INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74975, 2009, 'bio', 'Reconstructing spatiotemporal gene expression data from partial observations', 'Cartwright, Dustin A|Brady, Siobhan M|Orlando, David A|Sturmfels, Bernd|Benfey, Philip N', 'Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA 94704, USA. dustin@math.berkeley.edu', 'United States|United States|United States|United States|United States', 30, 'Computational Biology|Gene Expression|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btp437', '19608707', 'Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root, however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures.'),
(74976, 2009, 'bio', 'An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions', 'Miller, David J|Zhang, Yanxin|Yu, Guoqiang|Liu, Yongmei|Chen, Li|Langefeld, Carl D|Herrington, David|Wang, Yue', 'Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu|Department of Electrical Engineering, The Pennsylvania State University, USA. djmiller@engr.psu.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Entropy|Genetic Predisposition to Disease|Genome|Genome-Wide Association Study|Models, Statistical|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp435', '19608708', 'In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models.'),
(74977, 2009, 'bio', 'Caleydo: connecting pathways and gene expression', 'Streit, Marc|Lex, Alexander|Kalkusch, Michael|Zatloukal, Kurt|Schmalstieg, Dieter', 'Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 and Institute of Pathology, Medical University Graz, Auenbruggerplatz 25, 8010 Graz, Austria. caleydo@icg.tugraz.at|Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 and Institute of Pathology, Medical University Graz, Auenbruggerplatz 25, 8010 Graz, Austria. caleydo@icg.tugraz.at|Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 and Institute of Pathology, Medical University Graz, Auenbruggerplatz 25, 8010 Graz, Austria. caleydo@icg.tugraz.at|Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 and Institute of Pathology, Medical University Graz, Auenbruggerplatz 25, 8010 Graz, Austria. caleydo@icg.tugraz.at|Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 and Institute of Pathology, Medical University Graz, Auenbruggerplatz 25, 8010 Graz, Austria. caleydo@icg.tugraz.at', 'Austria|Austria|Austria|Austria|Austria', 25, 'Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp432', '19620095', 'Understanding the relationships between pathways and the altered expression of their components in disease conditions can be addressed in a visual data analysis process. Caleydo uses novel visualization techniques to support life science experts in their analysis of gene expression data in the context of pathways and functions of individual genes. Pathways and gene expression visualizations are placed in a 3D scene where selected entities (i.e. genes) are visually connected. This allows Caleydo to seamlessly integrate interactive gene expression visualization with cross-database pathway exploration.'),
(74978, 2009, 'bio', 'Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis', 'Kinoshita, Kengo|Obayashi, Takeshi', 'Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan. kinosita@hgc.jp|Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan. kinosita@hgc.jp', 'Japan|Japan', 0, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Plant', 'Journal Article', '10.1093/bioinformatics/btp442', '19620096', 'Recent improvements in DNA microarray techniques have made a large variety of gene expression data available in public databases. This data can be used to evaluate the strength of gene coexpression by calculating the correlation of expression patterns among different genes between many experiments. However, gene expression levels differ significantly across various tissues in higher organisms, as well as in different cellular location in eukaryotes in different cell state. Thus the usual correlation measure can only evaluate the difference of tissues or cellular localizations, and cannot adequately elucidate the functional relationship from the coexpression of genes.'),
(74979, 2009, 'bio', 'The SNP ratio test: pathway analysis of genome-wide association datasets', 'ODushlaine, Colm|Kenny, Elaine|Heron, Elizabeth A|Segurado, Ricardo|Gill, Michael|Morris, Derek W|Corvin, Aiden', 'Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com|Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Ireland. codushlaine@gmail.com', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 112, 'Computational Biology|Databases, Genetic|Genome|Genome-Wide Association Study|Humans|Parkinson Disease|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp448', '19620097', 'We present a tool that assesses the enrichment of significant associations from genome-wide association studies (GWAS) in a pathway context. The SNP ratio test (SRT) compares the proportion of significant to all SNPs within genes that are part of a pathway and computes an empirical P-value based on comparisons to ratios in datasets where the assignment of case/control status has been randomized. We applied the SRT to a Parkinsons disease GWAS dataset, using the KEGG database, revealing significance for Parkinsons disease and related pathways.'),
(74980, 2009, 'bio', 'Benchmarking homology detection procedures with low complexity filters', 'Forslund, Kristoffer|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden. kristoffer.forslund@sbc.su.se|Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden. kristoffer.forslund@sbc.su.se', 'Sweden|Sweden', 0, 'Amino Acid Sequence|Benchmarking|Computational Biology|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btp446', '19620098', 'Low-complexity sequence regions present a common problem in finding true homologs to a protein query sequence. Several solutions to this have been suggested, but a detailed comparison between these on challenging data has so far been lacking. A common benchmark for homology detection procedures is to use SCOP/ASTRAL domain sequences belonging to the same or different superfamilies, but these contain almost no low complexity sequences.'),
(74981, 2009, 'bio', 'FRODOCK: a new approach for fast rotational protein-protein docking', 'Garzon, José Ignacio|Lopéz-Blanco, José Ramón|Pons, Carles|Kovacs, Julio|Abagyan, Ruben|Fernandez-Recio, Juan|Chacon, Pablo', 'Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.|Centro de Investigaciones Biológicas, CSIC, Ramiro de Maeztu, 9. 28040 Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Computational Biology|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp447', '19620099', 'Prediction of protein-protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein-protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current omics world.'),
(74982, 2009, 'bio', 'An optimization model for metabolic pathways', 'Planes, F J|Beasley, J E', 'CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain.|CEIT and TECNUN, University of Navarra, Manuel de Lardizabal 15, 20018 San Sebastian, Spain.', 'Spain|Spain', 0, 'Computational Biology|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btp441', '19620100', 'Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.'),
(74983, 2009, 'bio', 'A new method for revealing correlated mutations under the structural and functional constraints in proteins', 'Lee, Byung-Chul|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, Korea.', '-|-', 24, 'Computational Biology|Evolution, Molecular|Mutation|Proteins|Sequence Alignment|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btp455', '19628501', 'Diverse studies have shown that correlated mutation (CM) is an important molecular evolutionary process alongside conservation. However, attempts to find the residue pairs that co-evolve under the structural and/or functional constraints are complicated by the fact that a large portion of covariance signals found in multiple sequence alignments arise from correlations due to common ancestry and stochastic noise.'),
(74984, 2009, 'bio', 'Moderated effect size and P-value combinations for microarray meta-analyses', 'Marot, Guillemette|Foulley, Jean-Louis|Mayer, Claus-Dieter|Jaffrézic, Florence', 'INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France. guillemette.marot@jouy.inra.fr|INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France. guillemette.marot@jouy.inra.fr|INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France. guillemette.marot@jouy.inra.fr|INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France. guillemette.marot@jouy.inra.fr', 'France|France|France|France', 0, 'Animals|Computer Simulation|Gene Expression Profiling|Humans|Male|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms', 'Journal Article', '10.1093/bioinformatics/btp444', '19628502', 'With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.'),
(74985, 2009, 'bio', 'Modified variational Bayes EM estimation of hidden Markov tree model of cell lineages', 'Olariu, Victor|Coca, Daniel|Billings, Stephen A|Tonge, Peter|Gokhale, Paul|Andrews, Peter W|Kadirkamanathan, Visakan', 'Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.|Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Cell Lineage|Humans|Markov Chains|Pluripotent Stem Cells', 'Journal Article', '10.1093/bioinformatics/btp456', '19628503', 'Human pluripotent stem cell lines persist in culture as a heterogeneous population of SSEA3 positive and SSEA3 negative cells. Tracking individual stem cells in real time can elucidate the kinetics of cells switching between the SSEA3 positive and negative substates. However, identifying a cells substate at all time points within a cell lineage tree is technically difficult.'),
(74986, 2009, 'bio', 'PathBuilder--open source software for annotating and developing pathway resources', 'Kandasamy, Kumaran|Keerthikumar, Shivakumar|Raju, Rajesh|Keshava Prasad, T S|Ramachandra, Y L|Mohan, Sujatha|Pandey, Akhilesh', 'Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.|Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.', 'India|India|India|India|India|India|India', 28, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp453', '19628504', 'We have developed PathBuilder, an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein-protein interactions, enzyme-substrate relationships and protein translocation events either manually or through automated importing of data from other databases. Salient features of PathBuilder include automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets.'),
(74987, 2009, 'bio', 'A modified LOESS normalization applied to microRNA arrays: a comparative evaluation', 'Risso, Davide|Massa, Maria Sofia|Chiogna, Monica|Romualdi, Chiara', 'Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.|Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.|Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.|Department of Statistical Sciences, University of Padova, via C. Battisti 241 and Department of Biology, University of Padova, via U. Bassi 58/B, 35121 Padova, Italy.', 'Italy|Italy|Italy|Italy', 31, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|MicroRNAs|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp443', '19628505', 'Microarray normalization is a fundamental step in removing systematic bias and noise variability caused by technical and experimental artefacts. Several approaches, suitable for large-scale genome arrays, have been proposed and shown to be effective in the reduction of systematic errors. Most of these methodologies are based on specific assumptions that are reasonable for whole-genome arrays, but possibly unsuitable for small microRNA (miRNA) platforms. In this work, we propose a novel normalization (loessM), and we investigate, through simulated and real datasets, the influence that normalizations for two-colour miRNA arrays have on the identification of differentially expressed genes.'),
(74988, 2009, 'bio', 'Enhancement of beta-sheet assembly by cooperative hydrogen bonds potential', 'Levy-Moonshine, Ami|Amir, El-Ad David|Keasar, Chen', 'Department of Computer Science and Department of Life Sciences, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel.|Department of Computer Science and Department of Life Sciences, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel.|Department of Computer Science and Department of Life Sciences, Ben-Gurion University of the Negev, 84105 Beer-Sheva, Israel.', 'Israel|Israel|Israel', 0, 'Computer Simulation|Databases, Protein|Hydrogen Bonding|Models, Molecular|Protein Folding|Protein Structure, Secondary|Proteins|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btp449', '19628506', 'The roughness of energy landscapes is a major obstacle to protein structure prediction, since it forces conformational searches to spend much time struggling to escape numerous traps. Specifically, beta-sheet formation is prone to stray, since many possible combinations of hydrogen bonds are dead ends in terms of beta-sheet assembly. It has been shown that cooperative terms for backbone hydrogen bonds ease this problem by augmenting hydrogen bond patterns that are consistent with beta sheets. Here, we present a novel cooperative hydrogen-bond term that is both effective in promoting beta sheets and computationally efficient. In addition, the new term is differentiable and operates on all-atom protein models.'),
(74989, 2009, 'bio', 'iBioSim: a tool for the analysis and design of genetic circuits', 'Myers, Chris J|Barker, Nathan|Jones, Kevin|Kuwahara, Hiroyuki|Madsen, Curtis|Nguyen, Nam-Phuong D', 'Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu|Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA. myers@ece.utah.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Gene Expression Profiling|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp457', '19628507', 'iBioSim is a tool that supports learning of genetic circuit models, efficient abstraction-based analysis of these models and the design of synthetic genetic circuits. iBioSim includes project management features and a graphical user interface that facilitate the development and maintenance of genetic circuit models as well as both experimental and simulation data records.'),
(74990, 2009, 'bio', 'Path: a tool to facilitate pathway-based genetic association analysis', 'Zamar, David|Tripp, Ben|Ellis, George|Daley, Denise', 'James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada.|James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada.|James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada.|James Hogg iCAPTURE Center, University of British Columbia (UBC), Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada', 14, 'Computational Biology|Databases, Factual|Genome-Wide Association Study|Internet|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp431', '19628508', 'Traditional methods of genetic study design and analysis work well under the scenario that a handful of single nucleotide polymorphisms (SNPs) independently contribute to the risk of disease. For complex diseases, susceptibility may be determined not by a single SNP, but rather a complex interplay between SNPs. For large studies involving hundreds of thousands of SNPs, a brute force search of all possible combinations of SNPs associated with disease is not only inefficient, but also results in a multiple testing paradigm, whereby larger and larger sample sizes are needed to maintain statistical power. Pathway-based methods are an example of one of the many approaches in identifying a subset of SNPs to test for interaction. To help determine which SNP-SNP interactions to test, we developed Path, a software application designed to help researchers interface their data with biological information from several bioinformatics resources. To this end, our application brings together currently available information from nine online bioinformatics resources including the National Center for Biotechnology Information (NCBI), Online Mendelian Inheritance in Man (OMIM), Kyoto Encyclopedia of Genes and Genomes (KEGG), UCSC Genome Browser, Seattle SNPs, PharmGKB, Genetic Association Database, the Single Nucleotide Polymorphism database (dbSNP) and the Innate Immune Database (IIDB).'),
(74991, 2009, 'bio', 'Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster', 'Marco, Antonio|Konikoff, Charlotte|Karr, Timothy L|Kumar, Sudhir', 'Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. amarcoca@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. amarcoca@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. amarcoca@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. amarcoca@asu.edu', 'United States|United States|United States|United States', 31, 'Animals|Binding Sites|Computational Biology|Drosophila melanogaster|Gene Expression|Genomics|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp462', '19633094', 'In functional genomics, it is frequently useful to correlate expression levels of genes to identify transcription factor binding sites (TFBS) via the presence of common sequence motifs. The underlying assumption is that co-expressed genes are more likely to contain shared TFBS and, thus, TFBS can be identified computationally. Indeed, gene pairs with a very high expression correlation show a significant excess of shared binding sites in yeast. We have tested this assumption in a more complex organism, Drosophila melanogaster, by using experimentally determined TFBS and microarray expression data. We have also examined the reverse relationship between the expression correlation and the extent of TFBS sharing.'),
(74992, 2009, 'bio', 'A System for Information Management in BioMedical Studies--SIMBioMS', 'Krestyaninova, Maria|Zarins, Andris|Viksna, Juris|Kurbatova, Natalja|Rucevskis, Peteris|Neogi, Sudeshna Guha|Gostev, Mike|Perheentupa, Teemu|Knuuttila, Juha|Barrett, Amy|Lappalainen, Ilkka|Rung, Johan|Podnieks, Karlis|Sarkans, Ugis|McCarthy, Mark I|Brazma, Alvis', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. support@simbioms.org', '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', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Information Management|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btp420', '19633095', 'SIMBioMS is a web-based open source software system for managing data and information in biomedical studies. It provides a solution for the collection, storage, management and retrieval of information about research subjects and biomedical samples, as well as experimental data obtained using a range of high-throughput technologies, including gene expression, genotyping, proteomics and metabonomics. The system can easily be customized and has proven to be successful in several large-scale multi-site collaborative projects. It is compatible with emerging functional genomics data standards and provides data import and export in accepted standard formats. Protocols for transferring data to durable archives at the European Bioinformatics Institute have been implemented.'),
(74993, 2009, 'bio', 'PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary trees', 'Lee, Bongshin|Nachmanson, Lev|Robertson, George|Carlson, Jonathan M|Heckerman, David', 'Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. bongshin@microsoft.com|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. bongshin@microsoft.com|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. bongshin@microsoft.com|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. bongshin@microsoft.com|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA. bongshin@microsoft.com', 'United States|United States|United States|United States|United States', 5, 'Computational Biology|Evolution, Molecular|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp454', '19633096', 'Evolutionary biologists are often interested in finding correlations among biological traits across a number of species, as such correlations may lead to testable hypotheses about the underlying function. Because some species are more closely related than others, computing and visualizing these correlations must be done in the context of the evolutionary tree that relates species. In this note, we introduce PhyloDet (short for PhyloDetective), an evolutionary tree visualization tool that enables biologists to visualize multiple traits mapped to the tree.'),
(74994, 2009, 'bio', 'Mining gene functional networks to improve mass-spectrometry-based protein identification', 'Ramakrishnan, Smriti R|Vogel, Christine|Kwon, Taejoon|Penalva, Luiz O|Marcotte, Edward M|Miranker, Daniel P', 'Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.|Department of Computer Sciences, 1 University Station C0500, The University of Texas at Austin, Austin, TX 78712, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Protein|Gene Regulatory Networks|Proteins|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btp461', '19633097', 'High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly.'),
(74995, 2009, 'bio', 'Accessible methods for the dynamic time-scale decomposition of biochemical systems', 'Surovtsova, Irina|Simus, Natalia|Lorenz, Thomas|König, Artjom|Sahle, Sven|Kummer, Ursula', 'Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de|Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de|Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de|Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de|Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de|Department of Modeling of Biological Processes, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. irina.surovtsova@bioquant.uni-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Algorithms|Computational Biology|Kinetics|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btp451', '19633098', 'The growing complexity of biochemical models asks for means to rationally dissect the networks into meaningful and rather independent subnetworks. Such foregoing should ensure an understanding of the system without any heuristics employed. Important for the success of such an approach is its accessibility and the clarity of the presentation of the results.'),
(74996, 2009, 'bio', 'Gene ranking and biomarker discovery under correlation', 'Zuber, Verena|Strimmer, Korbinian', 'Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.|Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.', 'Germany|Germany', 0, 'Computational Biology|Gene Expression Profiling|Genetic Markers|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp460', '19648135', 'Biomarker discovery and gene ranking is a standard task in genomic high-throughput analysis. Typically, the ordering of markers is based on a stabilized variant of the t-score, such as the moderated t or the SAM statistic. However, these procedures ignore gene-gene correlations, which may have a profound impact on the gene orderings and on the power of the subsequent tests.'),
(74997, 2009, 'bio', 'Network analyses in systems pharmacology', 'Berger, Seth I|Iyengar, Ravi', 'Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave Levy Place, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave Levy Place, New York, NY 10029, USA.', 'United States|United States', 0, 'Computational Biology|Drug Design|Drug Discovery|Gene Regulatory Networks|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp465', '19648136', 'Systems pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action as one of its approaches. By considering drug actions and side effects in the context of the regulatory networks within which the drug targets and disease gene products function, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Systems pharmacology can provide new approaches for drug discovery for complex diseases. The integrated approach used in systems pharmacology can allow for drug action to be considered in the context of the whole genome. Network-based studies are becoming an increasingly important tool in understanding the relationships between drug action and disease susceptibility genes. This review discusses how analysis of biological networks has contributed to the genesis of systems pharmacology and how these studies have improved global understanding of drug targets, suggested new targets and approaches for therapeutics, and provided a deeper understanding of the effects of drugs. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of existing medications.'),
(74998, 2009, 'bio', 'Methyl side-chain dynamics prediction based on protein structure', 'Carbonell, Pablo|del Sol, Antonio', 'Fujirebio Inc., Bioinformatics Research Unit, Research and Development Division, Komiya-cho, Hachioji-shi, Tokyo 192-0031, Japan.|Fujirebio Inc., Bioinformatics Research Unit, Research and Development Division, Komiya-cho, Hachioji-shi, Tokyo 192-0031, Japan.', 'Japan|Japan', 0, 'Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btp463', '19648137', 'Protein dynamics is believed to influence protein function through a variety of mechanisms, some of which are not fully understood. Thus, prediction of protein flexibility from sequence or structural characteristics would assist in comprehension of the ways dynamics is linked to function, and would be important in protein modeling and design. In particular, quantitative description of side-chain dynamics would allow us to understand the role of side-chain flexibility in different functional processes, such as protein-ligand and protein-protein interactions.'),
(74999, 2009, 'bio', 'RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data', 'Cloonan, Nicole|Xu, Qinying|Faulkner, Geoffrey J|Taylor, Darrin F|Tang, Dave T P|Kolle, Gabriel|Grimmond, Sean M', 'Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au|Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia 4072, Australia. n.cloonan@imb.uq.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Base Sequence|Computational Biology|Databases, Genetic|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp459', '19648138', 'Mapping of next-generation sequencing data derived from RNA samples (RNAseq) presents different genome mapping challenges than data derived from DNA. For example, tags that cross exon-junction boundaries will often not map to a reference genome, and the strand specificity of the data needs to be retained. Here we present RNA-MATE, a computational pipeline based on a recursive mapping strategy for placing strand specific RNAseq data onto a reference genome. Maximizing the mappable tags can provide significant savings in the cost of sequencing experiments. This pipeline provides an automatic and integrated way to align color-space sequencing data, collate this information and generate files for examining gene-expression data in a genomic context.'),
(75000, 2009, 'bio', 'A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge', 'Tian, Ze|Hwang, TaeHyun|Kuang, Rui', 'Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.|Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.|Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.', 'United States|United States|United States', 0, 'Algorithms|Comparative Genomic Hybridization|Computational Biology|Databases, Factual|Gene Expression|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btp467', '19648139', 'Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.'),
(75001, 2009, 'bio', 'metahdep: meta-analysis of hierarchically dependent gene expression studies', 'Stevens, John R|Nicholas, Gabriel', 'Department of Mathematics and Statistics, Utah State University, Logan, UT 84322-3900, USA. john.r.stevens@usu.edu|Department of Mathematics and Statistics, Utah State University, Logan, UT 84322-3900, USA. john.r.stevens@usu.edu', 'United States|United States', 11, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btp468', '19648140', 'The metahdep package contains tools for combining the results of multiple gene expression studies. Unlike previous microarray meta-analysis packages, metahdep accounts for both sampling and hierarchical dependence among studies, as well as fundamental covariate differences between studies.'),
(75002, 2009, 'bio', 'CMap 101: a comparative mapping application for the Internet', 'Youens-Clark, Ken|Faga, Ben|Yap, Immanuel V|Stein, Lincoln|Ware, Doreen', 'Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA. kclark@cshl.edu|Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA. kclark@cshl.edu|Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA. kclark@cshl.edu|Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA. kclark@cshl.edu|Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA. kclark@cshl.edu', 'United States|United States|United States|United States|United States', 58, 'Animals|Computational Biology|Internet|Software', 'Comparative Study', '10.1093/bioinformatics/btp458', '19648141', 'CMap is a web-based tool for displaying and comparing maps of any type and from any species. A user can compare an unlimited number of maps, view pair-wise comparisons of known correspondences, and search for maps or for features by name, species, type and accession. CMap is freely available, can run on a variety of database engines and uses only free and open software components.'),
(75003, 2009, 'bio', 'Upcoming challenges for multiple sequence alignment methods in the high-throughput era', 'Kemena, Carsten|Notredame, Cedric', 'Centre For Genomic Regulation, Pompeus Fabre University, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain.|Centre For Genomic Regulation, Pompeus Fabre University, Carrer del Doctor Aiguader 88, 08003 Barcelona, Spain.', 'Spain|Spain', 137, 'Algorithms|Amino Acid Sequence|Computational Biology|Genome|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp452', '19648142', 'This review focuses on recent trends in multiple sequence alignment tools. It describes the latest algorithmic improvements including the extension of consistency-based methods to the problem of template-based multiple sequence alignments. Some results are presented suggesting that template-based methods are significantly more accurate than simpler alternative methods. The validation of existing methods is also discussed at length with the detailed description of recent results and some suggestions for future validation strategies. The last part of the review addresses future challenges for multiple sequence alignment methods in the genomic era, most notably the need to cope with very large sequences, the need to integrate large amounts of experimental data, the need to accurately align non-coding and non-transcribed sequences and finally, the need to integrate many alternative methods and approaches.'),
(75004, 2009, 'bio', 'The Integrated Genome Browser: free software for distribution and exploration of genome-scale datasets', 'Nicol, John W|Helt, Gregg A|Blanchard, Steven G|Raja, Archana|Loraine, Ann E', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, North Carolina Research Campus, 600 Laureate Way, Kannapolis, NC 28081, USA.', 'United States|United States|United States|United States|United States', 430, 'Animals|Computational Biology|Databases, Genetic|Genome|Genomics|Humans|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btp472', '19654113', 'Experimental techniques that survey an entire genome demand flexible, highly interactive visualization tools that can display new data alongside foundation datasets, such as reference gene annotations. The Integrated Genome Browser (IGB) aims to meet this need. IGB is an open source, desktop graphical display tool implemented in Java that supports real-time zooming and panning through a genome, layout of genomic features and datasets in moveable, adjustable tiers, incremental or genome-scale data loading from remote web servers or local files, and dynamic manipulation of quantitative data via genome graphs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75005, 2009, 'bio', 'PubMed-EX: a web browser extension to enhance PubMed search with text mining features', 'Tsai, Richard Tzong-Han|Dai, Hong-Jie|Lai, Po-Ting|Huang, Chi-Hsin', 'Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw', '-|-|-|-', 12, 'Computational Biology|Data Mining|Databases, Factual|Information Storage and Retrieval|Internet|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp475', '19654114', 'PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EXs page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources.'),
(75006, 2009, 'bio', 'Multiple testing in genome-wide association studies via hidden Markov models', 'Wei, Zhi|Sun, Wenguang|Wang, Kai|Hakonarson, Hakon', 'Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. zhiwei@njit.edu|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. zhiwei@njit.edu|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. zhiwei@njit.edu|Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. zhiwei@njit.edu', 'Jersey|Jersey|Jersey|Jersey', 30, 'Animals|Databases, Genetic|Genome|Genome-Wide Association Study|Genomics|Humans|Markov Chains|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp476', '19654115', 'Genome-wide association studies (GWAS) interrogate common genetic variation across the entire human genome in an unbiased manner and hold promise in identifying genetic variants with moderate or weak effect sizes. However, conventional testing procedures, which are mostly P-value based, ignore the dependency and therefore suffer from loss of efficiency. The goal of this article is to exploit the dependency information among adjacent single nucleotide polymorphisms (SNPs) to improve the screening efficiency in GWAS.'),
(75007, 2009, 'bio', 'Genoscape: a Cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks', 'Clément-Ziza, Mathieu|Malabat, Christophe|Weber, Christian|Moszer, Ivan|Aittokallio, Tero|Letondal, Catherine|Rousseau, Sandrine', 'Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de|Biotechnology Center, TU Dresden, Dresden, Germany. Mathieu.clement-ziza@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Computational Biology|Gene Expression|Gene Expression Profiling|Genomics|Neural Networks (Computer)|Software', 'Journal Article', '10.1093/bioinformatics/btp464', '19654116', 'Genoscape is an open-source Cytoscape plug-in that visually integrates gene expression data sets from GenoScript, a transcriptomic database, and KEGG pathways into Cytoscape networks. The generated visualisation highlights gene expression changes and their statistical significance. The plug-in also allows one to browse GenoScript or import transcriptomic data from other sources through tab-separated text files. Genoscape has been successfully used by researchers to investigate the results of gene expression profiling experiments.'),
(75008, 2009, 'bio', 'A phylogenetic and Markov model approach for the reconstruction of mutational pathways of drug resistance', 'Buendia, Patricia|Cadwallader, Brice|DeGruttola, Victor', 'Department of Biology and Center for Computational Science, University of Miami, Miami, USA. pbuendia@miami.edu|Department of Biology and Center for Computational Science, University of Miami, Miami, USA. pbuendia@miami.edu|Department of Biology and Center for Computational Science, University of Miami, Miami, USA. pbuendia@miami.edu', 'United States|United States|United States', 10, 'Algorithms|Drug Resistance, Viral|HIV Infections|Markov Chains|Mutation|Phylogeny|Viruses', 'Journal Article', '10.1093/bioinformatics/btp466', '19654117', 'Modern HIV-1, hepatitis B virus and hepatitis C virus antiviral therapies have been successful at keeping viruses suppressed for prolonged periods of time, but therapy failures attributable to the emergence of drug resistant mutations continue to be a distressing reminder that no therapy can fully eradicate these viruses from their host organisms. To better understand the emergence of drug resistance, we combined phylogenetic and statistical models of viral evolution in a 2-phase computational approach that reconstructs mutational pathways of drug resistance.'),
(75009, 2009, 'bio', 'Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-20', 'Dehouck, Yves|Grosfils, Aline|Folch, Benjamin|Gilis, Dimitri|Bogaerts, Philippe|Rooman, Marianne', 'Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be|Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be|Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be|Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be|Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be|Bioinformatique génomique et structurale, Université Libre de Bruxelles. Av Fr. Roosevelt 50, CP165/61, 1050 Brussels, Belgium. ydehouck@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 208, 'Computational Biology|Databases, Protein|Mutation|Neural Networks (Computer)|Protein Folding|Protein Stability|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp445', '19654118', 'The rational design of proteins with modified properties, through amino acid substitutions, is of crucial importance in a large variety of applications. Given the huge number of possible substitutions, every protein engineering project would benefit strongly from the guidance of in silico methods able to predict rapidly, and with reasonable accuracy, the stability changes resulting from all possible mutations in a protein.'),
(75010, 2009, 'bio', 'ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data', 'Morgan, Martin|Anders, Simon|Lawrence, Michael|Aboyoun, Patrick|Pagès, Hervé|Gentleman, Robert', 'Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org|Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. mtmorgan@fhcrc.org', 'United States|United States|United States|United States|United States|United States', 248, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp450', '19654119', 'ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources.'),
(75011, 2009, 'bio', 'Flynet: a genomic resource for Drosophila melanogaster transcriptional regulatory networks', 'Tian, Feng|Shah, Parantu K|Liu, Xiangjun|Negre, Nicolas|Chen, Jia|Karpenko, Oleksiy|White, Kevin P|Grossman, Robert L', 'School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.|School of Medicine, Tsinghua University, Beijing, China 100084.', 'China|China|China|China|China|China|China|China', 3, 'Animals|Chromatin Immunoprecipitation|Computational Biology|Drosophila Proteins|Drosophila melanogaster|Gene Regulatory Networks|Genome|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp469', '19656951', 'The highly coordinated expression of thousands of genes in an organism is regulated by the concerted action of transcription factors, chromatin proteins and epigenetic mechanisms. High-throughput experimental data for genome wide in vivo protein-DNA interactions and epigenetic marks are becoming available from large projects, such as the model organism ENCyclopedia Of DNA Elements (modENCODE) and from individual labs. Dissemination and visualization of these datasets in an explorable form is an important challenge.'),
(75012, 2009, 'bio', 'Improving phylogenetic analyses by incorporating additional information from genetic sequence databases', 'Liang, Li-Jung|Weiss, Robert E|Redelings, Benjamin|Suchard, Marc A', 'Department of Medicine Statistics Core, UCLA School of Public Health, Los Angeles, CA 90095, USA. liangl@ucla.edu|Department of Medicine Statistics Core, UCLA School of Public Health, Los Angeles, CA 90095, USA. liangl@ucla.edu|Department of Medicine Statistics Core, UCLA School of Public Health, Los Angeles, CA 90095, USA. liangl@ucla.edu|Department of Medicine Statistics Core, UCLA School of Public Health, Los Angeles, CA 90095, USA. liangl@ucla.edu', 'United States|United States|United States|United States', 6, 'Algorithms|Computational Biology|Databases, Genetic|Markov Chains|Phylogeny|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btp473', '19661240', 'Statistical analyses of phylogenetic data culminate in uncertain estimates of underlying model parameters. Lack of additional data hinders the ability to reduce this uncertainty, as the original phylogenetic dataset is often complete, containing the entire gene or genome information available for the given set of taxa. Informative priors in a Bayesian analysis can reduce posterior uncertainty, however, publicly available phylogenetic software specifies vague priors for model parameters by default. We build objective and informative priors using hierarchical random effect models that combine additional datasets whose parameters are not of direct interest but are similar to the analysis of interest.'),
(75013, 2009, 'bio', 'R/Bioconductor software for Illuminas Infinium whole-genome genotyping BeadChips', 'Ritchie, Matthew E|Carvalho, Benilton S|Hetrick, Kurt N|Tavaré, Simon|Irizarry, Rafael A', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville Victoria 3052, Australia. mritchie@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia', 36, 'Algorithms|Computational Biology|Genome|Genotype|Software', 'Journal Article', '10.1093/bioinformatics/btp470', '19661241', 'Illumina produces a number of microarray-based technologies for human genotyping. An Infinium BeadChip is a two-color platform that types between 10(5) and 10(6) single nucleotide polymorphisms (SNPs) per sample. Despite being widely used, there is a shortage of open source software to process the raw intensities from this platform into genotype calls. To this end, we have developed the R/Bioconductor package crlmm for analyzing BeadChip data. After careful preprocessing, our software applies the CRLMM algorithm to produce genotype calls, confidence scores and other quality metrics at both the SNP and sample levels. We provide access to the raw summary-level intensity data, allowing users to develop their own methods for genotype calling or copy number analysis if they wish.'),
(75014, 2009, 'bio', 'Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model', 'Zhang, Chen|Wu, Chunguo|Blanzieri, Enrico|Zhou, You|Wang, Yan|Du, Wei|Liang, Yanchun', 'College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.|College of Computer Science and Technology, Jilin University, 130012 China.', 'China|China|China|China|China|China|China', 22, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp478', '19661242', 'Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations.'),
(75015, 2009, 'bio', 'Efficient SCOP-fold classification and retrieval using index-based protein substructure alignments', 'Chi, Pin-Hao|Pang, Bin|Korkin, Dmitry|Shyu, Chi-Ren', 'Medical and Biological Digital Library Research Lab, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.|Medical and Biological Digital Library Research Lab, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.|Medical and Biological Digital Library Research Lab, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.|Medical and Biological Digital Library Research Lab, Informatics Institute, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|Computational Biology|Databases, Protein|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp474', '19667079', 'To investigate structure-function relationships, life sciences researchers usually retrieve and classify proteins with similar substructures into the same fold. A manually constructed database, SCOP, is believed to be highly accurate, however, it is labor intensive. Another known method, DALI, is also precise but computationally expensive. We have developed an efficient algorithm, namely, index-based protein substructure alignment (IPSA), for protein-fold classification. IPSA constructs a two-layer indexing tree to quickly retrieve similar substructures in proteins and suggests possible folds by aligning these substructures.'),
(75016, 2009, 'bio', 'Double error shrinkage method for identifying protein binding sites observed by tiling arrays with limited replication', 'Kim, Youngchul|Bekiranov, Stefan|Lee, Jae K|Park, Taesung', 'Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.|Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.|Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.|Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States|United States|United States', 1, 'Binding Sites|Computational Biology|DNA Replication|Gene Expression Profiling|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp471', '19667080', 'ChIP-chip has been widely used for various genome-wide biological investigations. Given the small number of replicates (typically two to three) per biological sample, methods of analysis that control the variance are desirable but in short supply. We propose a double error shrinkage (DES) method by using moving average statistics based on local-pooled error estimates which effectively control both heterogeneous error variances and correlation structures of an extremely large number of individual probes on tiling arrays.'),
(75017, 2009, 'bio', 'Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks', 'Liu, Mei|Chen, Xue-Wen|Jothi, Raja', 'Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.|Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.|Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.', 'United States|United States|United States', 18, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Domains and Motifs|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btp480', '19667081', 'Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks, (ii) precise identification of binding sites, (iii) acquisition of insights into the causes of deleterious mutations at interaction sites, and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain-domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs.'),
(75018, 2009, 'bio', 'PineSAP--sequence alignment and SNP identification pipeline', 'Wegrzyn, Jill L|Lee, Jennifer M|Liechty, John|Neale, David B', 'Department of Plant Sciences, University of California, Davis, CA 95616, USA. jlwegrzyn@ucdavis.edu|Department of Plant Sciences, University of California, Davis, CA 95616, USA. jlwegrzyn@ucdavis.edu|Department of Plant Sciences, University of California, Davis, CA 95616, USA. jlwegrzyn@ucdavis.edu|Department of Plant Sciences, University of California, Davis, CA 95616, USA. jlwegrzyn@ucdavis.edu', 'United States|United States|United States|United States', 24, 'Computational Biology|Databases, Genetic|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp477', '19667082', 'The Pine Alignment and SNP Identification Pipeline (PineSAP) provides a high-throughput solution to single nucleotide polymorphism (SNP) prediction using multiple sequence alignments from re-sequencing data. This pipeline integrates a hybrid of customized scripting, existing utilities and machine learning in order to increase the speed and accuracy of SNP calls. The implementation of this pipeline results in significantly improved multiple sequence alignments and SNP identifications when compared with existing solutions. The use of machine learning in the SNP identifications extends the pipelines application to any eukaryotic species where full genome sequence information is unavailable.'),
(75019, 2009, 'bio', 'T-REKS: identification of Tandem REpeats in sequences with a K-meanS based algorithm', 'Jorda, Julien|Kajava, Andrey V', 'Centre de Recherches de Biochimie Macromoléculaire UMR 5237, CNRS, University of Montpellier 1 and 2, Montpellier, France. julien.jorda@crbm.cnrs.fr|Centre de Recherches de Biochimie Macromoléculaire UMR 5237, CNRS, University of Montpellier 1 and 2, Montpellier, France. julien.jorda@crbm.cnrs.fr', 'France|France', 95, 'Algorithms|Amino Acid Sequence|Base Sequence|Computational Biology|Databases, Genetic|Databases, Protein|Molecular Sequence Data|Proteins|Repetitive Sequences, Amino Acid|Sequence Analysis, Protein|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btp482', '19671691', 'Over the last years a number of evidences have been accumulated about high incidence of tandem repeats in proteins carrying fundamental biological functions and being related to a number of human diseases. At the same time, frequently, protein repeats are strongly degenerated during evolution and, therefore, cannot be easily identified. To solve this problem, several computer programs which were based on different algorithms have been developed. Nevertheless, our tests showed that there is still room for improvement of methods for accurate and rapid detection of tandem repeats in proteins.'),
(75020, 2009, 'bio', 'Partition function and base pairing probabilities for RNA-RNA interaction prediction', 'Huang, Fenix W D|Qin, Jing|Reidys, Christian M|Stadler, Peter F', 'Center for Combinatorics, LPMC-TJKLC, Nankai University Tianjin 300071, P.R. China.|Center for Combinatorics, LPMC-TJKLC, Nankai University Tianjin 300071, P.R. China.|Center for Combinatorics, LPMC-TJKLC, Nankai University Tianjin 300071, P.R. China.|Center for Combinatorics, LPMC-TJKLC, Nankai University Tianjin 300071, P.R. China.', 'China|China|China|China', 62, 'Algorithms|Base Pairing|Computational Biology|Databases, Genetic|Nucleic Acid Conformation|RNA|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btp481', '19671692', 'The RNA-RNA interaction problem (RIP) consists in finding the energetically optimal structure of two RNA molecules that bind to each other. The standard model allows secondary structures in both partners as well as additional base pairs between the two RNAs subject to certain restrictions that ensure that RIP is solvabale by a polynomial time dynamic programming algorithm. RNA-RNA binding, like RNA folding, is typically not dominated by the ground state structure. Instead, a large ensemble of alternative structures contributes to the interaction thermodynamics.'),
(75021, 2009, 'bio', 'Testing the hypothesis of tissue selectivity: the intersection-union test and a Bayesian approach', 'Van Deun, K|Hoijtink, H|Thorrez, L|Van Lommel, L|Schuit, F|Van Mechelen, I', 'Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Bayes Theorem|Computational Biology|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btp439', '19671693', 'Finding genes that are preferentially expressed in a particular tissue or condition is a problem that cannot be solved by standard statistical testing procedures. A relatively unknown procedure that can be used is the intersection-union test (IUT). However, two disadvantages of the IUT are that it is conservative and it conveys only the information of the least differing target tissue-other tissue pair.'),
(75022, 2009, 'bio', 'Comment on MeSH-up: effective MeSH text classification for improved document retrieval', 'Névéol, Aurélie|Mork, James G|Aronson, Alan R', 'Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|Center for Computational Systems Biology SymBioSys, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be', 'Belgium|Belgium|Belgium', 2, 'Abstracting and Indexing as Topic|Computational Biology|Information Storage and Retrieval|Medical Subject Headings|Vocabulary, Controlled', 'Comment', '10.1093/bioinformatics/btp483', '19671694', '-'),
(75023, 2009, 'bio', 'PerM: efficient mapping of short sequencing reads with periodic full sensitive spaced seeds', 'Chen, Yangho|Souaiaia, Tade|Chen, Ting', 'Program in Computational Biology and Bioinformatics, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089-2910, USA.|Program in Computational Biology and Bioinformatics, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089-2910, USA.|Program in Computational Biology and Bioinformatics, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089-2910, USA.', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp486', '19675096', 'The explosion of next-generation sequencing data has spawned the design of new algorithms and software tools to provide efficient mapping for different read lengths and sequencing technologies. In particular, ABIs sequencer (SOLiD system) poses a big computational challenge with its capacity to produce very large amounts of data, and its unique strategy of encoding sequence data into color signals.'),
(75024, 2009, 'bio', 'PopABC: a program to infer historical demographic parameters', 'Lopes, Joao S|Balding, David|Beaumont, Mark A', 'School of Biological Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG66AJ, UK. joao.lopes@reading.ac.uk|School of Biological Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG66AJ, UK. joao.lopes@reading.ac.uk|School of Biological Sciences, University of Reading, Whiteknights, PO Box 228, Reading RG66AJ, UK. joao.lopes@reading.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Computer Simulation|Demography|Evolution, Molecular|Genetics, Population|Software', 'Journal Article', '10.1093/bioinformatics/btp487', '19679678', 'PopABC is a computer package for inferring the pattern of demographic divergence of closely related populations and species. The software performs coalescent simulation in the framework of approximate Bayesian computation (ABC). PopABC can also be used to perform Bayesian model choice to discriminate between different demographic scenarios. The program can be used either for research or for education and teaching purposes.'),
(75025, 2009, 'bio', 'YADA: a tool for taking the most out of high-resolution spectra', 'Carvalho, Paulo C|Xu, Tao|Han, Xuemei|Cociorva, Daniel|Barbosa, Valmir C|Yates, John R', 'Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Caixa Postal 68511, 21941-972 Rio de Janeiro-RJ, Brazil. paulo@pcarvalho.com', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 55, 'Algorithms|Computational Biology|Mass Spectrometry|Peptides|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp489', '19684088', 'YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data.'),
(75026, 2009, 'bio', 'SnoopCGH: software for visualizing comparative genomic hybridization data', 'Almagro-Garcia, Jacob|Manske, Magnus|Carret, Celine|Campino, Susana|Auburn, Sarah|Macinnis, Bronwyn L|Maslen, Gareth|Pain, Arnab|Newbold, Christopher I|Kwiatkowski, Dominic P|Clark, Taane G', 'Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk|Wellcome Trust Sanger Institute, Hinxton, The Weatherall Institute of Molecular Medicine and Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. jg10@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Comparative Genomic Hybridization|Computational Biology|Computer Graphics|Gene Expression Profiling|Genome|Genome, Bacterial|Oligonucleotide Array Sequence Analysis|Plasmodium falciparum|Software', 'Journal Article', '10.1093/bioinformatics/btp488', '19687029', 'Array-based comparative genomic hybridization (CGH) technology is used to discover and validate genomic structural variation, including copy number variants, insertions, deletions and other structural variants (SVs). The visualization and summarization of the array CGH data outputs, potentially across many samples, is an important process in the identification and analysis of SVs. We have developed a software tool for SV analysis using data from array CGH technologies, which is also amenable to short-read sequence data.'),
(75027, 2009, 'bio', 'Statistical methods for gene set co-expression analysis', 'Choi, YounJeong|Kendziorski, Christina', 'Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA.|Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA.', 'United States|United States', 88, 'Computational Biology|Diabetes Mellitus, Type 2|Gene Expression Profiling|Humans|Lung Neoplasms|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp502', '19689953', 'The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.'),
(75028, 2009, 'bio', 'Improving peptide identification with single-stage mass spectrum peaks', 'He, Zengyou|Yu, Weichuan', 'Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eezyhe@ust.hk|Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eezyhe@ust.hk', 'China|China', 0, 'Computational Biology|Databases, Protein|Mass Spectrometry|Peptides|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp501', '19689954', 'Database searching is the major peptide identification method in shotgun proteomics. It searches tandem mass spectrometry (MS/MS) spectra against a protein database to identify target peptides. The success of such a database searching method relies on a scoring algorithm that can evaluate the quality of peptide-spectrum matches (PSMs) accurately. However, current scoring algorithms frequently generate inaccurate assignments due to variations and noises in the MS/MS spectra. To address this issue, we like to improve peptide identification by using additional information from other data sources.'),
(75029, 2009, 'bio', 'info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling', 'Defrance, Matthieu|van Helden, Jacques', 'Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. defrance@bigre.ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. defrance@bigre.ulb.ac.be', 'Belgium|Belgium', 0, 'Algorithms|Computational Biology|Entropy|Genomics|Likelihood Functions|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp490', '19689955', 'Discovering cis-regulatory elements in genome sequence remains a challenging issue. Several methods rely on the optimization of some target scoring function. The information content (IC) or relative entropy of the motif has proven to be a good estimator of transcription factor DNA binding affinity. However, these information-based metrics are usually used as a posteriori statistics rather than during the motif search process itself.'),
(75030, 2009, 'bio', 'CEAS: cis-regulatory element annotation system', 'Shin, Hyunjin|Liu, Tao|Manrai, Arjun K|Liu, X Shirley', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 294, 'Computational Biology|Databases, Genetic|Genome|Regulatory Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp479', '19689956', 'We present a tool designed to characterize genome-wide protein-DNA interaction patterns from ChIP-chip and ChIP-Seq data. This stand-alone extension of our web application CEAS (cis-regulatory element annotation system) provides summary statistics on ChIP enrichment in important genomic regions such as individual chromosomes, promoters, gene bodies or exons, and infers the genes most likely to be regulated by the binding factor under study. CEAS also enables biologists to visualize the average ChIP enrichment signals over specific genomic regions, particularly allowing observation of continuous and broad ChIP enrichment that might be too subtle to detect from ChIP peaks alone.'),
(75031, 2009, 'bio', 'The effects of probe binding affinity differences on gene expression measurements and how to deal with them', 'Dannemann, Michael|Lorenc, Anna|Hellmann, Ines|Khaitovich, Philipp|Lachmann, Michael', 'Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.|Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1093/bioinformatics/btp492', '19689957', 'When comparing gene expression levels between species or strains using microarrays, sequence differences between the groups can cause false identification of expression differences. Our simulated dataset shows that a sequence divergence of only 1% between species can lead to falsely reported expression differences for >50% of the transcripts-similar levels of effect have been reported previously in comparisons of human and chimpanzee expression. We propose a method for identifying probes that cause such false readings, using only the microarray data, so that problematic probes can be excluded from analysis. We then test the power of the method to detect sequence differences and to correct for falsely reported expression differences. Our method can detect 70% of the probes with sequence differences using human and chimpanzee data, while removing only 18% of probes with no sequence differences. Although only 70% of the probes with sequence differences are detected, the effect of removing probes on falsely reported expression differences is more dramatic: the method can remove 98% of the falsely reported expression differences from a simulated dataset. We argue that the method should be used even when sequence data are available.'),
(75032, 2009, 'bio', 'Correlating multiple SNPs and multiple disease phenotypes: penalized non-linear canonical correlation analysis', 'Waaijenborg, Sandra|Zwinderman, Aeilko H', 'Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands. s.waaijenborg@amc.uva.nl|Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands. s.waaijenborg@amc.uva.nl', 'Netherlands|Netherlands', 0, 'Computational Biology|Disease|Humans|Multivariate Analysis|Neoplasms|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp491', '19689958', 'Canonical correlation analysis (CCA) can be used to capture the underlying genetic background of a complex disease, by associating two datasets containing information about a patients phenotypical and genetic details. Often the genetic information is measured on a qualitative scale, consequently ordinary CCA cannot be applied to such data. Moreover, the size of the data in genetic studies can be enormous, thereby making the results difficult to interpret.'),
(75033, 2009, 'bio', 'Mobyle: a new full web bioinformatics framework', 'Néron, Bertrand|Ménager, Hervé|Maufrais, Corinne|Joly, Nicolas|Maupetit, Julien|Letort, Sébastien|Carrere, Sébastien|Tuffery, Pierre|Letondal, Catherine', 'Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr|Groupe Logiciels et Banques de Données, Institut Pasteur, 28, rue du Dr Roux, 75724 Paris Cedex, France. mobyle-support@pasteur.fr', 'France|France|France|France|France|France|France|France|France', 231, 'Computational Biology|Databases, Factual|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp493', '19689959', 'For the biologist, running bioinformatics analyses involves a time-consuming management of data and tools. Users need support to organize their work, retrieve parameters and reproduce their analyses. They also need to be able to combine their analytic tools using a safe data flow software mechanism. Finally, given that scientific tools can be difficult to install, it is particularly helpful for biologists to be able to use these tools through a web user interface. However, providing a web interface for a set of tools raises the problem that a single web portal cannot offer all the existing and possible services: it is the user, again, who has to cope with data copy among a number of different services. A framework enabling portal administrators to build a network of cooperating services would therefore clearly be beneficial.'),
(75034, 2009, 'bio', 'WEbcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale Ecoli model', 'Jung, Tae-Sung|Yeo, Hock Chuan|Reddy, Satty G|Cho, Wan-Sup|Lee, Dong-Yup', 'Department of Information Industrial Engineering, CBITRC, Chungbuk National University, Cheongju Chungbuk 361-763, Korea.|Department of Information Industrial Engineering, CBITRC, Chungbuk National University, Cheongju Chungbuk 361-763, Korea.|Department of Information Industrial Engineering, CBITRC, Chungbuk National University, Cheongju Chungbuk 361-763, Korea.|Department of Information Industrial Engineering, CBITRC, Chungbuk National University, Cheongju Chungbuk 361-763, Korea.|Department of Information Industrial Engineering, CBITRC, Chungbuk National University, Cheongju Chungbuk 361-763, Korea.', '-|-|-|-|-', 0, 'Computational Biology|Databases, Genetic|Escherichia coli|Genome, Bacterial|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp496', '19689960', 'WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions.'),
(75035, 2009, 'bio', 'CoCoa: a software tool for estimating the coefficient of coancestry from multilocus genotype data', 'Maenhout, Steven|De Baets, Bernard|Haesaert, Geert', 'Department of Biosciences and Landscape Architecture, University College Ghent, Voskenslaan 270, Ghent University, Coupure links 653, B-9000 Gent, Belgium. steven.maenhout@hogent.be|Department of Biosciences and Landscape Architecture, University College Ghent, Voskenslaan 270, Ghent University, Coupure links 653, B-9000 Gent, Belgium. steven.maenhout@hogent.be|Department of Biosciences and Landscape Architecture, University College Ghent, Voskenslaan 270, Ghent University, Coupure links 653, B-9000 Gent, Belgium. steven.maenhout@hogent.be', 'Belgium|Belgium|Belgium', 8, 'Computational Biology|Genotype|Phenotype|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btp499', '19689961', 'Phenotypic data collected in breeding programs and marker-trait association studies are often analyzed by means of linear mixed models. In these models, the covariance between the genetic background effects of all genotypes under study is modeled by means of pairwise coefficients of coancestry. Several marker-based coancestry estimation procedures allow to estimate this covariance matrix, but generally introduce a certain amount of bias when the examined genotypes are part of a breeding program. CoCoa implements the most commonly used marker-based coancestry estimation procedures and as such, allows to select the best fitting covariance structure for the phenotypic data at hand. This better model fit translates into an increased power and improved type I error control in association studies and an improved accuracy in phenotypic prediction studies. The presented software package also provides an implementation of the new Weighted Alikeness in State (WAIS) estimator for use in hybrid breeding programs. Besides several matrix manipulation tools, CoCoa implements two different bending heuristics, in case the inverse of an ill-conditioned coancestry matrix estimate is needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75036, 2009, 'bio', 'Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach', 'Kashima, Hisashi|Yamanishi, Yoshihiro|Kato, Tsuyoshi|Sugiyama, Masashi|Tsuda, Koji', 'IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp|IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp|IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp|IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp|IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Animals|Biological Evolution|Caenorhabditis elegans|Computational Biology|Gene Regulatory Networks|Genome|Helicobacter pylori|Metabolic Networks and Pathways|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp494', '19689962', 'The existing supervised methods for biological network inference work on each of the networks individually based only on intra-species information such as gene expression data. We believe that it will be more effective to use genomic data and cross-species evolutionary information from different species simultaneously, rather than to use the genomic data alone.'),
(75037, 2009, 'bio', 'Quantifying cancer progression with conjunctive Bayesian networks', 'Gerstung, Moritz|Baudis, Michael|Moch, Holger|Beerenwinkel, Niko', 'Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch|Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland. moritz.gerstung@bsse.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 68, 'Algorithms|Bayes Theorem|Computational Biology|Humans|Neoplasms', 'Journal Article', '10.1093/bioinformatics/btp505', '19692554', 'Cancer is an evolutionary process characterized by accumulating mutations. However, the precise timing and the order of genetic alterations that drive tumor progression remain enigmatic.'),
(75038, 2009, 'bio', 'MISTRAL: a tool for energy-based multiple structural alignment of proteins', 'Micheletti, Cristian|Orland, Henri', 'SISSA, CNR-INFM Democritos and Italian Institute of Technology, Via Beirut 2-4, 34014 Trieste, Italy. michelet@sissa.it|SISSA, CNR-INFM Democritos and Italian Institute of Technology, Via Beirut 2-4, 34014 Trieste, Italy. michelet@sissa.it', 'Italy|Italy', 21, 'Algorithms|Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btp506', '19692555', 'The steady growth of the number of available protein structures has constantly motivated the development of new algorithms for detecting structural correspondences in proteins. Detecting structural equivalences in two or more proteins is computationally demanding as it typically entails the exploration of the combinatorial space of all possible amino acid pairings in the parent proteins. The search is often aided by the introduction of various constraints such as considering protein fragments, rather than single amino acids, and/or seeking only sequential correspondences in the given proteins. An additional challenge is represented by the difficulty of associating to a given alignment, a reliable a priori measure of its statistical significance.'),
(75039, 2009, 'bio', 'TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples', 'Bandyopadhyay, Sanghamitra|Mitra, Ramkrishna', 'Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in|Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in', 'India|India', 0, 'Animals|Artificial Intelligence|Computational Biology|Databases, Nucleic Acid|Humans|Internet|MicroRNAs|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp503', '19692556', 'Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training.'),
(75040, 2009, 'bio', 'Metabolite and reaction inference based on enzyme specificities', 'de Groot, M J L|van Berlo, R J P|van Winden, W A|Verheijen, P J T|Reinders, M J T|de Ridder, D', 'The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.|The Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 13, 'Computational Biology|Databases, Factual|Enzymes|Glycolysis|Metabolic Networks and Pathways|Pentose Phosphate Pathway|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btp507', '19696044', 'Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra.'),
(75041, 2009, 'bio', 'ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes', 'Kristiansson, Erik|Hugenholtz, Philip|Dalevi, Daniel', 'Department of Zoology, University of Gothenburg, Department of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Göteborg, Sweden. kristiansson@zool.gu.se|Department of Zoology, University of Gothenburg, Department of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Göteborg, Sweden. kristiansson@zool.gu.se|Department of Zoology, University of Gothenburg, Department of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Göteborg, Sweden. kristiansson@zool.gu.se', 'Sweden|Sweden|Sweden', 0, 'Animals|Computational Biology|Databases, Genetic|Genomics|Humans|Metagenome|Metagenomics|Software', 'Comparative Study', '10.1093/bioinformatics/btp508', '19696045', 'Microorganisms are ubiquitous in nature and constitute intrinsic parts of almost every ecosystem. A culture-independent and powerful way to study microbial communities is metagenomics. In such studies, functional analysis is performed on fragmented genetic material from multiple species in the community. The recent advances in high-throughput sequencing have greatly increased the amount of data in metagenomic projects. At present, there is an urgent need for efficient statistical tools to analyse these data. We have created ShotgunFunctionalizeR, an R-package for functional comparison of metagenomes. The package contains tools for importing, annotating and visualizing metagenomic data produced by shotgun high-throughput sequencing. ShotgunFunctionalizeR contains several statistical procedures for assessing functional differences between samples, both for individual genes and for entire pathways. In addition to standard and previously published methods, we have developed and implemented a novel approach based on a Poisson model. This procedure is highly flexible and thus applicable to a wide range of different experimental designs. We demonstrate the potential of ShotgunFunctionalizeR by performing a regression analysis on metagenomes sampled at multiple depths in the Pacific Ocean.'),
(75042, 2009, 'bio', 'PiSQRD: a web server for decomposing proteins into quasi-rigid dynamical domains', 'Aleksiev, T|Potestio, R|Pontiggia, F|Cozzini, S|Micheletti, C', 'Scuola Internazionale Superiore di Studi Avanzati and eLab, CNR-INFM Democritos Simulation Centre, via Beirut 2-4, 34151 Trieste and Italian Institute of Technology, Genoa, Italy.|Scuola Internazionale Superiore di Studi Avanzati and eLab, CNR-INFM Democritos Simulation Centre, via Beirut 2-4, 34151 Trieste and Italian Institute of Technology, Genoa, Italy.|Scuola Internazionale Superiore di Studi Avanzati and eLab, CNR-INFM Democritos Simulation Centre, via Beirut 2-4, 34151 Trieste and Italian Institute of Technology, Genoa, Italy.|Scuola Internazionale Superiore di Studi Avanzati and eLab, CNR-INFM Democritos Simulation Centre, via Beirut 2-4, 34151 Trieste and Italian Institute of Technology, Genoa, Italy.|Scuola Internazionale Superiore di Studi Avanzati and eLab, CNR-INFM Democritos Simulation Centre, via Beirut 2-4, 34151 Trieste and Italian Institute of Technology, Genoa, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Databases, Protein|Internet|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp512', '19696046', 'The PiSQRD web resource can be used to subdivide protein structures in quasi-rigid dynamical domains. The latter are groups of amino acids behaving as approximately rigid units in the course of protein equilibrium fluctuations. The PiSQRD server takes as input a biomolecular structure and the desired fraction of protein internal fluctuations that must be accounted for by the relative rigid-body motion of the dynamical domains. Next, the lowest energy modes of fluctuation of the protein (optionally provided by the user) are calculated and used to identify the rigid subunits. The resulting optimal subdivision is returned through a web page containing both interactive graphics and detailed data output.'),
(75043, 2009, 'bio', 'A boosting approach to structure learning of graphs with and without prior knowledge', 'Anjum, Shahzia|Doucet, Arnaud|Holmes, Chris C', 'Medical Research Council, Harwell, UK. s.anjum@har.mrc.ac.uk|Medical Research Council, Harwell, UK. s.anjum@har.mrc.ac.uk|Medical Research Council, Harwell, UK. s.anjum@har.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Models, Statistical|Pattern Recognition, Automated|Proteome', 'Journal Article', '10.1093/bioinformatics/btp485', '19696047', 'Identifying the network structure through which genes and their products interact can help to elucidate normal cell physiology as well as the genetic architecture of pathological phenotypes. Recently, a number of gene network inference tools have appeared based on Gaussian graphical model representations. Following this, we introduce a novel Boosting approach to learn the structure of a high-dimensional Gaussian graphical model motivated by the applications in genomics. A particular emphasis is paid to the inclusion of partial prior knowledge on the structure of the graph. With the increasing availability of pathway information and large-scale gene expression datasets, we believe that conditioning on prior knowledge will be an important aspect in raising the statistical power of structural learning algorithms to infer true conditional dependencies.'),
(75044, 2009, 'bio', 'GRIMP: a web- and grid-based tool for high-speed analysis of large-scale genome-wide association using imputed data', 'Estrada, Karol|Abuseiris, Anis|Grosveld, Frank G|Uitterlinden, André G|Knoch, Tobias A|Rivadeneira, Fernando', 'Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.|Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.|Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.|Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.|Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.|Department of Internal Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015GE Rotterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Databases, Genetic|Genome|Genome-Wide Association Study|Genomics|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp497', '19700477', 'The current fast growth of genome-wide association studies (GWAS) combined with now common computationally expensive imputation requires the online access of large user groups to high-performance computing resources capable of analyzing rapidly and efficiently millions of genetic markers for ten thousands of individuals. Here, we present a web-based interface--called GRIMP--to run publicly available genetic software for extremely large GWAS on scalable super-computing grid infrastructures. This is of major importance for the enlargement of GWAS with the availability of whole-genome sequence data from the 1000 Genomes Project and for future whole-population efforts.'),
(75045, 2009, 'bio', 'A method for visualizing CellML models', 'Wimalaratne, S M|Halstead, M D B|Lloyd, C M|Cooling, M T|Crampin, E J|Nielsen, P F', 'Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz|Auckland Bioengineering Institute and Department of Engineering Science, The University of Auckland, 70 Symonds St, Auckland, New Zealand. sarala.dissanayake@auckland.ac.nz', 'New Zealand|New Zealand|New Zealand|New Zealand|New Zealand|New Zealand', 12, 'Algorithms|Computational Biology|Databases, Factual|Models, Biological|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btp495', '19703920', 'The Physiome Project was established in 1997 to develop tools to facilitate international collaboration in the physiological sciences and the sharing of biological models and experimental data. The CellML language was developed to represent and exchange mathematical models of biological processes. CellML models can be very complicated, making it difficult to interpret the underlying physical and biological concepts and relationships captured/described in the mathematical model.'),
(75046, 2009, 'bio', 'Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics', 'Aijö, Tarmo|Lähdesmäki, Harri', 'Department of Signal Processing, Tampere University of Technology, Tampere, Finland.|Department of Signal Processing, Tampere University of Technology, Tampere, Finland.', 'Finland|Finland', 60, 'Algorithms|Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Kinetics', 'Journal Article', '10.1093/bioinformatics/btp511', '19706742', 'Regulation of gene expression is fundamental to the operation of a cell. Revealing the structure and dynamics of a gene regulatory network (GRN) is of great interest and represents a considerably challenging computational problem. The GRN estimation problem is complicated by the fact that the number of gene expression measurements is typically extremely small when compared with the dimension of the biological system. Further, because the gene regulation process is intrinsically complex, commonly used parametric models can provide too simple description of the underlying phenomena and, thus, can be unreliable. In this article, we propose a novel methodology for the inference of GRNs from time-series and steady-state gene expression measurements. The presented framework is based on the use of Bayesian analysis with ordinary differential equations (ODEs) and non-parametric Gaussian process modeling for the transcriptional-level regulation.'),
(75047, 2009, 'bio', 'digeR: a graphical user interface R package for analyzing 2D-DIGE data', 'Fan, Yue|Murphy, T Brendan|Watson, R William G', 'UCD School of Medicine and Medical Science, UCD Conway Institute of Biomolecular and Biomolecular Research and UCD School of Mathematical Sciences, University College Dublin, Dublin 4, Dublin, Ireland.|UCD School of Medicine and Medical Science, UCD Conway Institute of Biomolecular and Biomolecular Research and UCD School of Mathematical Sciences, University College Dublin, Dublin 4, Dublin, Ireland.|UCD School of Medicine and Medical Science, UCD Conway Institute of Biomolecular and Biomolecular Research and UCD School of Mathematical Sciences, University College Dublin, Dublin 4, Dublin, Ireland.', 'Ireland|Ireland|Ireland', 0, 'Computer Graphics|Electrophoresis, Gel, Two-Dimensional|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp514', '19706743', '2D Difference In-Gel Electrophoresis (2D-DIGE) or 2D gel technology is being used as a routine proteomics technique for biomarker discovery. Analyzing such high-dimensional data requires multivariate analysis techniques to be applied. In addition, protein post-translational modification (PTM) information from the 2D gel data is usually overlooked. We report on an R package, digeR, with an easy to use graphical user interface for analyzing 2D-DIGE (2D gel) data. It provides a tool for visually looking for potential PTM changes from different biological states and support biomarker discovery through multivariate analysis techniques.'),
(75048, 2009, 'bio', 'A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation', 'Dong, Qiwen|Zhou, Shuigeng|Guan, Jihong', 'Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University and Department of Computer Science and Technology, Tongji University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University and Department of Computer Science and Technology, Tongji University, Shanghai, China.|Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University and Department of Computer Science and Technology, Tongji University, Shanghai, China.', 'China|China|China', 0, 'Algorithms|Computational Biology|Databases, Protein|Pattern Recognition, Automated|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btp500', '19706744', 'Fold recognition is an important step in protein structure and function prediction. Traditional sequence comparison methods fail to identify reliable homologies with low sequence identity, while the taxonomic methods are effective alternatives, but their prediction accuracies are around 70%, which are still relatively low for practical usage.'),
(75049, 2009, 'bio', 'integrOmics: an R package to unravel relationships between two omics datasets', 'Lê Cao, Kim-Anh|González, Ignacio|Déjean, Sébastien', 'Institute for Molecular Biosciences and ARC Centre of Excellence in Bioinformatics,The University of Queensland, Brisbane QLD 4072, Australia. k.lecao@uq.edu.au|Institute for Molecular Biosciences and ARC Centre of Excellence in Bioinformatics,The University of Queensland, Brisbane QLD 4072, Australia. k.lecao@uq.edu.au|Institute for Molecular Biosciences and ARC Centre of Excellence in Bioinformatics,The University of Queensland, Brisbane QLD 4072, Australia. k.lecao@uq.edu.au', 'Australia|Australia|Australia', 237, 'Computational Biology|Databases, Factual|Genomics|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp515', '19706745', 'With the availability of many omics data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated.'),
(75050, 2009, 'bio', 'Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus', 'Xia, Zhen|Jin, Gulei|Zhu, Jun|Zhou, Ruhong', 'Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.', 'China|China|China|China', 0, 'Evolution, Molecular|Genetic Variation|Genome, Viral|Humans|Influenza A Virus, H3N2 Subtype|Influenza, Human', 'Journal Article', '10.1093/bioinformatics/btp423', '19706746', 'Mapping the antigenic and genetic evolution pathways of influenza A is of critical importance in the vaccine development and drug design of influenza virus. In this article, we have analyzed more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations.'),
(75051, 2009, 'bio', 'Corrigendum for Elliott,B et al, PathCase pathways database system, Bioinformatics, Nov 2008, 24(21), pp 2526-2533', 'Elliott, Brendan|Kirac, Mustafa|Cakmak, Ali|Yavas, Gokhan|Mayes, Stephen|Cheng, En|Wang, Yuan|Gupta, Chirag|Ozsoyoglu, Gultekin|Ozsoyoglu, Zehra Meral', 'Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.|Institute of Bioinformatics, Zhejiang University, Hangzhou, PR China.', 'China|China|China|China|China|China|China|China|China|China', 0, 'Computational Biology|Databases, Factual|Metabolic Networks and Pathways|Software', 'Comment', '10.1093/bioinformatics/btp520', '19713414', '-'),
(75052, 2009, 'bio', 'ERNEST: a toolbox for chemical reaction network theory', 'Soranzo, Nicola|Altafini, Claudio', 'SISSA, 34151 Trieste, Italy.|SISSA, 34151 Trieste, Italy.', 'Italy|Italy', 14, 'Computational Biology|Models, Chemical|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btp513', '19713415', 'ERNEST Reaction Network Equilibria Study Toolbox is a MATLAB package which, by checking various different criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results obtained are independent of the rate constants of the reactions, and can be used for model discrimination.'),
(75053, 2009, 'bio', 'A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis', 'Keller, Andreas|Backes, Christina|Gerasch, Andreas|Kaufmann, Michael|Kohlbacher, Oliver|Meese, Eckart|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btp510', '19713416', 'Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks.'),
(75054, 2009, 'bio', 'UniMaP: finding unique mass and peptide signatures in the human proteome', 'Alexandridou, Anastasia|Tsangaris, George Th|Vougas, Konstantinos|Nikita, Konstantina|Spyrou, George', 'Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.|Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.|Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.|Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.|Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece.', 'Greece|Greece|Greece|Greece|Greece', 12, 'Computational Biology|Humans|Internet|Peptides|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp516', '19713417', 'The uniqueness of a measured molecular mass or peptide sequence plays a very important role in the fields of protein identification and peptide/protein-biomarker investigation. We present a publicly available web application that offers information concerning the uniqueness of one or more molecular masses and one or more peptide sequences in the human proteome. When a sequence is found to be unique in humans, the application is able to search across all species querying whether this sequence is unique, not only in humans but also in other species found in the Swiss-Prot Database. The application is also able to search for unique protein fragments derived computationally from enzymatic digestion driven by certain enzymes. Furthermore, the application can list all the unique masses and peptides of a given protein. Through this application, researchers are able to find unique tags, either on a molecular mass level or on a sequence level. These unique tags are remarkably important in research related to protein identification or biomarker discovery and measurements.'),
(75055, 2009, 'bio', 'Next generation software for functional trend analysis', 'Berriz, Gabriel F|Beaver, John E|Cenik, Can|Tasan, Murat|Roth, Frederick P', 'Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue and Center for Cancer Systems Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.|Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue and Center for Cancer Systems Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.|Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue and Center for Cancer Systems Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.|Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue and Center for Cancer Systems Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.|Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue and Center for Cancer Systems Biology, Dana Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Factual|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp498', '19717575', 'FuncAssociate is a web application that discovers properties enriched in lists of genes or proteins that emerge from large-scale experimentation. Here we describe an updated application with a new interface and several new features. For example, enrichment analysis can now be performed within multiple gene- and protein-naming systems. This feature avoids potentially serious translation artifacts to which other enrichment analysis strategies are subject.'),
(75056, 2009, 'bio', 'ANCHOR: web server for predicting protein binding regions in disordered proteins', 'Dosztányi, Zsuzsanna|Mészáros, Bálint|Simon, István', 'Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu', 'Hungary|Hungary|Hungary', 385, 'Amino Acid Sequence|Binding Sites|Computational Biology|Databases, Protein|Internet|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp518', '19717576', 'ANCHOR is a web-based implementation of an original method that takes a single amino acid sequence as an input and predicts protein binding regions that are disordered in isolation but can undergo disorder-to-order transition upon binding. The server incorporates the result of a general disorder prediction method, IUPred and can carry out simple motif searches as well.'),
(75057, 2009, 'bio', 'Comparative analysis and unification of domain-domain interaction networks', 'Björkholm, Patrik|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Center, Albanova, Stockholm University, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Center, Albanova, Stockholm University, 10691 Stockholm, Sweden.', 'Sweden|Sweden', 21, 'Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins', 'Comparative Study', '10.1093/bioinformatics/btp522', '19720675', 'Certain protein domains are known to preferentially interact with other domains. Several approaches have been proposed to predict domain-domain interactions, and over nine datasets are available. Our aim is to analyse the coverage and quality of the existing resources, as well as the extent of their overlap. With this knowledge, we have the opportunity to merge individual domain interaction networks to construct a comprehensive and reliable database.'),
(75058, 2009, 'bio', 'Identification of genes involved in the same pathways using a Hidden Markov Model-based approach', 'Senf, Alexander|Chen, Xue-Wen', 'Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.|Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA.', 'United States|United States', 0, 'Algorithms|Animals|Computational Biology|Drosophila|Gene Expression Profiling|Markov Chains|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp521', '19720676', 'The sequencing of whole genomes from various species has provided us with a wealth of genetic information. To make use of the vast amounts of data available today it is necessary to devise computer-based analysis techniques.'),
(75059, 2009, 'bio', 'Retrieve-ensembl-seq: user-friendly and large-scale retrieval of single or multi-genome sequences from Ensembl', 'Sand, Olivier|Thomas-Chollier, Morgane|van Helden, Jacques', 'Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. oly@bigre.ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. oly@bigre.ulb.ac.be|Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. oly@bigre.ulb.ac.be', 'Belgium|Belgium|Belgium', 10, 'Computational Biology|Database Management Systems|Databases, Factual|Genome|Genomics|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp519', '19720677', 'The preparation of an appropriate sequence dataset is the starting point of all genomic analyses. We present retrieve-ensembl-seq, an application that considerably eases the retrieval of sequences from the Ensembl database, via our user-friendly web site or web services. The user provides Ensembl identifiers or gene names, and the program returns corresponding upstream, downstream, intronic, exonic, UTR or whole gene sequences. retrieve-ensembl-seq also offers a multiple organism mode to retrieve sequences from homologous genes at any taxonomical level. And we introduce various original filters such as the masking of coding fragments and the avoidance of sequence redundancy for genes with multiple transcripts. retrieve-ensembl-seq is included in the software suite regulatory sequence analysis tools (RSAT), allowing instant submission of retrieved sequences to further analysis tools.'),
(75060, 2009, 'bio', 'CellClassifier: supervised learning of cellular phenotypes', 'Rämö, Pauli|Sacher, Raphael|Snijder, Berend|Begemann, Boris|Pelkmans, Lucas', 'Institute of Molecular Systems Biology, ETH Zürich, HPT E71, Wolfgang Pauli-Str. 16, 8093 Zürich, Switzerland.|Institute of Molecular Systems Biology, ETH Zürich, HPT E71, Wolfgang Pauli-Str. 16, 8093 Zürich, Switzerland.|Institute of Molecular Systems Biology, ETH Zürich, HPT E71, Wolfgang Pauli-Str. 16, 8093 Zürich, Switzerland.|Institute of Molecular Systems Biology, ETH Zürich, HPT E71, Wolfgang Pauli-Str. 16, 8093 Zürich, Switzerland.|Institute of Molecular Systems Biology, ETH Zürich, HPT E71, Wolfgang Pauli-Str. 16, 8093 Zürich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 64, 'Computational Biology|Internet|Phenotype|Proteome|Software', 'Journal Article', '10.1093/bioinformatics/btp524', '19729371', 'CellClassifier is a tool for classifying single-cell phenotypes in microscope images. It includes several unique and user-friendly features for classification using multiclass support vector machines'),
(75061, 2009, 'bio', 'Analyzing biological network parameters with CentiScaPe', 'Scardoni, Giovanni|Petterlini, Michele|Laudanna, Carlo', 'Center for Biomedical Computing, University of Verona, Strada le Grazie, 15 -37134 Verona, Italy. giovanni.scardoni@gmail.com|Center for Biomedical Computing, University of Verona, Strada le Grazie, 15 -37134 Verona, Italy. giovanni.scardoni@gmail.com|Center for Biomedical Computing, University of Verona, Strada le Grazie, 15 -37134 Verona, Italy. giovanni.scardoni@gmail.com', 'Italy|Italy|Italy', 0, 'Computational Biology|Databases, Factual|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btp517', '19729372', 'The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization.'),
(75062, 2009, 'bio', 'Saint: a lightweight integration environment for model annotation', 'Lister, Allyson L|Pocock, Matthew|Taschuk, Morgan|Wipat, Anil', 'Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University and School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK. a.l.lister@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University and School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK. a.l.lister@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University and School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK. a.l.lister@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University and School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK. a.l.lister@ncl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Computational Biology|Databases, Factual|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp523', '19734151', 'Saint is a web application which provides a lightweight annotation integration environment for quantitative biological models. The system enables modellers to rapidly mark up models with biological information derived from a range of data sources.'),
(75063, 2009, 'bio', 'Algorithms for optimal protein structure alignment', 'Poleksic, Aleksandar', 'Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614, USA. poleksic@cs.uni.edu', 'United States', 22, 'Algorithms|Computational Biology|Databases, Protein|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp530', '19734152', 'Structural alignment is an important tool for understanding the evolutionary relationships between proteins. However, finding the best pairwise structural alignment is difficult, due to the infinite number of possible superpositions of two structures. Unlike the sequence alignment problem, which has a polynomial time solution, the structural alignment problem has not been even classified as solvable.'),
(75064, 2009, 'bio', 'Bi-correlation clustering algorithm for determining a set of co-regulated genes', 'Bhattacharya, Anindya|De, Rajat K', 'Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata 700152, India.|Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata 700152, India.', 'India|India', 0, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Gene Expression|Gene Expression Profiling|Humans', 'Journal Article', '10.1093/bioinformatics/btp526', '19734153', 'Biclustering has been emerged as a powerful tool for identification of a group of co-expressed genes under a subset of experimental conditions (measurements) present in a gene expression dataset. Several biclustering algorithms have been proposed till date. In this article, we address some of the important shortcomings of these existing biclustering algorithms and propose a new correlation-based biclustering algorithm called bi-correlation clustering algorithm (BCCA).'),
(75065, 2009, 'bio', 'Automated inference of molecular mechanisms of disease from amino acid substitutions', 'Li, Biao|Krishnan, Vidhya G|Mort, Matthew E|Xin, Fuxiao|Kamati, Kishore K|Cooper, David N|Mooney, Sean D|Radivojac, Predrag', 'School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.', 'India|India|India|India|India|India|India|India', 490, 'Amino Acid Sequence|Amino Acid Substitution|Artificial Intelligence|Computational Biology|Databases, Protein|Humans|Molecular Sequence Data|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp528', '19734154', 'Advances in high-throughput genotyping and next generation sequencing have generated a vast amount of human genetic variation data. Single nucleotide substitutions within protein coding regions are of particular importance owing to their potential to give rise to amino acid substitutions that affect protein structure and function which may ultimately lead to a disease state. Over the last decade, a number of computational methods have been developed to predict whether such amino acid substitutions result in an altered phenotype. Although these methods are useful in practice, and accurate for their intended purpose, they are not well suited for providing probabilistic estimates of the underlying disease mechanism.'),
(75066, 2009, 'bio', 'Identifying related journals through log analysis', 'Lu, Zhiyong|Xie, Natalie|Wilbur, W John', 'National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20852, USA. luzh@ncbi.nlm.nih.gov|National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20852, USA. luzh@ncbi.nlm.nih.gov|National Center for Biotechnology Information (NCBI), 8600 Rockville Pike, Bethesda, MD 20852, USA. luzh@ncbi.nlm.nih.gov', 'United States|United States|United States', 9, 'Information Storage and Retrieval|Internet|Periodicals as Topic|PubMed|Publications', 'Journal Article', '10.1093/bioinformatics/btp529', '19734155', 'With the explosion of biomedical literature and the evolution of online and open access, scientists are reading more articles from a wider variety of journals. Thus, the list of core journals relevant to their research may be less obvious and may often change over time. To help researchers quickly identify appropriate journals to read and publish in, we developed a web application for finding related journals based on the analysis of PubMed log data.'),
(75067, 2009, 'bio', 'Updates to the RMAP short-read mapping software', 'Smith, Andrew D|Chung, Wen-Yu|Hodges, Emily|Kendall, Jude|Hannon, Greg|Hicks, James|Xuan, Zhenyu|Zhang, Michael Q', 'Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu|Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. andrewds@usc.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp533', '19736251', 'We report on a major new version of the RMAP software for mapping reads from short-read sequencing technology. General improvements to accuracy and space requirements are included, along with novel functionality. Included in the RMAP software package are tools for mapping paired-end reads, mapping using more sophisticated use of quality scores, collecting ambiguous mapping locations and mapping bisulfite-treated reads.'),
(75068, 2009, 'bio', 'Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data', 'Kayano, Mitsunori|Takigawa, Ichigaku|Shiga, Motoki|Tsuda, Koji|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan. kayano@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan. kayano@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan. kayano@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan. kayano@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan. kayano@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 17, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Genome|Genomics|Genotype|Logistic Models|Software', 'Journal Article', '10.1093/bioinformatics/btp531', '19736252', 'We address the issue of finding a three-way gene interaction, i.e. two interacting genes in expression under the genotypes of another gene, given a dataset in which expressions and genotypes are measured at once for each individual. This issue can be a general, switching mechanism in expression of two genes, being controlled by categories of another gene, and finding this type of interaction can be a key to elucidating complex biological systems. The most suitable method for this issue is likelihood ratio test using logistic regressions, which we call interaction test, but a serious problem of this test is computational intractability at a genome-wide level.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75069, 2009, 'bio', 'Predicting homologous signaling pathways using machine learning', 'Bostan, Babak|Greiner, Russell|Szafron, Duane|Lu, Paul', 'Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada. bioinfo@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada. bioinfo@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada. bioinfo@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, AB T6G2E8, Canada. bioinfo@cs.ualberta.ca', 'Canada|Canada|Canada|Canada', 7, 'Artificial Intelligence|Computational Biology|Databases, Protein|Information Storage and Retrieval|Proteins|Proteomics|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btp532', '19736253', 'In general, each cell signaling pathway involves many proteins, each with one or more specific roles. As they are essential components of cell activity, it is important to understand how these proteins work-and in particular, to determine which of the species proteins participate in each role. Experimentally determining this mapping of proteins to roles is difficult and time consuming. Fortunately, many pathways are similar across species, so we may be able to use known pathway information of one species to understand the corresponding pathway of another.'),
(75070, 2009, 'bio', 'TagDust--a program to eliminate artifacts from next generation sequencing data', 'Lassmann, Timo|Hayashizaki, Yoshihide|Daub, Carsten O', 'Omics Science Center, Riken Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. timolassmann@gmail.com|Omics Science Center, Riken Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. timolassmann@gmail.com|Omics Science Center, Riken Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan. timolassmann@gmail.com', 'Japan|Japan|Japan', 143, 'Algorithms|Computational Biology|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp527', '19737799', 'Next-generation parallel sequencing technologies produce large quantities of short sequence reads. Due to experimental procedures various types of artifacts are commonly sequenced alongside the targeted RNA or DNA sequences. Identification of such artifacts is important during the development of novel sequencing assays and for the downstream analysis of the sequenced libraries.'),
(75071, 2009, 'bio', 'massXpert 2: a cross-platform software environment for polymer chemistry modelling and simulation/analysis of mass spectrometric data', 'Rusconi, Filippo', 'Laboratoire de Biophysique, Muséum National dHistoire Naturelle, CNRS UMR7196-INSERM U565-MNHN USM0503, Paris, France. rusconi@mnhn.fr', 'France', 12, 'Algorithms|Biopolymers|Computer Graphics|Computer Simulation|Mass Spectrometry|Pattern Recognition, Automated|Polymers|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp504', '19740912', 'Since the middle of the 90s, mass spectrometry has evolved into an almost indispensable tool in structural studies on an ever-growing variety of (bio-)polymers, of which proteins, sugars and nucleic acids are the most prominent. Since the first public release of massXpert, the advances of mass spectrometry have motivated continuous and thorough maintenance of that software, in the form of two full software rewrites, culminating with massXpert 2, which we describe in this report. We shall describe the profound changes in massXpert that were performed so as to keep up with the technical advances in mass spectrometry since a decade.'),
(75072, 2009, 'bio', 'QuickGO: a web-based tool for Gene Ontology searching', 'Binns, David|Dimmer, Emily|Huntley, Rachael|Barrell, Daniel|ODonovan, Claire|Apweiler, Rolf', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. dbinns@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 394, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp536', '19744993', 'QuickGO is a web-based tool that allows easy browsing of the Gene Ontology (GO) and all associated electronic and manual GO annotations provided by the GO Consortium annotation groups QuickGO has been a popular GO browser for many years, but after a recent redevelopment it is now able to offer a greater range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation.'),
(75073, 2009, 'bio', 'Phenotypic categorization of genetic skin diseases reveals new relations between phenotypes, genes and pathways', 'Sadreyev, Ruslan I|Feramisco, Jamison D|Tsao, Hensin|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA.', 'United States|United States|United States|United States', 5, 'Computational Biology|Gene Expression Profiling|Genetic Predisposition to Disease|Genome, Human|Humans|Phenotype|Signal Transduction|Skin Diseases, Genetic|Telangiectasis|Transforming Growth Factor beta', 'Journal Article', '10.1093/bioinformatics/btp538', '19744994', 'Systematic analysis of connection between proteins, their cellular function and phenotypic manifestations in disease is a central problem of biological and clinical research. The solution to this problem requires the development of new approaches to link the rapidly growing dataset of gene-disease associations with the many complex and overlapping phenotypes of human disease.'),
(75074, 2009, 'bio', 'CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information', 'Bartoli, Lisa|Fariselli, Piero|Krogh, Anders|Casadio, Rita', 'Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.|Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 31, 'Computational Biology|Databases, Protein|Protein Conformation|Protein Interaction Mapping|Proteins|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btp539', '19744995', 'The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods.'),
(75075, 2009, 'bio', 'De novo computational prediction of non-coding RNA genes in prokaryotic genomes', 'Tran, Thao T|Zhou, Fengfeng|Marshburn, Sarah|Stead, Mark|Kushner, Sidney R|Xu, Ying', 'School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 36, 'Algorithms|Computational Biology|Databases, Genetic|Escherichia coli|Genome, Bacterial|Genomics|Operon|RNA, Bacterial|RNA, Untranslated', 'Journal Article', '10.1093/bioinformatics/btp537', '19744996', 'The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.'),
(75076, 2009, 'bio', '3D-SURFER: software for high-throughput protein surface comparison and analysis', 'La, David|Esquivel-Rodríguez, Juan|Venkatraman, Vishwesh|Li, Bin|Sael, Lee|Ueng, Stephen|Ahrendt, Steven|Kihara, Daisuke', 'Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 44, 'Binding Sites|Computational Biology|Databases, Protein|Protein Conformation|Proteins|Software|Surface Properties', 'Comparative Study', '10.1093/bioinformatics/btp542', '19759195', 'We present 3D-SURFER, a web-based tool designed to facilitate high-throughput comparison and characterization of proteins based on their surface shape. As each protein is effectively represented by a vector of 3D Zernike descriptors, comparison times for a query protein against the entire PDB take, on an average, only a couple of seconds. The web interface has been designed to be as interactive as possible with displays showing animated protein rotations, CATH codes and structural alignments using the CE program. In addition, geometrically interesting local features of the protein surface, such as pockets that often correspond to ligand binding sites as well as protrusions and flat regions can also be identified and visualized.'),
(75077, 2009, 'bio', 'A dictionary to identify small molecules and drugs in free text', 'Hettne, Kristina M|Stierum, Rob H|Schuemie, Martijn J|Hendriksen, Peter J M|Schijvenaars, Bob J A|Mulligen, Erik M van|Kleinjans, Jos|Kors, Jan A', 'Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl|Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands. k.hettne@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 88, 'Abstracting and Indexing as Topic|Computational Biology|Dictionaries as Topic|Dictionaries, Chemical as Topic|Information Storage and Retrieval|Natural Language Processing|Pharmaceutical Preparations|Software|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/btp535', '19759196', 'From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers.'),
(75078, 2009, 'bio', 'Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis', 'Shen, Ronglai|Olshen, Adam B|Ladanyi, Marc', 'Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. shenr@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. shenr@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. shenr@mskcc.org', 'United States|United States|United States', 296, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computational Biology|DNA Copy Number Variations|Databases, Genetic|Female|Gene Expression Profiling|Humans|Lung Neoplasms|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btp543', '19759197', 'The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment.'),
(75079, 2009, 'bio', 'Genome analysis with inter-nucleotide distances', 'Afreixo, Vera|Bastos, Carlos A C|Pinho, Armando J|Garcia, Sara P|Ferreira, Paulo J S G', 'Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. vera@ua.pt|Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. vera@ua.pt|Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. vera@ua.pt|Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. vera@ua.pt|Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal. vera@ua.pt', 'Portugal|Portugal|Portugal|Portugal|Portugal', 59, 'Algorithms|Base Sequence|DNA|Genome|Genomics|Nucleotides|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp546', '19759198', 'DNA sequences can be represented by sequences of four symbols, but it is often useful to convert the symbols into real or complex numbers for further analysis. Several mapping schemes have been used in the past, but they seem unrelated to any intrinsic characteristic of DNA. The objective of this work was to find a mapping scheme directly related to DNA characteristics and that would be useful in discriminating between different species. Mathematical models to explore DNA correlation structures may contribute to a better knowledge of the DNA and to find a concise DNA description.'),
(75080, 2009, 'bio', 'Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets', 'Anderson, Paul E|Raymer, Michael L|Kelly, Benjamin J|Reo, Nicholas V|DelRaso, Nicholas J|Doom, T E', 'Department of Computer Science and Engineering, Dayton, OH 45435, USA.|Department of Computer Science and Engineering, Dayton, OH 45435, USA.|Department of Computer Science and Engineering, Dayton, OH 45435, USA.|Department of Computer Science and Engineering, Dayton, OH 45435, USA.|Department of Computer Science and Engineering, Dayton, OH 45435, USA.|Department of Computer Science and Engineering, Dayton, OH 45435, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Algorithms|Computational Biology|Databases, Protein|Metabolomics|Nuclear Magnetic Resonance, Biomolecular|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp540', '19759199', 'Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori, therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis.'),
(75081, 2009, 'bio', 'Identifiability of isoform deconvolution from junction arrays and RNA-Seq', 'Hiller, David|Jiang, Hui|Xu, Weihong|Wong, Wing Hung', 'Department of Statistics, Stanford University, Stanford, CA 94305, USA.|Department of Statistics, Stanford University, Stanford, CA 94305, USA.|Department of Statistics, Stanford University, Stanford, CA 94305, USA.|Department of Statistics, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States', 43, 'Alternative Splicing|Animals|Base Sequence|Computational Biology|Gene Expression Profiling|Humans|Mice|Oligonucleotide Array Sequence Analysis|Protein Isoforms|RNA', 'Journal Article', '10.1093/bioinformatics/btp544', '19762346', 'Splice junction microarrays and RNA-seq are two popular ways of quantifying splice variants within a cell. Unfortunately, isoform expressions cannot always be determined from the expressions of individual exons and splice junctions. While this issue has been noted before, the extent of the problem on various platforms has not yet been explored, nor have potential remedies been presented.'),
(75082, 2009, 'bio', 'COMPASS: a program for generating serial samples under an infinite sites model', 'Jakobsson, Mattias', 'Department of Evolutionary Biology, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden. mattias.jakobsson@ebc.uu.se', 'Sweden', 8, 'Computational Biology|Databases, Genetic|Polymorphism, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btp534', '19762347', 'The program COMPASS can generate samples that have been collected at various points in time from a population that is evolving according to a Wright-Fisher model. The samples are generated using coalescence simulations permitting various demographic scenarios and the program uses an infinite sites model to generate polymorphism data for the samples. By generating serially sampled population-genetic data, COMPASS allows investigating properties of polymorphism data that has been collected at different time points, and aid in making inference from ancient polymorphism data.'),
(75083, 2009, 'bio', 'PoreLogo: a new tool to analyse, visualize and compare channels in transmembrane proteins', 'Oliva, Romina|Thornton, Janet M|Pellegrini-Calace, Marialuisa', 'Department of Applied Sciences, University of Parthenope of Naples, Centro Direzionale Isola C4 80143, Naples, Italy. romina.oliva@uniparthenope.it|Department of Applied Sciences, University of Parthenope of Naples, Centro Direzionale Isola C4 80143, Naples, Italy. romina.oliva@uniparthenope.it|Department of Applied Sciences, University of Parthenope of Naples, Centro Direzionale Isola C4 80143, Naples, Italy. romina.oliva@uniparthenope.it', 'Italy|Italy|Italy', 9, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Membrane Proteins|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btp545', '19762348', 'The increasing number of available atomic 3D structures of transmembrane channel proteins represents a valuable resource for better understanding their structure-function relationships and to eventually predict their selectivity. Herein, we present PoreLogo, an automatic tool for analysing, visualizing and comparing the amino acid composition of transmembrane channels and its conservation across the corresponding protein family.'),
(75084, 2009, 'bio', 'Understanding hydrogen-bond patterns in proteins using network motifs', 'Rahat, Ofer|Alon, Uri|Levy, Yaakov|Schreiber, Gideon', 'Department of Biological Chemistry, Department of Molecular Cell Biology and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Biological Chemistry, Department of Molecular Cell Biology and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Biological Chemistry, Department of Molecular Cell Biology and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.|Department of Biological Chemistry, Department of Molecular Cell Biology and Department of Structural Biology, Weizmann Institute of Science, Rehovot 76100, Israel.', 'Israel|Israel|Israel|Israel', 13, 'Amino Acid Motifs|Computational Biology|Crystallography, X-Ray|Hydrogen Bonding|Magnetic Resonance Spectroscopy|Muramidase|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Repressor Proteins|Viral Regulatory and Accessory Proteins|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btp541', '19767299', 'Protein structures can be viewed as networks of contacts (edges) between amino-acid residues (nodes). Here we dissect proteins into sub-graphs consisting of six nodes and their corresponding edges, with an edge being either a backbone hydrogen bond (H-bond) or a covalent interaction. Six thousand three hundred and twenty-two such sub-graphs were found in a large non-redundant dataset of high-resolution structures, from which 35 occur much more frequently than in a random model. Many of these significant sub-graphs (also called network motifs) correspond to sub-structures of alpha helices and beta-sheets, as expected. However, others correspond to more exotic sub-structures such as 3(10) helix, Schellman motif and motifs that were not defined previously. This topological characterization of patterns is very useful for producing a detailed differences map to compare protein structures. Here we analyzed in details the differences between NMR, molecular dynamics (MD) simulations and X-ray structures for Lysozyme, SH3 and the lambda repressor. In these cases, the same structures solved by NMR and simulated by MD showed small but consistent differences in their motif composition from the crystal structures, despite a very small root mean square deviation (RMSD) between them. This may be due to differences in the pair-wise energy functions used and the dynamic nature of these proteins.'),
(75085, 2009, 'bio', 'Client-side integration of life science literature resources', 'Easty, Richard|Nikolov, Nikolay', 'Computing Department, InfoLab21, Lancaster University, South Drive, Lancaster LA1 4WA, UK.|Computing Department, InfoLab21, Lancaster University, South Drive, Lancaster LA1 4WA, UK.', 'United Kingdom|United Kingdom', 2, 'Biological Science Disciplines|Computational Biology|Databases, Factual|Information Storage and Retrieval|Medicine in Literature|PubMed|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp550', '19767300', 'The online resources in the life sciences are characterized by a great fragmentation and one of the pressing issues of bioinformatics is making the integration of these resources a smoother and more flexible process than it is currently. Here we present i-cite, a browser extension, which implements a client-side model of integration which improves the navigation within the rapidly increasing life science literature and links terms from it to corresponding non-textual data.'),
(75086, 2009, 'bio', 'Reproducing the manual annotation of multiple sequence alignments using a SVM classifier', 'Blouin, Christian|Perry, Scott|Lavell, Allan|Susko, Edward|Roger, Andrew J', 'Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca|Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca|Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca|Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca|Department of Biochemistry and Molecular Biology, Dalhousie University, Sir Charles Tupper Medical Building, Halifax NS B3H 1X5, Canada. cblouin@cs.dal.ca', 'Canada|Canada|Canada|Canada|Canada', 8, 'Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp552', '19770262', 'Aligning protein sequences with the best possible accuracy requires sophisticated algorithms. Since the optimal alignment is not guaranteed to be the correct one, it is expected that even the best alignment will contain sites that do not respect the assumption of positional homology. Because formulating rules to identify these sites is difficult, it is common practice to manually remove them. Although considered necessary in some cases, manual editing is time consuming and not reproducible. We present here an automated editing method based on the classification of valid and invalid sites.'),
(75087, 2009, 'bio', 'How and when should interactome-derived clusters be used to predict functional modules and protein function?', 'Song, Jimin|Singh, Mona', 'Department of Computer Science & Lewis-Sigler Institute for Integrative Genomics Princeton University, Princeton, NJ 08544, USA.|Department of Computer Science & Lewis-Sigler Institute for Integrative Genomics Princeton University, Princeton, NJ 08544, USA.', 'United States|United States', 93, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Proteome|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp551', '19770263', 'Clustering of protein-protein interaction networks is one of the most common approaches for predicting functional modules, protein complexes and protein functions. But, how well does clustering perform at these tasks?'),
(75088, 2009, 'bio', 'MOODS: fast search for position weight matrix matches in DNA sequences', 'Korhonen, Janne|Martinmäki, Petri|Pizzi, Cinzia|Rastas, Pasi|Ukkonen, Esko', 'Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. janne.h.korhonen@helsinki.fi|Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. janne.h.korhonen@helsinki.fi|Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. janne.h.korhonen@helsinki.fi|Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. janne.h.korhonen@helsinki.fi|Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland. janne.h.korhonen@helsinki.fi', 'Finland|Finland|Finland|Finland|Finland', 74, 'Algorithms|Base Sequence|Computational Biology|Position-Specific Scoring Matrices|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp554', '19773334', 'MOODS (MOtif Occurrence Detection Suite) is a software package for matching position weight matrices against DNA sequences. MOODS implements state-of-the-art online matching algorithms, achieving considerably faster scanning speed than with a simple brute-force search. MOODS is written in C++, with bindings for the popular BioPerl and Biopython toolkits. It can easily be adapted for different purposes and integrated into existing workflows. It can also be used as a C++ library.'),
(75089, 2009, 'bio', 'HHsvm: fast and accurate classification of profile-profile matches identified by HHsearch', 'Dlakixc4x87, Mensur', 'Department of Microbiology, Montana State University, Bozeman, MT 59717-3520, USA. mdlakic@montana.edu', 'United States', 8, 'Computational Biology|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp555', '19773335', 'Recently developed profile-profile methods rival structural comparisons in their ability to detect homology between distantly related proteins. Despite this tremendous progress, many genuine relationships between protein families cannot be recognized as comparisons of their profiles result in scores that are statistically insignificant.'),
(75090, 2009, 'bio', 'Bayesian detection of non-sinusoidal periodic patterns in circadian expression data', 'Chudova, Darya|Ihler, Alexander|Lin, Kevin K|Andersen, Bogi|Smyth, Padhraic', 'Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com', 'United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Cell Cycle|Cell Cycle Proteins|Circadian Rhythm|Computational Biology|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btp547', '19773336', 'Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns.'),
(75091, 2009, 'bio', 'In response to Can sugars be produced from fatty acids? A test case for pathway analysis tools', 'Faust, Karoline|Croes, Didier|van Helden, Jacques', 'Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com|Department of Computer Science, University of California, Irvine, CA 92697, USA. dchudova@gmail.com', 'United States|United States|United States', 9, 'Acetyl Coenzyme A|Carbohydrate Metabolism|Carbohydrates|Computational Biology|Fatty Acids|Glucose-6-Phosphate|Humans|Metabolic Networks and Pathways', 'Comment', '10.1093/bioinformatics/btp557', '19776213', 'In their article entitled Can sugars be produced from fatty acids? A test case for pathway analysis tools de Figueiredo and co-authors assess the performance of three pathway prediction tools (METATOOL, PathFinding and Pathway Hunter Tool) using the synthesis of glucose-6-phosphate (G6P) from acetyl-CoA in humans as a test case. We think that this article is biased for three reasons: (i) the metabolic networks used as input for the respective tools were of very different sizes, (ii) the assessment is restricted to two study cases, (iii) developers are inherently more skilled to use their own tools than those developed by other people. We extended the analyses led by de Figueiredo and clearly show that the apparent superior performance of their tool (METATOOL) is partly due to the differences in input network sizes. We also see a conceptual problem in the comparison of tools that serve different purposes. In our opinion, metabolic path finding and elementary mode analysis are answering different biological questions, and should be considered as complementary rather than competitive approaches.'),
(75092, 2009, 'bio', 'SimCT: a generic tool to visualize ontology-based relationships for biological objects', 'Herrmann, Carl|Bérard, Sèverine|Tichit, Laurent', 'TAGC-U928 Inserm, Faculté des Sciences, Université de la Méditerranée, Campus de Luminy Case 928, Marseille, France. carl.herrmann@univmed.fr|TAGC-U928 Inserm, Faculté des Sciences, Université de la Méditerranée, Campus de Luminy Case 928, Marseille, France. carl.herrmann@univmed.fr|TAGC-U928 Inserm, Faculté des Sciences, Université de la Méditerranée, Campus de Luminy Case 928, Marseille, France. carl.herrmann@univmed.fr', 'France|France|France', 10, 'Computational Biology|Computer Graphics|Database Management Systems|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp553', '19776214', 'We present a web-based service, SimCT, which allows to graphically display the relationships between biological objects (e.g. genes or proteins) based on their annotations to a biomedical ontology. The result is presented as a tree of these objects, which can be viewed and explored through a specific java applet designed to highlight relevant features. Unlike the numerous tools that search for overrepresented terms, SimCT draws a simplified representation of biological terms present in the set of objects, and can be applied to any ontology for which annotation data is available. Being web-based, it does not require prior installation, and provides an intuitive, easy-to-use service.'),
(75093, 2009, 'bio', 'A report on the 2009 SIG on short read sequencing and algorithms (Short-SIG)', 'Brudno, Michael|Medvedev, Paul|Stoye, Jens|De La Vega, Francisco M', 'Department of Computer Science, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp525', '19783529', '-'),
(75094, 2009, 'bio', 'Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification', 'Nam, Hojung|Chung, Bong Chul|Kim, Younghoon|Lee, KiYoung|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.', '-|-|-|-|-', 0, 'Biomarkers, Tumor|Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Homovanillic Acid|Humans|Hydroxyindoleacetic Acid|Metabolomics|Phenylacetates|Proteomics|ROC Curve|Urea', 'Journal Article', '10.1093/bioinformatics/btp558', '19783829', 'For the early detection of cancer, highly sensitive and specific biomarkers are needed. Particularly, biomarkers in bio-fluids are relatively more useful because those can be used for non-biopsy tests. Although the altered metabolic activities of cancer cells have been observed in many studies, little is known about metabolic biomarkers for cancer screening. In this study, a systematic method is proposed for identifying metabolic biomarkers in urine samples by selecting candidate biomarkers from altered genome-wide gene expression signatures of cancer cells. Biomarkers identified by the present study have increased coherence and robustness because the significances of biomarkers are validated in both gene expression profiles and metabolic profiles.'),
(75095, 2009, 'bio', 'Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion', 'Agarwal, Shashank|Yu, Hong', 'University of Wisconsin, Milwaukee, Milwaukee WI 53211, USA.|University of Wisconsin, Milwaukee, Milwaukee WI 53211, USA.', 'United States|United States', 44, 'Abstracting and Indexing as Topic|Artificial Intelligence|Computational Biology|Databases, Factual|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btp548', '19783830', 'Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.'),
(75096, 2009, 'bio', 'Automatic assignment of reaction operators to enzymatic reactions', 'Leber, Markus|Egelhofer, Volker|Schomburg, Ida|Schomburg, Dietmar', 'Institute for Biochemistry, University of Cologne, Zülpicher Strasse 47, Germany.|Institute for Biochemistry, University of Cologne, Zülpicher Strasse 47, Germany.|Institute for Biochemistry, University of Cologne, Zülpicher Strasse 47, Germany.|Institute for Biochemistry, University of Cologne, Zülpicher Strasse 47, Germany.', 'Germany|Germany|Germany|Germany', 15, 'Biocatalysis|Computational Biology|Databases, Protein|Enzymes|Kinetics|Models, Theoretical|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btp549', '19783831', 'Enzymes are classified in a numerical classification scheme introduced by the Nomenclature Committee of the IUBMB based on the overall reaction chemistry. Due to the manifold of enzymatic reactions the system has become highly complex. Assignment of enzymes to the enzyme classes requires a detailed knowledge of the system and manual analysis. Frequently rearrangements and deletions of enzymes and sub-subclasses are necessary.'),
(75097, 2009, 'bio', 'Integration of heterogeneous expression data sets extends the role of the retinol pathway in diabetes and insulin resistance', 'Park, Peter J|Kong, Sek Won|Tebaldi, Toma|Lai, Weil R|Kasif, Simon|Kohane, Isaac S', 'Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Center of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Diabetes Mellitus, Type 2|Gene Expression Profiling|Humans|Insulin Resistance|Vitamin A', 'Journal Article', '10.1093/bioinformatics/btp559', '19786482', 'Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance.'),
(75098, 2009, 'bio', 'LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction', 'Kauffman, Chris|Karypis, George', 'Department of Computer Science, University of Minnesota, 117 Pleasant St SE, Room 464, Minneapolis, MN 55455, USA. kauffman@cs.umn.edu|Department of Computer Science, University of Minnesota, 117 Pleasant St SE, Room 464, Minneapolis, MN 55455, USA. kauffman@cs.umn.edu', 'United States|United States', 0, 'Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Ligands|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp561', '19786483', 'Identifying residues that interact with ligands is useful as a first step to understanding protein function and as an aid to designing small molecules that target the protein for interaction. Several studies have shown that sequence features are very informative for this type of prediction, while structure features have also been useful when structure is available. We develop a sequence-based method, called LIBRUS, that combines homology-based transfer and direct prediction using machine learning and compare it to previous sequence-based work and current structure-based methods.'),
(75099, 2009, 'bio', 'Detection of new protein domains using co-occurrence: application to Plasmodium falciparum', 'Terrapon, Nicolas|Gascuel, Olivier|Maréchal, Eric|Bréehélin, Laurent', 'Méthodes et algorithmes pour la Bioinformatique, LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France.|Méthodes et algorithmes pour la Bioinformatique, LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France.|Méthodes et algorithmes pour la Bioinformatique, LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France.|Méthodes et algorithmes pour la Bioinformatique, LIRMM, Université Montpellier 2, CNRS, 161 rue Ada, 34392 Montpellier Cedex 5, France.', 'France|France|France|France', 0, 'Computational Biology|Databases, Protein|Genome, Protozoan|Markov Chains|Plasmodium falciparum|Protein Structure, Tertiary|Protozoan Proteins', 'Journal Article', '10.1093/bioinformatics/btp560', '19786484', 'Hidden Markov models (HMMs) have proved to be a powerful tool for protein domain identification in newly sequenced organisms. However, numerous domains may be missed in highly divergent proteins. This is the case for Plasmodium falciparum proteins, the main causal agent of human malaria.'),
(75100, 2009, 'bio', 'VDNA: the virtual DNA plug-in for VMD', 'Bishop, Thomas C', 'Center for Computational Science, Tulane University, Lindy Boggs Center Suite 500, New Orleans, LA 70118, USA. bishop@tulane.edu', 'United States', 6, 'Binding Sites|Computational Biology|DNA|Models, Molecular|Nucleic Acid Conformation|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btp566', '19789266', 'The DNA inter base pair step parameters (Tilt, Roll, Twist, Shift, Slide, Rise) are a standard internal coordinate representation of DNA. In the absence of bend and shear, it is relatively easy to mentally visualize how Twist and Rise generate the familiar double helix. More complex structures do not readily yield to such intuition. For this reason, we developed a plug-in for VMD that accepts a set of mathematical expressions as input and generates a coarse-grained model of DNA as output. This feature of VDNA appears to provide a unique approach to DNA modeling. Predefined expressions include: linear, sheared, bent and circular DNA, and models of the nucleosome superhelix, chromatin, thermal motion and nucleosome unwrapping.'),
(75101, 2009, 'bio', 'Lost in translation: an assessment and perspective for computational microRNA target identification', 'Alexiou, Panagiotis|Maragkakis, Manolis|Papadopoulos, Giorgos L|Reczko, Martin|Hatzigeorgiou, Artemis G', 'Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 166 72 Varkiza, Greece. p.alexiou@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 166 72 Varkiza, Greece. p.alexiou@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 166 72 Varkiza, Greece. p.alexiou@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 166 72 Varkiza, Greece. p.alexiou@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, 166 72 Varkiza, Greece. p.alexiou@fleming.gr', 'Greece|Greece|Greece|Greece|Greece', 0, 'Animals|Computational Biology|Humans|MicroRNAs|Proteins', 'Journal Article', '10.1093/bioinformatics/btp565', '19789267', 'MicroRNAs (miRNAs) are a class of short endogenously expressed RNA molecules that regulate gene expression by binding directly to the messenger RNA of protein coding genes. They have been found to confer a novel layer of genetic regulation in a wide range of biological processes. Computational miRNA target prediction remains one of the key means used to decipher the role of miRNAs in development and disease. Here we introduce the basic idea behind the experimental identification of miRNA targets and present some of the most widely used computational miRNA target identification programs. The review includes an assessment of the prediction quality of these programs and their combinations.'),
(75102, 2009, 'bio', 'EasyMIFS and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures', 'Ghersi, Dario|Sanchez, Roberto', 'Department of Structural and Chemical Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Structural and Chemical Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.', 'United States|United States', 0, 'Binding Sites|Computational Biology|Databases, Protein|Ligands|Models, Molecular|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp562', '19789268', 'SiteHound uses Molecular Interaction Fields (MIFs) produced by EasyMIFs to identify protein structure regions that show a high propensity for interaction with ligands. The type of binding site identified depends on the probe atom used in the MIF calculation. The input to EasyMIFs is a PDB file of a protein structure, the output MIF serves as input to SiteHound, which in turn produces a list of putative binding sites. Extensive testing of SiteHound for the detection of binding sites for drug-like molecules and phosphorylated ligands has been carried out.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75103, 2009, 'bio', 'The interwinding nature of protein-protein interfaces and its implication for protein complex formation', 'Yura, Kei|Hayward, Steven', 'Computational Biology, Graduate School of Humanities and Sciences, Center for Informational Biology, Ochanomizu University, 2-1-1 Otsuka, Bunkyo, Tokyo 112-8610, Japan. yura.kei@ocha.ac.jp|Computational Biology, Graduate School of Humanities and Sciences, Center for Informational Biology, Ochanomizu University, 2-1-1 Otsuka, Bunkyo, Tokyo 112-8610, Japan. yura.kei@ocha.ac.jp', 'Japan|Japan', 13, 'Binding Sites|Computational Biology|Databases, Protein|Multiprotein Complexes|Protein Conformation|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp563', '19789269', 'Structural features at protein-protein interfaces can be studied to understand protein-protein interactions. It was noticed that in a dataset of 45 multimeric proteins the interface could either be described as flat against flat or protruding/interwound. In the latter, residues within one chain were surrounded by those in other chains, whereas in the former they were not.'),
(75104, 2009, 'bio', 'Computing the shortest elementary flux modes in genome-scale metabolic networks', 'de Figueiredo, Luis F|Podhorski, Adam|Rubio, Angel|Kaleta, Christoph|Beasley, John E|Schuster, Stefan|Planes, Francisco J', 'Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, 07743 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Computer Simulation|Corynebacterium glutamicum|Escherichia coli|Genome, Bacterial|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btp564', '19793869', 'Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity.'),
(75105, 2009, 'bio', 'Processing and population genetic analysis of multigenic datasets with ProSeq3 software', 'Filatov, Dmitry A', 'Department of Plant Sciences, University of Oxford, South Parks Rd, Oxford OX1 3RB, UK. dmitry.filatov@plants.ox.ac.uk', 'United Kingdom', 57, 'Computational Biology|Genetics, Population|Humans|Polymorphism, Genetic|Population Groups|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp572', '19797407', 'The current tendency in molecular population genetics is to use increasing numbers of genes in the analysis. Here I describe a program for handling and population genetic analysis of DNA polymorphism data collected from multiple genes. The program includes a sequence/alignment editor and an internal relational database that simplify the preparation and manipulation of multigenic DNA polymorphism datasets. The most commonly used DNA polymorphism analyses are implemented in ProSeq3, facilitating population genetic analysis of large multigenic datasets. Extensive input/output options make ProSeq3 a convenient hub for sequence data processing and analysis.'),
(75106, 2009, 'bio', 'W-ChIPMotifs: a web application tool for de novo motif discovery from ChIP-based high-throughput data', 'Jin, Victor X|Apostolos, Jeff|Nagisetty, Naga Satya Venkateswara Ra|Farnham, Peggy J', 'Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu', 'United States|United States|United States|United States', 39, 'Amino Acid Motifs|Chromatin Immunoprecipitation|Computational Biology|Databases, Genetic|Internet|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp570', '19797408', 'W-ChIPMotifs is a web application tool that provides a user friendly interface for de novo motif discovery. The web tool is based on our previous ChIPMotifs program which is a de novo motif finding tool developed for ChIP-based high-throughput data and incorporated various ab initio motif discovery tools such as MEME, MaMF, Weeder and optimized the significance of the detected motifs by using a bootstrap resampling statistic method and a Fisher test. Use of a randomized statistical model like bootstrap resampling can significantly increase the accuracy of the detected motifs. In our web tool, we have modified the program in two aspects: (i) we have refined the P-value with a Bonferroni correction, (ii) we have incorporated the STAMP tool to infer phylogenetic information and to determine the detected motifs if they are novel and known using the TRANSFAC and JASPAR databases. A comprehensive result file is mailed to users.'),
(75107, 2009, 'bio', 'Functionally guided alignment of protein interaction networks for module detection', 'Ali, Waqar|Deane, Charlotte M', 'Department of Statistics, University of Oxford, OX1 3TG, UK. ali@stats.ox.ac.uk|Department of Statistics, University of Oxford, OX1 3TG, UK. ali@stats.ox.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp569', '19797409', 'Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.'),
(75108, 2009, 'bio', 'Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments', 'Albaum, Stefan P|Neuweger, Heiko|Fränzel, Benjamin|Lange, Sita|Mertens, Dominik|Trötschel, Christian|Wolters, Dirk|Kalinowski, Jörn|Nattkemper, Tim W|Goesmann, Alexander', 'Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. stefan.albaum@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computational Biology|Databases, Protein|Internet|Mass Spectrometry|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btp568', '19808875', 'The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles.'),
(75109, 2009, 'bio', 'CentroidAlign: fast and accurate aligner for structured RNAs by maximizing expected sum-of-pairs score', 'Hamada, Michiaki|Sato, Kengo|Kiryu, Hisanori|Mituyama, Toutai|Asai, Kiyoshi', 'Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan. hamada-michiaki@aist.go.jp|Mizuho Information & Research Institute, Inc, 2-3 Kanda-Nishikicho, Chiyoda-ku, Tokyo 101-8443, Japan. hamada-michiaki@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Base Sequence|Computational Biology|Databases, Genetic|RNA|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp580', '19808876', 'The importance of accurate and fast predictions of multiple alignments for RNA sequences has increased due to recent findings about functional non-coding RNAs. Recent studies suggest that maximizing the expected accuracy of predictions will be useful for many problems in bioinformatics.'),
(75110, 2009, 'bio', 'Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data', 'Degner, Jacob F|Marioni, John C|Pai, Athma A|Pickrell, Joseph K|Nkadori, Everlyne|Gilad, Yoav|Pritchard, Jonathan K', 'Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu|Department of Human Genetics, University of Chicago, 920 E. 58th St., CLSC 507, Chicago, IL 60637, USA. jdegner@uchicago.edu', 'United States|United States|United States|United States|United States|United States|United States', 293, 'Alleles|Base Sequence|Computational Biology|Gene Expression Profiling|Genome, Human|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp579', '19808877', 'Next-generation sequencing has become an important tool for genome-wide quantification of DNA and RNA. However, a major technical hurdle lies in the need to map short sequence reads back to their correct locations in a reference genome. Here, we investigate the impact of SNP variation on the reliability of read-mapping in the context of detecting allele-specific expression (ASE).'),
(75111, 2009, 'bio', 'Quantitative measurement of aging using image texture entropy', 'Shamir, Lior|Wolkow, Catherine A|Goldberg, Ilya G', 'Laboratory of Genetics, National Institute on Aging/NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. shamirl@mail.nih.gov|Laboratory of Genetics, National Institute on Aging/NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. shamirl@mail.nih.gov|Laboratory of Genetics, National Institute on Aging/NIH, 251 Bayview Boulevard, Baltimore, MD 21224, USA. shamirl@mail.nih.gov', 'United States|United States|United States', 50, 'Aging|Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Computational Biology|Databases, Factual|Entropy|Genes, Helminth|Longevity', 'Journal Article', '10.1093/bioinformatics/btp571', '19808878', 'A key element in understanding the aging of Caenorhabditis elegans is objective quantification of the morphological differences between younger and older animals. Here we propose to use the image texture entropy as an objective measurement that reflects the structural deterioration of the C. elegans muscle tissues during aging.'),
(75112, 2009, 'bio', 'Exon Array Analyzer: a web interface for Affymetrix exon array analysis', 'Gellert, Pascal|Uchida, Shizuka|Braun, Thomas', 'Max-Planck-Institute for Heart and Lung Research, Parkstrasse 1, 61231 Bad Nauheim, Germany.|Max-Planck-Institute for Heart and Lung Research, Parkstrasse 1, 61231 Bad Nauheim, Germany.|Max-Planck-Institute for Heart and Lung Research, Parkstrasse 1, 61231 Bad Nauheim, Germany.', 'Germany|Germany|Germany', 0, 'Computational Biology|Databases, Genetic|Exons|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btp577', '19808879', 'The Exon Array Analyzer (EAA) is a web server, which provides a user-friendly interface to identify alternative splicing events analyzed with Affymetrix Exon Arrays. The EAA implements the Splice Index algorithm to identify differential expressed exons. The use of various filters allows reduction of the number of false positive hits. Results are presented with detailed annotation information and graphics to identify splice events and to facilitate biological validations. To demonstrate the versatility of the EAA, we analyzed exon arrays of 11 different murine tissues using sample data provided by Affymetrix (http://www.affymetrix.com). Data from the heart were compared with other tissues to identify exons that undergo heart-specific alternatively splicing, resulting in the identification of 885 differentially expressed probe sets in 649 genes.'),
(75113, 2009, 'bio', 'HTqPCR: high-throughput analysis and visualization of quantitative real-time PCR data in R', 'Dvinge, Heidi|Bertone, Paul', 'EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom', 146, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btp578', '19808880', 'Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility.'),
(75114, 2009, 'bio', 'Pathway identification by network pruning in the metabolic network of Escherichia coli', 'Gerlee, P|Lizana, L|Sneppen, K', 'Niels Bohr Institute, Blegdamsvej 17, 2100, Copenhagen, Denmark. gerlee@nbi.dk|Niels Bohr Institute, Blegdamsvej 17, 2100, Copenhagen, Denmark. gerlee@nbi.dk|Niels Bohr Institute, Blegdamsvej 17, 2100, Copenhagen, Denmark. gerlee@nbi.dk', 'Denmark|Denmark|Denmark', 26, 'Computational Biology|Computer Simulation|Databases, Factual|Escherichia coli|Escherichia coli Proteins|Metabolic Networks and Pathways|Proteome', 'Journal Article', '10.1093/bioinformatics/btp575', '19808881', 'All metabolic networks contain metabolites, such as ATP and NAD, known as currency metabolites, which take part in many reactions. These are often removed in the study of these networks, but no consensus exists on what actually constitutes a currency metabolite, and it is also unclear how these highly connected nodes contribute to the global structure of the network.'),
(75115, 2009, 'bio', 'Adaptive multi-agent architecture for functional sequence motifs recognition', 'Zeng, Jia|Alhajj, Reda|Demetrick, Douglas', 'Department of Computer Science, University of Calgary, Calgary, AB, Canada. jzeng@ucalgary.ca|Department of Computer Science, University of Calgary, Calgary, AB, Canada. jzeng@ucalgary.ca|Department of Computer Science, University of Calgary, Calgary, AB, Canada. jzeng@ucalgary.ca', 'Canada|Canada|Canada', 2, 'Algorithms|Base Sequence|Computational Biology|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp567', '19808882', 'Accurate genome annotation or protein function prediction requires precise recognition of functional sequence motifs. Many computational motif prediction models have been proposed. Due to the complexity of the biological data, it may be desirable to apply an integrated approach that uses multiple models for analysis.'),
(75116, 2009, 'bio', 'ncRNAppi--a tool for identifying disease-related miRNA and siRNA targeting pathways', 'Ng, Ka-Lok|Liu, Hsueh-Chuan|Lee, Shan-Chih', 'Department of Bioinformatics, Asia University, 500 Lioufeng Road, Wufeng Shiang, Taichung, Taiwan 41354.|Department of Bioinformatics, Asia University, 500 Lioufeng Road, Wufeng Shiang, Taichung, Taiwan 41354.|Department of Bioinformatics, Asia University, 500 Lioufeng Road, Wufeng Shiang, Taichung, Taiwan 41354.', '-|-|-', 0, 'Computational Biology|Databases, Genetic|Genomics|Humans|Internet|MicroRNAs|Protein Interaction Mapping|RNA, Messenger|RNA, Small Interfering|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp574', '19808883', 'Currently, there are a number of databases which store microRNA (miRNA) information, and tools available which provide miRNA target prediction. In this article, we describe a novel web-based tool that integrate the miRNA-targeted mRNA data, protein-protein interactions (PPI) records, tissues, biochemical pathways, human disease and gene function information to establish a disease-related miRNA target pathway database. This database is unique in the sense that it links miRNA target genes with their PPI partners according to being tissue- and diseases-specific or both. The same approach is also applied to siRNA data. This database provides two types of searches: (i) tissue- and (ii) disease-specific miRNA (or siRNA) targeting pathways. The search allows one to identify tissue- or disease-specific miRNA (or siRNA) target genes PPI partners two levels beyond.'),
(75117, 2009, 'bio', 'Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models', 'Jonikas, Magdalena A|Radmer, Randall J|Altman, Russ B', 'Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.|Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.|Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States', 39, 'Binding Sites|Computational Biology|Databases, Genetic|Knowledge Bases|Models, Molecular|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1093/bioinformatics/btp576', '19812110', 'The recent development of methods for modeling RNA 3D structures using coarse-grain approaches creates a need to bridge low- and high-resolution modeling methods. Although they contain topological information, coarse-grain models lack atomic detail, which limits their utility for some applications.'),
(75118, 2009, 'bio', 'Inferring relative proportions of DNA variants from sequencing electropherograms', 'Carr, I M|Robinson, J I|Dimitriou, R|Markham, A F|Morgan, A W|Bonthron, D T', 'Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk|Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk|Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk|Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk|Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk|Leeds Institute of Molecular Medicine, Wellcome Trust Brenner Building, University of Leeds, St Jamess University Hospital, Beckett Street, Leeds LS9 7TF, UK. msjimc@leeds.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Base Sequence|Computational Biology|DNA|DNA Copy Number Variations|Genotype|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp583', '19819885', 'Determination of the relative copy number of single-nucleotide sequence variants (SNVs) within a DNA sample is a frequent experimental goal. Various methods can be applied to this problem, although hybridization-based approaches tend to suffer from high-setup cost and poor adaptability, while others (such as pyrosequencing) may not be accessible to all laboratories. The potential to extract relative copy number information from standard dye-terminator electropherograms has been little explored, yet this technology is cheap and widely accessible. Since several biologically important loci have paralogous copies that interfere with genotyping, and which may also display copy number variation (CNV), there are many situations in which determination of the relative copy number of SNVs is desirable.'),
(75119, 2009, 'bio', 'Predicting citation count of Bioinformatics papers within four years of publication', 'Ibáñez, Alfonso|Larrañaga, Pedro|Bielza, Concha', 'Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660 Madrid, Spain. aibanez@fi.upm.es|Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660 Madrid, Spain. aibanez@fi.upm.es|Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660 Madrid, Spain. aibanez@fi.upm.es', 'Spain|Spain|Spain', 0, 'Computational Biology|Periodicals as Topic|Publishing', 'Journal Article', '10.1093/bioinformatics/btp585', '19819886', 'Nowadays, publishers of scientific journals face the tough task of selecting high-quality articles that will attract as many readers as possible from a pool of articles. This is due to the growth of scientific output and literature. The possibility of a journal having a tool capable of predicting the citation count of an article within the first few years after publication would pave the way for new assessment systems.'),
(75120, 2009, 'bio', 'A Bayesian approach to the alignment of mass spectra', 'Kong, Xiaoxiao|Reilly, Cavan', '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', 10, 'Bayes Theorem|Computational Biology|Mass Spectrometry|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btp582', '19819887', 'The need to align spectra to correct for mass-to-charge experimental variation is a problem that arises in mass spectrometry (MS). Most of the MS-based proteomic data analysis methods involve a two-step approach, identify peaks first and then do the alignment and statistical inference on these identified peaks only. However, the peak identification step relies on prior information on the proteins of interest or a peak detection model, which are subject to error. Also numerous additional features such as peak shape and peak width are lost in simple peak detection, and these are informative for correcting mass variation in the alignment step.'),
(75121, 2009, 'bio', 'Efficient estimation of pairwise distances between genomes', 'Domazet-Loso, Mirjana|Haubold, Bernhard', 'Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Germany.|Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, 24306 Plön, Germany.', 'Germany|Germany', 34, 'Algorithms|Animals|Base Sequence|Databases, Genetic|Genome|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp590', '19825795', 'Genome comparison is central to contemporary genomics and typically relies on sequence alignment. However, genome-wide alignments are difficult to compute. We have, therefore, recently developed an accurate alignment-free estimator of the number of substitutions per site based on the lengths of exact matches between pairs of sequences. The previous implementation of this measure requires n(n-1) suffix tree constructions and traversals, where n is the number of sequences analyzed. This does not scale well for large n.'),
(75122, 2009, 'bio', 'Detailing regulatory networks through large scale data integration', 'Huttenhower, Curtis|Mutungu, K Tsheko|Indik, Natasha|Yang, Woongcheol|Schroeder, Mark|Forman, Joshua J|Troyanskaya, Olga G|Coller, Hilary A', 'Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 54, 'Algorithms|Bayes Theorem|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp588', '19825796', 'Much of a cells regulatory response to changing environments occurs at the transcriptional level. Particularly in higher organisms, transcription factors (TFs), microRNAs and epigenetic modifications can combine to form a complex regulatory network. Part of this system can be modeled as a collection of regulatory modules: co-regulated genes, the conditions under which they are co-regulated and sequence-level regulatory motifs.'),
(75123, 2009, 'bio', 'Response to comment on Can sugars be produced from fatty acids? A test case for pathway analysis tools', 'de Figueiredo, Luis F|Schuster, Stefan|Kaleta, Christoph|Fell, David A', 'Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.', 'United States|United States|United States|United States', 7, 'Carbohydrates|Computational Biology|Fatty Acids|Gluconeogenesis|Glycolysis|Metabolic Networks and Pathways', 'Comment', '10.1093/bioinformatics/btp591', '19825797', '-'),
(75124, 2009, 'bio', 'A novel method for mining highly imbalanced high-throughput screening data in PubChem', 'Li, Qingliang|Wang, Yanli|Bryant, Stephen H', 'National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 47, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Pattern Recognition, Automated|Small Molecule Libraries|United States', 'Journal Article', '10.1093/bioinformatics/btp589', '19825798', 'The comprehensive information of small molecules and their biological activities in PubChem brings great opportunities for academic researchers. However, mining high-throughput screening (HTS) assay data remains a great challenge given the very large data volume and the highly imbalanced nature with only small number of active compounds compared to inactive compounds. Therefore, there is currently a need for better strategies to work with HTS assay data. Moreover, as luciferase-based HTS technology is frequently exploited in the assays deposited in PubChem, constructing a computational model to distinguish and filter out potential interference compounds for these assays is another motivation.'),
(75125, 2009, 'bio', 'Significant speedup of database searches with HMMs by search space reduction with PSSM family models', 'Beckstette, Michael|Homann, Robert|Giegerich, Robert|Kurtz, Stefan', 'Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. beckstette@zbh.uni-hamburg.de|Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. beckstette@zbh.uni-hamburg.de|Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. beckstette@zbh.uni-hamburg.de|Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. beckstette@zbh.uni-hamburg.de', 'Germany|Germany|Germany|Germany', 7, 'Algorithms|Computational Biology|Databases, Protein|Markov Chains|Pattern Recognition, Automated|Position-Specific Scoring Matrices|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp593', '19828575', 'Profile hidden Markov models (pHMMs) are currently the most popular modeling concept for protein families. They provide sensitive family descriptors, and sequence database searching with pHMMs has become a standard task in todays genome annotation pipelines. On the downside, searching with pHMMs is computationally expensive.'),
(75126, 2009, 'bio', 'Integrated analysis of copy number alterations and gene expression: a bivariate assessment of equally directed abnormalities', 'Schäfer, Martin|Schwender, Holger|Merk, Sylvia|Haferlach, Claudia|Ickstadt, Katja|Dugas, Martin', 'Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu|Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu|Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu|Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu|Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu|Collaborative Research Center 475, TU Dortmund University, Dortmund, Germany. martin.schaefer@udo.edu', 'Germany|Germany|Germany|Germany|Germany|Germany', 39, 'Computational Biology|Gene Dosage|Gene Expression|Gene Expression Profiling|Humans|Leukemia, Myeloid, Acute|Loss of Heterozygosity', 'Journal Article', '10.1093/bioinformatics/btp592', '19828576', 'The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material).'),
(75127, 2009, 'bio', 'ABWGAT: anchor-based whole genome analysis tool', 'Das, Sarbashis|Vishnoi, Anchal|Bhattacharya, Alok', 'Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi, 110067, India. dsarbashis@gmail.com|Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi, 110067, India. dsarbashis@gmail.com|Center for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi, 110067, India. dsarbashis@gmail.com', 'India|India|India', 2, 'Algorithms|Genome|Genomics|Software', 'Journal Article', '10.1093/bioinformatics/btp587', '19828577', 'Large numbers of genomes are being sequenced regularly and the rate will go up in future due to availability of new genome sequencing techniques. In order to understand genotype to phenotype relationships, it is necessary to identify sequence variations at the genomic level. Alignment of a pair of genomes and parsing the alignment data is an accepted approach for identification of variations. Though there are a number of tools available for whole-genome alignment, none of these allows automatic parsing of the alignment and identification of different kinds of genomic variants with high degree of sensitivity. Here we present a simple web-based interface for whole genome comparison named ABWGAT (Anchor-Based Whole Genome Analysis Tool) that is simple to use. The output is a list of variations such as SNVs, indels, repeat expansion and inversion.'),
(75128, 2009, 'bio', 'NAViGaTOR: Network Analysis, Visualization and Graphing Toronto', 'Brown, Kevin R|Otasek, David|Ali, Muhammad|McGuffin, Michael J|Xie, Wing|Devani, Baiju|Toch, Ian Lawson van|Jurisica, Igor', 'Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.|Division of Signaling Biology, Ontario Cancer Institute and University Health Network, Toronto, ON.', '-|-|-|-|-|-|-|-', 141, 'Computational Biology|Computer Graphics|Database Management Systems|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp595', '19837718', 'NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture.'),
(75129, 2009, 'bio', 'INTERSNP: genome-wide interaction analysis guided by a priori information', 'Herold, Christine|Steffens, Michael|Brockschmidt, Felix F|Baur, Max P|Becker, Tim', 'Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de|Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de|Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de|Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de|Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Germany. herold@imbie.meb.uni-bonn.de', 'Germany|Germany|Germany|Germany|Germany', 98, 'Databases, Genetic|Genome|Genome-Wide Association Study|Genomics|Logistic Models|Monte Carlo Method|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp596', '19837719', 'Genome-wide association studies (GWAS) have lead to the identification of hundreds of genomic regions associated with complex diseases. Nevertheless, a large fraction of their heritability remains unexplained. Interaction between genetic variants is one of several putative explanations for the case of missing heritability and, therefore, a compelling next analysis step. However, genome-wide interaction analysis (GWIA) of all pairs of SNPs from a standard marker panel is computationally unfeasible without massive parallelization. Furthermore, GWIA of all SNP triples is utopian. In order to overcome these computational constraints, we present a GWIA approach that selects combinations of SNPs for interaction analysis based on a priori information. Sources of information are statistical evidence (single marker association at a moderate level), genetic relevance (genomic location) and biologic relevance (SNP function class and pathway information). We introduce the software package INTERSNP that implements a logistic regression framework as well as log-linear models for joint analysis of multiple SNPs. Automatic handling of SNP annotation and pathways from the KEGG database is provided. In addition, Monte Carlo simulations to judge genome-wide significance are implemented. We introduce various meaningful GWIA strategies that can be conducted using INTERSNP. Typical examples are, for instance, the analysis of all pairs of non-synonymous SNPs, or, the analysis of all combinations of three SNPs that lie in a common pathway and that are among the top 50,000 single-marker results. We demonstrate the feasibility of these and other GWIA strategies by application to a GWAS dataset and discuss promising results.'),
(75130, 2010, 'bio', 'GeneE: gene and protein query expansion with disambiguation', 'Schuemie, Martijn J|Kang, Ning|Hekkelman, Maarten L|Kors, Jan A', 'Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. m.schuemie@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. m.schuemie@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. m.schuemie@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. m.schuemie@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Genes|Information Storage and Retrieval|Internet|Natural Language Processing|Pattern Recognition, Automated|Proteins|PubMed|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btp597', '19837720', 'When referring to genes, authors often use synonyms instead of the official gene symbols. In order to accurately retrieve as many relevant documents as possible, we have developed GeneE, a web application that expands a gene query to include all known synonyms, and adds disambiguation information for ambiguous terms, before forwarding the query to either PubMed, Google or Jane. The query expansion algorithm is also available as a web service.'),
(75131, 2009, 'bio', 'CRISPI: a CRISPR interactive database', 'Rousseau, Christine|Gonnet, Mathieu|Le Romancer, Marc|Nicolas, Jacques', 'IRISA-INRIA, Campus de Beaulieu, 35042 Rennes cedex, France. croussea@irisa.fr, jnicolas@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35042 Rennes cedex, France. croussea@irisa.fr, jnicolas@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35042 Rennes cedex, France. croussea@irisa.fr, jnicolas@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35042 Rennes cedex, France. croussea@irisa.fr, jnicolas@irisa.fr', 'France|France|France|France', 0, 'Archaea|Bacteria|Databases, Nucleic Acid|Evolution, Molecular|Genome, Archaeal|Genome, Bacterial|Genomics|Repetitive Sequences, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btp586', '19846435', 'The CRISPR genomic structures (Clustered Regularly Interspaced Short Palindromic Repeats) form a family of repeats that is largely present in archaea and frequent in bacteria. On the basis of a formal model of CRISPR using very few parameters, a systematic study of all their occurrences in all available genomes of Archaea and Bacteria has been carried out. This has resulted in a relational database, CRISPI, which also includes a complete repertory of associated CRISPR-associated genes (CAS). A user-friendly web interface with many graphical tools and functions allows users to extract results, find CRISPR in personal sequences or calculate sequence similarity with spacers.'),
(75132, 2010, 'bio', 'Reporting bias when using real data sets to analyze classification performance', 'Yousefi, Mohammadmahdi R|Hua, Jianping|Sima, Chao|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Artifacts|Artificial Intelligence|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp605', '19846436', 'It is commonplace for authors to propose a new classification rule, either the operator construction part or feature selection, and demonstrate its performance on real data sets, which often come from high-dimensional studies, such as from gene-expression microarrays, with small samples. Owing to the variability in feature selection and error estimation, individual reported performances are highly imprecise. Hence, if only the best test results are reported, then these will be biased relative to the overall performance of the proposed procedure.'),
(75133, 2010, 'bio', 'Accurate confidence aware clustering of array CGH tumor profiles', 'van Houte, Bart P P|Heringa, Jaap', 'Centre for Integrative Bioinformatics VU (IBIVU), Faculty of Sciences and Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), Faculty of Sciences and Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands', 5, 'Algorithms|Chromosome Mapping|Cluster Analysis|Confidence Intervals|Humans|In Situ Hybridization|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btp603', '19846437', 'Chromosomal aberrations tend to be characteristic for given (sub)types of cancer. Such aberrations can be detected with array comparative genomic hybridization (aCGH). Clustering aCGH tumor profiles aids in identifying chromosomal regions of interest and provides useful diagnostic information on the cancer type. An important issue here is to what extent individual aCGH tumor profiles can be reliably assigned to clusters associated with a given cancer type.'),
(75134, 2010, 'bio', 'Targeted interrogation of copy number variation using SCIMMkit', 'Zerr, Troy|Cooper, Gregory M|Eichler, Evan E|Nickerson, Deborah A', 'Department of Genome Sciences and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. troyz@u.washington.edu|Department of Genome Sciences and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. troyz@u.washington.edu|Department of Genome Sciences and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. troyz@u.washington.edu|Department of Genome Sciences and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. troyz@u.washington.edu', 'United States|United States|United States|United States', 6, 'Algorithms|Base Sequence|DNA Mutational Analysis|Gene Dosage|Gene Targeting|Genetic Variation|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp606', '19846438', 'Copy number variants (CNVs) contribute substantially to human genomic diversity, and development of accurate and efficient methods for CNV genotyping is a central problem in exploring human genotype-phenotype associations. SCIMMkit provides a robust, integrated implementation of three previously validated algorithms [SCIMM (SNP-Conditional Mixture Modeling), SCIMM-Search and SCOUT (SNP-Conditional OUTlier detection)] for targeted interrogation of CNVs using Illumina Infinium II and GoldenGate SNP assays. SCIMMkit is applicable to standardized genome-wide SNP arrays and customized multiplexed SNP panels, providing economy, efficiency and flexibility in experimental design.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75135, 2009, 'bio', 'G-compass: a web-based comparative genome browser between human and other vertebrate genomes', 'Kawahara, Yoshihiro|Sakate, Ryuichi|Matsuya, Akihiro|Murakami, Katsuhiko|Sato, Yoshiharu|Zhang, Hao|Gojobori, Takashi|Itoh, Takeshi|Imanishi, Tadashi', 'Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.|Japan Biological Information Research Center, Japan Biological Informatics Consortium, Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Animals|DNA Copy Number Variations|Databases, Genetic|Genome|Genomics|Humans|Internet|Software|Vertebrates', 'Comparative Study', '10.1093/bioinformatics/btp594', '19846439', 'G-compass is designed for efficient comparative genome analysis between human and other vertebrate genomes. The current version of G-compass allows us to browse two corresponding genomic regions between human and another species in parallel. One-to-one evolutionarily conserved regions (i.e. orthologous regions) between species are highlighted along the genomes. Information such as locations of duplicated regions, copy number variations and mammalian ultra-conserved elements is also provided. These features of G-compass enable us to easily determine patterns of genomic rearrangements and changes in gene orders through evolutionary time. Since G-compass is a satellite database of H-InvDB, which is a comprehensive annotation resource for human genes and transcripts, users can easily refer to manually curated functional annotations and other abundant biological information for each human transcript. G-compass is expected to be a valuable tool for comparing human and model organisms and promoting the exchange of functional information.'),
(75136, 2010, 'bio', 'Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere', 'Yu, Jian|Zhou, Yong|Tanaka, Isao|Yao, Min', 'Graduate School of Life Science, Hokkaido University, Kita-Ku Kita-10 Nishi-8, Sapporo 0600810, Japan.|Graduate School of Life Science, Hokkaido University, Kita-Ku Kita-10 Nishi-8, Sapporo 0600810, Japan.|Graduate School of Life Science, Hokkaido University, Kita-Ku Kita-10 Nishi-8, Sapporo 0600810, Japan.|Graduate School of Life Science, Hokkaido University, Kita-Ku Kita-10 Nishi-8, Sapporo 0600810, Japan.', 'Japan|Japan|Japan|Japan', 137, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp599', '19846440', 'Prediction of ligand binding sites of proteins is significant as it can provide insight into biological functions and reaction mechanisms of proteins. It is also a prerequisite for protein-ligand docking and an important step in structure-based drug design.'),
(75137, 2010, 'bio', 'Evaluation of linguistic features useful in extraction of interactions from PubMed, application to annotating known, high-throughput and predicted interactions in I2D', 'Niu, Yun|Otasek, David|Jurisica, Igor', 'Ontario Cancer Institute, UHN, 101 College Street, Toronto, Ontario M5G1L7, Canada. yniu@uhnres.utoronto.ca|Ontario Cancer Institute, UHN, 101 College Street, Toronto, Ontario M5G1L7, Canada. yniu@uhnres.utoronto.ca|Ontario Cancer Institute, UHN, 101 College Street, Toronto, Ontario M5G1L7, Canada. yniu@uhnres.utoronto.ca', 'Canada|Canada|Canada', 52, 'Artificial Intelligence|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Protein Interaction Mapping|Proteins|PubMed|Semantics', 'Journal Article', '10.1093/bioinformatics/btp602', '19850753', 'Identification and characterization of protein-protein interactions (PPIs) is one of the key aims in biological research. While previous research in text mining has made substantial progress in automatic PPI detection from literature, the need to improve the precision and recall of the process remains. More accurate PPI detection will also improve the ability to extract experimental data related to PPIs and provide multiple evidence for each interaction.'),
(75138, 2010, 'bio', 'The gputools package enables GPU computing in R', 'Buckner, Joshua|Wilson, Justin|Seligman, Mark|Athey, Brian|Watson, Stanley|Meng, Fan', 'Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu', 'United States|United States|United States|United States|United States|United States', 30, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btp608', '19850754', 'By default, the R statistical environment does not make use of parallelism. Researchers may resort to expensive solutions such as cluster hardware for large analysis tasks. Graphics processing units (GPUs) provide an inexpensive and computationally powerful alternative. Using R and the CUDA toolkit from Nvidia, we have implemented several functions commonly used in microarray gene expression analysis for GPU-equipped computers.'),
(75139, 2010, 'bio', 'Identification of non-Hodgkins lymphoma prognosis signatures using the CTGDR method', 'Ma, Shuangge|Zhang, Yawei|Huang, Jian|Han, Xuesong|Holford, Theodore|Lan, Qing|Rothman, Nathaniel|Boyle, Peter|Zheng, Tongzhang', 'School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Algorithms|Cluster Analysis|DNA Mutational Analysis|DNA, Neoplasm|Genetic Predisposition to Disease|Humans|Lymphoma, Non-Hodgkin|Polymorphism, Single Nucleotide|Prognosis|Reproducibility of Results|Risk Assessment|Risk Factors|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btp604', '19850755', 'Although NHL (non-Hodgkins lymphoma) is the fifth leading cause of cancer incidence and mortality in the USA, it remains poorly understood and is largely incurable. Biomedical studies have shown that genomic variations, measured with SNPs (single nucleotide polymorphisms) in genes, may have independent predictive power for disease-free survival in NHL patients beyond clinical measurements.'),
(75140, 2010, 'bio', 'Ancestors 10: a web server for ancestral sequence reconstruction', 'Diallo, Abdoulaye Banire|Makarenkov, Vladimir|Blanchette, Mathieu', 'Department of Computer Science, Université du Québec à Montréal, PO. Box 8888 Downtown Station, Montreal, QC H3C3P8, Canada. diallo.abdoulaye@uqam.ca|Department of Computer Science, Université du Québec à Montréal, PO. Box 8888 Downtown Station, Montreal, QC H3C3P8, Canada. diallo.abdoulaye@uqam.ca|Department of Computer Science, Université du Québec à Montréal, PO. Box 8888 Downtown Station, Montreal, QC H3C3P8, Canada. diallo.abdoulaye@uqam.ca', 'Canada|Canada|Canada', 25, 'Animals|Base Sequence|Chromosome Mapping|Genome|Humans|Internet|Molecular Sequence Data|Pedigree|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp600', '19850756', 'The computational inference of ancestral genomes consists of five difficult steps: identifying syntenic regions, inferring ancestral arrangement of syntenic regions, aligning multiple sequences, reconstructing the insertion and deletion history and finally inferring substitutions. Each of these steps have received lot of attention in the past years. However, there currently exists no framework that integrates all of the different steps in an easy workflow. Here, we introduce Ancestors 1.0, a web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure. It implements several alignment algorithms, an indel maximum likelihood solver and a context-dependent maximum likelihood substitution inference algorithm. The results presented by the server include the posterior probabilities for the last two steps of the ancestral genome reconstruction and the expected error rate of each ancestral base prediction.'),
(75141, 2010, 'bio', 'Seed-based INTARNA prediction combined with GFP-reporter system identifies mRNA targets of the small RNA Yfr1', 'Richter, Andreas S|Schleberger, Christian|Backofen, Rolf|Steglich, Claudia', 'Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, Freiburg D-79110, Germany.|Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, Freiburg D-79110, Germany.|Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, Freiburg D-79110, Germany.|Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, Freiburg D-79110, Germany.', 'Germany|Germany|Germany|Germany', 40, 'Base Sequence|Binding Sites|Gene Targeting|Genome, Bacterial|Green Fluorescent Proteins|Molecular Sequence Data|Prochlorococcus|RNA, Bacterial|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp609', '19850757', 'Prochlorococcus possesses the smallest genome of all sequenced photoautotrophs. Although the number of regulatory proteins in the genome is very small, the relative number of small regulatory RNAs is comparable with that of other bacteria. The compact genome size of Prochlorococcus offers an ideal system to search for targets of small RNAs (sRNAs) and to refine existing target prediction algorithms.'),
(75142, 2010, 'bio', 'Annotation confidence score for genome annotation: a genome comparison approach', 'Yang, Youngik|Gilbert, Donald|Kim, Sun', 'School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.', 'India|India|India', 8, 'Algorithms|Base Sequence|Chromosome Mapping|Confidence Intervals|Data Interpretation, Statistical|Genome|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp613', '19855104', 'The massively parallel sequencing technology can be used by small research labs to generate genome sequences of their research interest. However, annotation of genomes still relies on the manual process, which becomes a serious bottleneck to the high-throughput genome projects. Recently, automatic annotation methods are increasingly more accurate, but there are several issues. One important challenge in using automatic annotation methods is to distinguish annotation quality of ORFs or genes. The availability of such annotation quality of genes can reduce the human labor cost dramatically since manual inspection can focus only on genes with low-annotation quality scores.'),
(75143, 2010, 'bio', 'DEGseq: an R package for identifying differentially expressed genes from RNA-seq data', 'Wang, Likun|Feng, Zhixing|Wang, Xi|Wang, Xiaowo|Zhang, Xuegong', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.', 'China|China|China|China|China', 1869, 'Algorithms|Base Sequence|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Programming Languages|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp612', '19855105', 'High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference.'),
(75144, 2010, 'bio', 'NGSView: an extensible open source editor for next-generation sequencing data', 'Arner, Erik|Hayashizaki, Yoshihide|Daub, Carsten O', 'RIKEN Omics Science Center, RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. arner@gsc.riken.jp|RIKEN Omics Science Center, RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. arner@gsc.riken.jp|RIKEN Omics Science Center, RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. arner@gsc.riken.jp', 'Japan|Japan|Japan', 8, 'Algorithms|Computer Graphics|Documentation|Information Storage and Retrieval|Internet|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp611', '19855106', 'High-throughput sequencing technologies introduce novel demands on tools available for data analysis. We have developed NGSView (Next Generation Sequence View), a generally applicable, flexible and extensible next-generation sequence alignment editor. The software allows for visualization and manipulation of millions of sequences simultaneously on a desktop computer, through a graphical interface. NGSView is available under an open source license and can be extended through a well documented API.'),
(75145, 2010, 'bio', 'Computational protein profile similarity screening for quantitative mass spectrometry experiments', 'Kirchner, Marc|Renard, Bernhard Y|Köthe, Ullrich|Pappin, Darryl J|Hamprecht, Fred A|Steen, Hanno|Steen, Judith A J', 'Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.|Department of Pathology, Proteomics Center, Childrens Hospital Boston, Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 12, 'Algorithms|Amino Acid Sequence|Gene Expression Profiling|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btp607', '19861354', 'The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis workflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account.'),
(75146, 2010, 'bio', 'The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing', 'Clement, Nathan L|Snell, Quinn|Clement, Mark J|Hollenhorst, Peter C|Purwar, Jahnvi|Graves, Barbara J|Cairns, Bradley R|Johnson, W Evan', 'Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com|Department of Computer Science, Department of Statistics, Brigham Young University, Provo, UT 84602, USA. nathanlclement@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 48, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Data Interpretation, Statistical|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp614', '19861355', 'The advent of next-generation sequencing technologies has increased the accuracy and quantity of sequence data, opening the door to greater opportunities in genomic research.'),
(75147, 2009, 'bio', 'Estimating population haplotype frequencies from pooled SNP data using incomplete database information', 'Pirinen, Matti', 'Department of Mathematics and Statistics, University of Helsinki, PO Box 68, 00014, Helsinki, Finland. matti.pirinen@iki.fi', 'Finland', 11, 'Algorithms|Computational Biology|Databases, Genetic|Gene Frequency|Genotype|Haplotypes|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp584', '19861356', 'Information about haplotype structures gives a more detailed picture of genetic variation between individuals than single-locus analyses. Databases that contain the most frequent haplotypes of certain populations are developing rapidly (e.g. the HapMap database for single-nucleotide polymorphisms in humans). Utilization of such prior information about the prevailing haplotype structures makes it possible to estimate the haplotype frequencies also from large DNA pools. When genetic material from dozens of individuals is pooled together and analysed in a single genotyping, the overall number of genotypings and the costs of the genetic studies are reduced.'),
(75148, 2010, 'bio', 'Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations', 'Yu, W|Clyne, M|Khoury, M J|Gwinn, M', 'Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30345, USA. wby0@cdc.gov|Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30345, USA. wby0@cdc.gov|Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30345, USA. wby0@cdc.gov|Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA 30345, USA. wby0@cdc.gov', 'United States|United States|United States|United States', 122, 'Chromosome Mapping|Genetic Predisposition to Disease|Genome-Wide Association Study|Information Storage and Retrieval|Natural Language Processing|PubMed', 'Journal Article', '10.1093/bioinformatics/btp618', '19864262', 'We developed web-based applications that encourage the exploration of the literature on human genetic associations by using a database that is continuously updated from PubMed. These applications provide user-friendly interfaces for searching summarized information on human genetic associations, using either genes or diseases as the starting point.'),
(75149, 2010, 'bio', 'Predictive rule inference for epistatic interaction detection in genome-wide association studies', 'Wan, Xiang|Yang, Can|Yang, Qiang|Xue, Hong|Tang, Nelson L S|Yu, Weichuan', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China. eexiangw@ust.hk', 'China|China|China|China|China|China', 110, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1093/bioinformatics/btp622', '19880365', 'Under the current era of genome-wide association study (GWAS), finding epistatic interactions in the large volume of SNP data is a challenging and unsolved issue. Few of previous studies could handle genome-wide data due to the difficulties in searching the combinatorially explosive search space and statistically evaluating high-order epistatic interactions given the limited number of samples. In this work, we propose a novel learning approach (SNPRuler) based on the predictive rule inference to find disease-associated epistatic interactions.'),
(75150, 2010, 'bio', 'Bayesian model selection for characterizing genomic imprinting effects and patterns', 'Yang, Runqing|Wang, Xin|Wu, Zeyuan|Prows, Daniel R|Lin, Min', 'School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China. runqingyang@sjtu.edu.cn', 'China|China|China|China|China', 0, 'Bayes Theorem|Computer Simulation|Genomic Imprinting|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btp620', '19880366', 'Although imprinted genes have been ubiquitously observed in nature, statistical methodology still has not been systematically developed for jointly characterizing genomic imprinting effects and patterns. To detect imprinting genes influencing quantitative traits, the least square and maximum likelihood approaches for fitting a single quantitative trait loci (QTL) and Bayesian method for simultaneously modeling multiple QTLs have been adopted in various studies.'),
(75151, 2010, 'bio', 'inGAP: an integrated next-generation genome analysis pipeline', 'Qi, Ji|Zhao, Fangqing|Buboltz, Anne|Schuster, Stephan C', 'Pennsylvania State University, Center for Comparative Genomics and Bioinformatics, University Park, Pennsylvania 16802, USA.|Pennsylvania State University, Center for Comparative Genomics and Bioinformatics, University Park, Pennsylvania 16802, USA.|Pennsylvania State University, Center for Comparative Genomics and Bioinformatics, University Park, Pennsylvania 16802, USA.|Pennsylvania State University, Center for Comparative Genomics and Bioinformatics, University Park, Pennsylvania 16802, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genome|Molecular Sequence Data|Pancreatitis-Associated Proteins|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp615', '19880367', 'We develop a novel mining pipeline, Integrative Next-generation Genome Analysis Pipeline (inGAP), guided by a Bayesian principle to detect single nucleotide polymorphisms (SNPs), insertion/deletions (indels) by comparing high-throughput pyrosequencing reads with a reference genome of related organisms. inGAP can be applied to the mapping of both Roche/454 and Illumina reads with no restriction of read length. Experiments on simulated and experimental data show that this pipeline can achieve overall 97% accuracy in SNP detection and 94% in the finding of indels. All the detected SNPs/indels can be further evaluated by a graphical editor in our pipeline. inGAP also provides functions of multiple genomes comparison and assistance of bacterial genome assembly.'),
(75152, 2010, 'bio', 'MATICCE: mapping transitions in continuous character evolution', 'Hipp, Andrew L|Escudero, Marcial', 'Herbarium, The Morton Arboretum, 4100 Illinois Route 53, Lisle, IL 60532-1293, USA. ahipp@mortonarb.org|Herbarium, The Morton Arboretum, 4100 Illinois Route 53, Lisle, IL 60532-1293, USA. ahipp@mortonarb.org', 'United States|United States', 9, 'Algorithms|Animals|Biological Evolution|Chromosome Mapping|Computer Simulation|Genetic Variation|Humans|Models, Genetic|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btp625', '19880368', 'MATICCE is a new software package in the R language for mapping phylogenetic transitions in organismal traits that have continuous distributions. MATICCE integrates over phylogenetic and model uncertainty and provides simulation functions for visualizing evolutionary scenarios based on estimated parameter values.'),
(75153, 2010, 'bio', 'MicroRazerS: rapid alignment of small RNA reads', 'Emde, Anne-Katrin|Grunert, Marcel|Weese, David|Reinert, Knut|Sperling, Silke R', 'Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|MicroRNAs|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp601', '19880369', 'Deep sequencing has become the method of choice for determining the small RNA content of a cell. Mapping the sequenced reads onto their reference genome serves as the basis for all further analyses, namely for identification and quantification. A method frequently used is Mega BLAST followed by several filtering steps, even though it is slow and inefficient for this task. Also, none of the currently available short read aligners has established itself for the particular task of small RNA mapping.'),
(75154, 2010, 'bio', 'Pitfalls of supervised feature selection', 'Smialowski, Pawel|Frishman, Dmitrij|Kramer, Stefan', 'Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de|Department of Computer Science, Free University of Berlin, Takustr. 9, Berlin, Germany. emde@inf.fu-berlin.de', 'Germany|Germany|Germany', 101, 'Computational Biology|Protein Array Analysis|Proteins', 'Letter', '10.1093/bioinformatics/btp621', '19880370', '-'),
(75155, 2010, 'bio', 'Simulation-based model selection for dynamical systems in systems and population biology', 'Toni, Tina|Stumpf, Michael P H', 'Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW72AZ London, UK. ttoni@imperial.ac.uk|Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW72AZ London, UK. ttoni@imperial.ac.uk', 'United Kingdom|United Kingdom', 138, 'Algorithms|Animals|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Humans|Models, Biological|Models, Statistical|Population Dynamics', 'Journal Article', '10.1093/bioinformatics/btp619', '19880371', 'Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of, e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty.'),
(75156, 2009, 'bio', 'Model aggregation: a building-block approach to creating large macromolecular regulatory networks', 'Randhawa, Ranjit|Shaffer, Clifford A|Tyson, John J', 'Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.|Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.', 'United States|United States|United States', 0, 'Computational Biology|Gene Regulatory Networks|Models, Biological|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp581', '19880372', 'Models of regulatory networks become more difficult to construct and understand as they grow in size and complexity. Modelers naturally build large models from smaller components that each represent subsets of reactions within the larger network. To assist modelers in this process, we present model aggregation, which defines models in terms of components that are designed for the purpose of being combined.'),
(75157, 2010, 'bio', 'SimBoolNet--a Cytoscape plugin for dynamic simulation of signaling networks', 'Zheng, Jie|Zhang, David|Przytycki, Pawel F|Zielinski, Rafal|Capala, Jacek|Przytycka, Teresa M', 'National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM/NIH, Bethesda, MD, USA. zhengj@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States|United States', 40, 'Algorithms|Computer Simulation|Models, Biological|Models, Statistical|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btp617', '19887508', 'SimBoolNet is an open source Cytoscape plugin that simulates the dynamics of signaling transduction using Boolean networks. Given a user-specified level of stimulation to signal receptors, SimBoolNet simulates the response of downstream molecules and visualizes with animation and records the dynamic changes of the network. It can be used to generate hypotheses and facilitate experimental studies about causal relations and crosstalk among cellular signaling pathways.'),
(75158, 2010, 'bio', 'ARH: predicting splice variants from genome-wide data with modified entropy', 'Rasche, Axel|Herwig, Ralf', 'Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr. 63-73, D-14195 Berlin, Germany. rasche@molgen.mpg.de|Department of Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr. 63-73, D-14195 Berlin, Germany. rasche@molgen.mpg.de', 'Germany|Germany', 0, 'Algorithms|Alternative Splicing|Chromosome Mapping|DNA|Entropy|Exons|Genome|Oligonucleotide Array Sequence Analysis|RNA Splice Sites', 'Journal Article', '10.1093/bioinformatics/btp626', '19889797', 'Exon arrays allow the quantitative study of alternative splicing (AS) on a genome-wide scale. A variety of splicing prediction methods has been proposed for Affymetrix exon arrays mainly focusing on geometric correlation measures or analysis of variance. In this article, we introduce an information theoretic concept that is based on modification of the well-known entropy function.'),
(75159, 2010, 'bio', 'Modeling the interplay of single-stranded binding proteins and nucleic acid secondary structure', 'Forties, Robert A|Bundschuh, Ralf', 'Department of Physics, Center for RNA Biology and Department of Biochemistry, The Ohio State University, Columbus, OH, USA.|Department of Physics, Center for RNA Biology and Department of Biochemistry, The Ohio State University, Columbus, OH, USA.', 'United States|United States', 12, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|Nucleic Acids|Protein Binding|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp627', '19889798', 'There are many important proteins which bind single-stranded nucleic acids, such as the nucleocapsid protein in HIV and the RecA DNA repair protein in bacteria. The presence of such proteins can strongly alter the secondary structure of the nucleic acid molecules. Therefore, accurate modeling of the interaction between single-stranded nucleic acids and such proteins is essential to fully understand many biological processes.'),
(75160, 2010, 'bio', 'Methods for combining peptide intensities to estimate relative protein abundance', 'Carrillo, Brian|Yanofsky, Corey|Laboissiere, Sylvie|Nadon, Robert|Kearney, Robert E', 'Department of Biomedical Engineering, McGill University, Montreal, Canada.|Department of Biomedical Engineering, McGill University, Montreal, Canada.|Department of Biomedical Engineering, McGill University, Montreal, Canada.|Department of Biomedical Engineering, McGill University, Montreal, Canada.|Department of Biomedical Engineering, McGill University, Montreal, Canada.', 'Canada|Canada|Canada|Canada|Canada', 49, 'Algorithms|Amino Acid Sequence|Isotope Labeling|Molecular Sequence Data|Peptide Mapping|Proteins|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btp610', '19892804', 'Labeling techniques are being used increasingly to estimate relative protein abundances in quantitative proteomic studies. These techniques require the accurate measurement of correspondingly labeled peptide peak intensities to produce high-quality estimates of differential expression ratios. In mass spectrometers with counting detectors, the measurement noise varies with intensity and consequently accuracy increases with the number of ions detected. Consequently, the relative variability of peptide intensity measurements varies with intensity. This effect must be accounted for when combining information from multiple peptides to estimate relative protein abundance.'),
(75161, 2010, 'bio', 'GATE: software for the analysis and visualization of high-dimensional time series expression data', 'MacArthur, Ben D|Lachmann, Alexander|Lemischka, Ihor R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, New York, NY, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, New York, NY, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, New York, NY, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York, New York, NY, USA.', 'United States|United States|United States|United States', 24, 'Algorithms|Computer Graphics|Computer Simulation|Gene Expression Profiling|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp628', '19892805', 'We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures.'),
(75162, 2010, 'bio', 'Identification of microRNA activity by Targets Reverse EXpression', 'Volinia, Stefano|Visone, Rosa|Galasso, Marco|Rossi, Elda|Croce, Carlo M', 'DAMA, Data Mining for Analysis of Microarrays, Department of Morphology and Embryology, University of Ferrara, Italy. s.volinia@unife.it|DAMA, Data Mining for Analysis of Microarrays, Department of Morphology and Embryology, University of Ferrara, Italy. s.volinia@unife.it|DAMA, Data Mining for Analysis of Microarrays, Department of Morphology and Embryology, University of Ferrara, Italy. s.volinia@unife.it|DAMA, Data Mining for Analysis of Microarrays, Department of Morphology and Embryology, University of Ferrara, Italy. s.volinia@unife.it|DAMA, Data Mining for Analysis of Microarrays, Department of Morphology and Embryology, University of Ferrara, Italy. s.volinia@unife.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Base Sequence|Gene Targeting|Molecular Sequence Data|RNA, Messenger|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btp598', '19897564', 'Non-coding microRNAs (miRNAs) act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in miRNAs strongly motivates the need for increasing the options available to detect their cellular activity.'),
(75163, 2010, 'bio', 'Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments', 'McGuffin, Liam J|Roche, Daniel B', 'School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk|School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk', 'United Kingdom|United Kingdom', 74, 'Computational Biology|Databases, Protein|Models, Molecular|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1093/bioinformatics/btp629', '19897565', 'The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering- or consensus-based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality, however, they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ--a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilizing the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead.'),
(75164, 2010, 'bio', 'hPDI: a database of experimental human protein-DNA interactions', 'Xie, Zhi|Hu, Shaohui|Blackshaw, Seth|Zhu, Heng|Qian, Jiang', 'Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.|Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.', 'United States|United States|United States|United States|United States', 55, 'DNA|DNA-Binding Proteins|Databases, Protein|Humans|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btp631', '19900953', 'The human protein DNA Interactome (hPDI) database holds experimental protein-DNA interaction data for humans identified by protein microarray assays. The unique characteristics of hPDI are that it contains consensus DNA-binding sequences not only for nearly 500 human transcription factors but also for >500 unconventional DNA-binding proteins, which are completely uncharacterized previously. Users can browse, search and download a subset or the entire data via a web interface. This database is freely accessible for any academic purposes.'),
(75165, 2010, 'bio', 'Quantifying uncertainty in genotype calls', 'Carvalho, Benilton S|Louis, Thomas A|Irizarry, Rafael A', 'Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.|Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Genome-Wide Association Study|Genotype|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp624', '19906825', 'Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Microarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs) simultaneously. The starting point for the statistical analyses used by GWAS to determine association between loci and disease is making genotype calls (AA, AB or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays and different sample batches have substantial influence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability can adversely affect the quality of findings reported by the GWAS.'),
(75166, 2010, 'bio', 'Co-expression networks: graph properties and topological comparisons', 'Xulvi-Brunet, Ramon|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.', 'United States|United States', 42, 'Computational Biology|Databases, Genetic|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Comparative Study', '10.1093/bioinformatics/btp632', '19910304', 'Microarray-based gene expression data have been generated widely to study different biological processes and systems. Gene co-expression networks are often used to extract information about groups of genes that are functionally related or co-regulated. However, the structural properties of such co-expression networks have not been rigorously studied and fully compared with known biological networks. In this article, we aim at investigating the structural properties of co-expression networks inferred for the species Saccharomyces Cerevisiae and comparing them with the topological properties of the known, well-established transcriptional network, MIPS physical network and protein-protein interaction (PPI) network of yeast.'),
(75167, 2010, 'bio', 'Target prediction and a statistical sampling algorithm for RNA-RNA interaction', 'Huang, Fenix W D|Qin, Jing|Reidys, Christian M|Stadler, Peter F', 'Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China.|Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China.|Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China.|Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China.', 'China|China|China|China', 36, 'Algorithms|Binding Sites|Computational Biology|Databases, Genetic|MicroRNAs|Models, Molecular|Models, Statistical|Nucleic Acid Conformation|RNA|RNA, Small Interfering|Software', 'Journal Article', '10.1093/bioinformatics/btp635', '19910305', 'It has been proven that the accessibility of the target sites has a critical influence on RNA-RNA binding, in general and the specificity and efficiency of miRNAs and siRNAs, in particular. Recently, O(N(6)) time and O(N(4)) space dynamic programming (DP) algorithms have become available that compute the partition function of RNA-RNA interaction complexes, thereby providing detailed insights into their thermodynamic properties.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75168, 2010, 'bio', 'Identification of substrates for Ser/Thr kinases using residue-based statistical pair potentials', 'Kumar, Narendra|Mohanty, Debasisa', 'National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India.|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India.', 'India|India', 0, 'Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Protein-Serine-Threonine Kinases|Sequence Analysis, Protein|Serine|Substrate Specificity|Threonine', 'Journal Article', '10.1093/bioinformatics/btp633', '19910306', 'In silico methods are being widely used for identifying substrates for various kinases and deciphering cell signaling networks. However, most of the available phosphorylation site prediction methods use motifs or profiles derived from a known data set of kinase substrates and hence, their applicability is limited to only those kinase families for which experimental substrate data is available. This prompted us to develop a novel multi-scale structure-based approach which does not require training using experimental substrate data.'),
(75169, 2010, 'bio', 'Automatic clustering of docking poses in virtual screening process using self-organizing map', 'Bouvier, Guillaume|Evrard-Todeschi, Nathalie|Girault, Jean-Pierre|Bertho, Gildas', 'Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Unité Mixte de Recherche 8601, Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes, 45 rue des Saints-Pères, 75006 Paris, France.|Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Unité Mixte de Recherche 8601, Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes, 45 rue des Saints-Pères, 75006 Paris, France.|Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Unité Mixte de Recherche 8601, Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes, 45 rue des Saints-Pères, 75006 Paris, France.|Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Unité Mixte de Recherche 8601, Centre National de la Recherche Scientifique (CNRS), Université Paris Descartes, 45 rue des Saints-Pères, 75006 Paris, France.', 'France|France|France|France', 49, 'Algorithms|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Pharmaceutical Preparations|Protein Binding|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp623', '19910307', 'Scoring functions provided by the docking software are still a major limiting factor in virtual screening (VS) process to classify compounds. Score analysis of the docking is not able to find out all active compounds. This is due to a bad estimation of the ligand binding energies. Making the assumption that active compounds should have specific contacts with their target to display activity, it would be possible to discriminate active compounds from inactive ones with careful analysis of interatomic contacts between the molecule and the target. However, compounds clustering is very tedious due to the large number of contacts extracted from the different conformations proposed by docking experiments.'),
(75170, 2010, 'bio', 'edgeR: a Bioconductor package for differential expression analysis of digital gene expression data', 'Robinson, Mark D|McCarthy, Davis J|Smyth, Gordon K', 'Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au|Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au|Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au', 'Australia|Australia|Australia', 11941, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Programming Languages|Signal Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btp616', '19910308', 'It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.'),
(75171, 2010, 'bio', 'PyNAST: a flexible tool for aligning sequences to a template alignment', 'Caporaso, J Gregory|Bittinger, Kyle|Bushman, Frederic D|DeSantis, Todd Z|Andersen, Gary L|Knight, Rob', 'Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO, USA.', 'United States|United States|United States|United States|United States|United States', 2284, 'Algorithms|Databases, Genetic|Sequence Alignment|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp636', '19914921', 'The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API).'),
(75172, 2010, 'bio', 'PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks', 'Li, Fei|Li, Peng|Xu, Wenjian|Peng, Yuxing|Bo, Xiaochen|Wang, Shengqi', 'Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.', 'China|China|China|China|China|China', 17, 'Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp634', '19914922', 'The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets.'),
(75173, 2010, 'bio', 'AQUA: automated quality improvement for multiple sequence alignments', 'Muller, Jean|Creevey, Christopher J|Thompson, Julie D|Arendt, Detlev|Bork, Peer', 'European Molecular Biology Laboratory, Meyerhofstrasse 1, 69012 Heidelberg, Germany. muller@embl.de|European Molecular Biology Laboratory, Meyerhofstrasse 1, 69012 Heidelberg, Germany. muller@embl.de|European Molecular Biology Laboratory, Meyerhofstrasse 1, 69012 Heidelberg, Germany. muller@embl.de|European Molecular Biology Laboratory, Meyerhofstrasse 1, 69012 Heidelberg, Germany. muller@embl.de|European Molecular Biology Laboratory, Meyerhofstrasse 1, 69012 Heidelberg, Germany. muller@embl.de', 'Germany|Germany|Germany|Germany|Germany', 35, 'Computational Biology|Databases, Protein|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp651', '19926669', 'Multiple sequence alignment (MSA) is a central tool in most modern biology studies. However, despite generations of valuable tools, human experts are still able to improve automatically generated MSAs. In an effort to automatically identify the most reliable MSA for a given protein family, we propose a very simple protocol, named AQUA for Automated quality improvement for multiple sequence alignments. Our current implementation relies on two alignment programs (MUSCLE and MAFFT), one refinement program (RASCAL) and one assessment program (NORMD), but other programs could be incorporated at any of the three steps.'),
(75174, 2010, 'bio', 'Pathway analysis using random forests with bivariate node-split for survival outcomes', 'Pang, Herbert|Datta, Debayan|Zhao, Hongyu', 'Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA. pathwayrf@gmail.com|Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA. pathwayrf@gmail.com|Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA. pathwayrf@gmail.com', 'United States|United States|United States', 30, 'Algorithms|Artificial Intelligence|Computational Biology|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btp640', '19933158', 'There is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted.'),
(75175, 2010, 'bio', 'LegumeTFDB: an integrative database of Glycine max, Lotus japonicus and Medicago truncatula transcription factors', 'Mochida, Keiichi|Yoshida, Takuhiro|Sakurai, Tetsuya|Yamaguchi-Shinozaki, Kazuko|Shinozaki, Kazuo|Tran, Lam-Son Phan', 'Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp|Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp|Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp|Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp|Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp|Plant Science Center, Gene Discovery Research Group, RIKEN, Yokohama 230-0045 Japan. mochida@psc.riken.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 57, 'Databases, Genetic|Databases, Protein|Fabaceae|Genome, Plant|Genomics|Lotus|Medicago truncatula|Plant Proteins|Software|Soybeans|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp645', '19933159', 'We have established a database named LegumeTFDB to provide access to transcription factor (TF) repertoires of three major legume species: soybean (Glycine max), Lotus japonicus and Medicago truncatula. LegumeTFDB integrates unique information for each TF gene and family, including sequence features, gene promoters, domain alignments, gene ontology (GO) assignment and sequence comparison data derived from comparative analysis with TFs found within legumes, in Arabidopsis, rice and poplar as well as with proteins in NCBI nr and UniProt. We also analyzed the promoter regions for all of the TFs to identify all types of cis-motifs provided by the PLACE database. Additionally, we supply hyperlinks to make available expression data of 2411 soybean TF genes. LegumeTFDB provides an important user-friendly public resource for comparative genomics and understanding of transcriptional regulation in agriculturally important legumes.'),
(75176, 2010, 'bio', 'MADS+: discovery of differential splicing events from Affymetrix exon junction array data', 'Shen, Shihao|Warzecha, Claude C|Carstens, Russ P|Xing, Yi', 'Department of Biostatistics, University of Iowa, Iowa City, IA, USA.|Department of Biostatistics, University of Iowa, Iowa City, IA, USA.|Department of Biostatistics, University of Iowa, Iowa City, IA, USA.|Department of Biostatistics, University of Iowa, Iowa City, IA, USA.', 'United States|United States|United States|United States', 28, 'Alternative Splicing|Exons|Humans|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp643', '19933160', 'The Affymetrix Human Exon Junction Array is a newly designed high-density exon-sensitive microarray for global analysis of alternative splicing. Contrary to the Affymetrix exon 1.0 array, which only contains four probes per exon and no probes for exon-exon junctions, this new junction array averages eight probes per probeset targeting all exons and exon-exon junctions observed in the human mRNA/EST transcripts, representing a significant increase in the probe density for alternative splicing events. Here, we present MADS+, a computational pipeline to detect differential splicing events from the Affymetrix exon junction array data. For each alternative splicing event, MADS+ evaluates the signals of probes targeting competing transcript isoforms to identify exons or splice sites with different levels of transcript inclusion between two sample groups. MADS+ is used routinely in our analysis of Affymetrix exon junction arrays and has a high accuracy in detecting differential splicing events. For example, in a study of the novel epithelial-specific splicing regulator ESRP1, MADS+ detects hundreds of exons whose inclusion levels are dependent on ESRP1, with a RT-PCR validation rate of 88.5% (153 validated out of 173 tested).'),
(75177, 2010, 'bio', 'Annotation and merging of SBML models with semanticSBML', 'Krause, Falko|Uhlendorf, Jannis|Lubitz, Timo|Schulz, Marvin|Klipp, Edda|Liebermeister, Wolfram', 'Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.|Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.|Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.|Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.|Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.|Theoretische Biophysik, Humboldt-Universität zu Berlin, Invalidenstrasse 42, D-10115 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 61, 'Computational Biology|Database Management Systems|Databases, Factual|Semantics|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp642', '19933161', 'Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail.'),
(75178, 2010, 'bio', 'SCAN: SNP and copy number annotation', 'Gamazon, Eric R|Zhang, Wei|Konkashbaev, Anuar|Duan, Shiwei|Kistner, Emily O|Nicolae, Dan L|Dolan, M Eileen|Cox, Nancy J', 'Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.|Department of Medicine, The University of Chicago, Chicago, IL, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 192, 'Computational Biology|Databases, Genetic|Gene Dosage|Genome-Wide Association Study|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btp644', '19933162', 'Genome-wide association studies (GWAS) generate relationships between hundreds of thousands of single nucleotide polymorphisms (SNPs) and complex phenotypes. The contribution of the traditionally overlooked copy number variations (CNVs) to complex traits is also being actively studied. To facilitate the interpretation of the data and the designing of follow-up experimental validations, we have developed a database that enables the sensible prioritization of these variants by combining several approaches, involving not only publicly available physical and functional annotations but also multilocus linkage disequilibrium (LD) annotations as well as annotations of expression quantitative trait loci (eQTLs).'),
(75179, 2010, 'bio', 'Bayesian variable selection for disease classification using gene expression data', 'Yang, Ai-Jun|Song, Xin-Yuan', 'Department of Statistics, The Chinese University of Hong Kong, Hong Kong, PR China. ajyang81@gmail.com|Department of Statistics, The Chinese University of Hong Kong, Hong Kong, PR China. ajyang81@gmail.com', 'China|China', 66, 'Bayes Theorem|Colonic Neoplasms|Databases, Genetic|Disease|Gene Expression Profiling|Humans|Leukemia|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp638', '19933163', 'An important application of gene expression microarray data is the classification of samples into categories. Accurate classification depends upon the method used to identify the most relevant genes. Owing to the large number of genes and relatively small sample size, the selection process can be unstable. Modification of existing methods for achieving better analysis of microarray data is needed.'),
(75180, 2010, 'bio', 'VIBE 20: visual integration for bayesian evaluation', 'Beagley, Nathaniel|Stratton, Kelly G|Webb-Robertson, Bobbie-Jo M', 'Computational Mathematics, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational Mathematics, Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Computational Mathematics, Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States', 9, 'Bayes Theorem|Computer Graphics|Database Management Systems|Databases, Factual|Genomics|Metabolomics|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp639', '19933164', 'Data fusion methods are powerful tools for evaluating experiments designed to discover measurable features of directly unobservable systems. We describe an interactive software platform, Visual Integration for Bayesian Evaluation, that ingests or creates bayesian posterior probability matrices, performs data fusion and allows the user to interactively evaluate the classification power of fusing various combinations of data sources, such as transcriptomic, proteomics, metabolomics, biochemistry and function.'),
(75181, 2010, 'bio', 'CYSTM, a novel cysteine-rich transmembrane module with a role in stress tolerance across eukaryotes', 'Venancio, Thiago M|Aravind, L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States', 22, 'Arabidopsis|Computational Biology|Cysteine|Databases, Protein|Eukaryota|Membrane Proteins|Saccharomyces cerevisiae|Schizosaccharomyces|Sequence Alignment|Sequence Analysis, Protein|Software|Stress, Physiological', 'Journal Article', '10.1093/bioinformatics/btp647', '19933165', 'Using sensitive sequence profile analysis, we identify a hitherto uncharacterized cysteine-rich, transmembrane (TM) module, CYSTM, found in a wide range of tail-anchored membrane proteins across eukaryotes. This superfamily includes Schizosaccharomyces Uvi15, Arabidopsis PCC1, Digtaria CDT1 and Saccharomyces proteins YDL012C and YDR210W, which have all been implicated in resistance/response to stress or pathogens. Based on the pattern of conserved cysteines and data from different chemical genetics studies, we suggest that CYSTM proteins might have critical role in responding to deleterious compounds at the plasma membrane via chelation or redox-based mechanisms. Thus, CYSTM proteins are likely to be part of a novel cellular protective mechanism that is widely active in eukaryotes, including humans.'),
(75182, 2010, 'bio', 'MAVEN: a tool for visualization and functional analysis of genome-wide association results', 'Narayanan, Kanchana|Li, Jing', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University Cleveland, OH, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University Cleveland, OH, USA.', 'United States|United States', 2, 'Computational Biology|Databases, Genetic|Genome|Genome-Wide Association Study|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp641', '19933166', 'We describe the features and implementation of a web application tool named MAVEN--for Management, Analysis, Visualization and rEsults shariNg of genome-wide association data using cutting edge technologies. Main capabilities include user data uploading and management, queries using a variety of criteria, visualization of results, interactive selections and seamless integration of users data with databases at the National Center for Biotechnology Information (NCBI) for functional annotations of single nucleotide polymorphisms (SNPs) and genes.'),
(75183, 2010, 'bio', 'mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets', 'Ritchie, William|Flamant, Stephane|Rasko, John E J', 'Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, NSW 2042, Australia.|Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, NSW 2042, Australia.|Gene and Stem Cell Therapy Program, Centenary Institute, University of Sydney, Sydney, NSW 2042, Australia.', 'Australia|Australia|Australia', 66, 'Classification|Computational Biology|Databases, Genetic|Gene Expression Profiling|MicroRNAs|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btp649', '19933167', 'microRNAs (miRNAs) are short non-coding RNAs that regulate gene expression by inhibiting target mRNA genes. Their tissue- and disease-specific expression patterns have immense therapeutic and diagnostic potential. To understand these patterns, a reliable compilation of miRNA and mRNA expression data is required to compare multiple tissue types. Moreover, with the appropriate statistical tools, such a resource could be interrogated to discover functionally related miRNA-mRNA pairs.'),
(75184, 2010, 'bio', 'Joint estimation of DNA copy number from multiple platforms', 'Zhang, Nancy R|Senbabaoglu, Yasin|Li, Jun Z', 'Department of Statistics, Stanford University, Stanford, CA, USA. nzhang@stanford.edu|Department of Statistics, Stanford University, Stanford, CA, USA. nzhang@stanford.edu|Department of Statistics, Stanford University, Stanford, CA, USA. nzhang@stanford.edu', 'United States|United States|United States', 15, 'Algorithms|Computational Biology|DNA|DNA Copy Number Variations|Databases, Genetic|Gene Expression Profiling|Genetic Variation', 'Journal Article', '10.1093/bioinformatics/btp653', '19933593', 'DNA copy number variants (CNVs) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization-based techniques have been developed for high-throughput measurement of DNA copy number. In many studies, multiple technical platforms or different versions of the same platform were used to interrogate the same samples, and it became necessary to pool information across these multiple sources to derive a consensus molecular profile for each sample. An integrated analysis is expected to maximize resolution and accuracy, yet currently there is no well-formulated statistical method to address the between-platform differences in probe coverage, assay methods, sensitivity and analytical complexity.'),
(75185, 2010, 'bio', 'MIRAGAA--a methodology for finding coordinated effects of microRNA expression changes and genome aberrations in cancer', 'Gaire, Raj K|Bailey, James|Bearfoot, Jennifer|Campbell, Ian G|Stuckey, Peter J|Haviv, Izhak', 'NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au|NICTA, Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, VIC 3010, Australia. rgaire@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Computational Biology|Gene Dosage|Genome, Human|Genomic Structural Variation|Genomics|Glioblastoma|Humans|MicroRNAs|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btp654', '19933823', 'Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner. Understanding this coordination may assist in identifying changes which significantly produce the same functional impact on cancer phenotype, and further identify genes that are universally required for cancer. Present methodologies for finding aberrations usually analyze single datasets, which cannot identify such pairs of coordinating genes and miRNAs.'),
(75186, 2010, 'bio', 'A personalized microRNA microarray normalization method using a logistic regression model', 'Wang, Bin|Wang, Xiao-Feng|Howell, Paul|Qian, Xuemin|Huang, Kun|Riker, Adam I|Ju, Jingfang|Xi, Yaguang', 'Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.|Department of Mathematics and Statistics, University of South Alabama, Mobile, AL 36688, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'HCT116 Cells|Humans|Logistic Models|MicroRNAs|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp655', '19933824', 'MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. Its significant effects have contributed to a number of critical biological events including cell proliferation, apoptosis development, as well as tumorigenesis. High-dimensional genomic discovery platforms (e.g. microarray) have been employed to evaluate the important roles of miRNAs by analyzing their expression profiling. However, because of the small total number of miRNAs and the absence of well-known endogenous controls, the traditional normalization methods for messenger RNA (mRNA) profiling analysis could not offer a suitable solution for miRNA analysis. The need for the establishment of new adaptive methods has come to the forefront.'),
(75187, 2010, 'bio', 'EGAN: exploratory gene association networks', 'Paquette, Jesse|Tokuyasu, Taku', 'Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143, USA. jesse.paquette@cc.ucsf.edu|Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143, USA. jesse.paquette@cc.ucsf.edu', 'United States|United States', 0, 'Computational Biology|Databases, Genetic|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btp656', '19933825', 'Exploratory Gene Association Networks (EGAN) is a Java desktop application that provides a point-and-click environment for contextual graph visualization of high-throughput assay results. By loading the entire network of genes, pathways, interactions, annotation terms and literature references directly into memory, EGAN allows a biologist to repeatedly query and interpret multiple experimental results without incurring additional delays for data download/integration. Other compelling features of EGAN include: support for diverse -omics technologies, a simple and interactive graph display, sortable/searchable data tables, links to external web resources including > or = 240 000 articles at PubMed, hypergeometric and GSEA-like enrichment statistics, pipeline-compatible automation via scripting and the ability to completely customize and/or supplement the network with new/proprietary data.'),
(75188, 2010, 'bio', 'Expertomica Cells: analysis of cell monolayer development', 'Levitner, Tomás|Timr, Stepán|Stys, Dalibor', 'Institute of Physical Biology, University of South Bohemia, Academic and University Centre, Zámek 136, 373 33 Nové Hrady, Czech Republic. levitner@ufb.jcu.cz|Institute of Physical Biology, University of South Bohemia, Academic and University Centre, Zámek 136, 373 33 Nové Hrady, Czech Republic. levitner@ufb.jcu.cz|Institute of Physical Biology, University of South Bohemia, Academic and University Centre, Zámek 136, 373 33 Nové Hrady, Czech Republic. levitner@ufb.jcu.cz', '-|-|-', 0, 'Cells|Computer Graphics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp637', '19939831', 'Expertomica Cells is a program for the creation and analysis of pedigree plots from time-lapse micrographs of cell monolayers. It enables recording the basic events in a cell cycle, cell neighbourhoods and spatial migration. The output is both numeric and graphical. The software helps to lower main hurdles in the manual analysis of cell monolayer development to practical limits, it reduces the operator processing time of typical experiment containing 5000 consecutive images from the usual 3 months to 3-10 h.'),
(75189, 2010, 'bio', 'Robust biomarker identification for cancer diagnosis with ensemble feature selection methods', 'Abeel, Thomas|Helleputte, Thibault|Van de Peer, Yves|Dupont, Pierre|Saeys, Yvan', 'Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Biomarkers, Tumor|Computational Biology|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp630', '19942583', 'Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method.'),
(75190, 2010, 'bio', 'CMap3D: a 3D visualization tool for comparative genetic maps', 'Duran, Chris|Boskovic, Zoran|Imelfort, Michael|Batley, Jacqueline|Hamilton, Nicholas A|Edwards, David', 'Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.|Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia', 13, 'Chromosome Mapping|Computational Biology|Computer Graphics|Databases, Genetic|Genome|Sequence Alignment|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btp646', '19942584', 'Genetic linkage mapping enables the study of genome organization and the association of heritable traits with regions of sequenced genomes. Comparative genetic mapping is particularly powerful as it allows translation of information between related genomes and gives an insight into genome evolution. A common tool for the storage, comparison and visualization of genetic maps is CMap. However, current visualization in CMap is limited to the comparison of adjacent aligned maps. To overcome this limitation, we have developed CMap3D, a tool to compare multiple genetic maps in three-dimensional space. CMap3D is based on a client/server model ensuring operability with current CMap data repositories. This tool can be applied to any species where genetic map information is available and enables rapid, direct comparison between multiple aligned maps.'),
(75191, 2010, 'bio', 'Over-optimism in bioinformatics research', 'Boulesteix, Anne-Laure', 'Australian Centre for Plant Functional Genomics, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia', 47, 'Computational Biology|Periodicals as Topic|Research', 'Letter', '10.1093/bioinformatics/btp648', '19942585', '-'),
(75192, 2010, 'bio', 'Kinase selectivity potential for inhibitors targeting the ATP binding site: a network analysis', 'Huang, Danzhi|Zhou, Ting|Lafleur, Karine|Nevado, Cristina|Caflisch, Amedeo', 'Department of Biochemistry, University of Zürich, Winterthurerstrasse 190. dhuang@bioc.uzh.ch|Department of Biochemistry, University of Zürich, Winterthurerstrasse 190. dhuang@bioc.uzh.ch|Department of Biochemistry, University of Zürich, Winterthurerstrasse 190. dhuang@bioc.uzh.ch|Department of Biochemistry, University of Zürich, Winterthurerstrasse 190. dhuang@bioc.uzh.ch|Department of Biochemistry, University of Zürich, Winterthurerstrasse 190. dhuang@bioc.uzh.ch', '-|-|-|-|-', 103, 'Adenosine Triphosphate|Binding Sites|Computational Biology|Humans|Models, Molecular|Protein Conformation|Protein Kinase Inhibitors|Protein Kinases|Sequence Analysis, Protein|Structure-Activity Relationship|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btp650', '19942586', 'MOTIVATION AND METHOD: Small-molecule inhibitors targeting the adenosine triphosphate (ATP) binding pocket of the catalytic domain of protein kinases have potential to become drugs devoid of (major) side effects, particularly if they bind selectively. Here, the sequences of the 518 human kinases are first mapped onto the structural alignment of 116 kinases of known three-dimensional structure. The multiple structure alignment is then used to encode the known strategies for developing selective inhibitors into a fingerprint. Finally, a network analysis is used to partition the kinases into clusters according to similarity of their fingerprints, i.e. physico-chemical characteristics of the residues responsible for selective binding.'),
(75193, 2010, 'bio', 'Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images', 'Mace, Daniel L|Varnado, Nicole|Zhang, Weiping|Frise, Erwin|Ohler, Uwe', 'Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA.|Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA.|Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA.|Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA.|Computational Biology and Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States', 23, 'Animals|Databases, Genetic|Drosophila|Gene Expression|Gene Expression Profiling|Nucleic Acid Hybridization|RNA', 'Comparative Study', '10.1093/bioinformatics/btp658', '19942587', 'Recent advancements in high-throughput imaging have created new large datasets with tens of thousands of gene expression images. Methods for capturing these spatial and/or temporal expression patterns include in situ hybridization or fluorescent reporter constructs or tags, and results are still frequently assessed by subjective qualitative comparisons. In order to deal with available large datasets, fully automated analysis methods must be developed to properly normalize and model spatial expression patterns.'),
(75194, 2010, 'bio', 'Globally, unrelated protein sequences appear random', 'Lavelle, Daniel T|Pearson, William R', 'Department of Biochemistry and Molecular Genetics, University of Virginia, Jordan Hall Box 800733, Charlottesville, VA 22908, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia, Jordan Hall Box 800733, Charlottesville, VA 22908, USA.', 'Jordan|Jordan', 11, 'Amino Acid Sequence|Databases, Protein|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btp660', '19948773', 'To test whether protein folding constraints and secondary structure sequence preferences significantly reduce the space of amino acid words in proteins, we compared the frequencies of four- and five-amino acid word clumps (independent words) in proteins to the frequencies predicted by four random sequence models.'),
(75195, 2010, 'bio', 'Ceres: software for the integrated analysis of transcription factor binding sites and nucleosome positions in Saccharomyces cerevisiae', 'Morris, Robert T|OConnor, Timothy R|Wyrick, John J', 'School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA 99164, USA.|School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA 99164, USA.|School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA 99164, USA.', 'United States|United States|United States', 11, 'Binding Sites|Chromatin|Computational Biology|Databases, Protein|Genome, Fungal|Nucleosomes|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btp657', '19959498', 'There is accumulating evidence that the chromatin environment of transcription factor (TF) binding sites in promoter regions has a critical influence on their regulatory potential. Recent studies have mapped TF binding sites and nucleosome positions throughout the yeast genome, however, there is a lack of computation tools to integrate these data types.'),
(75196, 2010, 'bio', 'The Gene Interaction Miner: a new tool for data mining contextual information for protein-protein interaction analysis', 'Ikin, Aaron|Riveros, Carlos|Moscato, Pablo|Mendes, Alexandre', 'School of Electrical Engineering and Computer Science The University of Newcastle, Callaghan, NSW, 2308, Australia.|School of Electrical Engineering and Computer Science The University of Newcastle, Callaghan, NSW, 2308, Australia.|School of Electrical Engineering and Computer Science The University of Newcastle, Callaghan, NSW, 2308, Australia.|School of Electrical Engineering and Computer Science The University of Newcastle, Callaghan, NSW, 2308, Australia.', 'Australia|Australia|Australia|Australia', 7, 'Binding Sites|Data Mining|Databases, Protein|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btp652', '19965878', 'This work was motivated by the need for an automated tool for discovery of genetic networks and the availability of extensive contextual protein-protein interaction information in the iHOP repository. At the moment, this information cannot be explored to its full potential due to the lack of software tools to reliably collect, process and display that information in a way that life scientists can quickly analyze genes of interest and search for potential interaction networks. Commercial tools can perform a similar job, but results appear to be less informative than those obtained using contextual information.'),
(75197, 2010, 'bio', 'Quantifying the biological significance of gene ontology biological processes--implications for the analysis of systems-wide data', 'Chagoyen, Monica|Pazos, Florencio', 'Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid, Spain.|Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC), C/ Darwin 3, 28049 Madrid, Spain.', 'Spain|Spain', 14, 'Biological Phenomena|Computational Biology|Databases, Protein|Gene Expression Profiling|Protein Interaction Mapping|Proteins|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btp663', '19965879', 'Gene Ontology (GO), the de facto standard for representing protein functional aspects, is being used beyond the primary goal for which it is designed: protein functional annotation. It is increasingly used to evaluate large sets of relationships between proteins, e.g. protein-protein interactions or mRNA co-expression, under the assumption that related proteins tend to have the same or similar GO terms. Nevertheless, this assumption only holds for terms representing functional groups with biological significance (classes), and not for the ones representing human-imposed aggregations or conceptualizations lacking a biological rationale (categories).'),
(75198, 2010, 'bio', 'DataPflex: a MATLAB-based tool for the manipulation and visualization of multidimensional datasets', 'Hendriks, Bart S|Espelin, Christopher W', 'Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. DataPflexinfo@gmail.com|Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. DataPflexinfo@gmail.com', 'United States|United States', 10, 'Computational Biology|Computer Graphics|Database Management Systems|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp667', '19965880', 'DataPflex is a MATLAB-based application that facilitates the manipulation and visualization of multidimensional datasets. The strength of DataPflex lies in the intuitive graphical user interface for the efficient incorporation, manipulation and visualization of high-dimensional data that can be generated by multiplexed protein measurement platforms including, but not limited to Luminex or Meso-Scale Discovery. Such data can generally be represented in the form of multidimensional datasets [for example (time x stimulation x inhibitor x inhibitor concentration x cell type x measurement)]. For cases where measurements are made in a combinational fashion across multiple dimensions, there is a need for a tool to efficiently manipulate and reorganize such data for visualization. DataPflex accepts data consisting of up to five arbitrary dimensions in addition to a measurement dimension. Data are imported from a simple .xls format and can be exported to MATLAB or .xls. Data dimensions can be reordered, subdivided, merged, normalized and visualized in the form of collections of line graphs, bar graphs, surface plots, heatmaps, IC50s and other custom plots. Open source implementation in MATLAB enables easy extension for custom plotting routines and integration with more sophisticated analysis tools.'),
(75199, 2010, 'bio', 'Tablet--next generation sequence assembly visualization', 'Milne, Iain|Bayer, Micha|Cardle, Linda|Shaw, Paul|Stephen, Gordon|Wright, Frank|Marshall, David', 'Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.|Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 441, 'Base Sequence|Computational Biology|Databases, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp666', '19965881', 'Tablet is a lightweight, high-performance graphical viewer for next-generation sequence assemblies and alignments. Supporting a range of input assembly formats, Tablet provides high-quality visualizations showing data in packed or stacked views, allowing instant access and navigation to any region of interest, and whole contig overviews and data summaries. Tablet is both multi-core aware and memory efficient, allowing it to handle assemblies containing millions of reads, even on a 32-bit desktop machine.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75200, 2010, 'bio', 'PathGen: a transitive gene pathway generator', 'Clement, Kendell|Gustafson, Nathaniel|Berbert, Amanda|Carroll, Hyrum|Merris, Christopher|Olsen, Ammon|Clement, Mark|Snell, Quinn|Allen, Jared|Roper, Randall J', 'Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.|Computer Science Department, Brigham Young University, Provo, Utah, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 4, 'Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genes|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/btp661', '19965882', 'Many online sources of gene interaction networks supply rich visual data regarding gene pathways that can aid in the study of biological processes, disease research and drug discovery. PathGen incorporates data from several sources to create transitive connections that span multiple gene interaction databases. Results are displayed in a comprehensible graphical format, showing gene interaction type and strength, database source and microarray expression data. These features make PathGen a valuable tool for in silico discovery of novel gene interaction pathways, which can be experimentally tested and verified. The usefulness of PathGen interaction analyses was validated using genes connected to the altered facial development related to Down syndrome.'),
(75201, 2010, 'bio', 'Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting', 'Temiz, N A|Trapp, A|Prokopyev, O A|Camacho, C J', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.', 'United States|United States|United States|United States', 9, 'Amino Acid Sequence|Base Sequence|Binding Sites|Computational Biology|DNA|Models, Molecular|Molecular Sequence Data|Mutation|Proteins|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/btp664', '19965883', 'A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger-DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.'),
(75202, 2010, 'bio', 'Biomarker detection in the integration of multiple multi-class genomic studies', 'Lu, Shuya|Li, Jia|Song, Chi|Shen, Kui|Tseng, George C', 'Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.|Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States', 30, 'Algorithms|Animals|Biomarkers|Databases, Genetic|Genome|Genomics|Humans|Mice|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp669', '19965884', 'Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored.'),
(75203, 2010, 'bio', 'Protein complex prediction based on simultaneous protein interaction network', 'Jung, Suk Hoon|Hyun, Bora|Jang, Woo-Hyuk|Hur, Hee-Young|Han, Dong-Soo', 'Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.|Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.|Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.|Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.|Department of Information & Communications Engineering, Korea Advanced Institute of Science and Technology, 119 Munjiro, Yuseong-gu, Daejeon, 305-714, Korea.', '-|-|-|-|-', 38, 'Binding Sites|Cluster Analysis|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btp668', '19965885', 'The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions.'),
(75204, 2010, 'bio', 'A new gene selection procedure based on the covariance distance', 'Hu, Rui|Qiu, Xing|Glazko, Galina', 'Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA. huruizg@hotmail.com|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA. huruizg@hotmail.com|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA. huruizg@hotmail.com', 'United States|United States|United States', 14, 'Computational Biology|Databases, Genetic|Genetic Variation|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1093/bioinformatics/btp672', '19996162', 'Very little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance.'),
(75205, 2010, 'bio', 'HIGEDA: a hierarchical gene-set genetics based algorithm for finding subtle motifs in biological sequences', 'Le, Thanh|Altman, Tom|Gardiner, Katheleen', 'Department of Computer Science and Engineering, Computational Biosciences Program, University of Colorado, Denver, CO, USA.|Department of Computer Science and Engineering, Computational Biosciences Program, University of Colorado, Denver, CO, USA.|Department of Computer Science and Engineering, Computational Biosciences Program, University of Colorado, Denver, CO, USA.', 'United States|United States|United States', 7, 'Algorithms|DNA|Databases, Genetic|Databases, Protein|Proteins|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp676', '19996163', 'Identification of motifs in biological sequences is a challenging problem because such motifs are often short, degenerate, and may contain gaps. Most algorithms that have been developed for motif-finding use the expectation-maximization (EM) algorithm iteratively. Although EM algorithms can converge quickly, they depend strongly on initialization parameters and can converge to local sub-optimal solutions. In addition, they cannot generate gapped motifs. The effectiveness of EM algorithms in motif finding can be improved by incorporating methods that choose different sets of initial parameters to enable escape from local optima, and that allow gapped alignments within motif models.'),
(75206, 2010, 'bio', 'Validation of double digest selective label database for sequenced prokaryotic genomes', 'Terletskiy, Valery|Tyshchenko, Valentina|Martinez-Ballesteros, Ilargi|Garaizar, Javier|Bikandi, Joseba', 'All Russian Research Institute of Farm Animal Genetics and Breeding, Moscowskoye sh. 55a, 196625, St. Petersburg-Pushkin, Russia.|All Russian Research Institute of Farm Animal Genetics and Breeding, Moscowskoye sh. 55a, 196625, St. Petersburg-Pushkin, Russia.|All Russian Research Institute of Farm Animal Genetics and Breeding, Moscowskoye sh. 55a, 196625, St. Petersburg-Pushkin, Russia.|All Russian Research Institute of Farm Animal Genetics and Breeding, Moscowskoye sh. 55a, 196625, St. Petersburg-Pushkin, Russia.|All Russian Research Institute of Farm Animal Genetics and Breeding, Moscowskoye sh. 55a, 196625, St. Petersburg-Pushkin, Russia.', '-|-|-|-|-', 0, 'Bacterial Proteins|Bacterial Typing Techniques|DNA, Bacterial|Databases, Genetic|Genome, Bacterial|Salmonella typhimurium|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp675', '19996164', 'A database for simulation of double digest selective label (DDSL) typing technique has been created and validated against a sequenced strain (Salmonella enterica serovar Typhimurium strain LT2). In silico bands were in agreement with experimental, and the technique was able to discriminate among strains belonging to the same species. When compared with other strain discrimination techniques, DDSL showed a higher discriminatory power. The database contains precomputed data which may be searched to retrieve experimental conditions for typing all up-to-dated sequenced prokaryotic microorganisms.'),
(75207, 2010, 'bio', 'Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana', 'Kiddle, Steven J|Windram, Oliver P F|McHattie, Stuart|Mead, Andrew|Beynon, Jim|Buchanan-Wollaston, Vicky|Denby, Katherine J|Mukherjee, Sach', 'Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk|Warwick Systems Biology Centre, Warwick University, CV4 7AL, UK. s.j.kiddle@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Arabidopsis|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btp673', '19996165', 'Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here.'),
(75208, 2010, 'bio', 'CellMC--a multiplatform model compiler for the Cell Broadband Engine and x86', 'Caulfield, Emmet|Hellander, Andreas', 'Department of Information Technology, Uppsala University, Box 337, SE-751 05, Uppsala, Sweden.|Department of Information Technology, Uppsala University, Box 337, SE-751 05, Uppsala, Sweden.', 'Sweden|Sweden', 0, 'Algorithms|Computational Biology|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp662', '19996166', 'Gillespies stochastic simulation algorithm (SSA) is often the most tractable method to study stochastic models of biochemical systems. The algorithm itself is very simple and a natural target for implementation on specialized architectures such as the Cell Broadband Engine (Cell/BE). We have developed CellMC, a multiplatform SBML model compiler implementing a vectorized version of SSA for use on Cell/BE or x86 PCs.'),
(75209, 2010, 'bio', 'ChiBE: interactive visualization and manipulation of BioPAX pathway models', 'Babur, Ozgun|Dogrusoz, Ugur|Demir, Emek|Sander, Chris', 'Center for Bioinformatics, Bilkent University, Ankara, Turkey.|Center for Bioinformatics, Bilkent University, Ankara, Turkey.|Center for Bioinformatics, Bilkent University, Ankara, Turkey.|Center for Bioinformatics, Bilkent University, Ankara, Turkey.', 'Turkey|Turkey|Turkey|Turkey', 29, 'Computational Biology|Computer Graphics|Databases, Factual|Information Storage and Retrieval|Internet|Models, Biological|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp665', '20007251', 'Representing models of cellular processes or pathways in a graphically rich form facilitates interpretation of biological observations and generation of new hypotheses. Solving biological problems using large pathway datasets requires software that can combine data mapping, querying and visualization as well as providing access to diverse data resources on the Internet. ChiBE is an open source software application that features user-friendly multi-view display, navigation and manipulation of pathway models in BioPAX format. Pathway views are rendered in a feature-rich format, and may be laid out and edited with state-of-the-art visualization methods, including compound or nested structures for visualizing cellular compartments and molecular complexes. Users can easily query and visualize pathways through an integrated Pathway Commons query tool and analyze molecular profiles in pathway context.'),
(75210, 2010, 'bio', 'A novel method for accurate one-dimensional protein structure prediction based on fragment matching', 'Zhou, Tuping|Shu, Nanjiang|Hovmöller, Sven', 'Division of Structural Chemistry, Stockholm University, Stockholm SE-106 91, Sweden.|Division of Structural Chemistry, Stockholm University, Stockholm SE-106 91, Sweden.|Division of Structural Chemistry, Stockholm University, Stockholm SE-106 91, Sweden.', 'Sweden|Sweden|Sweden', 0, 'Computational Biology|Databases, Protein|Models, Molecular|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btp679', '20007252', 'The precise prediction of one-dimensional (1D) protein structure as represented by the protein secondary structure and 1D string of discrete state of dihedral angles (i.e. Shape Strings) is a prerequisite for the successful prediction of three-dimensional (3D) structure as well as protein-protein interaction. We have developed a novel 1D structure prediction method, called Frag1D, based on a straightforward fragment matching algorithm and demonstrated its success in the prediction of three sets of 1D structural alphabets, i.e. the classical three-state secondary structure, three- and eight-state Shape Strings.'),
(75211, 2010, 'bio', 'PCIT: an R package for weighted gene co-expression networks based on partial correlation and information theory approaches', 'Watson-Haigh, Nathan S|Kadarmideen, Haja N|Reverter, Antonio', 'CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia. nathan.watson-haigh@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia. nathan.watson-haigh@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia. nathan.watson-haigh@csiro.au', 'Australia|Australia|Australia', 61, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Software', 'Journal Article', '10.1093/bioinformatics/btp674', '20007253', 'We make the PCIT algorithm, used for detecting meaningful gene-gene associations in co-expression networks, available as an R package. Automatic detection of a suitable parallel environment is used such that scripts are portable between parallel and non-parallel environments with no modification of the script.'),
(75212, 2010, 'bio', 'ConceptGen: a gene set enrichment and gene set relation mapping tool', 'Sartor, Maureen A|Mahavisno, Vasudeva|Keshamouni, Venkateshwar G|Cavalcoli, James|Wright, Zachary|Karnovsky, Alla|Kuick, Rork|Jagadish, H V|Mirel, Barbara|Weymouth, Terry|Athey, Brian|Omenn, Gilbert S', 'Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.edu|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. sartorma@umich.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, 'Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Male|Neoplasm Metastasis|Oligonucleotide Array Sequence Analysis|Pancreatic Neoplasms|Pattern Recognition, Automated|Software|Transforming Growth Factor beta', 'Journal Article', '10.1093/bioinformatics/btp683', '20007254', 'The elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality.'),
(75213, 2010, 'bio', 'On the beta-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics', 'Pham, Thang V|Piersma, Sander R|Warmoes, Marc|Jimenez, Connie R', 'OncoProteomics Laboratory, Department Medical Oncology, VUmc-Cancer Center Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. t.pham@vumc.nl|OncoProteomics Laboratory, Department Medical Oncology, VUmc-Cancer Center Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. t.pham@vumc.nl|OncoProteomics Laboratory, Department Medical Oncology, VUmc-Cancer Center Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. t.pham@vumc.nl|OncoProteomics Laboratory, Department Medical Oncology, VUmc-Cancer Center Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. t.pham@vumc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 113, 'Databases, Protein|Models, Statistical|Proteins|Proteome|Proteomics|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btp677', '20007255', 'Spectral count data generated from label-free tandem mass spectrometry-based proteomic experiments can be used to quantify proteins abundances reliably. Comparing spectral count data from different sample groups such as control and disease is an essential step in statistical analysis for the determination of altered protein level and biomarker discovery. The Fishers exact test, the G-test, the t-test and the local-pooled-error technique (LPE) are commonly used for differential analysis of spectral count data. However, our initial experiments in two cancer studies show that the current methods are unable to declare at 95% confidence level a number of protein markers that have been judged to be differential on the basis of the biology of the disease and the spectral count numbers. A shortcoming of these tests is that they do not take into account within- and between-sample variations together. Hence, our aim is to improve upon existing techniques by incorporating both the within- and between-sample variations.'),
(75214, 2010, 'bio', 'Dynamically weighted clustering with noise set', 'Shen, Yijing|Sun, Wei|Li, Ker-Chau', 'Department of Statistics at University of California, Los Angeles, CA 90095, USA.|Department of Statistics at University of California, Los Angeles, CA 90095, USA.|Department of Statistics at University of California, Los Angeles, CA 90095, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genes, Fungal|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btp671', '20007256', 'Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest. On the opposite side of clustering, there are genes that have distinct expression profiles and do not co-express with other genes. Identification of these scattered genes could enhance the performance of clustering methods.'),
(75215, 2010, 'bio', 'MARTA: a suite of Java-based tools for assigning taxonomic status to DNA sequences', 'Horton, Matthew|Bodenhausen, Natacha|Bergelson, Joy', 'Department of Ecology and Evolution, Chicago, IL 60637, USA. mhorton@uchicago.edu|Department of Ecology and Evolution, Chicago, IL 60637, USA. mhorton@uchicago.edu|Department of Ecology and Evolution, Chicago, IL 60637, USA. mhorton@uchicago.edu', 'United States|United States|United States', 20, 'Base Sequence|Databases, Genetic|Genomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btp682', '20007739', 'We have created a suite of Java-based software to better provide taxonomic assignments to DNA sequences. We anticipate that the program will be useful for protistologists, virologists, mycologists and other microbial ecologists. The program relies on NCBI utilities including the BLAST software and Taxonomy database and is easily manipulated at the command-line to specify a BLAST candidates query-coverage or percent identity requirements, other options include the ability to set minimal consensus requirements (%) for each of the eight major taxonomic ranks (Domain, Kingdom, Phylum, ...) and whether to consider lower scoring candidates when the top-hit lacks taxonomic classification.'),
(75216, 2010, 'bio', 'Tmod: toolbox of motif discovery', 'Sun, Hanchang|Yuan, Yuan|Wu, Yibo|Liu, Hui|Liu, Jun S|Xie, Hongwei', 'Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.|Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.|Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.|Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.|Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.|Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.', 'China|China|China|China|China|China', 0, 'Algorithms|Amino Acid Motifs|Computational Biology|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp681', '20007740', 'Motif discovery is an important topic in computational transcriptional regulation studies. In the past decade, many researchers have contributed to the field and many de novo motif-finding tools have been developed, each may have a different strength. However, most of these tools do not have a user-friendly interface and their results are not easily comparable. We present a software called Toolbox of Motif Discovery (Tmod) for Windows operating systems. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. Tmod provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs. Tmod is developed in C++ with the support of Microsoft Foundation Classes and Cygwin. Tmod can also be easily expanded to include future algorithms.'),
(75217, 2010, 'bio', 'QDD: a user-friendly program to select microsatellite markers and design primers from large sequencing projects', 'Meglécz, Emese|Costedoat, Caroline|Dubut, Vincent|Gilles, André|Malausa, Thibaut|Pech, Nicolas|Martin, Jean-François', 'Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr|Aix-Marseille Université, CNRS, IRD, UMR 6116-IMEP, Equipe Evolution, Génome et Environnement, Centre Saint-Charles, Case 36, 3 Place Victor Hugo, 13331 Marseille Cedex 3, France. emese.meglecz@univ-provence.fr', 'France|France|France|France|France|France|France', 446, 'Base Sequence|Computational Biology|DNA Primers|Databases, Genetic|Microsatellite Repeats|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btp670', '20007741', 'QDD is an open access program providing a user-friendly tool for microsatellite detection and primer design from large sets of DNA sequences. The program is designed to deal with all steps of treatment of raw sequences obtained from pyrosequencing of enriched DNA libraries, but it is also applicable to data obtained through other sequencing methods, using FASTA files as input. The following tasks are completed by QDD: tag sorting, adapter/vector removal, elimination of redundant sequences, detection of possible genomic multicopies (duplicated loci or transposable elements), stringent selection of target microsatellites and customizable primer design. It can treat up to one million sequences of a few hundred base pairs in the tag-sorting step, and up to 50,000 sequences in a single input file for the steps involving estimation of sequence similarity.'),
(75218, 2010, 'bio', 'WebPARE: web-computing for inferring genetic or transcriptional interactions', 'Chuang, Cheng-Long|Wu, Jia-Hong|Cheng, Chi-Sheng|Shieh, Grace S', 'Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan and Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan and Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan and Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan and Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.', '-|-|-|-', 0, 'Computational Biology|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btp684', '20007742', 'Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways, this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs.'),
(75219, 2010, 'bio', 'sORF finder: a program package to identify small open reading frames with high coding potential', 'Hanada, Kousuke|Akiyama, Kenji|Sakurai, Tetsuya|Toyoda, Tetsuro|Shinozaki, Kazuo|Shiu, Shin-Han', 'Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp|Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp|Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp|Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp|Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp|Plant Science Center, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. kohanada@psc.riken.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 55, 'Amino Acid Sequence|Computational Biology|Databases, Genetic|Open Reading Frames|Software', 'Journal Article', '10.1093/bioinformatics/btp688', '20008477', 'sORF finder is a program package for identifying small open reading frames (sORFs) with high-coding potential. This application allows the identification of coding sORFs according to the nucleotide composition bias among coding sequences and the potential functional constraint at the amino acid level through evaluation of synonymous and non-synonymous substitution rates.'),
(75220, 2010, 'bio', 'Estimating DNA coverage and abundance in metagenomes using a gamma approximation', 'Hooper, Sean D|Dalevi, Daniel|Pati, Amrita|Mavromatis, Konstantinos|Ivanova, Natalia N|Kyrpides, Nikos C', 'Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se|Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se|Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se|Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se|Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se|Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. sean.d.hooper@genpat.uu.se', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|DNA|Databases, Genetic|Metagenome|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp687', '20008478', 'Shotgun sequencing generates large numbers of short DNA reads from either an isolated organism or, in the case of metagenomics projects, from the aggregate genome of a microbial community. These reads are then assembled based on overlapping sequences into larger, contiguous sequences (contigs). The feasibility of assembly and the coverage achieved (reads per nucleotide or distinct sequence of nucleotides) depend on several factors: the number of reads sequenced, the read length and the relative abundances of their source genomes in the microbial community. A low coverage suggests that most of the genomic DNA in the sample has not been sequenced, but it is often difficult to estimate either the extent of the uncaptured diversity or the amount of additional sequencing that would be most efficacious. In this work, we regard a metagenome as a population of DNA fragments (bins), each of which may be covered by one or more reads. We employ a gamma distribution to model this bin population due to its flexibility and ease of use. When a gamma approximation can be found that adequately fits the data, we may estimate the number of bins that were not sequenced and that could potentially be revealed by additional sequencing. We evaluated the performance of this model using simulated metagenomes and demonstrate its applicability on three recent metagenomic datasets.'),
(75221, 2010, 'bio', 'Mixture-model based estimation of gene expression variance from public database improves identification of differentially expressed genes in small sized microarray data', 'Kim, Mingoo|Cho, Sung Bum|Kim, Ju Han', 'Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 110-799, Korea.|Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 110-799, Korea.|Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 110-799, Korea.', '-|-|-', 9, 'Bayes Theorem|Databases, Genetic|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp685', '20015947', 'The small number of samples in many microarray experiments is a challenge for the correct identification of differentially expressed gens (DEGs) by conventional statistical means. Information from public microarray databases can help more efficient identification of DEGs. To model various experimental conditions of a public microarray database, we applied Gaussian mixture model and extracted bi- or tri-modal distributions of gene expression. Prior variance of Baldis Bayesian framework was estimate for the analysis of the small sample-sized datasets.'),
(75222, 2010, 'bio', 'r2cat: synteny plots and comparative assembly', 'Husemann, Peter|Stoye, Jens', 'Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany. peter.husemann@cebitec.uni-bielefeld.de|Genome Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany. peter.husemann@cebitec.uni-bielefeld.de', 'Germany|Germany', 89, 'Databases, Genetic|Genome|Genomics|Sequence Analysis, DNA|Software|Synteny', 'Journal Article', '10.1093/bioinformatics/btp690', '20015948', 'Recent parallel pyrosequencing methods and the increasing number of finished genomes encourage the sequencing and investigation of closely related strains. Although the sequencing itself becomes easier and cheaper with each machine generation, the finishing of the genomes remains difficult. Instead of the desired whole genomic sequence, a set of contigs is the result of the assembly. In this applications note, we present the tool r2cat (related reference contig arrangement tool) that helps in the task of comparative assembly and also provides an interactive visualization for synteny inspection.'),
(75223, 2010, 'bio', 'RNAsnoop: efficient target prediction for H/ACA snoRNAs', 'Tafer, Hakim|Kehr, Stephanie|Hertel, Jana|Hofacker, Ivo L|Stadler, Peter F', 'Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at|Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria. htafer@tbi.univie.ac.at', 'Austria|Austria|Austria|Austria|Austria', 39, 'Algorithms|Binding Sites|Genomics|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Ribosomal|RNA, Small Nucleolar|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btp680', '20015949', 'Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many orphan snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms.'),
(75224, 2010, 'bio', 'FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence', 'Garbuzynskiy, Sergiy O|Lobanov, Michail Yu|Galzitskaya, Oxana V', 'Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.|Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region 142290, Russia.', '-|-|-', 194, 'Amyloid|Binding Sites|Computational Biology|Databases, Protein|Hydrogen Bonding|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp691', '20019059', 'Amyloidogenic regions in polypeptide chains are very important because such regions are responsible for amyloid formation and aggregation. It is useful to be able to predict positions of amyloidogenic regions in protein chains.'),
(75225, 2010, 'bio', 'Copy number variant detection in inbred strains from short read sequence data', 'Simpson, Jared T|McIntyre, Rebecca E|Adams, David J|Durbin, Richard', 'Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK.|Wellcome Trust Sanger Institute, Hinxton, CB10 1HH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Base Sequence|DNA Copy Number Variations|Gene Dosage|Genetic Variation|Mice|Mice, Inbred Strains|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp693', '20022973', 'We have developed an algorithm to detect copy number variants (CNVs) in homozygous organisms, such as inbred laboratory strains of mice, from short read sequence data. Our novel approach exploits the fact that inbred mice are homozygous at virtually every position in the genome to detect CNVs using a hidden Markov model (HMM). This HMM uses both the density of sequence reads mapped to the genome, and the rate of apparent heterozygous single nucleotide polymorphisms, to determine genomic copy number. We tested our algorithm on short read sequence data generated from re-sequencing chromosome 17 of the mouse strains A/J and CAST/EiJ with the Illumina platform. In total, we identified 118 copy number variants (43 for A/J and 75 for CAST/EiJ). We investigated the performance of our algorithm through comparison to CNVs previously identified by array-comparative genomic hybridization (array CGH). We performed quantitative-PCR validation on a subset of the calls that differed from the array CGH data sets.'),
(75226, 2010, 'bio', 'webMGR: an online tool for the multiple genome rearrangement problem', 'Lin, Chi Ho|Zhao, Hao|Lowcay, Sean Harry|Shahab, Atif|Bourque, Guillaume', 'Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, Singapore 138672.|Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, Singapore 138672.|Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, Singapore 138672.|Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, Singapore 138672.|Genome Institute of Singapore, 60 Biopolis Street, #02-01, Genome, Singapore 138672.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 11, 'Algorithms|Computational Biology|Databases, Genetic|Gene Rearrangement|Genome|Internet|Phylogeny|Software|Synteny', 'Journal Article', '10.1093/bioinformatics/btp689', '20022974', 'The algorithm MGR enables the reconstruction of rearrangement phylogenies based on gene or synteny block order in multiple genomes. Although MGR has been successfully applied to study the evolution of different sets of species, its utilization has been hampered by the prohibitive running time for some applications. In the current work, we have designed new heuristics that significantly speed up the tool without compromising its accuracy. Moreover, we have developed a web server (webMGR) that includes elaborate web output to facilitate navigation through the results.'),
(75227, 2010, 'bio', 'RNA-Seq gene expression estimation with read mapping uncertainty', 'Li, Bo|Ruotti, Victor|Stewart, Ron M|Thomson, James A|Dewey, Colin N', 'Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin, Madison, WI 53706, USA.', 'United States|United States|United States|United States|United States', 534, 'Algorithms|Animals|Base Sequence|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Genome|Mice|Sequence Analysis, RNA|Software|Zea mays', 'Journal Article', '10.1093/bioinformatics/btp692', '20022975', 'RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(75228, 2010, 'bio', 'GonadSAGE: a comprehensive SAGE database for transcript discovery on male embryonic gonad development', 'Lee, Tin-Lap|Li, Yunmin|Cheung, Hoi-Hung|Claus, Janek|Singh, Sumeeta|Sastry, Chandan|Rennert, Owen M|Lau, Yun-Fai Chris|Chan, Wai-Yee', 'Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov|Section on Developmental Genomics, Laboratory of Clinical Genomics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA. leetl@mail.nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 11, 'Animals|Databases, Genetic|Embryonic Development|Gene Expression|Genomics|Gonads|Male|Mice|Software', 'Journal Article', '10.1093/bioinformatics/btp695', '20028690', 'Serial analysis of gene expression (SAGE) provides an alternative, with additional advantages, to microarray gene expression studies. GonadSAGE is the first publicly available web-based SAGE database on male gonad development that covers six male mouse embryonic gonad stages, including E10.5, E11.5, E12.5, E13.5, E15.5 and E17.5. The sequence coverage of each SAGE library is beyond 150K, which is the most extensive sequence-based male gonadal transcriptome to date. An interactive web interface with customizable parameters is provided for analyzing male gonad transcriptome information. Furthermore, the data can be visualized and analyzed with the other genomic features in the UCSC genome browser. It represents an integrated platform that leads to a better understanding of male gonad development, and allows discovery of related novel targets and regulatory pathways.'),
(75229, 2010, 'bio', 'Power to detect selective allelic amplification in genome-wide scans of tumor data', 'Dewal, Ninad|Freedman, Matthew L|LaFramboise, Thomas|Peer, Itsik', 'Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. ninad.dewal@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. ninad.dewal@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. ninad.dewal@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. ninad.dewal@dbmi.columbia.edu', 'United States|United States|United States|United States', 7, 'Algorithms|Alleles|Gene Amplification|Gene Dosage|Genome, Human|Haplotypes|Humans|Neoplasms|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp694', '20031965', 'Somatic amplification of particular genomic regions and selection of cellular lineages with such amplifications drives tumor development. However, pinpointing genes under such selection has been difficult due to the large span of these regions. Our recently-developed method, the amplification distortion test (ADT), identifies specific nucleotide alleles and haplotypes that confer better survival for tumor cells when somatically amplified. In this work, we focus on evaluating ADTs power to detect such causal variants across a variety of tumor dataset scenarios.'),
(75230, 2010, 'bio', 'DCDB: drug combination database', 'Liu, Yanbin|Hu, Bin|Fu, Chengxin|Chen, Xin', 'Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.', 'China|China|China|China', 57, 'Computational Biology|Databases, Factual|Drug Combinations|Drug Design|Drug Industry|Internet|PubMed|United States|United States Food and Drug Administration', 'Journal Article', '10.1093/bioinformatics/btp697', '20031966', 'Rapid advances in pharmaceutical sciences have brought ever-increasing interests in combined therapies for better clinical efficacy and safety, especially in cases of complicated and refractory diseases. Innovative experimental technologies and theoretical frameworks are being actively developed for multicomponent drug research. In this work, we present the Drug Combination Database, with aims to facilitate analyses of known drug combinations, to summarize patterns of beneficial drug interactions, and to provide a basis for theoretical modeling and simulation of such drug interactions. Its current version (1.0) collected 499 approved or investigational drug combinations, including 40 unsuccessful drug combinations, involving 485 individual drugs, from >6000 references.'),
(75231, 2010, 'bio', 'Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data', 'Xie, Benhuai|Pan, Wei|Shen, Xiaotong', 'Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.|Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.|Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.', 'United States|United States|United States', 23, 'Cluster Analysis|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp707', '20031967', 'Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices.'),
(75232, 2010, 'bio', 'CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data', 'Zhang, Qunyuan|Ding, Li|Larson, David E|Koboldt, Daniel C|McLellan, Michael D|Chen, Ken|Shi, Xiaoqi|Kraja, Aldi|Mardis, Elaine R|Wilson, Richard K|Borecki, Ingrid B|Province, Michael A', 'Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.edu|Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA. qunyuan@wustl.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', 46, 'Computational Biology|DNA|Databases, Genetic|Gene Dosage|Gene Expression Profiling|Genetics, Population|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btp708', '20031968', 'DNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly used methods for RCNA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden, as well as a loss of the overall statistical power due to segmentation and discretization of individual samples data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient and particularly suitable for high-resolution and large-population studies.'),
(75233, 2010, 'bio', 'Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways', 'Tepper, Naama|Shlomi, Tomer', 'Department of Computer Science, Technion-IIT, Haifa 32000, Israel. naamat@cs.technion.ac.il|Department of Computer Science, Technion-IIT, Haifa 32000, Israel. naamat@cs.technion.ac.il', 'Israel|Israel', 142, 'Computational Biology|Escherichia coli|Genome, Bacterial|Metabolic Networks and Pathways', 'Journal Article', '10.1093/bioinformatics/btp704', '20031969', 'Computational modeling in metabolic engineering involves the prediction of genetic manipulations that would lead to optimized microbial strains, maximizing the production rate of chemicals of interest. Various computational methods are based on constraint-based modeling, which enables to anticipate the effect of genetic manipulations on cellular metabolism considering a genome-scale metabolic network. However, current methods do not account for the presence of competing pathways in a metabolic network that may diverge metabolic flux away from producing a required chemical, resulting in lower (or even zero) chemical production rates in reality-making these methods somewhat over optimistic.'),
(75234, 2010, 'bio', 'Pandora, a pathway and network discovery approach based on common biological evidence', 'Zhang, Kelvin Xi|Ouellette, B F Francis', 'Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.|Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada.', 'Canada|Canada', 9, 'Algorithms|Computational Biology|Databases, Factual|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btp701', '20031970', 'Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information.'),
(75235, 2010, 'bio', 'Bisque: a platform for bioimage analysis and management', 'Kvilekval, Kristian|Fedorov, Dmitry|Obara, Boguslaw|Singh, Ambuj|Manjunath, B S', 'Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara, CA, USA. kris@cs.ucsb.edu|Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara, CA, USA. kris@cs.ucsb.edu|Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara, CA, USA. kris@cs.ucsb.edu|Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara, CA, USA. kris@cs.ucsb.edu|Center for Bio-Image Informatics, Electrical and Computer Engineering Department, University of California Santa Barbara, CA, USA. kris@cs.ucsb.edu', 'United States|United States|United States|United States|United States', 118, 'Computational Biology|Databases, Factual|Diagnostic Imaging|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btp699', '20031971', 'Advances in the field of microscopy have brought about the need for better image management and analysis solutions. Novel imaging techniques have created vast stores of images and metadata that are difficult to organize, search, process and analyze. These tasks are further complicated by conflicting and proprietary image and metadata formats, that impede analyzing and sharing of images and any associated data. These obstacles have resulted in research resources being locked away in digital media and file cabinets. Current image management systems do not address the pressing needs of researchers who must quantify image data on a regular basis.'),
(75236, 2010, 'bio', 'DR-Integrator: a new analytic tool for integrating DNA copy number and gene expression data', 'Salari, Keyan|Tibshirani, Robert|Pollack, Jonathan R', 'Department of Pathology, Stanford University, Stanford, CA, USA. ksalari@stanford.edu|Department of Pathology, Stanford University, Stanford, CA, USA. ksalari@stanford.edu|Department of Pathology, Stanford University, Stanford, CA, USA. ksalari@stanford.edu', 'United States|United States|United States', 25, 'DNA Copy Number Variations|Gene Dosage|Gene Expression|Genome|Software', 'Journal Article', '10.1093/bioinformatics/btp702', '20031972', 'DNA copy number alterations (CNA) frequently underlie gene expression changes by increasing or decreasing gene dosage. However, only a subset of genes with altered dosage exhibit concordant changes in gene expression. This subset is likely to be enriched for oncogenes and tumor suppressor genes, and can be identified by integrating these two layers of genome-scale data. We introduce DNA/RNA-Integrator (DR-Integrator), a statistical software tool to perform integrative analyses on paired DNA copy number and gene expression data. DR-Integrator identifies genes with significant correlations between DNA copy number and gene expression, and implements a supervised analysis that captures genes with significant alterations in both DNA copy number and gene expression between two sample classes.'),
(75237, 2010, 'bio', 'Limited contribution of stem-loop potential to symmetry of single-stranded genomic DNA', 'Zhang, Shang-Hong|Huang, Ya-Zhi', 'The Key Laboratory of Gene Engineering of Ministry of Education, and Biotechnology Research Center, Sun Yat-sen University, Guangzhou 510275, China. lsszsh@mail.sysu.edu.cn|The Key Laboratory of Gene Engineering of Ministry of Education, and Biotechnology Research Center, Sun Yat-sen University, Guangzhou 510275, China. lsszsh@mail.sysu.edu.cn', 'China|China', 24, 'Computational Biology|DNA, Single-Stranded|Genes, Bacterial|Genome, Bacterial|Nucleic Acid Conformation|Oligodeoxyribonucleotides', 'Journal Article', '10.1093/bioinformatics/btp703', '20031973', 'The phenomenon of strand symmetry, which may provide clues to genome evolution, exists in all prokaryotic and eukaryotic genomes studied. Several possible mechanisms for its origins have been proposed, including: no strand biases for mutation and selection, strand inversion and selection of stem-loop structures. However, the relative contributions of these mechanisms to strand symmetry are not clear. In this article, we studied specifically the role of stem-loop potential of single-stranded DNA in strand symmetry.'),
(75238, 2010, 'bio', 'BRAT: bisulfite-treated reads analysis tool', 'Harris, Elena Y|Ponts, Nadia|Levchuk, Aleksandr|Roch, Karine Le|Lonardi, Stefano', 'Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu|Department of Computer Science, University of California, Riverside, CA 92521, USA. elenah@cs.ucr.edu', 'United States|United States|United States|United States|United States', 51, 'Gene Expression Profiling|Genome|Genomics|Software|Sulfites', 'Journal Article', '10.1093/bioinformatics/btp706', '20031974', 'We present a new, accurate and efficient tool for mapping short reads obtained from the Illumina Genome Analyzer following sodium bisulfite conversion. Our tool, BRAT, supports single and paired-end reads and handles input files containing reads and mates of different lengths. BRAT is faster, maps more unique paired-end reads and has higher accuracy than existing programs. The software package includes tools to end-trim low-quality bases of the reads and to report nucleotide counts for mapped reads on the reference genome.'),
(75239, 2010, 'bio', 'emPAI Calc--for the estimation of protein abundance from large-scale identification data by liquid chromatography-tandem mass spectrometry', 'Shinoda, Kosaku|Tomita, Masaru|Ishihama, Yasushi', 'Human Metabolome Technologies, Inc., Tsuruoka, Yamagata, Japan.|Human Metabolome Technologies, Inc., Tsuruoka, Yamagata, Japan.|Human Metabolome Technologies, Inc., Tsuruoka, Yamagata, Japan.', 'Japan|Japan|Japan', 105, 'Algorithms|Chromatography, Liquid|Databases, Protein|Proteins|Proteomics|Sequence Analysis, Protein|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btp700', '20031975', 'emPAI Calc is an open-source web application for the estimation of protein abundance. It uses the correlation between the number of identified peptides and protein abundance in mass spectrometry-based proteomic experiments. The program is the first implementation of our previously reported emPAI algorithm, it calculates the emPAI from the protein identification results obtained by database search engines such as Mascot.'),
(75240, 2010, 'bio', 'Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects', 'Scharl, Theresa|Grü, Bettinan|Leisch, Friedrich', 'Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at|Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at|Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at', 'Austria|Austria|Austria', 22, 'Computational Biology|Databases, Genetic|Escherichia coli|Gene Expression|Gene Expression Profiling|Linear Models|Models, Statistical', 'Evaluation Studies', '10.1093/bioinformatics/btp686', '20040587', 'Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli.'),
(75241, 2010, 'bio', 'GWAF: an R package for genome-wide association analyses with family data', 'Chen, Ming-Huei|Yang, Qiong', 'Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.|Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA.', 'United States|United States', 178, 'Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Genomics|Genotype|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp710', '20040588', 'GWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Studys SNP Health Association Resource project.'),
(75242, 2010, 'bio', 'jORCA: easily integrating bioinformatics Web Services', 'Martín-Requena, Victoria|Ríos, Javier|García, Maximiliano|Ramírez, Sergio|Trelles, Oswaldo', 'Computer Architecture Department, University of Malaga, 29071 Málaga, Spain.|Computer Architecture Department, University of Malaga, 29071 Málaga, Spain.|Computer Architecture Department, University of Malaga, 29071 Málaga, Spain.|Computer Architecture Department, University of Malaga, 29071 Málaga, Spain.|Computer Architecture Department, University of Malaga, 29071 Málaga, Spain.', 'Spain|Spain|Spain|Spain|Spain', 25, 'Computational Biology|Information Dissemination|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp709', '20047879', 'Web services technology is becoming the option of choice to deploy bioinformatics tools that are universally available. One of the major strengths of this approach is that it supports machine-to-machine interoperability over a network. However, a weakness of this approach is that various Web Services differ in their definition and invocation protocols, as well as their communication and data formats-and this presents a barrier to service interoperability.'),
(75243, 2010, 'bio', 'Functional embedding for the classification of gene expression profiles', 'Wu, Ping-Shi|Müller, Hans-Georg', 'Department of Mathematics, Lehigh University, Bethlehem, PA 18015, USA. psw205@lehigh.edu|Department of Mathematics, Lehigh University, Bethlehem, PA 18015, USA. psw205@lehigh.edu', 'United States|United States', 8, 'Biomarkers, Tumor|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btp711', '20053838', 'Low sample size n high-dimensional large p data with n<p are commonly encountered in genomics and statistical genetics. Ill-conditioning of the variance-covariance matrix for such data renders the traditional multivariate data analytical approaches unattractive. On the other side, functional data analysis (FDA) approaches are designed for infinite-dimensional data and therefore may have potential for the analysis of large p data. We herein propose a functional embedding (FEM) technique, which exploits the interface between multivariate and functional data, aiming at borrowing strength across the sample through FDA techniques in order to resolve the difficulties caused by the high dimension p.'),
(75244, 2010, 'bio', 'GWAS analyzer: integrating genotype, phenotype and public annotation data for genome-wide association study analysis', 'Fong, Christine|Ko, Dennis C|Wasnick, Michael|Radey, Matthew|Miller, Samuel I|Brittnacher, Mitchell', 'Department of Immunology, University of Washington, Seattle, Washington 98195, USA.|Department of Immunology, University of Washington, Seattle, Washington 98195, USA.|Department of Immunology, University of Washington, Seattle, Washington 98195, USA.|Department of Immunology, University of Washington, Seattle, Washington 98195, USA.|Department of Immunology, University of Washington, Seattle, Washington 98195, USA.|Department of Immunology, University of Washington, Seattle, Washington 98195, USA.', 'United States|United States|United States|United States|United States|United States', 22, 'Computational Biology|Databases, Genetic|Genome|Genome-Wide Association Study|Genotype|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btp714', '20053839', 'Genome-wide association studies are beginning to elucidate how our genetic differences contribute to susceptibility and severity of disease. While computational tools have previously been developed to support various aspects of genome-wide association studies, there is currently a need for informatics solutions that facilitate the integration of data from multiple sources.'),
(75245, 2010, 'bio', 'Disambiguating the species of biomedical named entities using natural language parsers', 'Wang, Xinglong|Tsujii, Junichi|Ananiadou, Sophia', 'National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk|National Centre for Text Mining, School of Computer Science, University of Manchester, Manchester, UK. xinglong.wang@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Data Mining|Databases, Factual|Natural Language Processing|Species Specificity|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btq002', '20053840', 'Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers.'),
(75246, 2010, 'bio', 'Bioinformatics challenges for genome-wide association studies', 'Moore, Jason H|Asselbergs, Folkert W|Williams, Scott M', 'Department of Genetics, Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu|Department of Genetics, Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu|Department of Genetics, Department of Community and Family Medicine, Dartmouth Medical School, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu', 'Lebanon|Lebanon|Lebanon', 0, 'Algorithms|Computational Biology|Data Mining|Databases, Genetic|Genome, Human|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btp713', '20053841', 'The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype-phenotype relationship that is characterized by significant heterogeneity and gene-gene and gene-environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.'),
(75247, 2010, 'bio', 'Xper2: introducing e-taxonomy', 'Ung, Visotheary|Dubus, Guillaume|Zaragüeta-Bagils, René|Vignes-Lebbe, Régine', 'UPMC, Université Pierre et Marie Curie, UMR 7207 (CR2P), Centre de Recherche sur la Paléobiodiversité et les Paléoenvironnements, Paris, France. visotheary.riviere-ung@snv.jussieu.fr|UPMC, Université Pierre et Marie Curie, UMR 7207 (CR2P), Centre de Recherche sur la Paléobiodiversité et les Paléoenvironnements, Paris, France. visotheary.riviere-ung@snv.jussieu.fr|UPMC, Université Pierre et Marie Curie, UMR 7207 (CR2P), Centre de Recherche sur la Paléobiodiversité et les Paléoenvironnements, Paris, France. visotheary.riviere-ung@snv.jussieu.fr|UPMC, Université Pierre et Marie Curie, UMR 7207 (CR2P), Centre de Recherche sur la Paléobiodiversité et les Paléoenvironnements, Paris, France. visotheary.riviere-ung@snv.jussieu.fr', 'France|France|France|France', 42, 'Classification|Computer Graphics|Databases, Factual|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1093/bioinformatics/btp715', '20053842', 'Computer Aided Identification systems provide users with the resources to relate morpho-anatomic observations with taxa names and to subsequently access other knowledge about the organisms. They have the ability to manage descriptive data and make identifications through interactive keys. They are essential for both authors and users of biodiversity information. Xper(2) version 2.0 is one of the most user-friendly tools in its category and provides a complete environment dedicated to taxonomic management.'),
(75248, 2010, 'bio', 'dbTEU: a protein database of trace element utilization', 'Zhang, Yan|Gladyshev, Vadim N', 'Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA.|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States', 15, 'Cobalt|Copper|Databases, Protein|Molybdenum|Nickel|Proteomics|Selenium|Software|Trace Elements', 'Journal Article', '10.1093/bioinformatics/btp705', '20053843', 'Biological trace elements are required for numerous biological processes and by all organisms. We describe a database, dbTEU (DataBase of Trace Element Utilization), that features known transporters and user proteins for five trace elements (copper, molybdenum, nickel, cobalt and selenium) and represents sequenced organisms from the three domains of life. The manually curated dbTEU currently includes approximately 16,500 proteins from >700 organisms, and offers interactive trace element, protein, organism and sequence search and browse tools.'),
(75249, 2010, 'bio', 'CD-HIT Suite: a web server for clustering and comparing biological sequences', 'Huang, Ying|Niu, Beifang|Gao, Ying|Fu, Limin|Li, Weizhong', 'California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, CA, USA.', 'United States|United States|United States|United States|United States', 1085, 'Cluster Analysis|Computational Biology|Databases, Genetic|Internet|Sequence Alignment|Sequence Analysis|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btq003', '20053844', 'CD-HIT is a widely used program for clustering and comparing large biological sequence datasets. In order to further assist the CD-HIT users, we significantly improved this program with more functions and better accuracy, scalability and flexibility. Most importantly, we developed a new web server, CD-HIT Suite, for clustering a user-uploaded sequence dataset or comparing it to another dataset at different identity levels. Users can now interactively explore the clusters within web browsers. We also provide downloadable clusters for several public databases (NCBI NR, Swissprot and PDB) at different identity levels.'),
(75250, 2010, 'bio', 'PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta', 'Chaudhury, Sidhartha|Lyskov, Sergey|Gray, Jeffrey J', 'Program in Molecular Biophysics, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.|Program in Molecular Biophysics, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.|Program in Molecular Biophysics, Sidney Kimmel Comprehensive Cancer Center and Institute of Computational Medicine, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.', 'United States|United States|United States', 0, 'Algorithms|Internet|Models, Molecular|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq007', '20061306', 'PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design.'),
(75251, 2010, 'bio', 'BamView: viewing mapped read alignment data in the context of the reference sequence', 'Carver, Tim|Böhme, Ulrike|Otto, Thomas D|Parkhill, Julian|Berriman, Matthew', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. artemis@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Base Sequence|Databases, Genetic|Genome|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btq010', '20071372', 'BamView is an interactive Java application for visualizing the large amounts of data stored for sequence reads which are aligned against a reference genome sequence. It supports the BAM (Binary Alignment/Map) format. It can be used in a number of contexts including SNP calling and structural annotation. BamView has also been integrated into Artemis so that the reads can be viewed in the context of the nucleotide sequence and genomic features.'),
(75252, 2010, 'bio', 'COPS benchmark: interactive analysis of database search methods', 'Frank, Karl|Gruber, Markus|Sippl, Manfred J', 'Center of Applied Molecular Engineering, Division of Bioinformatics, Department of Molecular Biology, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, Division of Bioinformatics, Department of Molecular Biology, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, Division of Bioinformatics, Department of Molecular Biology, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.', 'Austria|Austria|Austria', 7, 'Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btp712', '20080504', 'The performance of sequence database search methods is usually judged by receiver operating characteristic (ROC) analysis. The proper interpretation of the results obtained and a fair comparison across different methods critically depends on the properties of the data set used for such an analysis, in particular, each query must have the same number of true positives and true negatives. Here, we present a novel web service based on a dataset specifically designed for ROC analysis and the investigation of alignment quality. The data set is derived from a quantitative classification of protein structures (COPS), while analysis and results are presented through an intuitive web interface. The analysis provides details such as false positives per query, and visualization of the structural similarity between query and targets. Most importantly, results obtained for a specific alignment method are immediately related to those obtained for several popular standard sequence alignment methods.'),
(75253, 2010, 'bio', 'Fast and accurate long-read alignment with Burrows-Wheeler transform', 'Li, Heng|Durbin, Richard', 'Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Base Sequence|Genome, Human|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btp698', '20080505', 'Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time.'),
(75254, 2010, 'bio', 'A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes', 'Markitsis, Anastasios|Lai, Yinglei', 'Department of Statistics, The George Washington University, Washington, DC 20052, USA.|Department of Statistics, The George Washington University, Washington, DC 20052, USA.', 'United States|United States', 16, 'Computer Simulation|Databases, Genetic|False Positive Reactions|Gene Expression Profiling|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btq001', '20080506', 'The proportion of non-differentially expressed genes (pi(0)) is an important quantity in microarray data analysis. Although many statistical methods have been proposed for its estimation, it is still necessary to develop more efficient methods.'),
(75255, 2010, 'bio', 'Active site prediction using evolutionary and structural information', 'Sankararaman, Sriram|Sha, Fei|Kirsch, Jack F|Jordan, Michael I|Sjölander, Kimmen', 'Computer Science Division, University of California, Berkeley, USA.|Computer Science Division, University of California, Berkeley, USA.|Computer Science Division, University of California, Berkeley, USA.|Computer Science Division, University of California, Berkeley, USA.|Computer Science Division, University of California, Berkeley, USA.', 'United States|United States|United States|United States|United States', 0, 'Binding Sites|Catalysis|Catalytic Domain|Databases, Protein|Evolution, Molecular|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btq008', '20080507', 'The identification of catalytic residues is a key step in understanding the function of enzymes. While a variety of computational methods have been developed for this task, accuracies have remained fairly low. The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark. Here we present a new method, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites.'),
(75256, 2010, 'bio', 'MSMSpdbb: providing protein databases of closely related organisms to improve proteomic characterization of prokaryotic microbes', 'de Souza, Gustavo A|Arntzen, Magnus Ø|Wiker, Harald G', 'The Gade Institute, Section for Microbiology and Immunology, University of Bergen, Norway.|The Gade Institute, Section for Microbiology and Immunology, University of Bergen, Norway.|The Gade Institute, Section for Microbiology and Immunology, University of Bergen, Norway.', 'Norway|Norway|Norway', 22, 'Databases, Protein|Prokaryotic Cells|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btq004', '20080508', 'The Microbial Proteomic Resource (MPR) is a repository service that contains non-redundant protein databases of related bacterial strains, which were generated through an in-house developed software called Multi-Strain Mass Spectrometry Prokaryotic DataBase Builder (MSMSpdbb). MSMSpdbb merges and clusters protein sequences inferred from genomic sequences, and provide a protein list in FASTA format that covers for divergence in gene annotation, translational start site choice and presence of single nucleotide polymorphisms and other mutations.'),
(75257, 2010, 'bio', 'pegas: an R package for population genetics with an integrated-modular approach', 'Paradis, Emmanuel', 'Institut de Recherche pour le Développement, UR175 CAVIAR, BP 5095, 361 rue Jean-François Breton, F-34196 Montpellier Cédex 5, France. emmanuel.paradis@ird.fr', 'France', 771, 'Computational Biology|Databases, Genetic|Genetics, Population|Haplotypes|Software', 'Journal Article', '10.1093/bioinformatics/btp696', '20080509', 'pegas (Population and Evolutionary Genetics Analysis System) is a new package for the analysis of population genetic data. It is written in R and is integrated with two other existing R packages (ape and adegenet). pegas provides functions for standard population genetic methods, as well as low-level functions for developing new methods. The flexible and efficient graphical capabilities of R are used for plotting haplotype networks as well as for other functionalities. pegas emphasizes the need to further develop an integrated-modular approach for software dedicated to the analysis of population genetic data.'),
(75258, 2010, 'bio', 'NEMO: a tool for analyzing gene and chromosome territory distributions from 3D-FISH experiments', 'Iannuccelli, E|Mompart, F|Gellin, J|Lahbib-Mansais, Y|Yerle, M|Boudier, T', 'Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr|Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr|Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr|Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr|Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr|Institut National de la Recherche Agronomique, Laboratoire de Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan, France. eddie.iannuccelli@toulouse.inra.fr', 'France|France|France|France|France|France', 0, 'Cell Nucleus|Chromosomes|Genes|Image Processing, Computer-Assisted|In Situ Hybridization, Fluorescence|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btq013', '20080510', 'Three-dimensional fluorescence in situ hybridization (3D-FISH) is used to study the organization and the positioning of chromosomes or specific sequences such as genes or RNA in cell nuclei. Many different programs (commercial or free) allow image analysis for 3D-FISH experiments. One of the more efficient open-source programs for automatically processing 3D-FISH microscopy images is Smart 3D-FISH, an ImageJ plug-in designed to automatically analyze distances between genes. One of the drawbacks of Smart 3D-FISH is that it has a rather basic user interface and produces its results in various text and image files thus making the data post-processing step time consuming. We developed a new Smart 3D-FISH graphical user interface, NEMO, which provides all information in the same place so that results can be checked and validated efficiently. NEMO gives users the ability to drive their experiments analysis in either automatic, semi-automatic or manual detection mode. We also tuned Smart 3D-FISH to better analyze chromosome territories.'),
(75259, 2010, 'bio', 'Faster computation of exact RNA shape probabilities', 'Janssen, Stefan|Giegerich, Robert', 'Practical Computer Science, Faculty of Technology, Bielefeld University, D-33615 Bielefeld, Germany.|Practical Computer Science, Faculty of Technology, Bielefeld University, D-33615 Bielefeld, Germany.', 'Germany|Germany', 16, 'Base Sequence|Computational Biology|Databases, Genetic|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btq014', '20080511', 'Abstract shape analysis allows efficient computation of a representative sample of low-energy foldings of an RNA molecule. More comprehensive information is obtained by computing shape probabilities, accumulating the Boltzmann probabilities of all structures within each abstract shape. Such information is superior to free energies because it is independent of sequence length and base composition. However, up to this point, computation of shape probabilities evaluates all shapes simultaneously and comes with a computation cost which is exponential in the length of the sequence.');
