Japan.|The Systems Biology Institute, Tokyo, Japan.|The Systems Biology Institute, Tokyo, Japan.', 'Japan|Japan|Japan|Japan', 44, 'Computational Biology|Databases, Factual|Gene Regulatory Networks|Information Storage and Retrieval|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq143', '20371497', 'Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members.'),
(75387, 2010, 'bio', 'Alignment-free local structural search by writhe decomposition', 'Zhi, Degui|Shatsky, Maxim|Brenner, Steven E', 'Department of Plant and Microbial Biology, UC Berkeley and Physical Biosciences Division, LBNL, Berkeley, CA 94720, USA. dzhi@compbio.berkeley.edu|Department of Plant and Microbial Biology, UC Berkeley and Physical Biosciences Division, LBNL, Berkeley, CA 94720, USA. dzhi@compbio.berkeley.edu|Department of Plant and Microbial Biology, UC Berkeley and Physical Biosciences Division, LBNL, Berkeley, CA 94720, USA. dzhi@compbio.berkeley.edu', 'United States|United States|United States', 6, 'Algorithms|Computational Biology|Computer Simulation|Databases, Factual|Databases, Protein|Models, Statistical|Molecular Conformation|Probability|Protein Structure, Secondary|Proteins|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq127', '20371498', 'Rapid methods for protein structure search enable biological discoveries based on flexibly defined structural similarity, unleashing the power of the ever greater number of solved protein structures. Projection methods show promise for the development of fast structural database search solutions. Projection methods map a structure to a point in a high-dimensional space and compare two structures by measuring distance between their projected points. These methods offer a tremendous increase in speed over residue-level structural alignment methods. However, current projection methods are not practical, partly because they are unable to identify local similarities.'),
(75388, 2010, 'bio', 'partDSA: deletion/substitution/addition algorithm for partitioning the covariate space in prediction', 'Molinaro, Annette M|Lostritto, Karen|van der Laan, Mark', 'Division of Biostatistics, Yale University Schools of Public Health and Medicine, 60 College St., New Haven, CT 06519, USA. annette.molinaro@yale.edu|Division of Biostatistics, Yale University Schools of Public Health and Medicine, 60 College St., New Haven, CT 06519, USA. annette.molinaro@yale.edu|Division of Biostatistics, Yale University Schools of Public Health and Medicine, 60 College St., New Haven, CT 06519, USA. annette.molinaro@yale.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btq142', '20375111', 'Until now, much of the focus in cancer has been on biomarker discovery and generating lists of univariately significant genes, as well as epidemiological and clinical measures. These approaches, although significant on their own, are not effective for elucidating the synergistic qualities of the numerous components in complex diseases. These components do not act one at a time, but rather in concert with numerous others. A compelling need exists to develop analytically sound and computationally advanced methods that elucidate a more biologically meaningful understanding of the mechanisms of cancer initiation and progression by taking these interactions into account.'),
(75389, 2010, 'bio', 'Multilevel support vector regression analysis to identify condition-specific regulatory networks', 'Chen, Li|Xuan, Jianhua|Riggins, Rebecca B|Wang, Yue|Hoffman, Eric P|Clarke, Robert', 'Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Estrogens|Gene Expression Profiling|Gene Regulatory Networks|Humans|Regression Analysis|Saccharomyces cerevisiae|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btq144', '20375112', 'The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification.'),
(75390, 2010, 'bio', 'FastEpistasis: a high performance computing solution for quantitative trait epistasis', 'Schüpbach, Thierry|Xenarios, Ioannis|Bergmann, Sven|Kapur, Karen', 'Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, Molecular Modeling Group, Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 72, 'Computing Methodologies|Epistasis, Genetic|Genome-Wide Association Study|Genomics|Humans|Phenotype|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btq147', '20375113', 'Genome-wide association studies have become widely used tools to study effects of genetic variants on complex diseases. While it is of great interest to extend existing analysis methods by considering interaction effects between pairs of loci, the large number of possible tests presents a significant computational challenge. The number of computations is further multiplied in the study of gene expression quantitative trait mapping, in which tests are performed for thousands of gene phenotypes simultaneously.'),
(75391, 2010, 'bio', 'Small RNAs in angiosperms: sequence characteristics, distribution and generation', 'Chen, Dijun|Meng, Yijun|Ma, Xiaoxia|Mao, Chuanzao|Bai, Youhuang|Cao, Junjie|Gu, Haibin|Wu, Ping|Chen, Ming', 'Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.|Department of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Sciences and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310058, P. R. China.', 'China|China|China|China|China|China|China|China|China', 0, 'Arabidopsis|Base Sequence|Chromosomes, Plant|Computational Biology|Genes, Plant|Genome, Plant|Magnoliopsida|MicroRNAs|Oryza|RNA, Antisense|RNA, Plant|RNA, Small Interfering|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btq150', '20378553', 'High-throughput sequencing (HTS) has opened up a new era for small RNA (sRNA) exploration. Using HTS data for a global survey of sRNAs in 26 angiosperms, elevated GC contents were detected in the monocots, whereas the 5()-terminal compositions were quite uniform among the angiosperms. Chromosome-wide distribution patterns of sRNAs were investigated by using scrolling-window analysis. We performed de novo natural antisense transcript (NAT) prediction, and found that the overlapping regions of trans-NATs, but not cis-NATs, were hotspots for sRNA generation. One cis-NAT generates phased natural antisense short interfering RNAs (nat-siRNAs) specifically from flowers in Arabidopsis, while one in rice produces phased nat-siRNAs from grains, suggesting their organ-specific regulatory roles.'),
(75392, 2010, 'bio', 'Structural variation analysis with strobe reads', 'Ritz, Anna|Bashir, Ali|Raphael, Benjamin J', 'Department of Computer Science, Brown University, Providence, RI 02912, USA.|Department of Computer Science, Brown University, Providence, RI 02912, USA.|Department of Computer Science, Brown University, Providence, RI 02912, USA.', 'United States|United States|United States', 26, 'Base Sequence|DNA|Databases, Genetic|Genetic Variation|Genome, Human|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq153', '20378554', 'Structural variation including deletions, duplications and rearrangements of DNA sequence are an important contributor to genome variation in many organisms. In human, many structural variants are found in complex and highly repetitive regions of the genome making their identification difficult. A new sequencing technology called strobe sequencing generates strobe reads containing multiple subreads from a single contiguous fragment of DNA. Strobe reads thus generalize the concept of paired reads, or mate pairs, that have been routinely used for structural variant detection. Strobe sequencing holds promise for unraveling complex variants that have been difficult to characterize with current sequencing technologies.'),
(75393, 2010, 'bio', 'Correction of sequencing errors in a mixed set of reads', 'Salmela, Leena', 'Department of Computer Science, PO Box 68 (Gustaf Hällströmin katu 2b), FI-00014 University of Helsinki, Finland. leena.salmela@cs.helsinki.fi', 'Finland', 74, 'Algorithms|Base Sequence|Computational Biology|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq151', '20378555', 'High-throughput sequencing technologies produce large sets of short reads that may contain errors. These sequencing errors make de novo assembly challenging. Error correction aims to reduce the error rate prior assembly. Many de novo sequencing projects use reads from several sequencing technologies to get the benefits of all used technologies and to alleviate their shortcomings. However, combining such a mixed set of reads is problematic as many tools are specific to one sequencing platform. The SOLiD sequencing platform is especially problematic in this regard because of the two base color coding of the reads. Therefore, new tools for working with mixed read sets are needed.'),
(75394, 2010, 'bio', 'VSDocker: a tool for parallel high-throughput virtual screening using AutoDock on Windows-based computer clusters', 'Prakhov, Nikita D|Chernorudskiy, Alexander L|Gainullin, Murat R', 'Faculty of Biology, Nizhny Novgorod State University, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia. nikita.prakhov@gmail.com|Faculty of Biology, Nizhny Novgorod State University, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia. nikita.prakhov@gmail.com|Faculty of Biology, Nizhny Novgorod State University, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia. nikita.prakhov@gmail.com', '-|-|-', 29, 'Algorithms|Computational Biology|Computer Graphics|Ligands|Software', 'Journal Article', '10.1093/bioinformatics/btq149', '20378556', 'VSDocker is an original program that allows using AutoDock4 for optimized virtual ligand screening on computer clusters or multiprocessor workstations. This tool is the first implementation of parallel high-performance virtual screening of ligands for MS Windows-based computer systems.'),
(75395, 2010, 'bio', 'A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval', 'Swamidass, S Joshua|Azencott, Chloé-Agathe|Daily, Kenny|Baldi, Pierre', 'Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA.|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA.|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA.|Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63110, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Area Under Curve|Computational Biology|Drug Discovery|Models, Statistical|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btq140', '20378557', 'The performance of classifiers is often assessed using Receiver Operating Characteristic ROC [or (AC) accumulation curve or enrichment curve] curves and the corresponding areas under the curves (AUCs). However, in many fundamental problems ranging from information retrieval to drug discovery, only the very top of the ranked list of predictions is of any interest and ROCs and AUCs are not very useful. New metrics, visualizations and optimization tools are needed to address this early retrieval problem.'),
(75396, 2010, 'bio', 'BoolNet--an R package for generation, reconstruction and analysis of Boolean networks', 'Müssel, Christoph|Hopfensitz, Martin|Kestler, Hans A', 'Institute of Neural Information Processing, University of Ulm and Department of Internal Medicine I, University Hospital Ulm, 89081 Ulm, Germany.|Institute of Neural Information Processing, University of Ulm and Department of Internal Medicine I, University Hospital Ulm, 89081 Ulm, Germany.|Institute of Neural Information Processing, University of Ulm and Department of Internal Medicine I, University Hospital Ulm, 89081 Ulm, Germany.', 'Germany|Germany|Germany', 200, 'Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btq124', '20378558', 'As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines.'),
(75397, 2010, 'bio', 'Detection and characterization of novel sequence insertions using paired-end next-generation sequencing', 'Hajirasouliha, Iman|Hormozdiari, Fereydoun|Alkan, Can|Kidd, Jeffrey M|Birol, Inanc|Eichler, Evan E|Sahinalp, S Cenk', 'Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Databases, Genetic|Genetic Variation|Genome, Human|Genomics|Humans|Models, Genetic|Mutagenesis, Insertional|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq152', '20385726', 'In the past few years, human genome structural variation discovery has enjoyed increased attention from the genomics research community. Many studies were published to characterize short insertions, deletions, duplications and inversions, and associate copy number variants (CNVs) with disease. Detection of new sequence insertions requires sequence data, however, the detectable sequence length with read-pair analysis is limited by the insert size. Thus, longer sequence insertions that contribute to our genetic makeup are not extensively researched.'),
(75398, 2010, 'bio', 'Permutation importance: a corrected feature importance measure', 'Altmann, Andrxc3xa9|Toloxc5x9fi, Laura|Sander, Oliver|Lengauer, Thomas', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrxc3xbccken, Germany. altmann@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrxc3xbccken, Germany. altmann@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrxc3xbccken, Germany. altmann@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrxc3xbccken, Germany. altmann@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany', 209, 'Algorithms|Artificial Intelligence|Computational Biology|Linear Models|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btq134', '20385727', 'In life sciences, interpretability of machine learning models is as important as their prediction accuracy. Linear models are probably the most frequently used methods for assessing feature relevance, despite their relative inflexibility. However, in the past years effective estimators of feature relevance have been derived for highly complex or non-parametric models such as support vector machines and RandomForest (RF) models. Recently, it has been observed that RF models are biased in such a way that categorical variables with a large number of categories are preferred.'),
(75399, 2010, 'bio', 'Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation', 'Liebermeister, Wolfram|Uhlendorf, Jannis|Klipp, Edda', 'Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany. wolfram.liebermeister@biologie.hu-berlin.de|Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany. wolfram.liebermeister@biologie.hu-berlin.de|Institut für Biologie, Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany. wolfram.liebermeister@biologie.hu-berlin.de', 'Germany|Germany|Germany', 61, 'Algorithms|Enzymes|Kinetics|Metabolic Networks and Pathways|Systems Biology|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btq141', '20385728', 'Standard rate laws are a key requisite for systematically turning metabolic networks into kinetic models. They should provide simple, general and biochemically plausible formulae for reaction velocities and reaction elasticities. At the same time, they need to respect thermodynamic relations between the kinetic constants and the metabolic fluxes and concentrations.'),
(75400, 2010, 'bio', 'Multi-objective tag SNPs selection using evolutionary algorithms', 'Ting, Chuan-Kang|Lin, Wei-Ting|Huang, Yao-Ting', 'Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan. ckting@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan. ckting@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan. ckting@cs.ccu.edu.tw', '-|-|-', 16, 'Algorithms|Computational Biology|Databases, Genetic|Genotype|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq158', '20385729', 'Integrated analysis of single nucleotide polymorphisms (SNPs) and structure variations showed that the extent of linkage disequilibrium is common across different types of genetic variants. A subset of SNPs (called tag SNPs) is sufficient for capturing alleles of bi-allelic and even multi-allelic variants. However, accuracy and power of tag SNPs are affected by several factors, including genotyping failure, errors and tagging bias of certain alleles. In addition, different sets of tag SNPs should be selected for fulfilling requirements of various genotyping platforms and projects.'),
(75401, 2010, 'bio', 'Quality assessment of protein model-structures using evolutionary conservation', 'Kalman, Matan|Ben-Tal, Nir', 'Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.|Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.', 'Israel|Israel', 0, 'Biological Evolution|Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq114', '20385730', 'Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface.'),
(75402, 2010, 'bio', 'A computational analysis of the antigenic properties of haemagglutinin in influenza A H3N2', 'Lees, William D|Moss, David S|Shepherd, Adrian J', 'Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. wlees01@mail.cryst.bbk.ac.uk|Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. wlees01@mail.cryst.bbk.ac.uk|Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. wlees01@mail.cryst.bbk.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 28, 'Amino Acid Sequence|Antigenic Variation|Binding Sites, Antibody|Computational Biology|Hemagglutinin Glycoproteins, Influenza Virus|Humans|Influenza A Virus, H3N2 Subtype|Influenza, Human|Models, Molecular|Protein Conformation', 'Journal Article', '10.1093/bioinformatics/btq160', '20388627', 'Modelling antigenic shift in influenza A H3N2 can help to predict the efficiency of vaccines. The virus is known to exhibit sudden jumps in antigenic distance, and prediction of such novel strains from amino acid sequence differences remains a challenge.'),
(75403, 2010, 'bio', 'Picante: R tools for integrating phylogenies and ecology', 'Kembel, Steven W|Cowan, Peter D|Helmus, Matthew R|Cornwell, William K|Morlon, Helene|Ackerly, David D|Blomberg, Simon P|Webb, Campbell O', 'Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.|Center for Ecology and Evolutionary Biology, University of Oregon, Eugene, OR, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biodiversity|Ecology|Phenotype|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btq166', '20395285', 'Picante is a software package that provides a comprehensive set of tools for analyzing the phylogenetic and trait diversity of ecological communities. The package calculates phylogenetic diversity metrics, performs trait comparative analyses, manipulates phenotypic and phylogenetic data, and performs tests for phylogenetic signal in trait distributions, community structure and species interactions.'),
(75404, 2010, 'bio', 'A hidden Markov support vector machine framework incorporating profile geometry learning for identifying microbial RNA in tiling array data', 'Yu, Wen-Han|Høvik, Hedda|Chen, Tsute', 'Department of Molecular Genetics, The Forsyth Institute, Boston, MA 02115, USA.|Department of Molecular Genetics, The Forsyth Institute, Boston, MA 02115, USA.|Department of Molecular Genetics, The Forsyth Institute, Boston, MA 02115, USA.', 'United States|United States|United States', 8, 'Artificial Intelligence|Gene Expression Profiling|Genome, Bacterial|Markov Chains|Oligonucleotide Array Sequence Analysis|RNA, Bacterial', 'Journal Article', '10.1093/bioinformatics/btq162', '20395286', 'RNA expression signals detected by high-density genomic tiling microarrays contain comprehensive transcriptomic information of the target organism. Current methods for determining the RNA transcription units are still computation intense and lack the discriminative power. This article describes an efficient and accurate methodology to reveal complicated transcriptional architecture, including small regulatory RNAs, in microbial transcriptome profiles.'),
(75405, 2010, 'bio', 'HIV classification using the coalescent theory', 'Bulla, Ingo|Schultz, Anne-Kathrin|Schreiber, Fabian|Zhang, Ming|Leitner, Thomas|Korber, Bette|Morgenstern, Burkhard|Stanke, Mario', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ibulla@uni-goettingen.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 8, 'Algorithms|HIV|HIV-1|Markov Chains|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq159', '20400454', 'Existing coalescent models and phylogenetic tools based on them are not designed for studying the genealogy of sequences like those of HIV, since in HIV recombinants with multiple cross-over points between the parental strains frequently arise. Hence, ambiguous cases in the classification of HIV sequences into subtypes and circulating recombinant forms (CRFs) have been treated with ad hoc methods in lack of tools based on a comprehensive coalescent model accounting for complex recombination patterns.'),
(75406, 2010, 'bio', 'ParaSAM: a parallelized version of the significance analysis of microarrays algorithm', 'Sharma, Ashok|Zhao, Jieping|Podolsky, Robert|McIndoe, Richard A', 'Center for Biotechnology and Genomic Medicine, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA.|Center for Biotechnology and Genomic Medicine, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA.|Center for Biotechnology and Genomic Medicine, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA.|Center for Biotechnology and Genomic Medicine, School of Medicine, Medical College of Georgia, Augusta, GA 30912, USA.', 'Georgia|Georgia|Georgia|Georgia', 1, 'Algorithms|Databases, Factual|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btq161', '20400455', 'Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements.'),
(75407, 2010, 'bio', 'EuGène-maize: a web site for maize gene prediction', 'Montalent, Pierre|Joets, Johann', 'INRA, UMR 0320 / UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.|INRA, UMR 0320 / UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.', 'France|France', 1, 'Algorithms|DNA, Complementary|Gene Expression Profiling|Genes, Plant|Genotype|Markov Chains|Models, Genetic|Probability|Programming Languages|Sequence Alignment|Software|Zea mays', 'Journal Article', '10.1093/bioinformatics/btq123', '20400755', 'A large part of the maize B73 genome sequence is now available and emerging sequencing technologies will offer cheap and easy ways to sequence areas of interest from many other maize genotypes. One of the steps required to turn these sequences into valuable information is gene content prediction. To date, there is no publicly available gene predictor specifically trained for maize sequences. To this end, we have chosen to train the EuGène software that can combine several sources of evidence into a consolidated gene model prediction.'),
(75408, 2010, 'bio', 'Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences', 'Ling, Zhi-Qiang|Wang, Yi|Mukaisho, Kenichi|Hattori, Takanori|Tatsuta, Takeshi|Ge, Ming-Hua|Jin, Li|Mao, Wei-Min|Sugihara, Hiroyuki', 'Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.|Zhejiang Cancer Research Institute, Zhejiang Province Cancer Hospital, Banshanqiao Guangji Road 38, Hangzhou 310022, China.', 'China|China|China|China|China|China|China|China|China', 10, 'Biomarkers, Tumor|Cell Line, Tumor|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction', 'Journal Article', '10.1093/bioinformatics/btq163', '20400756', 'Tests of differentially expressed genes (DEGs) from microarray experiments are based on the null hypothesis that genes that are irrelevant to the phenotype/stimulus are expressed equally in the target and control samples. However, this strict hypothesis is not always true, as there can be several transcriptomic background differences between target and control samples, including different cell/tissue types, different cell cycle stages and different biological donors. These differences lead to increased false positives, which have little biological/medical significance.'),
(75409, 2010, 'bio', 'CNAnova: a new approach for finding recurrent copy number abnormalities in cancer SNP microarray data', 'Ivakhno, Sergii|Tavaré, Simon', 'Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. sergii.ivakhno@cancer.org.uk|Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. sergii.ivakhno@cancer.org.uk', 'United Kingdom|United Kingdom', 6, 'Algorithms|Databases, Genetic|Gene Dosage|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btq145', '20403815', 'The current generation of single nucleotide polymorphism (SNP) arrays allows measurement of copy number aberrations (CNAs) in cancer at more than one million locations in the genome in hundreds of tumour samples. Most research has focused on single-sample CNA discovery, the so-called segmentation problem. The availability of high-density, large sample-size SNP array datasets makes the identification of recurrent copy number changes in cancer, an important issue that can be addressed using the cross-sample information.'),
(75410, 2010, 'bio', 'Inferring combined CNV/SNP haplotypes from genotype data', 'Su, Shu-Yi|Asher, Julian E|Jarvelin, Marjo-Riita|Froguel, Phillipe|Blakemore, Alexandra I F|Balding, David J|Coin, Lachlan J M', 'Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London W2 1PG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 27, 'Alleles|Chromosomes, Human, Pair 2|DNA Copy Number Variations|Genome, Human|Genotype|Haplotypes|Humans|Male|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq157', '20406911', 'Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate.'),
(75411, 2010, 'bio', 'Supersplat--spliced RNA-seq alignment', 'Bryant, Douglas W|Shen, Rongkun|Priest, Henry D|Wong, Weng-Keen|Mockler, Todd C', 'Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.|Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.|Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.|Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.|Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97331, USA.', 'United States|United States|United States|United States|United States', 30, 'Base Sequence|Genomics|RNA Splicing|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btq206', '20410051', 'High-throughput sequencing technologies have recently made deep interrogation of expressed transcript sequences practical, both economically and temporally. Identification of intron/exon boundaries is an essential part of genome annotation, yet remains a challenge. Here, we present supersplat, a method for unbiased splice-junction discovery through empirical RNA-seq data.'),
(75412, 2010, 'bio', 'Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets', 'Park, Inho|Lee, Kwang H|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea.', '-|-|-', 10, 'Algorithms|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genes, Neoplasm|Humans|Male|Neoplasms|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms', 'Journal Article', '10.1093/bioinformatics/btq207', '20410052', 'Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently.'),
(75413, 2010, 'bio', 'Meta-analysis for pathway enrichment analysis when combining multiple genomic studies', 'Shen, Kui|Tseng, George C', 'Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.|Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.', 'United States|United States', 0, 'Cell Line, Tumor|Databases, Genetic|Gene Expression Profiling|Genome|Genomics|Humans|Lung Neoplasms|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq148', '20410053', 'Many pathway analysis (or gene set enrichment analysis) methods have been developed to identify enriched pathways under different biological states within a genomic study. As more and more microarray datasets accumulate, meta-analysis methods have also been developed to integrate information among multiple studies. Currently, most meta-analysis methods for combining genomic studies focus on biomarker detection and meta-analysis for pathway analysis has not been systematically pursued.'),
(75414, 2010, 'bio', 'NetRaVE: constructing dependency networks using sparse linear regression', 'Phatak, A|Kiiveri, H|Clemmensen, L H|Wilson, W J', 'CSIRO Mathematical & Information Sciences, Private Bag 5, Wembley, WA, Australia.|CSIRO Mathematical & Information Sciences, Private Bag 5, Wembley, WA, Australia.|CSIRO Mathematical & Information Sciences, Private Bag 5, Wembley, WA, Australia.|CSIRO Mathematical & Information Sciences, Private Bag 5, Wembley, WA, Australia.', 'Australia|Australia|Australia|Australia', 2, 'Algorithms|Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq168', '20410054', 'NetRaVE is a small suite of R functions for generating dependency networks using sparse regression methods. Such networks provide an alternative to interpreting top n lists of genes arising out of an analysis of microarray data, and they provide a means of organizing and visualizing the resulting information in a manner that may suggest relationships between genes.'),
(75415, 2010, 'bio', 'Ergatis: a web interface and scalable software system for bioinformatics workflows', 'Orvis, Joshua|Crabtree, Jonathan|Galens, Kevin|Gussman, Aaron|Inman, Jason M|Lee, Eduardo|Nampally, Sreenath|Riley, David|Sundaram, Jaideep P|Felix, Victor|Whitty, Brett|Mahurkar, Anup|Wortman, Jennifer|White, Owen|Angiuoli, Samuel V', 'Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA. jorvis@users.sourceforge.net', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 73, 'Computational Biology|Databases, Genetic|Databases, Protein|Internet|Software|Workflow', 'Journal Article', '10.1093/bioinformatics/btq167', '20413634', 'The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users.'),
(75416, 2010, 'bio', 'HangOut: generating clean PSI-BLAST profiles for domains with long insertions', 'Kim, Bong-Hyun|Cong, Qian|Grishin, Nick V', 'Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA. kim@chop.swmed.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA. kim@chop.swmed.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA. kim@chop.swmed.edu', 'United States|United States|United States', 6, 'Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btq208', '20413635', 'Profile-based similarity search is an essential step in structure-function studies of proteins. However, inclusion of non-homologous sequence segments into a profile causes its corruption and results in false positives. Profile corruption is common in multidomain proteins, and single domains with long insertions are a significant source of errors. We developed a procedure (HangOut) that, for a single domain with specified insertion position, cleans erroneously extended PSI-BLAST alignments to generate better profiles.'),
(75417, 2010, 'bio', 'PepC: proteomics software for identifying differentially expressed proteins based on spectral counting', 'Heinecke, N L|Pratt, B S|Vaisar, T|Becker, L', 'Insilicos LLC, Seattle, WA 98109, USA.|Insilicos LLC, Seattle, WA 98109, USA.|Insilicos LLC, Seattle, WA 98109, USA.|Insilicos LLC, Seattle, WA 98109, USA.', 'United States|United States|United States|United States', 0, 'Databases, Protein|Mass Spectrometry|Proteins|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq171', '20413636', 'Identifying biologically significant changes in protein abundance between two conditions is a key issue when analyzing proteomic data. One widely used approach centers on spectral counting, a label-free method that sums all the tandem mass spectra for a protein observed in an analysis. To assess the significance of the results, we recently combined the t-test and G-test, with random permutation analysis, and we validated this approach biochemically. To automate the statistical method, we developed PepC, a software program that balances the trade-off between the number of differentially expressed proteins identified and the false discovery rate. This tool can be applied to a wide range of proteomic datasets, making data analysis rapid, reproducible and easily interpretable by proteomics specialists and non-specialists alike.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75418, 2010, 'bio', 'FABIA: factor analysis for bicluster acquisition', 'Hochreiter, Sepp|Bodenhofer, Ulrich|Heusel, Martin|Mayr, Andreas|Mitterecker, Andreas|Kasim, Adetayo|Khamiakova, Tatsiana|Van Sanden, Suzy|Lin, Dan|Talloen, Willem|Bijnens, Luc|Göhlmann, Hinrich W H|Shkedy, Ziv|Clevert, Djork-Arné', 'Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler University, Linz, Austria. hochreit@bioinf.jku.at', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Aust', 0, 'Algorithms|Factor Analysis, Statistical|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btq227', '20418340', 'Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called FABIA: Factor Analysis for Bicluster Acquisition. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques.'),
(75419, 2010, 'bio', 'CGHnormaliter: a Bioconductor package for normalization of array CGH data with many CNAs', 'van Houte, Bart P P|Binsl, Thomas W|Hettling, Hannes|Heringa, Jaap', 'Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 4, 'Comparative Genomic Hybridization|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btq155', '20418341', 'CGHnormaliter is a package for normalization of array comparative genomic hybridization (aCGH) data. It uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). CGHnormaliter is integrated in the Bioconductor environment allowing a smooth link to visualization tools and further data analysis.'),
(75420, 2010, 'bio', 'A CoD-based reduction algorithm for designing stationary control policies on Boolean networks', 'Ghaffari, Noushin|Ivanov, Ivan|Qian, Xiaoning|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. nghaffari@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. nghaffari@tamu.edu', 'United States|United States|United States|United States', 23, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Probability', 'Journal Article', '10.1093/bioinformatics/btq225', '20421196', 'Gene regulatory networks serve as models from which to derive therapeutic intervention strategies, in particular, stationary control policies over time that shift the probability mass of the steady state distribution (SSD) away from states associated with undesirable phenotypes. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, network reduction is a fundamental issue for intervention.'),
(75421, 2010, 'bio', 'Finding metabolic pathways using atom tracking', 'Heath, Allison P|Bennett, George N|Kavraki, Lydia E', 'Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btq223', '20421197', 'Finding novel or non-standard metabolic pathways, possibly spanning multiple species, has important applications in fields such as metabolic engineering, metabolic network analysis and metabolic network reconstruction. Traditionally, this has been a manual process, but the large volume of metabolic data now available has created a need for computational tools to automatically identify biologically relevant pathways.'),
(75422, 2010, 'bio', 'DendroPy: a Python library for phylogenetic computing', 'Sukumaran, Jeet|Holder, Mark T', 'Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, USA. jeet@ku.edu|Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, USA. jeet@ku.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|Databases, Factual|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btq228', '20421198', 'DendroPy is a cross-platform library for the Python programming language that provides for object-oriented reading, writing, simulation and manipulation of phylogenetic data, with an emphasis on phylogenetic tree operations. DendroPy uses a splits-hash mapping to perform rapid calculations of tree distances, similarities and shape under various metrics. It contains rich simulation routines to generate trees under a number of different phylogenetic and coalescent models. DendroPys data simulation and manipulation facilities, in conjunction with its support of a broad range of phylogenetic data formats (NEXUS, Newick, PHYLIP, FASTA, NeXML, etc.), allow it to serve a useful role in various phyloinformatics and phylogeographic pipelines.'),
(75423, 2010, 'bio', 'Parallelization of the MAFFT multiple sequence alignment program', 'Katoh, Kazutaka|Toh, Hiroyuki', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan. kazutaka.katoh@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan. kazutaka.katoh@aist.go.jp', 'Japan|Japan', 460, 'Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq224', '20427515', 'Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement, of the MAFFT MSA program were parallelized using the POSIX Threads library. Two natural parallelization strategies (best-first and simple hill-climbing) were implemented for the iterative refinement stage. Based on comparisons of the objective scores and benchmark scores between the two approaches, we selected a simple hill-climbing approach as the default.'),
(75424, 2010, 'bio', 'Modeling RNA loops using sequence homology and geometric constraints', 'Schudoma, Christian|May, Patrick|Walther, Dirk', 'Bioinformatics Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. schudoma@mpimp-golm.mpg.de|Bioinformatics Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. schudoma@mpimp-golm.mpg.de|Bioinformatics Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. schudoma@mpimp-golm.mpg.de', 'Germany|Germany|Germany', 0, 'Models, Chemical|Nucleic Acid Conformation|RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btq236', '20427516', 'RNA loop regions are essential structural elements of RNA molecules influencing both their structural and functional properties. We developed RLooM, a web application for homology-based modeling of RNA loops utilizing template structures extracted from the PDB. RLooM allows the insertion and replacement of loop structures of a desired sequence into an existing RNA structure. Furthermore, a comprehensive database of loops in RNA structures can be accessed through the web interface.'),
(75425, 2010, 'bio', 'Semantic integration of data on transcriptional regulation', 'Baitaluk, Michael|Ponomarenko, Julia', 'San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. baitaluk@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. baitaluk@sdsc.edu', 'United States|United States', 12, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btq231', '20427517', 'Experimental and predicted data concerning gene transcriptional regulation are distributed among many heterogeneous sources. However, there are no resources to integrate these data automatically or to provide a one-stop shop experience for users seeking information essential for deciphering and modeling gene regulatory networks.'),
(75426, 2010, 'bio', 'ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking', 'Wilkerson, Matthew D|Hayes, D Neil', 'Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. mwilkers@med.unc.edu|Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. mwilkers@med.unc.edu', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Data Mining|Gene Expression Profiling|Principal Component Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btq170', '20427518', 'Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery.'),
(75427, 2010, 'bio', 'TAPIR, a web server for the prediction of plant microRNA targets, including target mimics', 'Bonnet, Eric|He, Ying|Billiau, Kenny|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, B-9052 Gent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 70, 'Biomimetics|Internet|MicroRNAs|RNA, Plant|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btq233', '20430753', 'We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools.'),
(75428, 2010, 'bio', 'The Genomedata format for storing large-scale functional genomics data', 'Hoffman, Michael M|Buske, Orion J|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.|Department of Genome Sciences, University of Washington, PO Box 355065, Seattle, WA 98195-5065, USA.', 'United States|United States|United States', 11, 'Databases, Genetic|Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btq164', '20435580', 'We present a format for efficient storage of multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. We have also developed utilities to load data into this format. We show that retrieving data from this format is more than 2900 times faster than a naive approach using wiggle files.'),
(75429, 2010, 'bio', 'Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases', 'Wang, Dong|Wang, Juan|Lu, Ming|Song, Fei|Cui, Qinghua', 'Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, 38 Xueyuan Road, Beijing 100191, China.', 'China|China|China|China|China', 320, 'Databases, Nucleic Acid|Disease|Genetic Markers|Humans|MicroRNAs', 'Journal Article', '10.1093/bioinformatics/btq241', '20439255', 'It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level.'),
(75430, 2010, 'bio', 'Deuteration distribution estimation with improved sequence coverage for HX/MS experiments', 'Lou, Xinghua|Kirchner, Marc|Renard, Bernhard Y|Köthe, Ullrich|Boppel, Sebastian|Graf, Christian|Lee, Chung-Tien|Steen, Judith A J|Steen, Hanno|Mayer, Matthias P|Hamprecht, Fred A', 'Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 31, 'Algorithms|Deuterium|Deuterium Exchange Measurement|Mass Spectrometry|Proteins|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq165', '20439256', 'Time-resolved hydrogen exchange (HX) followed by mass spectrometry (MS) is a key technology for studying protein structure, dynamics and interactions. HX experiments deliver a time-dependent distribution of deuteration levels of peptide sequences of the protein of interest. The robust and complete estimation of this distribution for as many peptide fragments as possible is instrumental to understanding dynamic protein-level HX behavior. Currently, this data interpretation step still is a bottleneck in the overall HX/MS workflow.'),
(75431, 2010, 'bio', 'A low-polynomial algorithm for assembling clusters of orthologous groups from intergenomic symmetric best matches', 'Kristensen, David M|Kannan, Lavanya|Coleman, Michael K|Wolf, Yuri I|Sorokin, Alexander|Koonin, Eugene V|Mushegian, Arcady', 'Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org|Department of Binformatics, Stowers Institute for Medical Research, Kansas City, MO 64110, USA. dmk@stowers.org', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Genome|Genomics', 'Journal Article', '10.1093/bioinformatics/btq229', '20439257', 'Identifying orthologous genes in multiple genomes is a fundamental task in comparative genomics. Construction of intergenomic symmetrical best matches (SymBets) and joining them into clusters is a popular method of ortholog definition, embodied in several software programs. Despite their wide use, the computational complexity of these programs has not been thoroughly examined.'),
(75432, 2010, 'bio', 'Learning combinatorial transcriptional dynamics from gene expression data', 'Opper, Manfred|Sanguinetti, Guido', 'Department of Computer Science, Technische Universität Berlin D-10587 Berlin, Germany.|Department of Computer Science, Technische Universität Berlin D-10587 Berlin, Germany.', 'Germany|Germany', 24, 'Fungal Proteins|Gene Expression|Gene Expression Regulation|Kinetics|Models, Biological|Promoter Regions, Genetic|RNA, Messenger|Ribosomal Proteins|Transcription Factors|Transcription, Genetic|Yeasts', 'Journal Article', '10.1093/bioinformatics/btq244', '20444835', 'mRNA transcriptional dynamics is governed by a complex network of transcription factor (TF) proteins. Experimental and theoretical analysis of this process is hindered by the fact that measurements of TF activity in vivo is very challenging. Current models that jointly infer TF activities and model parameters rely on either of the two main simplifying assumptions: either the dynamics is simplified (e.g. assuming quasi-steady state) or the interactions between TFs are ignored, resulting in models accounting for a single TF.'),
(75433, 2010, 'bio', 'CHOYCE: a web server for constrained homology modelling with cryoEM maps', 'Rawi, Reda|Whitmore, Lee|Topf, Maya', 'Institute of Structural and Molecular Biology, Crystallography, Department of Biological Sciences, Birkbeck College, University of London, London, UK. reda.rawi@uni-due.de|Institute of Structural and Molecular Biology, Crystallography, Department of Biological Sciences, Birkbeck College, University of London, London, UK. reda.rawi@uni-due.de|Institute of Structural and Molecular Biology, Crystallography, Department of Biological Sciences, Birkbeck College, University of London, London, UK. reda.rawi@uni-due.de', 'United Kingdom|United Kingdom|United Kingdom', 6, 'Animals|Cattle|Computational Biology|Cryoelectron Microscopy|Electron Transport Complex IV|Models, Molecular|Proteins|Receptors for Activated C Kinase|Receptors, Cell Surface|Saccharomyces cerevisiae|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btq237', '20444836', 'CHOYCE is a web server for homology modelling of protein components and the fitting of those components into cryo electron microscopy (cryoEM) maps of their assemblies. It provides an interactive approach to improving the selection of models based on the quality of their fit into the EM map.'),
(75434, 2010, 'bio', 'SensSB: a software toolbox for the development and sensitivity analysis of systems biology models', 'Rodriguez-Fernandez, Maria|Banga, Julio R', '(Bio)Process Engineering Group, IIM-CSIC C/Eduardo Cabello 6, 36208 Vigo, Spain. mrodriguez@iim.csic.es|(Bio)Process Engineering Group, IIM-CSIC C/Eduardo Cabello 6, 36208 Vigo, Spain. mrodriguez@iim.csic.es', 'Spain|Spain', 0, 'Internet|Models, Biological|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq242', '20444837', 'SensSB (Sensitivity Analysis for Systems Biology) is an easy to use, MATLAB-based software toolbox, which integrates several local and global sensitivity methods that can be applied to a wide variety of biological models. In addition to addressing the sensitivity analysis problem, SensSB aims to cover all the steps involved during the modeling process. The main features of SensSB are: (i) derivative and variance-based global sensitivity analysis, (ii) pseudo-global identifiability analysis, (iii) optimal experimental design (OED) based on global sensitivities, (iv) robust parameter estimation, (v) local sensitivity and identifiability analysis, (vi) confidence intervals of the estimated parameters and (vii) OED based on the Fisher Information Matrix (FIM). SensSB is also able to import models in the Systems Biology Mark-up Language (SBML) format. Several examples from simple analytical functions to more complex biological pathways have been implemented and can be downloaded together with the toolbox. The importance of using sensitivity analysis techniques for identifying unessential parameters and designing new experiments is quantified by increased identifiability metrics of the models and decreased confidence intervals of the estimated parameters.'),
(75435, 2010, 'bio', 'LCE: a link-based cluster ensemble method for improved gene expression data analysis', 'Iam-on, Natthakan|Boongoen, Tossapon|Garrett, Simon', 'Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, UK. nii07@aber.ac.uk|Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, UK. nii07@aber.ac.uk|Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, UK. nii07@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 78, 'Algorithms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btq226', '20444838', 'It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically combining multiple data partitions from different clusterings to improve both the robustness and quality of the clustering result. However, many existing ensemble techniques use an association matrix to summarize sample-cluster co-occurrence statistics, and relations within an ensemble are encapsulated only at coarse level, while those existing among clusters are completely neglected. Discovering these missing associations may greatly extend the capability of the ensemble methodology for microarray data clustering.'),
(75436, 2010, 'bio', 'Polynomial algebra of discrete models in systems biology', 'Veliz-Cuba, Alan|Jarrah, Abdul Salam|Laubenbacher, Reinhard', 'Virginia Bioinformatics Institute, Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. alanavc@vt.edu|Virginia Bioinformatics Institute, Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. alanavc@vt.edu|Virginia Bioinformatics Institute, Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA. alanavc@vt.edu', 'United States|United States|United States', 0, 'Algorithms|Models, Biological|Models, Statistical|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq240', '20448137', 'An increasing number of discrete mathematical models are being published in Systems Biology, ranging from Boolean network models to logical models and Petri nets. They are used to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation.'),
(75437, 2010, 'bio', 'Computing biological functions using BioPsi, a formal description of biological processes based on elementary bricks of actions', 'Pérès, Sabine|Felicori, Liza|Rialle, Stéphanie|Jobard, Elodie|Molina, Franck', 'Sysdiag CNRS Bio-Rad UMR 3145, Cap delta/Parc euromédecine, 1682 rue de la Valsière CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS Bio-Rad UMR 3145, Cap delta/Parc euromédecine, 1682 rue de la Valsière CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS Bio-Rad UMR 3145, Cap delta/Parc euromédecine, 1682 rue de la Valsière CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS Bio-Rad UMR 3145, Cap delta/Parc euromédecine, 1682 rue de la Valsière CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS Bio-Rad UMR 3145, Cap delta/Parc euromédecine, 1682 rue de la Valsière CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr', 'France|France|France|France|France', 0, 'Biological Phenomena|Computational Biology|Glycolysis|Peptide Hydrolases|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btq169', '20448138', 'In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes.'),
(75438, 2010, 'bio', 'UMA and MABP domains throw light on receptor endocytosis and selection of endosomal cargoes', 'de Souza, Robson F|Aravind, L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States', 16, 'Amino Acid Sequence|Carrier Proteins|Endocytosis|Endosomal Sorting Complexes Required for Transport|Endosomes|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btq235', '20448139', 'Interactions of the ESCRT complexes are critical for endosomal trafficking. We identify two domains with potential significance for this process. The MABP domain present in metazoan ESCRT-I/MVB12 subunits, Crag, a regulator of protein sorting, and bacterial pore-forming proteins might mediate novel membrane interactions in trafficking. The UBAP1-MVB12-associated UMA domain found in MVB12 and UBAP1 defines a novel adaptor that might recruit diverse targets to ESCRT-I.'),
(75439, 2010, 'bio', 'Arcadia: a visualization tool for metabolic pathways', 'Villéger, Alice C|Pettifer, Stephen R|Kell, Douglas B', 'School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. alice.villeger@manchester.ac.uk|School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. alice.villeger@manchester.ac.uk|School of Chemistry and Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. alice.villeger@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Computer Graphics|Databases, Factual|Metabolic Networks and Pathways|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq154', '20453003', 'Arcadia translates text-based descriptions of biological networks (SBML files) into standardized diagrams (SBGN PD maps). Users can view the same model from different perspectives and easily alter the layout to emulate traditional textbook representations.'),
(75440, 2010, 'bio', 'Discover regulatory DNA elements using chromatin signatures and artificial neural network', 'Firpi, Hiram A|Ucar, Duygu|Tan, Kai', 'Department of Internal Medicine, University of Iowa, 2294 CBRB, 285 Newton Road, Iowa City, IA 52242, USA.|Department of Internal Medicine, University of Iowa, 2294 CBRB, 285 Newton Road, Iowa City, IA 52242, USA.|Department of Internal Medicine, University of Iowa, 2294 CBRB, 285 Newton Road, Iowa City, IA 52242, USA.', 'United States|United States|United States', 87, 'Algorithms|CD4-Positive T-Lymphocytes|Chromatin|Gene Expression Regulation|HeLa Cells|Histone Code|Histones|Humans|Neural Networks (Computer)|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btq248', '20453004', 'Recent large-scale chromatin states mapping efforts have revealed characteristic chromatin modification signatures for various types of functional DNA elements. Given the important influence of chromatin states on gene regulation and the rapid accumulation of genome-wide chromatin modification data, there is a pressing need for computational methods to analyze these data in order to identify functional DNA elements. However, existing computational tools do not exploit data transformation and feature extraction as a means to achieve a more accurate prediction.'),
(75441, 2010, 'bio', 'Repitools: an R package for the analysis of enrichment-based epigenomic data', 'Statham, Aaron L|Strbenac, Dario|Coolen, Marcel W|Stirzaker, Clare|Clark, Susan J|Robinson, Mark D', 'Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.|Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.|Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.|Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.|Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.|Epigenetics Laboratory, Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 41, 'DNA Methylation|Epigenesis, Genetic|Genomics|Histones|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btq247', '20457667', 'Epigenetics, the study of heritable somatic phenotypic changes not related to DNA sequence, has emerged as a critical component of the landscape of gene regulation. The epigenetic layers, such as DNA methylation, histone modifications and nuclear architecture are now being extensively studied in many cell types and disease settings. Few software tools exist to summarize and interpret these datasets. We have created a toolbox of procedures to interrogate and visualize epigenomic data (both array- and sequencing-based) and make available a software package for the cross-platform R language.'),
(75442, 2010, 'bio', 'Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming', 'Ibarra, Ignacio L|Melo, Francisco', 'Laboratorio de Bioinformática Molecular, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.|Laboratorio de Bioinformática Molecular, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.', 'Chile|Chile', 0, 'Algorithms|Amino Acid Sequence|Base Sequence|Computational Biology|Programming Languages|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq252', '20472540', 'Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications.'),
(75443, 2010, 'bio', 'Classification of DNA sequences using Bloom filters', 'Stranneheim, Henrik|Käller, Max|Allander, Tobias|Andersson, Björn|Arvestad, Lars|Lundeberg, Joakim', 'Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se|Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se|Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se|Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se|Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se|Science for Life Laboratory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. henrik.stranneheim@biotech.kth.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 35, 'Algorithms|Genome, Mitochondrial|Humans|Metagenome|Respiratory System|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq230', '20472541', 'New generation sequencing technologies producing increasingly complex datasets demand new efficient and specialized sequence analysis algorithms. Often, it is only the novel sequences in a complex dataset that are of interest and the superfluous sequences need to be removed.'),
(75444, 2010, 'bio', 'The Newick utilities: high-throughput phylogenetic tree processing in the UNIX shell', 'Junier, Thomas|Zdobnov, Evgeny M', 'Department of Genetic Medicine and Development, University of Geneva Medical School, 1 rue Michel-Servet, 1211 Geneva, Switzerland. thomas.junier@unige.ch|Department of Genetic Medicine and Development, University of Geneva Medical School, 1 rue Michel-Servet, 1211 Geneva, Switzerland. thomas.junier@unige.ch', 'Switzerland|Switzerland', 162, 'Computational Biology|Computer Graphics|Likelihood Functions|Phylogeny|Programming Languages|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq243', '20472542', 'We present a suite of Unix shell programs for processing any number of phylogenetic trees of any size. They perform frequently-used tree operations without requiring user interaction. They also allow tree drawing as scalable vector graphics (SVG), suitable for high-quality presentations and further editing, and as ASCII graphics for command-line inspection. As an example we include an implementation of bootscanning, a procedure for finding recombination breakpoints in viral genomes.'),
(75445, 2010, 'bio', 'PSORTb 30: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes', 'Yu, Nancy Y|Wagner, James R|Laird, Matthew R|Melli, Gabor|Rey, Sébastien|Lo, Raymond|Dao, Phuong|Sahinalp, S Cenk|Ester, Martin|Foster, Leonard J|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Archaea|Archaeal Proteins|Bacteria|Bacterial Proteins|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btq249', '20472543', 'PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program.'),
(75446, 2010, 'bio', 'Characterization of the N-ATPase, a distinct, laterally transferred Na+-translocating form of the bacterial F-type membrane ATPase', 'Dibrova, Daria V|Galperin, Michael Y|Mulkidjanian, Armen Y', 'School of Physics, University of Osnabrück, D-49069 Osnabrück, Germany.|School of Physics, University of Osnabrück, D-49069 Osnabrück, Germany.|School of Physics, University of Osnabrück, D-49069 Osnabrück, Germany.', 'Germany|Germany|Germany', 36, 'Adenosine Triphosphatases|Bacterial Proteins|Cation Transport Proteins|Genome, Bacterial', 'Journal Article', '10.1093/bioinformatics/btq234', '20472544', 'An analysis of the distribution of the Na(+)-translocating ATPases/ATP synthases among microbial genomes identified an atypical form of the F(1)F(o)-type ATPase that is present in the archaea Methanosarcina barkeri and M. acetivorans, in a number of phylogenetically diverse marine and halotolerant bacteria and in pathogens Burkholderia spp. In complete genomes, representatives of this form (referred to here as N-ATPase) are always present as second copies, in addition to the typical proton-translocating ATP synthases. The N-ATPase is encoded by a highly conserved atpDCQRBEFAG operon and its subunits cluster separately from the equivalent subunits of the typical F-type ATPases. N-ATPase c subunits carry a full set of sodium-binding residues, indicating that most of these enzymes are Na(+)-translocating ATPases that likely confer on their hosts the ability to extrude Na(+) ions. Other distinctive properties of the N-ATPase operons include the absence of the delta subunit from its cytoplasmic sector and the presence of two additional membrane subunits, AtpQ (formerly gene 1) and AtpR (formerly gene X). We argue that N-ATPases are an early-diverging branch of membrane ATPases that, similarly to the eukaryotic V-type ATPases, do not synthesize ATP.'),
(75447, 2010, 'bio', 'Identifying duplicate content using statistically improbable phrases', 'Errami, Mounir|Sun, Zhaohui|George, Angela C|Long, Tara C|Skinner, Michael A|Wren, Jonathan D|Garner, Harold R', 'Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu|Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Databases, Factual|Duplicate Publication as Topic|MEDLINE|Plagiarism|PubMed|Search Engine|United States', 'Journal Article', '10.1093/bioinformatics/btq146', '20472545', 'Document similarity metrics such as PubMeds Find related articles feature, which have been primarily used to identify studies with similar topics, can now also be used to detect duplicated or potentially plagiarized papers within literature reference databases. However, the CPU-intensive nature of document comparison has limited MEDLINE text similarity studies to the comparison of abstracts, which constitute only a small fraction of a publications total text. Extending searches to include text archived by online search engines would drastically increase comparison ability. For large-scale studies, submitting short phrases encased in direct quotes to search engines for exact matches would be optimal for both individual queries and programmatic interfaces. We have derived a method of analyzing statistically improbable phrases (SIPs) for assistance in identifying duplicate content.'),
(75448, 2010, 'bio', 'Ligand-binding site prediction of proteins based on known fragment-fragment interactions', 'Kasahara, Kota|Kinoshita, Kengo|Takagi, Toshihisa', 'Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. kasahara@cb.k.u-tokyo.ac.jp|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. kasahara@cb.k.u-tokyo.ac.jp|Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. kasahara@cb.k.u-tokyo.ac.jp', 'Japan|Japan|Japan', 14, 'Algorithms|Binding Sites|Databases, Protein|Ligands|Models, Molecular|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btq232', '20472546', 'The identification of putative ligand-binding sites on proteins is important for the prediction of protein function. Knowledge-based approaches using structure databases have become interesting, because of the recent increase in structural information. Approaches using binding motif information are particularly effective. However, they can only be applied to well-known ligands that frequently appear in the structure databases.'),
(75449, 2010, 'bio', 'Prediction of protein-RNA binding sites by a random forest method with combined features', 'Liu, Zhi-Ping|Wu, Ling-Yun|Wang, Yong|Zhang, Xiang-Sun|Chen, Luonan', 'Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.', 'China|China|China|China|China', 0, 'Artificial Intelligence|Computational Biology|RNA|RNA-Binding Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq253', '20483814', 'Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide.'),
(75450, 2010, 'bio', 'Pathgroups, a dynamic data structure for genome reconstruction problems', 'Zheng, Chunfang', 'Département dinformatique et de recherche opérationnelle, Université de Montréal, Canada. chunfang313@gmail.com', 'Canada', 12, 'Algorithms|Computational Biology|Gene Order|Genome, Fungal|Phylogeny|Saccharomyces cerevisiae|Yeasts', 'Journal Article', '10.1093/bioinformatics/btq255', '20483815', 'Ancestral gene order reconstruction problems, including the median problem, quartet construction, small phylogeny, guided genome halving and genome aliquoting, are NP hard. Available heuristics dedicated to each of these problems are computationally costly for even small instances.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75451, 2010, 'bio', 'A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury', 'Baumgartner, Christian|Lewis, Gregory D|Netzer, Michael|Pfeifer, Bernhard|Gerszten, Robert E', 'Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria. christian.baumgartner@umit.at|Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria. christian.baumgartner@umit.at|Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria. christian.baumgartner@umit.at|Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria. christian.baumgartner@umit.at|Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria. christian.baumgartner@umit.at', 'Austria|Austria|Austria|Austria|Austria', 0, 'Biomarkers|Cohort Studies|Data Mining|Humans|Kinetics|Mass Spectrometry|Myocardial Infarction|Myocardium', 'Journal Article', '10.1093/bioinformatics/btq254', '20483816', 'The discovery of new and unexpected biomarkers in cardiovascular disease is a highly data-driven process that requires the complementary power of modern metabolite profiling technologies, bioinformatics and biostatistics. Clinical biomarkers of early myocardial injury are lacking. A prospective biomarker cohort study was carried out to identify, categorize and profile kinetic patterns of early metabolic biomarkers of planned myocardial infarction (PMI) and spontaneous (SMI) myocardial infarction. We applied a targeted mass spectrometry (MS)-based metabolite profiling platform to serial blood samples drawn from carefully phenotyped patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy serving as a human model of PMI. Patients with SMI and patients undergoing catheterization without induction of myocardial infarction served as positive and negative controls to assess generalizability of markers identified in PMI.'),
(75452, 2010, 'bio', 'Automated analysis of protein subcellular location in time series images', 'Hu, Yanhua|Osuna-Highley, Elvira|Hua, Juchang|Nowicki, Theodore Scott|Stolz, Robert|McKayle, Camille|Murphy, Robert F', 'Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States|United States|United States|United States', 11, 'Animals|Fibroblasts|Fourier Analysis|Imaging, Three-Dimensional|Mice|Microscopy, Fluorescence|NIH 3T3 Cells|Proteins', 'Journal Article', '10.1093/bioinformatics/btq239', '20484328', 'Image analysis, machine learning and statistical modeling have become well established for the automatic recognition and comparison of the subcellular locations of proteins in microscope images. By using a comprehensive set of features describing static images, major subcellular patterns can be distinguished with near perfect accuracy. We now extend this work to time series images, which contain both spatial and temporal information. The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone.'),
(75453, 2010, 'bio', 'FuncBase: a resource for quantitative gene function annotation', 'Beaver, John E|Tasan, Murat|Gibbons, Francis D|Tian, Weidong|Hughes, Timothy R|Roth, Frederick P', 'Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.|Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Factual|Genes|Internet|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btq265', '20495000', 'Computational gene function prediction can serve to focus experimental resources on high-priority experimental tasks. FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations. Quantitative annotations of genes, including fungal and mammalian genes, with Gene Ontology terms are accompanied by a community feedback system. Evidence underlying function annotations is shown. For example, a custom Cytoscape viewer shows functional linkage graphs relevant to the gene or function of interest. FuncBase provides links to external resources, and may be accessed directly or via links from species-specific databases.'),
(75454, 2010, 'bio', 'Machine learning based prediction for peptide drift times in ion mobility spectrometry', 'Shah, Anuj R|Agarwal, Khushbu|Baker, Erin S|Singhal, Mudita|Mayampurath, Anoop M|Ibrahim, Yehia M|Kangas, Lars J|Monroe, Matthew E|Zhao, Rui|Belov, Mikhail E|Anderson, Gordon A|Smith, Richard D', 'Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.|Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, 999 Battelle Boulevard, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 26, 'Artificial Intelligence|Computational Biology|Ions|Mass Spectrometry|Peptides|Proteomics|Software|Spectrum Analysis', 'Journal Article', '10.1093/bioinformatics/btq245', '20495001', 'Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptides drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting.'),
(75455, 2010, 'bio', 'GPCR-OKB: the G Protein Coupled Receptor Oligomer Knowledge Base', 'Khelashvili, George|Dorff, Kevin|Shan, Jufang|Camacho-Artacho, Marta|Skrabanek, Lucy|Vroling, Bas|Bouvier, Michel|Devi, Lakshmi A|George, Susan R|Javitch, Jonathan A|Lohse, Martin J|Milligan, Graeme|Neubig, Richard R|Palczewski, Krzysztof|Parmentier, Marc|Pin, Jean-Philippe|Vriend, Gerrit|Campagne, Fabien|Filizola, Marta', 'Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 0, 'Databases, Factual|Internet|Knowledge Bases|Receptors, G-Protein-Coupled|Software', 'Journal Article', '10.1093/bioinformatics/btq264', '20501551', 'Rapid expansion of available data about G Protein Coupled Receptor (GPCR) dimers/oligomers over the past few years requires an effective system to organize this information electronically. Based on an ontology derived from a community dialog involving colleagues using experimental and computational methodologies, we developed the GPCR-Oligomerization Knowledge Base (GPCR-OKB). GPCR-OKB is a system that supports browsing and searching for GPCR oligomer data. Such data were manually derived from the literature. While focused on GPCR oligomers, GPCR-OKB is seamlessly connected to GPCRDB, facilitating the correlation of information about GPCR protomers and oligomers.'),
(75456, 2010, 'bio', 'A parallel algorithm to compute chemical organizations in biological networks', 'Centler, Florian|Kaleta, Christoph|Speroni di Fenizio, Pietro|Dittrich, Peter', 'Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany. florian.centler@ufz.de|Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany. florian.centler@ufz.de|Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany. florian.centler@ufz.de|Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstrasse 15, D-04318 Leipzig, Germany. florian.centler@ufz.de', 'Germany|Germany|Germany|Germany', 6, 'Algorithms|Models, Biological|Models, Chemical|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq263', '20501552', 'Analysing genome-scale in silico models with stoichiometry-based methods is computationally demanding. The current algorithms to compute chemical organizations in chemical reaction networks are limited to small-scale networks, prohibiting a thorough analysis of large models. Here, we introduce a parallelized version of the constructive algorithm to determine chemical organizations. The algorithm is implemented in the Standard C programming language and parallelized using the message passing interface (MPI) protocol. The resulting code can be executed on computer clusters making use of an arbitrary number of processors. The algorithm is parallelized in an embarrassing parallel manner, providing good scalability.'),
(75457, 2010, 'bio', 'Revealing differences in gene network inference algorithms on the network level by ensemble methods', 'Altay, Gökmen|Emmert-Streib, Frank', 'Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK.|Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|B-Lymphocytes|Gene Regulatory Networks|Genes, myc|Genomics', 'Journal Article', '10.1093/bioinformatics/btq259', '20501553', 'The inference of regulatory networks from large-scale expression data holds great promise because of the potentially causal interpretation of these networks. However, due to the difficulty to establish reliable methods based on observational data there is so far only incomplete knowledge about possibilities and limitations of such inference methods in this context.'),
(75458, 2010, 'bio', 'COMPASSS (COMplex PAttern of Sequence Search Software), a simple and effective tool for mining complex motifs in whole genomes', 'Maccari, Giuseppe|Gemignani, Federica|Landi, Stefano', 'Laboratory of Microbiology and Genetics - Ospedale di Circolo e Fondazione Macchi, University of Insubria, Viale Borri 57 21100 Varese, Italy. gpmaccari@gmail.com|Laboratory of Microbiology and Genetics - Ospedale di Circolo e Fondazione Macchi, University of Insubria, Viale Borri 57 21100 Varese, Italy. gpmaccari@gmail.com|Laboratory of Microbiology and Genetics - Ospedale di Circolo e Fondazione Macchi, University of Insubria, Viale Borri 57 21100 Varese, Italy. gpmaccari@gmail.com', 'Italy|Italy|Italy', 0, 'Algorithms|Base Sequence|Data Mining|Genome|Internet|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq258', '20501554', 'The complete sequencing of the human genome shows that only 1% of the entire genome encodes for proteins. The major part of the genome is made up of non-coding DNA, regulatory elements and junk DNA. Transcriptional regulation plays a central role in a multitude of critical cellular processes and responses, and it is a central force in the development and differentiation of multicellular organisms. Identifying regulatory elements is one of the major tasks in this challenge. To accomplish this task, we developed a solid and simple suite that allows direct access to genomic database and immediate result check. We introduce COMPASSS (COMplex PAttern of Sequence Search Software), a simple and effective tool for motif search in entire genomes. Motifs can be partially degenerated and interrupted by spacers of variable length.'),
(75459, 2010, 'bio', 'A quality control algorithm for filtering SNPs in genome-wide association studies', 'Pongpanich, Monnat|Sullivan, Patrick F|Tzeng, Jung-Ying', 'Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA.|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA.|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA.', 'United States|United States|United States', 15, 'Algorithms|Genome|Genome-Wide Association Study|Polymorphism, Single Nucleotide|Quality Control', 'Journal Article', '10.1093/bioinformatics/btq272', '20501555', 'The quality control (QC) filtering of single nucleotide polymorphisms (SNPs) is an important step in genome-wide association studies to minimize potential false findings. SNP QC commonly uses expert-guided filters based on QC variables [e.g. Hardy-Weinberg equilibrium, missing proportion (MSP) and minor allele frequency (MAF)] to remove SNPs with insufficient genotyping quality. The rationale of the expert filters is sensible and concrete, but its implementation requires arbitrary thresholds and does not jointly consider all QC features.'),
(75460, 2010, 'bio', 'DA 10: parameter estimation of biological pathways using data assimilation approach', 'Koh, Chuan Hock|Nagasaki, Masao|Saito, Ayumu|Wong, Limsoon|Miyano, Satoru', 'NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597.|NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597.|NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597.|NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597.|NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 9, 'Databases, Factual|Models, Biological|Monte Carlo Method|Software', 'Journal Article', '10.1093/bioinformatics/btq276', '20505000', 'Data assimilation (DA) is a computational approach that estimates unknown parameters in a pathway model using time-course information. Particle filtering, the underlying method used, is a well-established statistical method that approximates the joint posterior distributions of parameters by using sequentially generated Monte Carlo samples. In this article, we report the release of Java-based software (DA 1.0) with an intuitive and user-friendly interface to allow users to carry out parameters estimation using DA.'),
(75461, 2010, 'bio', 'Predicting post-translational lysine acetylation using support vector machines', 'Gnad, Florian|Ren, Shubin|Choudhary, Chunaram|Cox, Jürgen|Mann, Matthias', 'Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.|Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.|Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.|Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.|Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.', 'Germany|Germany|Germany|Germany|Germany', 47, 'Acetylation|Algorithms|Humans|Lysine|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1093/bioinformatics/btq260', '20505001', 'Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues.'),
(75462, 2010, 'bio', 'Threshold Average Precision (TAP-k): a measure of retrieval designed for bioinformatics', 'Carroll, Hyrum D|Kann, Maricel G|Sheetlin, Sergey L|Spouge, John L', 'National Center for Biotechnology Information, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 26, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btq270', '20505002', 'Since database retrieval is a fundamental operation, the measurement of retrieval efficacy is critical to progress in bioinformatics. This article points out some issues with current methods of measuring retrieval efficacy and suggests some improvements. In particular, many studies have used the pooled receiver operating characteristic for n irrelevant records (ROC(n)) score, the area under the ROC curve (AUC) of a pooled ROC curve, truncated at n irrelevant records. Unfortunately, the pooled ROC(n) score does not faithfully reflect actual usage of retrieval algorithms. Additionally, a pooled ROC(n) score can be very sensitive to retrieval results from as little as a single query.'),
(75463, 2010, 'bio', 'Prediction of protease substrates using sequence and structure features', 'Barkan, David T|Hostetter, Daniel R|Mahrus, Sami|Pieper, Ursula|Wells, James A|Craik, Charles S|Sali, Andrej', 'Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.|Graduate Group in Bioinformatics, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.', 'United States|United States|United States|United States|United States|United States|United States', 45, 'Caspases|Computational Biology|Ligands|Peptide Hydrolases|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq267', '20505003', 'Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.'),
(75464, 2010, 'bio', 'On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data', 'Schwarz, Daniel F|König, Inke R|Ziegler, Andreas', 'Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Strasse 1, 23562 Lübeck, Germany.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Strasse 1, 23562 Lübeck, Germany.|Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Maria-Goeppert-Strasse 1, 23562 Lübeck, Germany.', 'Germany|Germany|Germany', 161, 'Crohn Disease|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btq257', '20505004', 'Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohns disease. This may partly be due to the low power of standard statistical approaches to detect gene-gene and gene-environment interactions when small marginal effects are present. A promising alternative is Random Forests, which have already been successfully applied in candidate gene analyses. Important single nucleotide polymorphisms are detected by permutation importance measures. To this day, the application to GWA data was highly cumbersome with existing implementations because of the high computational burden.'),
(75465, 2010, 'bio', 'A UIMA wrapper for the NCBO annotator', 'Roeder, Christophe|Jonquet, Clement|Shah, Nigam H|Baumgartner, William A|Verspoor, Karin|Hunter, Lawrence', 'Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, CO, USA. chris.roeder@ucdenver.edu', 'United States|United States|United States|United States|United States|United States', 9, 'Data Mining|Databases, Factual|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq250', '20505005', 'The Unstructured Information Management Architecture (UIMA) framework and web services are emerging as useful tools for integrating biomedical text mining tools. This note describes our work, which wraps the National Center for Biomedical Ontology (NCBO) Annotator-an ontology-based annotation service-to make it available as a component in UIMA workflows.'),
(75466, 2010, 'bio', 'Evoker: a visualization tool for genotype intensity data', 'Morris, James A|Randall, Joshua C|Maller, Julian B|Barrett, Jeffrey C', 'Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Computer Graphics|Databases, Genetic|Genome-Wide Association Study|Genotype|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btq280', '20507892', 'Genome-wide association studies (GWAS), which produce huge volumes of data, are now being carried out by many groups around the world, creating a need for user-friendly tools for data quality control (QC) and analysis. One critical aspect of GWAS QC is evaluating genotype cluster plots to verify sensible genotype calling in putatively associated single nucleotide polymorphisms (SNPs). Evoker is a tool for visualizing genotype cluster plots, and provides a solution to the computational and storage problems related to working with such large datasets.'),
(75467, 2010, 'bio', 'MODEVO: exploring modularity and evolution of protein interaction networks', 'Woxc5xbaniak, Michaxc5x82|Tiuryn, Jerzy|Dutkowski, Janusz', 'Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.|Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.|Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.', 'Poland|Poland|Poland', 13, 'Algorithms|Databases, Protein|Evolution, Molecular|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq274', '20507893', 'Interrogating protein complexes and pathways in an evolutionary context provides insights into the formation of the basic functional components of the cell. We developed two independent Cytoscape plugins that can be cooperatively used to map evolving protein interaction networks at the module level. The APCluster plugin implements a recent affinity propagation (AP) algorithm for graph clustering and can be applied to decompose networks into coherent modules. The NetworkEvolution plugin provides the capability to visualize selected modules in consecutive evolutionary stages.'),
(75468, 2010, 'bio', 'Integrative platform to translate gene sets to networks', 'Laakso, Marko|Hautaniemi, Sampsa', 'Computational Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Program, University of Helsinki, 00014 University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Program, University of Helsinki, 00014 University of Helsinki, Finland.', 'Finland|Finland', 21, 'Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genes|Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btq277', '20507894', 'We have implemented a computational platform (Moksiskaan) that integrates pathway, protein-protein interaction, genome and literature mining data to result in comprehensive networks for a list of genes or proteins. Moksiskaan is able to generate hypothetical pathways for these genes or proteins as well as estimate their activation statuses using regulation information in pathway repositories. An automatically generated result document provides a detailed description of the query genes, biological processes and drug targets. Moksiskaan networks can be downloaded to Cytoscape for further analysis. To demonstrate the utility of Moksiskaan, we use gene microarray and clinical data from >200 glioblastoma multiforme primary tumor samples and translate the resulting set of 124 survival-associated genes to a network.'),
(75469, 2010, 'bio', 'Fast integration of heterogeneous data sources for predicting gene function with limited annotation', 'Mostafavi, Sara|Morris, Quaid', 'Department of Computer Science and Center for Cellular and Biomolecular Research, University of Toronto, Canada. smostafavi@cs.toronto.edu|Department of Computer Science and Center for Cellular and Biomolecular Research, University of Toronto, Canada. smostafavi@cs.toronto.edu', 'Canada|Canada', 0, 'Algorithms|Animals|Data Collection|Databases, Genetic|Gene Expression Profiling|Genes|Genomics|Humans|Linear Models|Mice', 'Journal Article', '10.1093/bioinformatics/btq262', '20507895', 'Many algorithms that integrate multiple functional association networks for predicting gene function construct a composite network as a weighted sum of the individual networks and then use the composite network to predict gene function. The weight assigned to an individual network represents the usefulness of that network in predicting a given gene function. However, because many categories of gene function have a small number of annotations, the process of assigning these network weights is prone to overfitting.'),
(75470, 2010, 'bio', 'geWorkbench: an open source platform for integrative genomics', 'Floratos, Aris|Smith, Kenneth|Ji, Zhou|Watkinson, John|Califano, Andrea', 'Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York, NY 10032, USA. geworkbench@c2b2.columbia.edu|Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York, NY 10032, USA. geworkbench@c2b2.columbia.edu|Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York, NY 10032, USA. geworkbench@c2b2.columbia.edu|Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York, NY 10032, USA. geworkbench@c2b2.columbia.edu|Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Ave, New York, NY 10032, USA. geworkbench@c2b2.columbia.edu', 'United States|United States|United States|United States|United States', 64, 'Genomics|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq282', '20511363', 'geWorkbench (genomics Workbench) is an open source Java desktop application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomics domains (gene expression, sequence, protein structure and systems biology). More than 70 distinct plug-in modules are currently available implementing both classical analyses (several variants of clustering, classification, homology detection, etc.) as well as state of the art algorithms for the reverse engineering of regulatory networks and for protein structure prediction, among many others. geWorkbench leverages standards-based middleware technologies to provide seamless access to remote data, annotation and computational servers, thus, enabling researchers with limited local resources to benefit from available public infrastructure.'),
(75471, 2010, 'bio', 'Global analysis of microarray data reveals intrinsic properties in gene expression and tissue selectivity', 'Kim, Changsik|Choi, Jiwon|Park, Hyunjin|Park, Yunsun|Park, Jungsun|Park, Taesung|Cho, Kwanghui|Yang, Young|Yoon, Sukjoon', 'Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.|Department of Biological Sciences, Sookmyung Womens University, Seoul, Republic of Korea.', '-|-|-|-|-|-|-|-|-', 7, 'Breast Neoplasms|Databases, Genetic|Female|Gene Expression|Gene Expression Profiling|Genomics|Humans|Internet|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq279', '20511364', 'It is expected that individual genes have intrinsically different variability in the global expressional trend among them. Thus, the consideration of gene-specific expressional properties will help us to distinguish target-selective gene expression over non-selective over-expression.'),
(75472, 2010, 'bio', 'FastMEDUSA: a parallelized tool to infer gene regulatory networks', 'Bozdag, Serdar|Li, Aiguo|Wuchty, Stefan|Fine, Howard A', 'Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Diseases and Stroke, Bethesda, MD 20892, USA.|Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Diseases and Stroke, Bethesda, MD 20892, USA.|Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Diseases and Stroke, Bethesda, MD 20892, USA.|Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Diseases and Stroke, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States', 0, 'Gene Expression Profiling|Gene Regulatory Networks|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq275', '20513661', 'In order to construct gene regulatory networks of higher organisms from gene expression and promoter sequence data efficiently, we developed FastMEDUSA. In this parallelized version of the regulatory network-modeling tool MEDUSA, expression and sequence data are shared among a user-defined number of processors on a single multi-core machine or cluster. Our results show that FastMEDUSA allows a more efficient utilization of computational resources. While the determination of a regulatory network of brain tumor in Homo sapiens takes 12 days with MEDUSA, FastMEDUSA obtained the same results in 6 h by utilizing 100 processors.'),
(75473, 2010, 'bio', 'Gap5--editing the billion fragment sequence assembly', 'Bonfield, James K|Whitwham, Andrew', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK. jkb@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK. jkb@sanger.ac.uk', 'United Kingdom|United Kingdom', 121, 'Base Sequence|Databases, Factual|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq268', '20513662', 'Existing sequence assembly editors struggle with the volumes of data now readily available from the latest generation of DNA sequencing instruments.'),
(75474, 2010, 'bio', 'DETECT--a density estimation tool for enzyme classification and its application to Plasmodium falciparum', 'Hung, Stacy S|Wasmuth, James|Sanford, Christopher|Parkinson, John', 'Program in Molecular Structure and Function, Hospital for Sick Children, 15-704 MaRS TMDT East, 101 College Street, Toronto, ON M5G 1L7, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, 15-704 MaRS TMDT East, 101 College Street, Toronto, ON M5G 1L7, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, 15-704 MaRS TMDT East, 101 College Street, Toronto, ON M5G 1L7, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, 15-704 MaRS TMDT East, 101 College Street, Toronto, ON M5G 1L7, Canada.', 'Canada|Canada|Canada|Canada', 25, 'Databases, Protein|Enzymes|Genomics|Plasmodium falciparum|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btq266', '20513663', 'A major challenge in genomics is the accurate annotation of component genes. Enzymes are typically predicted using homology-based search methods, where the membership of a protein to an enzyme family is based on single-sequence comparisons. As such, these methods are often error-prone and lack useful measures of reliability for the prediction.'),
(75475, 2010, 'bio', 'CisGenome Browser: a flexible tool for genomic data visualization', 'Jiang, Hui|Wang, Fan|Dyer, Nigel P|Wong, Wing Hung', 'Department of Statistics, Stanford University, Stanford, CA 94305, USA. jiangh@stanford.edu|Department of Statistics, Stanford University, Stanford, CA 94305, USA. jiangh@stanford.edu|Department of Statistics, Stanford University, Stanford, CA 94305, USA. jiangh@stanford.edu|Department of Statistics, Stanford University, Stanford, CA 94305, USA. jiangh@stanford.edu', 'United States|United States|United States|United States', 0, 'Computer Graphics|Genome|Genomics|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq286', '20513664', 'We present an open source, platform independent tool, called CisGenome Browser, which can work together with any other data analysis program to serve as a flexible component for genomic data visualization. It can also work by itself as a standalone genome browser. By working as a light-weight web server, CisGenome Browser is a convenient tool for data sharing between labs. It has features that are specifically designed for ultra high-throughput sequencing data visualization.'),
(75476, 2010, 'bio', 'SLIMS--a user-friendly sample operations and inventory management system for genotyping labs', 'Van Rossum, Thea|Tripp, Ben|Daley, Denise', 'James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada V6Z1Y6.|James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada V6Z1Y6.|James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada V6Z1Y6.', 'Canada|Canada|Canada', 0, 'Database Management Systems|Databases, Factual|Genotype|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq271', '20513665', 'We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors, however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers.'),
(75477, 2010, 'bio', 'Homolonto: generating homology relationships by pairwise alignment of ontologies and application to vertebrate anatomy', 'Parmentier, Gilles|Bastian, Frederic B|Robinson-Rechavi, Marc', 'Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.|Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Animals|Databases, Factual|Gene Expression|Humans|Sequence Alignment|Software|Vertebrates', 'Journal Article', '10.1093/bioinformatics/btq283', '20519284', 'The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relations between ontologies describing different species.'),
(75478, 2010, 'bio', 'A computational genomics pipeline for prokaryotic sequencing projects', 'Kislyuk, Andrey O|Katz, Lee S|Agrawal, Sonia|Hagen, Matthew S|Conley, Andrew B|Jayaraman, Pushkala|Nelakuditi, Viswateja|Humphrey, Jay C|Sammons, Scott A|Govil, Dhwani|Mair, Raydel D|Tatti, Kathleen M|Tondella, Maria L|Harcourt, Brian H|Mayer, Leonard W|Jordan, I King', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Geor', 51, 'Bordetella bronchiseptica|Genome, Bacterial|Genomics|Georgia|Neisseria meningitidis|Prokaryotic Cells|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq284', '20519285', 'New sequencing technologies have accelerated research on prokaryotic genomes and have made genome sequencing operations outside major genome sequencing centers routine. However, no off-the-shelf solution exists for the combined assembly, gene prediction, genome annotation and data presentation necessary to interpret sequencing data. The resulting requirement to invest significant resources into custom informatics support for genome sequencing projects remains a major impediment to the accessibility of high-throughput sequence data.'),
(75479, 2010, 'bio', 'Structure-based variable selection for survival data', 'Lagani, Vincenzo|Tsamardinos, Ioannis', 'Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH) and Computer Science Department, University of Crete, Heraklion, Greece. vlagani@ics.forth.gr|Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH) and Computer Science Department, University of Crete, Heraklion, Greece. vlagani@ics.forth.gr', 'Greece|Greece', 19, 'Algorithms|Data Mining|Humans|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btq261', '20519286', 'Variable selection is a typical approach used for molecular-signature and biomarker discovery, however, its application to survival data is often complicated by censored samples. We propose a new algorithm for variable selection suitable for the analysis of high-dimensional, right-censored data called Survival Max-Min Parents and Children (SMMPC). The algorithm is conceptually simple, scalable, based on the theory of Bayesian networks (BNs) and the Markov blanket and extends the corresponding algorithm (MMPC) for classification tasks. The selected variables have a structural interpretation: if T is the survival time (in general the time-to-event), SMMPC returns the variables adjacent to T in the BN representing the data distribution. The selected variables also have a causal interpretation that we discuss.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75480, 2010, 'bio', 'A more precise characterization of chaperonin substrates', 'Raineri, Emanuele|Ribeca, Paolo|Serrano, Luis|Maier, Tobias', 'CNAG Centro Nacional de Análisis Genómico, Parc Cientific de Barcelona, Baldiri Reixac 4, E-08028 Barcelona, Spain.|CNAG Centro Nacional de Análisis Genómico, Parc Cientific de Barcelona, Baldiri Reixac 4, E-08028 Barcelona, Spain.|CNAG Centro Nacional de Análisis Genómico, Parc Cientific de Barcelona, Baldiri Reixac 4, E-08028 Barcelona, Spain.|CNAG Centro Nacional de Análisis Genómico, Parc Cientific de Barcelona, Baldiri Reixac 4, E-08028 Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Chaperonin 60|Chaperonins|Computational Biology|Evolution, Molecular|Kinetics|Protein Folding|Proteome', 'Journal Article', '10.1093/bioinformatics/btq287', '20519287', 'Molecular chaperones prevent the aggregation of their substrate proteins and thereby ensure that they reach their functional native state. The bacterial GroEL/ES chaperonin system is understood in great detail on a structural, mechanistic and functional level, its interactors in Escherichia coli have been identified and characterized. However, a long-standing question in the field is: What makes a protein a chaperone substrate?'),
(75481, 2010, 'bio', 'Exploring the potential of template-based modelling', 'Lance, Braddon K|Deane, Charlotte M|Wood, Graham R', 'Department of Statistics, Macquarie University, North Ryde, Australia. braddon.lance@mq.edu.au|Department of Statistics, Macquarie University, North Ryde, Australia. braddon.lance@mq.edu.au|Department of Statistics, Macquarie University, North Ryde, Australia. braddon.lance@mq.edu.au', 'Australia|Australia|Australia', 10, 'Algorithms|Computational Biology|Models, Molecular|Proteins|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btq294', '20525820', 'Template-based modelling can approximate the unknown structure of a target protein using an homologous template structure. The core of the resulting prediction then comprises the structural regions conserved between template and target. Target prediction could be improved by rigidly repositioning such single template, structurally conserved fragment regions. The purpose of this article is to quantify the extent to which such improvements are possible and to relate this extent to properties of the target, the template and their alignment.'),
(75482, 2010, 'bio', 'Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model', 'Youn, Ahrim|Reiss, David J|Stuetzle, Werner', 'National Cancer Institute, Bethesda, MD 20892, USA. youna2@mail.nih.gov|National Cancer Institute, Bethesda, MD 20892, USA. youna2@mail.nih.gov|National Cancer Institute, Bethesda, MD 20892, USA. youna2@mail.nih.gov', 'United States|United States|United States', 0, 'Algorithms|Cell Cycle|Cell Cycle Proteins|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq289', '20525821', 'We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.'),
(75483, 2010, 'bio', 'Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function', 'Zhao, Huiying|Yang, Yuedong|Zhou, Yaoqi', 'School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University Purdue University, Indianapolis, IN 46202, USA.', 'India|India|India', 0, 'Computational Biology|DNA|DNA-Binding Proteins|Models, Molecular|Physical Phenomena|Protein Folding|Protein Structure, Tertiary', 'Journal Article', '10.1093/bioinformatics/btq295', '20525822', 'Template-based prediction of DNA binding proteins requires not only structural similarity between target and template structures but also prediction of binding affinity between the target and DNA to ensure binding. Here, we propose to predict protein-DNA binding affinity by introducing a new volume-fraction correction to a statistical energy function based on a distance-scaled, finite, ideal-gas reference (DFIRE) state.'),
(75484, 2010, 'bio', 'adephylo: new tools for investigating the phylogenetic signal in biological traits', 'Jombart, Thibaut|Balloux, François|Dray, Stéphane', 'Department of Infectious Disease Epidemiology, Imperial College-Faculty of Medicine, MRC Centre for Outbreak Analysis and Modelling, St Marys Campus, Norfolk Place, London, UK. t.jombart@imperial.ac.uk|Department of Infectious Disease Epidemiology, Imperial College-Faculty of Medicine, MRC Centre for Outbreak Analysis and Modelling, St Marys Campus, Norfolk Place, London, UK. t.jombart@imperial.ac.uk|Department of Infectious Disease Epidemiology, Imperial College-Faculty of Medicine, MRC Centre for Outbreak Analysis and Modelling, St Marys Campus, Norfolk Place, London, UK. t.jombart@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 151, 'Computational Biology|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btq292', '20525823', 'adephylo is a package for the R software dedicated to the analysis of comparative evolutionary data. Phylogenetic comparative methods initially aimed at accounting for or removing the effects of phylogenetic signal in the analysis of biological traits. However, recent approaches have shown that considerable information can be gathered from the study of the phylogenetic signal. In particular, close examination of phylogenetic structures can unveil interesting evolutionary patterns. For this purpose, we developed the package adephylo that provides tools for quantifying and describing the phylogenetic structures of biological traits. adephylo implements tests of phylogenetic signal, phylogenetic distances and proximities, and novel methods for describing further univariate and multivariate phylogenetic structures. These tools open up new perspectives in the analysis of evolutionary comparative data.'),
(75485, 2010, 'bio', 'COMA server for protein distant homology search', 'Margelevicius, Mindaugas|Laganeckas, Mindaugas|Venclovas, Ceslovas', 'Institute of Biotechnology, Graicixc5xabno 8, Vilnius, Lithuania.|Institute of Biotechnology, Graicixc5xabno 8, Vilnius, Lithuania.|Institute of Biotechnology, Graicixc5xabno 8, Vilnius, Lithuania.', 'Lithuania|Lithuania|Lithuania', 0, 'Computational Biology|Internet|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq306', '20529888', 'Detection of distant homology is a widely used computational approach for studying protein evolution, structure and function. Here, we report a homology search web server based on sequence profile-profile comparison. The user may perform searches in one of several regularly updated profile databases using either a single sequence or a multiple sequence alignment as an input. The same profile databases can also be downloaded for local use. The capabilities of the server are illustrated with the identification of new members of the highly diverse PD-(D/E)XK nuclease superfamily.'),
(75486, 2010, 'bio', 'ACNE: a summarization method to estimate allele-specific copy numbers for Affymetrix SNP arrays', 'Ortiz-Estevez, Maria|Bengtsson, Henrik|Rubio, Angel', 'Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.|Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.|Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Alleles|Computational Biology|Gene Dosage|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq300', '20529889', 'Current algorithms for estimating DNA copy numbers (CNs) borrow concepts from gene expression analysis methods. However, single nucleotide polymorphism (SNP) arrays have special characteristics that, if taken into account, can improve the overall performance. For example, cross hybridization between alleles occurs in SNP probe pairs. In addition, most of the current CN methods are focused on total CNs, while it has been shown that allele-specific CNs are of paramount importance for some studies. Therefore, we have developed a summarization method that estimates high-quality allele-specific CNs.'),
(75487, 2010, 'bio', 'Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites', 'Murakami, Yoichi|Mizuguchi, Kenji', 'National Institute of Biomedical Innovation, Osaka, Japan. yoichi@nibio.go.jp|National Institute of Biomedical Innovation, Osaka, Japan. yoichi@nibio.go.jp', 'Japan|Japan', 112, 'Binding Sites|Computational Biology|Position-Specific Scoring Matrices|Proteins|Reproducibility of Results|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btq302', '20529890', 'The limited availability of protein structures often restricts the functional annotation of proteins and the identification of their protein-protein interaction sites. Computational methods to identify interaction sites from protein sequences alone are, therefore, required for unraveling the functions of many proteins. This article describes a new method (PSIVER) to predict interaction sites, i.e. residues binding to other proteins, in protein sequences. Only sequence features (position-specific scoring matrix and predicted accessibility) are used for training a Naïve Bayes classifier (NBC), and conditional probabilities of each sequence feature are estimated using a kernel density estimation method (KDE).'),
(75488, 2010, 'bio', 'Efficient learning of microbial genotype-phenotype association rules', 'MacDonald, Norman J|Beiko, Robert G', 'Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.|Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.', 'Canada|Canada', 17, 'Algorithms|Genetic Association Studies|Genome, Bacterial|Software', 'Journal Article', '10.1093/bioinformatics/btq305', '20529891', 'Finding biologically causative genotype-phenotype associations from whole-genome data is difficult due to the large gene feature space to mine, the potential for interactions among genes and phylogenetic correlations between genomes. Associations within phylogenetically distinct organisms with unusual molecular mechanisms underlying their phenotype may be particularly difficult to assess.'),
(75489, 2010, 'bio', 'MiRror: a combinatorial analysis web tool for ensembles of microRNAs and their targets', 'Friedman, Yitzhak|Naamati, Guy|Linial, Michal', 'Department of Biological Chemistry, Sudarsky Center for Computational Biology and School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.|Department of Biological Chemistry, Sudarsky Center for Computational Biology and School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.|Department of Biological Chemistry, Sudarsky Center for Computational Biology and School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.', 'Israel|Israel|Israel', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Internet|MicroRNAs', 'Journal Article', '10.1093/bioinformatics/btq298', '20529892', 'The miRror application provides insights on microRNA (miRNA) regulation. It is based on the notion of a combinatorial regulation by an ensemble of miRNAs or genes. miRror integrates predictions from a dozen of miRNA resources that are based on complementary algorithms into a unified statistical framework. For miRNAs set as input, the online tool provides a ranked list of targets, based on set of resources selected by the user, according to their significance of being coordinately regulated. Symmetrically, a set of genes can be used as input to suggest a set of miRNAs. The user can restrict the analysis for the preferred tissue or cell line. miRror is suitable for analyzing results from miRNAs profiling, proteomics and gene expression arrays.'),
(75490, 2010, 'bio', 'Robust unmixing of tumor states in array comparative genomic hybridization data', 'Tolliver, David|Tsourakakis, Charalampos|Subramanian, Ayshwarya|Shackney, Stanley|Schwartz, Russell', 'Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213, USA. tolliver@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213, USA. tolliver@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213, USA. tolliver@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213, USA. tolliver@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh PA 15213, USA. tolliver@cs.cmu.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Comparative Genomic Hybridization|Gene Dosage|Gene Expression Profiling|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btq213', '20529894', 'Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data.'),
(75491, 2010, 'bio', 'SuperTriplets: a triplet-based supertree approach to phylogenomics', 'Ranwez, Vincent|Criscuolo, Alexis|Douzery, Emmanuel J P', 'Université Montpellier 2, CC064, Place Eugène Bataillon, 34 095 Montpellier Cedex 05, France. vincent.ranwez@univ-montp2.fr|Université Montpellier 2, CC064, Place Eugène Bataillon, 34 095 Montpellier Cedex 05, France. vincent.ranwez@univ-montp2.fr|Université Montpellier 2, CC064, Place Eugène Bataillon, 34 095 Montpellier Cedex 05, France. vincent.ranwez@univ-montp2.fr', 'France|France|France', 42, 'Algorithms|Biological Evolution|Computer Simulation|Genomics|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btq196', '20529895', 'Phylogenetic tree-building methods use molecular data to represent the evolutionary history of genes and taxa. A recurrent problem is to reconcile the various phylogenies built from different genomic sequences into a single one. This task is generally conducted by a two-step approach whereby a binary representation of the initial trees is first inferred and then a maximum parsimony (MP) analysis is performed on it. This binary representation uses a decomposition of all source trees that is usually based on clades, but that can also be based on triplets or quartets. The relative performances of these representations have been discussed but are difficult to assess since both are limited to relatively small datasets.'),
(75492, 2010, 'bio', 'Phylogenetic networks do not need to be complex: using fewer reticulations to represent conflicting clusters', 'van Iersel, Leo|Kelk, Steven|Rupp, Regula|Huson, Daniel', 'Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. l.j.j.v.iersel@gmail.com|Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. l.j.j.v.iersel@gmail.com|Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. l.j.j.v.iersel@gmail.com|Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. l.j.j.v.iersel@gmail.com', 'New Zealand|New Zealand|New Zealand|New Zealand', 0, 'Algorithms|Cluster Analysis|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btq202', '20529896', 'Phylogenetic trees are widely used to display estimates of how groups of species are evolved. Each phylogenetic tree can be seen as a collection of clusters, subgroups of the species that evolved from a common ancestor. When phylogenetic trees are obtained for several datasets (e.g. for different genes), then their clusters are often contradicting. Consequently, the set of all clusters of such a dataset cannot be combined into a single phylogenetic tree. Phylogenetic networks are a generalization of phylogenetic trees that can be used to display more complex evolutionary histories, including reticulate events, such as hybridizations, recombinations and horizontal gene transfers. Here, we present the new Cass algorithm that can combine any set of clusters into a phylogenetic network. We show that the networks constructed by Cass are usually simpler than networks constructed by other available methods. Moreover, we show that Cass is guaranteed to produce a network with at most two reticulations per biconnected component, whenever such a network exists. We have implemented Cass and integrated it into the freely available Dendroscope software.'),
(75493, 2010, 'bio', 'Automated tracking and analysis of centrosomes in early Caenorhabditis elegans embryos', 'Jaensch, Steffen|Decker, Markus|Hyman, Anthony A|Myers, Eugene W', 'Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. jaensch@mpi-cbg.de|Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. jaensch@mpi-cbg.de|Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. jaensch@mpi-cbg.de|Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. jaensch@mpi-cbg.de', 'Germany|Germany|Germany|Germany', 0, 'Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Cell Division|Cell Lineage|Centrosome|Embryo, Nonmammalian|Microscopy, Fluorescence|Spindle Apparatus', 'Journal Article', '10.1093/bioinformatics/btq190', '20529897', 'The centrosome is a dynamic structure in animal cells that serves as a microtubule organizing center during mitosis and also regulates cell-cycle progression and sets polarity cues. Automated and reliable tracking of centrosomes is essential for genetic screens that study the process of centrosome assembly and maturation in the nematode Caenorhabditis elegans.'),
(75494, 2010, 'bio', 'Time and memory efficient likelihood-based tree searches on phylogenomic alignments with missing data', 'Stamatakis, Alexandros|Alachiotis, Nikolaos', 'The Exelixis Lab (I12), Department of Computer Science, Technische Universität München, Boltzmannstr. 3, D-85748, Garching b. München, Germany. stamatak@cs.tum.edu|The Exelixis Lab (I12), Department of Computer Science, Technische Universität München, Boltzmannstr. 3, D-85748, Garching b. München, Germany. stamatak@cs.tum.edu', 'Germany|Germany', 0, 'Algorithms|Evolution, Molecular|Genomics|Likelihood Functions|Phylogeny|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btq205', '20529898', 'The current molecular data explosion poses new challenges for large-scale phylogenomic analyses that can comprise hundreds or even thousands of genes. A property that characterizes phylogenomic datasets is that they tend to be gappy, i.e. can contain taxa with (many and disparate) missing genes. In current phylogenomic analyses, this type of alignment gappyness that is induced by missing data frequently exceeds 90%. We present and implement a generally applicable mechanism that allows for reducing memory footprints of likelihood-based [maximum likelihood (ML) or Bayesian] phylogenomic analyses proportional to the amount of missing data in the alignment. We also introduce a set of algorithmic rules to efficiently conduct tree searches via subtree pruning and re-grafting moves using this mechanism.'),
(75495, 2010, 'bio', 'Close lower and upper bounds for the minimum reticulate network of multiple phylogenetic trees', 'Wu, Yufeng', 'Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA. ywu@engr.uconn.edu', 'United States', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btq198', '20529899', 'Reticulate network is a model for displaying and quantifying the effects of complex reticulate processes on the evolutionary history of species undergoing reticulate evolution. A central computational problem on reticulate networks is: given a set of phylogenetic trees (each for some region of the genomes), reconstruct the most parsimonious reticulate network (called the minimum reticulate network) that combines the topological information contained in the given trees. This problem is well-known to be NP-hard. Thus, existing approaches for this problem either work with only two input trees or make simplifying topological assumptions.'),
(75496, 2010, 'bio', 'Inferring combinatorial association logic networks in multimodal genome-wide screens', 'de Ridder, Jeroen|Gerrits, Alice|Bot, Jan|de Haan, Gerald|Reinders, Marcel|Wessels, Lodewyk', 'Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.|Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Algorithms|Animals|Genetic Loci|Genome|Genomics|Genotype|Mice|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btq211', '20529900', 'We propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels.'),
(75497, 2010, 'bio', 'Discovering transcriptional modules by Bayesian data integration', 'Savage, Richard S|Ghahramani, Zoubin|Griffin, Jim E|de la Cruz, Bernard J|Wild, David L', 'Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.|Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.|Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.|Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.|Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Binding Sites|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae Proteins|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btq210', '20529901', 'We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets.'),
(75498, 2010, 'bio', 'Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation', 'Yang, Rendong|Su, Zhen', 'Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.', 'China|China', 88, 'Algorithms|Arabidopsis|Circadian Rhythm|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btq189', '20529902', 'Circadian rhythms are prevalent in most organisms. Identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Such genes are largely detected by searching periodic patterns in microarray data. However, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise. This makes circadian rhythmic analysis of temporal microarray data very challenging.'),
(75499, 2010, 'bio', 'Estimating genome-wide IBD sharing from SNP data via an efficient hidden Markov model of LD with application to gene mapping', 'Bercovici, Sivan|Meek, Christopher|Wexler, Ydo|Geiger, Dan', 'Technion-Israel Institute of Technology, Computer Science Department Haifa, 32000 Israel. sberco@gmail.com|Technion-Israel Institute of Technology, Computer Science Department Haifa, 32000 Israel. sberco@gmail.com|Technion-Israel Institute of Technology, Computer Science Department Haifa, 32000 Israel. sberco@gmail.com|Technion-Israel Institute of Technology, Computer Science Department Haifa, 32000 Israel. sberco@gmail.com', 'Israel|Israel|Israel|Israel', 0, 'Algorithms|Chromosome Mapping|Genome|Genome-Wide Association Study|Genomics|Linkage Disequilibrium|Markov Chains|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq204', '20529903', 'Association analysis is the method of choice for studying complex multifactorial diseases. The premise of this method is that affected persons contain some common genomic regions with similar SNP alleles and such areas will be found in this analysis. An important disadvantage of GWA studies is that it does not distinguish between genomic areas that are inherited from a common ancestor [identical by descent (IBD)] and areas that are identical merely by state [identical by state (IBS)]. Clearly, areas that can be marked with higher probability as IBD and have the same correlation with the disease status of identical areas that are more probably only IBS, are better candidates to be causative, and yet this distinction is not encoded in standard association analysis.'),
(75500, 2010, 'bio', 'Optimal algorithms for haplotype assembly from whole-genome sequence data', 'He, Dan|Choi, Arthur|Pipatsrisawat, Knot|Darwiche, Adnan|Eskin, Eleazar', 'Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu', 'United States|United States|United States|United States|United States', 88, 'Algorithms|Base Sequence|Genome|Genomics|Haplotypes', 'Journal Article', '10.1093/bioinformatics/btq215', '20529904', 'Haplotype inference is an important step for many types of analyses of genetic variation in the human genome. Traditional approaches for obtaining haplotypes involve collecting genotype information from a population of individuals and then applying a haplotype inference algorithm. The development of high-throughput sequencing technologies allows for an alternative strategy to obtain haplotypes by combining sequence fragments. The problem of haplotype assembly is the problem of assembling the two haplotypes for a chromosome given the collection of such fragments, or reads, and their locations in the haplotypes, which are pre-determined by mapping the reads to a reference genome. Errors in reads significantly increase the difficulty of the problem and it has been shown that the problem is NP-hard even for reads of length 2. Existing greedy and stochastic algorithms are not guaranteed to find the optimal solutions for the haplotype assembly problem.'),
(75501, 2010, 'bio', 'Efficient identification of identical-by-descent status in pedigrees with many untyped individuals', 'Li, Xin|Yin, Xiaolin|Li, Jing', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.', 'United States|United States|United States', 21, 'Computer Simulation|Genetic Linkage|Haplotypes|Inheritance Patterns|Linkage Disequilibrium|Markov Chains|Pedigree|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq222', '20529905', 'Inference of identical-by-descent (IBD) probabilities is the key in family-based linkage analysis. Using high-density single nucleotide polymorphism (SNP) markers, one can almost always infer haplotype configurations of each member in a family given all individuals being typed. Consequently, the IBD status can be obtained directly from haplotype configurations. However, in reality, many family members are not typed due to practical reasons. The problem of IBD/haplotype inference is much harder when treating untyped individuals as missing.'),
(75502, 2010, 'bio', 'Efficient genome ancestry inference in complex pedigrees with inbreeding', 'Liu, Eric Yi|Zhang, Qi|McMillan, Leonard|de Villena, Fernando Pardo-Manuel|Wang, Wei', 'Department of Computer Science, University of North Carolina at Chapel Hill, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, USA.|Department of Computer Science, University of North Carolina at Chapel Hill, USA.', 'United States|United States|United States|United States|United States', 35, 'Animals|Genetic Variation|Genome|Genomics|Genotype|Inbreeding|Pedigree|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btq187', '20529906', 'High-density SNP data of model animal resources provides opportunities for fine-resolution genetic variation studies. These genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals. In this article, we investigate the problem of inferring the most probable ancestry of resulting genotypes, given a set of founder genotypes. Due to computational difficulty, existing methods either handle only small pedigree data or disregard the pedigree structure. However, large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computation.'),
(75503, 2010, 'bio', 'Multi-population GWA mapping via multi-task regularized regression', 'Puniyani, Kriti|Kim, Seyoung|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 30, 'Algorithms|Chromosome Mapping|Genetic Markers|Genome|Genome-Wide Association Study|Humans|Phenotype|Population Groups|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btq191', '20529908', 'Population heterogeneity through admixing of different founder populations can produce spurious associations in genome-wide association studies that are linked to the population structure rather than the phenotype. Since samples from the same population generally co-evolve, different populations may or may not share the same genetic underpinnings for the seemingly common phenotype. Our goal is to develop a unified framework for detecting causal genetic markers through a joint association analysis of multiple populations.'),
(75504, 2010, 'bio', 'A soft kinetic data structure for lesion border detection', 'Kockara, Sinan|Mete, Mutlu|Yip, Vincent|Lee, Brendan|Aydin, Kemal', 'Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu|Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu|Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu|Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu|Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu', 'United States|United States|United States|United States|United States', 9, 'Algorithms|Humans|Image Interpretation, Computer-Assisted|Pattern Recognition, Automated|Skin Neoplasms', 'Journal Article', '10.1093/bioinformatics/btq178', '20529909', 'The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach-graph spanner-for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented.'),
(75505, 2010, 'bio', 'TEAM: efficient two-locus epistasis tests in human genome-wide association study', 'Zhang, Xiang|Huang, Shunping|Zou, Fei|Wang, Wei', 'Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu', 'United States|United States|United States|United States', 119, 'Algorithms|Epistasis, Genetic|Genome, Human|Genome-Wide Association Study|Genomics|Humans|Population Groups|Software', 'Journal Article', '10.1093/bioinformatics/btq186', '20529910', 'As a promising tool for identifying genetic markers underlying phenotypic differences, genome-wide association study (GWAS) has been extensively investigated in recent years. In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus study since many diseases are known to be complex traits. A brute force search is infeasible for epistasis detection in the genome-wide scale because of the intensive computational burden. Existing epistasis detection algorithms are designed for dataset consisting of homozygous markers and small sample size. In human study, however, the genotype may be heterozygous, and number of individuals can be up to thousands. Thus, existing methods are not readily applicable to human datasets. In this article, we propose an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS. Our algorithm is exhaustive, i.e. it does not ignore any epistatic interaction. Utilizing the minimum spanning tree structure, the algorithm incrementally updates the contingency tables for epistatic tests without scanning all individuals. Our algorithm has broader applicability and is more efficient than existing methods for large sample study. It supports any statistical test that is based on contingency tables, and enables both family-wise error rate and false discovery rate controlling. Extensive experiments show that our algorithm only needs to examine a small portion of the individuals to update the contingency tables, and it achieves at least an order of magnitude speed up over the brute force approach.'),
(75506, 2010, 'bio', 'Modularity and directionality in genetic interaction maps', 'Jaimovich, Ariel|Rinott, Ruty|Schuldiner, Maya|Margalit, Hanah|Friedman, Nir', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.', 'Israel|Israel|Israel|Israel|Israel', 25, 'Algorithms|Computational Biology|Gene Regulatory Networks|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btq197', '20529911', 'Genetic interactions between genes reflect functional relationships caused by a wide range of molecular mechanisms. Large-scale genetic interaction assays lead to a wealth of information about the functional relations between genes. However, the vast number of observed interactions, along with experimental noise, makes the interpretation of such assays a major challenge.'),
(75507, 2010, 'bio', 'Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM', 'Vaske, Charles J|Benz, Stephen C|Sanborn, J Zachary|Earl, Dent|Szeto, Christopher|Zhu, Jingchun|Haussler, David|Stuart, Joshua M', 'Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.|Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 429, 'Breast Neoplasms|DNA Copy Number Variations|Female|Gene Expression Profiling|Genomics|Glioblastoma|Humans|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btq182', '20529912', 'High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.'),
(75508, 2010, 'bio', 'Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework', 'Yamanishi, Yoshihiro|Kotera, Masaaki|Kanehisa, Minoru|Goto, Susumu', 'Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honore, F-77305 Fontainebleau Cedex, Institut Curie, F-75248, INSERM U900, F-75248, Paris, France. yoshihiro.yamanishi@ensmp.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honore, F-77305 Fontainebleau Cedex, Institut Curie, F-75248, INSERM U900, F-75248, Paris, France. yoshihiro.yamanishi@ensmp.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honore, F-77305 Fontainebleau Cedex, Institut Curie, F-75248, INSERM U900, F-75248, Paris, France. yoshihiro.yamanishi@ensmp.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honore, F-77305 Fontainebleau Cedex, Institut Curie, F-75248, INSERM U900, F-75248, Paris, France. yoshihiro.yamanishi@ensmp.fr', 'France|France|France|France', 290, 'Binding Sites|Drug Discovery|Drug Interactions|Genomics|Ion Channels|Narcotic Antagonists|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btq176', '20529913', 'In silico prediction of drug-target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.'),
(75509, 2010, 'bio', 'Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model', 'Yizhak, Keren|Benyamini, Tomer|Liebermeister, Wolfram|Ruppin, Eytan|Shlomi, Tomer', 'The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel. kerenyiz@post.tau.ac.il|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel. kerenyiz@post.tau.ac.il|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel. kerenyiz@post.tau.ac.il|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel. kerenyiz@post.tau.ac.il|The Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel. kerenyiz@post.tau.ac.il', 'Israel|Israel|Israel|Israel|Israel', 160, 'Escherichia coli|Genome|Metabolic Networks and Pathways|Metabolomics|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq183', '20529914', 'The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.'),
(75510, 2010, 'bio', 'A fast mathematical programming procedure for simultaneous fitting of assembly components into cryoEM density maps', 'Zhang, Shihua|Vasishtan, Daven|Xu, Min|Topf, Maya|Alber, Frank', 'Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States', 19, 'Algorithms|Computational Biology|Cryoelectron Microscopy|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btq201', '20529915', 'Single-particle cryo electron microscopy (cryoEM) typically produces density maps of macromolecular assemblies at intermediate to low resolution (approximately 5-30 A). By fitting high-resolution structures of assembly components into these maps, pseudo-atomic models can be obtained. Optimizing the quality-of-fit of all components simultaneously is challenging due to the large search space that makes the exhaustive search over all possible component configurations computationally unfeasible.'),
(75511, 2010, 'bio', 'Markov dynamic models for long-timescale protein motion', 'Chiang, Tsung-Han|Hsu, David|Latombe, Jean-Claude', 'Department of Computer Science, National University of Singapore, Singapore 117417, Singapore. chiangts@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore 117417, Singapore. chiangts@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore 117417, Singapore. chiangts@comp.nus.edu.sg', 'Singapore|Singapore|Singapore', 12, 'Markov Chains|Molecular Dynamics Simulation|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btq177', '20529916', 'Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.'),
(75512, 2010, 'bio', 'Thermodynamics of RNA structures by Wang-Landau sampling', 'Lou, Feng|Clote, Peter', 'Laboratoire de Recherche en Informatique, Université Paris-Sud XI, bât. 490, 91405 Orsay cedex, France.|Laboratoire de Recherche en Informatique, Université Paris-Sud XI, bât. 490, 91405 Orsay cedex, France.', 'France|France', 10, 'Algorithms|Computational Biology|Monte Carlo Method|Nucleic Acid Conformation|RNA|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btq218', '20529917', 'Thermodynamics-based dynamic programming RNA secondary structure algorithms have been of immense importance in molecular biology, where applications range from the detection of novel selenoproteins using expressed sequence tag (EST) data, to the determination of microRNA genes and their targets. Dynamic programming algorithms have been developed to compute the minimum free energy secondary structure and partition function of a given RNA sequence, the minimum free-energy and partition function for the hybridization of two RNA molecules, etc. However, the applicability of dynamic programming methods depends on disallowing certain types of interactions (pseudoknots, zig-zags, etc.), as their inclusion renders structure prediction an nondeterministic polynomial time (NP)-complete problem. Nevertheless, such interactions have been observed in X-ray structures.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75513, 2010, 'bio', 'Recognition of beta-structural motifs using hidden Markov models trained with simulated evolution', 'Kumar, Anoop|Cowen, Lenore', 'Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu|Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu', 'United States|United States', 14, 'Amino Acid Motifs|Evolution, Molecular|Hydrogen Bonding|Markov Chains|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btq199', '20529918', 'One of the most successful methods to date for recognizing protein sequences that are evolutionarily related, has been profile hidden Markov models. However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta-sheets. We thus explore methods for incorporating pairwise dependencies into these models.'),
(75514, 2010, 'bio', 'A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images', 'Lin, Yu-Shi|Lin, Chung-Chih|Tsai, Yuh-Show|Ku, Tien-Chuan|Huang, Yi-Hung|Hsu, Chun-Nan', 'Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.|Institute of Information Science, Academia Sinica, Taipei, Taiwan.', '-|-|-|-|-|-', 8, 'Acetogenins|Calibration|Cellular Structures|Computational Biology|Image Interpretation, Computer-Assisted|Mitochondria', 'Journal Article', '10.1093/bioinformatics/btq194', '20529919', 'High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise.'),
(75515, 2010, 'bio', 'Low-homology protein threading', 'Peng, Jian|Xu, Jinbo', 'Toyota Technological Institute at Chicago, IL 60637, USA.|Toyota Technological Institute at Chicago, IL 60637, USA.', 'United States|United States', 60, 'Algorithms|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq192', '20529920', 'The challenge of template-based modeling lies in the recognition of correct templates and generation of accurate sequence-template alignments. Homologous information has proved to be very powerful in detecting remote homologs, as demonstrated by the state-of-the-art profile-based method HHpred. However, HHpred does not fare well when proteins under consideration are low-homology. A protein is low-homology if we cannot obtain sufficient amount of homologous information for it from existing protein sequence databases.'),
(75516, 2010, 'bio', 'MOTIF-EM: an automated computational tool for identifying conserved regions in CryoEM structures', 'Saha, Mitul|Levitt, Michael|Chiu, Wah', 'NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305, USA. mitul@cs.stanford.edu|NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305, USA. mitul@cs.stanford.edu|NIH Center for Biomedical Computation, Stanford University, Stanford, CA 94305, USA. mitul@cs.stanford.edu', 'United States|United States|United States', 9, 'Algorithms|Computational Biology|Cryoelectron Microscopy|Software', 'Journal Article', '10.1093/bioinformatics/btq195', '20529921', 'We present a new, first-of-its-kind, fully automated computational tool MOTIF-EM for identifying regions or domains or motifs in cryoEM maps of large macromolecular assemblies (such as chaperonins, viruses, etc.) that remain conformationally conserved. As a by-product, regions in structures that are not conserved are revealed: this can indicate local molecular flexibility related to biological activity. MOTIF-EM takes cryoEM volumetric maps as inputs. The technique used by MOTIF-EM to detect conserved sub-structures is inspired by a recent breakthrough in 2D object recognition. The technique works by constructing rotationally invariant, low-dimensional representations of local regions in the input cryoEM maps. Correspondences are established between the reduced representations (by comparing them using a simple metric) across the input maps. The correspondences are clustered using hash tables and graph theory is used to retrieve conserved structural domains or motifs. MOTIF-EM has been used to extract conserved domains occurring in large macromolecular assembly maps, including as those of viruses P22 and epsilon 15, Ribosome 70S, GroEL, that remain structurally conserved in different functional states. Our method can also been used to build atomic models for some maps. We also used MOTIF-EM to identify the conserved folds shared among dsDNA bacteriophages HK97, Epsilon 15, and ô29, though they have low-sequence similarity.'),
(75517, 2010, 'bio', 'Fragment-free approach to protein folding using conditional neural fields', 'Zhao, Feng|Peng, Jian|Xu, Jinbo', 'Toyota Technological Institute, Chicago, IL 60637, USA.|Toyota Technological Institute, Chicago, IL 60637, USA.|Toyota Technological Institute, Chicago, IL 60637, USA.', 'United States|United States|United States', 21, 'Algorithms|Databases, Protein|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btq193', '20529922', 'One of the major bottlenecks with ab initio protein folding is an effective conformation sampling algorithm that can generate native-like conformations quickly. The popular fragment assembly method generates conformations by restricting the local conformations of a protein to short structural fragments in the PDB. This method may limit conformations to a subspace to which the native fold does not belong because (i) a protein with really new fold may contain some structural fragments not in the PDB and (ii) the discrete nature of fragments may prevent them from building a native-like fold. Previously we have developed a conditional random fields (CRF) method for fragment-free protein folding that can sample conformations in a continuous space and demonstrated that this CRF method compares favorably to the popular fragment assembly method. However, the CRF method is still limited by its capability of generating conformations compatible with a sequence.'),
(75518, 2010, 'bio', 'A statistical method for the detection of variants from next-generation resequencing of DNA pools', 'Bansal, Vikas', 'Scripps Genomic Medicine, Scripps Translational Science Institute, La Jolla, CA 92037, USA. vbansal@scripps.edu', 'United States', 120, 'Algorithms|Base Sequence|Data Interpretation, Statistical|Genetic Variation|Genome|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btq214', '20529923', 'Next-generation sequencing technologies have enabled the sequencing of several human genomes in their entirety. However, the routine resequencing of complete genomes remains infeasible. The massive capacity of next-generation sequencers can be harnessed for sequencing specific genomic regions in hundreds to thousands of individuals. Sequencing-based association studies are currently limited by the low level of multiplexing offered by sequencing platforms. Pooled sequencing represents a cost-effective approach for studying rare variants in large populations. To utilize the power of DNA pooling, it is important to accurately identify sequence variants from pooled sequencing data. Detection of rare variants from pooled sequencing represents a different challenge than detection of variants from individual sequencing.'),
(75519, 2010, 'bio', 'Model-based detection of alternative splicing signals', 'Barash, Yoseph|Blencowe, Benjamin J|Frey, Brendan J', 'Banting and Best Department of Medical Research, University of Toronto, ON, Canada. yoseph@psi.utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, ON, Canada. yoseph@psi.utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, ON, Canada. yoseph@psi.utoronto.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Alternative Splicing|Exons|Gene Expression Profiling|Models, Statistical|RNA Splicing', 'Journal Article', '10.1093/bioinformatics/btq200', '20529924', 'Transcripts from approximately 95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases.'),
(75520, 2010, 'bio', 'A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data', 'Chen, Xiaoyu|Hoffman, Michael M|Bilmes, Jeff A|Hesselberth, Jay R|Noble, William S', 'Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.|Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States', 31, 'Algorithms|Bayes Theorem|Binding Sites|Genome|Molecular Sequence Data|Protein Footprinting|Protein Interaction Mapping|Saccharomyces cerevisiae|Transcription Factors|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btq175', '20529925', 'A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.'),
(75521, 2010, 'bio', 'VARiD: a variation detection framework for color-space and letter-space platforms', 'Dalca, Adrian V|Rumble, Stephen M|Levy, Samuel|Brudno, Michael', 'Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. varid@cs.toronto.edu|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. varid@cs.toronto.edu|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. varid@cs.toronto.edu|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. varid@cs.toronto.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq184', '20529926', 'High-throughput sequencing (HTS) technologies are transforming the study of genomic variation. The various HTS technologies have different sequencing biases and error rates, and while most HTS technologies sequence the residues of the genome directly, generating base calls for each position, the Applied Biosystems SOLiD platform generates dibase-coded (color space) sequences. While combining data from the various platforms should increase the accuracy of variation detection, to date there are only a few tools that can identify variants from color space data, and none that can analyze color space and regular (letter space) data together.'),
(75522, 2010, 'bio', 'Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery', 'Hormozdiari, Fereydoun|Hajirasouliha, Iman|Dao, Phuong|Hach, Faraz|Yorukoglu, Deniz|Alkan, Can|Eichler, Evan E|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|DNA Transposable Elements|Genome|Genomics', 'Journal Article', '10.1093/bioinformatics/btq216', '20529927', 'Recent years have witnessed an increase in research activity for the detection of structural variants (SVs) and their association to human disease. The advent of next-generation sequencing technologies make it possible to extend the scope of structural variation studies to a point previously unimaginable as exemplified by the 1000 Genomes Project. Although various computational methods have been described for the detection of SVs, no such algorithm is yet fully capable of discovering transposon insertions, a very important class of SVs to the study of human evolution and disease. In this article, we provide a complete and novel formulation to discover both loci and classes of transposons inserted into genomes sequenced with high-throughput sequencing technologies. In addition, we also present conflict resolution improvements to our earlier combinatorial SV detection algorithm (VariationHunter) by taking the diploid nature of the human genome into consideration. We test our algorithms with simulated data from the Venter genome (HuRef) and are able to discover >85% of transposon insertion events with precision of >90%. We also demonstrate that our conflict resolution algorithm (denoted as VariationHunter-CR) outperforms current state of the art (such as original VariationHunter, BreakDancer and MoDIL) algorithms when tested on the genome of the Yoruba African individual (NA18507).'),
(75523, 2010, 'bio', 'TRStalker: an efficient heuristic for finding fuzzy tandem repeats', 'Pellegrini, Marco|Renda, M Elena|Vecchio, Alessio', 'CNR, Istituto di Informatica e Telematica, Via Moruzzi 1, 56124 Pisa, Italy. marco.pellegrini@iit.cnr.it|CNR, Istituto di Informatica e Telematica, Via Moruzzi 1, 56124 Pisa, Italy. marco.pellegrini@iit.cnr.it|CNR, Istituto di Informatica e Telematica, Via Moruzzi 1, 56124 Pisa, Italy. marco.pellegrini@iit.cnr.it', 'Italy|Italy|Italy', 0, 'Algorithms|Genome|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btq209', '20529928', 'Genomes in higher eukaryotic organisms contain a substantial amount of repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage and are characterized by close spatial contiguity. They play an important role in several molecular regulatory mechanisms, and also in several diseases (e.g. in the group of trinucleotide repeat disorders). While for TRs with a low or medium level of divergence the current methods are rather effective, the problem of detecting TRs with higher divergence (fuzzy TRs) is still open. The detection of fuzzy TRs is propaedeutic to enriching our view of their role in regulatory mechanisms and diseases. Fuzzy TRs are also important as tools to shed light on the evolutionary history of the genome, where higher divergence correlates with more remote duplication events.'),
(75524, 2010, 'bio', 'Efficient construction of an assembly string graph using the FM-index', 'Simpson, Jared T|Durbin, Richard', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK. js18@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK. js18@sanger.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq217', '20529929', 'Sequence assembly is a difficult problem whose importance has grown again recently as the cost of sequencing has dramatically dropped. Most new sequence assembly software has started by building a de Bruijn graph, avoiding the overlap-based methods used previously because of the computational cost and complexity of these with very large numbers of short reads. Here, we show how to use suffix array-based methods that have formed the basis of recent very fast sequence mapping algorithms to find overlaps and generate assembly string graphs asymptotically faster than previously described algorithms.'),
(75525, 2010, 'bio', 'PathText: a text mining integrator for biological pathway visualizations', 'Kemper, Brian|Matsuzaki, Takuya|Matsuoka, Yukiko|Tsuruoka, Yoshimasa|Kitano, Hiroaki|Ananiadou, Sophia|Tsujii, Junichi', 'Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com|Department of Computer Science, University of Tokyo, Tokyo, Japan. brian@monrovian.com', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 41, 'Biological Phenomena|Data Mining|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq221', '20529930', 'Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations.'),
(75526, 2010, 'bio', 'Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model', 'Peng, Hanchuan|Ruan, Zongcai|Atasoy, Deniz|Sternson, Scott', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org|Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA. pengh@janelia.hhmi.org', 'United States|United States|United States|United States', 71, 'Animals|Caenorhabditis elegans|Drosophila|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Mice|Neurons', 'Journal Article', '10.1093/bioinformatics/btq212', '20529931', 'Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.'),
(75527, 2010, 'bio', 'Complex event extraction at PubMed scale', 'Björne, Jari|Ginter, Filip|Pyysalo, Sampo|Tsujii, Junichi|Salakoski, Tapio', 'Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. jari.bjorne@utu.fi', 'Finland|Finland|Finland|Finland|Finland', 0, 'Data Mining|Natural Language Processing|PubMed|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btq180', '20529932', 'There has recently been a notable shift in biomedical information extraction (IE) from relation models toward the more expressive event model, facilitated by the maturation of basic tools for biomedical text analysis and the availability of manually annotated resources. The event model allows detailed representation of complex natural language statements and can support a number of advanced text mining applications ranging from semantic search to pathway extraction. A recent collaborative evaluation demonstrated the potential of event extraction systems, yet there have so far been no studies of the generalization ability of the systems nor the feasibility of large-scale extraction.'),
(75528, 2010, 'bio', 'Multivariate multi-way analysis of multi-source data', 'Huopaniemi, Ilkka|Suvitaival, Tommi|Nikkilä, Janne|Oresic, Matej|Kaski, Samuel', 'Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, PO Box 15400, FI-00076 Aalto, Espoo, Finland. ilkka.huopaniemi@tkk.fi|Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, PO Box 15400, FI-00076 Aalto, Espoo, Finland. ilkka.huopaniemi@tkk.fi|Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, PO Box 15400, FI-00076 Aalto, Espoo, Finland. ilkka.huopaniemi@tkk.fi|Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, PO Box 15400, FI-00076 Aalto, Espoo, Finland. ilkka.huopaniemi@tkk.fi|Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, PO Box 15400, FI-00076 Aalto, Espoo, Finland. ilkka.huopaniemi@tkk.fi', 'Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Analysis of Variance|Data Collection|Gene Expression Profiling|Multivariate Analysis', 'Journal Article', '10.1093/bioinformatics/btq174', '20529933', 'Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently increasingly important experiments where data is obtained from multiple sources. Common examples of such settings include integrated analysis of metabolic and gene expression profiles, or metabolic profiles from several tissues in our case, in a controlled multi-way experimental setup where disease status, medical treatment, gender and time-series are usual covariates.'),
(75529, 2010, 'bio', 'Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate', 'Ye, Ding|Fu, Yan|Sun, Rui-Xiang|Wang, Hai-Peng|Yuan, Zuo-Fei|Chi, Hao|He, Si-Min', 'Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.', 'China|China|China|China|China|China|China', 68, 'Databases, Protein|Mass Spectrometry|Protein Processing, Post-Translational|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/btq185', '20529934', 'Identification of post-translationally modified proteins has become one of the central issues of current proteomics. Spectral library search is a new and promising computational approach to mass spectrometry-based protein identification. However, its potential in identification of unanticipated post-translational modifications has rarely been explored. The existing spectral library search tools are designed to match the query spectrum to the reference library spectra with the same peptide mass. Thus, spectra of peptides with unanticipated modifications cannot be identified.'),
(75530, 2010, 'bio', 'SPEX2: automated concise extraction of spatial gene expression patterns from Fly embryo ISH images', 'Puniyani, Kriti|Faloutsos, Christos|Xing, Eric P', 'School of Computer Science, Carnegie Mellon Unversity, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon Unversity, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon Unversity, Pittsburgh, PA, USA.', 'United States|United States|United States', 0, 'Animals|Drosophila|Gene Expression|Gene Expression Profiling|Image Processing, Computer-Assisted|In Situ Hybridization|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/btq172', '20529936', 'Microarray profiling of mRNA abundance is often ill suited for temporal-spatial analysis of gene expressions in multicellular organisms such as Drosophila. Recent progress in image-based genome-scale profiling of whole-body mRNA patterns via in situ hybridization (ISH) calls for development of accurate and automatic image analysis systems to facilitate efficient mining of complex temporal-spatial mRNA patterns, which will be essential for functional genomics and network inference in higher organisms.'),
(75531, 2010, 'bio', 'As-rigid-as-possible mosaicking and serial section registration of large ssTEM datasets', 'Saalfeld, Stephan|Cardona, Albert|Hartenstein, Volker|Tomanxc4x8dxc3xa1k, Pavel', 'Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.|Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.', 'Germany|Germany|Germany|Germany', 88, 'Animals|Databases, Factual|Drosophila|Imaging, Three-Dimensional|Microscopy, Electron, Transmission', 'Journal Article', '10.1093/bioinformatics/btq219', '20529937', 'Tiled serial section Transmission Electron Microscopy (ssTEM) is increasingly used to describe high-resolution anatomy of large biological specimens. In particular in neurobiology, TEM is indispensable for analysis of synaptic connectivity in the brain. Registration of ssTEM image mosaics has to recover the 3D continuity and geometrical properties of the specimen in presence of various distortions that are applied to the tissue during sectioning, staining and imaging. These include staining artifacts, mechanical deformation, missing sections and the fact that structures may appear dissimilar in consecutive sections.'),
(75532, 2010, 'bio', 'Reconstruction of the neuromuscular junction connectome', 'Srinivasan, Ranga|Li, Qing|Zhou, Xiaobo|Lu, Ju|Lichtman, Jeff|Wong, Stephen T C', 'Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.|Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.|Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.|Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.|Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.|Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Imaging in Neurosciences, The Methodist Hospital Research Institute, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Axons|Humans|Imaging, Three-Dimensional|Microscopy, Confocal|Neuromuscular Junction|Software|Synapses', 'Journal Article', '10.1093/bioinformatics/btq179', '20529938', 'Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets.'),
(75533, 2010, 'bio', 'Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing', 'Coelho, Luis Pedro|Peng, Tao|Murphy, Robert F', 'Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 0, 'Algorithms|Fluorescent Dyes|Image Processing, Computer-Assisted|Proteins|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btq220', '20529939', 'Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them.'),
(75534, 2010, 'bio', 'Using semantic web rules to reason on an ontology of pseudogenes', 'Holford, Matthew E|Khurana, Ekta|Cheung, Kei-Hoi|Gerstein, Mark', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. matthew.holford@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. matthew.holford@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. matthew.holford@yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. matthew.holford@yale.edu', 'United States|United States|United States|United States', 19, 'Databases, Factual|Information Storage and Retrieval|Internet|Pseudogenes|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btq173', '20529940', 'Recent years have seen the development of a wide range of biomedical ontologies. Notable among these is Sequence Ontology (SO) which offers a rich hierarchy of terms and relationships that can be used to annotate genomic data. Well-designed formal ontologies allow data to be reasoned upon in a consistent and logically sound way and can lead to the discovery of new relationships. The Semantic Web Rules Language (SWRL) augments the capabilities of a reasoner by allowing the creation of conditional rules. To date, however, formal reasoning, especially the use of SWRL rules, has not been widely used in biomedicine.'),
(75535, 2010, 'bio', 'Assessing the functional coherence of gene sets with metrics based on the Gene Ontology graph', 'Richards, Adam J|Muller, Brian|Shotwell, Matthew|Cowart, L Ashley|Rohrer, Bäerbel|Lu, Xinghua', 'Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes|Humans|Software', 'Journal Article', '10.1093/bioinformatics/btq203', '20529941', 'The results of initial analyses for many high-throughput technologies commonly take the form of gene or protein sets, and one of the ensuing tasks is to evaluate the functional coherence of these sets. The study of gene set function most commonly makes use of controlled vocabulary in the form of ontology annotations. For a given gene set, the statistical significance of observing these annotations or enrichment may be tested using a number of methods. Instead of testing for significance of individual terms, this study is concerned with the task of assessing the global functional coherence of gene sets, for which novel metrics and statistical methods have been devised.'),
(75536, 2010, 'bio', 'Semi-automated ontology generation within OBO-Edit', 'Wächter, Thomas|Schroeder, Michael', 'Biotechnology Center, Technische Universität Dresden, 01062 Dresden, Germany. thomas.waechter@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, 01062 Dresden, Germany. thomas.waechter@biotec.tu-dresden.de', 'Germany|Germany', 0, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Internet|Programming Languages|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btq188', '20529942', 'Ontologies and taxonomies have proven highly beneficial for biocuration. The Open Biomedical Ontology (OBO) Foundry alone lists over 90 ontologies mainly built with OBO-Edit. Creating and maintaining such ontologies is a labour-intensive, difficult, manual process. Automating parts of it is of great importance for the further development of ontologies and for biocuration.'),
(75537, 2010, 'bio', 'Sparse multitask regression for identifying common mechanism of response to therapeutic targets', 'Zhang, Kai|Gray, Joe W|Parvin, Bahram', 'Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.|Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.', 'United States|United States|United States', 0, 'Breast Neoplasms|Female|Gene Expression|Gene Expression Profiling|Genes, cdc|Humans|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btq181', '20529943', 'Molecular association of phenotypic responses is an important step in hypothesis generation and for initiating design of new experiments. Current practices for associating gene expression data with multidimensional phenotypic data are typically (i) performed one-to-one, i.e. each gene is examined independently with a phenotypic index and (ii) tested with one stress condition at a time, i.e. different perturbations are analyzed separately. As a result, the complex coordination among the genes responsible for a phenotypic profile is potentially lost. More importantly, univariate analysis can potentially hide new insights into common mechanism of response.'),
(75538, 2010, 'bio', 'Testing multiple gene interactions by the ordered combinatorial partitioning method in case-control studies', 'Hua, Xing|Zhang, Han|Zhang, Hong|Yang, Yaning|Kuk, Anthony Y C', 'Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.|Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.|Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.|Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.|Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China.', 'China|China|China|China|China', 6, 'Breast Neoplasms|Case-Control Studies|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Female|Genes|Genotype|Humans', 'Journal Article', '10.1093/bioinformatics/btq290', '20538724', 'The multifactor-dimensionality reduction (MDR) method has been widely used in multi-locus interaction analysis. It reduces dimensionality by partitioning the multi-locus genotypes into a high-risk group and a low-risk group according to whether the genotype-specific risk ratio exceeds a fixed threshold or not. Alternatively, one can maximize the chi(2) value exhaustively over all possible ways of partitioning the multi-locus genotypes into two groups, and we aim to show that this is computationally feasible.'),
(75539, 2010, 'bio', 'cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data', 'Zhou, Fengfeng|Xu, Ying', 'Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia', 0, 'Base Sequence|Chromosomes|Metagenome|Metagenomics|Plasmids|Software', 'Evaluation Studies', '10.1093/bioinformatics/btq299', '20538725', 'Huge amount of metagenomic sequence data have been produced as a result of the rapidly increasing efforts worldwide in studying microbial communities as a whole. Most, if not all, sequenced metagenomes are complex mixtures of chromosomal and plasmid sequence fragments from multiple organisms, possibly from different kingdoms. Computational methods for prediction of genomic elements such as genes are significantly different for chromosomes and plasmids, hence raising the need for separation of chromosomal from plasmid sequences in a metagenome. We present a program for classification of a metagenome set into chromosomal and plasmid sequences, based on their distinguishing pentamer frequencies. On a large training set consisting of all the sequenced prokaryotic chromosomes and plasmids, the program achieves approximately 92% in classification accuracy. On a large set of simulated metagenomes with sequence lengths ranging from 300 bp to 100 kbp, the program has classification accuracy from 64.45% to 88.75%. On a large independent test set, the program achieves 88.29% classification accuracy.'),
(75540, 2010, 'bio', 'A highly accurate statistical approach for the prediction of transmembrane beta-barrels', 'Freeman, Thomas C|Wimley, William C', 'Department of Biochemistry, Tulane University Health Sciences Center, New Orleans, LA 70112, USA.|Department of Biochemistry, Tulane University Health Sciences Center, New Orleans, LA 70112, USA.', 'United States|United States', 41, 'Algorithms|Data Interpretation, Statistical|Membrane Proteins|Protein Structure, Secondary|Software', 'Evaluation Studies', '10.1093/bioinformatics/btq308', '20538726', 'Transmembrane beta-barrels (TMBBs) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from nutrient acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. However, membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches and current prediction methods are still unreliable.'),
(75541, 2010, 'bio', 'PESDserv: a server for high-throughput comparison of protein binding site surfaces', 'Das, Sourav|Krein, Michael P|Breneman, Curt M', 'Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA.|Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA.|Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180, USA.', 'United States|United States|United States', 10, 'Algorithms|Binding Sites|Computational Biology|Computers|Databases, Protein|Ligands|Protein Binding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btq288', '20538727', 'Structure-based approaches complement ligand-based approaches for lead-discovery and cross-reactivity prediction. We present to the scientific community a web server for comparing the surface of a ligand bound site of a protein against a ligand bound site surface database of 106 796 sites. The web server implements the property encoded shape distributions (PESD) algorithm for surface comparison. A typical virtual screen takes 5 min to complete. The output provides a ranked list of sites (by site similarity), hyperlinked to the corresponding entries in the PDB and PDBeChem databases.'),
(75542, 2010, 'bio', 'LOX: inferring Level Of eXpression from diverse methods of census sequencing', 'Zhang, Zhang|López-Giráldez, Francesc|Townsend, Jeffrey P', 'Department of Ecology and Evolutionary Biology and Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Ecology and Evolutionary Biology and Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Department of Ecology and Evolutionary Biology and Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.', 'United States|United States|United States', 21, 'Computational Biology|Expressed Sequence Tags|Gene Expression Profiling|Gene Expression Regulation, Fungal|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btq303', '20538728', 'We present LOX (Level Of eXpression) that estimates the Level Of gene eXpression from high-throughput-expressed sequence datasets with multiple treatments or samples. Unlike most analyses, LOX incorporates a gene bias model that facilitates integration of diverse transcriptomic sequencing data that arises when transcriptomic data have been produced using diverse experimental methodologies. LOX integrates overall sequence count tallies normalized by total expressed sequence count to provide expression levels for each gene relative to all treatments as well as Bayesian credible intervals.'),
(75543, 2010, 'bio', 'Large-scale benchmark of Endeavour using MetaCore maps', 'Schuierer, Sven|Tranchevent, Léon-Charles|Dengler, Uwe|Moreau, Yves', 'Novartis Pharma AG, Postfach, Basel, Switzerland. sven.schuierer@novartis.com|Novartis Pharma AG, Postfach, Basel, Switzerland. sven.schuierer@novartis.com|Novartis Pharma AG, Postfach, Basel, Switzerland. sven.schuierer@novartis.com|Novartis Pharma AG, Postfach, Basel, Switzerland. sven.schuierer@novartis.com', 'Switzerland|Switzerland|Switzerland|Switzerland', 20, 'Benchmarking|Biomarkers|Computational Biology|Genes|Genome|Humans|Internet|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/btq307', '20538729', 'Endeavour is a tool that detects the most promising genes within large lists of candidates with respect to a biological process of interest and by combining several genomic data sources. We have benchmarked Endeavour using 450 pathway maps and 826 disease marker sets from MetaCore of GeneGo, Inc. containing a total of 9911 and 12 432 genes, respectively. We obtained an area under the receiver operating characteristic curves of 0.97 for pathway and of 0.91 for disease gene sets. These results indicate that Endeavour can be used to efficiently prioritize candidate genes for pathways and diseases.'),
(75544, 2010, 'bio', 'Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery', 'Korcsmáros, Tamás|Farkas, Illés J|Szalay, Máté S|Rovó, Petra|Fazekas, Dávid|Spiró, Zoltán|Böde, Csaba|Lenti, Katalin|Vellai, Tibor|Csermely, Péter', 'Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.|Department of Genetics, Eötvös University, Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 60, 'Animals|Caenorhabditis elegans|Databases, Protein|Drosophila melanogaster|Drug Discovery|Humans|Intracellular Signaling Peptides and Proteins|Neoplasms|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btq310', '20542890', 'Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult.'),
(75545, 2010, 'bio', 'DARNED: a DAtabase of RNa EDiting in humans', 'Kiran, Anmol|Baranov, Pavel V', 'Biochemistry Department, University College Cork, Ireland.|Biochemistry Department, University College Cork, Ireland.', 'Ireland|Ireland', 140, 'Base Sequence|Databases, Genetic|Genome, Human|Humans|RNA|RNA Editing|Software', 'Journal Article', '10.1093/bioinformatics/btq285', '20547637', 'RNA editing is a phenomenon, which is responsible for the alteration of particular nucleotides in RNA sequences relative to their genomic templates. Recently, a large number of RNA editing instances in humans have been identified using bioinformatic screens and high-throughput experimental investigations utilizing next-generation sequencing technologies. However, the available data on RNA editing are not uniform and difficult to access.');
