INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73396, 2007, 'bio', 'Cyclone: java-based querying and computing with Pathway/Genome databases', 'Le Fèvre, François|Smidtas, Serge|Schächter, Vincent', 'Computational Systems Biology Group, Genoscope/CNRS-UMR8030, 2 rue Gaston Crémieux, 91057 Evry Cedex, France.|Computational Systems Biology Group, Genoscope/CNRS-UMR8030, 2 rue Gaston Crémieux, 91057 Evry Cedex, France.|Computational Systems Biology Group, Genoscope/CNRS-UMR8030, 2 rue Gaston Crémieux, 91057 Evry Cedex, France.', 'France|France|France', 10, 'Computational Biology|Databases, Factual|Databases, Genetic|Metabolic Networks and Pathways|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btm107', '17392333', 'Cyclone aims at facilitating the use of BioCyc, a collection of Pathway/Genome Databases (PGDBs). Cyclone provides a fully extensible Java Object API to analyze and visualize these data. Cyclone can read and write PGDBs, and can write its own data in the CycloneML format. This format is automatically generated from the BioCyc ontology by Cyclone itself, ensuring continued compatibility. Cyclone objects can also be stored in a relational database CycloneDB. Queries can be written in SQL, and in an intuitive and concise object-oriented query language, Hibernate Query Language (HQL). In addition, Cyclone interfaces easily with Java software including the Eclipse IDE for HQL edition, the Jung API for graph algorithms or Cytoscape for graph visualization.'),
(73397, 2007, 'bio', 'SCEPTRANS: an online tool for analyzing periodic transcription in yeast', 'Kudlicki, Andrzej|Rowicka, Maga|Otwinowski, Zbyszek', 'University of Texas Southwestern Medical Center, Department of Biochemistry, Dallas, TX 75390-8816, USA.|University of Texas Southwestern Medical Center, Department of Biochemistry, Dallas, TX 75390-8816, USA.|University of Texas Southwestern Medical Center, Department of Biochemistry, Dallas, TX 75390-8816, USA.', 'United States|United States|United States', 27, 'Cell Cycle|DNA, Fungal|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Periodicity|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm126', '17400726', 'SCEPTRANS is designed for analysis of microarray timecourse data related to periodic phenomena in the budding yeast. The server allows for easy viewing of temporal profiles of multiple genes in a number of datasets. Additional functionality includes searching for coexpressed genes, periodicity and correlation analysis, integrating functional annotation and localization data as well as advanced operations on sets of genes.'),
(73398, 2007, 'bio', 'PROTMAP2D: visualization, comparison and analysis of 2D maps of protein structure', 'Pietal, Michal J|Tuszynska, Irina|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, Warsaw, Poland.|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, Warsaw, Poland.', 'Poland|Poland|Poland', 20, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm124', '17400727', 'Protein structure comparison is a fundamental problem in structural biology and bioinformatics. Two-dimensional maps of distances between residues in the structure contain sufficient information to restore the 3D representation, while maps of contacts reveal characteristic patterns of interactions between secondary and super-secondary structures and are very attractive for visual analysis. The overlap of 2D maps of two structures can be easily calculated, providing a sensitive measure of protein structure similarity. PROTMAP2D is a software tool for calculation of contact and distance maps based on user-defined criteria, quantitative comparison of pairs or series of contact maps (e.g. alternative models of the same protein, model versus native structure, different trajectories from molecular dynamics simulations, etc.) and visualization of the results.'),
(73399, 2007, 'bio', 'Management, presentation and interpretation of genome scans using GSCANDB', 'Taylor, Martin|Valdar, William|Kumar, Ashish|Flint, Jonathan|Mott, Richard', 'Wellcome Trust Centre for Human Genetics, Oxford, UK.|Wellcome Trust Centre for Human Genetics, Oxford, UK.|Wellcome Trust Centre for Human Genetics, Oxford, UK.|Wellcome Trust Centre for Human Genetics, Oxford, UK.|Wellcome Trust Centre for Human Genetics, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Animals|Computer Graphics|Database Management Systems|Databases, Genetic|Genetic Linkage|Genome|Genome, Human|Humans|Information Storage and Retrieval|Mice|Models, Genetic|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/btm123', '17400728', 'Advances in high-throughput genotyping have made it possible to carry out genome-wide association studies using very high densities of genetic markers. This has led to the problem of the storage, management, quality control, presentation and interpretation of results. In order to achieve a successful outcome, it may be necessary to analyse the data in different ways and compare the results with genome annotations and other genome scans.'),
(73400, 2007, 'bio', 'Functional evaluation of domain-domain interactions and human protein interaction networks', 'Schlicker, Andreas|Huthmacher, Carola|Ramírez, Fidel|Lengauer, Thomas|Albrecht, Mario', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany', 33, 'Binding Sites|Computer Simulation|Humans|Models, Biological|Models, Chemical|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm012', '17456608', 'Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms.'),
(73401, 2007, 'bio', 'TFmodeller: comparative modelling of protein-DNA complexes', 'Contreras-Moreira, Bruno|Branger, Pierre-Alain|Collado-Vides, Julio', 'Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av.Universidad, s/n, 62210 Cuernavaca, Morelos, México. contrera@ccg.unam.mx|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av.Universidad, s/n, 62210 Cuernavaca, Morelos, México. contrera@ccg.unam.mx|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av.Universidad, s/n, 62210 Cuernavaca, Morelos, México. contrera@ccg.unam.mx', '-|-|-', 22, 'Algorithms|Binding Sites|Computer Simulation|DNA|DNA-Binding Proteins|Databases, Protein|Models, Chemical|Protein Binding|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btm148', '17459960', 'Interactions between proteins and DNA molecules lie at the core of the fundamental cellular processes such as transcriptional regulation. Some of these interactions have been experimentally described at atomic scale, but the molecular details of many others remain to be discovered. TFmodeller exploits the current knowledge about protein-DNA interfaces contained in the Protein Data Bank and uses it to model similar interfaces related by homology. Results are emailed to the user and include an evolutionary contact matrix, a schematic representation of the putative binding interface and atomic coordinates of the modelled complex. The library of complexes used by TFmodeller is updated on a weekly basis and is available for download.'),
(73402, 2007, 'bio', 'Murlet: a practical multiple alignment tool for structural RNA sequences', 'Kiryu, Hisanori|Tabei, Yasuo|Kin, Taishin|Asai, Kiyoshi', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. kiryu-h@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. kiryu-h@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. kiryu-h@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. kiryu-h@aist.go.jp', 'Japan|Japan|Japan|Japan', 52, 'Algorithms|Base Sequence|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btm146', '17459961', 'Structural RNA genes exhibit unique evolutionary patterns that are designed to conserve their secondary structures, these patterns should be taken into account while constructing accurate multiple alignments of RNA genes. The Sankoff algorithm is a natural alignment algorithm that includes the effect of base-pair covariation in the alignment model. However, the extremely high computational cost of the Sankoff algorithm precludes its application to most RNA sequences.'),
(73403, 2007, 'bio', 'Mapping the genetic architecture of complex traits in experimental populations', 'Yang, Jian|Zhu, Jun|Williams, Robert W', 'Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, PR China.', 'China|China|China', 249, 'Algorithms|Animals|Chromosome Mapping|Chromosomes, Mammalian|Chromosomes, Plant|Computational Biology|Computer Simulation|Crosses, Genetic|Epistasis, Genetic|Genetic Markers|Genetic Variation|Genome|Genome, Plant|Genotype|Haplotypes|Likelihood Functions|Markov Chains|Mice|Mice, Inbred C57BL|Mice, Inbred DBA|Models, Genetic|Monte Carlo Method|Oryza|Phenotype|Quantitative Trait, Heritable|Regression Analysis|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btm143', '17459962', 'Understanding how interactions among set of genes affect diverse phenotypes is having a greater impact on biomedical research, agriculture and evolutionary biology. Mapping and characterizing the isolated effects of single quantitative trait locus (QTL) is a first step, but we also need to assemble networks of QTLs and define non-additive interactions (epistasis) together with a host of potential environmental modulators. In this article, we present a full-QTL model with which to explore the genetic architecture of complex trait in multiple environments. Our model includes the effects of multiple QTLs, epistasis, QTL-by-environment interactions and epistasis-by-environment interactions. A new mapping strategy, including marker interval selection, detection of marker interval interactions and genome scans, is used to evaluate putative locations of multiple QTLs and their interactions. All the mapping procedures are performed in the framework of mixed linear model that are flexible to model environmental factors regardless of fix or random effects being assumed. An F-statistic based on Henderson method III is used for hypothesis tests. This method is less computationally greedy than corresponding likelihood ratio test. In each of the mapping procedures, permutation testing is exploited to control for genome-wide false positive rate, and model selection is used to reduce ghost peaks in F-statistic profile. Parameters of the full-QTL model are estimated using a Bayesian method via Gibbs sampling. Monte Carlo simulations help define the reliability and efficiency of the method. Two real-world phenotypes (BXD mouse olfactory bulb weight data and rice yield data) are used as exemplars to demonstrate our methods.'),
(73404, 2007, 'bio', 'GENOME: a rapid coalescent-based whole genome simulator', 'Liang, Liming|Zöllner, Sebastian|Abecasis, Gonçalo R', 'Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. lianglim@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. lianglim@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. lianglim@umich.edu', 'United States|United States|United States', 0, 'Alleles|Computational Biology|Computer Simulation|Gene Frequency|Genome|Genome, Human|Humans|Models, Genetic|Mutation|Programming Languages|Recombination, Genetic|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btm138', '17459963', 'GENOME proposes a rapid coalescent-based approach to simulate whole genome data. In addition to features of standard coalescent simulators, the program allows for recombination rates to vary along the genome and for flexible population histories. Within small regions, we have evaluated samples simulated by GENOME to verify that GENOME provides the expected LD patterns and frequency spectra. The program can be used to study the sampling properties of any statistic for a whole genome study.'),
(73405, 2007, 'bio', 'CTX-BLAST: context sensitive version of protein BLAST', 'Gambin, Anna|Wojtalewicz, Piotr', 'Institute of Informatics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland. aniag@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland. aniag@mimuw.edu.pl', 'Poland|Poland', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Statistical|Molecular Sequence Data|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btm136', '17459964', 'We present a software tool CTX-BLAST that incorporates contextual alignment model into the popular protein BLAST program. Our alignment tool allows us to investigate the effect of context-dependency in the protein alignment much more efficient than using previous dynamic algorithms. The software makes use of non-symmetric contextual substitution tables and calculates the statistical significance of a given alignment according to the contextual statistical model.'),
(73406, 2007, 'bio', 'TOPD/FMTS: a new software to compare phylogenetic trees', 'Puigbò, Pere|Garcia-Vallvé, Santiago|McInerney, James O', 'Evolutionary Genomics Group, Biochemistry and Biotechnology Department, Rovira i Virgili University, Tarragona, Spain. ppuigbo@urv.cat|Evolutionary Genomics Group, Biochemistry and Biotechnology Department, Rovira i Virgili University, Tarragona, Spain. ppuigbo@urv.cat|Evolutionary Genomics Group, Biochemistry and Biotechnology Department, Rovira i Virgili University, Tarragona, Spain. ppuigbo@urv.cat', 'Spain|Spain|Spain', 96, 'Algorithms|Computational Biology|Evolution, Molecular|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/btm135', '17459965', 'TOPD/FMTS has been developed to evaluate similarities and differences between phylogenetic trees. The software implements several new algorithms (including the Disagree method that returns the taxa, that disagree between two trees and the Nodal method that compares two trees using nodal information) and several previously described methods (such as the Partition method, Triplets or Quartets) to compare phylogenetic trees. One of the novelties of this software is that the FMTS (From Multiple to Single) program allows the comparison of trees that contain both orthologs and paralogs. Each option is also complemented with a randomization analysis to test the null hypothesis that the similarity between two trees is not better than chance expectation.'),
(73407, 2007, 'bio', 'A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays', 'Xiao, Yuanyuan|Segal, Mark R|Yang, Y H|Yeh, Ru-Fang', 'Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA. yxiao@itsa.ucsf.edu|Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA. yxiao@itsa.ucsf.edu|Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA. yxiao@itsa.ucsf.edu|Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA. yxiao@itsa.ucsf.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Alleles|Cluster Analysis|Computational Biology|Genotype|Haplotypes|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btm131', '17459966', 'Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls.'),
(73408, 2007, 'bio', 'Simultaneous and exact interval estimates for the contrast of two groups based on an extremely high dimensional variable: application to mass spec data', 'Park, Yuhyun|Downing, Sean R|Kim, Dohyun|Hahn, William C|Li, Cheng|Kantoff, Philip W|Wei, L J', 'Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com|Department of Biostatistics, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. parkyuhyun@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 10, 'Algorithms|Artifacts|Biomarkers|Biomarkers, Tumor|Computational Biology|Female|Humans|Male|Mass Spectrometry|Ovarian Neoplasms|Prostatic Neoplasms|Protein Array Analysis|Proteome|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Comparative Study', '10.1093/bioinformatics/btm130', '17459967', 'Analysis of high-throughput proteomic/genomic data, in particular, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) data and microarray data, has led to a multitude of techniques aimed at identifying potential biomarkers. Most of the statistical techniques for comparing two groups are based on qualitative measures such as P-value. A quantitative way such as interval estimation for the contrasts of two groups is more appealing.'),
(73409, 2007, 'bio', 'A quantitative model for linking two disparate sets of articles in MEDLINE', 'Torvik, Vetle I|Smalheiser, Neil R', 'Department of Psychiatry and Psychiatric Institute (MC912), University of Illinois-Chicago, Chicago, IL 60612, USA.|Department of Psychiatry and Psychiatric Institute (MC912), University of Illinois-Chicago, Chicago, IL 60612, USA.', 'United States|United States', 15, 'Algorithms|Artificial Intelligence|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btm161', '17463015', 'Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator.'),
(73410, 2007, 'bio', 'Faspad: fast signaling pathway detection', 'Hüffner, Falk|Wernicke, Sebastian|Zichner, Thomas', 'Institut für Informatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. hueffner@minet.uni-jena.de|Institut für Informatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. hueffner@minet.uni-jena.de|Institut für Informatik, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany. hueffner@minet.uni-jena.de', 'Germany|Germany|Germany', 14, 'Algorithms|Computer Simulation|Gene Expression|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btm160', '17463016', 'Faspad is a user-friendly tool that detects candidates for linear signaling pathways in protein interaction networks based on an approach by Scott et al. (Journal of Computational Biology, 2006). Using recent algorithmic insights, it can solve the underlying NP-hard problem quite fast: for protein networks of typical size (several thousand nodes), pathway candidates of length up to 13 proteins can be found within seconds and with a 99.9% probability of optimality. Faspad graphically displays all candidates that are found, for evaluation and comparison purposes, an overlay of several candidates and the surrounding network context can also be shown.'),
(73411, 2007, 'bio', 'SciRoKo: a new tool for whole genome microsatellite search and investigation', 'Kofler, Robert|Schlötterer, Christian|Lelley, Tamas', 'University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology IFA-Tulln, Institute of Biotechnology in Plant Production, 3430 Tulln, Austria. robert.kofler@boku.ac.at|University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology IFA-Tulln, Institute of Biotechnology in Plant Production, 3430 Tulln, Austria. robert.kofler@boku.ac.at|University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology IFA-Tulln, Institute of Biotechnology in Plant Production, 3430 Tulln, Austria. robert.kofler@boku.ac.at', 'Austria|Austria|Austria', 242, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Microsatellite Repeats|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btm157', '17463017', 'SciRoKo is a user-friendly software tool for the identification of microsatellites in genomic sequences. The combination of an extremely fast search algorithm with a built-in summary statistic tool makes SciRoKo an excellent tool for full genome analysis. Compared to other already existing tools, SciRoKo also allows the analysis of compound microsatellites.'),
(73412, 2007, 'bio', 'The qualitative and time-dependent character of spatial relations in biomedical ontologies', 'Bittner, Thomas|Goldberg, Louis J', 'Department of Philosophy, University at Buffalo, USA. bittner3@buffalo.edu|Department of Philosophy, University at Buffalo, USA. bittner3@buffalo.edu', 'United States|United States', 8, 'Algorithms|Anatomy|Biomedical Engineering|Computer Simulation|Humans|Image Interpretation, Computer-Assisted|Models, Anatomic|Temporomandibular Joint|Terminology as Topic|Time Factors|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btm155', '17463018', 'The formal representation of mereological aspects of canonical anatomy (parthood relations) is relatively well understood. The formal representation of other aspects of canonical anatomy, such as connectedness and adjacency relations between anatomical parts, their shape and size as well as the spatial arrangement of anatomical parts within larger anatomical structures are, however, much less well understood and represented in existing computational anatomical and bio-medical ontologies only insufficiently.'),
(73413, 2007, 'bio', 'SNP detection exploiting multiple sources of redundancy in large EST collections improves validation rates', 'Hayes, Ben J|Nilsen, Kjetil|Berg, Paul R|Grindflek, Eli|Lien, Sigbjørn', 'Animal Genetics and Genomics, Primary Industries Research Victoria, 475 Mickleham Rd, Attwood, Victoria, Australia 3049. ben.hayes@dpi.vic.gov.au|Animal Genetics and Genomics, Primary Industries Research Victoria, 475 Mickleham Rd, Attwood, Victoria, Australia 3049. ben.hayes@dpi.vic.gov.au|Animal Genetics and Genomics, Primary Industries Research Victoria, 475 Mickleham Rd, Attwood, Victoria, Australia 3049. ben.hayes@dpi.vic.gov.au|Animal Genetics and Genomics, Primary Industries Research Victoria, 475 Mickleham Rd, Attwood, Victoria, Australia 3049. ben.hayes@dpi.vic.gov.au|Animal Genetics and Genomics, Primary Industries Research Victoria, 475 Mickleham Rd, Attwood, Victoria, Australia 3049. ben.hayes@dpi.vic.gov.au', 'Australia|Australia|Australia|Australia|Australia', 26, 'Animals|Artifacts|Atlantic Ocean|Base Sequence|DNA Mutational Analysis|Databases, Genetic|Expressed Sequence Tags|Molecular Sequence Data|Polymorphism, Single Nucleotide|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Swine|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btm154', '17463019', 'Single nucleotide polymorphism (SNP) detection exploiting redundancy in expressed sequence tag (EST) collections that arises from the presence of transcripts of the same gene from different individuals has been used to generate large collections of SNPs for many species. A second source of redundancy, namely that EST collections can contain multiple transcripts of the same gene from the same individual, can be exploited to distinguish true SNPs from sequencing error. In this article, we demonstrate with Atlantic salmon and pig EST collections that splitting the EST collection in two, detecting SNPs in both subsets, then accepting only cross-validated SNPs increases validation rates.'),
(73414, 2007, 'bio', 'OSLay: optimal syntenic layout of unfinished assemblies', 'Richter, Daniel C|Schuster, Stephan C|Huson, Daniel H', 'Center for Bioinformatics (ZBIT), Institute for Computer Science, Tübingen University, 72076 Tübingen, Germany. drichter@informatik.uni-tuebingen.de|Center for Bioinformatics (ZBIT), Institute for Computer Science, Tübingen University, 72076 Tübingen, Germany. drichter@informatik.uni-tuebingen.de|Center for Bioinformatics (ZBIT), Institute for Computer Science, Tübingen University, 72076 Tübingen, Germany. drichter@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 63, 'Algorithms|Computer Graphics|Contig Mapping|DNA Primers|Sequence Analysis, DNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btm153', '17463020', 'The whole genome shotgun approach to genome sequencing results in a collection of contigs that must be ordered and oriented to facilitate efficient gap closure. We present a new tool OSLay that uses synteny between matching sequences in a target assembly and a reference assembly to layout the contigs (or scaffolds) in the target assembly. The underlying algorithm is based on maximum weight matching. The tool provides an interactive visualization of the computed layout and the result can be imported into the assembly editing tool Consed to support the design of primer pairs for gap closure.'),
(73415, 2007, 'bio', 'Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method', 'Fujita, A|Sato, J R|Garay-Malpartida, H M|Morettin, P A|Sogayar, M C|Ferreira, C E', 'Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.|Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.|Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.|Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.|Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.|Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 53, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Regression Analysis|Signal Transduction|Time Factors', 'Journal Article', '10.1093/bioinformatics/btm151', '17463021', 'A variety of biological cellular processes are achieved through a variety of extracellular regulators, signal transduction, protein-protein interactions and differential gene expression. Understanding of the mechanisms underlying these processes requires detailed molecular description of the protein and gene networks involved. To better understand these molecular networks, we propose a statistical method to estimate time-varying gene regulatory networks from time series microarray data. One well known problem when inferring connectivity in gene regulatory networks is the fact that the relationships found constitute correlations that do not allow inferring causation, for which, a priori biological knowledge is required. Moreover, it is also necessary to know the time period at which this causation occurs. Here, we present the Dynamic Vector Autoregressive model as a solution to these problems.'),
(73416, 2007, 'bio', 'A network-based method for target selection in metabolic networks', 'Guimerà, R|Sales-Pardo, M|Amaral, L A N', 'Northwestern Institute on Complex Systems and Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA. rguimera@northwestern.edu|Northwestern Institute on Complex Systems and Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA. rguimera@northwestern.edu|Northwestern Institute on Complex Systems and Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA. rguimera@northwestern.edu', 'United States|United States|United States', 0, 'Algorithms|Bacteria|Bacterial Proteins|Drug Delivery Systems|Drug Design|Models, Biological|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm150', '17463022', 'The lack of new antimicrobials, combined with increasing microbial resistance to old ones, poses a serious threat to public health. With hundreds of genomes sequenced, systems biology promises to help in solving this problem by uncovering new drug targets.'),
(73417, 2007, 'bio', 'Meta-analysis of gene expression data: a predictor-based approach', 'Fishel, Irit|Kaufman, Alon|Ruppin, Eytan', 'School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel.|School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel.|School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel.', 'Israel|Israel|Israel', 0, 'Algorithms|Biomarkers, Tumor|Database Management Systems|Databases, Protein|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Information Storage and Retrieval|Lung Neoplasms|Meta-Analysis as Topic|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm149', '17463023', 'With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately.'),
(73418, 2007, 'bio', 'An efficient method for the detection and elimination of systematic error in high-throughput screening', 'Makarenkov, Vladimir|Zentilli, Pablo|Kevorkov, Dmytro|Gagarin, Andrei|Malo, Nathalie|Nadon, Robert', 'Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca|Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca|Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca|Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca|Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca|Department dinformatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada. makarenkov.vladimir@uqam.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 75, 'Artifacts|Biological Assay|Data Interpretation, Statistical|Drug Design|Drug Evaluation, Preclinical|Sensitivity and Specificity|Technology, Pharmaceutical', 'Journal Article', '10.1093/bioinformatics/btm145', '17463024', 'High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS.'),
(73419, 2007, 'bio', 'Group SCAD regression analysis for microarray time course gene expression data', 'Wang, Lifeng|Chen, Guang|Li, Hongzhe', 'Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States|United States', 130, 'Cell Cycle|Chromatin Immunoprecipitation|Computer Simulation|Gene Expression|Gene Expression Regulation|Oligonucleotide Array Sequence Analysis|Regression Analysis|Saccharomyces cerevisiae|Time Factors|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm125', '17463025', 'Since many important biological systems or processes are dynamic systems, it is important to study the gene expression patterns over time in a genomic scale in order to capture the dynamic behavior of gene expression. Microarray technologies have made it possible to measure the gene expression levels of essentially all the genes during a given biological process. In order to determine the transcriptional factors (TFs) involved in gene regulation during a given biological process, we propose to develop a functional response model with varying coefficients in order to model the transcriptional effects on gene expression levels and to develop a group smoothly clipped absolute deviation (SCAD) regression procedure for selecting the TFs with varying coefficients that are involved in gene regulation during a biological process.'),
(73420, 2007, 'bio', 'iHMMune-align: hidden Markov model-based alignment and identification of germline genes in rearranged immunoglobulin gene sequences', 'Gaëta, Bruno A|Malming, Harald R|Jackson, Katherine J L|Bain, Michael E|Wilson, Patrick|Collins, Andrew M', 'School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney, NSW 2052, Australia. bgaeta@cse.unsw.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Gene Rearrangement|Genes, Immunoglobulin|Germ-Line Mutation|Immunoglobulins|Markov Chains|Models, Genetic|Models, Immunological|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btm147', '17463026', 'Immunoglobulin heavy chain (IGH) genes in mature B lymphocytes are the result of recombination of IGHV, IGHD and IGHJ germline genes, followed by somatic mutation. The correct identification of the germline genes that make up a variable VH domain is essential to our understanding of the process of antibody diversity generation as well as to clinical investigations of some leukaemias and lymphomas.'),
(73421, 2007, 'bio', 'An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks', 'Ching, Wai-Ki|Zhang, Shuqin|Ng, Michael K|Akutsu, Tatsuya', 'Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong.|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong.|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong.|Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 65, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression Regulation|Markov Chains|Models, Genetic|Models, Statistical|Monte Carlo Method|Probability|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btm142', '17463027', 'Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2(n)-by-2(n) where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method.'),
(73422, 2007, 'bio', 'Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer', 'Huang, D|Chow, Tommy W S', 'Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR. ifkorf@ucdavis.edu|Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR. ifkorf@ucdavis.edu', 'Hong Kong|Hong Kong', 0, 'Artificial Intelligence|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Male|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms', 'Comparative Study', '10.1093/bioinformatics/btm141', '17463028', 'Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed.'),
(73423, 2007, 'bio', 'Two-stage designs applying methods differing in costs', 'Goll, Alexandra|Bauer, Peter', 'Section of Medical Statistics, Medical University of Vienna, Vienna, Austria. alexandra.goll@meduniwien.ac.at|Section of Medical Statistics, Medical University of Vienna, Vienna, Austria. alexandra.goll@meduniwien.ac.at', 'Austria|Austria', 5, 'Chromatography, Liquid|Computational Biology|Costs and Cost Analysis|Data Interpretation, Statistical|Enzyme-Linked Immunosorbent Assay|Gene Expression Profiling|Mass Spectrometry|Models, Statistical|Proteomics|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm140', '17463029', 'Two-stage pilot and integrated designs are powerful tools for investigating large numbers of hypotheses. Asymptotically, optimal two-stage designs controlling the familywise error or false discovery rate are considered when costs and effect sizes per measurement differ between stages and total costs are constrained.'),
(73424, 2007, 'bio', 'DITOP: drug-induced toxicity related protein database', 'Zhang, Jing-Xian|Huang, Wei-Juan|Zeng, Jing-Hua|Huang, Wen-Hui|Wang, Yi|Zhao, Rui|Han, Bu-Cong|Liu, Qing-Feng|Chen, Yu-Zong|Ji, Zhi-Liang', 'Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.|Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian, PR China.', 'China|China|China|China|China|China|China|China|China|China', 30, 'Adverse Drug Reaction Reporting Systems|Databases, Protein|Drug-Related Side Effects and Adverse Reactions|Pharmaceutical Preparations|Proteins', 'Journal Article', '10.1093/bioinformatics/btm139', '17463030', 'Drug-induced toxicity related proteins (DITRPs) are proteins that mediate adverse drug reactions (ADRs) or toxicities through their binding to drugs or reactive metabolites. Collection of these proteins facilitates better understanding of the molecular mechanisms of drug-induced toxicity and the rational drug discovery. Drug-induced toxicity related protein database (DITOP) is such a database that is intending to provide comprehensive information of DITRPs. Currently, DITOP contains 1501 records, covering 618 distinct literature-reported DITRPs, 529 drugs/ligands and 418 distinct toxicity terms. These proteins were confirmed experimentally to interact with drugs or their reactive metabolites, thus directly or indirectly cause adverse effects or toxicities. Five major types of drug-induced toxicities or ADRs are included in DITOP, which are the idiosyncratic adverse drug reactions, the dose-dependent toxicities, the drug-drug interactions, the immune-mediated adverse drug effects (IMADEs) and the toxicities caused by genetic susceptibility. Molecular mechanisms underlying the toxicity and cross-links to related resources are also provided while available. Moreover, a series of user-friendly interfaces were designed for flexible retrieval of DITRPs-related information. The DITOP can be accessed freely at http://bioinf.xmu.edu.cn/databases/ADR/index.html.'),
(73425, 2007, 'bio', 'Human recombination rates are increased around accelerated conserved regions--evidence for continued selection?', 'Freudenberg, Jan|Fu, Ying-Hui|Ptácek, Louis J', 'University of California San Francisco, Department of Neurology, Institute of Human Genetics, San Francisco, CA 94158-2922, USA. jan.freudenberg@ucsf.edu|University of California San Francisco, Department of Neurology, Institute of Human Genetics, San Francisco, CA 94158-2922, USA. jan.freudenberg@ucsf.edu|University of California San Francisco, Department of Neurology, Institute of Human Genetics, San Francisco, CA 94158-2922, USA. jan.freudenberg@ucsf.edu', 'United States|United States|United States', 0, 'Base Pairing|Base Sequence|Conserved Sequence|Evolution, Molecular|Humans|Recombination, Genetic|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btm137', '17463031', 'We hypothesized that recombination rates might be increased at genetic loci that are subject to more intense selection. Here, we test this hypothesis by using a recently published set of accelerated conserved regions and fine-scale recombination rate estimates provided by the HapMap project.'),
(73426, 2007, 'bio', 'Libaffy: software for processing Affymetrix GeneChip data', 'Eschrich, Steven A|Hoerter, Andrew M', 'H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Steven.Eschrich@moffitt.org|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA. Steven.Eschrich@moffitt.org', 'United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm127', '17463032', 'Affymetrix GeneChip microarrays are increasingly used in gene expression studies and in greater number. A software library was developed that supports Affymetrix file formats and implements two popular summary algorithms (MAS5.0 and RMA). The library is modular in design for integration into larger systems and processing pipelines. Additionally, a graphical interface (GENE) was developed to allow end-user access to the functionality within the library.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73427, 2007, 'bio', 'Alignment of molecular networks by integer quadratic programming', 'Zhenping, Li|Zhang, Shihua|Wang, Yong|Zhang, Xiang-Sun|Chen, Luonan', 'Beijing Wuzi University, Beijing 101149, China.|Beijing Wuzi University, Beijing 101149, China.|Beijing Wuzi University, Beijing 101149, China.|Beijing Wuzi University, Beijing 101149, China.|Beijing Wuzi University, Beijing 101149, China.', 'China|China|China|China|China', 85, 'Algorithms|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Biological|Programming, Linear|Proteome|Sequence Alignment|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm156', '17468121', 'With more and more data on molecular networks (e.g. protein interaction networks, gene regulatory networks and metabolic networks) available, the discovery of conserved patterns or signaling pathways by comparing various kinds of networks among different species or within a species becomes an increasingly important problem. However, most of the conventional approaches either restrict comparative analysis to special structures, such as pathways, or adopt heuristic algorithms due to computational burden.'),
(73428, 2007, 'bio', 'GAzer: gene set analyzer', 'Kim, Sang-Bae|Yang, Sungjin|Kim, Seon-Kyu|Kim, Sang Cheol|Woo, Hyun Goo|Volsky, David J|Kim, Seon-Young|Chu, In-Sun', 'Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea.', '-|-|-|-|-|-|-|-', 32, 'Algorithms|Animals|Gene Expression Profiling|Humans|Internet|Mice|Multigene Family|Oligonucleotide Array Sequence Analysis|Rats|Software|Systems Integration|User-Computer Interface|Yeasts', 'Journal Article', '10.1093/bioinformatics/btm144', '17468122', 'Gene Set Analyzer (GAzer) is a web-based integrated gene set analysis tool covering previously reported parametric and non-parametric models. Based on a simulation test for the reported algorithms, we classified and implemented three main statistical methods consisting of the z-statistic, gene permutation and sample permutation for ten gene set categories including Gene Ontology (GO) for human, mouse, rat and yeast. This tool identifies significantly altered gene sets scored by z-statistics and P-values from the z-test or permutation test and provides q-values and Bonferroni P-values to correct multiple hypothesis testing. GAzer allows users to observe changes in expression of each gene in a gene set or to see the significance of the gene sets containing a gene(s) of interest, thus allowing interactive data analysis both at the gene and gene set level. Moreover, GAzer offers extensive annotation for each gene.'),
(73429, 2007, 'bio', 'SPACER: identification of cis-regulatory elements with non-contiguous critical residues', 'Chakravarty, Arijit|Carlson, Jonathan M|Khetani, Radhika S|DeZiel, Charles E|Gross, Robert H', 'Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA.|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA.|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA.|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA.|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Molecular Sequence Data|Protein Binding|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm041', '17470480', 'Many transcription factors bind to sites that are long and loosely related to each other. De novo identification of such motifs is computationally challenging. In this article, we propose a novel semi-greedy algorithm over the space of all IUPAC degenerate strings to identify the most over-represented highly degenerate motifs.'),
(73430, 2007, 'bio', 'Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach', 'Pihur, Vasyl|Datta, Susmita|Datta, Somnath', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.', 'United States|United States|United States', 106, 'Algorithms|Artificial Intelligence|Cluster Analysis|Entropy|Gene Expression Profiling|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1093/bioinformatics/btm158', '17483500', 'Biologists often employ clustering techniques in the explorative phase of microarray data analysis to discover relevant biological groupings. Given the availability of numerous clustering algorithms in the machine-learning literature, an user might want to select one that performs the best for his/her data set or application. While various validation measures have been proposed over the years to judge the quality of clusters produced by a given clustering algorithm including their biological relevance, unfortunately, a given clustering algorithm can perform poorly under one validation measure while outperforming many other algorithms under another validation measure. A manual synthesis of results from multiple validation measures is nearly impossible in practice, especially, when a large number of clustering algorithms are to be compared using several measures. An automated and objective way of reconciling the rankings is needed.'),
(73431, 2007, 'bio', 'Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis', 'Kim, Hyunsoo|Park, Haesun', 'College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA. hskim@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA. hskim@cc.gatech.edu', 'Georgia|Georgia', 0, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Entropy|Factor Analysis, Statistical|Gene Expression|Humans|Least-Squares Analysis|Microarray Analysis|Neoplasms|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btm134', '17483501', 'Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.'),
(73432, 2007, 'bio', 'Genomix: a method for combining gene-finders predictions, which uses evolutionary conservation of sequence and intron-exon structure', 'Coghlan, Avril|Durbin, Richard', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. alc@sanger.ac.uk', 'United Kingdom|United Kingdom', 0, 'Animals|Base Sequence|Caenorhabditis|Computational Biology|Conserved Sequence|DNA|DNA, Intergenic|Exons|Genome, Helminth|Introns|Models, Genetic|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm133', '17483502', 'Correct gene predictions are crucial for most analyses of genomes. However, in the absence of transcript data, gene prediction is still challenging. One way to improve gene-finding accuracy in such genomes is to combine the exons predicted by several gene-finders, so that gene-finders that make uncorrelated errors can correct each other.'),
(73433, 2007, 'bio', 'PQuad--a visual analysis platform for proteomic data exploration of microbial organisms', 'Webb-Robertson, Bobbie-Jo M|Peterson, Elena S|Singhal, Mudita|Klicker, Kyle R|Oehmen, Christopher S|Adkins, Joshua N|Havre, Susan L', 'Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. bobbie-jo.webb-robertson@pnl.gov', 'United States|United States|United States|United States|United States|United States|United States', 2, 'Algorithms|Bacterial Physiological Phenomena|Computer Graphics|Gene Expression Profiling|Proteome|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm132', '17483503', 'The visual Platform for Proteomics Peptide and Protein data exploration (PQuad) is a multi-resolution environment that visually integrates genomic and proteomic data for prokaryotic systems, overlays categorical annotation and compares differential expression experiments. PQuad requires Java 1.5 and has been tested to run across different operating systems.'),
(73434, 2007, 'bio', 'A Markov random field model for network-based analysis of genomic data', 'Wei, Zhi|Li, Hongzhe', 'Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.|Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States', 0, 'Adult|Aged|Aged, 80 and over|Algorithms|Bayes Theorem|Breast Neoplasms|Computer Simulation|Databases, Genetic|Female|Gene Expression Regulation|Genome, Human|Humans|Likelihood Functions|Markov Chains|Middle Aged|Models, Genetic|Models, Statistical|Neoplasm Metastasis|Neoplasm Recurrence, Local|Oligonucleotide Array Sequence Analysis|Phenotype|Sensitivity and Specificity|Survival Analysis|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm129', '17483504', 'A central problem in genomic research is the identification of genes and pathways involved in diseases and other biological processes. The genes identified or the univariate test statistics are often linked to known biological pathways through gene set enrichment analysis in order to identify the pathways involved. However, most of the procedures for identifying differentially expressed (DE) genes do not utilize the known pathway information in the phase of identifying such genes. In this article, we develop a Markov random field (MRF)-based method for identifying genes and subnetworks that are related to diseases. Such a procedure models the dependency of the DE patterns of genes on the networks using a local discrete MRF model.'),
(73435, 2007, 'bio', 'BioDownloader: bioinformatics downloads and updates in a few clicks', 'Shapovalov, Maxim V|Canutescu, Adrian A|Dunbrack, Roland L', 'Fox Chase Cancer Center, Philadelphia PA 19111 USA.|Fox Chase Cancer Center, Philadelphia PA 19111 USA.|Fox Chase Cancer Center, Philadelphia PA 19111 USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm120', '17483505', 'There are many ftp or http servers storing data required for biological research. While some download applications are available, there is no user-friendly download application with a graphical interface specifically designed and adapted to meet the requirements of bioinformatics. BioDownloader is a program for downloading and updating files from ftp and http servers. It is optimized to work robustly with large numbers of files. It allows the selective retrieval of only the required files (batch downloads, multiple file masks, ls-lR file parsing, recursive search, recent updates, etc.). BioDownloader has a built-in repository containing the settings for common bioinformatics file-synchronization needs, including the Protein Data Bank (PDB) and National Center for Biotechnology Information (NCBI) databases. It can post-process downloaded files, including archive extraction and file conversions.'),
(73436, 2007, 'bio', 'Automated Improvement of Domain ANnotations using context analysis of domain arrangements (AIDAN)', 'Beaussart, Francois|Weiner, January|Bornberg-Bauer, Erich', 'Evolutionary Bioinformatics, Institute for Evolution and Biodiversity, Westfälische Wilhelms University, Schlossplatz 4, D48149 Münster, Germany.|Evolutionary Bioinformatics, Institute for Evolution and Biodiversity, Westfälische Wilhelms University, Schlossplatz 4, D48149 Münster, Germany.|Evolutionary Bioinformatics, Institute for Evolution and Biodiversity, Westfälische Wilhelms University, Schlossplatz 4, D48149 Münster, Germany.', 'Germany|Germany|Germany', 12, 'Algorithms|Automation|Cluster Analysis|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm240', '17483506', 'Since protein domains are the units of evolution, databases of domain signatures such as ProDom or Pfam enable both a sensitive and selective sequence analysis. However, manually curated databases have a low coverage and automatically generated ones often miss relationships which have not yet been discovered between domains or cannot display similarities between domains which have drifted apart.'),
(73437, 2007, 'bio', 'Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms', 'Tai, Feng|Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building (MMC 303), Minneapolis, MN 55455-0378, USA.|Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building (MMC 303), Minneapolis, MN 55455-0378, USA.', 'United States|United States', 46, 'Algorithms|Breast Neoplasms|Computational Biology|Female|Gene Expression Regulation, Neoplastic|Humans|Least-Squares Analysis|Male|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Prostatic Neoplasms|Regression Analysis|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btm234', '17483507', 'In the context of sample (e.g. tumor) classifications with microarray gene expression data, many methods have been proposed. However, almost all the methods ignore existing biological knowledge and treat all the genes equally a priori. On the other hand, because some genes have been identified by previous studies to have biological functions or to be involved in pathways related to the outcome (e.g. cancer), incorporating this type of prior knowledge into a classifier can potentially improve both the predictive performance and interpretability of the resulting model.'),
(73438, 2007, 'bio', 'Phenotypic clustering of yeast mutants based on kinetochore microtubule dynamics', 'Jaqaman, K|Dorn, J F|Marco, E|Sorger, P K|Danuser, G', 'Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. kjaqaman@scripps.edu|Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. kjaqaman@scripps.edu|Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. kjaqaman@scripps.edu|Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. kjaqaman@scripps.edu|Department of Cell Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. kjaqaman@scripps.edu', 'United States|United States|United States|United States|United States', 1, 'Algorithms|Cluster Analysis|Kinetics|Kinetochores|Microtubules|Models, Biological|Mutation|Phenotype|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Temperature', 'Journal Article', '10.1093/bioinformatics/btm230', '17483508', 'Kinetochores are multiprotein complexes which mediate chromosome attachment to microtubules (MTs) of the mitotic spindle. They regulate MT dynamics during chromosome segregation. Our goal is to identify groups of kinetochore proteins with similar effects on MT dynamics, revealing pathways through which kinetochore proteins transform chemical and mechanical input signals into cues of MT regulation.'),
(73439, 2007, 'bio', 'Detection of generic spaced motifs using submotif pattern mining', 'Wijaya, Edward|Rajaraman, Kanagasabai|Yiu, Siu-Ming|Sung, Wing-Kin', 'Institute for Infocomm Research, Singapore.|Institute for Infocomm Research, Singapore.|Institute for Infocomm Research, Singapore.|Institute for Infocomm Research, Singapore.', 'Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Computational Biology|Models, Statistical|Pattern Recognition, Automated|Protein Binding|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btm118', '17483509', 'Identification of motifs is one of the critical stages in studying the regulatory interactions of genes. Motifs can have complicated patterns. In particular, spaced motifs, an important class of motifs, consist of several short segments separated by spacers of different lengths. Locating spaced motifs is not trivial. Existing motif-finding algorithms are either designed for monad motifs (short contiguous patterns with some mismatches) or have assumptions on the spacer lengths or can only handle at most two segments. An effective motif finder for generic spaced motifs is highly desirable.'),
(73440, 2007, 'bio', 'Bioinformatics software for biologists in the genomics era', 'Kumar, Sudhir|Dudley, Joel', 'Center for Evolutionary Functional Genomics, The Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, Arizona 85287-5301, USA. s.kumar@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, Arizona 85287-5301, USA. s.kumar@asu.edu', 'United States|United States', 50, 'Algorithms|Animals|Biology|Computational Biology|Computers|Genome|Genomics|Humans|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm239', '17485425', 'The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.'),
(73441, 2007, 'bio', 'CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering', 'Xiang, Zuoshuang|Qin, Zhaohui S|He, Yongqun', 'Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Internet|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm238', '17485426', 'CRCView is a user-friendly point-and-click web server for analyzing and visualizing microarray gene expression data using a Dirichlet process mixture model-based clustering algorithm. CRCView is designed to clustering genes based on their expression profiles. It allows flexible input data format, rich graphical illustration as well as integrated GO term based annotation/interpretation of clustering results.'),
(73442, 2007, 'bio', 'Genetdes: automatic design of transcriptional networks', 'Rodrigo, Guillermo|Carrera, Javier|Jaramillo, Alfonso', 'Dep. de Matematica Aplicada, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain.|Dep. de Matematica Aplicada, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain.|Dep. de Matematica Aplicada, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain.', 'Spain|Spain|Spain', 59, 'Algorithms|Automation|Computational Biology|Computer Simulation|Kinetics|Models, Biological|Models, Statistical|Oscillometry|Promoter Regions, Genetic|Software|Systems Biology|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm237', '17485427', 'The rational design of biological networks with prescribed functions is limited to gene circuits of a few genes. Larger networks involve complex interactions with many parameters and the use of automated computational tools can be very valuable. We propose a new tool to design transcriptional networks with targeted behavior that could be used to better understand the design principles of genetic circuits.'),
(73443, 2007, 'bio', 'ChromatinDB: a database of genome-wide histone modification patterns for Saccharomyces cerevisiae', 'OConnor, Timothy R|Wyrick, John J', 'School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA.|School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA.', 'United States|United States', 0, 'Chromatin|Chromatin Immunoprecipitation|Computational Biology|Computer Graphics|Databases, Factual|Genome, Fungal|Histone Acetyltransferases|Histones|Models, Genetic|Models, Statistical|Open Reading Frames|Promoter Regions, Genetic|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btm236', '17485428', 'Covalent modifications to histone proteins play a critical role in regulating gene transcription. Previous studies have used chromatin immunoprecipitation (ChIP) based microarray assays to profile genomic regions that are enriched or depleted for a particular histone modification. Such studies have been conducted extensively in the yeast Saccharomyces cerevisiae, but currently no comprehensive data repositories or analysis tools are available for these data sets. For this reason, we have developed the ChromatinDB database, which contains genome-wide ChIP data for 22 different histones or histone modifications in S. cerevisiae. ChromatinDB provides novel tools to facilitate the visualization and statistical analysis of chromatin features for user-selected gene sets.'),
(73444, 2007, 'bio', 'CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure', 'Jakobsson, Mattias|Rosenberg, Noah A', 'Center for Computational Medicine and Biology, Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA. mjakob@umich.edu|Center for Computational Medicine and Biology, Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA. mjakob@umich.edu', 'United States|United States', 0, 'Algorithms|Animals|Chickens|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Genetics, Population|Genotype|Models, Genetic|Models, Statistical|Population Dynamics|Software', 'Journal Article', '10.1093/bioinformatics/btm233', '17485429', 'Clustering of individuals into populations on the basis of multilocus genotypes is informative in a variety of settings. In population-genetic clustering algorithms, such as BAPS, STRUCTURE and TESS, individual multilocus genotypes are partitioned over a set of clusters, often using unsupervised approaches that involve stochastic simulation. As a result, replicate cluster analyses of the same data may produce several distinct solutions for estimated cluster membership coefficients, even though the same initial conditions were used. Major differences among clustering solutions have two main sources: (1) label switching of clusters across replicates, caused by the arbitrary way in which clusters in an unsupervised analysis are labeled, and (2) genuine multimodality, truly distinct solutions across replicates.'),
(73445, 2007, 'bio', 'Assessment of survival prediction models based on microarray data', 'Schumacher, Martin|Binder, Harald|Gerds, Thomas', 'Department of Medical Biometry and Statistics, Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. sec@imbi.uni-freiburg.de|Department of Medical Biometry and Statistics, Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. sec@imbi.uni-freiburg.de|Department of Medical Biometry and Statistics, Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. sec@imbi.uni-freiburg.de', 'Germany|Germany|Germany', 72, 'Computational Biology|Humans|Likelihood Functions|Lymphoma, B-Cell|Lymphoma, Large B-Cell, Diffuse|Models, Statistical|Oligonucleotide Array Sequence Analysis|Prognosis|Proportional Hazards Models|Regression Analysis|Reproducibility of Results|Risk|Time Factors|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btm232', '17485430', 'In the process of developing risk prediction models, various steps of model building and model selection are involved. If this process is not adequately controlled, overfitting may result in serious overoptimism leading to potentially erroneous conclusions.'),
(73446, 2007, 'bio', 'Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data', 'Soranzo, Nicola|Bianconi, Ginestra|Altafini, Claudio', 'SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.|SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.|SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Biomedical Engineering|Computer Simulation|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm163', '17485431', 'Inferring a gene regulatory network exclusively from microarray expression profiles is a difficult but important task. The aim of this work is to compare the predictive power of some of the most popular algorithms in different conditions (like data taken at equilibrium or time courses) and on both synthetic and real microarray data. We are in particular interested in comparing similarity measures both of linear type (like correlations and partial correlations) and of non-linear type (mutual information and conditional mutual information), and in investigating the underdetermined case (less samples than genes).'),
(73447, 2007, 'bio', 'CALIB: a Bioconductor package for estimating absolute expression levels from two-color microarray data', 'Zhao, Hui|Engelen, Kristof|De Moor, Bart|Marchal, Kathleen', 'CMPG-BioI, Department of Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Leuven-Hevelee, Belgium.|CMPG-BioI, Department of Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Leuven-Hevelee, Belgium.|CMPG-BioI, Department of Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Leuven-Hevelee, Belgium.|CMPG-BioI, Department of Microbial and Molecular Systems, K.U.Leuven, Kasteelpark Arenberg 20, B-3001 Leuven-Hevelee, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 3, 'Algorithms|Calibration|Gene Expression Profiling|Gene Targeting|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Software', 'Evaluation Studies', '10.1093/bioinformatics/btm159', '17485432', 'In this article we describe a new Bioconductor package CALIB for normalization of two-color microarray data. This approach is based on the measurements of external controls and estimates an absolute target level for each gene and condition pair, as opposed to working with log-ratios as a relative measure of expression. Moreover, this method makes no assumptions regarding the distribution of gene expression divergence.'),
(73448, 2007, 'bio', 'AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes', 'Dicks, E|Teague, J W|Stephens, P|Raine, K|Yates, A|Mattocks, C|Tarpey, P|Butler, A|Menzies, A|Richardson, D|Jenkinson, A|Davies, H|Edkins, S|Forbes, S|Gray, K|Greenman, C|Shepherd, R|Stratton, M R|Futreal, P A|Wooster, R', 'Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.|Cancer Genome Project, Wellcome Trust Sanger Institute, Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 13, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|DNA, Neoplasm|Genetic Predisposition to Disease|Genetic Testing|Genetic Variation|Humans|Molecular Sequence Data|Neoplasms|Software', 'Evaluation Studies', '10.1093/bioinformatics/btm152', '17485433', 'The undertaking of large-scale DNA sequencing screens for somatic variants in human cancers requires accurate and rapid processing of traces for variants. Due to their often aneuploid nature and admixed normal tissue, heterozygous variants found in primary cancers are often subtle and difficult to detect. To address these issues, we have developed a mutation detection algorithm, AutoCSA, specifically optimized for the high throughput screening of cancer samples.'),
(73449, 2007, 'bio', 'SQUINT: a multiple alignment program and editor', 'Goode, Matthew G|Rodrigo, Allen G', 'Bioinformatics Institute, University of Auckland, Auckland, New Zealand.|Bioinformatics Institute, University of Auckland, Auckland, New Zealand.', 'New Zealand|New Zealand', 18, 'Amino Acid Sequence|Base Sequence|Computational Biology|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btm128', '17485434', 'SQUINT is a sequence alignment tool, and combines both automated progressive sequence alignment with facilities for manual editing. The program imports nucleotide or amino acid sequence multiple alignment files in standard formats, and permits users to view two translations of the same multiple alignment simultaneously. Edits in one view are instantaneously reflected in the other, and the scoring cost of the changes are shown in real-time. Progressive multiple alignments, using a variety of alignment parameters, can be performed on any block of sequences, including blocks embedded in the existing alignment.'),
(73450, 2007, 'bio', 'Persistent voids: a new structural metric for membrane fusion', 'Kasson, Peter M|Zomorodian, Afra|Park, Sanghyun|Singhal, Nina|Guibas, Leonidas J|Pande, Vijay S', 'Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu|Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu|Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu|Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu|Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu|Medical Scientist Training Program, Stanford University, Stanford, CA 94305, USA. kasson@cmgm.stanford-edu', 'United States|United States|United States|United States|United States|United States', 43, 'Animals|Computational Biology|Computer Simulation|Markov Chains|Membrane Fluidity|Membrane Fusion|Models, Biological|Models, Statistical|Phosphatidylethanolamines|Software|Solvents|Synapses|Synaptic Transmission|Viruses', 'Journal Article', '10.1093/bioinformatics/btm250', '17488753', 'Membrane fusion constitutes a key stage in cellular processes such as synaptic neurotransmission and infection by enveloped viruses. Current experimental assays for fusion have thus far been unable to resolve early fusion events in fine structural detail. We have previously used molecular dynamics simulations to develop mechanistic models of fusion by small lipid vesicles. Here, we introduce a novel structural measurement of vesicle topology and fusion geometry: persistent voids.'),
(73451, 2007, 'bio', 'STOP: searching for transcription factor motifs using gene expression', 'Hertzberg, Libi|Izraeli, Shai|Domany, Eytan', 'Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Tel Hashomer, Israel.|Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Tel Hashomer, Israel.|Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Tel Hashomer, Israel.', 'Israel|Israel|Israel', 0, 'Algorithms|Amino Acid Motifs|Animals|Computational Biology|Gene Expression Regulation|Genomics|Humans|Mice|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Software|Species Specificity|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm249', '17488754', 'Existing computational methods that identify transcription factor (TF) binding sites on a genes promoter are plagued by significant inaccuracies. Binding of a TF to a particular sequence is assessed by comparing its similarity score, obtained from the TFs known position weight matrix (PWM), to a threshold. If the similarity score is above the threshold, the sequence is considered a putative binding site. Determining this threshold is a central part of the problem, for which no satisfactory biologically based solution exists.'),
(73452, 2007, 'bio', 'Fast model-based protein homology detection without alignment', 'Hochreiter, Sepp|Heusel, Martin|Obermayer, Klaus', 'Institute of Bioinformatics, Johannes Kepler Universität Linz, 4040 Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler Universität Linz, 4040 Linz, Austria. hochreit@bioinf.jku.at|Institute of Bioinformatics, Johannes Kepler Universität Linz, 4040 Linz, Austria. hochreit@bioinf.jku.at', 'Austria|Austria|Austria', 15, 'Algorithms|Amino Acid Motifs|Computational Biology|Computers|Data Interpretation, Statistical|Genome|Genomics|Models, Statistical|Models, Theoretical|Proteins|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm247', '17488755', 'As more genomes are sequenced, the demand for fast gene classification techniques is increasing. To analyze a newly sequenced genome, first the genes are identified and translated into amino acid sequences which are then classified into structural or functional classes. The best-performing protein classification methods are based on protein homology detection using sequence alignment methods. Alignment methods have recently been enhanced by discriminative methods like support vector machines (SVMs) as well as by position-specific scoring matrices (PSSM) as obtained from PSI-BLAST. However, alignment methods are time consuming if a new sequence must be compared to many known sequences-the same holds for SVMs. Even more time consuming is to construct a PSSM for the new sequence. The best-performing methods would take about 25 days on present-day computers to classify the sequences of a new genome (20,000 genes) as belonging to just one specific class--however, there are hundreds of classes. Another shortcoming of alignment algorithms is that they do not build a model of the positive class but measure the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are popular classification methods which build a model of the positive class but they show low classification performance. The advantage of a model is that it can be analyzed for chemical properties common to the class members to obtain new insights into protein function and structure. We propose a fast model-based recurrent neural network for protein homology detection, the Long Short-Term Memory (LSTM). LSTM automatically extracts indicative patterns for the positive class, but in contrast to profile methods it also extracts negative patterns and uses correlations between all detected patterns for classification. LSTM is capable to automatically extract useful local and global sequence statistics like hydrophobicity, polarity, volume, polarizability and combine them with a pattern. These properties make LSTM complementary to alignment-based approaches as it does not use predefined similarity measures like BLOSUM or PAM matrices.'),
(73453, 2007, 'bio', 'MODELER4SIMCOAL2: a user-friendly, extensible modeler of demography and linked loci for coalescent simulations', 'Antao, T|Beja-Pereira, A|Luikart, G', 'CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, Universidade do Porto, Portugal. tra@fc.up.pt|CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, Universidade do Porto, Portugal. tra@fc.up.pt|CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, Universidade do Porto, Portugal. tra@fc.up.pt', 'Portugal|Portugal|Portugal', 5, 'Biology|Chromosomes|Computational Biology|Computer Simulation|Demography|Genetics, Population|Genotype|Models, Statistical|Models, Theoretical|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm243', '17488756', 'MODELER4SIMCOAL2 (M4S2) is an extensible graphical tool to model linked loci and population demographies. M4S2 is easy to use, allowing for the modeling of complicated scenarios, making coalescent simulation modeling accessible to biologists with limited computer skills. The software includes an extension system allowing for new models to be created, published and downloaded from the Internet.'),
(73454, 2007, 'bio', 'Madeline 20 PDE: a new program for local and web-based pedigree drawing', 'Trager, Edward H|Khanna, Ritu|Marrs, Adrian|Siden, Lawrence|Branham, Kari E H|Swaroop, Anand|Richards, Julia E', 'Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com|Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan 48105, USA. ed.trager@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 23, 'Algorithms|Computational Biology|Computer Graphics|Computers|Genetics|Humans|Internet|Pedigree|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm242', '17488757', 'The Madeline 2.0 Pedigree Drawing Engine (PDE) is a pedigree drawing program for use in linkage and family-based association studies. The program is designed to handle large and complex pedigrees with an emphasis on readability and aesthetics. For complex pedigrees, we use a hybrid algorithm in which consanguinous loops are drawn as cyclic graphs whenever possible, but we resort to acyclic graphs when matings can no longer be connected without line crossings. A similar hybrid approach is used to avoid line crossings for matings between distant descendants of different founding groups. Written in object-oriented C++ and released under the GNU General Public License (GPL), Madeline 2.0 PDE reads input files specified on the command line and generates pedigree drawings without user interaction. Pedigree output in scalable vector graphics (SVG) format can be viewed in browsers with native SVG rendering support or in vector graphics editors. We provide an easy-to-use public web service, which is experimental and still under development.'),
(73455, 2007, 'bio', 'A phylogenetic Gibbs sampler that yields centroid solutions for cis-regulatory site prediction', 'Newberg, Lee A|Thompson, William A|Conlan, Sean|Smith, Thomas M|McCue, Lee Ann|Lawrence, Charles E', 'The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.|The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.|The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.|The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.|The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.|The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Automation|Computational Biology|Conserved Sequence|Genome|Genome, Bacterial|Genomics|Humans|Mice|Models, Statistical|Phylogeny|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btm241', '17488758', 'Identification of functionally conserved regulatory elements in sequence data from closely related organisms is becoming feasible, due to the rapid growth of public sequence databases. Closely related organisms are most likely to have common regulatory motifs, however, the recent speciation of such organisms results in the high degree of correlation in their genome sequences, confounding the detection of functional elements. Additionally, alignment algorithms that use optimization techniques are limited to the detection of a single alignment that may not be representative. Comparative-genomics studies must be able to address the phylogenetic correlation in the data and efficiently explore the alignment space, in order to make specific and biologically relevant predictions.'),
(73456, 2007, 'bio', 'MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data', 'Zhou, Xin|Tuck, David P', 'Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA.|Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA.', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btm036', '17494773', 'Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve binary gene selection problems. Several groups have extended SVM-RFE to solve multiclass problems using one-versus-all techniques. However, the genes selected from one binary gene selection problem may reduce the classification performance in other binary problems.'),
(73457, 2007, 'bio', 'Nucleotide composition string selection in HIV-1 subtyping using whole genomes', 'Wu, Xiaomeng|Cai, Zhipeng|Wan, Xiu-Feng|Hoang, Tin|Goebel, Randy|Lin, Guohui', 'Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Base Composition|Computational Biology|Evolution, Molecular|Genetic Techniques|Genetic Vectors|Genome, Viral|Genomics|HIV-1|Models, Statistical|Nucleotides|Phylogeny|RNA|Recombination, Genetic|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btm248', '17495995', 'The availability of the whole genomic sequences of HIV-1 viruses provides an excellent resource for studying the HIV-1 phylogenies using all the genetic materials. However, such huge volumes of data create computational challenges in both memory consumption and CPU usage.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73458, 2007, 'bio', 'Biological network mapping and source signal deduction', 'Brynildsen, Mark P|Wu, Tung-Yun|Jang, Shi-Shang|Liao, James C', 'Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|Bayes Theorem|Chromatin Immunoprecipitation|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Fungal Proteins|Gene Expression Profiling|Genes, Fungal|Metabolic Networks and Pathways|Models, Statistical|Protein Interaction Mapping|Saccharomyces cerevisiae|Spectrophotometry, Ultraviolet', 'Journal Article', '10.1093/bioinformatics/btm246', '17495996', 'Many biological networks, including transcriptional regulation, metabolism, and the absorbance spectra of metabolite mixtures, can be represented in a bipartite fashion. Key to understanding these bipartite networks are the network architecture and governing source signals. Such information is often implicitly imbedded in the data. Here we develop a technique, network component mapping (NCM), to deduce bipartite network connectivity and regulatory signals from data without any need for prior information.'),
(73459, 2007, 'bio', 'PLMaddon: a power-law module for the Matlab SBToolbox', 'Vera, Julio|Sun, Cheng|Oertel, Yvonne|Wolkenhauer, Olaf', 'Systems Biology and Bioinformatics Group, Department of Computer Science. University of Rostock, 18051 Rostock, Germany. olaf.wolkenhauer@uni-rostock.de|Systems Biology and Bioinformatics Group, Department of Computer Science. University of Rostock, 18051 Rostock, Germany. olaf.wolkenhauer@uni-rostock.de|Systems Biology and Bioinformatics Group, Department of Computer Science. University of Rostock, 18051 Rostock, Germany. olaf.wolkenhauer@uni-rostock.de|Systems Biology and Bioinformatics Group, Department of Computer Science. University of Rostock, 18051 Rostock, Germany. olaf.wolkenhauer@uni-rostock.de', 'Germany|Germany|Germany|Germany', 3, 'Algorithms|Computer Simulation|Models, Biological|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btm245', '17495997', 'PLMaddon is a General Public License (GPL) software module designed to expand the current version of the SBToolbox (a Matlab toolbox for systems biology, www.sbtoolbox.org) with a set of functions for the analysis of power-law models, a specific class of kinetic models, set in ordinary differential equations (ODE) and in which the kinetic orders can have positive/negative non-integer values. The module includes functions to generate power-law Taylor expansions of other ODE models (e.g. Michaelis-Menten type models), as well as algorithms to estimate steady-states. The robustness and sensitivity of the models can also be analysed and visualized by computing the power-laws logarithmic gains and sensitivities.'),
(73460, 2007, 'bio', 'MutationFinder: a high-performance system for extracting point mutation mentions from text', 'Caporaso, J Gregory|Baumgartner, William A|Randolph, David A|Cohen, K Bretonnel|Hunter, Lawrence', 'Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com|Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com|Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com|Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com|Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com', 'United States|United States|United States|United States|United States', 99, 'Algorithms|Computational Biology|DNA Mutational Analysis|Databases, Bibliographic|Databases, Genetic|Databases, Protein|Genetic Techniques|Humans|Mutation|Pattern Recognition, Automated|Point Mutation|Publications|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btm235', '17495998', 'Discussion of point mutations is ubiquitous in biomedical literature, and manually compiling databases or literature on mutations in specific genes or proteins is tedious. We present an open-source, rule-based system, MutationFinder, for extracting point mutation mentions from text. On blind test data, it achieves nearly perfect precision and a markedly improved recall over a baseline.'),
(73461, 2007, 'bio', 'WilcoxCV: an R package for fast variable selection in cross-validation', 'Boulesteix, Anne-Laure', 'Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org', 'Germany', 0, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/btm162', '17495999', 'In the last few years, numerous methods have been proposed for microarray-based class prediction. Although many of them have been designed especially for the case n < p (much more variables than observations), preliminary variable selection is almost always necessary when the number of genes reaches several tens of thousands, as usual in recent data sets. In the two-class setting, the Wilcoxon rank sum test statistic is, with the t-statistic, one of the standard approaches for variable selection. It is well known that the variable selection step must be seen as a part of classifier construction and, as such, be performed based on training data only. When classifier accuracy is evaluated via cross-validation or Monte-Carlo cross-validation, it means that we have to perform p Wilcoxon or t-tests for each iteration, which becomes a daunting task for increasing p. As a consequence, many authors often perform variable selection only once using all the available data, which can induce a dramatic underestimation of error rate and thus lead to misleadingly reporting predictive power. We propose a very fast implementation of variable selection based on the Wilcoxon test for use in cross-validation and Monte Carlo cross-validation (also known as random splitting into learning and test sets). This implementation is based on a simple mathematical formula using only the ranks calculated from the original data set.'),
(73462, 2007, 'bio', 'Data reduction of isotope-resolved LC-MS spectra', 'Du, Peicheng|Sudha, Rajagopalan|Prystowsky, Michael B|Angeletti, Ruth Hogue', 'Albert Einstein College of Medicine, Bronx, NY 10461, USA. pdu@us.ibm.com|Albert Einstein College of Medicine, Bronx, NY 10461, USA. pdu@us.ibm.com|Albert Einstein College of Medicine, Bronx, NY 10461, USA. pdu@us.ibm.com|Albert Einstein College of Medicine, Bronx, NY 10461, USA. pdu@us.ibm.com', 'United States|United States|United States|United States', 24, 'Algorithms|Artifacts|Chromatography, Liquid|Data Compression|Databases, Protein|Isotope Labeling|Mass Spectrometry|Peptide Mapping|Proteome|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btm083', '17496000', 'Data reduction of liquid chromatography-mass spectrometry (LC-MS) spectra can be a challenge due to the inherent complexity of biological samples, noise and non-flat baseline. We present a new algorithm, LCMS-2D, for reliable data reduction of LC-MS proteomics data.'),
(73463, 2007, 'bio', 'OBO to OWL: a protege OWL tab to read/save OBO ontologies', 'Moreira, Dilvan A|Musen, Mark A', 'Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94002, USA. dilvan@stanford.edu|Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94002, USA. dilvan@stanford.edu', 'United States|United States', 35, 'Animals|Computational Biology|Computer Graphics|Computers|Database Management Systems|Databases, Genetic|Humans|Information Science|Information Storage and Retrieval|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm258', '17496317', 'The Open Biomedical Ontologies (OBO) format from the GO consortium is a very successful format for biomedical ontologies, including the Gene Ontology. But it lacks formal computational definitions for its constructs and tools, like DL reasoners, to facilitate ontology development/maintenance. We describe the OBO Converter, a Java tool to convert files from OBO format to Web Ontology Language (OWL) (and vice versa) that can also be used as a Protégé Tab plug-in. It uses the OBO to OWL mapping provided by the National Center for Biomedical Ontologies (NCBO) (a joint effort of OBO developers and OWL experts) and offers options to ease the task of saving/reading files in both formats.'),
(73464, 2007, 'bio', 'Rapid assessment of correlated amino acids from pair-to-pair (P2P) substitution matrices', 'Eyal, Eran|Pietrokovski, Shmuel|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA. eyal@ccbb.pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA. eyal@ccbb.pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA 15213, USA. eyal@ccbb.pitt.edu', 'United States|United States|United States', 0, 'Amino Acids|Automation|Catalytic Domain|Computational Biology|Computer Graphics|Models, Statistical|Mutation|Probability|Protein Structure, Tertiary|Proteins|Sequence Alignment|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm256', '17496318', 'Identification of correlated amino acids in proteins has been a topic of broad interest in view of its functional implications and importance in protein design. A new set of pair-to-pair (P2P) substitution matrices for amino acids was recently introduced as a useful tool for inferring information on such correlated sites. We present a website developed for automated application of these matrices for analysis of query sequences. The site offers options for graphical analysis of correlations, as well as visualization of correlated amino acids on representative, structurally characterized, members of the examined family of sequences.'),
(73465, 2007, 'bio', 'DoriC: a database of oriC regions in bacterial genomes', 'Gao, Feng|Zhang, Chun-Ting', 'Department of Physics, Tianjin University, Tianjin 300072, China.|Department of Physics, Tianjin University, Tianjin 300072, China.', 'China|China', 65, 'Algorithms|Base Composition|Chromosome Mapping|Chromosomes, Bacterial|Computational Biology|Databases, Genetic|Databases, Nucleic Acid|Genome, Bacterial|Internet|Phylogeny|Programming Languages|Replication Origin|Software|Sulfolobus solfataricus', 'Journal Article', '10.1093/bioinformatics/btm255', '17496319', 'Replication origins (oriCs) of bacterial genomes currently available in GenBank have been predicted by using a systematic method comprising the Z-curve analysis for nucleotide distribution asymmetry, DnaA box distribution, genes adjacent to candidate oriCs and phylogenetic relationships. These oriCs are organized into a MySQL database, DoriC, which provides extensive information and graphical views of the oriC regions. In addition, users can Blast a query sequence or even a whole genome against DoriC to find a homologous one. DoriC will be updated timely and the latest version is DoriC 1.8, in which oriCs of 425 genomes (468 chromosomes) are identified.'),
(73466, 2007, 'bio', 'GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor', 'Davis, Sean|Meltzer, Paul S', 'Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. sdavis2@mail.nih.gov|Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. sdavis2@mail.nih.gov', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computers|Data Interpretation, Statistical|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btm254', '17496320', 'Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data.'),
(73467, 2007, 'bio', 'BioMoby web services to support clustering of co-regulated genes based on similarity of promoter configurations', 'Kerhornou, Arnaud|Guigó, Roderic', 'Centre de Regulacio Genomica, Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta, 08003 Barcelona, Catalonia, Spain. arnaud.kerhornou@crg.es|Centre de Regulacio Genomica, Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta, 08003 Barcelona, Catalonia, Spain. arnaud.kerhornou@crg.es', 'Spain|Spain', 12, 'Algorithms|Automation|Binding Sites|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Internet|Models, Genetic|Promoter Regions, Genetic|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm252', '17496321', 'Here we present a computational protocol to analyze the promoter regions of a given set of co-expressed genes, and its implementation through the use of Web services technologies. This protocol aims to cluster a set of co-regulated genes in subsets of genes showing similar configurations of transcription factor binding sites. All the steps of this protocol have been developed as web services that are compliant with BioMoby specifications.'),
(73468, 2007, 'bio', 'Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein-protein interactions', 'Guharoy, Mainak|Chakrabarti, Pinak', 'Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India.|Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India.', 'India|India', 96, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm274', '17510165', 'The increasing amount of data on protein-protein interaction needs to be rationalized for deriving guidelines for the alteration or design of an interface between two proteins.'),
(73469, 2007, 'bio', 'The Cytochrome P450 Engineering Database: a navigation and prediction tool for the cytochrome P450 protein family', 'Fischer, Markus|Knoll, Michael|Sirim, Demet|Wagner, Florian|Funke, Sonja|Pleiss, Juergen', 'Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.|Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.|Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.|Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.|Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.|Department of Biochemistry & Molecular Biophysics, Columbia University, New York, NY 10032, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Graphics|Computer Simulation|Cytochrome P-450 Enzyme System|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Protein Engineering|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm268', '17510166', 'The Cytochrome P450 Engineering Database (CYPED) has been designed to serve as a tool for a comprehensive and systematic comparison of protein sequences and structures within the vast and diverse family of cytochrome P450 monooxygenases (CYPs). The CYPED currently integrates sequence and structure data of 3911 and 25 proteins, respectively. Proteins are grouped into homologous families and superfamilies according to Nelsons classification. Nonclassified CYP sequences are assigned by similarity. Functionally relevant residues are annotated. The web accessible version contains multisequence alignments, phylogenetic trees and HMM profiles. The CYPED is regularly updated and supplies all data for download. Thus, it provides a valuable data source for phylogenetic analysis, investigation of sequence-function relationships and the design of CYPs with improved biochemical properties.'),
(73470, 2007, 'bio', 'Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass spectrum/peptide sequence false match frequencies', 'Feng, Jian|Naiman, Daniel Q|Cooper, Bret', 'Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA.|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA.|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA.', 'United States|United States|United States', 30, 'Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Mass Spectrometry|Models, Chemical|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Pattern Recognition, Automated|Peptide Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm267', '17510167', 'In proteomics, reverse database searching is used to control the false match frequency for tandem mass spectrum/peptide sequence matches, but reversal creates sequences devoid of patterns that usually challenge database-search software.'),
(73471, 2007, 'bio', 'Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data', 'Nagamine, Nobuyoshi|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan.|Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan.', 'Japan|Japan', 66, 'Algorithms|Binding Sites|Computer Simulation|Data Interpretation, Statistical|Mass Spectrometry|Models, Chemical|Peptide Mapping|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm266', '17510168', 'Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability.'),
(73472, 2007, 'bio', 'Isolated ZP-N domains constitute the N-terminal extensions of Zona Pellucida proteins', 'Callebaut, Isabelle|Mornon, Jean-Paul|Monget, Philippe', 'Département de Biologie Structurale, IMPMC UMR 7590, Universités Pierre et Marie Curie-Paris6, Paris, France. isabelle.callebaut@impmc.jussieu.fr|Département de Biologie Structurale, IMPMC UMR 7590, Universités Pierre et Marie Curie-Paris6, Paris, France. isabelle.callebaut@impmc.jussieu.fr|Département de Biologie Structurale, IMPMC UMR 7590, Universités Pierre et Marie Curie-Paris6, Paris, France. isabelle.callebaut@impmc.jussieu.fr', 'France|France|France', 0, 'Amino Acid Sequence|Animals|Databases, Protein|Egg Proteins|Humans|Information Storage and Retrieval|Membrane Glycoproteins|Molecular Sequence Data|Protein Structure, Tertiary|Receptors, Cell Surface|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Species Specificity|Zona Pellucida|Zona Pellucida Glycoproteins', 'Journal Article', '10.1093/bioinformatics/btm265', '17510169', 'Zona Pellucida (ZP) domains have been found in a wide variety of extracellular proteins, in which they play essential role for polymerization. They are shared by the ZP proteins, which constitute the extracellular coat of animal eggs. Except from ZP3, constituting the primary sperm receptor, the ZP proteins possess, in addition to their C-terminal ZP domains, N-terminal extensions, which are thought to play an important role in the species-specific gamete recognition. Here, we show that these extensions are made of single or multiple copies of a small globular domain, which can be significantly related to the N-terminal region of ZP domains (ZP-N domains). This finding brings new insights into the molecular evolution of ZP proteins, which may have evolved around a common ZP-N architecture, and more generally into the noticeable sequence diversity of ZP-N domains, which can be found as isolated subunits or tightly associated with ZP-C domains to form complete, canonical ZP domains.'),
(73473, 2007, 'bio', 'Computational identification of candidate loci for recessively inherited mutation using high-throughput SNP arrays', 'Laakso, Marko|Tuupanen, Sari|Karhu, Auli|Lehtonen, Rainer|Aaltonen, Lauri A|Hautaniemi, Sampsa', 'Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.|Computational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland', 1, 'Chromosome Mapping|Colorectal Neoplasms|DNA Mutational Analysis|Genes, Recessive|Genetic Predisposition to Disease|Genetic Variation|Humans|Mutation|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Quantitative Trait Loci|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm263', '17510170', 'Single nucleic polymorphisms (SNPs) are one of the most abundant genetic variations in the human genome. Recently, several platforms for high-throughput SNP analysis have become available, capable of measuring thousands of SNPs across the genome. Tools for analysing and visualizing these large genetic data sets in biologically relevant manner are rare. This hinders effective use of the SNP-array data in research on complex diseases, such as cancer.'),
(73474, 2007, 'bio', 'An evaluation of automated homology modelling methods at low target template sequence similarity', 'Dalton, James A R|Jackson, Richard M', 'Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.|Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.', 'United Kingdom|United Kingdom', 68, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software|Software Validation', 'Comparative Study', '10.1093/bioinformatics/btm262', '17510171', 'There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities </=50%.'),
(73475, 2007, 'bio', 'Error detection in SNP data by considering the likelihood of recombinational history implied by three-site combinations', 'Toleno, Donna M|Morrell, Peter L|Clegg, Michael T', 'Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA.|Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA.|Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697, USA.', 'United States|United States|United States', 0, 'Algorithms|Cloning, Molecular|Computational Biology|Computer Simulation|Genotype|Haplotypes|Heterozygote|Humans|Likelihood Functions|Models, Biological|Models, Genetic|Pedigree|Polymorphism, Genetic|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btm260', '17510172', 'Errors in nucleotide sequence and SNP genotyping data are problematic when inferring haplotypes. Previously published methods for error detection in haplotype data make use of pedigree information, however, for many samples, individuals are not related by pedigree. This article describes a method for detecting errors in haplotypes by considering the recombinational history implied by the patterns of variation, three SNPs at a time.'),
(73476, 2007, 'bio', 'T-Pile--a package for thermodynamic calculations for biomolecules', 'Gront, Dominik|Kolinski, Andrzej', 'Warsaw University, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland. dgront@chem.uw.edu.pl|Warsaw University, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland. dgront@chem.uw.edu.pl', 'Poland|Poland', 3, 'Algorithms|Biophysics|Computational Biology|Computers|Hot Temperature|Models, Theoretical|Molecular Conformation|Monte Carlo Method|Probability|Proteins|Software|Temperature|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btm259', '17510173', 'Molecular dynamics and Monte Carlo, usually conducted in canonical ensemble, deliver a plethora of biomolecular conformations. Proper analysis of the simulation data is a crucial part of biophysical and bioinformatics studies. Sequence alignment problem can be also formulated in terms of Boltzmann distribution. Therefore tools for efficient analysis of canonical ensemble data become extremely valuable. T-Pile package, presented here provides a user-friendly implementation of most important algorithms such as multihistogram analysis and reweighting technique. The package can be used in studies of virtually any system governed by Boltzmann distribution.'),
(73477, 2007, 'bio', 'Predicting functionally important residues from sequence conservation', 'Capra, John A|Singh, Mona', 'Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.|Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.', 'United States|United States', 339, 'Algorithms|Amino Acid Sequence|Amino Acids|Conserved Sequence|Evolution, Molecular|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Nucleic Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm270', '17519246', 'All residues in a protein are not equally important. Some are essential for the proper structure and function of the protein, whereas others can be readily replaced. Conservation analysis is one of the most widely used methods for predicting these functionally important residues in protein sequences.'),
(73478, 2007, 'bio', 'The evolutionary forest algorithm', 'Leman, Scotland C|Uyenoyama, Marcy K|Lavine, Michael|Chen, Yuguo', 'Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA. scotland@stat.duke.edu|Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA. scotland@stat.duke.edu|Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA. scotland@stat.duke.edu|Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA. scotland@stat.duke.edu', 'United States|United States|United States|United States', 5, 'Algorithms|Biological Evolution|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Genetics, Population|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm264', '17519247', 'Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.'),
(73479, 2007, 'bio', 'Assortative mixing in Protein Contact Networks and protein folding kinetics', 'Bagler, Ganesh|Sinha, Somdatta', 'Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad 500007, India.|Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad 500007, India.', 'India|India', 92, 'Allosteric Site|Amino Acid Motifs|Biochemistry|Cluster Analysis|Computational Biology|Databases, Protein|Imaging, Three-Dimensional|Kinetics|Models, Statistical|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Research Design', 'Journal Article', '10.1093/bioinformatics/btm257', '17519248', 'Starting from linear chains of amino acids, the spontaneous folding of proteins into their elaborate 3D structures is one of the remarkable examples of biological self-organization. We investigated native state structures of 30 single-domain, two-state proteins, from complex networks perspective, to understand the role of topological parameters in proteins folding kinetics, at two length scales--as Protein Contact Networks (PCNs) and their corresponding Long-range Interaction Networks (LINs) constructed by ignoring the short-range interactions.'),
(73480, 2007, 'bio', 'Recombination-filtered genomic datasets by information maximization', 'Woerner, August E|Cox, Murray P|Hammer, Michael F', 'Arizona Research Laboratories-Biotechnology, University of Arizona, Tucson, AZ 85721, USA.|Arizona Research Laboratories-Biotechnology, University of Arizona, Tucson, AZ 85721, USA.|Arizona Research Laboratories-Biotechnology, University of Arizona, Tucson, AZ 85721, USA.', 'United States|United States|United States', 161, 'Algorithms|Automation|Computational Biology|DNA|Databases, Genetic|Demography|Genetic Variation|Genomics|Humans|Models, Theoretical|Programming Languages|Recombination, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm253', '17519249', 'With the increasing amount of DNA sequence data available from natural populations, new computational methods are needed to efficiently process raw sequences into formats that are applicable to a variety of analytical methods. One highly successful approach to inferring aspects of demographic history is grounded in coalescent theory. Many of these methods restrict themselves to perfectly tree-like genealogies (i.e. regions with no observed recombination), because theoretical difficulties prevent ready statistical evaluation of recombining regions. However, determining which recombination-filtered dataset to analyze from a larger recombination-rich genomic region is a non-trivial problem. Current applications primarily aim to quantify recombination rates (rather than produce optimal recombination-filtered blocks), require significant manual intervention, and are impractical for multiple genomic datasets in high-throughput, automated research environments. Here, we present a fast, simple and automatable command-line program that extracts optimal recombination-filtered blocks (no four-gamete violations) from recombination-rich genomic re-sequence data.'),
(73481, 2007, 'bio', 'Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA', 'Nueda, María José|Conesa, Ana|Westerhuis, Johan A|Hoefsloot, Huub C J|Smilde, Age K|Talón, Manuel|Ferrer, Alberto', 'Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es|Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Spain. mj.nueda@ua.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 59, 'Algorithms|Analysis of Variance|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Time Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm251', '17519250', 'Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account.'),
(73482, 2007, 'bio', 'Short oligonucleotide probes containing G-stacks display abnormal binding affinity on Affymetrix microarrays', 'Wu, Chunlei|Zhao, Haitao|Baggerly, Keith|Carta, Roberto|Zhang, Li', 'Genomic Institute of Novartis Research Foundation, 10675 John Jay Hopkins Dr, San Diego, CA 92121, USA.|Genomic Institute of Novartis Research Foundation, 10675 John Jay Hopkins Dr, San Diego, CA 92121, USA.|Genomic Institute of Novartis Research Foundation, 10675 John Jay Hopkins Dr, San Diego, CA 92121, USA.|Genomic Institute of Novartis Research Foundation, 10675 John Jay Hopkins Dr, San Diego, CA 92121, USA.|Genomic Institute of Novartis Research Foundation, 10675 John Jay Hopkins Dr, San Diego, CA 92121, USA.', 'United States|United States|United States|United States|United States', 20, 'Affinity Labels|Algorithms|Base Sequence|Gene Targeting|In Situ Hybridization|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm271', '17537749', 'In microarray experiments, probe design is critical to the specific and accurate measurement of target concentrations. Current designs select suitable probes through in silico scanning of transcriptome/genome based on first principles. However, due to lack of tools, the observed microarray data have not been used to assess the performance of individual probes to provide feedback to improve future designs.'),
(73483, 2007, 'bio', 'StrBioLib: a Java library for development of custom computational structural biology applications', 'Chandonia, John-Marc', 'Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. JMChandonia@lbl.gov', 'United States', 8, 'Biopolymers|Computer Simulation|Models, Biological|Programming Languages|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm269', '17537750', 'StrBioLib is a library of Java classes useful for developing software for computational structural biology research. StrBioLib contains classes to represent and manipulate protein structures, biopolymer sequences, sets of biopolymer sequences, and alignments between biopolymers based on either sequence or structure. Interfaces are provided to interact with commonly used bioinformatics applications, including (psi)-blast, modeller, muscle and Primer3, and tools are provided to read and write many file formats used to represent bioinformatic data. The library includes a general-purpose neural network object with multiple training algorithms, the Hooke and Jeeves non-linear optimization algorithm, and tools for efficient C-style string parsing and formatting. StrBioLib is the basis for the Pred2ary secondary structure prediction program, is used to build the astral compendium for sequence and structure analysis, and has been extensively tested through use in many smaller projects. Examples and documentation are available at the site below.'),
(73484, 2007, 'bio', 'Clustering microarray-derived gene lists through implicit literature relationships', 'Burkart, Mark F|Wren, Jonathan D|Herschkowitz, Jason I|Perou, Charles M|Garner, Harold R', 'Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu|Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu|Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu|Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu|Department of Internal Medicine, The McDermott Center for Human Growth and Development, Division of Translational Research, The University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. mark.burkart@utsouthwestern.edu', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Cluster Analysis|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Periodicals as Topic|Proteome', 'Evaluation Studies', '10.1093/bioinformatics/btm261', '17537751', 'Microarrays rapidly generate large quantities of gene expression information, but interpreting such data within a biological context is still relatively complex and laborious. New methods that can identify functionally related genes via shared literature concepts will be useful in addressing these needs.'),
(73485, 2007, 'bio', 'Computational methods for diffusion-influenced biochemical reactions', 'Dobrzynski, Maciej|Rodríguez, Jordi Vidal|Kaandorp, Jaap A|Blom, Joke G', 'CWI (Center for Mathematics and Computer Science), Kruislaan 413 and Section Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, Amsterdam, The Netherlands. m.dobrzynski@cwi.nl|CWI (Center for Mathematics and Computer Science), Kruislaan 413 and Section Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, Amsterdam, The Netherlands. m.dobrzynski@cwi.nl|CWI (Center for Mathematics and Computer Science), Kruislaan 413 and Section Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, Amsterdam, The Netherlands. m.dobrzynski@cwi.nl|CWI (Center for Mathematics and Computer Science), Kruislaan 413 and Section Computational Science, Faculty of Science, University of Amsterdam, Kruislaan 403, Amsterdam, The Netherlands. m.dobrzynski@cwi.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 43, 'Algorithms|Biochemistry|Biopolymers|Computer Simulation|Diffusion|Models, Biological|Models, Chemical|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm278', '17537752', 'We compare stochastic computational methods accounting for space and discrete nature of reactants in biochemical systems. Implementations based on Brownian dynamics (BD) and the reaction-diffusion master equation are applied to a simplified gene expression model and to a signal transduction pathway in Escherichia coli.'),
(73486, 2007, 'bio', 'Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics', 'Verkhivker, Gennady M', 'Department of Pharmaceutical Chemistry, School of Pharmacy, Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047-1620, USA. verk@ku.edu', 'United States', 17, 'Amino Acid Sequence|Antineoplastic Agents|Binding Sites|Computer Simulation|Drug Delivery Systems|Humans|Models, Chemical|Models, Molecular|Molecular Sequence Data|Neoplasm Proteins|Neoplasms|Protein Binding|Protein Conformation|Protein-Tyrosine Kinases|Proteome|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm277', '17537753', 'Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence-structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence-structure-binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor-protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases.'),
(73487, 2007, 'bio', 'Mining co-regulated gene profiles for the detection of functional associations in gene expression data', 'Gyenesei, Attila|Wagner, Ulrich|Barkow-Oesterreicher, Simon|Stolte, Etzard|Schlapbach, Ralph', 'Knowledge and Data Analysis, Unilever Research Vlaardingen, Vlaardingen, The Netherlands.|Knowledge and Data Analysis, Unilever Research Vlaardingen, Vlaardingen, The Netherlands.|Knowledge and Data Analysis, Unilever Research Vlaardingen, Vlaardingen, The Netherlands.|Knowledge and Data Analysis, Unilever Research Vlaardingen, Vlaardingen, The Netherlands.|Knowledge and Data Analysis, Unilever Research Vlaardingen, Vlaardingen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 30, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Pattern Recognition, Automated|Proteome', 'Journal Article', '10.1093/bioinformatics/btm276', '17537754', 'Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. These methods, however, fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles. Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets display contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods.'),
(73488, 2007, 'bio', 'PALMA: mRNA to genome alignments using large margin algorithms', 'Schulze, Uta|Hepp, Bettina|Ong, Cheng Soon|Rätsch, Gunnar', 'Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Tübingen, Germany.', 'Germany|Germany|Germany|Germany', 9, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|Expressed Sequence Tags|Molecular Sequence Data|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btm275', '17537755', 'Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task.'),
(73489, 2007, 'bio', 'RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment', 'Xu, Xing|Ji, Yongmei|Stormo, Gary D', 'Department of Genetics, Washington University, School of Medicine, St. Louis, MO 63110, USA. xingxu@ural.wustl.edu|Department of Genetics, Washington University, School of Medicine, St. Louis, MO 63110, USA. xingxu@ural.wustl.edu|Department of Genetics, Washington University, School of Medicine, St. Louis, MO 63110, USA. xingxu@ural.wustl.edu', 'United States|United States|United States', 54, 'Algorithms|Base Sequence|Computer Simulation|Databases, Genetic|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sample Size|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btm272', '17537756', 'Non-coding RNA genes and RNA structural regulatory motifs play important roles in gene regulation and other cellular functions. They are often characterized by specific secondary structures that are critical to their functions and are often conserved in phylogenetically or functionally related sequences. Predicting common RNA secondary structures in multiple unaligned sequences remains a challenge in bioinformatics research.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73490, 2007, 'bio', 'Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications', 'Yu, Haiyuan|Jansen, Ronald|Stolovitzky, Gustavo|Gerstein, Mark', 'Department of Molecular Biophysics & Biochemistry, Yale University, PO Box 208114, New Haven, CT 06520, USA.|Department of Molecular Biophysics & Biochemistry, Yale University, PO Box 208114, New Haven, CT 06520, USA.|Department of Molecular Biophysics & Biochemistry, Yale University, PO Box 208114, New Haven, CT 06520, USA.|Department of Molecular Biophysics & Biochemistry, Yale University, PO Box 208114, New Haven, CT 06520, USA.', 'United States|United States|United States|United States', 40, 'Chromosome Mapping|Evolution, Molecular|Genetic Variation|Phylogeny|Proteins|Quantitative Trait Loci|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm291', '17540677', 'Many classifications of protein function such as Gene Ontology (GO) are organized in directed acyclic graph (DAG) structures. In these classifications, the proteins are terminal leaf nodes, the categories above them are functional annotations at various levels of specialization and the computation of a numerical measure of relatedness between two arbitrary proteins is an important proteomics problem. Moreover, analogous problems are important in other contexts in large-scale information organization--e.g. the Wikipedia online encyclopedia and the Yahoo and DMOZ web page classification schemes.'),
(73491, 2007, 'bio', 'VISDA: an open-source caBIG analytical tool for data clustering and beyond', 'Wang, Jiajing|Li, Huai|Zhu, Yitan|Yousef, Malik|Nebozhyn, Michael|Showe, Michael|Showe, Louise|Xuan, Jianhua|Clarke, Robert|Wang, Yue', '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.|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|United States|United States|United States|United States', 10, 'Algorithms|Artificial Intelligence|Cluster Analysis|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Evaluation Studies', '10.1093/bioinformatics/btm290', '17540678', 'VISDA (Visual Statistical Data Analyzer) is a caBIG analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical datasets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data.'),
(73492, 2007, 'bio', 'Statistical assessment of functional categories of genes deregulated in pathological conditions by using microarray data', 'Maglietta, R|Piepoli, A|Catalano, D|Licciulli, F|Carella, M|Liuni, S|Pesole, G|Perri, F|Ancona, N', 'Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 23, 'Biomarkers, Tumor|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Male|Multigene Family|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btm289', '17540679', 'A major challenge in current biomedical research is the identification of cellular processes deregulated in a given pathology through the analysis of gene expression profiles. To this end, predefined lists of genes, coding specific functions, are compared with a list of genes ordered according to their values of differential expression measured by suitable univariate statistics.'),
(73493, 2007, 'bio', 'Logistic regression for disease classification using microarray data: model selection in a large p and small n case', 'Liao, J G|Chin, Khew-Voon', 'Drexel University School of Public Health, Philadelphia, PA 19102, USA. jl544@drexel.edu|Drexel University School of Public Health, Philadelphia, PA 19102, USA. jl544@drexel.edu', 'United States|United States', 117, 'Algorithms|Biomarkers, Tumor|Data Interpretation, Statistical|Diagnosis, Computer-Assisted|Humans|Logistic Models|Models, Biological|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Regression Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm287', '17540680', 'Logistic regression is a standard method for building prediction models for a binary outcome and has been extended for disease classification with microarray data by many authors. A feature (gene) selection step, however, must be added to penalized logistic modeling due to a large number of genes and a small number of subjects. Model selection for this two-step approach requires new statistical tools because prediction error estimation ignoring the feature selection step can be severely downward biased. Generic methods such as cross-validation and non-parametric bootstrap can be very ineffective due to the big variability in the prediction error estimate.'),
(73494, 2007, 'bio', 'Tree Gibbs Sampler: identifying conserved motifs without aligning orthologous sequences', 'Cai, Xiaohui|Hu, Haiyan|Li, Xiaoman Shawn', 'Division of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.|Division of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.|Division of Biostatistics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.', 'India|India|India', 2, 'Algorithms|Amino Acid Motifs|Conserved Sequence|Evolution, Molecular|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btm282', '17540681', 'Tree Gibbs Sampler is a software for identifying motifs by simultaneously using the motif overrepresentation property and the motif evolutionary conservation property. It identifies motifs without depending on pre-aligned orthologous sequences, which makes it useful for the extraction of regulatory elements in multiple genomes of both closely related and distant species.'),
(73495, 2007, 'bio', 'Bayesian modelling of shared gene function', 'Sykacek, P|Clarkson, R|Print, C|Furlong, R|Micklem, G', 'Department of Biotechnology, BOKU University, Vienna, Austria. peter@sykacek.net|Department of Biotechnology, BOKU University, Vienna, Austria. peter@sykacek.net|Department of Biotechnology, BOKU University, Vienna, Austria. peter@sykacek.net|Department of Biotechnology, BOKU University, Vienna, Austria. peter@sykacek.net|Department of Biotechnology, BOKU University, Vienna, Austria. peter@sykacek.net', 'Austria|Austria|Austria|Austria|Austria', 0, 'Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm280', '17540682', 'Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms.'),
(73496, 2007, 'bio', 'Simple and fast alignment of metabolic pathways by exploiting local diversity', 'Wernicke, Sebastian|Rasche, Florian', 'Institut für Informatik, Friedrich-Schiller-Universität Jena, Jena, Germany.|Institut für Informatik, Friedrich-Schiller-Universität Jena, Jena, Germany.', 'Germany|Germany', 0, 'Algorithms|Computer Simulation|Gene Expression|Genetic Variation|Models, Biological|Pattern Recognition, Automated|Proteome|Sequence Alignment|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm279', '17540683', 'An important tool for analyzing biological networks is the ability to perform homology searches, i.e. given a pattern network one would like to be able to search for occurrences of similar (sub)networks within a set of host networks. In the context of metabolic pathways, Pinter et al. [Bioinformatics, 2005] proposed to solve this computationally hard problem by restricting it to the case where both the pattern and host networks are trees. This restriction, however, severely limits the applicability of their algorithm.'),
(73497, 2007, 'bio', 'POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions', 'Hirose, Shuichi|Shimizu, Kana|Kanai, Satoru|Kuroda, Yutaka|Noguchi, Tamotsu', 'PharmaDesign, Inc., Tokyo 104-0032, Japan. hirose-shuichi@aist.go.jp|PharmaDesign, Inc., Tokyo 104-0032, Japan. hirose-shuichi@aist.go.jp|PharmaDesign, Inc., Tokyo 104-0032, Japan. hirose-shuichi@aist.go.jp|PharmaDesign, Inc., Tokyo 104-0032, Japan. hirose-shuichi@aist.go.jp|PharmaDesign, Inc., Tokyo 104-0032, Japan. hirose-shuichi@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Folding|Protein Structure, Tertiary|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm302', '17545177', 'Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications.'),
(73498, 2007, 'bio', 'BioText Search Engine: beyond abstract search', 'Hearst, Marti A|Divoli, Anna|Guturu, Harendra|Ksikes, Alex|Nakov, Preslav|Wooldridge, Michael A|Ye, Jerry', 'School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu|School of Information, University of California, Berkeley, CA 94720, USA. hearst@ischool.berkeley.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biology|Database Management Systems|Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing', 'Journal Article', '10.1093/bioinformatics/btm301', '17545178', 'The BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. One novel feature is the ability to search and browse article figures and their captions. A grid view juxtaposes many different figures associated with the same keywords, providing new insight into the literature. An abstract/title search and list view shows at a glance many of the figures associated with each article. The interface is carefully designed according to usability principles and techniques. The search engine is a work in progress, and more functionality will be added over time.'),
(73499, 2007, 'bio', 'PIP: a database of potential intron polymorphism markers', 'Yang, Long|Jin, Gulei|Zhao, Xiangqian|Zheng, Yan|Xu, Zhaohua|Wu, Weiren', 'Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.|Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.|Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.|Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.|Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.|Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, 310029, China.', 'China|China|China|China|China|China', 68, 'Base Sequence|Chromosome Mapping|DNA, Plant|Databases, Genetic|Evolution, Molecular|Genetic Markers|Genetic Variation|Genome, Plant|Information Storage and Retrieval|Introns|Molecular Sequence Data|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btm296', '17545179', 'With the recent progress made in large-scale plant functional genome sequencing projects, a great amount of EST (express sequence tag) data is becoming available. With the help of complete genomic sequence information of model plants (rice and Arabidopsis), it is possible to predict the joints between adjacent exons after splicing (or termed intron positions for short) in homologous ESTs of other plants. This would allow developing potential intron polymorphism (PIP) markers in these plants by designing primers in exons flanking the target intron.'),
(73500, 2007, 'bio', 'OMA Browser--exploring orthologous relations across 352 complete genomes', 'Schneider, Adrian|Dessimoz, Christophe|Gonnet, Gaston H', 'Institute of Computational Science, ETH Zurich, Switzerland. schneadr@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. schneadr@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. schneadr@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland', 0, 'Base Pair Mismatch|Base Sequence|Chromosome Mapping|Computer Graphics|Conserved Sequence|Evolution, Molecular|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btm295', '17545180', 'Inference of the evolutionary relation between proteins, in particular the identification of orthologs, is a central problem in comparative genomics. Several large-scale efforts with various methodologies and scope tackle this problem, including OMA (the Orthologous MAtrix project).'),
(73501, 2007, 'bio', 'Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network', 'Guo, Zheng|Wang, Lei|Li, Yongjin|Gong, Xue|Yao, Chen|Ma, Wencai|Wang, Dong|Li, Yanhui|Zhu, Jing|Zhang, Min|Yang, Da|Rao, Shaoqi|Wang, Jing', 'Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn|Department of Bioinformatics, Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory, Harbin Medical University, Harbin 150086, China. guoz@ems.hrbmu.edu.cn', 'China|China|China|China|China|China|China|China|China|China|China|China|China', 107, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm294', '17545181', 'Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments.'),
(73502, 2007, 'bio', 'VIPER: an advanced software package to support high-throughput LC-MS peptide identification', 'Monroe, Matthew E|Tolixc4x87, Nikola|Jaitly, Navdeep|Shaw, Jason L|Adkins, Joshua N|Smith, Richard D', 'Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm281', '17545182', 'The accurate mass and time (AMT) tag approach is used for analysis of large scale experiments by combining information generated over multiple datasets and instrument types. The VIPER software package is one of the key components of the data processing pipeline and implements automated algorithms to discover LC-MS features, align and match these LC-MS features to a database of peptides previously identified in LC-MS/MS analyses, and identify and quantify pairs of isotopically labeled peptides.'),
(73503, 2007, 'bio', 'OBO-Edit--an ontology editor for biologists', 'Day-Richter, John|Harris, Midori A|Haendel, Melissa|Gene Ontology OBO-Edit Working Group|Lewis, Suzanna', 'Berkeley Bioinformatics and Ontology Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Berkeley Bioinformatics and Ontology Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Berkeley Bioinformatics and Ontology Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Berkeley Bioinformatics and Ontology Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Berkeley Bioinformatics and Ontology Project, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Documentation|Information Storage and Retrieval|Proteins|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm112', '17545183', 'OBO-Edit is an open source, platform-independent ontology editor developed and maintained by the Gene Ontology Consortium. Implemented in Java, OBO-Edit uses a graph-oriented approach to display and edit ontologies. OBO-Edit is particularly valuable for viewing and editing biomedical ontologies.'),
(73504, 2007, 'bio', 'Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models', 'Wong, Wendy S W|Nielsen, Rasmus', 'Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. sww8@cornell.edu|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. sww8@cornell.edu', 'United States|United States', 8, 'Animals|Artificial Intelligence|Base Sequence|Chromosome Mapping|Computer Simulation|Drosophila|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Phylogeny|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm299', '17550911', 'Finding the regulatory modules for transcription factors binding is an important step in elucidating the complex molecular mechanisms underlying regulation of gene expression. There are numerous methods available for solving this problem, however, very few of them take advantage of the increasing availability of comparative genomic data.'),
(73505, 2007, 'bio', 'Classification of small molecules by two- and three-dimensional decomposition kernels', 'Ceroni, Alessio|Costa, Fabrizio|Frasconi, Paolo', 'Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy.|Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy.|Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Biomarkers, Tumor|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Neoplasm Proteins|Neoplasms|Pattern Recognition, Automated|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm298', '17550912', 'Several kernel-based methods have been recently introduced for the classification of small molecules. Most available kernels on molecules are based on 2D representations obtained from chemical structures, but far less work has focused so far on the definition of effective kernels that can also exploit 3D information.'),
(73506, 2007, 'bio', 'Alvira: comparative genomics of viral strains', 'Enault, François|Fremez, Romain|Baranowski, Eric|Faraut, Thomas', 'Laboratoire de Génétique Cellulaire INRA UMR444, Chemin de Borde Rouge BP52627 31326 Castanet Tolosan Cedex, France. fenault@toulouse.inra.fr|Laboratoire de Génétique Cellulaire INRA UMR444, Chemin de Borde Rouge BP52627 31326 Castanet Tolosan Cedex, France. fenault@toulouse.inra.fr|Laboratoire de Génétique Cellulaire INRA UMR444, Chemin de Borde Rouge BP52627 31326 Castanet Tolosan Cedex, France. fenault@toulouse.inra.fr|Laboratoire de Génétique Cellulaire INRA UMR444, Chemin de Borde Rouge BP52627 31326 Castanet Tolosan Cedex, France. fenault@toulouse.inra.fr', 'France|France|France|France', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Genome, Viral|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface|Viruses', 'Journal Article', '10.1093/bioinformatics/btm293', '17550913', 'The Alvira tool is a general purpose multiple sequence alignment viewer with a special emphasis on the comparative analysis of viral genomes. This new tool has been devised specifically to address the problem of the simultaneous analysis of a large number of viral strains. The multiple alignment is embedded in a graph that can be explored at different levels of resolution.'),
(73507, 2007, 'bio', 'A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments', 'Zhou, Yiyong|Cras-Méneur, Corentin|Ohsugi, Mitsuru|Stormo, Gary D|Permutt, M Alan', 'Division of Endocrinology, Metabolism and Lipid Research, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. yyzhou@netra.wustl.edu|Division of Endocrinology, Metabolism and Lipid Research, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. yyzhou@netra.wustl.edu|Division of Endocrinology, Metabolism and Lipid Research, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. yyzhou@netra.wustl.edu|Division of Endocrinology, Metabolism and Lipid Research, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. yyzhou@netra.wustl.edu|Division of Endocrinology, Metabolism and Lipid Research, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA. yyzhou@netra.wustl.edu', 'United States|United States|United States|United States|United States', 18, 'Algorithms|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Multigene Family|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1093/bioinformatics/btm292', '17550914', 'Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed.'),
(73508, 2007, 'bio', 'dPattern: transcription factor binding site (TFBS) discovery in human genome using a discriminative pattern analysis', 'Bae, Seung-Hee|Tang, Haixu|Wu, Jing|Xie, Jun|Kim, Sun', 'Department of Computer Science, Indiana University - Bloomington, IN 47404, USA.|Department of Computer Science, Indiana University - Bloomington, IN 47404, USA.|Department of Computer Science, Indiana University - Bloomington, IN 47404, USA.|Department of Computer Science, Indiana University - Bloomington, IN 47404, USA.|Department of Computer Science, Indiana University - Bloomington, IN 47404, USA.', 'India|India|India|India|India', 6, 'Algorithms|Artificial Intelligence|Base Sequence|Binding Sites|Chromosome Mapping|Discriminant Analysis|Gene Expression Profiling|Genome, Human|Humans|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm288', '17550915', 'Transcription factor binding sites (TFBSs) are typically short in length, thus search with a profile model from known TFBSs produces many false positives. When combined with additional information, gene expression data in this article, sensitivity and specificity of TFBS search can be improved significantly.'),
(73509, 2007, 'bio', 'Significance analysis of groups of genes in expression profiling studies', 'Chen, James J|Lee, Taewon|Delongchamp, Robert R|Chen, Tao|Tsai, Chen-An', 'Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA. jchen@nctr.fda.gov|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA. jchen@nctr.fda.gov|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA. jchen@nctr.fda.gov|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA. jchen@nctr.fda.gov|Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, FDA, Jefferson, AR 72079, USA. jchen@nctr.fda.gov', 'United States|United States|United States|United States|United States', 22, 'Algorithms|Cell Differentiation|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Biological|Multigene Family|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm310', '17553853', 'Gene class testing (GCT) is a statistical approach to determine whether some functionally predefined classes of genes express differently under two experimental conditions. GCT computes the P-value of each gene class based on the null distribution and the gene classes are ranked for importance in accordance with their P-values. Currently, two null hypotheses have been considered: the Q1 hypothesis tests the relative strength of association with the phenotypes among the gene classes, and the Q2 hypothesis assesses the statistical significance. These two hypotheses are related but not equivalent.'),
(73510, 2007, 'bio', 'Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process', 'Elo, Laura L|Järvenpää, Henna|Oresic, Matej|Lahesmaa, Riitta|Aittokallio, Tero', 'Department of Mathematics, FI-20014 University of Turku, Finland. laliel@utu.fi|Department of Mathematics, FI-20014 University of Turku, Finland. laliel@utu.fi|Department of Mathematics, FI-20014 University of Turku, Finland. laliel@utu.fi|Department of Mathematics, FI-20014 University of Turku, Finland. laliel@utu.fi|Department of Mathematics, FI-20014 University of Turku, Finland. laliel@utu.fi', 'Finland|Finland|Finland|Finland|Finland', 50, 'Algorithms|Cell Differentiation|Computer Simulation|Gene Expression Profiling|Humans|Models, Biological|Multigene Family|Signal Transduction|T-Lymphocytes, Helper-Inducer', 'Journal Article', '10.1093/bioinformatics/btm309', '17553854', 'Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.'),
(73511, 2007, 'bio', 'The commonality of protein interaction networks determined in neurodegenerative disorders (NDDs)', 'Limviphuvadh, Vachiranee|Tanaka, Seigo|Goto, Susumu|Ueda, Kunihiro|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan', 21, 'Brain|Computer Simulation|Databases, Protein|Humans|Information Storage and Retrieval|Models, Neurological|Nerve Tissue Proteins|Neurodegenerative Diseases|Protein Interaction Mapping|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm307', '17553855', 'Neurodegenerative disorders (NDDs) are progressive and fatal disorders, which are commonly characterized by the intracellular or extracellular presence of abnormal protein aggregates. The identification and verification of proteins interacting with causative gene products are effective ways to understand their physiological and pathological functions. The objective of this research is to better understand common molecular pathogenic mechanisms in NDDs by employing protein-protein interaction networks, the domain characteristics commonly identified in NDDs and correlation among NDDs based on domain information.'),
(73512, 2007, 'bio', 'Characterization of mismatch and high-signal intensity probes associated with Affymetrix genechips', 'Wang, Yonghong|Miao, Ze-Hong|Pommier, Yves|Kawasaki, Ernest S|Player, Audrey', 'SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wangyong@mail.nih.gov|SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wangyong@mail.nih.gov|SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wangyong@mail.nih.gov|SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wangyong@mail.nih.gov|SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wangyong@mail.nih.gov', 'United States|United States|United States|United States|United States', 19, 'Algorithms|Base Pair Mismatch|DNA Probes|Equipment Design|Equipment Failure Analysis|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm306', '17553856', 'For Affymetrix microarray platforms, gene expression is determined by computing the difference in signal intensities between perfect match (PM) and mismatch (MM) probesets. Although the use of PM is not controversial, MM probesets have been associated with variance and ultimately inaccurate gene expression calls. A principal focus of this study was to investigate the nature of the MM signal intensities and demonstrate its contribution to the experimental results.'),
(73513, 2007, 'bio', 'Predicting survival from microarray data--a comparative study', 'Bøvelstad, H M|Nygård, S|Størvold, H L|Aldrin, M|Borgan, Ø|Frigessi, A|Lingjaerde, O C', 'Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no|Department of Mathematics, University of Oslo, Norway. hegembo@math.uio.no', 'Norway|Norway|Norway|Norway|Norway|Norway|Norway', 175, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Female|Forecasting|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Proportional Hazards Models|Reproducibility of Results|Sensitivity and Specificity|Survival Analysis|Survival Rate', 'Comparative Study', '10.1093/bioinformatics/btm305', '17553857', 'Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Coxs proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso.'),
(73514, 2007, 'bio', 'The MiSink Plugin: Cytoscape as a graphical interface to the Database of Interacting Proteins', 'Salwinski, Lukasz|Eisenberg, David', 'UCLA-DOE Institute for Genomics & Proteomics, Department of Chemistry, Howard Hughes Medical Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA. lukasz@mbi.ucla.edu|UCLA-DOE Institute for Genomics & Proteomics, Department of Chemistry, Howard Hughes Medical Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA. lukasz@mbi.ucla.edu', 'United States|United States', 0, 'Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Protein Interaction Mapping|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm304', '17553858', 'The MiSink Plugin converts Cytoscape, an open-source bioinformatics platform for network visualization, to a graphical interface for the database of interacting proteins (DIP: http://dip.doe-mbi.ucla.edu). Seamless integration is possible by providing bi-directional communication between Cytoscape and any Web site supplying data in XML or tab-delimited format.'),
(73515, 2007, 'bio', 'Boltzmann probability of RNA structural neighbors and riboswitch detection', 'Freyhult, Eva|Moulton, Vincent|Clote, Peter', 'Linnaeus Centre for Bioinformatics, Uppsala University, 75124 Uppsala, Sweden.|Linnaeus Centre for Bioinformatics, Uppsala University, 75124 Uppsala, Sweden.|Linnaeus Centre for Bioinformatics, Uppsala University, 75124 Uppsala, Sweden.', 'Sweden|Sweden|Sweden', 32, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Catalytic|Sequence Analysis, RNA|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm314', '17573364', 'We describe algorithms implemented in a new software package, RNAbor, to investigate structures in a neighborhood of an input secondary structure S of an RNA sequence s. The input structure could be the minimum free energy structure, the secondary structure obtained by analysis of the X-ray structure or by comparative sequence analysis, or an arbitrary intermediate structure.'),
(73516, 2007, 'bio', 'ViroBLAST: a stand-alone BLAST web server for flexible queries of multiple databases and users datasets', 'Deng, Wenjie|Nickle, David C|Learn, Gerald H|Maust, Brandon|Mullins, James I', 'Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States', 133, 'Algorithms|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm331', '17586542', 'ViroBLAST is a stand-alone BLAST web interface for nucleotide and amino acid sequence similarity searches. It extends the utility of BLAST to query against multiple sequence databases and user sequence datasets, and provides a friendly output to easily parse and navigate BLAST results. ViroBLAST is readily useful for all research areas that require BLAST functions and is available online and as a downloadable archive for independent installation.'),
(73517, 2007, 'bio', 'Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data', 'Zhu, Dongxiao|Li, Youjuan|Li, Hua', 'Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110, USA. doz@stowers-institute.org|Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110, USA. doz@stowers-institute.org|Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110, USA. doz@stowers-institute.org', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Genomics|Models, Genetic|Models, Statistical|Multivariate Analysis|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btm328', '17586543', 'Estimating pairwise correlation from replicated genome-scale (a.k.a. OMICS) data is fundamental to cluster functionally relevant biomolecules to a cellular pathway. The popular Pearson correlation coefficient estimates bivariate correlation by averaging over replicates. It is not completely satisfactory since it introduces strong bias while reducing variance. We propose a new multivariate correlation estimator that models all replicates as independent and identically distributed (i.i.d.) samples from the multivariate normal distribution. We derive the estimator by maximizing the likelihood function. For small sample data, we provide a resampling-based statistical inference procedure, and for moderate to large sample data, we provide an asymptotic statistical inference procedure based on the Likelihood Ratio Test (LRT). We demonstrate advantages of the new multivariate correlation estimator over Pearson bivariate correlation estimator using simulations and real-world data analysis examples.'),
(73518, 2007, 'bio', 'Detection of potential enzyme targets by metabolic modelling and optimization: application to a simple enzymopathy', 'Vera, Julio|Curto, Raul|Cascante, Marta|Torres, Néstor V', 'Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany. julio_vera_g@yahoo.es|Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany. julio_vera_g@yahoo.es|Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany. julio_vera_g@yahoo.es|Systems Biology and Bioinformatics Group, University of Rostock, Albert Einstein Street 21, 18051, Germany. julio_vera_g@yahoo.es', 'Germany|Germany|Germany|Germany', 29, 'Computer Simulation|Drug Delivery Systems|Drug Design|Enzyme Inhibitors|Humans|Hyperuricemia|Models, Biological|Multienzyme Complexes|Ribose-Phosphate Pyrophosphokinase', 'Journal Article', '10.1093/bioinformatics/btm326', '17586544', 'A very promising approach in drug discovery involves the integration of available biomedical data through mathematical modelling and data mining. We have developed a method called optimization program for drug discovery (OPDD) that allows new enzyme targets to be identified in enzymopathies through the integration of metabolic models and biomedical data in a mathematical optimization program. The method involves four steps: (i) collection of the necessary information about the metabolic system and disease, (ii) translation of the information into mathematical terms, (iii) computation of the optimization programs prioritizing the solutions that propose the inhibition of a reduced number of enzymes and (iv) application of additional biomedical criteria to select and classify the solutions. Each solution consists of a set of predicted values for metabolites, initial substrates and enzyme activities, which describe a biologically acceptable steady state of the system that shifts the pathologic state towards a healthy state.'),
(73519, 2007, 'bio', 'Interaction-site prediction for protein complexes: a critical assessment', 'Zhou, Huan-Xiang|Qin, Sanbo', 'Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA. zhou@sb.fsu.edu|Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida 32306, USA. zhou@sb.fsu.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm323', '17586545', 'Proteins function through interactions with other proteins and biomolecules. Protein-protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order.'),
(73520, 2007, 'bio', 'Annotation-based distance measures for patient subgroup discovery in clinical microarray studies', 'Lottaz, Claudio|Toedling, Joern|Spang, Rainer', 'Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Ihnestr. 73, D-14195 Berlin, Germany. claudio.lottaz@molgen.mpg.de|Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Ihnestr. 73, D-14195 Berlin, Germany. claudio.lottaz@molgen.mpg.de|Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Ihnestr. 73, D-14195 Berlin, Germany. claudio.lottaz@molgen.mpg.de', 'Germany|Germany|Germany', 10, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btm322', '17586546', 'Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering, however, can also stratify patients by similarity of their gene expression profiles, thereby defining novel disease entities based on molecular characteristics. Several distance-based cluster algorithms have been suggested, but little attention has been given to the distance measure between patients. Even with the Euclidean metric, including and excluding genes from the analysis leads to different distances between the same objects, and consequently different clustering results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73521, 2007, 'bio', 'MAnGO: an interactive R-based tool for two-colour microarray analysis', 'Marisa, Laetitia|Ichanté, Jean-Laurent|Reymond, Nancie|Aggerbeck, Lawrence|Delacroix, Hervé|Mucchielli-Giorgi, Marie-Hélène', 'Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA Microarray Platform, 91190 Gif-sur-Yvette, France.', 'France|France|France|France|France|France', 0, 'Algorithms|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btm321', '17586547', 'MAnGO (Microarray Analysis at the Gif/Orsay platform) is an interactive R-based tool for the analysis of two-colour microarray experiments. It is a compilation of various methods, which allows the user (1) to control data quality by detecting biases with a large number of visual representations, (2) to pre-process data (filtering and normalization) and (3) to carry out differential analyses. MAnGO is not only a turn-key tool, oriented towards biologists but also a flexible and adaptable R script oriented towards bioinformaticians.'),
(73522, 2007, 'bio', 'Constraint-based functional similarity of metabolic genes: going beyond network topology', 'Rokhlenko, Oleg|Shlomi, Tomer|Sharan, Roded|Ruppin, Eytan|Pinter, Ron Y', 'Department of Computer Science, Technion, Haifa 32000, Israel. olegro@cs.technion.ac.il|Department of Computer Science, Technion, Haifa 32000, Israel. olegro@cs.technion.ac.il|Department of Computer Science, Technion, Haifa 32000, Israel. olegro@cs.technion.ac.il|Department of Computer Science, Technion, Haifa 32000, Israel. olegro@cs.technion.ac.il|Department of Computer Science, Technion, Haifa 32000, Israel. olegro@cs.technion.ac.il', 'Israel|Israel|Israel|Israel|Israel', 12, 'Computer Simulation|Energy Metabolism|Models, Biological|Multigene Family|Protein Interaction Mapping|Saccharomyces cerevisiae|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm319', '17586548', 'Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data.'),
(73523, 2007, 'bio', 'Evaporative cooling feature selection for genotypic data involving interactions', 'McKinney, B A|Reif, D M|White, B C|Crowe, J E|Moore, J H', 'Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com|Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com|Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com|Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com|Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com', 'United States|United States|United States|United States|United States', 35, 'Base Sequence|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Databases, Genetic|Evolution, Molecular|Genotype|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm317', '17586549', 'The development of genome-wide capabilities for genotyping has led to the practical problem of identifying the minimum subset of genetic variants relevant to the classification of a phenotype. This challenge is especially difficult in the presence of attribute interactions, noise and small sample size.'),
(73524, 2007, 'bio', 'MareyMap: an R-based tool with graphical interface for estimating recombination rates', 'Rezvoy, Clément|Charif, Delphine|Guéguen, Laurent|Marais, Gabriel A B', 'Université de Lyon, France.|Université de Lyon, France.|Université de Lyon, France.|Université de Lyon, France.', 'France|France|France|France', 0, 'Chromosome Mapping|Computer Graphics|Computer Simulation|Genetic Variation|Models, Genetic|Programming Languages|Recombination, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm315', '17586550', 'Comparing genetic and physical maps (the so-called Marey map approach) is still the most widely used approach to estimate genome-wide recombination rates. Remarkably, there is no available bioinformatics tool specifically devoted to Marey map approach. Here, we developed such a tool called MareyMap based on GNU R and Tcl/Tk. MareyMap offers a user-friendly graphical interface and includes useful features, such as data cleaning process, sophisticated interpolation methods to estimate local rates, possibility of complex queries, various range of import-export files. Moreover, MareyMap comes with ready-to-use maps for human, Drosophila, Caenorhabditis elegans and Arabidopsis. MareyMap has been made so that it can be easily upgraded with new data and interpolation methods.'),
(73525, 2007, 'bio', 'PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets', 'Kim, Kyu-Won|Chung, Hun-Ki|Cho, Gyu-Taek|Ma, Kyung-Ho|Chandrabalan, Dorothy|Gwag, Jae-Gyun|Kim, Tae-San|Cho, Eun-Gi|Park, Yong-Jin', 'National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.|National Institute of Agricultural Biotechnology, 247 Seodun-dong, Suwon, 441-707, R.Korea.', '-|-|-|-|-|-|-|-|-', 144, 'Algorithms|Base Sequence|Gene Frequency|Molecular Sequence Data|Quantitative Trait Loci|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm313', '17586551', 'Core sets are necessary to ensure that access to useful alleles or characteristics retained in genebanks is guaranteed. We have successfully developed a computational tool named PowerCore that aims to support the development of core sets by reducing the redundancy of useful alleles and thus enhancing their richness.'),
(73526, 2007, 'bio', 'Visualization-based cancer microarray data classification analysis', 'Mramor, Minca|Leban, Gregor|Demsar, Janez|Zupan, Blaz', 'Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.', 'Slovenia|Slovenia|Slovenia|Slovenia', 47, 'Algorithms|Biomarkers, Tumor|Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Humans|Information Storage and Retrieval|Neoplasm Proteins|Neoplasms|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm312', '17586552', 'Methods for analyzing cancer microarray data often face two distinct challenges: the models they infer need to perform well when classifying new tissue samples while at the same time providing an insight into the patterns and gene interactions hidden in the data. State-of-the-art supervised data mining methods often cover well only one of these aspects, motivating the development of methods where predictive models with a solid classification performance would be easily communicated to the domain expert.'),
(73527, 2007, 'bio', 'Stochastic simulation GUI for biochemical networks', 'Vallabhajosyula, Ravishankar Rao|Sauro, Herbert M', 'Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu|Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu', 'United States|United States', 7, 'Algorithms|Computational Biology|Computer Graphics|Computer Simulation|Models, Biological|Programming Languages|Software|Stochastic Processes|Systems Biology|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm231', '17586553', 'This article describes the development of a useful graphical user interface for stochastic simulation of biochemical networks which allows model builders to run stochastic simulations of their models and perform statistical analysis on the results. These include the construction of correlations, power-spectral densities and transfer functions between selected inputs and outputs.'),
(73528, 2007, 'bio', 'BiVisu: software tool for bicluster detection and visualization', 'Cheng, K O|Law, N F|Siu, W C|Lau, T H', 'Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong. k.o.cheng@polyu.edu.hk|Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong. k.o.cheng@polyu.edu.hk|Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong. k.o.cheng@polyu.edu.hk|Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong. k.o.cheng@polyu.edu.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 50, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm338', '17586826', 'BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed.'),
(73529, 2007, 'bio', 'HaploBuild: an algorithm to construct non-contiguous associated haplotypes in family based genetic studies', 'Laramie, Jason M|Wilk, Jemma B|DeStefano, Anita L|Myers, Richard H', 'Department of Neurology, Boston University School of Medicine, Boston, MA, USA. laramiej@bu.edu|Department of Neurology, Boston University School of Medicine, Boston, MA, USA. laramiej@bu.edu|Department of Neurology, Boston University School of Medicine, Boston, MA, USA. laramiej@bu.edu|Department of Neurology, Boston University School of Medicine, Boston, MA, USA. laramiej@bu.edu', 'United States|United States|United States|United States', 10, 'Algorithms|Biological Evolution|Contig Mapping|Family|Genetic Linkage|Genetic Variation|Genetics, Population|Haplotypes|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btm316', '17586827', 'We have created a program that searches densely genotyped regions for associated non-contiguous haplotypes using a standard family based haplotype association test. This program was designed to expand upon the sliding window methodologies commonly used for haplotype construction by allowing the association of subsets of single nucleotide polymorphisms (SNPs) to drive the construction of the haplotype. This strategy permits HaploBuild to construct more biologically relevant haplotypes that are not constrained by arbitrary length and contiguous orientation.'),
(73530, 2007, 'bio', 'beadarray: R classes and methods for Illumina bead-based data', 'Dunning, Mark J|Smith, Mike L|Ritchie, Matthew E|Tavaré, Simon', 'Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. md392@cam.ac.uk|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. md392@cam.ac.uk|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. md392@cam.ac.uk|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. md392@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 340, 'Algorithms|Databases, Genetic|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microspheres|Oligonucleotide Array Sequence Analysis|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm311', '17586828', 'The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illuminas, proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illuminas, BeadStudio software can also be read and analysed in the same manner.'),
(73531, 2007, 'bio', 'TASSEL: software for association mapping of complex traits in diverse samples', 'Bradbury, Peter J|Zhang, Zhiwu|Kroon, Dallas E|Casstevens, Terry M|Ramdoss, Yogesh|Buckler, Edward S', 'United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu|United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu|United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu|United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu|United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu|United States Department of Agriculture-Agricultural Research Service, Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. pjb39@cornell.edu', 'United States|United States|United States|United States|United States|United States', 2867, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Data Interpretation, Statistical|Genetic Variation|Linkage Disequilibrium|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btm308', '17586829', 'Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.'),
(73532, 2007, 'bio', 'Web-based GeneChip analysis system for large-scale collaborative projects', 'Dai, Manhong|Wang, Pinglang|Jakupovic, Elvis|Watson, Stanley J|Meng, Fan', 'Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States', 11, 'Computer Security|Cooperative Behavior|Database Management Systems|Databases, Genetic|Information Dissemination|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm297', '17586830', 'The Web-Based GeneChip Analysis System (WGAS) is developed to overcome limitations in analysis setup efficiency, data and procedure sharing, as well as security issues in existing commercial and public domain solutions. It also incorporates unique functions and resources for more accurate and flexible GeneChip analysis.'),
(73533, 2007, 'bio', 'Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data', 'Tseng, George C', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA. ctseng@pitt.edu', 'United States', 62, 'Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Pattern Recognition, Automated|Peptide Mapping|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btm320', '17597097', 'Cluster analysis is one of the most important data mining tools for investigating high-throughput biological data. The existence of many scattered objects that should not be clustered has been found to hinder performance of most traditional clustering algorithms in such a high-dimensional complex situation. Very often, additional prior knowledge from databases or previous experiments is also available in the analysis. Excluding scattered objects and incorporating existing prior information are desirable to enhance the clustering performance.'),
(73534, 2007, 'bio', 'Different packing of external residues can explain differences in the thermostability of proteins from thermophilic and mesophilic organisms', 'Glyakina, Anna V|Garbuzynskiy, Sergiy O|Lobanov, Michail Yu|Galzitskaya, Oxana V', 'Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.', '-|-|-|-', 71, 'Amino Acid Sequence|Bacterial Proteins|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Folding|Sequence Analysis, Protein|Temperature', 'Journal Article', '10.1093/bioinformatics/btm345', '17599925', 'Understanding the basis of protein stability in thermophilic organisms raises a general question: what structural properties of proteins are responsible for the higher thermostability of proteins from thermophilic organisms compared to proteins from mesophilic organisms?'),
(73535, 2007, 'bio', 'QPRIMER: a quick web-based application for designing conserved PCR primers from multigenome alignments', 'Kim, Namshin|Lee, Christopher', 'Center for Computational Biology, Molecular Biology Institute, University of California, Los Angeles, 90095-1570, USA.|Center for Computational Biology, Molecular Biology Institute, University of California, Los Angeles, 90095-1570, USA.', 'United States|United States', 14, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|DNA Primers|Internet|Molecular Sequence Data|Polymerase Chain Reaction|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btm343', '17599926', 'We have developed a quick web-based application for designing conserved genomic PCR and RT-PCR primers from multigenome alignments targeting specific exons or introns. We used Pygr (The Python Graph Database Framework for Bioinformatics) to query intervals from multigenome alignments, which gives us less than a millisecond access to any intervals of any genome within multigenome alignments. PRIMER3 was used to extract optimal primers from a gene of interest. QPRIMER creates an electronic genomic PCR image from a set of conserved primers as well as summary pages for primer alignments and products. QPRIMER supports human, mouse, rat, chicken, dog, zebrafish and fruit fly.'),
(73536, 2007, 'bio', 'Detection of eQTL modules mediated by activity levels of transcription factors', 'Sun, Wei|Yu, Tianwei|Li, Ker-Chau', 'Department of Statistics, University of California at Los Angeles, Los Angeles, California, USA.|Department of Statistics, University of California at Los Angeles, Los Angeles, California, USA.|Department of Statistics, University of California at Los Angeles, Los Angeles, California, USA.', 'United States|United States|United States', 0, 'Base Sequence|Chromosome Mapping|Molecular Sequence Data|Quantitative Trait Loci|Sequence Analysis, DNA|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btm327', '17599927', 'Studies of gene expression quantitative trait loci (eQTL) in different organisms have shown the existence of eQTL hot spots: each being a small segment of DNA sequence that harbors the eQTL of a large number of genes. Two questions of great interest about eQTL hot spots arise: (1) which gene within the hot spot is responsible for the linkages, i.e. which gene is the quantitative trait gene (QTG)? (2) How does a QTG affect the expression levels of many genes linked to it? Answers to the first question can be offered by available biological evidence or by statistical methods. The second question is harder to address. One simple situation is that the QTG encodes a transcription factor (TF), which regulates the expression of genes linked to it. However, previous results have shown that TFs are not overrepresented in the eQTL hot spots. In this article, we consider the scenario that the propagation of genetic perturbation from a QTG to other linked genes is mediated by the TF activity. We develop a procedure to detect the eQTL modules (eQTL hot spots together with linked genes) that are compatible with this scenario.'),
(73537, 2007, 'bio', 'Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential', 'Zhang, Zhuqing|Chen, Hao|Lai, Luhua', 'Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.|Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.|Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.', 'China|China|China', 75, 'Amino Acid Sequence|Amino Acids|Amyloid|Data Interpretation, Statistical|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm325', '17599928', 'Experimental evidence suggests that certain short protein segments have stronger amyloidogenic propensities than others. Identification of the fibril-forming segments of proteins is crucial for understanding diseases associated with protein misfolding and for finding favorable targets for therapeutic strategies.'),
(73538, 2007, 'bio', 'Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network', 'Asgary, Mehdi Poursheikhali|Jahandideh, Samad|Abdolmaleki, Parviz|Kazemnejad, Anoshirvan', 'Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.|Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.|Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.|Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.', '-|-|-|-', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Logistic Models|Models, Chemical|Models, Molecular|Molecular Sequence Data|Multivariate Analysis|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Conformation|Regression Analysis|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm324', '17599929', 'So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn.'),
(73539, 2007, 'bio', 'Non-parametric quantification of protein lysate arrays', 'Hu, Jianhua|He, Xuming|Baggerly, Keith A|Coombes, Kevin R|Hennessy, Bryan T J|Mills, Gordon B', 'Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org|Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org|Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org|Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org|Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org|Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, TX, USA. jhu@mdanderson.org', 'United States|United States|United States|United States|United States|United States', 144, 'Algorithms|Gene Expression Profiling|Peptide Mapping|Protein Array Analysis|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm283', '17599930', 'Proteins play a crucial role in biological activity, so much can be learned from measuring protein expression and post-translational modification quantitatively. The reverse-phase protein lysate arrays allow us to quantify the relative expression levels of a protein in many different cellular samples simultaneously. Existing approaches to quantify protein arrays use parametric response curves fit to dilution series data. The results can be biased when the parametric function does not fit the data.'),
(73540, 2007, 'bio', 'A statistical approach using network structure in the prediction of protein characteristics', 'Chen, Pao-Yang|Deane, Charlotte M|Reinert, Gesine', 'Department of Statistics, University of Oxford, Oxford, OX1 3TG, UK. pchen@stats.ox.ac.uk|Department of Statistics, University of Oxford, Oxford, OX1 3TG, UK. pchen@stats.ox.ac.uk|Department of Statistics, University of Oxford, Oxford, OX1 3TG, UK. pchen@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Protein Interaction Mapping|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm342', '17599931', 'The Majority Vote approach has demonstrated that protein-protein interactions can be used to predict the structure or function of a protein. In this article we propose a novel method for the prediction of such protein characteristics based on frequencies of pairwise interactions. In addition, we study a second new approach using the pattern frequencies of triplets of proteins, thus for the first time taking network structure explicitly into account. Both these methods are extended to jointly consider multiple organisms and multiple characteristics.'),
(73541, 2007, 'bio', 'ChemDB update--full-text search and virtual chemical space', 'Chen, Jonathan H|Linstead, Erik|Swamidass, S Joshua|Wang, Dennis|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, USA.', 'United States|United States|United States|United States|United States', 78, 'Artificial Intelligence|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Organic Chemicals|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm341', '17599932', 'ChemDB is a chemical database containing nearly 5M commercially available small molecules, important for use as synthetic building blocks, probes in systems biology and as leads for the discovery of drugs and other useful compounds. The data is publicly available over the web for download and for targeted searches using a variety of powerful methods. The chemical data includes predicted or experimentally determined physicochemical properties, such as 3D structure, melting temperature and solubility. Recent developments include optimization of chemical structure (and substructure) retrieval algorithms, enabling full database searches in less than a second. A text-based search engine allows efficient searching of compounds based on over 65M annotations from over 150 vendors. When searching for chemicals by name, fuzzy text matching capabilities yield productive results even when the correct spelling of a chemical name is unknown, taking advantage of both systematic and common names. Finally, built in reaction models enable searches through virtual chemical space, consisting of hypothetical products readily synthesizable from the building blocks in ChemDB.'),
(73542, 2007, 'bio', 'Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer', 'Buness, Andreas|Kuner, Ruprecht|Ruschhaupt, Markus|Poustka, Annemarie|Sültmann, Holger|Tresch, Achim', 'German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de|German Cancer Research Center (DKFZ), Department of Molecular Genome Analysis, 69120 Heidelberg, Germany. a.buness@gmx.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 23, 'Biomarkers, Tumor|Breast Neoplasms|Chromosome Aberrations|Chromosome Mapping|Female|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btm340', '17599933', 'In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy.'),
(73543, 2007, 'bio', 'Genomic mutation consequence calculator', 'Major, John E', 'Memorial Sloan-Kettering Computational Biology Center, 1275 York Avenue, Box 460, New York, NY 10021, USA. majorj@mskcc.org', 'United States', 12, 'Base Sequence|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Genetic Variation|Models, Genetic|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm339', '17599934', 'The genomic mutation consequence calculator (GMCC) is a tool that will reliably and quickly calculate the consequence of arbitrary genomic mutations. GMCC also reports supporting annotations for the specified genomic region. The particular strength of the GMCC is it works in genomic space, not simply in spliced transcript space as some similar tools do. Within gene features, GMCC can report on the effects on splice site, UTR and coding regions in all isoforms affected by the mutation. A considerable number of genomic annotations are also reported, including: genomic conservation score, known SNPs, COSMIC mutations, disease associations and others. The manual interface also offers link outs to various external databases and resources. In batch mode, GMCC returns a csv file which can easily be parsed by the end user.'),
(73544, 2007, 'bio', 'Improving gene quantification by adjustable spot-image restoration', 'Daskalakis, Antonis|Cavouras, Dionisis|Bougioukos, Panagiotis|Kostopoulos, Spiros|Glotsos, Dimitris|Kalatzis, Ioannis|Kagadis, George C|Argyropoulos, Christos|Nikiforidis, George', 'Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr|Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, 265 04 Rio, Greece. daskalakis@med.upatras.gr', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece', 0, 'Algorithms|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm337', '17599935', 'One of the major factors that complicate the task of microarray image analysis is that microarray images are distorted by various types of noise. In this study a robust framework is proposed, designed to take into account the effect of noise in microarray images in order to assist the demanding task of microarray image analysis. The proposed framework, incorporates in the microarray image processing pipeline a novel combination of spot adjustable image analysis and processing techniques and consists of the following stages: (1) gridding for facilitating spot identification, (2) clustering (unsupervised discrimination between spot and background pixels) applied to spot image for automatic local noise assessment, (3) modeling of local image restoration process for spot image conditioning (adjustable wiener restoration using an empirically determined degradation function), (4) automatic spot segmentation employing seeded-region-growing, (5) intensity extraction and (6) assessment of the reproducibility (real data) and the validity (simulated data) of the extracted gene expression levels.'),
(73545, 2007, 'bio', 'Prediction of Ras-effector interactions using position energy matrices', 'Kiel, Christina|Serrano, Luis', 'EMBL-CRG Systems Biology Unit, CRG-Centre de Regulacio Genomica, Dr Aiguader 88, 08003 Barcelona, Spain. christina.kiel@crg.es|EMBL-CRG Systems Biology Unit, CRG-Centre de Regulacio Genomica, Dr Aiguader 88, 08003 Barcelona, Spain. christina.kiel@crg.es', 'Spain|Spain', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Energy Transfer|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|ras Proteins', 'Journal Article', '10.1093/bioinformatics/btm336', '17599936', 'One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences.'),
(73546, 2007, 'bio', 'CASVM: web server for SVM-based prediction of caspase substrates cleavage sites', 'Wee, Lawrence J K|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Caspases|Enzyme Activation|Internet|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Sequence Alignment|Sequence Analysis, Protein|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btm334', '17599937', 'Caspases belong to a unique class of cysteine proteases which function as critical effectors of apoptosis, inflammation and other important cellular processes. Caspases cleave substrates at specific tetrapeptide sites after a highly conserved aspartic acid residue. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. We have recently developed a support vector machines (SVM) method to address this issue. Our algorithm achieved an accuracy ranging from 81.25 to 97.92%, making it one of the best methods currently available. CASVM is the web server implementation of our SVM algorithms, written in Perl and hosted on a Linux platform. The server can be used for predicting non-canonical caspase substrate cleavage sites. We have also included a relational database containing experimentally verified caspase substrates retrievable using accession IDs, keywords or sequence similarity.'),
(73547, 2007, 'bio', 'EuGene Analyzer a tool for integrating gene expression data with pathway databases', 'Cavalieri, Duccio|Castagnini, Cinzia|Toti, Simona|Maciag, Karolina|Kelder, Thomas|Gambineri, Luca|Angioli, Samuele|Dolara, Piero', 'Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it|Department of Preclinical and Clinical Pharmacology, Department of Statistics, University of Florence, Florence, Italy. duccio.cavalieri@unifi.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 31, 'Algorithms|Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm333', '17599938', 'Eu.Gene Analyzer is an easy-to-use, stand-alone application that allows rapid and powerful microarray data analysis in the context of biological pathways. Its intuitive graphical user interface makes it an easy and flexible tool, even for the first-time user. Eu.Gene supports a variety of array platforms, organisms and pathway ontologies, transparently deals with multiple nomenclature systems and seamlessly integrates data from different sources. Two different statistical methods, the Fisher Exact Test and the Gene Set Enrichment Analysis (GSEA), are implemented to identify biological pathways transcriptionally affected under experimental conditions. A suite of tools is offered to define, visualize and share custom non-redundant pathway sets. In conclusion, Eu.Gene Analyzer is a new software application that takes advantage of information from multiple pathway databases to build a comprehensive interpretation of experimental results in a simple, intuitive environment.'),
(73548, 2007, 'bio', 'Context-sensitive data integration and prediction of biological networks', 'Myers, Chad L|Troyanskaya, Olga G', 'Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ, USA.', 'United States|United States', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btm332', '17599939', 'Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context.'),
(73549, 2007, 'bio', 'POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix', 'Shimizu, Kana|Hirose, Shuichi|Noguchi, Tamotsu', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. poodle@cbrc.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. poodle@cbrc.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. poodle@cbrc.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Amino Acids|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Protein Conformation|Protein Denaturation|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btm330', '17599940', 'Protein disorder is characterized by a lack of a stable 3D structure, and is considered to be involved in a number of important protein functions such as regulatory and signalling events. We developed a web application, the POODLE-S, which predicts the disordered region from amino acid sequences by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.'),
(73550, 2007, 'bio', 'RefPlus: an R package extending the RMA Algorithm', 'Harbron, Chris|Chang, Kai-Ming|South, Marie C', 'Statistical Sciences, AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK. Chris.Harbron@AstraZeneca.Com|Statistical Sciences, AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK. Chris.Harbron@AstraZeneca.Com|Statistical Sciences, AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK. Chris.Harbron@AstraZeneca.Com', 'United Kingdom|United Kingdom|United Kingdom', 24, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btm357', '17623700', 'RMA has become a widely used methodology to pre-process Affymetrix gene expression microarrays. A limitation of RMA is that the calculated probeset intensities change when a set of microarrays is re-pre-processed after the inclusion of additional microarrays into the analysis set. Here we report the availability of the RefPlus package containing functions to perform the Extrapolation Strategy and Extrapolation Averaging algorithms which address these issues.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73551, 2007, 'bio', 'A quantitative genotype algorithm reflecting H5N1 Avian influenza niches', 'Wan, Xiu-Feng|Chen, Guorong|Luo, Feng|Emch, Michael|Donis, Ruben', 'Department of Microbiology, Miami University, Oxford, OH 45056, USA. wanhenry@yahoo.com|Department of Microbiology, Miami University, Oxford, OH 45056, USA. wanhenry@yahoo.com|Department of Microbiology, Miami University, Oxford, OH 45056, USA. wanhenry@yahoo.com|Department of Microbiology, Miami University, Oxford, OH 45056, USA. wanhenry@yahoo.com|Department of Microbiology, Miami University, Oxford, OH 45056, USA. wanhenry@yahoo.com', 'United States|United States|United States|United States|United States', 21, 'Animals|Biological Evolution|Birds|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Genotype|Influenza A Virus, H5N1 Subtype|Influenza in Birds|Reassortant Viruses|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm354', '17623701', 'Computational genotyping analyses are critical for characterizing molecular evolutionary footprints, thus providing important information for designing the strategies of influenza prevention and control. Most of the current methods that are available are based on multiple sequence alignment and phylogenetic tree construction, which are time consuming and limited by the number of taxa. Arbitrarily defining genotypes further complicates the interpretation of genotyping results.'),
(73552, 2007, 'bio', 'EcoProDB: the Escherichia coli protein database', 'Yun, Hongseok|Lee, Jeong Wook|Jeong, Joonwoo|Chung, Jaesung|Park, Jong Myoung|Myoung, Han Na|Lee, Sang Yup', 'Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.|Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Daejeon 305-701, Korea.', '-|-|-|-|-|-|-', 0, 'Computer Graphics|Database Management Systems|Databases, Protein|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Information Storage and Retrieval|Internet|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm351', '17623702', 'EcoProDB is a web-based database for comparative proteomics of Escherichia coli. The database contains information on E. coli proteins identified on 2D gels along with other resources collected from various databases and published literature, with a special feature of showing the expression levels of E. coli proteins under different genetic and environmental conditions. It also provides comparative information of subcellular localization, theoretical 2D map, experimental 2D map and integrated protein information via an interactive web interface and application such as the Map Browser. Users can also upload their own 2D gels, extract core information associated with the proteins and 2D gel results from different experiments and consequently generate new knowledge and hypotheses for further studies.'),
(73553, 2007, 'bio', 'HMMoC--a compiler for hidden Markov models', 'Lunter, Gerton', 'MRC Functional Genetics Unit, Department of Physiology, Anatomy and Genetics, South Parks Road, OX1 3TG, University of Oxford, UK. gerton.lunter@dpag.ox.ac.uk', 'United Kingdom', 20, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Simulation|Markov Chains|Models, Biological|Models, Statistical|Pattern Recognition, Automated|Programming Languages|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btm350', '17623703', 'Hidden Markov models are widely applied within computational biology. The large data sets and complex models involved demand optimized implementations, while efficient exploration of model space requires rapid prototyping. These requirements are not met by existing solutions, and hand-coding is time-consuming and error-prone. Here, I present a compiler that takes over the mechanical process of implementing HMM algorithms, by translating high-level XML descriptions into efficient C++ implementations. The compiler is highly customizable, produces efficient and bug-free code, and includes several optimizations.'),
(73554, 2007, 'bio', 'An approach to predict transcription factor DNA binding site specificity based upon gene and transcription factor functional categorization', 'Qian, Ziliang|Lu, Lingyi|Liu, XiaoJun|Cai, Yu-Dong|Li, Yixue', 'CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.|CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100039, China.', 'China|China|China|China|China', 0, 'Algorithms|Base Sequence|Binding Sites|DNA|Molecular Sequence Data|Protein Binding|Sensitivity and Specificity|Sequence Analysis, DNA|Structure-Activity Relationship|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btm348', '17623704', 'To understand transcription regulatory mechanisms, it is indispensable to investigate transcription factor (TF) DNA binding preferences. We noted that the generally acknowledged information of functional annotations of TFs as well as that of their target genes should provide useful hints in determining TF DNA binding preferences.'),
(73555, 2008, 'bio', 'Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood', 'Kelley, Ryan|Feizi, Hoda|Ideker, Trey', 'Program in Bioinformatics and Department of Bioengineering, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. rmkelley@ucsd.edu|Program in Bioinformatics and Department of Bioengineering, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. rmkelley@ucsd.edu|Program in Bioinformatics and Department of Bioengineering, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. rmkelley@ucsd.edu', 'United States|United States|United States', 10, 'Algorithms|Artifacts|Fluorescent Dyes|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|Likelihood Functions|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btm347', '17623705', 'In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes.'),
(73556, 2007, 'bio', 'structureViz: linking Cytoscape and UCSF Chimera', 'Morris, John H|Huang, Conrad C|Babbitt, Patricia C|Ferrin, Thomas E', 'Department of Pharmaceutical Chemistry, University of California, San Francisco, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California, San Francisco, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California, San Francisco, USA. scooter@cgl.ucsf.edu|Department of Pharmaceutical Chemistry, University of California, San Francisco, USA. scooter@cgl.ucsf.edu', 'United States|United States|United States|United States', 0, 'Computer Graphics|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Sequence Analysis, Protein|Signal Transduction|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm329', '17623706', 'structureViz is a Cytoscape plug-in that links the visualization of biological networks provided by Cytoscape with the visualization and analysis of macromolecular structures and sequences provided by UCSF Chimera. When combined with Cytoscape and Chimera, structureViz provides the first tool that links these two critical aspects of computational analysis in a straightforward manner. structureViz includes commands to open structures in Chimera and align them using Chimeras sequence-structure analysis tools. When a structure is opened, structureViz provides an alternative interface to Chimera: the Cytoscape Molecular Structure Navigator. This interface uses a tree-based paradigm to allow users to select and affect the display of models, chains and residues, mostly through the use of context menus.'),
(73557, 2007, 'bio', 'Quantitative quality-assessment techniques to compare fractionation and depletion methods in SELDI-TOF mass spectrometry experiments', 'Harezlak, Jaroslaw|Wang, Mike|Christiani, David|Lin, Xihong', 'Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue Boston, MA 02115, USA. harezlak@post.harvard.edu|Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue Boston, MA 02115, USA. harezlak@post.harvard.edu|Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue Boston, MA 02115, USA. harezlak@post.harvard.edu|Department of Biostatistics, Harvard School of Public Health 655 Huntington Avenue Boston, MA 02115, USA. harezlak@post.harvard.edu', 'United States|United States|United States|United States', 9, 'Biomarkers, Tumor|Blood Chemical Analysis|Humans|Lung Neoplasms|Male|Middle Aged|Neoplasm Proteins|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Specimen Handling|Spectrometry, Mass, Electrospray Ionization|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Comparative Study', '10.1093/bioinformatics/btm346', '17626063', 'Mass spectrometry (MS), such as the surface-enhanced laser desorption and ionization time-of-flight (SELDI-TOF) MS, provides a potentially promising proteomic technology for biomarker discovery. An important matter for such a technology to be used routinely is its reproducibility. It is of significant interest to develop quantitative measures to evaluate the quality and reliability of different experimental methods.'),
(73558, 2007, 'bio', 'Exploring biological network structure using exponential random graph models', 'Saul, Zachary M|Filkov, Vladimir', 'Department of Computer Science, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA. saul@cs.ucdavis.edu|Department of Computer Science, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA. saul@cs.ucdavis.edu', 'United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm370', '17644557', 'The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus on identifying simple, but prominent, structural components, as such components are easier to understand, and, once identified, can be used as building blocks to succinctly describe the network.'),
(73559, 2007, 'bio', 'GSEA-P: a desktop application for Gene Set Enrichment Analysis', 'Subramanian, Aravind|Kuehn, Heidi|Gould, Joshua|Tamayo, Pablo|Mesirov, Jill P', 'Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.', 'United States|United States|United States|United States|United States', 571, 'Algorithms|Computer Graphics|Computer Simulation|Gene Expression Profiling|Models, Biological|Programming Languages|Proteins|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm369', '17644558', 'Gene Set Enrichment Analysis (GSEA) is a computational method that assesses whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. We report the availability of a new version of the Java based software (GSEA-P 2.0) that represents a major improvement on the previous release through the addition of a leading edge analysis component, seamless integration with the Molecular Signature Database (MSigDB) and an embedded browser that allows users to search for gene sets and map them to a variety of microarray platform formats. This functionality makes it possible for users to directly import gene sets from MSigDB for analysis with GSEA. We have also improved the visualizations in GSEA-P 2.0 and added links to a new form of concise gene set annotations called Gene Set Cards. These additions, as well as other improvements suggested by over 3500 users who have downloaded the software over the past year have been incorporated into this new release of the GSEA-P Java desktop program.'),
(73560, 2007, 'bio', 'Analysis of array CGH data for cancer studies using fused quantile regression', 'Li, Youjuan|Zhu, Ji', 'Department of Statistics, University of Michigan, Michigan, USA.|Department of Statistics, University of Michigan, Michigan, USA.', 'United States|United States', 0, 'Chromosome Mapping|Diagnosis, Computer-Assisted|Gene Dosage|Genetic Predisposition to Disease|Genetic Testing|Humans|In Situ Hybridization, Fluorescence|Neoplasms|Oligonucleotide Array Sequence Analysis|Regression Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm364', '17644559', 'The identification of DNA copy number changes provides insights that may advance our understanding of initiation and progression of cancer. Array-based comparative genomic hybridization (array-CGH) has emerged as a technique allowing high-throughput genome-wide scanning for chromosomal aberrations. A number of statistical methods have been proposed for the analysis of array-CGH data. In this article, we consider a fused quantile regression model based on three motivations: (1) quantile regression may provide a more comprehensive picture for the ratio profile of copy numbers than the standard mean regression approach, (2) for simplicity, most available methods assume uniform spacing between neighboring clones, while incorporating the information of physical locations of clones may be helpful and (3) most current methods have a set of tuning parameters that must be carefully tuned, which introduces complexity to the implementation.'),
(73561, 2007, 'bio', 'APID2NET: unified interactome graphic analyzer', 'Hernandez-Toro, Juan|Prieto, Carlos|De las Rivas, Javier', 'Bioinformatics and Functional Genomics Research Group, Cancer Research Center (IBMCC-CIC, CSIC-USAL), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (IBMCC-CIC, CSIC-USAL), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (IBMCC-CIC, CSIC-USAL), Salamanca, Spain.', 'Spain|Spain|Spain', 63, 'Binding Sites|Computer Graphics|Computer Simulation|Internet|Models, Biological|Protein Binding|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm373', '17644818', 'Exploration and analysis of interactome networks at systems level requires unification of the biomolecular elements and annotations that come from many different high-throughput or small-scale proteomic experiments. Only such integration can provide a non-redundant and consistent identification of proteins and interactions. APID2NET is a new tool that works with Cytoscape to allow surfing unified interactome data by querying APID server (http://bioinfow.dep.usal.es/apid/) to provide interactive analysis of protein-protein interaction (PPI) networks. The program is designed to visualize, explore and analyze the proteins and interactions retrieved, including the annotations and attributes associated to them, such as: GO terms, InterPro domains, experimental methods that validate each interaction, PubMed IDs, UniProt IDs, etc. The tool provides interactive graphical representation of the networks with all Cytoscape capabilities, plus new automatic tools to find concurrent functional and structural attributes along all protein pairs in a network.'),
(73562, 2007, 'bio', 'miniTUBA: medical inference by network integration of temporal data using Bayesian analysis', 'Xiang, Zuoshuang|Minter, Rebecca M|Bi, Xiaoming|Woolf, Peter J|He, Yongqun', 'Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.|Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA.', 'United States|United States|United States|United States|United States', 14, 'Algorithms|Artificial Intelligence|Bayes Theorem|Databases, Factual|Decision Support Systems, Clinical|Diagnosis, Computer-Assisted|Medical Informatics|Neural Networks (Computer)|Pattern Recognition, Automated|Software|Systems Integration|Time Factors', 'Journal Article', '10.1093/bioinformatics/btm372', '17644819', 'Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data.'),
(73563, 2007, 'bio', 'Haplotype inference for present-absent genotype data using previously identified haplotypes and haplotype patterns', 'Yoo, Yun Joo|Tang, Jianming|Kaslow, Richard A|Zhang, Kui', 'Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.', 'United States|United States|United States|United States', 0, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genotype|Haplotypes|Molecular Sequence Data|Pattern Recognition, Automated|Receptors, Immunologic|Receptors, KIR|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm371', '17644820', 'Killer immunoglobulin-like receptor (KIR) genes vary considerably in their presence or absence on a specific regional haplotype. Because presence or absence of these genes is largely detected using locus-specific genotyping technology, the distinction between homozygosity and hemizygosity is often ambiguous. The performance of methods for haplotype inference (e.g. PL-EM, PHASE) for KIR genes may be compromised due to the large portion of ambiguous data. At the same time, many haplotypes or partial haplotype patterns have been previously identified and can be incorporated to facilitate haplotype inference for unphased genotype data. To accommodate the increased ambiguity of present-absent genotyping of KIR genes, we developed a hybrid approach combining a greedy algorithm with the Expectation-Maximization (EM) method for haplotype inference based on previously identified haplotypes and haplotype patterns.'),
(73564, 2007, 'bio', 'Multiway analysis of epilepsy tensors', 'Acar, Evrim|Aykut-Bingol, Canan|Bingol, Haluk|Bro, Rasmus|Yener, Bülent', 'Computer Science Department, Rensselaer Polytechnic Institute, NY, USA. acare@cs.rpi.edu|Computer Science Department, Rensselaer Polytechnic Institute, NY, USA. acare@cs.rpi.edu|Computer Science Department, Rensselaer Polytechnic Institute, NY, USA. acare@cs.rpi.edu|Computer Science Department, Rensselaer Polytechnic Institute, NY, USA. acare@cs.rpi.edu|Computer Science Department, Rensselaer Polytechnic Institute, NY, USA. acare@cs.rpi.edu', 'United States|United States|United States|United States|United States', 169, 'Artificial Intelligence|Brain Mapping|Diagnosis, Computer-Assisted|Electroencephalography|Epilepsy|Humans|Multivariate Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btm210', '17646285', 'The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization.'),
(73565, 2007, 'bio', 'Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants', 'Danziger, Samuel A|Zeng, Jue|Wang, Ying|Brachmann, Rainer K|Lathrop, Richard H', 'Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.|Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.|Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.|Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.|Department of Biomedical Engineering, University of California, Irvine, California 92697, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Artificial Intelligence|Biomarkers, Tumor|DNA Mutational Analysis|Humans|Mutation|Neoplasm Proteins|Neoplasms|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btm166', '17646286', 'Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for rapid discovery of biological function. To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants for their ability to build classifiers that predict protein function. The best of these techniques, Maximum Curiosity, improved the baseline accuracy of 56-77%. This article shows that Active Learning is a useful tool for biomedical research, and provides a case study of interest to others facing similar discovery challenges.'),
(73566, 2007, 'bio', 'Structural templates predict novel protein interactions and targets from pancreas tumour gene expression data', 'Dawelbait, Gihan|Winter, Christof|Zhang, Yanju|Pilarsky, Christian|Grützmann, Robert|Heinrich, Jörg-Christian|Schroeder, Michael', 'Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.|Bioinformatics Group, Biotechnological Centre, TU Dresden, Dresden, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Biomarkers, Tumor|Carcinoma, Pancreatic Ductal|Computer Simulation|Drug Delivery Systems|Gene Expression Profiling|Gene Targeting|Humans|Models, Biological|Neoplasm Proteins|Pancreatic Neoplasms|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm188', '17646287', 'Pancreatic ductal adenocarcinoma (PDAC) eludes early detection and is characterized by its aggressiveness and resistance to current therapies. A number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue. To identify molecular markers and suitable targets, these genes have been mapped to protein interactions to gain an understanding at systems level.'),
(73567, 2007, 'bio', 'Kernel-based data fusion for gene prioritization', 'De Bie, Tijl|Tranchevent, Léon-Charles|van Oeffelen, Liesbeth M M|Moreau, Yves', 'Department of Engineering Mathematics, University of Bristol, University Walk, BS8 1TR, Bristol, UK. tijl.debie@gmail.com|Department of Engineering Mathematics, University of Bristol, University Walk, BS8 1TR, Bristol, UK. tijl.debie@gmail.com|Department of Engineering Mathematics, University of Bristol, University Walk, BS8 1TR, Bristol, UK. tijl.debie@gmail.com|Department of Engineering Mathematics, University of Bristol, University Walk, BS8 1TR, Bristol, UK. tijl.debie@gmail.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 99, 'Biomarkers|Chromosome Mapping|Computer Simulation|Database Management Systems|Databases, Factual|Disease Susceptibility|Genetic Predisposition to Disease|Humans|Information Storage and Retrieval|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btm187', '17646288', 'Hunting disease genes is a problem of primary importance in biomedical research. Biologists usually approach this problem in two steps: first a set of candidate genes is identified using traditional positional cloning or high-throughput genomics techniques, second, these genes are further investigated and validated in the wet lab, one by one. To speed up discovery and limit the number of costly wet lab experiments, biologists must test the candidate genes starting with the most probable candidates. So far, biologists have relied on literature studies, extensive queries to multiple databases and hunches about expected properties of the disease gene to determine such an ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et al., 2006), which performs this task automatically by relying on different genome-wide data sources, such as Gene Ontology, literature, microarray, sequence and more.'),
(73568, 2007, 'bio', 'Co-occurrence analysis of insertional mutagenesis data reveals cooperating oncogenes', 'de Ridder, Jeroen|Kool, Jaap|Uren, Anthony|Bot, Jan|Wessels, Lodewyk|Reinders, Marcel', 'Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.|Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.|Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.|Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.|Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.|Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computer Simulation|Databases, Genetic|Models, Biological|Multigene Family|Mutagenesis, Insertional|Neoplasms|Oncogene Proteins|Oncogenes|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm202', '17646289', 'Cancers are caused by an accumulation of multiple independent mutations that collectively deregulate cellular pathways, e.g. such as those regulating cell division and cell-death. The publicly available Retroviral Tagged Cancer Gene Database (RTCGD) contains the data of many insertional mutagenesis screens, in which the virally induced mutations result in tumor formation in mice. The insertion loci therefore indicate the location of putative cancer genes. Additionally, the presence of multiple independent insertions within one tumor hints towards a cooperation between the insertionally mutated genes. In this study we focus on the detection of statistically significant co-mutations.'),
(73569, 2007, 'bio', 'Cotranslational protein folding--fact or fiction?', 'Deane, Charlotte M|Dong, Mingqiang|Huard, Fabien P E|Lance, Braddon K|Wood, Graham R', 'Department of Statistics, 1 South Park Road, Oxford University, Oxford OX1 3TG, UK.|Department of Statistics, 1 South Park Road, Oxford University, Oxford OX1 3TG, UK.|Department of Statistics, 1 South Park Road, Oxford University, Oxford OX1 3TG, UK.|Department of Statistics, 1 South Park Road, Oxford University, Oxford OX1 3TG, UK.|Department of Statistics, 1 South Park Road, Oxford University, Oxford OX1 3TG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 25, 'Amino Acid Sequence|Computer Simulation|Databases, Protein|Models, Biological|Models, Chemical|Molecular Sequence Data|Protein Biosynthesis|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btm175', '17646290', 'Experimentalists have amassed extensive evidence over the past four decades that proteins appear to fold during production by the ribosome. Protein structure prediction methods, however, do not incorporate this property of folding. A thorough study to find the fingerprint of such sequential folding is the first step towards using it in folding algorithms, so assisting structure prediction.'),
(73570, 2007, 'bio', 'Identification of functional modules from conserved ancestral protein-protein interactions', 'Dutkowski, Janusz|Tiuryn, Jerzy', 'Institute of Informatics, Warsaw University, Poland. januszd@mimuw.edu.pl|Institute of Informatics, Warsaw University, Poland. januszd@mimuw.edu.pl', 'Poland|Poland', 70, 'Biological Evolution|Computer Simulation|Conserved Sequence|Evolution, Molecular|Models, Biological|Protein Interaction Mapping|Proteome|Sequence Homology, Nucleic Acid|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm194', '17646291', 'The increasing availability of large-scale protein-protein interaction (PPI) data has fueled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison.'),
(73571, 2007, 'bio', 'Computational prediction of host-pathogen protein-protein interactions', 'Dyer, Matthew D|Murali, T M|Sobral, Bruno W', 'Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. dyermd@vbi.vt.edu|Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. dyermd@vbi.vt.edu|Genetics, Bioinformatics and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. dyermd@vbi.vt.edu', 'United States|United States|United States', 54, 'Animals|Binding Sites|Host-Parasite Interactions|Humans|Plasmodium falciparum|Protein Binding|Protein Interaction Mapping|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm208', '17646292', 'Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics.'),
(73572, 2007, 'bio', 'GPDTI: a Genetic Programming Decision Tree induction method to find epistatic effects in common complex diseases', 'Estrada-Gil, Jesús K|Fernández-López, Juan C|Hernández-Lemus, Enrique|Silva-Zolezzi, Irma|Hidalgo-Miranda, Alfredo|Jiménez-Sánchez, Gerardo|Vallejo-Clemente, Edgar E', 'Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx|Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx', 'Mexico|Mexico|Mexico|Mexico|Mexico|Mexico|Mexico', 16, 'Algorithms|Animals|Chromosome Mapping|Decision Support Techniques|Epistasis, Genetic|Genetic Predisposition to Disease|Genetic Testing|Genetics, Population|Humans|Models, Genetic|Penetrance', 'Journal Article', '10.1093/bioinformatics/btm205', '17646293', 'The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities.'),
(73573, 2007, 'bio', 'Anisotropic fluctuations of amino acids in protein structures: insights from X-ray crystallography and elastic network models', 'Eyal, Eran|Chennubhotla, Chakra|Yang, Lee-Wei|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, Suite 3064, Biomedical Science Tower 3, 3051 Fifth Ave., Pittsburgh, PA 15213, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Suite 3064, Biomedical Science Tower 3, 3051 Fifth Ave., Pittsburgh, PA 15213, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Suite 3064, Biomedical Science Tower 3, 3051 Fifth Ave., Pittsburgh, PA 15213, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Suite 3064, Biomedical Science Tower 3, 3051 Fifth Ave., Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 56, 'Amino Acids|Anisotropy|Computer Simulation|Crystallography, X-Ray|Elasticity|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm186', '17646294', 'A common practice in X-ray crystallographic structure refinement has been to model atomic displacements or thermal fluctuations as isotropic motions. Recent high-resolution data reveal, however, significant departures from isotropy, described by anisotropic displacement parameters (ADPs) modeled for individual atoms. Yet, ADPs are currently reported for a limited set of structures, only.'),
(73574, 2007, 'bio', 'Dead-end elimination with backbone flexibility', 'Georgiev, Ivelin|Donald, Bruce R', 'Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.', 'United States|United States', 63, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm197', '17646295', 'Dead-End Elimination (DEE) is a powerful algorithm capable of reducing the search space for structure-based protein design by a combinatorial factor. By using a fixed backbone template, a rotamer library, and a potential energy function, DEE identifies and prunes rotamer choices that are provably not part of the Global Minimum Energy Conformation (GMEC), effectively eliminating the majority of the conformations that must be subsequently enumerated to obtain the GMEC. Since a fixed-backbone model biases the algorithm predictions against protein sequences for which even small backbone movements may result in a significantly enhanced stability, the incorporation of backbone flexibility can improve the accuracy of the design predictions. If explicit backbone flexibility is incorporated into the model, however, the traditional DEE criteria can no longer guarantee that the flexible-backbone GMEC, the lowest-energy conformation when the backbone is allowed to flex, will not be pruned.'),
(73575, 2007, 'bio', 'Efficient parameter estimation for RNA secondary structure prediction', 'Andronescu, Mirela|Condon, Anne|Hoos, Holger H|Mathews, David H|Murphy, Kevin P', 'Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada. andrones@cs.ubc.ca', 'Canada|Canada|Canada|Canada|Canada', 101, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btm223', '17646296', 'Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data.'),
(73576, 2007, 'bio', 'Optimized design and assessment of whole genome tiling arrays', 'Gräf, Stefan|Nielsen, Fiona G G|Kurtz, Stefan|Huynen, Martijn A|Birney, Ewan|Stunnenberg, Henk|Flicek, Paul', 'EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.|EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 51, 'Algorithms|Chromosome Mapping|Computer-Aided Design|DNA Probes|Equipment Design|Equipment Failure Analysis|Microarray Analysis|Quality Control|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm200', '17646297', 'Recent advances in microarray technologies have made it feasible to interrogate whole genomes with tiling arrays and this technique is rapidly becoming one of the most important high-throughput functional genomics assays. For large mammalian genomes, analyzing oligonucleotide tiling array data is complicated by the presence of non-unique sequences on the array, which increases the overall noise in the data and may lead to false positive results due to cross-hybridization. The ability to create custom microarrays using maskless array synthesis has led us to consider ways to optimize array design characteristics for improving data quality and analysis. We have identified a number of design parameters to be optimized including uniqueness of the probe sequences within the whole genome, melting temperature and self-hybridization potential.'),
(73577, 2007, 'bio', 'Using genome-context data to identify specific types of functional associations in pathway/genome databases', 'Green, Michelle L|Karp, Peter D', 'Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International, Menlo Park, CA 94025, USA.', 'United States|United States', 19, 'Algorithms|Chromosome Mapping|Databases, Genetic|Enzymes|Information Storage and Retrieval|Proteome|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm213', '17646298', 'Hundreds of genes lacking homology to any protein of known function are sequenced every day. Genome-context methods have proved useful in providing clues about functional annotations for many proteins. However, genome-context methods detect many biological types of functional associations, and do not identify which type of functional association they have found.'),
(73578, 2007, 'bio', 'Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations', 'Huang, Jim C|Kannan, Anitha|Winn, John', 'Probabilistic and Statistical Inference Group, University of Toronto, Toronto, ON, M5S 3G4, Canada.|Probabilistic and Statistical Inference Group, University of Toronto, Toronto, ON, M5S 3G4, Canada.|Probabilistic and Statistical Inference Group, University of Toronto, Toronto, ON, M5S 3G4, Canada.', 'Canada|Canada|Canada', 4, 'Bayes Theorem|Biological Evolution|Chromosome Mapping|Genetic Markers|Genetic Variation|Genetics, Population|Haplotypes|Humans|Models, Genetic|Phenotype|Quantitative Trait, Heritable|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btm217', '17646299', 'With the recent availability of large-scale data sets profiling single nucleotide polymorphisms (SNPs) and quantitative traits data across different human subpopulations, there has been much attention directed towards discovering patterns of genetic variation and their connection to gene regulation and the onset/progression of disease. While previous work has focused primarily on correlating individual SNP markers with gene expression and disease, it has been suggested that using haplotype blocks instead of individual markers can significantly increase statistical power.'),
(73579, 2007, 'bio', 'Systematic discovery of functional modules and context-specific functional annotation of human genome', 'Huang, Yu|Li, Haifeng|Hu, Haiyan|Yan, Xifeng|Waterman, Michael S|Huang, Haiyan|Zhou, Xianghong Jasmine', 'Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.|Molecuolar and Computational Biology, University of Southern California, Los Angeles, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 61, 'Algorithms|Chromosome Mapping|Gene Expression|Gene Expression Profiling|Genome, Human|Humans|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm222', '17646300', 'The rapid accumulation of microarray datasets provides unique opportunities to perform systematic functional characterization of the human genome. We designed a graph-based approach to integrate cross-platform microarray data, and extract recurrent expression patterns. A series of microarray datasets can be modeled as a series of co-expression networks, in which we search for frequently occurring network patterns. The integrative approach provides three major advantages over the commonly used microarray analysis methods: (1) enhance signal to noise separation (2) identify functionally related genes without co-expression and (3) provide a way to predict gene functions in a context-specific way.'),
(73580, 2007, 'bio', 'Reconstruction of highly heterogeneous gene-content evolution across the three domains of life', 'Iwasaki, Wataru|Takagi, Toshihisa', 'Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. iwasaki@cb.k.u-tokyo.ac.jp|Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. iwasaki@cb.k.u-tokyo.ac.jp', 'Japan|Japan', 18, 'Animals|Biological Evolution|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Gene Components|Genetic Variation|Genetics, Population|Humans|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btm165', '17646301', 'Reconstruction of gene-content evolutionary history is fundamental in studying the evolution of genomes and biological systems. To reconstruct plausible evolutionary history, rates of gene gain/loss should be estimated by considering the high level of heterogeneity: e.g. genome duplication and parasitization, respectively, result in high rates of gene gain and loss. Gene-content evolution reconstruction methods that consider this heterogeneity and that are both effective in estimating the rates of gene gain and loss and sufficiently efficient to analyze abundant genomic data had not been developed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73581, 2007, 'bio', 'Different mechanistic requirements for prokaryotic and eukaryotic chaperonins: a lattice study', 'Jacob, Etai|Horovitz, Amnon|Unger, Ron', 'The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel.|The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel.|The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel.', 'Israel|Israel|Israel', 23, 'Algorithms|Animals|Chaperonins|Computer Simulation|Eukaryotic Cells|Humans|Models, Chemical|Prokaryotic Cells|Protein Folding|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btm180', '17646302', 'The folding of many proteins in vivo and in vitro is assisted by molecular chaperones. A well-characterized molecular chaperone system is the chaperonin GroEL/GroES from Escherichia coli which has a homolog found in the eukaryotic cytosol called CCT. All chaperonins have a ring structure with a cavity in which the substrate protein folds. An interesting difference between prokaryotic and eukaryotic chaperonins is in the nature of the ATP-mediated conformational changes that their ring structures undergo during their reaction cycle. Prokaryotic chaperonins are known to exhibit a highly cooperative concerted change of their cavity surface while in eukaryotic chaperonins the change is sequential. Approximately 70% of proteins in eukaryotic cells are multi-domain whereas in prokaryotes single-domain proteins are more common. Thus, it was suggested that the different modes of action of prokaryotic and eukaryotic chaperonins can be explained by the need of eukaryotic chaperonins to facilitate folding of multi-domain proteins.'),
(73582, 2007, 'bio', 'A statistical method for alignment-free comparison of regulatory sequences', 'Kantorovitz, Miriam R|Robinson, Gene E|Sinha, Saurabh', 'Department of Computer Science, University of Illinois, Urbana-Champaign, Illinois, USA.|Department of Computer Science, University of Illinois, Urbana-Champaign, Illinois, USA.|Department of Computer Science, University of Illinois, Urbana-Champaign, Illinois, USA.', 'United States|United States|United States', 115, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Genetic|Models, Statistical|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btm211', '17646303', 'The similarity of two biological sequences has traditionally been assessed within the well-established framework of alignment. Here we focus on the task of identifying functional relationships between cis-regulatory sequences that are non-orthologous or greatly diverged. Alignment-free measures of sequence similarity are required in this regime.'),
(73583, 2007, 'bio', 'Learning to extract relations for protein annotation', 'Kim, Jee-Hyub|Mitchell, Alex|Attwood, Teresa K|Hilario, Melanie', 'Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland. jee.kim@cui.unige.ch|Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland. jee.kim@cui.unige.ch|Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland. jee.kim@cui.unige.ch|Artificial Intelligence Laboratory, University of Geneva, CH-1211 Geneva 4, Switzerland. jee.kim@cui.unige.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 9, 'Artificial Intelligence|Databases, Protein|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm168', '17646304', 'Protein annotation is a task that describes protein X in terms of topic Y. Usually, this is constructed using information from the biomedical literature. Until now, most of literature-based protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever more rapidly, manual annotation becomes more difficult, and there is increasing need to automate the process. Recently, information extraction (IE) has been used to address this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real-world scenarios such as in the biomedical domain. In this article, we describe an IE system that requires only sentences labelled according to their relevance or not to a given topic by domain experts.'),
(73584, 2007, 'bio', 'Identification of new drug classification terms in textual resources', 'Kolárik, Corinna|Hofmann-Apitius, Martin|Zimmermann, Marc|Fluck, Juliane', 'Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. corinna.kolarik@scai.fraunhofer.de|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. corinna.kolarik@scai.fraunhofer.de|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. corinna.kolarik@scai.fraunhofer.de|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. corinna.kolarik@scai.fraunhofer.de', 'Germany|Germany|Germany|Germany', 29, 'Artificial Intelligence|Database Management Systems|Databases, Factual|Drug Information Services|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btm196', '17646305', 'Knowledge about biological effects of small molecules helps in the understanding of biological processes and supports the development of new therapeutic agents. DrugBank is a high quality database providing such information about drugs that contains annotation of drug effects and classification of therapeutic effects. However, to broaden the scope of such a database in classifying and annotating drugs, systems for automatic extraction of classification terms and the corresponding annotation of drugs are needed. We have developed an approach for the identification of new terms used in unstructured text that provide information about drug properties. It is based on the identification and extraction of phrases corresponding to lexico-syntactic patterns--so-called Hearst patterns that contain drug names and directly related drug annotation terms. Such phrases could be identified with a high performance in DrugBank text (0.89 F-score) and in Medline abstracts (0.83 F-score). In comparison to DrugBank annotation terminology, a huge amount of new drug annotation terms could be found. The evaluation of terms extracted from Medline showed that 29-53% of them are new valid drug property terms. They could be assigned to existing and new drug property classes not provided by the DrugBank drug annotation. We come to the conclusion that our system can support database content update by providing additionally drug descriptions of pharmacological effects not yet found in databases like DrugBank. Moreover, we propose that automatic normalization of terms improves the annotation and the retrieval of relevant database entries.'),
(73585, 2007, 'bio', 'A geometric approach for the alignment of liquid chromatography-mass spectrometry data', 'Lange, Eva|Gröpl, Clemens|Schulz-Trieglaff, Ole|Leinenbach, Andreas|Huber, Christian|Reinert, Knut', 'Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de|Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de|Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de|Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de|Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de|Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany. lange@inf.fu-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 64, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Mass Spectrometry|Peptide Mapping|Proteome|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btm209', '17646306', 'Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments.'),
(73586, 2007, 'bio', 'Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks', 'Lim, Wei Keat|Wang, Kai|Lefebvre, Celine|Califano, Andrea', 'Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic 5th Floor, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic 5th Floor, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic 5th Floor, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic 5th Floor, New York, NY 10032, USA.', 'United States|United States|United States|United States', 135, 'Benchmarking|Biomedical Engineering|Gene Expression|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Protein Engineering|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction|Software Validation|United States', 'Comparative Study', '10.1093/bioinformatics/btm201', '17646307', 'An increasingly common application of gene expression profile data is the reverse engineering of cellular networks. However, common procedures to normalize expression profiles generated using the Affymetrix GeneChips technology were originally developed for a rather different purpose, namely the accurate measure of differential gene expression between two or more phenotypes. As a result, current evaluation strategies lack comprehensive metrics to assess the suitability of available normalization procedures for reverse engineering and, in general, for measuring correlation between the expression profiles of a gene pair.'),
(73587, 2007, 'bio', 'Probabilistic whole-genome alignments reveal high indel rates in the human and mouse genomes', 'Lunter, Gerton', 'MRC Functional Genetics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3QX, United Kingdom. Gerton.lunter@dpag.ox.ac.uk', 'United Kingdom', 41, 'Algorithms|Animals|Base Composition|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|DNA Transposable Elements|Evolution, Molecular|Gene Deletion|Genome|Genome, Human|Humans|Mice|Models, Genetic|Models, Statistical|Molecular Sequence Data|Mutation|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm185', '17646308', 'The two mutation processes that have the largest impact on genome evolution at small scales are substitutions, and sequence insertions and deletions (indels). While the former have been studied extensively, indels have received less attention, and in particular, the problem of inferring indel rates between pairs of divergent sequence remains unsolved. Here, I describe a novel and accurate method for estimating neutral indel rates between divergent pairs of genomes.'),
(73588, 2007, 'bio', 'An ensemble framework for clustering protein-protein interaction networks', 'Asur, Sitaram|Ucar, Duygu|Parthasarathy, Srinivasan', 'Department of Computer Science and Engineering, Ohio State University, USA.|Department of Computer Science and Engineering, Ohio State University, USA.|Department of Computer Science and Engineering, Ohio State University, USA.', 'United States|United States|United States', 140, 'Cluster Analysis|Computer Simulation|Models, Biological|Multigene Family|Principal Component Analysis|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm212', '17646309', 'Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network.'),
(73589, 2007, 'bio', 'Inferring protein-DNA dependencies using motif alignments and mutual information', 'Mahony, Shaun|Auron, Philip E|Benos, Panayiotis V', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, USA. shaun.mahony@ccbb.pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, USA. shaun.mahony@ccbb.pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, USA. shaun.mahony@ccbb.pitt.edu', 'United States|United States|United States', 23, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Computational Biology|DNA|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm215', '17646310', 'Mutual information can be used to explore covarying positions in biological sequences. In the past, it has been successfully used to infer RNA secondary structure conformations from multiple sequence alignments. In this study, we show that the same principles allow the discovery of transcription factor amino acids that are coevolving with nucleotides in their DNA-binding targets.'),
(73590, 2007, 'bio', 'Nested effects models for high-dimensional phenotyping screens', 'Markowetz, Florian|Kostka, Dennis|Troyanskaya, Olga G|Spang, Rainer', '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.|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|United States|United States', 79, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Phenotype|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btm178', '17646311', 'In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. Comprehensive analysis of perturbation effects is one of the most powerful techniques for attributing functions to genes, but not much work has been done so far to adapt statistical and computational methodology to the specific needs of large-scale and high-dimensional phenotyping screens.'),
(73591, 2007, 'bio', 'Biases induced by pooling samples in microarray experiments', 'Mary-Huard, Tristan|Daudin, Jean-Jacques|Baccini, Michela|Biggeri, Annibale|Bar-Hen, Avner', 'UMR AgroParisTech/INRA MIA 518, 16 rue Claude Bernard 75231 Paris Cedex 5, France. maryhuar@inapg.fr|UMR AgroParisTech/INRA MIA 518, 16 rue Claude Bernard 75231 Paris Cedex 5, France. maryhuar@inapg.fr|UMR AgroParisTech/INRA MIA 518, 16 rue Claude Bernard 75231 Paris Cedex 5, France. maryhuar@inapg.fr|UMR AgroParisTech/INRA MIA 518, 16 rue Claude Bernard 75231 Paris Cedex 5, France. maryhuar@inapg.fr|UMR AgroParisTech/INRA MIA 518, 16 rue Claude Bernard 75231 Paris Cedex 5, France. maryhuar@inapg.fr', 'France|France|France|France|France', 30, 'Artifacts|Computer Simulation|Data Interpretation, Statistical|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Research Design|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm182', '17646312', 'If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view.'),
(73592, 2007, 'bio', 'Towards realistic codon models: among site variability and dependency of synonymous and non-synonymous rates', 'Mayrose, Itay|Doron-Faigenboim, Adi|Bacharach, Eran|Pupko, Tal', 'The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel- Aviv University, Tel Aviv 69978, Israel.|The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel- Aviv University, Tel Aviv 69978, Israel.|The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel- Aviv University, Tel Aviv 69978, Israel.|The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel- Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel', 47, 'Base Sequence|Codon|Computer Simulation|DNA Mutational Analysis|Gene Frequency|Genetic Variation|HIV-1|Models, Genetic|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btm176', '17646313', 'Codon evolutionary models are widely used to infer the selection forces acting on a protein. The non-synonymous to synonymous rate ratio (denoted by Ka/Ks) is used to infer specific positions that are under purifying or positive selection. Current evolutionary models usually assume that only the non-synonymous rates vary among sites while the synonymous substitution rates are constant. This assumption ignores the possibility of selection forces acting at the DNA or mRNA levels. Towards a more realistic description of sequence evolution, we present a model that accounts for among-site-variation of both synonymous and non-synonymous substitution rates. Furthermore, we alleviate the widespread assumption that positions evolve independently of each other. Thus, possible sources of bias caused by random fluctuations in either the synonymous or non-synonymous rate estimations at a single site is removed. Our model is based on two hidden Markov models that operate on the spatial dimension: one describes the dependency between adjacent non-synonymous rates while the other describes the dependency between adjacent synonymous rates. The presented model is applied to study the selection pressure across the HIV-1 genome. The new model better describes the evolution of all HIV-1 genes, as compared to current codon models. Using both simulations and real data analyses, we illustrate that accounting for synonymous rate variability and dependency greatly increases the accuracy of Ka/Ks estimation and in particular of positively selected sites. Finally, we discuss the applicability of the developed model to infer the selection forces in regulatory and overlapping regions of the HIV-1 genome.'),
(73593, 2007, 'bio', 'Using dynamic programming to create isotopic distribution maps from mass spectra', 'McIlwain, Sean|Page, David|Huttlin, Edward L|Sussman, Michael R', 'Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu', 'United States|United States|United States|United States', 13, 'Algorithms|Computer Simulation|Isotope Labeling|Mass Spectrometry|Models, Chemical|Pattern Recognition, Automated|Peptide Mapping|Radioisotopes|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm198', '17646314', 'This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification.'),
(73594, 2007, 'bio', 'A Chado case study: an ontology-based modular schema for representing genome-associated biological information', 'Mungall, Christopher J|Emmert, David B|FlyBase Consortium', 'Lawrence Berkeley National Laboratory, Lawrence Berkeley National Lab, Mail Stop 64R0121, Berkeley, CA 94720, USA. cjm@fruitfly.org|Lawrence Berkeley National Laboratory, Lawrence Berkeley National Lab, Mail Stop 64R0121, Berkeley, CA 94720, USA. cjm@fruitfly.org|Lawrence Berkeley National Laboratory, Lawrence Berkeley National Lab, Mail Stop 64R0121, Berkeley, CA 94720, USA. cjm@fruitfly.org', 'United States|United States|United States', 162, 'Algorithms|Chromosome Mapping|Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm189', '17646315', 'A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused.'),
(73595, 2007, 'bio', 'Prediction of DNA-binding residues from sequence', 'Ofran, Yanay|Mysore, Venkatesh|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA. yo135@columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA. yo135@columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA. yo135@columbia.edu', 'United States|United States|United States', 120, 'Algorithms|Amino Acid Sequence|Amino Acids|Computer Simulation|DNA|DNA-Binding Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm174', '17646316', 'Thousands of proteins are known to bind to DNA, for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.'),
(73596, 2007, 'bio', 'Genomic characterization of perturbation sensitivity', 'Ohn, Jung Hun|Kim, Jihun|Kim, Ju Han', 'Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Korea.|Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Korea.|Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110-799, Korea.', '-|-|-', 14, 'Chromosome Mapping|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Sensitivity and Specificity|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm172', '17646317', 'In determining the function of a gene, it provides much information to observe the changes in a biological system after disruption of the gene of interest through its knockout. Thanks to the microarray technology, it is now possible to profile transcriptional changes of the whole genome, thus differentiating genes that are significantly affected by the knockout. Based on microarray experiments of hundreds of different knockouts, we assigned the so called, Perturbation Sensitivity, to the Saccharomyces cerevisiae genome by the frequency of significant changes in the transcript level in hundreds of knockout conditions. Biologically, it reflects the degree of a genes sensitivity to external perturbations.'),
(73597, 2007, 'bio', 'SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments', 'Ourfali, Oved|Shlomi, Tomer|Ideker, Trey|Ruppin, Eytan|Sharan, Roded', 'School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.|School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.|School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.|School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.|School of Computer Science, School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel', 88, 'Algorithms|Causality|Computer Simulation|Gene Expression Regulation|Gene Silencing|Models, Biological|Proteome|Research Design|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btm170', '17646318', 'The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.'),
(73598, 2007, 'bio', 'Connecting quantitative regulatory-network models to the genome', 'Pan, Yue|Durfee, Tim|Bockhorst, Joseph|Craven, Mark', 'Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. ypan@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. ypan@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. ypan@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA. ypan@cs.wisc.edu', 'United States|United States|United States|United States', 10, 'Algorithms|Chromosome Mapping|Computer Simulation|Gene Expression Regulation|Models, Biological|Proteome|Research Design|Signal Transduction|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btm228', '17646319', 'An important task in computational biology is to infer, using background knowledge and high-throughput data sources, models of cellular processes such as gene regulation. Nachman et al. have developed an approach to inferring gene-regulatory networks that represents quantitative transcription rates, and simultaneously estimates both the kinetic parameters that govern these rates and the activity levels of unobserved regulators that control them. This approach is appealing in that it provides a more detailed and realistic description of how a genes regulators influence its level of expression than alternative methods. We have developed an extension to this approach that involves representing and learning the key kinetic parameters as functions of features in the genomic sequence. The primary motivation for our approach is that it provides a more mechanistic representation of the regulatory relationships being modeled.'),
(73599, 2007, 'bio', 'Functional annotation of regulatory pathways', 'Pandey, Jayesh|Koyutürk, Mehmet|Kim, Yohan|Szpankowski, Wojciech|Subramaniam, Shankar|Grama, Ananth', 'Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu', 'United States|United States|United States|United States|United States|United States', 18, 'Algorithms|Computer Simulation|Documentation|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btm203', '17646320', 'Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level.'),
(73600, 2007, 'bio', 'SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation', 'Panitz, Frank|Stengaard, Henrik|Hornshøj, Henrik|Gorodkin, Jan|Hedegaard, Jakob|Cirera, Susanna|Thomsen, Bo|Madsen, Lone B|Høj, Anette|Vingborg, Rikke K|Zahn, Bujie|Wang, Xuegang|Wang, Xuefei|Wernersson, Rasmus|Jørgensen, Claus B|Scheibye-Knudsen, Karsten|Arvin, Troels|Lumholdt, Steen|Sawera, Milena|Green, Trine|Nielsen, Bente J|Havgaard, Jakob H|Brunak, Søren|Fredholm, Merete|Bendixen, Christian', 'Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.|Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, DK-8830 Tjele, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denm', 25, 'Algorithms|Animals|Computer Graphics|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Documentation|Expressed Sequence Tags|Information Storage and Retrieval|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|Swine|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm192', '17646321', 'Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators.'),
(73601, 2007, 'bio', 'Locomotif: from graphical motif description to RNA motif search', 'Reeder, Janina|Reeder, Jens|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, D-33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, D-33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, D-33615 Bielefeld, Germany.', 'Germany|Germany|Germany', 34, 'Algorithms|Base Sequence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btm179', '17646322', 'Motivated by the recent rise of interest in small regulatory RNAs, we present Locomotif--a new approach for locating RNA motifs that goes beyond the previous ones in three ways: (1) motif search is based on efficient dynamic programming algorithms, incorporating the established thermodynamic model of RNA secondary structure formation. (2) motifs are described graphically, using a Java-based editor, and search algorithms are derived from the graphics in a fully automatic way. The editor allows us to draw secondary structures, annotated with size and sequence information. They closely resemble the established, but informal way in which RNA motifs are communicated in the literature. Thus, the learning effort for Locomotif users is minimal. (3) Locomotif employs a client-server approach. Motifs are designed by the user locally. Search programs are generated and compiled on a bioinformatics server. They are made available both for execution on the server, and for download as C source code plus an appropriate makefile.'),
(73602, 2007, 'bio', 'Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows', 'Roberts, Adam|McMillan, Leonard|Wang, Wei|Parker, Joel|Rusyn, Ivan|Threadgill, David', 'Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States|United States|United States', 48, 'Algorithms|Artifacts|Chromosome Mapping|DNA Mutational Analysis|Genetic Variation|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm220', '17646323', 'Typical high-throughput genotyping techniques produce numerous missing calls that confound subsequent analyses, such as disease association studies. Common remedies for this problem include removing affected markers and/or samples or, otherwise, imputing the missing data. On small marker sets imputation is frequently based on a vote of the K-nearest-neighbor (KNN) haplotypes, but this technique is neither practical nor justifiable for large datasets.'),
(73603, 2007, 'bio', 'Adaptive torsion-angle quasi-statics: a general simulation method with applications to protein structure analysis and design', 'Rossi, Romain|Isorce, Mathieu|Morin, Sandy|Flocard, Julien|Arumugam, Karthik|Crouzy, Serge|Vivaudou, Michel|Redon, Stephane', 'i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.|i3D-INRIA Rhône-Alpes, 655 avenue de lEurope, 38334 Saint-Ismier Cedex, France.', 'France|France|France|France|France|France|France|France', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Drug Design|Kinetics|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Rotation|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm191', '17646324', 'The cost of molecular quasi-statics or dynamics simulations increases with the size of the simulated systems, which is a problem when studying biological phenomena that involve large molecules over long time scales. To address this problem, one has often to either increase the processing power (which might be expensive), or make arbitrary simplifications to the system (which might bias the study).'),
(73604, 2007, 'bio', 'Manual curation is not sufficient for annotation of genomic databases', 'Baumgartner, William A|Cohen, K Bretonnel|Fox, Lynne M|Acquaah-Mensah, George|Hunter, Lawrence', 'Center for Computational Pharmacology, University of Colorado School of Medicine, USA.|Center for Computational Pharmacology, University of Colorado School of Medicine, USA.|Center for Computational Pharmacology, University of Colorado School of Medicine, USA.|Center for Computational Pharmacology, University of Colorado School of Medicine, USA.|Center for Computational Pharmacology, University of Colorado School of Medicine, USA.', 'United States|United States|United States|United States|United States', 151, 'Chromosome Mapping|Databases, Protein|Documentation|Genomics|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm229', '17646325', 'Knowledge base construction has been an area of intense activity and great importance in the growth of computational biology. However, there is little or no history of work on the subject of evaluation of knowledge bases, either with respect to their contents or with respect to the processes by which they are constructed. This article proposes the application of a metric from software engineering known as the found/fixed graph to the problem of evaluating the processes by which genomic knowledge bases are built, as well as the completeness of their contents.'),
(73605, 2007, 'bio', 'Translation initiation site prediction on a genomic scale: beauty in simplicity', 'Saeys, Yvan|Abeel, Thomas|Degroeve, Sven|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, Technologiepark 927, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be|Department of Plant Systems Biology, VIB, Technologiepark 927, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be|Department of Plant Systems Biology, VIB, Technologiepark 927, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be|Department of Plant Systems Biology, VIB, Technologiepark 927, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Chromosome Mapping|Chromosomes, Human, Pair 21|Computer Simulation|Humans|Models, Genetic|Pattern Recognition, Automated|Peptide Chain Initiation, Translational|Reproducibility of Results|Sensitivity and Specificity|Transcription Initiation Site', 'Comparative Study', '10.1093/bioinformatics/btm177', '17646326', 'The correct identification of translation initiation sites (TIS) remains a challenging problem for computational methods that automatically try to solve this problem. Furthermore, the lions share of these computational techniques focuses on the identification of TIS in transcript data. However, in the gene prediction context the identification of TIS occurs on the genomic level, which makes things even harder because at the genome level many more pseudo-TIS occur, resulting in models that achieve a higher number of false positive predictions.'),
(73606, 2007, 'bio', 'Negation of protein-protein interactions: analysis and extraction', 'Sanchez-Graillet, Olivia|Poesio, Massimo', 'Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK. osanch@essex.ac.uk|Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK. osanch@essex.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm184', '17646327', 'Negative information about protein-protein interactions--from uncertainty about the occurrence of an interaction to knowledge that it did not occur--is often of great use to biologists and could lead to important discoveries. Yet, to our knowledge, no proposals focusing on extracting such information have been proposed in the text mining literature.'),
(73607, 2007, 'bio', 'Polyploids, genome halving and phylogeny', 'Sankoff, David|Zheng, Chunfang|Zhu, Qian', 'Department of Mathematics and Statistics, University of Ottawa, Ottawa K1N 6N5, Canada. sankoff@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, Ottawa K1N 6N5, Canada. sankoff@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, Ottawa K1N 6N5, Canada. sankoff@uottawa.ca', 'Canada|Canada|Canada', 14, 'Algorithms|Biological Evolution|Chromosome Mapping|Evolution, Molecular|Gene Rearrangement|Genetic Variation|Phylogeny|Polymorphism, Genetic|Polyploidy|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm169', '17646328', 'Autopolyploidization and allopolyploidization events multiply the number of chromosomes and genomic content. Genome rearrangement phylogenetics requires that all genomes analyzed have the same set of orthologs, so that it is not possible to include diploid and polyploid genomes in the same phylogeny.'),
(73608, 2007, 'bio', 'Nucleotide variation of regulatory motifs may lead to distinct expression patterns', 'Segal, Liat|Lapidot, Michal|Solan, Zach|Ruppin, Eytan|Pilpel, Yitzhak|Horn, David', 'Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Gene Expression|Genetic Variation|Nucleotides|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm183', '17646329', 'Current methodologies for the selection of putative transcription factor binding sites (TFBS) rely on various assumptions such as over-representation of motifs occurring on gene promoters, and the use of motif descriptions such as consensus or position-specific scoring matrices (PSSMs). In order to avoid bias introduced by such assumptions, we apply an unsupervised motif extraction (MEX) algorithm to sequences of promoters. The extracted motifs are assessed for their likely cis-regulatory function by calculating the expression coherence (EC) of the corresponding genes, across a set of biological conditions.'),
(73609, 2007, 'bio', 'Modeling recurrent DNA copy number alterations in array CGH data', 'Shah, Sohrab P|Lam, Wan L|Ng, Raymond T|Murphy, Kevin P', 'Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca', 'Canada|Canada|Canada|Canada', 54, 'Chromosome Mapping|Computer Simulation|Gene Dosage|Genetic Variation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm221', '17646330', 'Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples.'),
(73610, 2007, 'bio', 'Continuous hidden process model for time series expression experiments', 'Shi, Yanxin|Klustein, Michael|Simon, Itamar|Mitchell, Tom|Bar-Joseph, Ziv', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression|Gene Expression Profiling|Markov Chains|Models, Biological|Multigene Family|Pattern Recognition, Automated|Signal Transduction|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/btm218', '17646331', 'When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up- or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of the activity levels of many biological processes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73611, 2007, 'bio', 'Annotating gene function by combining expression data with a modular gene network', 'Shiga, Motoki|Takigawa, Ichigaku|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.', 'Japan|Japan|Japan', 0, 'Artificial Intelligence|Databases, Genetic|Gene Expression|Gene Expression Profiling|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btm173', '17646332', 'A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks.'),
(73612, 2007, 'bio', 'Spectrum: joint Bayesian inference of population structure and recombination events', 'Sohn, Kyung-Ah|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States', 10, 'Algorithms|Bayes Theorem|Chromosome Mapping|Evolution, Molecular|Genetic Variation|Genetics, Population|Models, Genetic|Polymorphism, Single Nucleotide|Recombination, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btm171', '17646333', 'While genetic properties such as linkage disequilibrium (LD) and population structure are closely related under a common inheritance process, the statistical methodologies developed so far mostly deal with LD analysis and structural inference separately, using specialized models that do not capture their statistical and genetic relationships. Also, most of these approaches ignore the inherent uncertainty in the genetic complexity of the data and rely on inflexible models built on a closed genetic space. These limitations may make it difficult to infer detailed and consistent structural information from rich genomic data such as populational single nucleotide polymorphisms (SNP) profiles.'),
(73613, 2007, 'bio', 'Reconstructing sibling relationships in wild populations', 'Berger-Wolf, Tanya Y|Sheikh, Saad I|DasGupta, Bhaskar|Ashley, Mary V|Caballero, Isabel C|Chaovalitwongse, Wanpracha|Putrevu, S Lahari', 'Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA. tanyabw@uic.edu', 'United States|United States|United States|United States|United States|United States|United States', 51, 'Animals|Chromosome Mapping|Computer Simulation|Genetic Markers|Genetic Testing|Genetics, Population|Heredity|Humans|Microsatellite Repeats|Models, Genetic|Pedigree|Quantitative Trait, Heritable|Siblings', 'Evaluation Studies', '10.1093/bioinformatics/btm219', '17646334', 'Reconstruction of sibling relationships from genetic data is an important component of many biological applications. In particular, the growing application of molecular markers (microsatellites) to study wild populations of plant and animals has created the need for new computational methods of establishing pedigree relationships, such as sibgroups, among individuals in these populations. Most current methods for sibship reconstruction from microsatellite data use statistical and heuristic techniques that rely on a priori knowledge about various parameter distributions. Moreover, these methods are designed for data with large number of sampled loci and small family groups, both of which typically do not hold for wild populations. We present a deterministic technique that parsimoniously reconstructs sibling groups using only Mendelian laws of inheritance. We validate our approach using both simulated and real biological data and compare it to other methods. Our method is highly accurate on real data and compares favorably with other methods on simulated data with few loci and large family groups. It is the only method that does not rely on a priori knowledge about the population under study. Thus, our method is particularly appropriate for reconstructing sibling groups in wild populations.'),
(73614, 2007, 'bio', 'Gene selection via the BAHSIC family of algorithms', 'Song, Le|Bedo, Justin|Borgwardt, Karsten M|Gretton, Arthur|Smola, Alex', 'National ICT Australia and Australian National University, Canberra, Australia.|National ICT Australia and Australian National University, Canberra, Australia.|National ICT Australia and Australian National University, Canberra, Australia.|National ICT Australia and Australian National University, Canberra, Australia.|National ICT Australia and Australian National University, Canberra, Australia.', 'Australia|Australia|Australia|Australia|Australia', 5, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm216', '17646335', 'Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the Hilbert-Schmidt independence criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems.'),
(73615, 2007, 'bio', 'Computational modeling of Caenorhabditis elegans vulval induction', 'Sun, Xiaoyun|Hong, Pengyu', 'Department of Computer Science, National Center of Behavioral Genomics, 415 South Street, Waltham, MA 02454, USA.|Department of Computer Science, National Center of Behavioral Genomics, 415 South Street, Waltham, MA 02454, USA.', 'United States|United States', 18, 'Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Embryonic Induction|Female|Gene Expression Regulation, Developmental|Models, Biological|Organogenesis|Signal Transduction|Vulva', 'Journal Article', '10.1093/bioinformatics/btm214', '17646336', 'Caenorhabditis elegans vulval development is a paradigmatic example of animal organogenesis with extensive experimental data. During vulval induction, each of the six multipotent vulval precursor cells (VPCs) commits to one of three fates (primary, secondary, tertiary). The precise primary-secondary-tertiary formation of VPC fates is controlled by a network of intercellular signaling, intracellular signal transduction and transcriptional regulation. The construction of mathematical models for this network will enable hypothesis generation, biological mechanism discovery and system behavior analysis.'),
(73616, 2007, 'bio', 'From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis', 'Sundaresh, Suman|Randall, Arlo|Unal, Berkay|Petersen, Jeannine M|Belisle, John T|Hartley, M Gill|Duffield, Melanie|Titball, Richard W|Davies, D Huw|Felgner, Philip L|Baldi, Pierre', 'School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 72, 'Algorithms|Antigens, Bacterial|Artificial Intelligence|Francisella tularensis|Humans|Pattern Recognition, Automated|Protein Array Analysis|Sensitivity and Specificity|Serologic Tests', 'Journal Article', '10.1093/bioinformatics/btm207', '17646338', 'An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development.'),
(73617, 2007, 'bio', 'Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles', 'Sysi-Aho, Marko|Vehtari, Aki|Velagapudi, Vidya R|Westerbacka, Jukka|Yetukuri, Laxman|Bergholm, Robert|Taskinen, Marja-Riitta|Yki-Järvinen, Hannele|Oresic, Matej', 'VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi|VTT Technical Research Centre of Finland, Espoo, FI-02044 VTT. marko.sysi-aho@vtt.fi', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 20, 'Algorithms|Bayes Theorem|Gene Expression Profiling|Humans|Lipids|Lipoproteins|Metabolic Syndrome|Microarray Analysis', 'Journal Article', '10.1093/bioinformatics/btm181', '17646339', 'Serum lipids have been traditionally studied in the context of lipoprotein particles. Todays emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.'),
(73618, 2007, 'bio', 'Information theory applied to the sparse gene ontology annotation network to predict novel gene function', 'Tao, Ying|Sam, Lee|Li, Jianrong|Friedman, Carol|Lussier, Yves A', 'Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032, USA.|Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC5, New York, NY 10032, USA.', 'United States|United States|United States|United States|United States', 123, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Information Theory|Proteins|Structure-Activity Relationship|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btm195', '17646340', 'Despite advances in the gene annotation process, the functions of a large portion of gene products remain insufficiently characterized. In addition, the in silico prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or functional genomic approaches. To our knowledge, no prediction method has been demonstrated to be highly accurate for sparsely annotated GO terms (those associated to fewer than 10 genes).'),
(73619, 2007, 'bio', 'Kinetics analysis methods for approximate folding landscapes', 'Tapia, Lydia|Tang, Xinyu|Thomas, Shawna|Amato, Nancy M', 'Parasol Lab, Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.|Parasol Lab, Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.|Parasol Lab, Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.|Parasol Lab, Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States', 16, 'Algorithms|Computer Simulation|Kinetics|Models, Chemical|Models, Molecular|Motion|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm199', '17646341', 'Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants.'),
(73620, 2007, 'bio', 'Automatic genome-wide reconstruction of phylogenetic gene trees', 'Wapinski, Ilan|Pfeffer, Avi|Friedman, Nir|Regev, Aviv', 'Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Broad Institute of MIT and Harvard, Cambridge, MA, USA.|Broad Institute of MIT and Harvard, Cambridge, MA, USA.', 'United States|United States|United States|United States', 103, 'Algorithms|Chromosome Mapping|Computer Simulation|Conserved Sequence|Evolution, Molecular|Gene Duplication|Genetic Variation|Models, Genetic|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm193', '17646342', 'Gene duplication and divergence is a major evolutionary force. Despite the growing number of fully sequenced genomes, methods for investigating these events on a genome-wide scale are still in their infancy. Here, we present SYNERGY, a novel and scalable algorithm that uses sequence similarity and a given species phylogeny to reconstruct the underlying evolutionary history of all genes in a large group of species. In doing so, SYNERGY resolves homology relations and accurately distinguishes orthologs from paralogs. We applied our approach to a set of nine fully sequenced fungal genomes spanning 150 million years, generating a genome-wide catalog of orthologous groups and corresponding gene trees. Our results are highly accurate when compared to a manually curated gold standard, and are robust to the quality of input according to a novel jackknife confidence scoring. The reconstructed gene trees provide a comprehensive view of gene evolution on a genomic scale. Our approach can be applied to any set of sequenced eukaryotic species with a known phylogeny, and opens the way to systematic studies of the evolution of individual genes, molecular systems and whole genomes.'),
(73621, 2007, 'bio', 'Multiple alignment by aligning alignments', 'Wheeler, Travis J|Kececioglu, John D', 'Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA. twheeler@cs.arizona.edu|Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA. twheeler@cs.arizona.edu', 'United States|United States', 149, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm226', '17646343', 'Multiple sequence alignment is a fundamental task in bioinformatics. Current tools typically form an initial alignment by merging subalignments, and then polish this alignment by repeated splitting and merging of subalignments to obtain an improved final alignment. In general this form-and-polish strategy consists of several stages, and a profusion of methods have been tried at every stage. We carefully investigate: (1) how to utilize a new algorithm for aligning alignments that optimally solves the common subproblem of merging subalignments, and (2) what is the best choice of method for each stage to obtain the highest quality alignment.'),
(73622, 2007, 'bio', 'Semiparametric functional mapping of quantitative trait loci governing long-term HIV dynamics', 'Wu, Song|Yang, Jie|Wu, Rongling', 'Department of Statistics, University of Florida, Gainesville, FL 32611, USA.|Department of Statistics, University of Florida, Gainesville, FL 32611, USA.|Department of Statistics, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States', 12, 'Algorithms|Computer Simulation|Genes, Viral|Genetic Predisposition to Disease|HIV|HIV Infections|Humans|Models, Genetic|Quantitative Trait Loci|Viral Load', 'Journal Article', '10.1093/bioinformatics/btm164', '17646344', 'Functional mapping has proven to be powerful for characterizing quantitative trait loci (QTL) that control complex dynamic traits. More recently, functional mapping has been extended to identify the host QTL responsible for HIV dynamics by incorporating a parametric bi-exponential function for earlier stages of viral load trajectories. However, existing functional mapping cannot be used to map long-term HIV dynamics because no mathematical functions are available for later stages of HIV dynamic changes.'),
(73623, 2007, 'bio', 'Supervised reconstruction of biological networks with local models', 'Bleakley, Kevin|Biau, Gérard|Vert, Jean-Philippe', 'Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, CC 051, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France.|Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, CC 051, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France.|Institut de Mathématiques et de Modélisation de Montpellier, UMR CNRS 5149, Equipe de Probabilités et Statistique, Université Montpellier II, CC 051, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France.', 'France|France|France', 70, 'Algorithms|Artificial Intelligence|Computer Simulation|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm204', '17646345', 'Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest.'),
(73624, 2007, 'bio', 'A graph-based approach to systematically reconstruct human transcriptional regulatory modules', 'Yan, Xifeng|Mehan, Michael R|Huang, Yu|Waterman, Michael S|Yu, Philip S|Zhou, Xianghong Jasmine', 'IBM T. J. Watson Research Center, Hawthorne, NY, USA.|IBM T. J. Watson Research Center, Hawthorne, NY, USA.|IBM T. J. Watson Research Center, Hawthorne, NY, USA.|IBM T. J. Watson Research Center, Hawthorne, NY, USA.|IBM T. J. Watson Research Center, Hawthorne, NY, USA.|IBM T. J. Watson Research Center, Hawthorne, NY, USA.', 'United States|United States|United States|United States|United States|United States', 64, 'Algorithms|Base Sequence|Binding Sites|Chromosome Mapping|Computer Graphics|Genome, Human|Humans|Molecular Sequence Data|Protein Binding|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm227', '17646346', 'A major challenge in studying gene regulation is to systematically reconstruct transcription regulatory modules, which are defined as sets of genes that are regulated by a common set of transcription factors. A commonly used approach for transcription module reconstruction is to derive coexpression clusters from a microarray dataset. However, such results often contain false positives because genes from many transcription modules may be simultaneously perturbed upon a given type of conditions. In this study, we propose and validate that genes, which form a coexpression cluster in multiple microarray datasets across diverse conditions, are more likely to form a transcription module. However, identifying genes coexpressed in a subset of many microarray datasets is not a trivial computational problem.'),
(73625, 2007, 'bio', 'Automated image analysis of protein localization in budding yeast', 'Chen, Shann-Ching|Zhao, Ting|Gordon, Geoffrey J|Murphy, Robert F', 'Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 56, 'Algorithms|Fungal Proteins|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Saccharomycetales|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btm206', '17646347', 'The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast.'),
(73626, 2007, 'bio', 'RankMotif++: a motif-search algorithm that accounts for relative ranks of K-mers in binding transcription factors', 'Chen, Xiaoyu|Hughes, Timothy R|Morris, Quaid', 'Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.|Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.|Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada', 45, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Molecular Sequence Data|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm224', '17646348', 'The sequence specificity of DNA-binding proteins is typically represented as a position weight matrix in which each base position contributes independently to relative affinity. Assessment of the accuracy and broad applicability of this representation has been limited by the lack of extensive DNA-binding data. However, new microarray techniques, in which preferences for all possible K-mers are measured, enable a broad comparison of both motif representation and methods for motif discovery. Here, we consider the problem of accounting for all of the binding data in such experiments, rather than the highest affinity binding data. We introduce the RankMotif++, an algorithm designed for finding motifs whenever sequences are associated with a semi-quantitative measure of protein-DNA-binding affinity. RankMotif++ learns motif models by maximizing the likelihood of a set of binding preferences under a probabilistic model of how sequence binding affinity translates into binding preference observations. Because RankMotif++ makes few assumptions about the relationship between binding affinity and the semi-quantitative readout, it is applicable to a wide variety of experimental assays of DNA-binding preference.'),
(73627, 2007, 'bio', 'A physical model for tiling array analysis', 'Chung, Ho-Ryun|Kostka, Dennis|Vingron, Martin', 'Max-Planck-Institut für molekulare Genetik, Ihnestrasse 63-73, 14195 Berlin, Germany. chung@molgen.mpg.de|Max-Planck-Institut für molekulare Genetik, Ihnestrasse 63-73, 14195 Berlin, Germany. chung@molgen.mpg.de|Max-Planck-Institut für molekulare Genetik, Ihnestrasse 63-73, 14195 Berlin, Germany. chung@molgen.mpg.de', 'Germany|Germany|Germany', 7, 'Algorithms|Chromatin Immunoprecipitation|Computer Simulation|DNA Fragmentation|DNA Probes|Databases, Genetic|Microarray Analysis|Models, Genetic', 'Journal Article', '10.1093/bioinformatics/btm167', '17646349', 'Chromatin immunoprecipitation (ChIP) is a powerful experimental approach to identify in vivo binding sites of sequence-specific transcription factors (TFs). These experiments are designed to specifically enrich DNA fragments that are bound to the TF. Tiling arrays have become more and more popular for the identification of these DNA fragments. However, many studies showed that only a fraction of the identified DNA fragments contains bona fide binding sites for the TF, suggesting that indirect binding mechanisms play a very important role. We explored the possibility that the lack of binding sites can also be explained by problems in identifying ChIP-enriched DNA fragments from the measured intensities.'),
(73628, 2007, 'bio', 'In search of lost introns', 'Csurös, Miklós|Holey, J Andrew|Rogozin, Igor B', 'Department of Computer Science and Operations Research, Université de Montréal, Québec, Canada. csuros@iro.umontreal.ca|Department of Computer Science and Operations Research, Université de Montréal, Québec, Canada. csuros@iro.umontreal.ca|Department of Computer Science and Operations Research, Université de Montréal, Québec, Canada. csuros@iro.umontreal.ca', 'Canada|Canada|Canada', 22, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Introns|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm190', '17646350', 'Many fundamental questions concerning the emergence and subsequent evolution of eukaryotic exon-intron organization are still unsettled. Genome-scale comparative studies, which can shed light on crucial aspects of eukaryotic evolution, require adequate computational tools. We describe novel computational methods for studying spliceosomal intron evolution. Our goal is to give a reliable characterization of the dynamics of intron evolution. Our algorithmic innovations address the identification of orthologous introns, and the likelihood-based analysis of intron data. We discuss a compression method for the evaluation of the likelihood function, which is noteworthy for phylogenetic likelihood problems in general. We prove that after O(n l) preprocessing time, subsequent evaluations take O(n l/log l) time almost surely in the Yule-Harding random model of n-taxon phylogenies, where l is the input sequence length. We illustrate the practicality of our methods by compiling and analyzing a data set involving 18 eukaryotes, which is more than in any other study to date. The study yields the surprising result that ancestral eukaryotes were fairly intron-rich. For example, the bilaterian ancestor is estimated to have had more than 90% as many introns as vertebrates do now.'),
(73629, 2007, 'bio', 'Homology search for genes', 'Cui, Xuefeng|Vinar, Tomás|Brejová, Brona|Shasha, Dennis|Li, Ming', 'Cheriton School of Computer Science, University of Waterloo, Ontario, Canada N2L 3G1.|Cheriton School of Computer Science, University of Waterloo, Ontario, Canada N2L 3G1.|Cheriton School of Computer Science, University of Waterloo, Ontario, Canada N2L 3G1.|Cheriton School of Computer Science, University of Waterloo, Ontario, Canada N2L 3G1.|Cheriton School of Computer Science, University of Waterloo, Ontario, Canada N2L 3G1.', 'Canada|Canada|Canada|Canada|Canada', 13, 'Algorithms|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Proteome|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btm225', '17646351', 'Life science researchers often require an exhaustive list of protein coding genes similar to a given query gene. To find such genes, homology search tools, such as BLAST or PatternHunter, return a set of high-scoring pairs (HSPs). These HSPs then need to be correlated with existing sequence annotations, or assembled manually into putative gene structures. This process is error-prone and labor-intensive, especially in genomes without reliable gene annotation.'),
(73630, 2007, 'bio', 'Polyketide synthase genes and the natural products potential of Dictyostelium discoideum', 'Zucko, J|Skunca, N|Curk, T|Zupan, B|Long, P F|Cullum, J|Kessin, R H|Hranueli, D', 'Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.|Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.', 'Croatia|Croatia|Croatia|Croatia|Croatia|Croatia|Croatia|Croatia', 30, 'Amino Acid Sequence|Animals|Biological Products|Chromosome Mapping|Dictyostelium|Molecular Sequence Data|Multigene Family|Polyketide Synthases|Protein Structure, Tertiary|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btm381', '17660200', 'The genome of the social amoeba Dictyostelium discoideum contains an unusually large number of polyketide synthase (PKS) genes. An analysis of the genes is a first step towards understanding the biological roles of their products and exploiting novel products.'),
(73631, 2007, 'bio', 'Comparative modeling without implicit sequence alignments', 'Kolinski, Andrzej|Gront, Dominik', 'University of Warsaw, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland.|University of Warsaw, Faculty of Chemistry, Pasteura 1 02-093 Warsaw, Poland.', 'Poland|Poland', 11, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btm380', '17660201', 'The number of known protein sequences is about thousand times larger than the number of experimentally solved 3D structures. For more than half of the protein sequences a close or distant structural analog could be identified. The key starting point in a classical comparative modeling is to generate the best possible sequence alignment with a template or templates. With decreasing sequence similarity, the number of errors in the alignments increases and these errors are the main causes of the decreasing accuracy of the molecular models generated. Here we propose a new approach to comparative modeling, which does not require the implicit alignment - the model building phase explores geometric, evolutionary and physical properties of a template (or templates).'),
(73632, 2007, 'bio', 'Consensus Data Mining (CDM) Protein Secondary Structure Prediction Server: combining GOR V and Fragment Database Mining (FDM)', 'Cheng, Haitao|Sen, Taner Z|Jernigan, Robert L|Kloczkowski, Andrzej', 'Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA.|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA.|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA.|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States', 27, 'Computer Simulation|Consensus Sequence|Databases, Protein|Information Storage and Retrieval|Internet|Models, Chemical|Models, Molecular|Peptide Fragments|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm379', '17660202', 'One of the challenges in protein secondary structure prediction is to overcome the cross-validated 80% prediction accuracy barrier. Here, we propose a novel approach to surpass this barrier. Instead of using a single algorithm that relies on a limited data set for training, we combine two complementary methods having different strengths: Fragment Database Mining (FDM) and GOR V. FDM harnesses the availability of the known protein structures in the Protein Data Bank and provides highly accurate secondary structure predictions when sequentially similar structural fragments are identified. In contrast, the GOR V algorithm is based on information theory, Bayesian statistics, and PSI-BLAST multiple sequence alignments to predict the secondary structure of residues inside a sliding window along a protein chain. A combination of these two different methods benefits from the large number of structures in the PDB and significantly improves the secondary structure prediction accuracy, resulting in Q3 ranging from 67.5 to 93.2%, depending on the availability of highly similar fragments in the Protein Data Bank.'),
(73633, 2007, 'bio', 'BLISS 20: a web-based tool for predicting conserved regulatory modules in distantly-related orthologous sequences', 'Meng, Hailong|Banerjee, Arunava|Zhou, Lei', 'Department of Computer and Information Science, College of Engineering, UFL, Gainesville, FL 32611, USA.|Department of Computer and Information Science, College of Engineering, UFL, Gainesville, FL 32611, USA.|Department of Computer and Information Science, College of Engineering, UFL, Gainesville, FL 32611, USA.', 'United States|United States|United States', 3, 'Algorithms|Base Sequence|Binding Sites|Conserved Sequence|Evolution, Molecular|Internet|Molecular Sequence Data|Protein Binding|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm368', '17660203', 'BLISS 2.0 is a web-based application for identifying conserved regulatory modules in distantly related orthologous sequences. Unlike existing approaches, it performs the cross-genome comparison at the binding site level. Experimental results on simulated and real world data indicate that BLISS 2.0 can identify conserved regulatory modules from sequences with little overall similarity at the DNA sequence level.'),
(73634, 2007, 'bio', 'Quality estimation of multiple sequence alignments by Bayesian hypothesis testing', 'Tomovic, Andrija|Oakeley, Edward J', 'Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerestrasse 66, CH-4056 Basel.|Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerestrasse 66, CH-4056 Basel.', '-|-', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btm366', '17660204', 'In this work we present a web-based tool for estimating multiple alignment quality using Bayesian hypothesis testing. The proposed method is very simple, easily implemented and not time consuming with a linear complexity. We evaluated method against a series of different alignments (a set of random and biologically derived alignments) and compared the results with tools based on classical statistical methods (such as sFFT and csFFT). Taking correlation coefficient as an objective criterion of the true quality, we found that Bayesian hypothesis testing performed better on average than the classical methods we tested. This approach may be used independently or as a component of any tool in computational biology which is based on the statistical estimation of alignment quality.'),
(73635, 2007, 'bio', 'sMOL Explorer: an open source, web-enabled database and exploration tool for Small MOLecules datasets', 'Ingsriswang, Supawadee|Pacharawongsakda, Eakasit', 'Information Systems Laboratory, BIOTEC Central Research Unit, National Center for Genetic Engineering and Biotechnology (BIOTEC), Klongluang, Pathumthani, 12120, Thailand. supawadee@biotec.or.th|Information Systems Laboratory, BIOTEC Central Research Unit, National Center for Genetic Engineering and Biotechnology (BIOTEC), Klongluang, Pathumthani, 12120, Thailand. supawadee@biotec.or.th', 'Thailand|Thailand', 0, 'Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Models, Chemical|Models, Molecular|Molecular Weight|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btm363', '17660205', 'sMOL Explorer is a 2D ligand-based computational tool that provides three major functionalities: data management, information retrieval and extraction and statistical analysis and data mining through Web interface. With sMOL Explorer, users can create personal databases by adding each small molecule via a drawing interface or uploading the data files from internal and external projects into the sMOL database. Then, the database can be browsed and queried with textual and structural similarity search. The molecule can also be submitted to search against external public databases including PubChem, KEGG, DrugBank and eMolecules. Moreover, users can easily access a variety of data mining tools from Weka and R packages to perform analysis including (1) finding the frequent substructure, (2) clustering the molecular fingerprints, (3) identifying and removing irrelevant attributes from the data and (4) building the classification model of biological activity.'),
(73636, 2007, 'bio', 'Robust smooth segmentation approach for array CGH data analysis', 'Huang, Jian|Gusnanto, Arief|OSullivan, Kathleen|Staaf, Johan|Borg, Ake|Pawitan, Yudi', 'Statistical Laboratory, Department of Statistics, University College Cork, Ireland.|Statistical Laboratory, Department of Statistics, University College Cork, Ireland.|Statistical Laboratory, Department of Statistics, University College Cork, Ireland.|Statistical Laboratory, Department of Statistics, University College Cork, Ireland.|Statistical Laboratory, Department of Statistics, University College Cork, Ireland.|Statistical Laboratory, Department of Statistics, University College Cork, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 32, 'Algorithms|Chromosome Mapping|Gene Dosage|In Situ Hybridization, Fluorescence|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btm359', '17660206', 'Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach.'),
(73637, 2007, 'bio', 'AssociationDB: web-based exploration of genomic association', 'Seelow, Dominik|Hoffmann, Katrin|Lindner, Tom H', 'Institute for Medical Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, D-13353 Berlin, Germany. dominik.seelow@charite.de|Institute for Medical Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, D-13353 Berlin, Germany. dominik.seelow@charite.de|Institute for Medical Genetics, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, D-13353 Berlin, Germany. dominik.seelow@charite.de', 'Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Humans|Internet|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm376', '17660207', 'Genome-wide association studies use hundreds of thousands of markers making it challenging to present and finally interpret the results. We developed a graphical, web-based solution for an interactive exploration of the results of case-control studies, with a tight integration of related gene information and tissue-specific expression data. Association results are presented as physical position-based vertical bars with known genes included as horizontal bars at their respective physical positions. The interface allows the specification of filtering criteria for the association data and highlights potentially interesting genes with user-specified terms occurring in their reports or with relevant expression patterns. Pop-up windows and hyperlinks provide drill-down capabilities and quick access to relevant data AssociationDB can either be used as a stand-alone solution or as a front-end joining association results obtained by other software with genomic information.'),
(73638, 2007, 'bio', 'Modular decomposition of metabolic reaction networks based on flux analysis and pathway projection', 'Yoon, Jeongah|Si, Yaguang|Nolan, Ryan|Lee, Kyongbum', 'Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.|Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.|Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.|Department of Chemical and Biological Engineering, Tufts University, Medford, MA 02155, USA.', 'United States|United States|United States|United States', 0, 'Adipocytes|Algorithms|Computer Simulation|Lipid Metabolism|Metabolic Clearance Rate|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm374', '17660208', 'The rational decomposition of biochemical networks into sub-structures has emerged as a useful approach to study the design of these complex systems. A biochemical network is characterized by an inhomogeneous connectivity distribution, which gives rise to several organizational features, including modularity. To what extent the connectivity-based modules reflect the functional organization of the network remains to be further explored. In this work, we examine the influence of physiological perturbations on the modular organization of cellular metabolism.'),
(73639, 2007, 'bio', 'Robustness analysis and tuning of synthetic gene networks', 'Batt, Grégory|Yordanov, Boyan|Weiss, Ron|Belta, Calin', 'Centers for Information and Systems Engineering and for BioDynamics, Boston University, Boston, MA, USA. gregory.batt@imag.fr|Centers for Information and Systems Engineering and for BioDynamics, Boston University, Boston, MA, USA. gregory.batt@imag.fr|Centers for Information and Systems Engineering and for BioDynamics, Boston University, Boston, MA, USA. gregory.batt@imag.fr|Centers for Information and Systems Engineering and for BioDynamics, Boston University, Boston, MA, USA. gregory.batt@imag.fr', 'United States|United States|United States|United States', 98, 'Algorithms|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Gene Expression|Gene Expression Profiling|Models, Biological|Signal Transduction|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm362', '17660209', 'The goal of synthetic biology is to design and construct biological systems that present a desired behavior. The construction of synthetic gene networks implementing simple functions has demonstrated the feasibility of this approach. However, the design of these networks is difficult, notably because existing techniques and tools are not adapted to deal with uncertainties on molecular concentrations and parameter values.'),
(73640, 2007, 'bio', 'Selection and validation of normalization methods for c-DNA microarrays using within-array replications', 'Fan, Jianqing|Niu, Yue', 'Department of Operations Research and Financial Engineering Princeton University, Princeton, NJ 08544, USA. jqfan@princeton.edu|Department of Operations Research and Financial Engineering Princeton University, Princeton, NJ 08544, USA. jqfan@princeton.edu', 'United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|United States', 'Evaluation Studies', '10.1093/bioinformatics/btm361', '17660210', 'Normalization of microarray data is essential for multiple-array analyses. Several normalization protocols have been proposed based on different biological or statistical assumptions. A fundamental problem arises whether they have effectively normalized arrays. In addition, for a given array, the question arises how to choose a method to most effectively normalize the microarray data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73641, 2007, 'bio', 'AffyProbeMiner: a web resource for computing or retrieving accurately redefined Affymetrix probe sets', 'Liu, Hongfang|Zeeberg, Barry R|Qu, Gang|Koru, A Gunes|Ferrucci, Alessandro|Kahn, Ari|Ryan, Michael C|Nuhanovic, Antej|Munson, Peter J|Reinhold, William C|Kane, David W|Weinstein, John N', 'Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. hl224@georgetown.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Base Sequence|DNA Probes|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm360', '17660211', 'Affymetrix microarrays are widely used to measure global expression of mRNA transcripts. That technology is based on the concept of a probe set. Individual probes within a probe set were originally designated by Affymetrix to hybridize with the same unique mRNA transcript. Because of increasing accuracy in knowledge of genomic sequences, however, a substantial number of the manufacturers original probe groupings and mappings are now known to be inaccurate and must be corrected. Otherwise, analysis and interpretation of an Affymetrix microarray experiment will be in error.'),
(73642, 2007, 'bio', 'Flavitrack: an annotated database of flavivirus sequences', 'Misra, Milind|Schein, Catherine H', 'Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas 77555-0857, USA. chschein@utmb.edu|Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, Texas 77555-0857, USA. chschein@utmb.edu', 'United States|United States', 15, 'Amino Acid Sequence|Base Sequence|DNA, Viral|Database Management Systems|Databases, Genetic|Documentation|Evolution, Molecular|Flavivirus|Genome, Viral|Information Storage and Retrieval|Internet|Molecular Sequence Data|Sequence Analysis|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btm383', '17660525', 'Properly annotated sequence data for flaviviruses, which cause diseases, such as tick-borne encephalitis (TBE), dengue fever (DF), West Nile (WN) and yellow fever (YF), can aid in the design of antiviral drugs and vaccines to prevent their spread. Flavitrack was designed to help identify conserved sequence motifs, interpret mutational and structural data and track evolution of phenotypic properties.'),
(73643, 2007, 'bio', 'Data-based identifiability analysis of non-linear dynamical models', 'Hengl, S|Kreutz, C|Timmer, J|Maiwald, T', 'Physics Institute, University of Freiburg, Hermann Herder Strasse 3, 79104 Freiburg i.Br., Germany. hengl@fdm.uni-freiburg.de|Physics Institute, University of Freiburg, Hermann Herder Strasse 3, 79104 Freiburg i.Br., Germany. hengl@fdm.uni-freiburg.de|Physics Institute, University of Freiburg, Hermann Herder Strasse 3, 79104 Freiburg i.Br., Germany. hengl@fdm.uni-freiburg.de|Physics Institute, University of Freiburg, Hermann Herder Strasse 3, 79104 Freiburg i.Br., Germany. hengl@fdm.uni-freiburg.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Factual|Information Storage and Retrieval|Models, Biological|Nonlinear Dynamics|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm382', '17660526', 'Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect.'),
(73644, 2007, 'bio', 'Bayesian inference for dynamic transcriptional regulation, the Hes1 system as a case study', 'Heron, Elizabeth A|Finkenstädt, Bärbel|Rand, David A', 'Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK.|Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 31, 'Artificial Intelligence|Basic Helix-Loop-Helix Transcription Factors|Bayes Theorem|Biological Clocks|Computer Simulation|Gene Expression Regulation|Homeodomain Proteins|Models, Genetic|Pattern Recognition, Automated|Transcription Factor HES-1|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btm367', '17660527', 'In this study, we address the problem of estimating the parameters of regulatory networks and provide the first application of Markov chain Monte Carlo (MCMC) methods to experimental data. As a case study, we consider a stochastic model of the Hes1 system expressed in terms of stochastic differential equations (SDEs) to which rigorous likelihood methods of inference can be applied. When fitting continuous-time stochastic models to discretely observed time series the lengths of the sampling intervals are important, and much of our study addresses the problem when the data are sparse.'),
(73645, 2007, 'bio', 'DNA reference alignment benchmarks based on tertiary structure of encoded proteins', 'Carroll, Hyrum|Beckstead, Wesley|OConnor, Timothy|Ebbert, Mark|Clement, Mark|Snell, Quinn|McClellan, David', 'Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com|Computer Science Department, Brigham Young University, Provo, Utah 84602, USA. hyrumdc@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Benchmarking|DNA|Genetic Code|Protein Structure, Tertiary|Proteins|Reference Values|Sequence Alignment|Sequence Analysis, DNA|United States', 'Evaluation Studies', '10.1093/bioinformatics/btm389', '17686799', 'Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences.'),
(73646, 2007, 'bio', 'Query-driven module discovery in microarray data', 'Dhollander, Thomas|Sheng, Qizheng|Lemmens, Karen|De Moor, Bart|Marchal, Kathleen|Moreau, Yves', 'Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Leuven, Belgium. thomas.dhollander@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 23, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm387', '17686800', 'Existing (bi)clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions.'),
(73647, 2007, 'bio', 'Improved model-based, platform-independent feature extraction for mass spectrometry', 'Noy, Karin|Fasulo, Daniel', 'Integrated Data System Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA.|Integrated Data System Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btm385', '17698491', 'Mass spectrometry (MS) is increasingly being used for biomedical research. The typical analysis of MS data consists of several steps. Feature extraction is a crucial step since subsequent analyses are performed only on the detected features. Current methodologies applied to low-resolution MS, in which features are peaks or wavelet functions, are parameter-sensitive and inaccurate in the sense that peaks and wavelet functions do not directly correspond to the underlying molecules under observation. In high-resolution MS, the model-based approach is more appealing as it can provide a better representation of the MS signals by incorporating information about peak shapes and isotopic distributions. Current model-based techniques are computationally expensive, various algorithms have been proposed to improve the computational efficiency of this paradigm. However, these methods cannot deal well with overlapping features, especially when they are merged to create one broad peak. In addition, no method has been proven to perform well across different MS platforms.'),
(73648, 2007, 'bio', 'Expression ratio evaluation in two-colour microarray experiments is significantly improved by correcting image misalignment', 'Tang, Thomas|François, Nicolas|Glatigny, Annie|Agier, Nicolas|Mucchielli, Marie-Hélène|Aggerbeck, Lawrence|Delacroix, Hervé', 'Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS UPR2167 and Gif/Orsay DNA MicroArray Platform (GODMAP), 91190 Gif-sur-Yvette, France. tang@cgm.cnrs-gif.fr', 'France|France|France|France|France|France|France', 14, 'Artifacts|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm399', '17698492', 'Two-colour microarrays are widely used to perform transcriptome analysis. In most cases, it appears that the red and green images resulting from the scan of a microarray slide are slightly shifted one with respect to the other. To increase the robustness of the measurement of the fluorescent emission intensities, multiple acquisitions with the same or different PMT gains can be used. In these cases, a systematic correction of image shift is required.'),
(73649, 2007, 'bio', 'Learning string similarity measures for gene/protein name dictionary look-up using logistic regression', 'Tsuruoka, Yoshimasa|McNaught, John|Tsujii, Junichi|Ananiadou, Sophia', 'School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Databases, Protein|Genes|Information Storage and Retrieval|Logistic Models|Natural Language Processing|Proteins|Regression Analysis|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btm393', '17698493', 'One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed.'),
(73650, 2007, 'bio', 'PhylArray: phylogenetic probe design algorithm for microarray', 'Militon, Cécile|Rimour, Sébastien|Missaoui, Mohieddine|Biderre, Corinne|Barra, Vincent|Hill, David|Moné, Anne|Gagne, Geneviève|Meier, Harald|Peyretaillade, Eric|Peyret, Pierre', 'Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.|Génomique Intégrée des Interactions Microbiennes, Laboratoire de Biologie des Protistes, UMR CNRS 6023, Blaise Pascal University, 24 avenue des Landais, Campus des Cézeaux, France.', 'France|France|France|France|France|France|France|France|France|France|France', 27, 'Algorithms|Base Sequence|DNA Probes|Gene Targeting|In Situ Hybridization|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btm392', '17698494', 'Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial black-box, the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality.'),
(73651, 2007, 'bio', 'pIPHULA--parallel inference of population parameters using a likelihood approach', 'Schmidt, Heiko A|von Haeseler, Arndt|Buschbom, Jutta', 'Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz Laboratories (MFPL), Vienna, Austria.|Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz Laboratories (MFPL), Vienna, Austria.|Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz Laboratories (MFPL), Vienna, Austria.', 'Austria|Austria|Austria', 0, 'Algorithms|Biological Evolution|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Evolution, Molecular|Genetics, Population|Likelihood Functions|Models, Genetic|Models, Statistical|Population Growth|Software', 'Journal Article', '10.1093/bioinformatics/btm391', '17698495', 'pIPHULA is the parallel program to estimate the parameters of a realistic model of population growth.'),
(73652, 2007, 'bio', 'Mining complex genotypic features for predicting HIV-1 drug resistance', 'Saigo, Hiroto|Uno, Takeaki|Tsuda, Koji', 'Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.', 'Germany|Germany|Germany', 35, 'Algorithms|Antiviral Agents|Base Sequence|DNA Mutational Analysis|Databases, Genetic|Drug Resistance, Viral|Genotype|HIV-1|Information Storage and Retrieval|Molecular Sequence Data', 'Journal Article', '10.1093/bioinformatics/btm353', '17698858', 'Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.'),
(73653, 2007, 'bio', 'Natively unstructured regions in proteins identified from contact predictions', 'Schlessinger, Avner|Punta, Marco|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA. as2067@columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA. as2067@columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA. as2067@columbia.edu', 'United States|United States|United States', 93, 'Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btm349', '17709338', 'Natively unstructured (also dubbed intrinsically disordered) regions in proteins lack a defined 3D structure under physiological conditions and often adopt regular structures under particular conditions. Proteins with such regions are overly abundant in eukaryotes, they may increase functional complexity of organisms and they usually evade structure determination in the unbound form. Low propensity for the formation of internal residue contacts has been previously used to predict natively unstructured regions.'),
(73654, 2007, 'bio', 'A Laboratory Information Management System (LIMS) for a high throughput genetic platform aimed at candidate gene mutation screening', 'Voegele, C|Tavtigian, S V|de Silva, D|Cuber, S|Thomas, A|Le Calvez-Kelm, F', 'International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr|International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr|International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr|International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr|International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr|International Agency for Research on Cancer, Lyon, France. voegele@iarc.fr', 'France|France|France|France|France|France', 18, 'Clinical Laboratory Techniques|Computer Communication Networks|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Genetic Testing|Information Storage and Retrieval|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm365', '17709339', 'High throughput mutation screening in an automated environment generates large data sets that have to be organized and stored reliably. Complex multistep workflows require strict process management and careful data tracking. We have developed a Laboratory Information Management Systems (LIMS) tailored to high throughput candidate gene mutation scanning and resequencing that respects these requirements. Designed with a client/server architecture, our system is platform independent and based on open-source tools from the database to the web application development strategy. Flexible, expandable and secure, the LIMS, by communicating with most of the laboratory instruments and robots, tracks samples and laboratory information, capturing data at every step of our automated mutation screening workflow. An important feature of our LIMS is that it enables tracking of information through a laboratory workflow where the process at one step is contingent on results from a previous step.'),
(73655, 2007, 'bio', 'Con-Struct Map: a comparative contact map analysis tool', 'Chung, Jo-Lan|Beaver, John E|Scheeff, Eric D|Bourne, Philip E', 'Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States', 9, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Graphics|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm356', '17709340', 'Con-Struct Map is a graphical tool for the comparative study of protein structures. The tool detects potential conserved residue contacts shared by multiple protein structures by superimposing their contact maps according to a multiple structure alignment. In general, Con-Struct Map allows the study of structural changes resulting from, e.g. sequence substitutions, or alternatively, the study of conserved components of a structure framework across structurally aligned proteins. Specific applications include the study of sequence-structure relationship in distantly related proteins and the comparisons of wild type and mutant proteins.'),
(73656, 2007, 'bio', 'Methods of remote homology detection can be combined to increase coverage by 10% in the midnight zone', 'Reid, Adam James|Yeats, Corin|Orengo, Christine Anne', 'Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK. reid@bioichem.ucl.ac.uk|Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK. reid@bioichem.ucl.ac.uk|Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK. reid@bioichem.ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Evolution, Molecular|Molecular Sequence Data|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btm355', '17709341', 'A recent development in sequence-based remote homologue detection is the introduction of profile-profile comparison methods. These are more powerful than previous technologies and can detect potentially homologous relationships missed by structural classifications such as CATH and SCOP. As structural classifications traditionally act as the gold standard of homology this poses a challenge in benchmarking them.'),
(73657, 2007, 'bio', 'Wordom: a program for efficient analysis of molecular dynamics simulations', 'Seeber, Michele|Cecchini, Marco|Rao, Francesco|Settanni, Giovanni|Caflisch, Amedeo', 'Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland.|Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland.|Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland.|Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland.|Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 200, 'Algorithms|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm378', '17717034', 'Wordom is a versatile program for manipulation of molecular dynamics trajectories and efficient analysis of simulations. Original tools in Wordom include a procedure to evaluate significance of sampling for principal component analysis as well as modules for clustering multiple conformations and evaluation of order parameters for folding and aggregation. The program was developed with special emphasis on user-friendliness, effortless addition of new modules and efficient handling of large sets of trajectories.'),
(73658, 2007, 'bio', 'Sliding MinPD: building evolutionary networks of serial samples via an automated recombination detection approach', 'Buendia, Patricia|Narasimhan, Giri', 'Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA.|Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University, Miami, FL 33199, USA.', 'United States|United States', 0, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genome, Viral|Molecular Sequence Data|Phylogeny|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btm413', '17717035', 'Traditional phylogenetic methods assume tree-like evolutionary models and are likely to perform poorly when provided with sequence data from fast-evolving, recombining viruses. Furthermore, these methods assume that all the sequence data are from contemporaneous taxa, which is not valid for serially-sampled data. A more general approach is proposed here, referred to as the Sliding MinPD method, that reconstructs evolutionary networks for serially-sampled sequences in the presence of recombination.'),
(73659, 2007, 'bio', 'CTree: comparison of clusters between phylogenetic trees made easy', 'Archer, John|Robertson, David L', 'Faculty of Life Science, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. john.archer@postgrad.manchester.ac.uk|Faculty of Life Science, University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK. john.archer@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom', 21, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Graphics|Computer Simulation|Genome, Viral|Models, Genetic|Phylogeny|Software|User-Computer Interface|Viruses', 'Journal Article', '10.1093/bioinformatics/btm410', '17717036', 'CTree has been designed for the quantification of clusters within viral phylogenetic tree topologies. Clusters are stored as individual data structures from which statistical data, such as the Subtype Diversity Ratio (SDR), Subtype Diversity Variance (SDV) and pairwise distances can be extracted. This simplifies the quantification of tree topologies in relation to inter- and intra-cluster diversity. Here the novel features incorporated within CTree, including the implementation of a heuristic algorithm for identifying clusters, are outlined along with the more usual features found within general tree viewing software.'),
(73660, 2007, 'bio', 'LICORN: learning cooperative regulation networks from gene expression data', 'Elati, Mohamed|Neuvial, Pierre|Bolotin-Fukuhara, Monique|Barillot, Emmanuel|Radvanyi, François|Rouveirol, Céline', 'LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr|LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr|LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr|LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr|LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr|LRI, CNRS UMR 8623, bât 490, Université Paris Sud, 91405 F-Orsay, France. mohamed.elati@curie.fr', 'France|France|France|France|France|France', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Proteome|RNA|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btm352', '17720703', 'One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels.'),
(73661, 2007, 'bio', 'A review of feature selection techniques in bioinformatics', 'Saeys, Yvan|Inza, Iñaki|Larrañaga, Pedro', 'Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be|Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be|Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be', 'Belgium|Belgium|Belgium', 2910, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Simulation|Gene Expression Profiling|Models, Biological|Pattern Recognition, Automated|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btm344', '17720704', 'Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.'),
(73662, 2007, 'bio', 'An improved algorithm for clustering gene expression data', 'Bandyopadhyay, Sanghamitra|Mukhopadhyay, Anirban|Maulik, Ujjwal', 'Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, India.|Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, India.|Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, India.', 'India|India|India', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Data Interpretation, Statistical|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btm418', '17720981', 'Recent advancements in microarray technology allows simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and imprecision. In this article, a two-stage clustering algorithm, which employs a recently proposed variable string length genetic scheme and a multiobjective genetic clustering algorithm, is proposed. It is based on the novel concept of points having significant membership to multiple classes. An iterated version of the well-known Fuzzy C-Means is also utilized for clustering.'),
(73663, 2007, 'bio', 'A comparison of background correction methods for two-colour microarrays', 'Ritchie, Matthew E|Silver, Jeremy|Oshlack, Alicia|Holmes, Melissa|Diyagama, Dileepa|Holloway, Andrew|Smyth, Gordon K', 'Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.|Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 675, 'Artifacts|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/btm412', '17720982', 'Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments.'),
(73664, 2007, 'bio', 'MeTaDoR: a comprehensive resource for membrane targeting domains and their host proteins', 'Bhardwaj, Nitin|Stahelin, Robert V|Zhao, Guijun|Cho, Wonhwa|Lu, Hui', 'Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.|Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.', 'United States|United States|United States|United States|United States', 17, 'Binding Sites|Databases, Protein|Information Storage and Retrieval|Membrane Microdomains|Membrane Proteins|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm395', '17720983', 'Protein-lipid interactions play a central role in cellular signaling and membrane trafficking and at the core of these interactions are domains specialized in lipid binding and membrane targeting. Considering the importance of these domains, we have created MeTaDoR, a comprehensive resource dedicated to membrane targeting domains (MTDs).'),
(73665, 2007, 'bio', 'Tools for visually exploring biological networks', 'Suderman, Matthew|Hallett, Michael', 'McGill Centre for Bioinformatics, 3775 University Street, Montreal, QCH3A 2B4, Canada. msuder@mcb.mcgill.ca|McGill Centre for Bioinformatics, 3775 University Street, Montreal, QCH3A 2B4, Canada. msuder@mcb.mcgill.ca', 'Canada|Canada', 172, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm401', '17720984', 'Many tools exist for visually exploring biological networks including well-known examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of these tools is to go beyond static representations of cellular state, towards a more dynamic model of cellular processes through the incorporation of gene expression data, subcellular localization information and time-dependent behavior. We provide a comprehensive review of the relative advantages and disadvantages of existing systems with two goals in mind: to aid researchers in efficiently identifying the appropriate existing tools for data visualization, to describe the necessary and realistic goals for the next generation of visualization tools. In view of the first goal, we provide in the Supplementary Material a systematic comparison of more than 35 existing tools in terms of over 25 different features.'),
(73666, 2007, 'bio', 'Metal reduction kinetics in Shewanella', 'Lall, Raman|Mitchell, Julie', 'BACTER Institute, University of Wisconsin-Madison, Wisconsin 53706, USA. rl8q@cms.mail.virginia.edu|BACTER Institute, University of Wisconsin-Madison, Wisconsin 53706, USA. rl8q@cms.mail.virginia.edu', 'United States|United States', 0, 'Biotransformation|Computer Simulation|Metals|Models, Biological|Models, Chemical|Oxidation-Reduction|Shewanella', 'Journal Article', '10.1093/bioinformatics/btm400', '17720985', 'Metal reduction kinetics have been studied in cultures of dissimilatory metal reducing bacteria which include the Shewanella oneidensis strain MR-1. Estimation of system parameters from time-series data faces obstructions in the implementation depending on the choice of the mathematical model that captures the observed dynamics. The modeling of metal reduction is often based on Michaelis-Menten equations. These models are often developed using initial in vitro reaction rates and seldom match with in vivo reduction profiles.'),
(73667, 2007, 'bio', 'Identification of compositionally distinct regions in genomes using the centroid method', 'Rajan, Issaac|Aravamuthan, Sarang|Mande, Sharmila S', 'Life Sciences Research, Advanced Technology Centre, Tata Consultancy Services, Hyderabad 500 081, Andhra Pradesh, India.|Life Sciences Research, Advanced Technology Centre, Tata Consultancy Services, Hyderabad 500 081, Andhra Pradesh, India.|Life Sciences Research, Advanced Technology Centre, Tata Consultancy Services, Hyderabad 500 081, Andhra Pradesh, India.', 'India|India|India', 19, 'Algorithms|Base Sequence|Chromosome Mapping|Genome, Bacterial|Genomic Islands|Molecular Sequence Data|Sequence Analysis, DNA|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btm405', '17724060', 'It is known that most genomic regions of special interest, e.g. horizontally acquired sequences, genomic islands, etc. have distinct word (m-mer) compositions. Most of the earlier work along this direction, addressed di- and tri-nucleotide compositions. We present an approach that can be applied to analyze compositions of any given word size. The method, called the centroid approach, can reveal compositionally distinct regions in genomic sequences for any given word size.'),
(73668, 2007, 'bio', 'Exploring the functional landscape of gene expression: directed search of large microarray compendia', 'Hibbs, Matthew A|Hess, David C|Myers, Chad L|Huttenhower, Curtis|Li, Kai|Troyanskaya, Olga G', 'Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Database Management Systems|Databases, Protein|Gene Expression|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btm403', '17724061', 'The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium.'),
(73669, 2007, 'bio', 'TMpro web server and web service: transmembrane helix prediction through amino acid property analysis', 'Ganapathiraju, Madhavi|Jursa, Christopher Jon|Karimi, Hassan A|Klein-Seetharaman, Judith', 'Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States', 0, 'Computer Simulation|Internet|Membrane Proteins|Models, Chemical|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btm398', '17724062', 'TMpro is a transmembrane (TM) helix prediction algorithm that uses language processing methodology for TM segment identification. It is primarily based on the analysis of statistical distributions of properties of amino acids in transmembrane segments. This article describes the availability of TMpro on the internet via a web interface. The key features of the interface are: (i) output is generated in multiple formats including a user-interactive graphical chart which allows comparison of TMpro predicted segment locations with other labeled segments input by the user, such as predictions from other methods. (ii) Up to 5000 sequences can be submitted at a time for prediction. (iii) TMpro is available as a web server and is published as a web service so that the method can be accessed by users as well as other services depending on the need for data integration.');
