INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74203, 2008, 'bio', 'Bayesian survival analysis in genetic association studies', 'Tachmazidou, Ioanna|Andrew, Toby|Verzilli, Claudio J|Johnson, Michael R|De Iorio, Maria', 'Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, UK. ioanna.tachmazidou@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'ATP-Binding Cassette Transporters|Bayes Theorem|Computer Simulation|Epilepsy|Genetic Predisposition to Disease|Genome, Human|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btn351', '18617538', 'Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations.'),
(74204, 2008, 'bio', 'Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli', 'Covert, Markus W|Xiao, Nan|Chen, Tiffany J|Karr, Jonathan R', 'Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305-5444, USA. covert@stanford.edu|Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305-5444, USA. covert@stanford.edu|Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305-5444, USA. covert@stanford.edu|Department of Bioengineering, Stanford University, 318 Campus Drive, Stanford, CA 94305-5444, USA. covert@stanford.edu', 'United States|United States|United States|United States', 205, 'Computer Simulation|Escherichia coli|Gene Expression Regulation, Bacterial|Models, Biological|Proteome|Signal Transduction|Systems Integration|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn352', '18621757', 'The effort to build a whole-cell model requires the development of new modeling approaches, and in particular, the integration of models for different types of processes, each of which may be best described using different representation. Flux-balance analysis (FBA) has been useful for large-scale analysis of metabolic networks, and methods have been developed to incorporate transcriptional regulation (regulatory FBA, or rFBA). Of current interest is the integration of these approaches with detailed models based on ordinary differential equations (ODEs).'),
(74205, 2008, 'bio', 'Classification with reject option in gene expression data', 'Hanczar, Blaise|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.', 'United States|United States', 35, 'Algorithms|Artifacts|Artificial Intelligence|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn349', '18621758', 'The classification methods typically used in bioinformatics classify all examples, even if the classification is ambiguous, for instance, when the example is close to the separating hyperplane in linear classification. For medical applications, it may be better to classify an example only when there is a sufficiently high degree of accuracy, rather than classify all examples with decent accuracy. Moreover, when all examples are classified, the classification rule has no control over the accuracy of the classifier, the algorithm just aims to produce a classifier with the smallest error rate possible. In our approach, we fix the accuracy of the classifier and thereby choose a desired risk of error.'),
(74206, 2008, 'bio', 'Metagenomics reveals our incomplete knowledge of global diversity', 'Pignatelli, Miguel|Aparicio, Gabriel|Blanquer, Ignacio|Hernández, Vicente|Moya, Andrés|Tamames, Javier', '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.|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|United States|United States', 31, 'Biodiversity|Ecosystem|Genetic Variation|Genomics', 'Letter', '10.1093/bioinformatics/btn355', '18625611', '-'),
(74207, 2008, 'bio', 'Semantic reclassification of the UMLS concepts', 'Fan, Jung-Wei|Friedman, Carol', 'Department of Biomedical Informatics, Columbia University, 622 W 168th St, VC5, New York, NY10032, USA. fan@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 W 168th St, VC5, New York, NY10032, USA. fan@dbmi.columbia.edu', 'United States|United States', 0, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Semantics|Terminology as Topic|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/btn343', '18625612', 'Accurate semantic classification is valuable for text mining and knowledge-based tasks that perform inference based on semantic classes. To benefit applications using the semantic classification of the Unified Medical Language System (UMLS) concepts, we automatically reclassified the concepts based on their lexical and contextual features. The new classification is useful for auditing the original UMLS semantic classification and for building biomedical text mining applications.'),
(74208, 2009, 'bio', 'The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis', 'Koziol, James A|Feng, Anne C|Jia, Zhenyu|Wang, Yipeng|Goodison, Seven|McClelland, Michael|Mercola, Dan', 'The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.|The Scripps Research Institute, La Jolla, San Diego, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 20, 'Cohort Studies|Computational Biology|Disease Progression|Disease-Free Survival|Genes, Neoplasm|Humans|Male|Prognosis|Proportional Hazards Models|Prostatectomy|Prostatic Neoplasms|Recurrence|Risk Factors|Time Factors', 'Journal Article', '10.1093/bioinformatics/btn354', '18628288', 'Classification and regression trees have long been used for cancer diagnosis and prognosis. Nevertheless, instability and variable selection bias, as well as overfitting, are well-known problems of tree-based methods. In this article, we investigate whether ensemble tree classifiers can ameliorate these difficulties, using data from two recent studies of radical prostatectomy in prostate cancer.'),
(74209, 2008, 'bio', 'PathCluster: a framework for gene set-based hierarchical clustering', 'Kim, Tae-Min|Yim, Seon-Hee|Jeong, Yong-Bok|Jung, Yu-Chae|Chung, Yeun-Jun', 'Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.|Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.|Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.|Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.|Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.', '-|-|-|-|-', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Multigene Family|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn357', '18628289', 'Gene clustering and gene set-based functional analysis are widely used for the analysis of expression profiles. The development of a comprehensive method jointly combining the two methods would allow for greater biological insights.'),
(74210, 2008, 'bio', 'Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein', 'Lara, James|Wohlhueter, Robert M|Dimitrova, Zoya|Khudyakov, Yury E', 'Division of Viral Hepatitis and Biotechnology Core Facility, Division of Scientific Resources, Centers for Disease Control and Prevention, 1600 Clifton Road MS A33, Atlanta, GA, 30333, USA. jlara@cdc.gov|Division of Viral Hepatitis and Biotechnology Core Facility, Division of Scientific Resources, Centers for Disease Control and Prevention, 1600 Clifton Road MS A33, Atlanta, GA, 30333, USA. jlara@cdc.gov|Division of Viral Hepatitis and Biotechnology Core Facility, Division of Scientific Resources, Centers for Disease Control and Prevention, 1600 Clifton Road MS A33, Atlanta, GA, 30333, USA. jlara@cdc.gov|Division of Viral Hepatitis and Biotechnology Core Facility, Division of Scientific Resources, Centers for Disease Control and Prevention, 1600 Clifton Road MS A33, Atlanta, GA, 30333, USA. jlara@cdc.gov', 'United States|United States|United States|United States', 11, 'Algorithms|Amino Acid Sequence|Antigen-Antibody Complex|Antigens|Epitope Mapping|Molecular Sequence Data|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Conformation|Sequence Analysis, Protein|Structure-Activity Relationship|Viral Nonstructural Proteins', 'Journal Article', '10.1093/bioinformatics/btn339', '18628290', 'Insufficient knowledge of general principles for accurate quantitative inference of biological properties from sequences is a major obstacle in the rationale design of proteins with predetermined activities. Due to this deficiency, protein engineering frequently relies on the use of computational approaches focused on the identification of quantitative structure-activity relationship (SAR) for each specific task. In the current article, a computational model was developed to define SAR for a major conformational antigenic epitope of the hepatitis C virus (HCV) non-structural protein 3 (NS3) in order to facilitate a rationale design of HCV antigens with improved diagnostically relevant properties.'),
(74211, 2008, 'bio', 'Apparently low reproducibility of true differential expression discoveries in microarray studies', 'Zhang, Min|Yao, Chen|Guo, Zheng|Zou, Jinfeng|Zhang, Lin|Xiao, Hui|Wang, Dong|Yang, Da|Gong, Xue|Zhu, Jing|Li, Yanhui|Li, Xia', 'School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.|School of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btn365', '18632747', 'Differentially expressed gene (DEG) lists detected from different microarray studies for a same disease are often highly inconsistent. Even in technical replicate tests using identical samples, DEG detection still shows very low reproducibility. It is often believed that current small microarray studies will largely introduce false discoveries.'),
(74212, 2008, 'bio', 'EPoS: a modular software framework for phylogenetic analysis', 'Griebel, Thasso|Brinkmeyer, Malte|Böcker, Sebastian', 'Faculty of Mathematics and Computer Science, Friedrich-Schiller-University Jena, 07743 Jena, Germany. thasso@minet.uni-jena.de|Faculty of Mathematics and Computer Science, Friedrich-Schiller-University Jena, 07743 Jena, Germany. thasso@minet.uni-jena.de|Faculty of Mathematics and Computer Science, Friedrich-Schiller-University Jena, 07743 Jena, Germany. thasso@minet.uni-jena.de', 'Germany|Germany|Germany', 37, 'Algorithms|Computational Biology|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn364', '18632748', 'Estimating Phylogenies of Species (EPoS) is a modular software framework for phylogenetic analysis, visualization and data management. It provides a plugin-based system that integrates a storage facility, a rich user interface and the ability to easily incorporate new methods, functions and visualizations. EPoS ships with persistent data management, a set of well-known phylogenetic algorithms and a multitude of tree visualization methods and layouts. Implemented algorithms cover distance-based tree construction, consensus trees and various graph-based supertree methods. The rendering system can be customized for, say, different edge and node styles.'),
(74213, 2008, 'bio', 'Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis', 'Masso, Majid|Vaisman, Iosif I', 'Department of Bioinformatics and Computational Biology, Laboratory for Structural Bioinformatics, George Mason University, 10900 University Blvd, MSN 5B3, Manassas, VA 20110, USA.|Department of Bioinformatics and Computational Biology, Laboratory for Structural Bioinformatics, George Mason University, 10900 University Blvd, MSN 5B3, Manassas, VA 20110, USA.', 'United States|United States', 115, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Simulation|Databases, Protein|Models, Molecular|Mutagenesis|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship|Thermodynamics', 'Evaluation Studies', '10.1093/bioinformatics/btn353', '18632749', 'Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (DeltaDeltaG) and denaturant (DeltaDeltaG(H2O)) denaturations, as well as mutant thermal stability (DeltaT(m)), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal.'),
(74214, 2008, 'bio', 'Automated annotation of Drosophila gene expression patterns using a controlled vocabulary', 'Ji, Shuiwang|Sun, Liang|Jin, Rong|Kumar, Sudhir|Ye, Jieping', 'Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.|Department of Computer Science and Engineering, Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Documentation|Drosophila Proteins|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Signal Transduction|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn347', '18632750', 'Regulation of gene expression in space and time directs its localization to a specific subset of cells during development. Systematic determination of the spatiotemporal dynamics of gene expression plays an important role in understanding the regulatory networks driving development. An atlas for the gene expression patterns of fruit fly Drosophila melanogaster has been created by whole-mount in situ hybridization, and it documents the dynamic changes of gene expression pattern during Drosophila embryogenesis. The spatial and temporal patterns of gene expression are integrated by anatomical terms from a controlled vocabulary linking together intermediate tissues developed from one another. Currently, the terms are assigned to patterns manually. However, the number of patterns generated by high-throughput in situ hybridization is rapidly increasing. It is, therefore, tempting to approach this problem by employing computational methods.'),
(74215, 2008, 'bio', 'BioBayes: a software package for Bayesian inference in systems biology', 'Vyshemirsky, Vladislav|Girolami, Mark', 'Department of Computing Science, University of Glasgow, G12 8QQ, UK. vvv@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, G12 8QQ, UK. vvv@dcs.gla.ac.uk', 'United Kingdom|United Kingdom', 34, 'Algorithms|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn338', '18632751', 'There are several levels of uncertainty involved in the mathematical modelling of biochemical systems. There often may be a degree of uncertainty about the values of kinetic parameters, about the general structure of the model and about the behaviour of biochemical species which cannot be observed directly. The methods of Bayesian inference provide a consistent framework for modelling and predicting in these uncertain conditions. We present a software package for applying the Bayesian inferential methodology to problems in systems biology.'),
(74216, 2008, 'bio', 'Considering dependence among genes and markers for false discovery control in eQTL mapping', 'Chen, Liang|Tong, Tiejun|Zhao, Hongyu', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA. liang.chen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA. liang.chen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA. liang.chen@usc.edu', 'United States|United States|United States', 10, 'Chromosome Mapping|Computer Simulation|Genetic Markers|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btn373', '18635565', 'Multiple comparison adjustment is a significant and challenging statistical issue in large-scale biological studies. In previous studies, dependence among genes is largely ignored. However, such dependence may be strong for some genomic-scale studies such as genetical genomics [also called expression quantitative trait loci (eQTL) mapping] in which thousands of genes are treated as quantitative traits and mapped to different genetical markers. Besides the dependence among markers, the dependence among the expression levels of genes can also have a significant impact on data analysis and interpretation.'),
(74217, 2008, 'bio', 'ProCope--protein complex prediction and evaluation', 'Krumsiek, Jan|Friedel, Caroline C|Zimmer, Ralf', 'Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany.|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany.|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Databases, Protein|Models, Biological|Multiprotein Complexes|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn376', '18635566', 'Recent advances in high-throughput technology have increased the quantity of available data on protein complexes and stimulated the development of many new prediction methods. In this article, we present ProCope, a Java software suite for the prediction and evaluation of protein complexes from affinity purification experiments which integrates the major methods for calculating interaction scores and predicting protein complexes published over the last years. Methods can be accessed via a graphical user interface, command line tools and a Java API. Using ProCope, existing algorithms can be applied quickly and reproducibly on new experimental results, individual steps of the different algorithms can be combined in new and innovative ways and new methods can be implemented and integrated in the existing prediction framework.'),
(74218, 2008, 'bio', 'A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?', 'Haibe-Kains, B|Desmedt, C|Sotiriou, C|Bontempi, G', 'Machine Learning Group, Department of Computer Science, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be|Machine Learning Group, Department of Computer Science, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be|Machine Learning Group, Department of Computer Science, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be|Machine Learning Group, Department of Computer Science, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. bhaibeka@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium', 136, 'Algorithms|Breast Neoplasms|Female|Gene Expression Profiling|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Phenotype|Survival Analysis', 'Comparative Study', '10.1093/bioinformatics/btn374', '18635567', 'Survival prediction of breast cancer (BC) patients independently of treatment, also known as prognostication, is a complex task since clinically similar breast tumors, in addition to be molecularly heterogeneous, may exhibit different clinical outcomes. In recent years, the analysis of gene expression profiles by means of sophisticated data mining tools emerged as a promising technology to bring additional insights into BC biology and to improve the quality of prognostication. The aim of this work is to assess quantitatively the accuracy of prediction obtained with state-of-the-art data analysis techniques for BC microarray data through an independent and thorough framework.'),
(74219, 2008, 'bio', 'Development of a two-dimensional agent-based model for chronic chagasic cardiomyopathy after stem cell transplantation', 'Galvão, Viviane|Miranda, José Garcia Vivas|Ribeiro-dos-Santos, Ricardo', 'Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, BA, Brazil. vivianegalvao@uefs.br|Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, BA, Brazil. vivianegalvao@uefs.br|Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, BA, Brazil. vivianegalvao@uefs.br', 'Brazil|Brazil|Brazil', 15, 'Adult Stem Cells|Animals|Bone Marrow Transplantation|Chagas Cardiomyopathy|Computer Simulation|Disease Models, Animal|Kinetics|Mice|Myocardium|Stem Cell Transplantation', 'Journal Article', '10.1093/bioinformatics/btn362', '18635568', 'A significant issue in stem cell therapy is to understand the role of this type of cell in the tissue regeneration. To explain this mechanism, an experimental study has quantified that the bone marrow cell transplantation decreases the number of inflammatory cells and reduces the fibrosis area in chagasic mice. Using this experimental data, we have developed an agent-based computational model to investigate the regeneration of the chronic chagasic cardiomyopathy after bone marrow stem cell transplantation.'),
(74220, 2008, 'bio', 'Sircah: a tool for the detection and visualization of alternative transcripts', 'Harrington, Eoghan D|Bork, Peer', 'Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.|Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.', 'Germany|Germany', 7, 'Algorithms|Base Sequence|Computer Graphics|Molecular Sequence Data|RNA Splice Sites|Sequence Analysis, DNA|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn361', '18635569', 'Sircah is a flexible tool for the detection, analysis and visualization of alternative transcripts. It takes as input gene models or spliced alignments and creates a database of alternative transcription events: alternative transcription initiation and polyadenylation, alternative 3 and 5 splice-site usage, skipped exons and retained introns. The results can be visualized in a variety of ways, allowing the creation of publication quality images.'),
(74221, 2008, 'bio', 'mlegp: statistical analysis for computer models of biological systems using R', 'Dancik, Garrett M|Dorman, Karin S', 'Program in Bioinformatics & Computational Biology, Department of Statistics and Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50010, USA.|Program in Bioinformatics & Computational Biology, Department of Statistics and Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50010, USA.', 'United States|United States', 14, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Normal Distribution|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btn329', '18635570', 'Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of important model inputs.'),
(74222, 2008, 'bio', 'MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence', 'Lippi, Marco|Passerini, Andrea|Punta, Marco|Rost, Burkhard|Frasconi, Paolo', 'Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.|Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.|Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.|Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.|Dipartimento di Sistemi e Informatica, Machine Learning and Neural Networks Group, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy.', 'Italy|Italy|Italy|Italy|Italy', 42, 'Amino Acid Sequence|Binding Sites|Computational Biology|Computer Simulation|Cysteine|Databases, Protein|Disulfides|Histidine|Internet|Metalloproteins|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn371', '18635571', 'The web server MetalDetector classifies histidine residues in proteins into one of two states (free or metal bound) and cysteines into one of three states (free, metal bound or disulfide bridged). A decision tree integrates predictions from two previously developed methods (DISULFIND and Metal Ligand Predictor). Cross-validated performance assessment indicates that our server predicts disulfide bonding state at 88.6% precision and 85.1% recall, while it identifies cysteines and histidines in transition metal-binding sites at 79.9% precision and 76.8% recall, and at 60.8% precision and 40.7% recall, respectively.'),
(74223, 2008, 'bio', 'The protein information and property explorer: an easy-to-use, rich-client web application for the management and functional analysis of proteomic data', 'Ramos, H|Shannon, P|Aebersold, R', 'Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103-8904, USA.|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103-8904, USA.|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103-8904, USA.', 'United States|United States|United States', 25, 'Computational Biology|Databases, Protein|Information Storage and Retrieval|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn363', '18635572', 'Mass spectrometry experiments in the field of proteomics produce lists containing tens to thousands of identified proteins. With the protein information and property explorer (PIPE), the biologist can acquire functional annotations for these proteins and explore the enrichment of the list, or fraction thereof, with respect to functional classes. These protein lists may be saved for access at a later time or different location. The PIPE is interoperable with the Firegoose and the Gaggle, permitting wide-ranging data exploration and analysis. The PIPE is a rich-client web application which uses AJAX capabilities provided by the Google Web Toolkit, and server-side data storage using Hibernate.'),
(74224, 2008, 'bio', 'MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization', 'Blum, Torsten|Kohlbacher, Oliver', 'Division for Simulation of Biological Systems, ZBIT/WSI, University of Tübingen, Tübingen, Germany. blum@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, ZBIT/WSI, University of Tübingen, Tübingen, Germany. blum@informatik.uni-tuebingen.de', 'Germany|Germany', 55, 'Algorithms|Computational Biology|Computer Graphics|Databases, Genetic|Information Storage and Retrieval|Metabolic Networks and Pathways|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn360', '18635573', 'We present MetaRoute, an efficient search algorithm based on atom mapping rules and path weighting schemes that returns relevant or textbook-like routes between a source and a product metabolite within seconds for genome-scale networks. Its speed allows the algorithm to be used interactively through a web interface to visualize relevant routes and local networks for one or multiple organisms based on data from KEGG.'),
(74225, 2008, 'bio', 'Message-passing algorithms for the prediction of protein domain interactions from protein-protein interaction data', 'Iqbal, Mudassar|Freitas, Alex A|Johnson, Colin G|Vergassola, Massimo', 'Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, UK. mi26@kent.ac.uk|Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, UK. mi26@kent.ac.uk|Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, UK. mi26@kent.ac.uk|Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, UK. mi26@kent.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Amino Acid Motifs|Binding Sites|Databases, Protein|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btn366', '18641010', 'Cellular processes often hinge upon specific interactions among proteins, and knowledge of these processes at a system level constitutes a major goal of proteomics. In particular, a greater understanding of protein-protein interactions can be gained via a more detailed investigation of the protein domain interactions that mediate the interactions of proteins. Existing high-throughput experimental techniques assay protein-protein interactions, yet they do not provide any direct information on the interactions among domains. Inferences concerning the latter can be made by analysis of the domain composition of a set of proteins and their interaction map. This inference problem is non-trivial, however, due to the high level of noise generally present in experimental data concerning protein-protein interactions. This noise leads to contradictions, i.e. the impossibility of having a pattern of domain interactions compatible with the protein-protein interaction map.'),
(74226, 2008, 'bio', 'Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction', 'Fenner, Kathrin|Gao, Junfeng|Kramer, Stefan|Ellis, Lynda|Wackett, Larry', 'Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland. kathrin.fenner@eawag.ch|Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland. kathrin.fenner@eawag.ch|Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland. kathrin.fenner@eawag.ch|Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland. kathrin.fenner@eawag.ch|Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland. kathrin.fenner@eawag.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 41, 'Biodegradation, Environmental|Biotransformation|Computational Biology|Databases, Factual|Environmental Pollutants|Internet|Minnesota|Reproducibility of Results|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn378', '18641402', 'The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion.'),
(74227, 2008, 'bio', 'PIGS: automatic prediction of antibody structures', 'Marcatili, Paolo|Rosi, Alessandra|Tramontano, Anna', 'Department of Biochemical Sciences and Istituto Pasteur Fondazione Cenci Bolognetti, Sapienza University, P.le A. Moro 5, 00185 Rome, Italy.|Department of Biochemical Sciences and Istituto Pasteur Fondazione Cenci Bolognetti, Sapienza University, P.le A. Moro 5, 00185 Rome, Italy.|Department of Biochemical Sciences and Istituto Pasteur Fondazione Cenci Bolognetti, Sapienza University, P.le A. Moro 5, 00185 Rome, Italy.', 'Italy|Italy|Italy', 122, 'Algorithms|Amino Acid Sequence|Antibodies|Computer Simulation|Immunoglobulin Variable Region|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn341', '18641403', 'We describe a web server for the automatic prediction of immunoglobulin variable domains based on the canonical structure model. The server is user-friendly and flexible. It allows the user to select the templates for the frameworks and the loops using different strategies. The final output is a full-fledged 3D model of the variable domains of the target immunoglobulin.'),
(74228, 2008, 'bio', 'A knowledge-based approach to predict intragenic deletions or duplications', 'Kalari, Krishna R|Casavant, Thomas L|Scheetz, Todd E', 'Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, Iowa, USA.|Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, Iowa, USA.|Department of Biomedical Engineering, Center for Bioinformatics and Computational Biology, University of Iowa, Iowa, USA.', 'United States|United States|United States', 2, 'Artificial Intelligence|Computational Biology|Gene Deletion|Gene Duplication|Genome, Human|Humans|Sequence Deletion', 'Journal Article', '10.1093/bioinformatics/btn370', '18647756', 'Despite recent improvements in high-throughput or classic molecular biology approaches it is still challenging to identify intermediate resolution genomic variations (50 bp to 50 kb). Although array-based technologies can be used to detect copy number variations in the human genome they are biased to detect only the largest such deletions or duplications. Several studies have identified deletions or duplications occurring within a gene that directly cause or predispose to disease. We have developed a novel computational system, SPeeDD (system to prioritize deletions or duplications) that utilizes machine learning techniques to predict likely candidate regions that delete or duplicate exon(s) within a gene.'),
(74229, 2008, 'bio', 'Enriched random forests', 'Amaratunga, Dhammika|Cabrera, Javier|Lee, Yung-Seop', 'Department of Nonclinical Biostatistics, Johnson & Johnson PRD LLC, Raritan, NJ 08869, USA. damaratu@prdus.jnj.com|Department of Nonclinical Biostatistics, Johnson & Johnson PRD LLC, Raritan, NJ 08869, USA. damaratu@prdus.jnj.com|Department of Nonclinical Biostatistics, Johnson & Johnson PRD LLC, Raritan, NJ 08869, USA. damaratu@prdus.jnj.com', 'United States|United States|United States', 100, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn356', '18650208', 'Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly. In such instances, the procedure can be improved by reducing the contribution of trees whose nodes are populated by non-informative features. To some extent, this can be achieved by prefiltering, but we propose a novel, yet simple, adjustment that has demonstrably superior performance: choose the eligible subsets at each node by weighted random sampling instead of simple random sampling, with the weights tilted in favor of the informative features. This results in an enriched random forest. We illustrate the superior performance of this procedure in several actual microarray datasets.'),
(74230, 2008, 'bio', 'GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population', 'Giannoulatou, Eleni|Yau, Christopher|Colella, Stefano|Ragoussis, Jiannis|Holmes, Christopher C', 'Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX13TG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 53, 'Algorithms|Cluster Analysis|Computational Biology|Genotype|Humans|Models, Statistical|Polymorphism, Single Nucleotide|Population Groups', 'Journal Article', '10.1093/bioinformatics/btn386', '18653518', 'Current genotyping algorithms typically call genotypes by clustering allele-specific intensity data on a single nucleotide polymorphism (SNP) by SNP basis. This approach assumes the availability of a large number of control samples that have been sampled on the same array and platform. We have developed a SNP genotyping algorithm for the Illumina Infinium SNP genotyping assay that is entirely within-sample and does not require the need for a population of control samples nor parameters derived from such a population. Our algorithm exhibits high concordance with current methods and >99% call accuracy on HapMap samples. The ability to call genotypes using only within-sample information makes the method computationally light and practical for studies involving small sample sizes and provides a valuable independent quality control metric for other population-based approaches.'),
(74231, 2008, 'bio', 'An integrated approach for automating validation of extracted ion chromatographic peaks', 'Nelson, William D|Viele, Kert|Lynn, Bert C', 'Department of Biology, University of Kentucky, Lexington, KY 40506, USA. wnels2@uky.edu|Department of Biology, University of Kentucky, Lexington, KY 40506, USA. wnels2@uky.edu|Department of Biology, University of Kentucky, Lexington, KY 40506, USA. wnels2@uky.edu', 'United States|United States|United States', 2, 'Algorithms|Databases, Protein|Isotope Labeling|Mass Spectrometry|Proteins|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btn385', '18653519', 'Accurate determination of extracted ion chromatographic peak areas in isotope-labeled quantitative proteomics is difficult to automate. Manual validation of identified peaks is typically required. We have integrated a peak confidence scoring algorithm into existing tools which are compatible with analysis pipelines based on the standards from the Institute for Systems Biology. This algorithm automatically excludes incorrectly identified peaks, improving the accuracy of the final protein expression ratio calculation.'),
(74232, 2008, 'bio', 'Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference', 'Morcos, Faruck|Lamanna, Charles|Sikora, Marcin|Izaguirre, Jesús', 'Department of Computer Science and Engineering and Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. cytoprophet@cse.nd.edu|Department of Computer Science and Engineering and Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. cytoprophet@cse.nd.edu|Department of Computer Science and Engineering and Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. cytoprophet@cse.nd.edu|Department of Computer Science and Engineering and Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. cytoprophet@cse.nd.edu', 'United States|United States|United States|United States', 9, 'Algorithms|Likelihood Functions|Myxococcus xanthus|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn380', '18653520', 'Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM), the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophets website.'),
(74233, 2008, 'bio', 'Robust and efficient identification of biomarkers by classifying features on graphs', 'Hwang, TaeHyun|Sicotte, Hugues|Tian, Ze|Wu, Baolin|Kocher, Jean-Pierre|Wigle, Dennis A|Kumar, Vipin|Kuang, Rui', 'Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.|Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Bioinformatics Core, Mayo Clinic College of Medicine, Rochester, MN, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 27, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Databases, Protein|Female|Gene Expression Profiling|Humans|Neoplasm Proteins', 'Evaluation Studies', '10.1093/bioinformatics/btn383', '18653521', 'A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74234, 2008, 'bio', 'hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework', 'Abo, Ryan|Knight, Stacey|Wong, Jathine|Cox, Angela|Camp, Nicola J', 'Department of Biomedical Informatics, University of Utah, UT, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, UT, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, UT, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, UT, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, UT, USA. ryan.abo@hsc.utah.edu', 'United States|United States|United States|United States|United States', 15, 'Computer Simulation|Haplotypes|Monte Carlo Method|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btn359', '18653522', 'Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous, they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions.'),
(74235, 2008, 'bio', 'DrugViz: a Cytoscape plugin for visualizing and analyzing small molecule drugs in biological networks', 'Xiong, Bing|Liu, Ke|Wu, Jie|Burk, David L|Jiang, Hualiang|Shen, Jingkang', 'State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.|State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Pudong, Shanghai, China.', 'China|China|China|China|China|China', 0, 'Computer Graphics|Computer Simulation|Databases, Factual|Databases, Protein|Drug Design|Pharmaceutical Preparations|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn389', '18658180', 'DrugViz is a Cytoscape plugin that is designed to visualize and analyze small molecules within the framework of the interactome. DrugViz can import drug-target network information in an extended SIF file format to Cytoscape and display the two-dimensional (2D) structures of small molecule nodes in a unified visualization environment. It also can identify small molecule nodes by means of three different 2D structure searching methods, namely isomorphism, substructure and fingerprint-based similarity searches. After selections, users can furthermore conduct a two-side clustering analysis on drugs and targets, which allows for a detailed analysis of the active compounds in the network, and elucidate relationships between these drugs and targets. DrugViz represents a new tool for the analysis of data from chemogenomics, metabolomics and systems biology.'),
(74236, 2008, 'bio', 'dbNEI20: building multilayer network for drug-NEI-disease', 'Zhang, Jing|Ma, Tao|Li, Yanda|Li, Shao', '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', 0, 'Animals|Computational Biology|Databases, Genetic|Drug Therapy|Gene Regulatory Networks|Humans|Models, Biological|Neuroimmunomodulation|Neurosecretory Systems|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn388', '18658181', 'The neuro-endocrine-immune (NEI) system plays a critical regulatory role in modulating host homeostasis and optimizing health. We created the dbNEI 2 years ago to collect NEI molecules and interactions. For transferring the conceptual NEI to the systematic NEI network and uncovering the NEIs medical function, we updated the dbNEI 2.0 in three ways: (i) extended NEI molecules to 2242 genes and 7657 chemical compounds by using gene ontology-based (GO-based) data mining strategy, (ii) added multilayer interactions of NEI molecules including KEGG signal transduction and metabolic pathways, HPRD protein-protein interactions (PPI), transcription factor and microRNA regulations and (iii) connected 611 drugs and 823 diseases through multilayer NEI interactions. The reconstructed drug-NEI-disease network will facilitate the systematic study of NEI system.'),
(74237, 2008, 'bio', 'The CellML Model Repository', 'Lloyd, Catherine M|Lawson, James R|Hunter, Peter J|Nielsen, Poul F', 'Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand. c.lloyd@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand. c.lloyd@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand. c.lloyd@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand. c.lloyd@auckland.ac.nz', 'New Zealand|New Zealand|New Zealand|New Zealand', 171, 'Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Models, Biological|Proteins', 'Journal Article', '10.1093/bioinformatics/btn390', '18658182', 'The CellML Model Repository provides free access to over 330 biological models. The vast majority of these models are derived from published, peer-reviewed papers. Model curation is an important and ongoing process to ensure the CellML model is able to accurately reproduce the published results. As the CellML community grows, and more people add their models to the repository, model annotation will become increasingly important to facilitate data searches and information retrieval.'),
(74238, 2008, 'bio', 'ExactFDR: exact computation of false discovery rate estimate in case-control association studies', 'Wojcik, Jérôme|Forner, Karl', 'Department of Bioinformatics, Merck Serono Geneva Research Center, 1202 Geneva, Switzerland. Jerome.wojcik@merckserono.net|Department of Bioinformatics, Merck Serono Geneva Research Center, 1202 Geneva, Switzerland. Jerome.wojcik@merckserono.net', 'Switzerland|Switzerland', 2, 'Algorithms|Case-Control Studies|Computational Biology|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Genetic Predisposition to Disease|Genome, Human|Humans|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btn379', '18662924', 'Genome-wide association studies require accurate and fast statistical methods to identify relevant signals from the background noise generated by a huge number of simultaneously tested hypotheses. It is now commonly accepted that exact computations of association probability value (P-value) are preferred to chi(2) and permutation-based approximations. Following the same principle, the ExactFDR software package improves speed and accuracy of the permutation-based false discovery rate (FDR) estimation method by replacing the permutation-based estimation of the null distribution by the generalization of the algorithm used for computing individual exact P-values. It provides a quick and accurate non-conservative estimator of the proportion of false positives in a given selection of markers, and is therefore an efficient and pragmatic tool for the analysis of genome-wide association studies.'),
(74239, 2008, 'bio', 'Mixture models for protein structure ensembles', 'Hirsch, Michael|Habeck, Michael', 'Department of Empirical Inference, Max-Planck-Institute for Biological Cybernetics, Spemannstrasse 38, 72076 Tübingen, Germany.|Department of Empirical Inference, Max-Planck-Institute for Biological Cybernetics, Spemannstrasse 38, 72076 Tübingen, Germany.', 'Germany|Germany', 6, 'Algorithms|Computer Simulation|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btn396', '18662925', 'Protein structure ensembles provide important insight into the dynamics and function of a protein and contain information that is not captured with a single static structure. However, it is not clear a priori to what extent the variability within an ensemble is caused by internal structural changes. Additional variability results from overall translations and rotations of the molecule. And most experimental data do not provide information to relate the structures to a common reference frame. To report meaningful values of intrinsic dynamics, structural precision, conformational entropy, etc., it is therefore important to disentangle local from global conformational heterogeneity.'),
(74240, 2008, 'bio', 'Site-specific evolutionary rates in proteins are better modeled as non-independent and strictly relative', 'Fernandes, Andrew D|Atchley, William R', 'Department of Biochemistry, The University of Western Ontario, London, Ontario N6A5C1, Canada. andrew@fernandes.org|Department of Biochemistry, The University of Western Ontario, London, Ontario N6A5C1, Canada. andrew@fernandes.org', 'Canada|Canada', 5, 'Algorithms|Animals|Basic-Leucine Zipper Transcription Factors|Computational Biology|Databases, Protein|Evolution, Molecular|Genes, myc|Humans|Proteins|Sequence Alignment|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/btn395', '18662926', 'In a nucleotide or amino acid sequence, not all sites evolve at the same rate, due to differing selective constraints at each site. Currently in computational molecular evolution, models incorporating rate heterogeneity always share two assumptions. First, the rate of evolution at each site is assumed to be independent of every other site. Second, the values of these rates are assumed to be drawn from a known prior distribution. Although often assumed to be small, the actual effect of these assumptions has not been previously quantified in the literature.'),
(74241, 2008, 'bio', 'HELIQUEST: a web server to screen sequences with specific alpha-helical properties', 'Gautier, Romain|Douguet, Dominique|Antonny, Bruno|Drin, Guillaume', 'Institut de Pharmacologie Moléculaire et Cellulaire, Université de Nice Sophia Antipolis and CNRS, Valbonne, France. gautier@ipmc.cnrs.fr|Institut de Pharmacologie Moléculaire et Cellulaire, Université de Nice Sophia Antipolis and CNRS, Valbonne, France. gautier@ipmc.cnrs.fr|Institut de Pharmacologie Moléculaire et Cellulaire, Université de Nice Sophia Antipolis and CNRS, Valbonne, France. gautier@ipmc.cnrs.fr|Institut de Pharmacologie Moléculaire et Cellulaire, Université de Nice Sophia Antipolis and CNRS, Valbonne, France. gautier@ipmc.cnrs.fr', 'France|France|France|France', 443, 'Computer Simulation|Databases, Protein|Internet|Models, Biological|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn392', '18662927', 'HELIQUEST calculates the physicochemical properties and amino acid composition of an alpha-helix and screens databank to identify protein segments possessing similar features. This server is also dedicated to mutating helices manually or automatically by genetic algorithm to design analogues of defined features.'),
(74242, 2008, 'bio', 'Is there an acceleration of the CpG transition rate during the mammalian radiation?', 'Peifer, M|Karro, J E|von Grünberg, H H', 'Institute of Chemistry, Karl-Franzens University Graz, Graz, Austria. peifer@uni-graz.at|Institute of Chemistry, Karl-Franzens University Graz, Graz, Austria. peifer@uni-graz.at|Institute of Chemistry, Karl-Franzens University Graz, Graz, Austria. peifer@uni-graz.at', 'Austria|Austria|Austria', 5, 'Algorithms|Animals|CpG Islands|DNA Methylation|Evolution, Molecular|Genome|Genome, Human|Humans|Mammals|Markov Chains|Mice', 'Journal Article', '10.1093/bioinformatics/btn391', '18662928', 'In this article we build a model of the CpG dinucleotide substitution rate and use it to challenge the claim that, that rate underwent a sudden mammalian-specific increase approximately 90 million years ago. The evidence supporting this hypothesis comes from the application of a model of neutral substitution rates able to account for elevated CpG dinucleotide substitution rates. With the initial goal of improving that models accuracy, we introduced a modification enabling us to account for boundary effects arising by the truncation of the Markov field, as well as improving the optimization procedure required for estimating the substitution rates.'),
(74243, 2008, 'bio', 'Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler', 'Grzegorczyk, Marco|Husmeier, Dirk|Edwards, Kieron D|Ghazal, Peter|Millar, Andrew J', 'School of Biological Sciences, The University of Edinburgh, Swann Building, Kings Buildings, Edinburgh, UK. marco@bioss.ac.uk|School of Biological Sciences, The University of Edinburgh, Swann Building, Kings Buildings, Edinburgh, UK. marco@bioss.ac.uk|School of Biological Sciences, The University of Edinburgh, Swann Building, Kings Buildings, Edinburgh, UK. marco@bioss.ac.uk|School of Biological Sciences, The University of Edinburgh, Swann Building, Kings Buildings, Edinburgh, UK. marco@bioss.ac.uk|School of Biological Sciences, The University of Edinburgh, Swann Building, Kings Buildings, Edinburgh, UK. marco@bioss.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Arabidopsis|Bayes Theorem|Circadian Rhythm|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Macrophages|Models, Genetic|Models, Statistical|Proteome', 'Journal Article', '10.1093/bioinformatics/btn367', '18664467', 'The objective of the present article is to propose and evaluate a probabilistic approach based on Bayesian networks for modelling non-homogeneous and non-linear gene regulatory processes. The method is based on a mixture model, using latent variables to assign individual measurements to different classes. The practical inference follows the Bayesian paradigm and samples the network structure, the number of classes and the assignment of latent variables from the posterior distribution with Markov Chain Monte Carlo (MCMC), using the recently proposed allocation sampler as an alternative to RJMCMC.'),
(74244, 2008, 'bio', 'Efficient whole-genome association mapping using local phylogenies for unphased genotype data', 'Ding, Zhihong|Mailund, Thomas|Song, Yun S', 'Department of Computer Science, University of California, Davis, USA.|Department of Computer Science, University of California, Davis, USA.|Department of Computer Science, University of California, Davis, USA.', 'United States|United States|United States', 9, 'Animals|Chromosome Mapping|Computer Simulation|Genomics|Genotype|Haplotypes|Humans|Phylogeny', 'Journal Article', '10.1093/bioinformatics/btn406', '18667442', 'Recent advances in genotyping technology has made data acquisition for whole-genome association study cost effective, and a current active area of research is developing efficient methods to analyze such large-scale datasets. Most sophisticated association mapping methods that are currently available take phased haplotype data as input. However, phase information is not readily available from sequencing methods and inferring the phase via computational approaches is time-consuming, taking days to phase a single chromosome.'),
(74245, 2008, 'bio', 'A fast Bayesian change point analysis for the segmentation of microarray data', 'Erdman, Chandra|Emerson, John W', 'Department of Statistics, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511, USA. chandra.erdman@yale.edu|Department of Statistics, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511, USA. chandra.erdman@yale.edu', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Chromosome Aberrations|Chromosome Mapping|Databases, Genetic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn404', '18667443', 'The ability to detect regions of genetic alteration is of great importance in cancer research. These alterations can take the form of large chromosomal gains and losses as well as smaller amplifications and deletions. The detection of such regions allows researchers to identify genes involved in cancer progression, and to fully understand differences between cancer and non-cancer tissue. The Bayesian method proposed by Barry and Hartigan is well suited for the analysis of such change point problems. In our previous article we introduced the R package bcp (Bayesian change point), an MCMC implementation of Barry and Hartigans method. In a simulation study and real data examples, bcp is shown to both accurately detect change points and estimate segment means. Earlier versions of bcp (prior to 2.0) are O(n(2)) in speed and O(n) in memory (where n is the number of observations), and run in approximately 45 min for a sequence of length 10 000. With the high resolution of newer microarrays, the number of computations in the O(n(2)) algorithm is prohibitively time-intensive.'),
(74246, 2008, 'bio', 'An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data', 'Xu, Han|Wei, Chia-Lin|Lin, Feng|Sung, Wing-Kin', 'Computational & Mathematical Biology Group, Genome Institute of Singapore, 138672 Singapore.|Computational & Mathematical Biology Group, Genome Institute of Singapore, 138672 Singapore.|Computational & Mathematical Biology Group, Genome Institute of Singapore, 138672 Singapore.|Computational & Mathematical Biology Group, Genome Institute of Singapore, 138672 Singapore.', 'Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Animals|Binding Sites|Chromatin Immunoprecipitation|Genome|Genomics|Histones|Markov Chains|Mice', 'Journal Article', '10.1093/bioinformatics/btn402', '18667444', 'Epigenetic modifications are one of the critical factors to regulate gene expression and genome function. Among different epigenetic modifications, the differential histone modification sites (DHMSs) are of great interest to study the dynamic nature of epigenetic and gene expression regulations among various cell types, stages or environmental responses. To capture the histone modifications at whole genome scale, ChIP-seq technology is becoming a robust and comprehensive approach. Thus the DHMSs are potentially identifiable by comparing two ChIP-seq libraries. However, little has been addressed on this issue in literature.'),
(74247, 2008, 'bio', 'Protein-ligand interaction prediction: an improved chemogenomics approach', 'Jacob, Laurent|Vert, Jean-Philippe', 'Mines ParisTech, Centre for Computational Biology, 35 rue Saint Honoré, F-77305 Fontainebleau, Institut Curie and INSERM, U900, F-75248, Paris, France. laurent.jacob@ensmp.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint Honoré, F-77305 Fontainebleau, Institut Curie and INSERM, U900, F-75248, Paris, France. laurent.jacob@ensmp.fr', 'France|France', 214, 'Algorithms|Binding Sites|Databases, Protein|Drug Design|Genomics|Ion Channels|Ligands|Proteins|Receptors, G-Protein-Coupled|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn409', '18676415', 'Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-based virtual screening allows the construction of predictive models by learning to discriminate known ligands from non-ligands. However, the accuracy of ligand-based models quickly degrades when the number of known ligands decreases, and in particular the approach is not applicable for orphan receptors with no known ligand.'),
(74248, 2008, 'bio', 'Genome-scale classification of metabolic reactions and assignment of EC numbers with self-organizing maps', 'Latino, Diogo A R S|Zhang, Qing-You|Aires-de-Sousa, João', 'CQFB, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|CQFB, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.|CQFB, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.', 'Portugal|Portugal|Portugal', 28, 'Computational Biology|Databases, Protein|Enzymes|Genome|Genomics|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btn405', '18676416', 'The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer-aided validation of classification systems, to genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Comparison of metabolic reactions has been mostly based on Enzyme Commission (EC) numbers, which are extremely useful and widespread, but not always straightforward to apply, and often problematic when an enzyme catalyzes several reactions, when the same reaction is catalyzed by different enzymes, when official full EC numbers are unavailable or when reactions are not catalyzed by enzymes. Different methods should be available to compare metabolic reactions. Simultaneously, methods are required for the automatic assignment of EC numbers to reactions still not officially classified.'),
(74249, 2008, 'bio', 'Large-scale computation of elementary flux modes with bit pattern trees', 'Terzer, Marco|Stelling, Jörg', 'Institute of Computational Science and Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland.|Institute of Computational Science and Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland.', 'Switzerland|Switzerland', 203, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Escherichia coli|Metabolism|Proteome|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn401', '18676417', 'Elementary flux modes (EFMs)--non-decomposable minimal pathways--are commonly accepted tools for metabolic network analysis under steady state conditions. Valid states of the network are linear superpositions of elementary modes shaping a polyhedral cone (the flux cone), which is a well-studied convex set in computational geometry. Computing EFMs is thus basically equivalent to extreme ray enumeration of polyhedral cones. This is a combinatorial problem with poorly scaling algorithms, preventing the large-scale analysis of metabolic networks so far.'),
(74250, 2008, 'bio', 'Predicting pathway membership via domain signatures', 'Fröhlich, Holger|Fellmann, Mark|Sültmann, Holger|Poustka, Annemarie|Beissbarth, Tim', 'German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Structure, Tertiary|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn403', '18676972', 'Functional characterization of genes is of great importance for the understanding of complex cellular processes. Valuable information for this purpose can be obtained from pathway databases, like KEGG. However, only a small fraction of genes is annotated with pathway information up to now. In contrast, information on contained protein domains can be obtained for a significantly higher number of genes, e.g. from the InterPro database.'),
(74251, 2008, 'bio', 'Optimizing amino acid groupings for GPCR classification', 'Davies, Matthew N|Secker, Andrew|Freitas, Alex A|Clark, Edward|Timmis, Jon|Flower, Darren R', 'Edward Jenner Institute, Compton, Newbury, Berkshire, UK.|Edward Jenner Institute, Compton, Newbury, Berkshire, UK.|Edward Jenner Institute, Compton, Newbury, Berkshire, UK.|Edward Jenner Institute, Compton, Newbury, Berkshire, UK.|Edward Jenner Institute, Compton, Newbury, Berkshire, UK.|Edward Jenner Institute, Compton, Newbury, Berkshire, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acids|Artificial Intelligence|Computational Biology|Databases, Protein|Receptors, G-Protein-Coupled|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn382', '18676973', 'There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings.'),
(74252, 2008, 'bio', 'Model-based prediction of sequence alignment quality', 'Ahola, Virpi|Aittokallio, Tero|Vihinen, Mauno|Uusipaikka, Esa', 'Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland. virpi.ahola@mtt.fi|Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland. virpi.ahola@mtt.fi|Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland. virpi.ahola@mtt.fi|Biotechnology and Food Research, MTT Agrifood Research Finland, FI-31600 Jokioinen, Finland. virpi.ahola@mtt.fi', 'Finland|Finland|Finland|Finland', 13, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Models, Statistical|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btn414', '18678587', 'Multiple sequence alignment (MSA) is an essential prerequisite for many sequence analysis methods and valuable tool itself for describing relationships between protein sequences. Since the success of the sequence analysis is highly dependent on the reliability of alignments, measures for assessing the quality of alignments are highly requisite.'),
(74253, 2008, 'bio', 'Powerful fusion: PSI-BLAST and consensus sequences', 'Przybylski, Dariusz|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA. dariusz@mit.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA. dariusz@mit.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Consensus Sequence|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btn384', '18678588', 'A typical PSI-BLAST search consists of iterative scanning and alignment of a large sequence database during which a scoring profile is progressively built and refined. Such a profile can also be stored and used to search against a different database of sequences. Using it to search against a database of consensus rather than native sequences is a simple add-on that boosts performance surprisingly well. The improvement comes at a price: we hypothesized that random alignment score statistics would differ between native and consensus sequences. Thus PSI-BLAST-based profile searches against consensus sequences might incorrectly estimate statistical significance of alignment scores. In addition, iterative searches against consensus databases may fail. Here, we addressed these challenges in an attempt to harness the full power of the combination of PSI-BLAST and consensus sequences.'),
(74254, 2008, 'bio', 'VistaClara: an expression browser plug-in for Cytoscape', 'Kincaid, Robert|Kuchinsky, Allan|Creech, Michael', 'Agilent Laboratories, Santa Clara, CA 95051, USA. robert_kincaid@agilent.com|Agilent Laboratories, Santa Clara, CA 95051, USA. robert_kincaid@agilent.com|Agilent Laboratories, Santa Clara, CA 95051, USA. robert_kincaid@agilent.com', 'United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Databases, Protein|Gene Expression|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn368', '18678589', 'VistaClara is a plug-in for Cytoscape which provides a more flexible means to visualize gene and protein expression within a network context. An extended attribute browser is provided in the form of a graphical and interactive permutation matrix that resembles the heat map displays popular in gene-expression analysis. This extended browser permits a variety of display options and interactions not currently available in Cytoscape.'),
(74255, 2008, 'bio', 'Phylocom: software for the analysis of phylogenetic community structure and trait evolution', 'Webb, Campbell O|Ackerly, David D|Kembel, Steven W', 'Arnold Arboretum, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA. cwebb@oeb.harvard.edu|Arnold Arboretum, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA. cwebb@oeb.harvard.edu|Arnold Arboretum, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA. cwebb@oeb.harvard.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btn358', '18678590', 'The increasing availability of phylogenetic and trait data for communities of co-occurring species has created a need for software that integrates ecological and evolutionary analyses. Capabilities: Phylocom calculates numerous metrics of phylogenetic community structure and trait similarity within communities. Hypothesis testing is implemented using several null models. Within the same framework, it measures phylogenetic signal and correlated evolution for species traits. A range of utility functions allow community and phylogenetic data manipulation, tree and trait generation, and integration into scientific workflows.'),
(74256, 2008, 'bio', 'Comment on Network-constrained regularization and variable selection for analysis of genomic data', 'Binder, Harald|Schumacher, Martin', 'Arnold Arboretum, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA. cwebb@oeb.harvard.edu|Arnold Arboretum, Harvard University, 22 Divinity Avenue, Cambridge, MA 02138, USA. cwebb@oeb.harvard.edu', 'United States|United States', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genome|Glioblastoma|Humans|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Comment', '10.1093/bioinformatics/btn412', '18682424', '-'),
(74257, 2008, 'bio', 'BLogo: a tool for visualization of bias in biological sequences', 'Li, Wencheng|Yang, Bo|Liang, Shaoguang|Wang, Yonghua|Whiteley, Chris|Cao, Yicheng|Wang, Xiaoning', 'School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.|School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510641, China.', 'China|China|China|China|China|China|China', 10, 'Algorithms|Base Sequence|Computational Biology|Open Reading Frames|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn407', '18682425', 'Blogo is a web-based tool that detects and displays statistically significant position-specific sequence bias with reduced background noise. The over-represented and under-represented symbols in a particular position are shown above and below the zero line. When the sequences are in open reading frames, the background frequency of nucleotides could be calculated separately for the three positions of a codon, thus greatly reducing the background noise. The chi(2)-test or Fishers exact test is used to evaluate the statistical significance of every symbol in every position and only those that are significant are highlighted in the resulting logo. The perl source code of the program is freely available and can be run locally.'),
(74258, 2008, 'bio', 'ZOOM! Zillions of oligos mapped', 'Lin, Hao|Zhang, Zefeng|Zhang, Michael Q|Ma, Bin|Li, Ming', 'Institute for Computing Technology, Chinese Academy of Sciences, Beijing, China.|Institute for Computing Technology, Chinese Academy of Sciences, Beijing, China.|Institute for Computing Technology, Chinese Academy of Sciences, Beijing, China.|Institute for Computing Technology, Chinese Academy of Sciences, Beijing, China.|Institute for Computing Technology, Chinese Academy of Sciences, Beijing, China.', 'China|China|China|China|China', 0, 'Algorithms|Contig Mapping|Genome, Human|Humans|Oligonucleotides|Sequence Alignment|Sequence Analysis, DNA|Software|Software Validation', 'Journal Article', '10.1093/bioinformatics/btn416', '18684737', 'The next generation sequencing technologies are generating billions of short reads daily. Resequencing and personalized medicine need much faster software to map these deep sequencing reads to a reference genome, to identify SNPs or rare transcripts.'),
(74259, 2008, 'bio', 'PK/DB: database for pharmacokinetic properties and predictive in silico ADME models', 'Moda, Tiago L|Torres, Leonardo G|Carrara, Alexandre E|Andricopulo, Adriano D', 'Laboratory of Computational and Medicinal Chemistry, Center for Structural Molecular Biotechnology, Institute of Physics of São Carlos, University of São Paulo, São Carlos-SP 13566-970, Brazil.|Laboratory of Computational and Medicinal Chemistry, Center for Structural Molecular Biotechnology, Institute of Physics of São Carlos, University of São Paulo, São Carlos-SP 13566-970, Brazil.|Laboratory of Computational and Medicinal Chemistry, Center for Structural Molecular Biotechnology, Institute of Physics of São Carlos, University of São Paulo, São Carlos-SP 13566-970, Brazil.|Laboratory of Computational and Medicinal Chemistry, Center for Structural Molecular Biotechnology, Institute of Physics of São Carlos, University of São Paulo, São Carlos-SP 13566-970, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 58, 'Absorption|Biological Transport|Computer Simulation|Databases, Factual|Humans|Intestinal Absorption|Metabolic Networks and Pathways|Models, Biological|Pharmaceutical Preparations|Pharmacokinetics', 'Journal Article', '10.1093/bioinformatics/btn415', '18684738', 'The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, blood-brain barrier and water solubility).'),
(74260, 2008, 'bio', 'Fast and accurate search for non-coding RNA pseudoknot structures in genomes', 'Huang, Zhibin|Wu, Yong|Robertson, Joseph|Feng, Liang|Malmberg, Russell L|Cai, Liming', 'Department of Computer Science, University of Georgia, Athens, GA 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA 30602, USA.|Department of Computer Science, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 15, 'Algorithms|Animals|Base Sequence|Genomics|Humans|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/btn393', '18687694', 'Searching genomes for non-coding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknot-free structures, ncRNA search can be effective based on the covariance model and CYK-type dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structure-sequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute a best alignment in time O(k(t)n) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that, without degrading the accuracy, only a small number of stem candidates need to be examined and a tree decomposition of a small tree width can always be found for the structure graph.'),
(74261, 2008, 'bio', 'Peptide Finder: mapping measured molecular masses to peptides and proteins', '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', 0, 'Databases, Protein|Molecular Weight|Peptides|Proteins|Proteomics|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btn413', '18687695', 'The identification of unknown amino acid sequences of peptides as well as protein identification is of great significance in proteomics. Here, we present a publicly available web application that facilitates a high resolution mapping of measured molecular masses to peptides and proteins, irrespectively of the enzyme/digestion method used. Furthermore, multi-filtering may be applied in terms of measured mass tolerance, molecular mass and isoelectric point range as well as pattern matching to refine the results. This approach serves complementary to the existing solutions for protein identification and gives insights in novel peptides discovery and protein identification at the cases where the identification scores from the other approaches may be below significance threshold. Peptide Finder has been proven useful in proteomics procedures with experimental data from MALDI-TOF.'),
(74262, 2008, 'bio', 'On the frequency of copy number variants', 'Ionita-Laza, Iuliana|Laird, Nan M|Raby, Benjamin A|Weiss, Scott T|Lange, Christoph', 'Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. iionita@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. iionita@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. iionita@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. iionita@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA. iionita@hsph.harvard.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Dosage|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Humans|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btn421', '18689430', 'Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission.'),
(74263, 2008, 'bio', 'BioJava: an open-source framework for bioinformatics', 'Holland, R C G|Down, T A|Pocock, M|Prlixc4x87, A|Huen, D|James, K|Foisy, S|Drxc3xa4ger, A|Yates, A|Heuer, M|Schreiber, M J', 'European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.', '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|Nucleic Acid Conformation|Programming Languages|Protein Conformation|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn397', '18689808', 'BioJava is a mature open-source project that provides a framework for processing of biological data. BioJava contains powerful analysis and statistical routines, tools for parsing common file formats and packages for manipulating sequences and 3D structures. It enables rapid bioinformatics application development in the Java programming language.'),
(74264, 2008, 'bio', 'The aim of the conference is to provide a scientific forum for discussing the issues and advances in computational biology', 'Tramontano, Anna|ECCB08 Steering Committee', 'European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.|European Bioinformatics Institute, EMBL-EBI, Genome Campus, Hinxton, Cambridgeshire, UK.', 'United Kingdom|United Kingdom', 1, 'Computational Biology', 'Editorial', '10.1093/bioinformatics/btn377', '18689809', '-'),
(74265, 2008, 'bio', 'Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites', 'Najmanovich, Rafael|Kurbatova, Natalja|Thornton, Janet', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. rafael.najmanovich@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. rafael.najmanovich@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. rafael.najmanovich@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Discriminant Analysis|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology', 'Journal Article', '10.1093/bioinformatics/btn263', '18689810', 'Current computational methods for the prediction of function from structure are restricted to the detection of similarities and subsequent transfer of functional annotation. In a significant minority of cases, global sequence or structural (fold) similarities do not provide clues about protein function. In these cases, one alternative is to detect local binding site similarities. These may still reflect more distant evolutionary relationships as well as unique physico-chemical constraints necessary for binding similar ligands, thus helping pinpoint the function. In the present work, we ask the following question: is it possible to discriminate within a dataset of non-homologous proteins those that bind similar ligands based on their binding site similarities?'),
(74266, 2008, 'bio', 'RNA structure alignment by a unit-vector approach', 'Capriotti, Emidio|Marti-Renom, Marc A', 'Bioinformatics and Genomics Department, Structural Genomics Unit, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Bioinformatics and Genomics Department, Structural Genomics Unit, Centro de Investigación Príncipe Felipe, Valencia, Spain.', 'Spain|Spain', 37, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btn288', '18689811', 'The recent discovery of tiny RNA molecules such as microRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74267, 2008, 'bio', 'Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining', 'Yu, Shi|Van Vooren, Steven|Tranchevent, Leon-Charles|De Moor, Bart|Moreau, Yves', 'Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be|Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be|Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be|Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be|Department of Electrical Engineering, Bioinformatics Group, SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Heverlee, Belgium. shi.yu@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Artificial Intelligence|Genes|Genetic Markers|Genetic Predisposition to Disease|Humans|Information Storage and Retrieval|MEDLINE|Natural Language Processing', 'Comparative Study', '10.1093/bioinformatics/btn291', '18689812', 'Computational gene prioritization methods are useful to help identify susceptibility genes potentially being involved in genetic disease. Recently, text mining techniques have been applied to extract prior knowledge from text-based genomic information sources and this knowledge can be used to improve the prioritization process. However, the effect of various vocabularies, representations and ranking algorithms on text mining for gene prioritization is still an issue that requires systematic and comparative studies. Therefore, a benchmark study about the vocabularies, representations and ranking algorithms in gene prioritization by text mining is discussed in this article.'),
(74268, 2008, 'bio', 'Inter-species normalization of gene mentions with GNAT', 'Hakenberg, Jörg|Plake, Conrad|Leaman, Robert|Schroeder, Michael|Gonzalez, Graciela', 'Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu', 'United States|United States|United States|United States|United States', 79, 'Algorithms|Artificial Intelligence|Databases, Genetic|Genes|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btn299', '18689813', 'Text mining in the biomedical domain aims at helping researchers to access information contained in scientific publications in a faster, easier and more complete way. One step towards this aim is the recognition of named entities and their subsequent normalization to database identifiers. Normalization helps to link objects of potential interest, such as genes, to detailed information not contained in a publication, it is also key for integrating different knowledge sources. From an information retrieval perspective, normalization facilitates indexing and querying. Gene mention normalization (GN) is particularly challenging given the high ambiguity of gene names: they refer to orthologous or entirely different genes, are named after phenotypes and other biomedical terms, or they resemble common English words.'),
(74269, 2008, 'bio', 'Analysis of segmental duplications via duplication distance', 'Kahn, Crystal L|Raphael, Benjamin J', 'Department of Computer Science, Brown University, Providence, RI, USA. clkahn@cs.brown.edu|Department of Computer Science, Brown University, Providence, RI, USA. clkahn@cs.brown.edu', 'United States|United States', 0, 'Base Sequence|Chromosome Mapping|Gene Duplication|Genetic Linkage|Genome, Human|Humans|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn292', '18689814', 'Segmental duplications are common in mammalian genomes, but their evolutionary origins remain mysterious. A major difficulty in analyzing segmental duplications is that many duplications are complex mosaics of fragments of numerous other segmental duplications.'),
(74270, 2008, 'bio', 'A fast and flexible method for the segmentation of aCGH data', 'Ben-Yaacov, Erez|Eldar, Yonina C', 'Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa Israel.|Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa Israel.', 'Israel|Israel', 60, 'Algorithms|Chromosome Mapping|DNA|Gene Dosage|In Situ Hybridization, Fluorescence|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn272', '18689815', 'Array Comparative Genomic Hybridization (aCGH) is used to scan the entire genome for variations in DNA copy number. A central task in the analysis of aCGH data is the segmentation into groups of probes sharing the same DNA copy number. Some well known segmentation methods suffer from very long running times, preventing interactive data analysis.'),
(74271, 2008, 'bio', 'PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database', 'Yang, Chia-Ying|Chang, Chao-Hui|Yu, Ya-Ling|Lin, Tsu-Chun Emma|Lee, Sheng-An|Yen, Chueh-Chuan|Yang, Jinn-Moon|Lai, Jin-Mei|Hong, Yi-Ren|Tseng, Tzu-Ling|Chao, Kun-Mao|Huang, Chi-Ying F', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, Republic of China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Binding Sites|Databases, Protein|Humans|Information Storage and Retrieval|Phosphoproteins|Phosphorylation|Phosphotransferases|Protein Binding|Protein Interaction Mapping|Proteome|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn297', '18689816', 'To fully understand how a protein kinase regulates biological processes, it is imperative to first identify its substrate(s) and interacting protein(s). However, of the 518 known human serine/threonine/tyrosine kinases, 35% of these have known substrates, while 14% of the kinases have identified substrate recognition motifs. In contrast, 85% of the kinases have protein-protein interaction (PPI) datasets, raising the possibility that we might reveal potential kinase-substrate pairs from these PPIs.'),
(74272, 2008, 'bio', 'Poisson adjacency distributions in genome comparison: multichromosomal, circular, signed and unsigned cases', 'Xu, Wei|Alain, Benoît|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada. wxu060@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada. wxu060@uottawa.ca|Department of Mathematics and Statistics, University of Ottawa, Ottawa, Ontario, Canada. wxu060@uottawa.ca', 'Canada|Canada|Canada', 10, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|DNA, Circular|Evolution, Molecular|Models, Genetic|Models, Statistical|Molecular Sequence Data|Poisson Distribution|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn295', '18689817', 'The number of common adjacencies of genetic markers, as a measure of the similarity of two genomes, has been widely used as indicator of evolutionary relatedness and as the basis for inferring phylogenetic relationships. Its probability distribution enables statistical tests in detecting whether significant evolutionary signal remains in the marker order. In this article, we derive the probability distributions of the number of adjacencies for a number of types of genome--signed or unsigned, circular or linear, single-chromosome or multichromosomal. Generating functions are found for singlechromosome cases, from which exact counts can be calculated. Probability approaches are adopted for multichromosomal cases, where we.nd the exact values for expectations and variances. In both cases, the limiting distributions are derived in term of numbers of adjacencies. For all unsigned cases, the limiting distribution is Poisson with parameter 2, for all signed cases, the limiting distribution is Poisson with parameter (1/2).'),
(74273, 2008, 'bio', 'HapCUT: an efficient and accurate algorithm for the haplotype assembly problem', 'Bansal, Vikas|Bafna, Vineet', 'Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0404, USA. vibansal@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0404, USA. vibansal@cs.ucsd.edu', 'United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Haplotypes|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn298', '18689818', 'The goal of the haplotype assembly problem is to reconstruct the two haplotypes (chromosomes) for an individual using a mix of sequenced fragments from the two chromosomes. This problem has been shown to be computationally intractable for various optimization criteria. Polynomial time algorithms have been proposed for restricted versions of the problem. In this article, we consider the haplotype assembly problem in the most general setting, i.e. fragments of any length and with an arbitrary number of gaps.'),
(74274, 2008, 'bio', 'Efficient representation and P-value computation for high-order Markov motifs', 'da Fonseca, Paulo G S|Guimarães, Katia S|Sagot, Marie-France', 'Centro de Informática, Universidade Federal de Pernambuco, 50732-970 Recife, Brazil. paguso@cin.ufpe.br|Centro de Informática, Universidade Federal de Pernambuco, 50732-970 Recife, Brazil. paguso@cin.ufpe.br|Centro de Informática, Universidade Federal de Pernambuco, 50732-970 Recife, Brazil. paguso@cin.ufpe.br', 'Brazil|Brazil|Brazil', 5, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Markov Chains|Models, Chemical|Models, Genetic|Models, Statistical|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn282', '18689819', 'Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Markov chainbased models generalize the PWM model by allowing for interposition dependencies to be considered, at the cost of substantial computational overhead, which may limit their application.'),
(74275, 2008, 'bio', 'Mining significant tree patterns in carbohydrate sugar chains', 'Hashimoto, Kosuke|Takigawa, Ichigaku|Shiga, Motoki|Kanehisa, Minoru|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Carbohydrate Sequence|Carbohydrates|Information Storage and Retrieval|Pattern Recognition, Automated|Polysaccharides', 'Journal Article', '10.1093/bioinformatics/btn293', '18689820', 'Carbohydrate sugar chains or glycans, the third major class of macromolecules, hold branch shaped tree structures. Glycan motifs are known to be two types: (1) conserved patterns called cores containing the root and (2) ubiquitous motifs which appear in external parts including leaves and are distributed over different glycan classes. Finding these glycan tree motifs is an important issue, but there have been no computational methods to capture these motifs efficiently.'),
(74276, 2008, 'bio', 'Optimal spliced alignments of short sequence reads', 'De Bona, Fabio|Ossowski, Stephan|Schneeberger, Korbinian|Rätsch, Gunnar', 'Friedrich Miescher Laboratory, Max Planck Society, Spemannstr 39, 72076 Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr 39, 72076 Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr 39, 72076 Tübingen, Germany.|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr 39, 72076 Tübingen, Germany.', 'Germany|Germany|Germany|Germany', 78, 'Algorithms|Base Sequence|Chromosome Mapping|DNA, Recombinant|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn300', '18689821', 'Next generation sequencing technologies open exciting new possibilities for genome and transcriptome sequencing. While reads produced by these technologies are relatively short and error prone compared to the Sanger method their throughput is several magnitudes higher. To utilize such reads for transcriptome sequencing and gene structure identification, one needs to be able to accurately align the sequence reads over intron boundaries. This represents a significant challenge given their short length and inherent high error rate.'),
(74277, 2008, 'bio', 'ChIPmix: mixture model of regressions for two-color ChIP-chip analysis', 'Martin-Magniette, Marie-Laure|Mary-Huard, Tristan|Bérard, Caroline|Robin, Stéphane', 'UMR AgroParisTech/INRA MIA 518, Paris Cedex 05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech/INRA MIA 518, Paris Cedex 05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech/INRA MIA 518, Paris Cedex 05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech/INRA MIA 518, Paris Cedex 05, France. marie_laure.martin@agroparistech.fr', 'France|France|France|France', 0, 'Algorithms|Base Sequence|Chromatin Immunoprecipitation|Computer Simulation|Data Interpretation, Statistical|Microscopy, Fluorescence, Multiphoton|Models, Genetic|Models, Statistical|Molecular Sequence Data|Regression Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn280', '18689822', 'Chromatin immunoprecipitation (ChIP) combined with DNA microarray is a high-throughput technology to investigate DNA-protein binding or chromatin/histone modifications. ChIP-chip data require adapted statistical method in order to identify enriched regions. All methods already proposed are based on the analysis of the log ratio (Ip/Input). Nevertheless, the assumption that the log ratio is a pertinent quantity to assess the probe status is not always veri.ed and it leads to a poor data interpretation.'),
(74278, 2008, 'bio', 'Segment-based multiple sequence alignment', 'Rausch, Tobias|Emde, Anne-Katrin|Weese, David|Döring, Andreas|Notredame, Cedric|Reinert, Knut', 'International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Ihnestr 63-73, 14195 Berlin, Germany. rausch@inf.fu-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn281', '18689823', 'Many multiple sequence alignment tools have been developed in the past, progressing either in speed or alignment accuracy. Given the importance and wide-spread use of alignment tools, progress in both categories is a contribution to the community and has driven research in the field so far.'),
(74279, 2008, 'bio', 'Connect the dots: exposing hidden protein family connections from the entire sequence tree', 'Loewenstein, Yaniv|Linial, Michal', 'School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. lonshy@cs.huji.ac.il|School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. lonshy@cs.huji.ac.il', 'Israel|Israel', 5, 'Algorithms|Conserved Sequence|Evolution, Molecular|Multigene Family|Proteins|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn301', '18689824', 'Mapping of remote evolutionary links is a classic computational problem of much interest. Relating protein families allows for functional and structural inference on uncharacterized families. Since sequences have diverged beyond reliable alignment, these are too remote to identify by conventional methods.'),
(74280, 2008, 'bio', 'Improved network-based identification of protein orthologs', 'Yosef, Nir|Sharan, Roded|Noble, William Stafford', 'School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il', 'Israel|Israel|Israel', 0, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn277', '18689825', 'Identifying protein orthologs is an important task that is receiving growing attention in the bioinformatics literature. Orthology detection provides a fundamental tool towards understanding protein evolution, predicting protein functions and interactions, aligning protein-protein interaction (PPI) networks of different species and detecting conserved modules within these networks.'),
(74281, 2008, 'bio', 'Comprehensive in silico mutagenesis highlights functionally important residues in proteins', 'Bromberg, Yana|Rost, Burkhard', 'Department of Biochemistry Molecular Biophysics, Columbia University, 630 West 168th St, New York, NY 10032, USA. yb2009@columbia.edu|Department of Biochemistry Molecular Biophysics, Columbia University, 630 West 168th St, New York, NY 10032, USA. yb2009@columbia.edu', 'United States|United States', 0, 'Amino Acid Sequence|Amino Acids|Binding Sites|Computer Simulation|Models, Chemical|Molecular Sequence Data|Mutagenesis, Site-Directed|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn268', '18689826', 'Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico.'),
(74282, 2008, 'bio', 'Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways', 'Willemsen, A M|Jansen, G A|Komen, J C|van Hooff, S|Waterham, H R|Brites, P M T|Wanders, R J A|van Kampen, A H C', 'Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.|Department of Clinical Chemistry and Pediatrics, Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, AZ Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Database Management Systems|Databases, Protein|Health Knowledge, Attitudes, Practice|Information Storage and Retrieval|Peroxisomes|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn274', '18689827', 'One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts minds and may be highly specific, heterogeneous, complex and voluminous.'),
(74283, 2008, 'bio', 'Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules', 'Saez-Rodriguez, Julio|Gayer, Stefan|Ginkel, Martin|Gilles, Ernst Dieter', 'Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstr 1, 39106 Magdeburg, Germany.|Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstr 1, 39106 Magdeburg, Germany.|Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstr 1, 39106 Magdeburg, Germany.|Max-Planck-Institute for Dynamics of Complex Technical Systems, Sandtorstr 1, 39106 Magdeburg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Kinetics|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn289', '18689828', 'The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism.'),
(74284, 2008, 'bio', 'From minimal signed circuits to the dynamics of Boolean regulatory networks', 'Remy, Elisabeth|Ruet, Paul', 'CNRS, Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France.|CNRS, Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France.', 'France|France', 42, 'Computer Simulation|Gene Expression Regulation|Logistic Models|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn287', '18689829', 'It is acknowledged that the presence of positive or negative circuits in regulatory networks such as genetic networks is linked to the emergence of significant dynamical properties such as multistability (involved in differentiation) and periodic oscillations (involved in homeostasis). Rules proposed by the biologist R. Thomas assert that these circuits are necessary for such dynamical properties. These rules have been studied by several authors. Their obvious interest is that they relate the rather simple information contained in the structure of the network (signed circuits) to its much more complex dynamical behaviour. We prove in this article a nontrivial converse of these rules, namely that certain positive or negative circuits in a regulatory graph are actually sufficient for the observation of a restricted form of the corresponding dynamical property, differentiation or homeostasis. More precisely, the crucial property that we require is that the circuit be globally minimal. We then apply these results to the vertebrate immune system, and show that the two minimal functional positive circuits of the model indeed behave as modules which combine to explain the presence of the three stable states corresponding to the Th0, Th1 and Th2 cells.'),
(74285, 2008, 'bio', 'Temporal logic patterns for querying dynamic models of cellular interaction networks', 'Monteiro, Pedro T|Ropers, Delphine|Mateescu, Radu|Freitas, Ana T|de Jong, Hidde', 'INRIA Grenoble, Rhône-Alpes, 655 Av de lEurope, Montbonnot, 38334 St Ismier Cedex, France.|INRIA Grenoble, Rhône-Alpes, 655 Av de lEurope, Montbonnot, 38334 St Ismier Cedex, France.|INRIA Grenoble, Rhône-Alpes, 655 Av de lEurope, Montbonnot, 38334 St Ismier Cedex, France.|INRIA Grenoble, Rhône-Alpes, 655 Av de lEurope, Montbonnot, 38334 St Ismier Cedex, France.|INRIA Grenoble, Rhône-Alpes, 655 Av de lEurope, Montbonnot, 38334 St Ismier Cedex, France.', 'France|France|France|France|France', 0, 'Algorithms|Computer Simulation|Logistic Models|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software|Software Validation', 'Evaluation Studies', '10.1093/bioinformatics/btn275', '18689830', 'Models of the dynamics of cellular interaction networks have become increasingly larger in recent years. Formal verification based on model checking provides a powerful technology to keep up with this increase in scale and complexity. The application of modelchecking approaches is hampered, however, by the difficulty for nonexpert users to formulate appropriate questions in temporal logic.'),
(74286, 2008, 'bio', 'Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc', 'González, Aitor|Chaouiya, Claudine|Thieffry, Denis', 'Institute for Virus Research, Kyoto University, Shogoin-Kawahara, Sakyo-ku, Kyoto, Japan.|Institute for Virus Research, Kyoto University, Shogoin-Kawahara, Sakyo-ku, Kyoto, Japan.|Institute for Virus Research, Kyoto University, Shogoin-Kawahara, Sakyo-ku, Kyoto, Japan.', 'Japan|Japan|Japan', 32, 'Animals|Body Patterning|Computer Simulation|Drosophila|Drosophila Proteins|Hedgehog Proteins|Logistic Models|Models, Biological|Signal Transduction|Wings, Animal', 'Journal Article', '10.1093/bioinformatics/btn266', '18689831', 'The development of most tissues and organs relies on a limited number of signal transduction pathways enabling the coordination of cellular differentiation. A proper understanding of the roles of signal transduction pathways requires the definition of formal models capturing the main qualitative features of these patterning processes. This is a challenging task because the underlying processes, diffusion, regulatory modifications, reception and sequestration of signalling molecules, transcriptional regulation of target genes, etc. are only partly characterized. In this context, qualitative models can be more readily proposed on the basis of available (molecular) genetic data. But this requires novel computational tools and proper qualitative representations of phenomena such as diffusion or sequestration. To assess the power and limits of a logical formalism in this context, we propose a multi-level model of the multi-cellular network involved in the definition of the anterior-posterior boundary during the development of the wing disc of Drosophila melanogaster. The morphogen Hedgehog (Hh) is the inter-cellular signal coordinating this process. It diffuses from the posterior compartment of the disc to activate its pathway in cells immediately anterior to the boundary. In these boundary cells, the Hh gradient induces target genes in distinct domains as a function of the Hh concentration. One target of Hh signalling is the gene coding for the receptor Patched (Ptc), which sequesters Hh and impedes further diffusion, thereby refining the boundary.'),
(74287, 2008, 'bio', 'Gain and loss of phosphorylation sites in human cancer', 'Radivojac, Predrag|Baenziger, Peter H|Kann, Maricel G|Mort, Matthew E|Hahn, Matthew W|Mooney, Sean D', 'School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.|School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.|School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.|School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.|School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.|School of Informatics, Indiana University, 901 East Tenth Street, Bloomington, IN 47408, USA.', 'India|India|India|India|India|India', 72, 'DNA Mutational Analysis|Genetic Variation|Humans|Neoplasm Proteins|Neoplasms|Phosphorylation|Phosphotransferases|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btn267', '18689832', 'Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem.'),
(74288, 2008, 'bio', 'Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data', 'Kronek, Louis-Philippe|Reddy, Anupama', 'G-SCOP, Grenoble-Science Conception Organization and Production, Grenoble, France. louis-philippe.kronek@g-scop.fr|G-SCOP, Grenoble-Science Conception Organization and Production, Grenoble, France. louis-philippe.kronek@g-scop.fr', 'France|France', 29, 'Bias|Computer Simulation|Data Interpretation, Statistical|Logistic Models|Prognosis|Proportional Hazards Models|Survival Analysis|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btn265', '18689833', 'Survival analysis involves predicting the time to event for patients in a dataset, based on a set of recorded attributes. In this study we focus on right-censored survival problems. Detecting high-degree interactions for the estimation of survival probability is a challenging problem in survival analysis from the statistical perspective.'),
(74289, 2008, 'bio', 'Functional coherence in domain interaction networks', 'Pandey, Jayesh|Koyutürk, Mehmet|Subramaniam, Shankar|Grama, Ananth', 'Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, USA. jpandey@cs.purdue.edu', 'United States|United States|United States|United States', 23, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Protein Structure, Tertiary|Proteome|Signal Transduction|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn296', '18689835', 'Extracting functional information from protein-protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain-domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks.'),
(74290, 2008, 'bio', 'ECCB 2008 conference proceedings, 22-26 September 2008, Cagliari, Italy', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1093/bioinformatics/btn369', '18689836', '-'),
(74291, 2008, 'bio', 'An integrative approach for predicting interactions of protein regions', 'Schelhorn, Sven-Eric|Lengauer, Thomas|Albrecht, Mario', 'Max Planck Institute for Informatics, Saarbrücken, Germany.|Max Planck Institute for Informatics, Saarbrücken, Germany.|Max Planck Institute for Informatics, Saarbrücken, Germany.', 'Germany|Germany|Germany', 12, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn290', '18689837', 'Protein-protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes.'),
(74292, 2008, 'bio', 'Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry', 'Käll, Lukas|Storey, John D|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States', 60, 'Algorithms|Amino Acid Sequence|Artifacts|Computer Simulation|Data Interpretation, Statistical|Mass Spectrometry|Models, Chemical|Models, Statistical|Molecular Sequence Data|Peptide Mapping|Peptides|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn294', '18689838', 'A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more dif.cult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution.'),
(74293, 2008, 'bio', 'Towards de novo identification of metabolites by analyzing tandem mass spectra', 'Böcker, Sebastian|Rasche, Florian', 'Chair for Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany.|Chair for Bioinformatics, Friedrich-Schiller-University Jena, Jena, Germany.', 'Germany|Germany', 87, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Gene Expression Profiling|Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btn270', '18689839', 'Mass spectrometry is among the most widely used technologies in proteomics and metabolomics. Being a high-throughput method, it produces large amounts of data that necessitates an automated analysis of the spectra. Clearly, database search methods for protein analysis can easily be adopted to analyze metabolite mass spectra. But for metabolites, de novo interpretation of spectra is even more important than for protein data, because metabolite spectra databases cover only a small fraction of naturally occurring metabolites: even the model plant Arabidopsis thaliana has a large number of enzymes whose substrates and products remain unknown. The field of bio-prospection searches biologically diverse areas for metabolites which might serve as pharmaceuticals. De novo identification of metabolite mass spectra requires new concepts and methods since, unlike proteins, metabolites possess a non-linear molecular structure.'),
(74294, 2008, 'bio', 'An environmental perspective on large-scale genome clustering based on metabolic capabilities', 'Kastenmüller, Gabi|Gasteiger, Johann|Mewes, Hans-Werner', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. g.kastenmueller@helmholtz-muenchen.de', 'Germany|Germany|Germany', 0, 'Biological Evolution|Chromosome Mapping|Cluster Analysis|Computer Simulation|Environment|Gene Expression Regulation|Genetic Variation|Models, Genetic|Proteome|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btn302', '18689840', 'In principle, an organisms ability to survive in a speci.c environment, is an observable result of the organisms regulatory and metabolic capabilities. Nonetheless, current knowledge about the global relation of the metabolisms and the niches of organisms is still limited.'),
(74295, 2008, 'bio', 'Fusing time series expression data through hybrid aggregation and hierarchical merge', 'Tsiporkova, Elena|Boeva, Veselka', 'Innovation Center, East Flanders, House of Economy, Seminariestraat and 2, B-9000 Ghent, Belgium.|Innovation Center, East Flanders, House of Economy, Seminariestraat and 2, B-9000 Ghent, Belgium.', 'Belgium|Belgium', 17, 'Algorithms|Bacterial Proteins|Cell Cycle|Cell Cycle Proteins|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Saccharomyces|Time Factors', 'Journal Article', '10.1093/bioinformatics/btn264', '18689841', 'A novel integration approach targeting the combination of multi-experiment time series expression data is proposed. A recursive hybrid aggregation algorithm is initially employed to extract a set of genes, which are eventually of interest for the biological phenomenon under study. Next, a hierarchical merge procedure is specifically developed for the purpose of fusing together the multiple-experiment expression pro.les of the selected genes. This employs dynamic time warping alignment techniques in order to account adequately for the potential phase shift between the different experiments. We subsequently demonstrate that the resulting gene expression pro.les consistently re.ect the behavior of the original expression pro.les in the different experiments.'),
(74296, 2008, 'bio', 'Annotation of metagenome short reads using proxygenes', 'Dalevi, Daniel|Ivanova, Natalia N|Mavromatis, Konstantinos|Hooper, Sean D|Szeto, Ernest|Hugenholtz, Philip|Kyrpides, Nikos C|Markowitz, Victor M', 'Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.|Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Bacterial Proteins|Base Sequence|Chromosome Mapping|Cluster Analysis|Molecular Sequence Data|Open Reading Frames|Proteome|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn276', '18689842', 'A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein.'),
(74297, 2008, 'bio', 'Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities', 'Gao, Pei|Honkela, Antti|Rattray, Magnus|Lawrence, Neil D', 'School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester.|School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester.|School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester.|School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, Manchester.', '-|-|-|-', 56, 'Computer Simulation|Models, Chemical|Models, Genetic|Models, Statistical|Normal Distribution|RNA, Messenger|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btn278', '18689843', 'Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalization of these latent chemical species through Gaussian process priors.'),
(74298, 2008, 'bio', 'SIRENE: supervised inference of regulatory networks', 'Mordelet, Fantine|Vert, Jean-Philippe', 'Ecole des Mines de Paris, ParisTech, Fontainebleau, France. Fantine.Mordelet@ensmp.fr|Ecole des Mines de Paris, ParisTech, Fontainebleau, France. Fantine.Mordelet@ensmp.fr', 'France|France', 0, 'Artificial Intelligence|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Pattern Recognition, Automated|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn273', '18689844', 'Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks is thus needed to understand the cells working mechanism, and can for example, be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level.'),
(74299, 2008, 'bio', 'PhyloDetect: a likelihood-based strategy for detecting microorganisms with diagnostic microarrays', 'Rehrauer, Hubert|Schönmann, Susan|Eberl, Leo|Schlapbach, Ralph', 'Department ofMicrobiology, Functional Genomics Center Zurich, University/ETH Zurich, University of Zurich, Zurich, Switzerland. Hubert.Rehrauer@fgcz.uzh.ch|Department ofMicrobiology, Functional Genomics Center Zurich, University/ETH Zurich, University of Zurich, Zurich, Switzerland. Hubert.Rehrauer@fgcz.uzh.ch|Department ofMicrobiology, Functional Genomics Center Zurich, University/ETH Zurich, University of Zurich, Zurich, Switzerland. Hubert.Rehrauer@fgcz.uzh.ch|Department ofMicrobiology, Functional Genomics Center Zurich, University/ETH Zurich, University of Zurich, Zurich, Switzerland. Hubert.Rehrauer@fgcz.uzh.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 12, 'Algorithms|Bacteria|Bacterial Infections|Colony Count, Microbial|DNA, Bacterial|Humans|Likelihood Functions|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn269', '18689845', 'Detection and identification of microbes using diagnostic arrays is still subject of ongoing research. Existing significance-based algorithms consider an organism detected even if a significant number of the microarray probes that match the organism are called absent in a hybridization. Further, they do generate redundant results if the target organisms show high sequence similarity and the microarray probes cannot discriminate all of them.'),
(74300, 2008, 'bio', 'Clinically driven semi-supervised class discovery in gene expression data', 'Steinfeld, Israel|Navon, Roy|Ardigò, Diego|Zavaroni, Ivana|Yakhini, Zohar', 'Agilent Laboratories, Tel Aviv, Israel.|Agilent Laboratories, Tel Aviv, Israel.|Agilent Laboratories, Tel Aviv, Israel.|Agilent Laboratories, Tel Aviv, Israel.|Agilent Laboratories, Tel Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Genetic Predisposition to Disease|Genetic Testing|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn279', '18689846', 'Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74301, 2008, 'bio', 'Protein structure alignment considering phenotypic plasticity', 'Csaba, Gergely|Birzele, Fabian|Zimmer, Ralf', 'Department of Informatics, Practical Informatics and Bioinformatics Group, Ludwig-Maximilians-University, Amalienstr 17, D-80333 Munich, Germany.|Department of Informatics, Practical Informatics and Bioinformatics Group, Ludwig-Maximilians-University, Amalienstr 17, D-80333 Munich, Germany.|Department of Informatics, Practical Informatics and Bioinformatics Group, Ludwig-Maximilians-University, Amalienstr 17, D-80333 Munich, Germany.', 'Germany|Germany|Germany', 35, 'Algorithms|Amino Acid Sequence|Base Sequence|Computer Simulation|Evolution, Molecular|Genetic Variation|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Phenotype|Proteins|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn271', '18689847', 'Protein structure comparison exhibits differences and similarities of proteins and protein families and may help to elucidate protein sequence and structure evolution. Despite many methods to score protein structure similarity with and without flexibility and to align proteins accurately based on their structures, a meaningful evolutionary distance measure and alignment method which models the cost of mutations, insertions and deletions occurring in protein sequences on the structure level is still missing.'),
(74302, 2008, 'bio', 'Arabidopsis thaliana regulatory element analyzer', 'Choudhury, Ananyo|Lahiri, Ansuman', 'Department of Biophysics, Molecular Biology and Genetics, University of Calcutta, 92 APC Road, Kolkata 700009, India. ananyo.c@gmail.com|Department of Biophysics, Molecular Biology and Genetics, University of Calcutta, 92 APC Road, Kolkata 700009, India. ananyo.c@gmail.com', 'India|India', 1, 'Arabidopsis|Computational Biology|Gene Expression Regulation, Plant|Genome, Plant|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btn417', '18694893', 'In the Arabidopsis thaliana regulatory element analyzer (AtREA) server, we have integrated sequence data, genome-wide expression data and functional annotation data in three application modules which will be useful to identify major regulatory targets of a user-provided cis-regulatory element (CRE), study different features of CRE distribution and evaluate the role of a set of CREs in the regulation of gene expression--independently as well as in combination with other user-provided CREs.'),
(74303, 2008, 'bio', 'Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data', 'Cruz-Marcelo, Alejandro|Guerra, Rudy|Vannucci, Marina|Li, Yiting|Lau, Ching C|Man, Tsz-Kwong', 'Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.|Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.|Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.|Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.|Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.|Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA.', 'United States|United States|United States|United States|United States|United States', 35, 'Algorithms|Humans|Protein Array Analysis|Proteome|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Comparative Study', '10.1093/bioinformatics/btn398', '18694894', 'Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order to locate the peaks and quantify their intensities, several pre-processing steps are required. Though different approaches to perform pre-processing have been proposed, there is no systematic study that compares their performance.'),
(74304, 2008, 'bio', 'Dasty2, an Ajax protein DAS client', 'Jimenez, Rafael C|Quinn, Antony F|Garcia, Alexander|Labarga, Alberto|ONeill, Kieran|Martinez, Fernando|Salazar, Gustavo A|Hermjakob, Henning', 'Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.|Proteomics Services Team, European Bioinformatics Institute, EMBL, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Computational Biology|Databases, Protein|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn387', '18694895', 'Dasty2 is a highly interactive web client integrating protein sequence annotations from currently more than 40 sources, using the distributed annotation system (DAS).'),
(74305, 2008, 'bio', 'MPSQ: a web tool for protein-state searching', 'Zheng, Siyuan|Sheng, Jia|Wang, Chuan|Wang, Xiaojing|Yu, Yao|Li, Yun|Michie, Alex|Dai, Jianliang|Zhong, Yang|Hao, Pei|Liu, Lei|Li, Yixue', 'Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.', 'China|China|China|China|China|China|China|China|China|China|China|China', 4, 'Databases, Protein|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn427', '18697766', 'MPSQ (multi-protein-states query) is a web-based tool for the discovery of protein states (e.g. biological interactions, covalent modifications, cellular localizations). In particular, large sets of genes can be used to search for enriched state transition network maps (NMs) and features facilitating the interpretation of genomic-scale experiments such as microarrays. One NM collects all the catalogued states of a protein as well as the mutual transitions between the states. For the returned NM, graph visualization is provided for easy understanding and to guide further analysis.'),
(74306, 2008, 'bio', 'The Synergizer service for translating gene, protein and other biological identifiers', 'Berriz, Gabriel F|Roth, Frederick P', 'Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.|Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.', 'United States|United States', 34, 'Algorithms|Databases, Factual|Databases, Genetic|Databases, Protein|Information Storage and Retrieval|Proteins', 'Journal Article', '10.1093/bioinformatics/btn424', '18697767', 'The Synergizer is a database and web service that provides translations of biological database identifiers. It is accessible both programmatically and interactively.'),
(74307, 2008, 'bio', 'MotifVoter: a novel ensemble method for fine-grained integration of generic motif finders', 'Wijaya, Edward|Yiu, Siu-Ming|Son, Ngo Thanh|Kanagasabai, Rajaraman|Sung, Wing-Kin', 'School of Computing, National University of Singapore, Singapore.|School of Computing, National University of Singapore, Singapore.|School of Computing, National University of Singapore, Singapore.|School of Computing, National University of Singapore, Singapore.|School of Computing, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Animals|Base Sequence|Binding Sites|Computational Biology|Humans|Molecular Sequence Data|Protein Structure, Tertiary|Regulatory Elements, Transcriptional|Software|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btn420', '18697768', 'Locating transcription factor binding sites (motifs) is a key step in understanding gene regulation. Based on Tompas benchmark study, the performance of current de novo motif finders is far from satisfactory (with sensitivity <or=0.222 and precision <or=0.307). The same study also shows that no motif finder performs consistently well over all datasets. Hence, it is not clear which finder one should use for a given dataset. To address this issue, a class of algorithms called ensemble methods have been proposed. Though the existing ensemble methods overall perform better than stand-alone motif finders, the improvement gained is not substantial. Our study reveals that these methods do not fully exploit the information obtained from the results of individual finders, resulting in minor improvement in sensitivity and poor precision.'),
(74308, 2008, 'bio', 'SeqMap: mapping massive amount of oligonucleotides to the genome', 'Jiang, Hui|Wong, Wing Hung', 'Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, USA.|Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, USA.', 'United States|United States', 287, 'Base Sequence|Chromosome Mapping|Gene Deletion|Genome|Genomics|Mutagenesis, Insertional|Oligonucleotides|Software', 'Journal Article', '10.1093/bioinformatics/btn429', '18697769', 'SeqMap is a tool for mapping large amount of short sequences to the genome. It is designed for finding all the places in a reference genome where each sequence may come from. This task is essential to the analysis of data from ultra high-throughput sequencing machines. With a carefully designed index-filtering algorithm and an efficient implementation, SeqMap can map tens of millions of short sequences to a genome of several billions of nucleotides. Multiple substitutions and insertions/deletions of the nucleotide bases in the sequences can be tolerated and therefore detected. SeqMap supports FASTA input format and various output formats, and provides command line options for tuning almost every aspect of the mapping process. A typical mapping can be done in a few hours on a desktop PC. Parallel use of SeqMap on a cluster is also very straightforward.'),
(74309, 2008, 'bio', 'Computational prediction of human proteins that can be secreted into the bloodstream', 'Cui, Juan|Liu, Qi|Puett, David|Xu, Ying', 'Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia', 0, 'Blood Proteins|Computational Biology|Humans|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn418', '18697770', 'We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them? We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved approximately 90% prediction sensitivity and approximately 98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi.'),
(74310, 2008, 'bio', 'Protein function prediction and annotation in an integrated environment powered by web services (AFAWE)', 'Jöcker, Anika|Hoffmann, Fabian|Groscurth, Andreas|Schoof, Heiko', 'Plant Computational Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.|Plant Computational Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.|Plant Computational Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.|Plant Computational Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Animals|Computational Biology|Databases, Protein|Humans|Internet|Medicago truncatula|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn394', '18697771', 'Many sequenced genes are mainly annotated through automatic transfer of annotation from similar sequences. Manual comparison of results or intermediate results from different tools can help avoid wrong annotations and give hints to the function of a gene even if none of the automated tools could return any result. AFAWE simplifies the task of manual functional annotation by running different tools and workflows for automatic function prediction and displaying the results in a way that facilitates comparison. Because all programs are executed as web services, AFAWE is easily extensible and can directly query primary databases, thereby always using the most up-to-date data sources. Visual filters help to distinguish trustworthy results from non-significant results. Furthermore, an interface to add detailed manual annotation to each gene is provided, which can be displayed to other users.'),
(74311, 2008, 'bio', 'Detection of stoichiometric inconsistencies in biomolecular models', 'Gevorgyan, Albert|Poolman, Mark G|Fell, David A', 'Cell Systems Modelling Group, School of Life Sciences, Oxford Brookes University, Oxford OX30BP, UK.|Cell Systems Modelling Group, School of Life Sciences, Oxford Brookes University, Oxford OX30BP, UK.|Cell Systems Modelling Group, School of Life Sciences, Oxford Brookes University, Oxford OX30BP, UK.', 'United Kingdom|United Kingdom|United Kingdom', 52, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Genome, Fungal|Metabolism|Models, Biological|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn425', '18697772', 'Metabolic modelling provides a mathematically rigorous basis for system-level analysis of biochemical networks. However, the growing sizes of metabolic models can lead to serious problems in their construction and validation. In this work, we describe a relatively poorly investigated type of modelling error, called stoichiometric inconsistencies. These errors are caused by incorrect definitions of reaction stoichiometries and result in conflicts between two fundamental physical constraints to be satisfied by any valid metabolic model: positivity of molecular masses of all metabolites and mass conservation in all interconversions.'),
(74312, 2008, 'bio', 'High-performance signal peptide prediction based on sequence alignment techniques', 'Frank, Karl|Sippl, Manfred J', 'Center of Applied Molecular Engineering, University of Salzburg, Jakob-Haringerstrasse 5, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, University of Salzburg, Jakob-Haringerstrasse 5, 5020 Salzburg, Austria.', 'Austria|Austria', 0, 'Computational Biology|Databases, Protein|Protein Sorting Signals|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn422', '18697773', 'The accuracy of current signal peptide predictors is outstanding. The most successful predictors are based on neural networks and hidden Markov models, reaching a sensitivity of 99% and an accuracy of 95%. Here, we demonstrate that the popular BLASTP alignment tool can be tuned for signal peptide prediction reaching the same high level of prediction success. Alignment-based techniques provide additional benefits. In spite of high success rates signal peptide predictors yield false predictions. Simple sequences like polyvaline, for example, are predicted as signal peptides. The general architecture of learning systems makes it difficult to trace the cause of such problems. This kind of false predictions can be recognized or avoided altogether by using sequence comparison techniques. Based on these results we have implemented a public web service, called Signal-BLAST. Predictions returned by Signal-BLAST are transparent and easy to analyze.'),
(74313, 2008, 'bio', 'Cancer outlier detection based on likelihood ratio test', 'Hu, Jianhua', 'Department of Biostatistics, Division of Quantitative Science, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA. jhu@mdanderson.org', 'United States', 0, 'Algorithms|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Likelihood Functions|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn372', '18697774', 'Microarray experiments can be used to help study the role of chromosomal translocation in cancer development through cancer outlier detection. The aim is to identify genes that are up- or down-regulated in a subset of cancer samples in comparison to normal samples.'),
(74314, 2008, 'bio', 'PRODECOMPv3: decompositions of NMR projections for protein backbone and side-chain assignments and structural studies', 'Staykova, Doroteya K|Fredriksson, Jonas|Billeter, Martin', 'Department of Chemistry, University of Gothenburg, P.O. Box 462, 405 30 Gothenburg, Sweden. doroteya@chem.gu.se|Department of Chemistry, University of Gothenburg, P.O. Box 462, 405 30 Gothenburg, Sweden. doroteya@chem.gu.se|Department of Chemistry, University of Gothenburg, P.O. Box 462, 405 30 Gothenburg, Sweden. doroteya@chem.gu.se', 'Sweden|Sweden|Sweden', 7, 'Algorithms|Nuclear Magnetic Resonance, Biomolecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn423', '18701530', 'PRODECOMP (projection decomposition) is an implementation of a multi-way decomposition algorithm for the analysis of two-dimensional projections of high-dimensional nuclear magnetic resonance spectra. The newest version, PRODECOMPv3, features a dramatic speedup, more reliable decompositions, a substantial reduction in memory demands, a new graphical user interface and integration into third-party software. These improvements extend the applicability of decompositions to novel types of NMR data on proteins, yielding backbone and side-chain assignments as well as structural information, and therewith enabling complete characterizations of proteins.'),
(74315, 2008, 'bio', 'GIMSAN: a Gibbs motif finder with significance analysis', 'Ng, Patrick|Keich, Uri', 'Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn408', '18703586', 'We present GIMSAN (GIbbsMarkov with Significance ANalysis): a novel tool for de novo motif finding. GIMSAN combines GibbsMarkov, our variant of the Gibbs Sampler, described here for the first time, with our recently introduced significance analysis.'),
(74316, 2008, 'bio', 'A novel CHHC Zn-finger domain found in spliceosomal proteins and tRNA modifying enzymes', 'Andreeva, Antonina|Tidow, Henning', 'MRC Centre for Protein Engineering, Cambridge, UK. tony@mrc-lmb.cam.ac.uk|MRC Centre for Protein Engineering, Cambridge, UK. tony@mrc-lmb.cam.ac.uk', 'United Kingdom|United Kingdom', 14, 'Binding Sites|Conserved Sequence|Humans|Protein Folding|Protein Structure, Tertiary|RNA, Transfer|Ribonucleoproteins, Small Nuclear|Spliceosomes|Zinc Fingers|tRNA Methyltransferases', 'Journal Article', '10.1093/bioinformatics/btn431', '18703587', 'We report a previously uncharacterized CHHC Zn-finger domain identified in spliceosomal U11-48K proteins, tRNA methyl-transferases TRM13 and gametocyte specific factors. We show that this domain behaves as an independent folding unit and that it stoichiometrically binds zinc in a one-to-one ratio. Based on the conserved sequence features we predict that this domain may function as a RNA recognition and binding module.'),
(74317, 2009, 'bio', 'Graphical methods for quantifying macromolecules through bright field imaging', 'Chang, Hang|DeFilippis, Rosa Anna|Tlsty, Thea D|Parvin, Bahram', 'Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov|Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov|Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov|Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. hchang@lbl.gov', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Humans|Image Enhancement|Image Processing, Computer-Assisted|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn426', '18703588', 'Bright field imaging of biological samples stained with antibodies and/or special stains provides a rapid protocol for visualizing various macromolecules. However, this method of sample staining and imaging is rarely employed for direct quantitative analysis due to variations in sample fixations, ambiguities introduced by color composition and the limited dynamic range of imaging instruments. We demonstrate that, through the decomposition of color signals, staining can be scored on a cell-by-cell basis. We have applied our method to fibroblasts grown from histologically normal breast tissue biopsies obtained from two distinct populations. Initially, nuclear regions are segmented through conversion of color images into gray scale, and detection of dark elliptic features. Subsequently, the strength of staining is quantified by a color decomposition model that is optimized by a graph cut algorithm. In rare cases where nuclear signal is significantly altered as a result of sample preparation, nuclear segmentation can be validated and corrected. Finally, segmented stained patterns are associated with each nuclear region following region-based tessellation. Compared to classical non-negative matrix factorization, proposed method: (i) improves color decomposition, (ii) has a better noise immunity, (iii) is more invariant to initial conditions and (iv) has a superior computing performance.'),
(74318, 2008, 'bio', 'Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison', 'Dai, Qi|Yang, Yanchun|Wang, Tianming', 'Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China. daiailiu2004@yahoo.com.cn|Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China. daiailiu2004@yahoo.com.cn|Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China. daiailiu2004@yahoo.com.cn', 'China|China|China', 58, 'Animals|Computational Biology|Databases, Nucleic Acid|Humans|Markov Chains|Phylogeny|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btn436', '18710871', 'Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r.'),
(74319, 2008, 'bio', 'spolTools: online utilities for analyzing spoligotypes of the Mycobacterium tuberculosis complex', 'Tang, Chaka|Reyes, Josephine F|Luciani, Fabio|Francis, Andrew R|Tanaka, Mark M', 'School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.|School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.|School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.|School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.|School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Australia.', 'Australia|Australia|Australia|Australia|Australia', 44, 'Animals|Computational Biology|Humans|Internet|Molecular Epidemiology|Mycobacterium tuberculosis|Software|Tuberculosis, Pulmonary', 'Journal Article', '10.1093/bioinformatics/btn434', '18710872', 'spolTools is a collection of online programs designed to manipulate and analyze spoligotype datasets of the Mycobacterium tuberculosis complex. These tools are integrated into a repository currently containing 1179 spoligotypes and 6278 isolates across 30 datasets. Users can search this database to export for external use or to pass on to the integrated tools. These tools include the computation of basic population genetic quantities, the visualization of clusters of spoligotype patterns based on an estimated evolutionary history and a procedure to predict emerging strains - genotypes associated with elevated transmission.'),
(74320, 2008, 'bio', 'Inference of structure in subdivided populations at low levels of genetic differentiation--the correlated allele frequencies model revisited', 'Guillot, Gilles', 'Department of Biology, Centre for Ecological and Evolutionary Synthesis, University of Oslo, P.O Box 1066 Blindern, 0316 Oslo, Norway. gilles.guillot@bio.uio.no', 'Norway', 0, 'Algorithms|Animals|Gene Frequency|Genetic Drift|Genetics, Population|Genotype|Markov Chains|Microsatellite Repeats|Monte Carlo Method|Mustelidae|Software', 'Journal Article', '10.1093/bioinformatics/btn419', '18710873', 'This article considers the problem of estimating population genetic subdivision from multilocus genotype data. A model is considered to make use of genotypes and possibly of spatial coordinates of sampled individuals. A particular attention is paid to the case of low genetic differentiation with the help of a previously described Bayesian clustering model where allele frequencies are assumed to be a priori correlated. Under this model, various problems of inference are considered, in particular the common and difficult, but still unaddressed, situation where the number of populations is unknown.'),
(74321, 2008, 'bio', 'Integrating expression data with domain interaction networks', 'Emig, Dorothea|Cline, Melissa S|Lengauer, Thomas|Albrecht, Mario', 'Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.|Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany', 7, 'Alternative Splicing|Databases, Protein|Humans|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn437', '18710874', 'Recent studies have revealed that alternative splicing plays an important role in the observed protein and interaction diversity. Special microarrays allow for measuring gene expression at the exon level and thus for studying alternative transcripts and their corresponding protein domain architecture. We have developed the Cytoscape plugin DomainGraph that enables the visualization and detailed study of domain-domain interactions forming protein interaction networks. In addition, the integration of exon expression data supports the analysis of alternative splicing events and the characterization of their effects on the protein and domain interaction network. Different expression patterns between human tissues or cells can be identified by comparing the generated domain graphs.'),
(74322, 2008, 'bio', 'Accurate sequence-based prediction of catalytic residues', 'Zhang, Tuo|Zhang, Hua|Chen, Ke|Shen, Shiyi|Ruan, Jishou|Kurgan, Lukasz', 'College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.|College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.|College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.|College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.|College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.|College of Mathematical Science and LPMC, Nankai University, Tianjin, PRC.', '-|-|-|-|-|-', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Catalysis|Catalytic Domain|Computational Biology|Databases, Protein|Hydrophobic and Hydrophilic Interactions|Protein Folding|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn433', '18710875', 'Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art sequence-based methods.'),
(74323, 2008, 'bio', 'Optimal contact map alignment of protein-protein interfaces', 'Pulim, Vinay|Berger, Bonnie|Bienkowska, Jadwiga', 'Computer Science and Artificial Intelligence Laboratory, MIT, MIT, Cambridge, USA.|Computer Science and Artificial Intelligence Laboratory, MIT, MIT, Cambridge, USA.|Computer Science and Artificial Intelligence Laboratory, MIT, MIT, Cambridge, USA.', 'United States|United States|United States', 17, 'Algorithms|Animals|Computational Biology|Databases, Protein|Humans|Multiprotein Complexes|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btn432', '18710876', 'The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPis use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence-structure alignment.'),
(74324, 2008, 'bio', 'A toolkit for analysing large-scale plant small RNA datasets', 'Moxon, Simon|Schwach, Frank|Dalmay, Tamas|Maclean, Dan|Studholme, David J|Moulton, Vincent', 'School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.|School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.|School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.|School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.|School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.|School of Computing Sciences, School of Biological Sciences, University of East Anglia, Norwich NR47TJ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Nucleic Acid|MicroRNAs|RNA, Plant|Software', 'Comparative Study', '10.1093/bioinformatics/btn428', '18713789', 'Recent developments in high-throughput sequencing technologies have generated considerable demand for tools to analyse large datasets of small RNA sequences. Here, we describe a suite of web-based tools for processing plant small RNA datasets. Our tools can be used to identify micro RNAs and their targets, compare expression levels in sRNA loci, and find putative trans-acting siRNA loci.'),
(74325, 2008, 'bio', 'ITFP: an integrated platform of mammalian transcription factors', 'Zheng, Guangyong|Tu, Kang|Yang, Qing|Xiong, Yun|Wei, Chaochun|Xie, Lu|Zhu, Yangyong|Li, Yixue', 'School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.|School of Life Sciences, Fudan University, Shanghai 200433, China.', 'China|China|China|China|China|China|China|China', 0, 'Animals|Computational Biology|Databases, Protein|Gene Regulatory Networks|Genome|Humans|Mammals|Mice|Rabbits|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn439', '18713790', 'Investigation of transcription factors (TFs) and their downstream regulated genes (targets) is a significant issue in post-genome era, which can provide a brand new vision for some vital biological process. However, information of TFs and their targets in mammalian is far from sufficient. Here, we developed an integrated TF platform (ITFP), which included abundant TFs and their targets of mammalian. In current release, ITFP includes 4105 putative TFs and 69 496 potential TF-target pairs for human, 3134 putative TFs and 37 040 potential TF-target pairs for mouse, and 1114 putative TFs and 18 055 potential TF-target pairs for rat. In short, ITFP will serve as an important resource for the research community of transcription and provide strong support for regulatory network study.'),
(74326, 2008, 'bio', 'Model-based analysis of interferon-beta induced signaling pathway', 'Smieja, Jaroslaw|Jamaluddin, Mohammad|Brasier, Allan R|Kimmel, Marek', 'Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland. jaroslaw.smieja@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland. jaroslaw.smieja@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland. jaroslaw.smieja@polsl.pl|Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland. jaroslaw.smieja@polsl.pl', 'Poland|Poland|Poland|Poland', 0, 'HeLa Cells|Humans|Interferon-beta|Janus Kinases|Models, Biological|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn400', '18713791', 'Interferon-beta induced JAK-STAT signaling pathways contribute to mucosal immune recognition and an anti-viral state. Though the main molecular mechanisms constituting these pathways are known, neither the detailed structure of the regulatory network, nor its dynamics has yet been investigated. The objective of this work is to build a mathematical model for the pathway that would serve two purposes: (1) to reproduce experimental results in simulation of both early and late response to Interferon-beta stimulation and (2) to explain experimental phenomena generating new hypotheses about regulatory mechanisms that cannot yet be tested experimentally.'),
(74327, 2008, 'bio', 'Analyzing gene perturbation screens with nested effects models in R and bioconductor', 'Fröhlich, Holger|Beissbarth, Tim|Tresch, Achim|Kostka, Dennis|Jacob, Juby|Spang, Rainer|Markowetz, F', 'German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.|German Cancer Research Center, INF 580, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 20, 'Algorithms|Gene Expression|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn446', '18718939', 'Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs.'),
(74328, 2008, 'bio', 'Novel pathway compendium analysis elucidates mechanism of pro-angiogenic synthetic small molecule', 'Wieghaus, Kristen A|Gianchandani, Erwin P|Paige, Mikell A|Brown, Milton L|Botchwey, Edward A|Papin, Jason A', 'Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Angiogenesis Inducing Agents|Cells, Cultured|Endothelial Cells|Gene Expression Profiling|Humans|Indoles|Matrix Metalloproteinase 14|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transforming Growth Factor beta|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1093/bioinformatics/btn451', '18718940', 'Computational techniques have been applied to experimental datasets to identify drug mode-of-action. A shortcoming of existing approaches is the requirement of large reference databases of compound expression profiles. Here, we developed a new pathway-based compendium analysis that couples multi-timepoint, controlled microarray data for a single compound with systems-based network analysis to elucidate drug mechanism more efficiently.'),
(74329, 2008, 'bio', 'Empirical profile mixture models for phylogenetic reconstruction', 'Quang, Le Si|Gascuel, Olivier|Lartillot, Nicolas', 'Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France.|Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France.|Méthodes et Algorithmes pour la Bioinformatique, LIRMM, CNRS-UM2, Montpellier Cedex 5, France.', 'France|France|France', 0, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Animals|Bayes Theorem|Computational Biology|Databases, Protein|Humans|Likelihood Functions|Phylogeny|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn445', '18718941', 'Previous studies have shown that accounting for site-specific amino acid replacement patterns using mixtures of stationary probability profiles offers a promising approach for improving the robustness of phylogenetic reconstructions in the presence of saturation. However, such profile mixture models were introduced only in a Bayesian context, and are not yet available in a maximum likelihood (ML) framework. In addition, these mixture models only perform well on large alignments, from which they can reliably learn the shapes of profiles, and their associated weights.'),
(74330, 2008, 'bio', 'Seeder: discriminative seeding DNA motif discovery', 'Fauteux, François|Blanchette, Mathieu|Strömvik, Martina V', 'Department of Plant Science, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.|Department of Plant Science, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.|Department of Plant Science, McGill University, Ste-Anne-de-Bellevue, Quebec, Canada.', 'Canada|Canada|Canada', 0, 'Algorithms|Animals|Arabidopsis|Base Sequence|Binding Sites|Computational Biology|Humans|Molecular Sequence Data|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn444', '18718942', 'The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches.'),
(74331, 2008, 'bio', 'cneViewer: a database of conserved non-coding elements for studies of tissue-specific gene regulation', 'Persampieri, Jason|Ritter, Deborah I|Lees, Daniel|Lehoczky, Jessica|Li, Qiang|Guo, Su|Chuang, Jeffrey H', 'Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA.', 'United States|United States|United States|United States|United States|United States|United States', 13, 'Animals|Base Sequence|Computational Biology|Conserved Sequence|DNA|Databases, Genetic|Gene Expression Regulation|Humans|Internet|Molecular Sequence Data|Regulatory Sequences, Nucleic Acid|Software|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btn443', '18718943', 'There are thousands of strongly conserved non-coding elements (CNEs) in vertebrate genomes, and their functions remain largely unknown. However, without biologically relevant criteria for prioritizing them, selecting a particular CNE sequences to study can be haphazard. To address this problem, we present cneViewer-a database and webtool that systematizes information on conserved non-coding DNA elements in zebrafish. A key feature here is the ability to search for CNEs that may be relevant to tissue-specific gene regulation, based on known developmental expression patterns of nearby genes. cneViewer provides this and other organizing features that significantly facilitate experimental design and CNE analysis.'),
(74332, 2008, 'bio', 'siDRM: an effective and generally applicable online siRNA design tool', 'Gong, Wuming|Ren, Yongliang|Zhou, Haiyan|Wang, Yejun|Kang, Shuli|Li, Tongbin', 'Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.', 'United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Databases, Nucleic Acid|RNA Interference|RNA, Small Interfering|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn442', '18718944', 'Small interfering RNAs (siRNAs) have become an indispensable tool for the investigation of gene functions. Most existing siRNA design tools were trained on datasets assembled from confined origins, incompatible with the diverse siRNA laboratory practice to which these tools will ultimately be applied. We have performed an updated analysis using the disjunctive rule merging (DRM) approach on a large and diverse dataset compiled from siRecords, and implemented the resulting rule sets in siDRM, a new online siRNA design tool. siDRM also implements a few high-sensitivity rule sets and fast rule sets, links to siRecords, and uses several filters to check unwanted detrimental effects, including innate immune responses, cell toxic effects and off-target activities in selecting siRNAs. A performance comparison using an independent dataset indicated that siDRM outperforms 19 existing siRNA design tools in identifying effective siRNAs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74333, 2008, 'bio', 'Local coherence in genetic interaction patterns reveals prevalent functional versatility', 'Pu, Shuye|Ronen, Karen|Vlasblom, James|Greenblatt, Jack|Wodak, Shoshana J', 'Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca|Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca|Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca|Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca|Molecular Structure and Function Program, Hospital for Sick Children, Toronto, ON, Canada. shuyepu@sickkids.ca', 'Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Computational Biology|Epistasis, Genetic|Gene Expression Profiling|Genes, Fungal|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btn440', '18718945', 'Epistatic or genetic interactions, representing the effects of mutating one gene on the phenotypes caused by mutations in one or more distinct genes, can be very helpful for uncovering functional relationships between genes. Recently, the epistatic miniarray profiles (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. For E-MAP data analysis, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles, and the resulting descriptions assign each gene to only one group, thereby ignoring the multifunctional roles played by most genes.'),
(74334, 2008, 'bio', 'Eigen-R2 for dissecting variation in high-dimensional studies', 'Chen, Lin S|Storey, John D', 'Lewis-Sigler Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.|Lewis-Sigler Institute and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.', 'United States|United States', 13, 'Algorithms|Gene Expression Profiling|Genetic Variation|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btn411', '18718946', 'We provide a new statistical algorithm and software package called eigen-R(2) for dissecting the variation of a high-dimensional biological dataset with respect to other measured variables of interest. We apply eigen-R(2) to two real-life examples and compare it with simply averaging R(2) over many features.'),
(74335, 2008, 'bio', 'Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores', 'Bogojeska, Jasmina|Alexa, Adrian|Altmann, André|Lengauer, Thomas|Rahnenführer, Jörg', 'Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. jasmina@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 7, 'Algorithms|Animals|Computer Simulation|Disease Progression|Drug Resistance, Viral|Evolution, Molecular|HIV|HIV Infections|Humans|Models, Genetic|Mutation|Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btn410', '18718947', 'In genetics, many evolutionary pathways can be modeled by the ordered accumulation of permanent changes. Mixture models of mutagenetic trees have been used to describe disease progression in cancer and in HIV. In cancer, progression is modeled by the accumulation of chromosomal gains and losses in tumor cells, in HIV, the accumulation of drug resistance-associated mutations in the viral genome is known to be associated with disease progression. From such evolutionary models, genetic progression scores can be derived that assign measures for the disease state to single patients. Rtreemix is an R package for estimating mixture models of evolutionary pathways from observed cross-sectional data and for estimating associated genetic progression scores. The package also provides extended functionality for estimating confidence intervals for estimated model parameters and for evaluating the stability of the estimated evolutionary mixture models.'),
(74336, 2008, 'bio', 'Multi-dimensional classification of biomedical text: toward automated, practical provision of high-utility text to diverse users', 'Shatkay, Hagit|Pan, Fengxia|Rzhetsky, Andrey|Wilbur, W John', 'The Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca|The Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca|The Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca|The Computational Biology and Machine Learning Lab, School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca', 'Canada|Canada|Canada|Canada', 65, 'Artificial Intelligence|Classification|Computational Biology|Databases as Topic|Databases, Factual|Guidelines as Topic|Periodicals as Topic|Terminology as Topic|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn381', '18718948', 'Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no average biologist client, different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks.'),
(74337, 2008, 'bio', 'An unsupervised conditional random fields approach for clustering gene expression time series', 'Li, Chang-Tsun|Yuan, Yinyin|Wilson, Roland', 'Department of Computer Science, University of Warwick, Coventry, UK. ctli@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. ctli@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. ctli@dcs.warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btn375', '18718949', 'There is a growing interest in extracting statistical patterns from gene expression time-series data, in which a key challenge is the development of stable and accurate probabilistic models. Currently popular models, however, would be computationally prohibitive unless some independence assumptions are made to describe large-scale data. We propose an unsupervised conditional random fields (CRF) model to overcome this problem by progressively infusing information into the labelling process through a small variable voting pool.'),
(74338, 2008, 'bio', 'Improving position-specific predictions of protein functional sites using phylogenetic motifs', 'Dukka, Bahadur K C|Livesay, Dennis R', 'Department of Computer Science and Bioinformatics Research Center, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Computer Science and Bioinformatics Research Center, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States', 15, 'Algorithms|Amino Acid Motifs|Binding Sites|Computational Biology|Conserved Sequence|Databases, Protein|Phylogeny|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn454', '18723520', 'Accurate computational prediction of protein functional sites is critical to maximizing the utility of recent high-throughput sequencing efforts. Among the available approaches, position-specific conservation scores remain among the most popular due to their accuracy and ease of computation. Unfortunately, high false positive rates remain a limiting factor. Using phylogenetic motifs (PMs), we have developed two combined (conservation + PMs) prediction schemes that significantly improve prediction accuracy.'),
(74339, 2008, 'bio', 'ProfDistS: (profile-) distance based phylogeny on sequence--structure alignments', 'Wolf, Matthias|Ruderisch, Benjamin|Dandekar, Thomas|Schultz, Jörg|Müller, Tobias', 'Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. matthias.wolf@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. matthias.wolf@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. matthias.wolf@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. matthias.wolf@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany. matthias.wolf@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany', 94, 'Algorithms|Phylogeny|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btn453', '18723521', 'The Profile Neighbor Joining (PNJ) algorithm as implemented in the software ProfDist is computationally efficient in reconstructing very large trees. Besides the huge amount of sequence data the structure is important in RNA alignment analysis and phylogenetic reconstruction.'),
(74340, 2008, 'bio', 'AMIN domains have a predicted role in localization of diverse periplasmic protein complexes', 'de Souza, Robson Francisco|Anantharaman, Vivek|de Souza, Sandro José|Aravind, L|Gueiros-Filho, Frederico J', 'Departamento de Bioquímica, Universidade de São Paulo, São Paulo, Brazil.|Departamento de Bioquímica, Universidade de São Paulo, São Paulo, Brazil.|Departamento de Bioquímica, Universidade de São Paulo, São Paulo, Brazil.|Departamento de Bioquímica, Universidade de São Paulo, São Paulo, Brazil.|Departamento de Bioquímica, Universidade de São Paulo, São Paulo, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil', 10, 'Amino Acid Sequence|Bacterial Proteins|Models, Genetic|Molecular Sequence Data|Periplasmic Proteins|Protein Structure, Secondary|Protein Structure, Tertiary|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btn449', '18723522', 'We describe AMIN (Amidase N-terminal domain), a novel protein domain found specifically in bacterial periplasmic proteins. AMIN domains are widely distributed among peptidoglycan hydrolases and transporter protein families. Based on experimental data, contextual information and phyletic profiles, we suggest that AMIN domains mediate the targeting of periplasmic or extracellular proteins to specific regions of the bacterial envelope.'),
(74341, 2008, 'bio', 'Optimal design of thermally stable proteins', 'Bannen, Ryan M|Suresh, Vanitha|Phillips, George N|Wright, Stephen J|Mitchell, Julie C', 'Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.|Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Computational Biology|Databases, Protein|Entropy|Protein Conformation|Protein Engineering|Proteins|Temperature', 'Journal Article', '10.1093/bioinformatics/btn450', '18723523', 'For many biotechnological purposes, it is desirable to redesign proteins to be more structurally and functionally stable at higher temperatures. For example, chemical reactions are intrinsically faster at higher temperatures, so using enzymes that are stable at higher temperatures would lead to more efficient industrial processes. We describe an innovative and computationally efficient method called Improved Configurational Entropy (ICE), which can be used to redesign a protein to be more thermally stable (i.e. stable at high temperatures). This can be accomplished by systematically modifying the amino acid sequence via local structural entropy (LSE) minimization. The minimization problem is modeled as a shortest path problem in an acyclic graph with nonnegative weights and is solved efficiently using Dijkstras method.'),
(74342, 2008, 'bio', 'Improving subcellular localization prediction using text classification and the gene ontology', 'Fyshe, Alona|Liu, Yifeng|Szafron, Duane|Greiner, Russ|Lu, Paul', 'Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada.', 'Canada|Canada|Canada|Canada|Canada', 40, 'Abstracting and Indexing as Topic|Classification|Computational Biology|Databases, Protein|Genes|Periodicals as Topic|Proteins|PubMed|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn463', '18728042', 'Each protein performs its functions within some specific locations in a cell. This subcellular location is important for understanding protein function and for facilitating its purification. There are now many computational techniques for predicting location based on sequence analysis and database information from homologs. A few recent techniques use text from biological abstracts: our goal is to improve the prediction accuracy of such text-based techniques. We identify three techniques for improving text-based prediction: a rule for ambiguous abstract removal, a mechanism for using synonyms from the Gene Ontology (GO) and a mechanism for using the GO hierarchy to generalize terms. We show that these three techniques can significantly improve the accuracy of protein subcellular location predictors that use text extracted from PubMed abstracts whose references are recorded in Swiss-Prot.'),
(74343, 2008, 'bio', 'Direct mapping and alignment of protein sequences onto genomic sequence', 'Gotoh, Osamu', 'Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 606-8501, Japan. o.gotoh@i.kyoto-u.ac.jp', 'Japan', 28, 'Algorithms|Amino Acid Sequence|DNA, Complementary|Exons|Genome|Genomics|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn460', '18728043', 'Finding protein-coding genes in a newly determined genomic sequence is the first step toward understanding the content written in the genome. Sequences of transcripts of homologous genes, if available, can considerably improve accuracy of prediction of genes and their structures, compared with that without such knowledge. As protein sequences are generally better conserved than nucleotide sequences, remote homologs can be used as templates, extending the applicability of evidence-based gene recognition methods. However, no tool seems to have been developed so far to simultaneously map and align a number of protein sequences on mammalian-sized genomic sequence.'),
(74344, 2008, 'bio', 'PathCase: pathways database system', 'Elliott, Brendan|Kirac, Mustafa|Cakmak, Ali|Yavas, Gokhan|Mayes, Stephen|Cheng, En|Wang, Yuan|Gupta, Chirag|Ozsoyoglu, Gultekin|Meral Ozsoyoglu, Zehra', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 32, 'Computer Simulation|Databases, Factual|Metabolic Networks and Pathways|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn459', '18728044', 'As the blueprints of cellular actions, biological pathways characterize the roles of genomic entities in various cellular mechanisms, and as such, their availability, manipulation and queriability over the web is important to facilitate ongoing biological research.'),
(74345, 2008, 'bio', 'GOfetcher: a database with complex searching facility for gene ontology', 'Pirooznia, Mehdi|Habib, Tanwir|Perkins, Edward J|Deng, Youping', 'Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA. mehdi.pirooznia@usm.edu|Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA. mehdi.pirooznia@usm.edu|Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA. mehdi.pirooznia@usm.edu|Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA. mehdi.pirooznia@usm.edu', 'United States|United States|United States|United States', 8, 'Database Management Systems|Databases, Genetic|Software|Species Specificity|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn441', '18728045', 'An important contribution to the Gene Ontology (GO) project is to develop tools that facilitate the creation, maintenance and use of ontologies. Several tools have been created for communicating and using the GO project. However, a limitation with most of these tools is that they suffer from lack of a comprehensive search facility. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: Quick Search, Advanced Search and Upload Files for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating GO information. The output data in GOfetcher can be saved into several different formats, including spreadsheet, comma-separated values and the extensible markup language (XML) format. The database is available at http://mcbc.usm.edu/gofetcher/.'),
(74346, 2008, 'bio', 'StatAlign: an extendable software package for joint Bayesian estimation of alignments and evolutionary trees', 'Novák, Adám|Miklós, István|Lyngsø, Rune|Hein, Jotun', 'Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK.|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 63, 'Bayes Theorem|Evolution, Molecular|Markov Chains|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btn457', '18753153', 'Bayesian analysis is one of the most popular methods in phylogenetic inference. The most commonly used methods fix a single multiple alignment and consider only substitutions as phylogenetically informative mutations, though alignments and phylogenies should be inferred jointly as insertions and deletions also carry informative signals. Methods addressing these issues have been developed only recently and there has not been so far a user-friendly program with a graphical interface that implements these methods.'),
(74347, 2008, 'bio', 'A semiparametric test to detect associations between quantitative traits and candidate genes in structured populations', 'Li, Meijuan|Reilly, Cavan|Hanson, Timothy', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0378, USA. meijuanl@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0378, USA. meijuanl@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0378, USA. meijuanl@biostat.umn.edu', 'United States|United States|United States', 9, 'Algorithms|Animals|Computer Simulation|Genetic Markers|Genetics, Population|Genotype|Humans|Quantitative Trait, Heritable', 'Comparative Study', '10.1093/bioinformatics/btn455', '18753154', 'Although population-based association mapping may be subject to the bias caused by population stratification, alternative methods that are robust to population stratification such as family-based linkage analysis have lower mapping resolution. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate genes and traits of interest. The association between a candidate gene and a quantitative trait is often evaluated via a regression model with inferred population structure variables as covariates, where the residual distribution is customarily assumed to be from a symmetric and unimodal parametric family, such as a Gaussian, although this may be inappropriate for the analysis of many real-life datasets.'),
(74348, 2008, 'bio', 'Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes', 'Chen, Xi|Wang, Lily|Smith, Jonathan D|Zhang, Bing', 'Department of Quantitative Health Sciences, The Cleveland Clinic, 9500 Euclid Ave. Cleveland, OH 44195, USA. chenx3@ccf.org|Department of Quantitative Health Sciences, The Cleveland Clinic, 9500 Euclid Ave. Cleveland, OH 44195, USA. chenx3@ccf.org|Department of Quantitative Health Sciences, The Cleveland Clinic, 9500 Euclid Ave. Cleveland, OH 44195, USA. chenx3@ccf.org|Department of Quantitative Health Sciences, The Cleveland Clinic, 9500 Euclid Ave. Cleveland, OH 44195, USA. chenx3@ccf.org', 'United States|United States|United States|United States', 49, 'Algorithms|Gene Expression Profiling|Microarray Analysis|Neoplasms|Principal Component Analysis|Software|Survival Analysis', 'Journal Article', '10.1093/bioinformatics/btn458', '18753155', 'Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variations in gene expression values. However, one limitation with PCA is that the latent variable identified by the first PC may be unrelated to outcome.'),
(74349, 2008, 'bio', 'SynBioSS: the synthetic biology modeling suite', 'Hill, Anthony D|Tomshine, Jonathan R|Weeding, Emma M B|Sotiropoulos, Vassilios|Kaznessis, Yiannis N', 'Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455-0132, USA.|Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455-0132, USA.', 'United States|United States|United States|United States|United States', 66, 'Algorithms|Computer Simulation|Databases, Genetic|Gene Regulatory Networks|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn468', '18757873', 'SynBioSS (Synthetic Biology Software Suite) is a suite of software for the modeling and simulation of synthetic genetic constructs. SynBioSS utilizes the registry of standard biological parts, a database of kinetic parameters, and both graphical and command-line interfaces to multiscale simulation algorithms.'),
(74350, 2008, 'bio', 'Biomolecular pleiomorphism probed by spatial interpolation of coarse models', 'Rusu, Mirabela|Birmanns, Stefan|Wriggers, Willy', 'School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, USA.|School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, USA.|School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, USA.', 'United States|United States|United States', 23, 'Algorithms|Binding Sites|Chaperonin 60|Computational Biology|DNA-Directed RNA Polymerases|Microscopy, Electron|Models, Molecular|Myosin Subfragments|Pattern Recognition, Automated|Protein Conformation|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btn461', '18757874', 'In low resolution structures of biological assemblies one can often observe conformational deviations that require a flexible rearrangement of structural domains fitted at the atomic level. We are evaluating interpolation methods for the flexible alignment of atomic models based on coarse models. Spatial interpolation is well established in image-processing and visualization to describe the overall deformation or warping of an object or an image. Combined with a coarse representation of the biological system by feature vectors, such methods can provide a flexible approximation of the molecular structure. We have compared three well-known interpolation techniques and evaluated the results by comparing them with constrained molecular dynamics. One method, inverse distance weighting interpolation, consistently produced models that were nearly indistinguishable on the alpha carbon level from the molecular dynamics results. The method is simple to apply and enables flexing of structures by non-expert modelers. This is useful for the basic interpretation of volumetric data in biological applications such as electron microscopy. The method can be used as a general interpretation tool for sparsely sampled motions derived from coarse models.'),
(74351, 2008, 'bio', 'PREDICT-2ND: a tool for generalized protein local structure prediction', 'Katzman, Sol|Barrett, Christian|Thiltgen, Grant|Karchin, Rachel|Karplus, Kevin', 'Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA.', 'United States|United States|United States|United States|United States', 33, 'Amino Acid Sequence|Molecular Sequence Data|Neural Networks (Computer)|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn438', '18757875', 'Predictions of protein local structure, derived from sequence alignment information alone, provide visualization tools for biologists to evaluate the importance of amino acid residue positions of interest in the absence of X-ray crystal/NMR structures or homology models. They are also useful as inputs to sequence analysis and modeling tools, such as hidden Markov models (HMMs), which can be used to search for homology in databases of known protein structure. In addition, local structure predictions can be used as a component of cost functions in genetic algorithms that predict protein tertiary structure. We have developed a program (predict-2nd) that trains multilayer neural networks and have applied it to numerous local structure alphabets, tuning network parameters such as the number of layers, the number of units in each layer and the window sizes of each layer. We have had the most success with four-layer networks, with gradually increasing window sizes at each layer.'),
(74352, 2008, 'bio', 'SNAP predicts effect of mutations on protein function', 'Bromberg, Yana|Yachdav, Guy|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. bromberg@rostlab.org|Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. bromberg@rostlab.org|Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. bromberg@rostlab.org', 'United States|United States|United States', 0, 'Amino Acid Substitution|Computational Biology|Databases, Protein|Humans|Mutation|Polymorphism, Single Nucleotide|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn435', '18757876', 'Many non-synonymous single nucleotide polymorphisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions.'),
(74353, 2008, 'bio', 'Bosque: integrated phylogenetic analysis software', 'Ramírez-Flandes, Salvador|Ulloa, Osvaldo', 'Departmento de Oceanografía, Universidad de Concepción, Casilla 160-C, Concepción, Chile. sram@profc.udec.cl|Departmento de Oceanografía, Universidad de Concepción, Casilla 160-C, Concepción, Chile. sram@profc.udec.cl', 'Chile|Chile', 0, 'Databases, Genetic|Phylogeny|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn466', '18762483', 'Phylogenetic analyses today involve dealing with computer files in different formats and often several computer programs. Although some widely used applications have integrated important functionalities for such analyses, they still work with local resources only: input/output files (users have to manage them) and local computing (users have sometimes to leave their programs, on their desktop computers, running for extended periods of time). To address these problems we have developed Bosque, a multi-platform client-server software that performs standard phylogenetic tasks either locally or remotely on servers, and integrates the results on a local relational database. Bosque performs sequence alignments and graphical visualization and editing of trees, thus providing a powerful environment that integrates all the steps of phylogenetic analyses.'),
(74354, 2008, 'bio', 'Comments on On correcting the overestimation of the permutation-based false discovery rate estimator', 'Xie, Yang', 'Departmento de Oceanografía, Universidad de Concepción, Casilla 160-C, Concepción, Chile. sram@profc.udec.cl', 'Chile', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Models, Statistical|Reproducibility of Results', 'Comment', '10.1093/bioinformatics/btn456', '18762484', '-'),
(74355, 2008, 'bio', 'MeltDB: a software platform for the analysis and integration of metabolomics experiment data', 'Neuweger, Heiko|Albaum, Stefan P|Dondrup, Michael|Persicke, Marcus|Watt, Tony|Niehaus, Karsten|Stoye, Jens|Goesmann, Alexander', 'International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de|International NRW Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany. hneuwege@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Database Management Systems|Genome, Bacterial|Metabolomics|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn452', '18765459', 'The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context.'),
(74356, 2008, 'bio', 'System estimation from metabolic time-series data', 'Goel, Gautam|Chou, I-Chun|Voit, Eberhard O', 'Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.|Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia', 61, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Proteome|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn470', '18772153', 'At the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether--or to what degree--the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency, circumvent undue compensation of errors, determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.'),
(74357, 2008, 'bio', 'FACTA: a text search engine for finding associated biomedical concepts', 'Tsuruoka, Yoshimasa|Tsujii, Junichi|Ananiadou, Sophia', 'School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Abstracting and Indexing as Topic|Database Management Systems|MEDLINE|Software', 'Journal Article', '10.1093/bioinformatics/btn469', '18772154', 'FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query. The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics. Unlike existing systems that provide similar functionality, FACTA pre-indexes not only the words but also the concepts mentioned in the documents, which enables the user to issue a flexible query (e.g. free keywords or Boolean combinations of keywords/concepts) and receive the results immediately even when the number of the documents that match the query is very large. The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts. The concept IDs and their names/synonyms for building the indexes were collected from several biomedical databases and thesauri, such as UniProt, BioThesaurus, UMLS, KEGG and DrugBank.'),
(74358, 2008, 'bio', 'Identification of the idiosyncratic bacterial protein tyrosine kinase (BY-kinase) family signature', 'Jadeau, Fanny|Bechet, Emmanuelle|Cozzone, Alain J|Deléage, Gilbert|Grangeasse, Christophe|Combet, Christophe', 'Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.|Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.|Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.|Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.|Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.|Institut de Biologie et Chimie des Protéines, UMR5086, CNRS, Université Lyon 1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon CEDEX 07, France.', 'France|France|France|France|France|France', 35, 'Amino Acid Motifs|Amino Acid Sequence|Bacterial Proteins|Computational Biology|Databases, Protein|Molecular Sequence Data|Protein-Tyrosine Kinases|Proteins|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/btn462', '18772155', 'Most of the protein tyrosine kinases found in bacteria have been recently classified in a new family, termed BY-kinase. Indeed, they share no sequence homology with their eukaryotic counterparts and have no known eukaryotic homologues. They are involved in several biological functions (e.g. capsule biosynthesis, antibiotic resistance, virulence mechanism). Thus, they can be considered interesting therapeutic targets to develop new drugs to treat infectious diseases. However, their identification is rendered difficult due to slow progress in their structural characterization and comes most often from biochemical experiments. Moreover BY-kinase sequences are related to many other bacterial proteins involved in several biological functions (e.g. ParA family proteins). Accordingly, their annotations in generalist databases, sequence analysis and classification remain partial and inhomogeneous and there is no bioinformatics resource dedicated to these proteins.'),
(74359, 2008, 'bio', 'varDB: a pathogen-specific sequence database of protein families involved in antigenic variation', 'Hayes, C Nelson|Diez, Diego|Joannin, Nicolas|Honda, Wataru|Kanehisa, Minoru|Wahlgren, Mats|Wheelock, Craig E|Goto, Susumu', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 13, 'Animals|Antigenic Variation|Bacteria|Databases, Genetic|Databases, Protein|Eukaryota|Fungi|Humans|Proteins|Viruses', 'Journal Article', '10.1093/bioinformatics/btn477', '18776192', 'Infectious diseases are a major threat to global public health and prosperity. The causative agents consist of a suite of pathogens, ranging from bacteria to viruses, including fungi, helminthes and protozoa. Although these organisms are extremely varied in their biological structure and interactions with the host, they share similar methods of evading the host immune system. Antigenic variation and drift are mechanisms by which pathogens change their exposed epitopes while maintaining protein function. Accordingly, these traits enable pathogens to establish chronic infections in the host. The varDB database was developed to serve as a central repository of protein and nucleotide sequences as well as associated features (e.g. field isolate data, clinical parameters, etc.) involved in antigenic variation. The data currently contained in varDB were mined from GenBank as well as multiple specialized data repositories (e.g. PlasmoDB, GiardiaDB). Family members and ortholog groups were identified using a hierarchical search strategy, including literature/author-based searches and HMM profiles. Included in the current release are>29,00 sequences from 39 gene families from 25 different pathogens. This resource will enable researchers to compare antigenic variation within and across taxa with the goal of identifying common mechanisms of pathogenicity to assist in the fight against a range of devastating diseases.'),
(74360, 2008, 'bio', 'INTREPID--INformation-theoretic TREe traversal for Protein functional site IDentification', 'Sankararaman, Sriram|Sjölander, Kimmen', 'Department of Electrical Engineering & Computer Science and Department of Bioengineering, University of California, Berkeley, USA. sriram_s@cs.berkeley.edu|Department of Electrical Engineering & Computer Science and Department of Bioengineering, University of California, Berkeley, USA. sriram_s@cs.berkeley.edu', 'United States|United States', 62, 'Algorithms|Binding Sites|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn474', '18776193', 'Identification of functionally important residues in proteins plays a significant role in biological discovery. Here, we present INTREPID--an information-theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments (MSAs). INTREPID uses a traversal of the phylogeny in combination with a positional conservation score, based on Jensen-Shannon divergence, to rank positions in an MSA. While knowledge of protein 3D structure can significantly improve the accuracy of functional site identification, since structural information is not available for a majority of proteins, INTREPID relies solely on sequence information. We evaluated INTREPID on two tasks: predicting catalytic residues and predicting specificity determinants.'),
(74361, 2008, 'bio', 'STREAM: Static Thermodynamic REgulAtory Model of transcription', 'Bauer, Denis C|Bailey, Timothy L', 'Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld. 4072, Australia.|Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld. 4072, Australia.', 'Australia|Australia', 2, 'Algorithms|Binding Sites|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Software|Thermodynamics|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn467', '18776194', 'Understanding the transcriptional regulation of a gene in detail is a crucial step towards uncovering and ultimately utilizing the regulatory grammar of the genome. Modeling transcriptional regulation using thermodynamic equations has become an increasingly important approach towards this goal. Here, we present stream, the first publicly available framework for modeling, visualizing and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of transcription factors (TFs), the TF binding sites (TFBSs) in a regulatory DNA region, and the transcription rate of the target gene, stream will optimize its parameters to generate a model that best fits the input data. This trained model can then be used to (a) validate that the given set of TFs is able to regulate the target gene and (b) to predict the transcription rate under different conditions (e.g. different tissues, knockout/additional TFs or mutated/missing TFBSs).'),
(74362, 2008, 'bio', 'DESHARKY: automatic design of metabolic pathways for optimal cell growth', 'Rodrigo, Guillermo|Carrera, Javier|Prather, Kristala Jones|Jaramillo, Alfonso', 'Instituto de Biologia Molecular y Celular de Plantas, CSIC, Spain.|Instituto de Biologia Molecular y Celular de Plantas, CSIC, Spain.|Instituto de Biologia Molecular y Celular de Plantas, CSIC, Spain.|Instituto de Biologia Molecular y Celular de Plantas, CSIC, Spain.', 'Spain|Spain|Spain|Spain', 70, 'Algorithms|Computational Biology|Databases, Protein|Escherichia coli|Metabolic Networks and Pathways|Phylogeny|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn471', '18776195', 'The biological solution for synthesis or remediation of organic compounds using living organisms, particularly bacteria and yeast, has been promoted because of the cost reduction with respect to the non-living chemical approach. In that way, computational frameworks can profit from the previous knowledge stored in large databases of compounds, enzymes and reactions. In addition, the cell behavior can be studied by modeling the cellular context.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74363, 2008, 'bio', 'ADZE: a rarefaction approach for counting alleles private to combinations of populations', 'Szpiech, Zachary A|Jakobsson, Mattias|Rosenberg, Noah A', 'Center for Computational Medicine and Biology, Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA. szpiechz@umich.edu|Center for Computational Medicine and Biology, Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA. szpiechz@umich.edu|Center for Computational Medicine and Biology, Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA. szpiechz@umich.edu', 'United States|United States|United States', 430, 'African Continental Ancestry Group|Alleles|Genetic Variation|Genetics, Population|Genotype|Humans|Microsatellite Repeats|Oceanic Ancestry Group|Software', 'Journal Article', '10.1093/bioinformatics/btn478', '18779233', 'Analysis of the distribution of alleles across populations is a useful tool for examining population diversity and relationships. However, sample sizes often differ across populations, sometimes making it difficult to assess allelic distributions across groups.'),
(74364, 2008, 'bio', 'OmicsViz: Cytoscape plug-in for visualizing omics data across species', 'Xia, Tian|Dickerson, Julie A', 'Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50010, USA.|Program of Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50010, USA.', 'United States|United States', 6, 'Algorithms|Computational Biology|Computer Graphics|Databases, Genetic|Gene Regulatory Networks|Genomics|Metabolic Networks and Pathways|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn473', '18779234', 'OmicsViz is a Cytoscape plug-in for mapping and visualizing large-scale omics datasets across species, including those with many-to-many mappings between homologs. This allows users to map their data onto pathways of related model organisms. Mapping schemas across species or different experimental protocols allow users to comparatively analyze the omics data. The data can also be visualized in parallel-coordinate plots.'),
(74365, 2008, 'bio', 'Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia', 'Cosgrove, Elissa J|Zhou, Yingchun|Gardner, Timothy S|Kolaczyk, Eric D', 'Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.', 'United States|United States|United States|United States', 23, 'Algorithms|Computer Simulation|Databases, Protein|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btn476', '18779235', 'DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation.'),
(74366, 2008, 'bio', 'Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks', 'Reverter, Antonio|Chan, Eva K F', 'CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, Brisbane, Queensland 4067, Australia. tony.reverter-gomez@csiro.au', 'Australia|Australia', 178, 'Algorithms|Animals|Cattle|Computer Simulation|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Humans|Information Theory|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn482', '18784117', 'We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.'),
(74367, 2008, 'bio', 'Chemical substructures that enrich for biological activity', 'Klekota, Justin|Roth, Frederick P', 'Harvard University Graduate Biophysics Program, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.|Harvard University Graduate Biophysics Program, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA.', 'United States|United States', 171, 'Animals|Cells, Cultured|Combinatorial Chemistry Techniques|Databases, Factual|Drug Design|Humans|Ligands|Pharmaceutical Preparations|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn479', '18784118', 'Certain chemical substructures are present in many drugs. This has led to the claim of privileged substructures which are predisposed to bioactivity. Because bias in screening library construction could explain this phenomenon, the existence of privilege has been controversial.'),
(74368, 2008, 'bio', 'F-Seq: a feature density estimator for high-throughput sequence tags', 'Boyle, Alan P|Guinney, Justin|Crawford, Gregory E|Furey, Terrence S', 'Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States', 230, 'Chromatin Immunoprecipitation|Deoxyribonuclease I|Genome|Sequence Analysis, DNA|Sequence Tagged Sites|Software', 'Journal Article', '10.1093/bioinformatics/btn480', '18784119', 'Tag sequencing using high-throughput sequencing technologies are now regularly employed to identify specific sequence features, such as transcription factor binding sites (ChIP-seq) or regions of open chromatin (DNase-seq). To intuitively summarize and display individual sequence data as an accurate and interpretable signal, we developed F-Seq, a software package that generates a continuous tag sequence density estimation allowing identification of biologically meaningful sites whose output can be displayed directly in the UCSC Genome Browser.'),
(74369, 2008, 'bio', 'Multi-spectra peptide sequencing and its applications to multistage mass spectrometry', 'Bandeira, Nuno|Olsen, Jesper V|Mann, Jesper V|Mann, Matthias|Pevzner, Pavel A', 'Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu', 'United States|United States|United States|United States|United States', 24, 'Computational Biology|Fungal Proteins|Peptides|Sequence Analysis, Protein|Tandem Mass Spectrometry|Yeasts', 'Journal Article', '10.1093/bioinformatics/btn184', '18785330', 'Despite a recent surge of interest in database-independent peptide identifications, accurate de novo peptide sequencing remains an elusive goal. While the recently introduced spectral network approach resulted in accurate peptide sequencing in low-complexity samples, its success depends on the chance of presence of spectra from overlapping peptides. On the other hand, while multistage mass spectrometry (collecting multiple MS 3 spectra from each MS 2 spectrum) can be applied to all spectra in a complex sample, there are currently no software tools for de novo peptide sequencing by multistage mass spectrometry. We describe a rigorous probabilistic framework for analyzing spectra of overlapping peptides and show how to apply it for multistage mass spectrometry. Our software results in both accurate de novo peptide sequencing from multistage mass spectra (despite the inferior quality of MS 3 spectra) and improved interpretation of spectral networks. We further study the problem of de novo peptide sequencing with accurate parent mass (but inaccurate fragment masses), the protocol that may soon become the dominant mode of spectral acquisition. Most existing peptide sequencing algorithms (based on the spectrum graph approach) do not track the accurate parent mass and are thus not equipped for solving this problem. We describe a de novo peptide sequencing algorithm aimed at this experimental protocol and show that it improves the sequencing accuracy on both tandem and multistage mass spectrometry.'),
(74370, 2008, 'bio', 'MPI-LIT: a literature-curated dataset of microbial binary protein--protein interactions', 'Rajagopala, Seesandra V|Goll, Johannes|Gowda, N D Deve|Sunil, Kumar C|Titz, Björn|Mukherjee, Arnab|Mary, Sharmila S|Raviswaran, Naresh|Poojari, Chetan S|Ramachandra, Srinivas|Shtivelband, Svetlana|Blazie, Stephen M|Hofmann, Julia|Uetz, Peter', 'J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org|J Craig Venter Institute, Rockville, MD 20850, USA. raja@jcvi.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Bacterial Proteins|Computational Biology|Databases, Protein|Protein Binding|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/btn481', '18786976', 'Prokaryotic protein-protein interactions are underrepresented in currently available databases. Here, we describe a gold standard dataset (MPI-LIT) focusing on microbial binary protein-protein interactions and associated experimental evidence that we have manually curated from 813 abstracts and full texts that were selected from an initial set of 36 852 abstracts. The MPI-LIT dataset comprises 1237 experimental descriptions that describe a non-redundant set of 746 interactions of which 659 (88%) are not reported in public databases. To estimate the curation quality, we compared our dataset with a union of microbial interaction data from IntAct, DIP, BIND and MINT. Among common abstracts, we achieve a sensitivity of up to 66% for interactions and 75% for experimental methods. Compared with these other datasets, MPI-LIT has the lowest fraction of interaction experiments per abstract (0.9) and the highest coverage of strains (92) and scientific articles (813). We compared methods that evaluate functional interactions among proteins (such as genomic context or co-expression) which are implemented in the STRING database. Most of these methods discriminate well between functionally relevant protein interactions (MPI-LIT) and high-throughput data.'),
(74371, 2008, 'bio', 'Gene set enrichment analysis using linear models and diagnostics', 'Oron, Assaf P|Jiang, Zhen|Gentleman, Robert', 'Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. assaf.oron@gmail.com|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. assaf.oron@gmail.com|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109-1024, USA. assaf.oron@gmail.com', 'United States|United States|United States', 36, 'Chromosomes|Gene Expression Profiling|Humans|Leukemia, Lymphoid|Linear Models|Models, Genetic|Phenotype', 'Journal Article', '10.1093/bioinformatics/btn465', '18790795', 'Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion.'),
(74372, 2008, 'bio', 'Specific alignment of structured RNA: stochastic grammars and sequence annealing', 'Bradley, Robert K|Pachter, Lior|Holmes, Ian', 'Biophysics Graduate Group, Department of Mathematics and Department of Bioengineering, University of California, Berkeley, CA 94720, USA.|Biophysics Graduate Group, Department of Mathematics and Department of Bioengineering, University of California, Berkeley, CA 94720, USA.|Biophysics Graduate Group, Department of Mathematics and Department of Bioengineering, University of California, Berkeley, CA 94720, USA.', 'United States|United States|United States', 28, 'Algorithms|Base Sequence|Computational Biology|Genome|Genomics|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bioinformatics/btn495', '18796475', 'Whole-genome screens suggest that eukaryotic genomes are dense with non-coding RNAs (ncRNAs). We introduce a novel approach to RNA multiple alignment which couples a generative probabilistic model of sequence and structure with an efficient sequence annealing approach for exploring the space of multiple alignments. This leads to a new software program, Stemloc-AMA, that is both accurate and specific in the alignment of multiple related RNA sequences.'),
(74373, 2008, 'bio', 'MonkeySNP: a web portal for non-human primate single nucleotide polymorphisms', 'Khouangsathiene, Samone|Pearson, Carlo|Street, Summer|Ferguson, Betsy|Dubay, Christopher', 'Oregon National Primate Research Center, Oregon Health & Science University, 505 N.W. 185th Avenue, Beaverton, OR 97006, USA.|Oregon National Primate Research Center, Oregon Health & Science University, 505 N.W. 185th Avenue, Beaverton, OR 97006, USA.|Oregon National Primate Research Center, Oregon Health & Science University, 505 N.W. 185th Avenue, Beaverton, OR 97006, USA.|Oregon National Primate Research Center, Oregon Health & Science University, 505 N.W. 185th Avenue, Beaverton, OR 97006, USA.|Oregon National Primate Research Center, Oregon Health & Science University, 505 N.W. 185th Avenue, Beaverton, OR 97006, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|CD40 Antigens|Computational Biology|Internet|Polymorphism, Single Nucleotide|Primates', 'Journal Article', '10.1093/bioinformatics/btn493', '18796476', 'MonkeySNP is a web-based resource created by the Genetic Resource and Informatics Program at the Oregon National Primate Research Center to facilitate access to non-human primate (NHP) single nucleotide polymorphisms (SNP) data. MonkeySNP is a mirror of the NCBI dbSNP database and contains additional NHP subpopulation genotype data and visual genotype displays to support SNP review and selection.'),
(74374, 2008, 'bio', 'tCal: transcriptional probability calculator using thermodynamic model', 'Zhou, Xin|Su, Zhen', 'College of Biological Sciences, China Agricultural University.|College of Biological Sciences, China Agricultural University.', 'China|China', 6, 'Computational Biology|Models, Genetic|Probability|Software|Thermodynamics|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn494', '18796477', 'Thermodynamic model has been proposed as a realistic model of gene transcriptional regulation in bacteria, in which transcription probability calculation is based on physical interpretation of biological knowledge. We extend the published results in this area, and derive a relatively new model which could serve as a generalized form of previous results. To facilitate the application in gene network modeling studies, we implement our model in a Python module, tCal. With minimum programming efforts, user can use tCal to build transcription units, and either compute the transcription probabilities, or construct SBML models of regulatory networks.'),
(74375, 2008, 'bio', 'TESE: generating specific protein structure test set ensembles', 'Sirocco, Francesco|Tosatto, Silvio C E', 'Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.|Department of Biology, University of Padova, Viale G. Colombo 3, 35131 Padova, Italy.', 'Italy|Italy', 9, 'Computational Biology|Databases, Protein|Internet|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btn488', '18796478', 'TESE is a web server for the generation of test sets of protein sequences and structures fulfilling a number of different criteria. At least three different use cases can be envisaged: (i) benchmarking of novel methods, (ii) test sets tailored for special needs and (iii) extending available datasets. The CATH structure classification is used to control structural/sequence redundancy and a variety of structural quality parameters can be used to interactively select protein subsets with specific characteristics, e.g. all X-ray structures of alpha-helical repeat proteins with more than 120 residues and resolution <2.0 A. The output includes FASTA-formatted sequences, PDB files and a clickable HTML index file containing images of the selected proteins. Multiple subsets for cross-validation are also supported.'),
(74376, 2008, 'bio', 'GOSLING: a rule-based protein annotator using BLAST and GO', 'Jones, Craig E|Schwerdt, Julian|Bretag, Tessa Arwen|Baumann, Ute|Brown, Alfred L', 'Australian Centre for Plant Functional Genomics, Waite Campus, University of Adelaide, South Australia. craig@cs.adelaide.edu.au|Australian Centre for Plant Functional Genomics, Waite Campus, University of Adelaide, South Australia. craig@cs.adelaide.edu.au|Australian Centre for Plant Functional Genomics, Waite Campus, University of Adelaide, South Australia. craig@cs.adelaide.edu.au|Australian Centre for Plant Functional Genomics, Waite Campus, University of Adelaide, South Australia. craig@cs.adelaide.edu.au|Australian Centre for Plant Functional Genomics, Waite Campus, University of Adelaide, South Australia. craig@cs.adelaide.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Internet|Proteins', 'Journal Article', '10.1093/bioinformatics/btn486', '18796479', 'GOSLING is a web-based protein function annotator that uses a decision tree-derived rule set to quickly predict Gene Ontology terms for a protein. A score is assigned to each term prediction that is indicative of the accuracy of the prediction. Due to its speed and accuracy GOSLING is ideally suited for high-throughput annotation tasks.'),
(74377, 2008, 'bio', 'FunNet: an integrative tool for exploring transcriptional interactions', 'Prifti, Edi|Zucker, Jean-Daniel|Clement, Karine|Henegar, Corneliu', 'INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique, Paris, France.|INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique, Paris, France.|INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique, Paris, France.|INSERM, UMR-S 872, Les Cordeliers, Eq. 7 Nutriomique, Paris, France.', 'France|France|France|France', 0, 'Computational Biology|Gene Expression Regulation|Gene Regulatory Networks|Humans|Internet|Software|Systems Biology|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn492', '18799481', 'We describe here an exploratory tool, called FunNet, which implements an original systems biology approach, aiming to improve the biological relevance of the modular interaction patterns identified in transcriptional co-expression networks. A suitable analytical model, involving two abstraction layers, has been devised to relate expression profiles to the knowledge on transcripts biological roles, extracted from genomic databases, into a comprehensive exploratory framework. This approach has been implemented into a user-friendly web tool to promote its open use by the community.'),
(74378, 2008, 'bio', 'GenMiner: mining non-redundant association rules from integrated gene expression data and annotations', 'Martinez, Ricardo|Pasquier, Nicolas|Pasquier, Claude', 'Laboratoire I3S, UNSA/CNRS UMR-6070, 2000 route des Lucioles, 06903 Valbonne, France.|Laboratoire I3S, UNSA/CNRS UMR-6070, 2000 route des Lucioles, 06903 Valbonne, France.|Laboratoire I3S, UNSA/CNRS UMR-6070, 2000 route des Lucioles, 06903 Valbonne, France.', 'France|France|France', 0, 'Algorithms|Computational Biology|Gene Expression|Software', 'Journal Article', '10.1093/bioinformatics/btn490', '18799482', 'GenMiner is an implementation of association rule discovery dedicated to the analysis of genomic data. It allows the analysis of datasets integrating multiple sources of biological data represented as both discrete values, such as gene annotations, and continuous values, such as gene expression measures. GenMiner implements the new NorDi (normal discretization) algorithm for normalizing and discretizing continuous values and takes advantage of the Close algorithm to efficiently generate minimal non-redundant association rules. Experiments show that execution time and memory usage of GenMiner are significantly smaller than those of the standard Apriori-based approach, as well as the number of extracted association rules.'),
(74379, 2008, 'bio', 'BEST: Bayesian estimation of species trees under the coalescent model', 'Liu, Liang', 'Musuem of Comparative Zoology, Harvard University, Cambridge, MA, USA. lliu@oeb.harvard.edu', 'United States', 281, 'Algorithms|Bayes Theorem|Computational Biology|Markov Chains|Models, Genetic|Monte Carlo Method|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/btn484', '18799483', 'BEST implements a Bayesian hierarchical model to jointly estimate gene trees and the species tree from multilocus sequences. It provides a new option for estimating species phylogenies within the popular Bayesian phylogenetic program MrBayes. The technique of simulated annealing is adopted along with Metropolis coupling as performed in MrBayes to improve the convergence rate of the Markov Chain Monte Carlo algorithm.'),
(74380, 2008, 'bio', 'A toolkit for capturing and sharing FuGE experiments', 'Belhajjame, Khalid|Jones, Andrew R|Paton, Norman W', 'School of Computer Science, University of Manchester, Oxford Road, Manchester, UK. khalid.belhajjame@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, UK. khalid.belhajjame@manchester.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, UK. khalid.belhajjame@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 7, 'Computational Biology|Genomics|Internet|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btn496', '18801749', 'The Functional Genomics Experiment Object Model (FuGE) supports modelling of experimental processes either directly or through extensions that specialize FuGE for use in specific contexts. FuGE applications commonly include components that capture, store and search experiment descriptions, where the requirements of different applications have much in common.'),
(74381, 2008, 'bio', 'Functional modules integrating essential cellular functions are predictive of the response of leukaemia cells to DNA damage', 'Sameith, Katrin|Antczak, Philipp|Marston, Elliot|Turan, Nil|Maier, Dieter|Stankovic, Tanja|Falciani, Francesco', 'School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.|School of Biosciences and Institute of Biomedical Research, CRUK Institute for Cancer Studies, University of Birmingham, Birmingham, B152TT, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Burkitt Lymphoma|DNA Damage|Humans|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btn489', '18801750', 'Childhood B-precursor lymphoblastic leukaemia (ALL) is the most common paediatric malignancy. Despite the fact that 80% of ALL patients respond to anti-cancer drugs, the patho-physiology of this disease is still not fully understood. mRNA expression-profiling studies that have been performed have not yet provided novel insights into the mechanisms behind cellular response to DNA damage. More powerful data analysis techniques may be required for identifying novel functional pathways involved in the cellular responses to DNA damage.'),
(74382, 2008, 'bio', 'Optimal stepwise experimental design for pairwise functional interaction studies', 'Casey, Fergal P|Cagney, Gerard|Krogan, Nevan J|Shields, Denis C', 'UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland. fergal.p.casey@gmail.com|UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland. fergal.p.casey@gmail.com|UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland. fergal.p.casey@gmail.com|UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland. fergal.p.casey@gmail.com', 'Ireland|Ireland|Ireland|Ireland', 10, 'Algorithms|Cluster Analysis|Computer Simulation|Models, Statistical|Oligonucleotide Array Sequence Analysis|Research Design', 'Journal Article', '10.1093/bioinformatics/btn472', '18805797', 'Pairwise experimental perturbation is increasingly used to probe gene and protein function because these studies offer powerful insight into the activity and regulation of biological systems. Symmetric two-dimensional datasets, such as pairwise genetic interactions are amenable to an optimally designed measurement procedure because of the equivalence of cases and conditions where fewer experimental measurements may be required to extract the underlying structure.'),
(74383, 2008, 'bio', 'MS-BID: a Java package for label-free LC-MS-based comparative proteomic analysis', 'Hwang, Daehee|Zhang, Ning|Lee, Hookeun|Yi, Eugene|Zhang, Hui|Lee, Inyoul Y|Hood, Leroy|Aebersold, Ruedi', 'Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr|Institute for Systems Biology, Seattle, WA 98103, USA. dhhwang@postech.ac.kr', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Chromatography, Liquid|Computational Biology|Mass Spectrometry|Proteomics|Software', 'Comparative Study', '10.1093/bioinformatics/btn491', '18805798', 'MS-BID (MS Biomarker Discovery Platform) is an integrative computational pipeline for biomarker discovery using LC-MS-based comparative proteomic analysis. This platform consists of several computational tools for: (i) detecting peptides in the collected patterns, (ii) matching detected peptides across a number of LC-MS datasets and (iii) selecting discriminatory peptides between classes of samples.'),
(74384, 2008, 'bio', '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 Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany. ldpf@minet.uni-jena.de,|Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany. ldpf@minet.uni-jena.de,|Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany. ldpf@minet.uni-jena.de,|Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany. ldpf@minet.uni-jena.de,', 'Germany|Germany|Germany|Germany', 0, 'Carbohydrate Metabolism|Citric Acid Cycle|Computational Biology|Fatty Acids|Gluconeogenesis|Glycolysis|Humans|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btn500', '18806269', 'In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis.'),
(74385, 2009, 'bio', 'Integrating and annotating the interactome using the MiMI plugin for cytoscape', 'Gao, Jing|Ade, Alex S|Tarcea, V Glenn|Weymouth, Terry E|Mirel, Barbara R|Jagadish, H V|States, David J', 'National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics, Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn501', '18812364', 'The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported.'),
(74386, 2008, 'bio', 'DOCKGROUND protein-protein docking decoy set', 'Liu, Shiyong|Gao, Ying|Vakser, Ilya A', 'Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, KS 66047, USA.|Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, KS 66047, USA.|Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, KS 66047, USA.', 'United States|United States|United States', 0, 'Computational Biology|Ligands|Protein Binding|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn497', '18812365', 'A protein-protein docking decoy set is built for the Dockground unbound benchmark set. The GRAMM-X docking scan was used to generate 100 non-native and at least one near-native match per complex for 61 complexes. The set is a publicly available resource for the development of scoring functions and knowledge-based potentials for protein docking methodologies.'),
(74387, 2008, 'bio', 'A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry', 'Osl, Melanie|Dreiseitl, Stephan|Pfeifer, Bernhard|Weinberger, Klaus|Klocker, Helmut|Bartsch, Georg|Schäfer, Georg|Tilg, Bernhard|Graber, Armin|Baumgartner, Christian', 'Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at|Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria. melanie.osl@umit.at', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 53, 'Algorithms|Biomarkers, Tumor|Cohort Studies|Humans|Male|Prostatic Neoplasms|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btn506', '18815183', 'Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management.'),
(74388, 2008, 'bio', 'Searching protein structure databases with DaliLite v3', 'Holm, L|Kääriäinen, S|Rosenström, P|Schenkel, A', 'Department of Biological and Environmental Sciences, and Institute of Biotechnology, P.O.Box 56 (Viikinkaari 5), 00014 University of Helsinki, Finland. liisa.holm@helsinki.fi|Department of Biological and Environmental Sciences, and Institute of Biotechnology, P.O.Box 56 (Viikinkaari 5), 00014 University of Helsinki, Finland. liisa.holm@helsinki.fi|Department of Biological and Environmental Sciences, and Institute of Biotechnology, P.O.Box 56 (Viikinkaari 5), 00014 University of Helsinki, Finland. liisa.holm@helsinki.fi|Department of Biological and Environmental Sciences, and Institute of Biotechnology, P.O.Box 56 (Viikinkaari 5), 00014 University of Helsinki, Finland. liisa.holm@helsinki.fi', 'Finland|Finland|Finland|Finland', 0, 'Algorithms|Databases, Protein|Internet|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn507', '18818215', 'The Red Queen said, It takes all the running you can do, to keep in the same place. Lewis Carrol'),
(74389, 2008, 'bio', 'Bayesian learning of biological pathways on genomic data assimilation', 'Yoshida, Ryo|Nagasaki, Masao|Yamaguchi, Rui|Imoto, Seiya|Miyano, Satoru|Higuchi, Tomoyuki', 'Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp|Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp|Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp|Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp|Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp|Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. yoshidar@ism.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Bayes Theorem|Circadian Rhythm|Computational Biology|Genome|Models, Genetic|Sensitivity and Specificity|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn483', '18818216', 'Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision.'),
(74390, 2008, 'bio', 'Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study', 'Fernández, Elmer A|Girotti, María R|López del Olmo, Juan A|Llera, Andrea S|Podhajcer, Osvaldo L|Cantet, Rodolfo J C|Balzarini, Mónica', 'School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar|School of Engineering, Intelligent Data Analysis Group, Catholic University of Córdoba, Argentina. elmer.fernandez@ucc.edu.ar', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 3, 'Carbocyanines|Cell Line, Tumor|Computational Biology|Electrophoresis, Gel, Two-Dimensional|Fluorescent Dyes|Humans|Image Processing, Computer-Assisted|Linear Models|Melanoma|Proteome|Proteomics', 'Journal Article', '10.1093/bioinformatics/btn508', '18818217', 'Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test. The analyses rarely accounts for other experimental effects, such as CyDye and gel effects, which could be important sources of noise while detecting treatment effects.'),
(74391, 2008, 'bio', 'Genome annotation in the presence of insertional RNA editing', 'Beargie, Christina|Liu, Tsunglin|Corriveau, Mark|Lee, Ha Youn|Gott, Jonatha|Bundschuh, Ralf', 'COSI, 333 West Broad Street, Columbus, OH 43215, USA.|COSI, 333 West Broad Street, Columbus, OH 43215, USA.|COSI, 333 West Broad Street, Columbus, OH 43215, USA.|COSI, 333 West Broad Street, Columbus, OH 43215, USA.|COSI, 333 West Broad Street, Columbus, OH 43215, USA.|COSI, 333 West Broad Street, Columbus, OH 43215, USA.', 'United States|United States|United States|United States|United States|United States', 12, 'Animals|Genome, Mitochondrial|Genome, Protozoan|Physarum polycephalum|RNA Editing', 'Journal Article', '10.1093/bioinformatics/btn487', '18819938', 'Insertional RNA editing renders gene prediction very difficult compared to organisms without such RNA editing. A case in point is the mitochondrial genome of Physarum polycephalum in which only about one-third of the number of genes that are to be expected given its length are annotated. Thus, gene prediction methods that explicitly take into account insertional editing are needed for successful annotation of such genomes.'),
(74392, 2008, 'bio', 'Identifying differentially expressed subnetworks with MMG', 'Noirel, Josselin|Sanguinetti, Guido|Wright, Phillip C', 'Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk|Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk|Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Models, Statistical|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn499', '18819939', 'Mixture model on graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins. It is remarkably robust to missing data, a feature particularly important for its use in quantitative proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend.'),
(74393, 2008, 'bio', 'Databases, data tombs and dust in the wind', 'Wren, Jonathan D|Bateman, Alex', 'Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk|Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Information Systems|Internet', 'Editorial', '10.1093/bioinformatics/btn464', '18819940', 'As biomedical data accumulates, the need to store, share and organize it grows. Consequently, the number of Internet-accessible databases has been rapidly growing on an annual basis. Bioinformatics regularly publishes descriptions of biomedically relevant databases, Nucleic Acids Research has published an annual database issue since 1996 and now a new open-access journal,'),
(74394, 2008, 'bio', 'Phylogenetic distances are encoded in networks of interacting pathways', 'Mazurie, Aurélien|Bonchev, Danail|Schwikowski, Benno|Buck, Gregory A', 'Systems Biology Group, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France. aurelien.mazurie@pasteur.fr|Systems Biology Group, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France. aurelien.mazurie@pasteur.fr|Systems Biology Group, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France. aurelien.mazurie@pasteur.fr|Systems Biology Group, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France. aurelien.mazurie@pasteur.fr', 'France|France|France|France', 0, 'Metabolomics|Models, Biological|Phylogeny|RNA, Ribosomal, 16S', 'Journal Article', '10.1093/bioinformatics/btn503', '18820265', 'Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange.'),
(74395, 2009, 'bio', 'BNFinder: exact and efficient method for learning Bayesian networks', 'Wilczyxc5x84ski, Bartek|Dojer, Norbert', 'Institute of Informatics, University of Warsaw, Poland.|Institute of Informatics, University of Warsaw, Poland.', 'Poland|Poland', 64, 'Algorithms|Bayes Theorem|Computer Simulation|Information Storage and Retrieval|Software', 'Journal Article', '10.1093/bioinformatics/btn505', '18826957', 'Bayesian methods are widely used in many different areas of research. Recently, it has become a very popular tool for biological network reconstruction, due to its ability to handle noisy data. Even though there are many software packages allowing for Bayesian network reconstruction, only few of them are freely available to researchers. Moreover, they usually require at least basic programming abilities, which restricts their potential user base. Our goal was to provide software which would be freely available, efficient and usable to non-programmers.'),
(74396, 2008, 'bio', 'MSX-3D: a tool to validate 3D protein models using mass spectrometry', 'Heymann, Michaël|Paramelle, David|Subra, Gilles|Forest, Eric|Martinez, Jean|Geourjon, Christophe|Deléage, Gilbert', 'Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.|Institut de Biologie et Chimie des Protéines (IBCP) UMR5086 CNRS, Université Lyon 1, France.', 'France|France|France|France|France|France|France', 15, 'Computer Simulation|Databases, Protein|Mass Spectrometry|Models, Molecular|Protein Conformation|Proteins|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn510', '18826958', 'The technique of chemical cross-linking followed by mass spectrometry has proven to bring valuable information about the protein structure and interactions between proteic subunits. It is an effective and efficient way to experimentally investigate some aspects of a protein structure when NMR and X-ray crystallography data are lacking.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74397, 2008, 'bio', 'Smarter clustering methods for SNP genotype calling', 'Lin, Yan|Tseng, George C|Cheong, Soo Yeon|Bean, Lora J H|Sherman, Stephanie L|Feingold, Eleanor', 'Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu|Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu|Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu|Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu|Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu|Department of Biostatistics, Department of Medicine, Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. liny@upmc.edu', 'United States|United States|United States|United States|United States|United States', 16, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Genotype|Polymorphism, Single Nucleotide', 'Comparative Study', '10.1093/bioinformatics/btn509', '18826959', 'Most genotyping technologies for single nucleotide polymorphism (SNP) markers use standard clustering methods to call the SNP genotypes. These methods are not always optimal in distinguishing the genotype clusters of a SNP because they do not take advantage of specific features of the genotype calling problem. In particular, when family data are available, pedigree information is ignored. Furthermore, prior information about the distribution of the measurements for each cluster can be used to choose an appropriate model-based clustering method and can significantly improve the genotype calls. One special genotyping problem that has never been discussed in the literature is that of genotyping of trisomic individuals, such as individuals with Down syndrome. Calling trisomic genotypes is a more complicated problem, and the addition of external information becomes very important.'),
(74398, 2008, 'bio', 'Physical protein-protein interactions predicted from microarrays', 'Soong, Ta-Tsen|Wrzeszczynski, Kazimierz O|Rost, Burkhard', 'Columbia University Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. ts2186@columbia.edu|Columbia University Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. ts2186@columbia.edu|Columbia University Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. ts2186@columbia.edu', 'United States|United States|United States', 28, 'Artificial Intelligence|Computational Biology|Oligonucleotide Array Sequence Analysis|Protein Binding|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btn498', '18829707', 'Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task.'),
(74399, 2008, 'bio', 'Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation', 'Cornuet, Jean-Marie|Santos, Filipe|Beaumont, Mark A|Robert, Christian P|Marin, Jean-Michel|Balding, David J|Guillemaud, Thomas|Estoup, Arnaud', 'Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 479, 'Algorithms|Bayes Theorem|Evolution, Molecular|Genetics, Population|Humans|Population Groups|Software', 'Journal Article', '10.1093/bioinformatics/btn514', '18842597', 'Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC.'),
(74400, 2008, 'bio', 'Efficient mapping of Applied Biosystems SOLiD sequence data to a reference genome for functional genomic applications', 'Ondov, Brian D|Varadarajan, Anjana|Passalacqua, Karla D|Bergman, Nicholas H', 'School of Biology, Georgia Institute of Technology, 310 Ferst Dr., Atlanta, GA 30332-0230, USA.|School of Biology, Georgia Institute of Technology, 310 Ferst Dr., Atlanta, GA 30332-0230, USA.|School of Biology, Georgia Institute of Technology, 310 Ferst Dr., Atlanta, GA 30332-0230, USA.|School of Biology, Georgia Institute of Technology, 310 Ferst Dr., Atlanta, GA 30332-0230, USA.', 'Georgia|Georgia|Georgia|Georgia', 60, 'Databases, Genetic|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn512', '18842598', 'Here, we report the development of SOCS (short oligonucleotide color space), a program designed for efficient and flexible mapping of Applied Biosystems SOLiD sequence data onto a reference genome. SOCS performs its mapping within the context of color space, and it maximizes usable data by allowing a user-specified number of mismatches. Sequence census functions facilitate a variety of functional genomics applications, including transcriptome mapping and profiling, as well as ChIP-Seq.'),
(74401, 2008, 'bio', 'GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus', 'Zhu, Yuelin|Davis, Sean|Stephens, Robert|Meltzer, Paul S|Chen, Yidong', 'Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.|Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States|United States', 72, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn520', '18842599', 'The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data in GEO can be challenging. We have developed GEOmetadb in an attempt to make querying the GEO metadata both easier and more powerful. All GEO metadata records as well as the relationships between them are parsed and stored in a local MySQL database. A powerful, flexible web search interface with several convenient utilities provides query capabilities not available via NCBI tools. In addition, a Bioconductor package, GEOmetadb that utilizes a SQLite export of the entire GEOmetadb database is also available, rendering the entire GEO database accessible with full power of SQL-based queries from within R.'),
(74402, 2008, 'bio', 'The Burkholderia Genome Database: facilitating flexible queries and comparative analyses', 'Winsor, Geoffrey L|Khaira, Bhavjinder|Van Rossum, Thea|Lo, Raymond|Whiteside, Matthew D|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.', 'Canada|Canada|Canada|Canada|Canada|Canada', 149, 'Burkholderia|Databases, Genetic|Genome, Bacterial|Internet|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btn524', '18842600', 'As the genome sequences of multiple strains of a given bacterial species are obtained, more generalized bacterial genome databases may be complemented by databases that are focused on providing more information geared for a distinct bacterial phylogenetic group and its associated research community. The Burkholderia Genome Database represents a model for such a database, providing a powerful, user-friendly search and comparative analysis interface that contains features not found in other genome databases. It contains continually updated, curated and tracked information about Burkholderia cepacia complex genome annotations, plus other Burkholderia species genomes for comparison, providing a high-quality resource for its targeted cystic fibrosis research community.'),
(74403, 2008, 'bio', 'A flexible forward simulator for populations subject to selection and demography', 'Hernandez, Ryan D', 'Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14850, USA. rhernandez@uchicago.edu', 'United States', 146, 'Algorithms|Biological Evolution|Computer Simulation|Demography|Genetics, Population|Genome, Human|Humans|Models, Genetic|Population|Selection, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btn522', '18842601', 'This article introduces a new forward population genetic simulation program that can efficiently generate samples from populations with complex demographic histories under various models of natural selection. The program (SFS_CODE) is highly flexible, allowing the user to simulate realistic genomic regions with several loci evolving according to a variety of mutation models (from simple to context-dependent), and allows for insertions and deletions. Each locus can be annotated as either coding or non-coding, sex-linked or autosomal, selected or neutral, and have an arbitrary linkage structure (from completely linked to independent).'),
(74404, 2008, 'bio', 'A novel method for comparing topological models of protein structures enhanced with ligand information', 'Veeramalai, Mallika|Gilbert, David', 'Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK. mallikav@burnham.org|Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK. mallikav@burnham.org', 'United Kingdom|United Kingdom', 12, 'Algorithms|Animals|Computational Biology|Databases, Protein|Humans|Ligands|Protein Conformation|Proteins', 'Comparative Study', '10.1093/bioinformatics/btn518', '18842602', 'We introduce TOPS+ strings, a highly abstract string-based model of protein topology that permits efficient computation of structure comparison, and can optionally represent ligand information. In this model, we consider loops as secondary structure elements (SSEs) as well as helices and strands, in addition we represent ligands as first class objects. Interactions between SSEs and between SSEs and ligands are described by incoming/outgoing arcs and ligand arcs, respectively, and SSEs are annotated with arc interaction direction and type. We are able to abstract away from the ligands themselves, to give a model characterized by a regular grammar rather than the context sensitive grammar of the original TOPS model. Our TOPS+ strings model is sufficiently descriptive to obtain biologically meaningful results and has the advantage of permitting fast string-based structure matching and comparison as well as avoiding issues of Non-deterministic Polynomial time (NP)-completeness associated with graph problems. Our structure comparison method is computationally more efficient in identifying distantly related proteins than BLAST, CLUSTALW, SSAP and TOPS because of the compact and abstract string-based representation of protein structure which records both topological and biochemical information including the functionally important loop regions of the protein structures. The accuracy of our comparison method is comparable with that of TOPS. Also, we have demonstrated that our TOPS+ strings method out-performs the TOPS method for the ligand-dependent protein structures and provides biologically meaningful results.'),
(74405, 2008, 'bio', 'CNVDetector: locating copy number variations using array CGH data', 'Chen, Peng-An|Liu, Hsiao-Fei|Chao, Kun-Mao', 'Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics and Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics and Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan.|Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics and Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan.', '-|-|-', 11, 'Algorithms|Comparative Genomic Hybridization|Gene Dosage|Genetic Variation|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn517', '18842603', 'CNVDetector is a program for locating copy number variations (CNVs) in a single genome. CNVDetector has several merits: (i) it can deal with the array comparative genomic hybridization data even if the noise is not normally distributed, (ii) it has a linear time kernel, (iii) its parameters can be easily selected, (iv) it evaluates the statistical significance for each CNV calling.'),
(74406, 2008, 'bio', 'Systems-level modeling of cellular glycosylation reaction networks: O-linked glycan formation on natural selectin ligands', 'Liu, Gang|Marathe, Dhananjay D|Matta, Khushi L|Neelamegham, Sriram', 'Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.|Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.|Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.|Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.', 'United States|United States|United States|United States', 29, 'Computer Simulation|Gene Regulatory Networks|Glycomics|Glycosylation|Ligands|Oligosaccharides|Polysaccharides|Selectins|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn515', '18842604', 'The emerging field of Glycomics requires the development of systems-based modeling strategies to relate glycosyltransferase gene expression and enzyme activity with carbohydrate structure and function.'),
(74407, 2008, 'bio', 'IPEP: an in silico tool to examine proteolytic peptides for mass spectrometry', 'Lu, Dihui|Liu, Richard Z|Izumi, Victoria|Fenstermacher, David|Haura, Eric B|Koomen, John|Eschrich, Steven A', 'H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL 33612, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Protein|Peptides|Proteome|Proteomics|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1093/bioinformatics/btn511', '18842605', 'Peptide-based proteomics supports identification and quantification as well as localization of post-translational modifications (PTMs) within proteins extracted from biological samples. The bottom-up approach involves the digestion of proteins into peptide fragments that can be detected and sequenced with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). A web-based application, iPEP, was developed to compare the effectiveness of different proteolytic digests in detecting specific sequences. Furthermore, peptide populations can be examined to help optimize detection of certain groups of proteins relative to the proteome and the digested peptidome. The application reports proteolytic peptide sequences, theoretical molecular weights and functional annotations using Gene Ontology (GO) terms. The iPEP tool can assist with experimental design by maximizing the detection of proteins, consensus sites and modified residues of interest for individual proteins or as part of large-scale proteomic assays.'),
(74408, 2008, 'bio', 'Impact of dimeric organization of enzyme on its function: the case of photosynthetic water splitting', 'Jablonsky, Jiri|Susila, Petr|Lazar, Dusan', 'Laboratory of Biophysics, Faculty of Science, Palacky University, Tr. Svobody 26, 771 46 Olomouc, Czech Republic.|Laboratory of Biophysics, Faculty of Science, Palacky University, Tr. Svobody 26, 771 46 Olomouc, Czech Republic.|Laboratory of Biophysics, Faculty of Science, Palacky University, Tr. Svobody 26, 771 46 Olomouc, Czech Republic.', '-|-|-', 0, 'Computer Simulation|Dimerization|Models, Molecular|Oxygen|Photosystem II Protein Complex|Water', 'Journal Article', '10.1093/bioinformatics/btn530', '18845578', 'It is a question of whether the supramolecular organization of the protein complex has an impact on its function, or not. In the case of the photosystem II (PSII), water splitting might be influenced by cooperation of the PSIIs. Since PSII is the source of the atmospheric oxygen and because better understanding of the water splitting may contribute to the effective use of water as an alternative energy source, possible cooperation should be analyzed and discussed.'),
(74409, 2008, 'bio', 'Stochastic dynamics of genetic networks: modelling and parameter identification', 'Cinquemani, Eugenio|Milias-Argeitis, Andreas|Summers, Sean|Lygeros, John', 'Automatic Control Laboratory, ETH, 8092 Zurich, Switzerland. cinquemani@control.ee.ethz.ch|Automatic Control Laboratory, ETH, 8092 Zurich, Switzerland. cinquemani@control.ee.ethz.ch|Automatic Control Laboratory, ETH, 8092 Zurich, Switzerland. cinquemani@control.ee.ethz.ch|Automatic Control Laboratory, ETH, 8092 Zurich, Switzerland. cinquemani@control.ee.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 19, 'Algorithms|Computer Simulation|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Kinetics|Models, Genetic|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn527', '18845579', 'Identification of regulatory networks is typically based on deterministic models of gene expression. Increasing experimental evidence suggests that the gene regulation process is intrinsically random. To ensure accurate and thorough processing of the experimental data, stochasticity must be explicitly accounted for both at the modelling stage and in the design of the identification algorithms.'),
(74410, 2008, 'bio', 'FABSIM: a software for generating FST distributions with various ascertainment biases', 'Ramírez-Soriano, Anna|Calafell, Francesc', 'IBE, Institute of Evolutionary Biology (UPF-CSIC), CEXS-UPF-PRBB. Doctor Aiguader, 88. 08003 Barcelona, Catalonia, Spain.|IBE, Institute of Evolutionary Biology (UPF-CSIC), CEXS-UPF-PRBB. Doctor Aiguader, 88. 08003 Barcelona, Catalonia, Spain.', 'Spain|Spain', 4, 'Base Sequence|Bias|Gene Frequency|Humans|Polymorphism, Single Nucleotide|Selection, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn531', '18845580', 'We have developed a software that applies ascertainment bias on simulated DNA sequences and calculates F(ST) on them, so they can be used to generate neutral distributions that are appropriate to test whether the genetic differentiation of a particular gene between populations is compatible with neutral evolution, or, on the contrary, suggests local adaptation by natural selection.'),
(74411, 2008, 'bio', 'Artemis and ACT: viewing, annotating and comparing sequences stored in a relational database', 'Carver, Tim|Berriman, Matthew|Tivey, Adrian|Patel, Chinmay|Böhme, Ulrike|Barrell, Barclay G|Parkhill, Julian|Rajandream, Marie-Adèle', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 410, 'Databases, Genetic|Databases, Nucleic Acid|Genomics|Software', 'Comparative Study', '10.1093/bioinformatics/btn529', '18845581', 'Artemis and Artemis Comparison Tool (ACT) have become mainstream tools for viewing and annotating sequence data, particularly for microbial genomes. Since its first release, Artemis has been continuously developed and supported with additional functionality for editing and analysing sequences based on feedback from an active user community of laboratory biologists and professional annotators. Nevertheless, its utility has been somewhat restricted by its limitation to reading and writing from flat files. Therefore, a new version of Artemis has been developed, which reads from and writes to a relational database schema, and allows users to annotate more complex, often large and fragmented, genome sequences.'),
(74412, 2008, 'bio', 'Faster exact Markovian probability functions for motif occurrences: a DFA-only approach', 'Ribeca, Paolo|Raineri, Emanuele', 'Bioinformatics and Genomics Unit, Center for Genomic Regulation, C/ Dr.Aiguader 88, E08003 Barcelona, Spain. paolo.ribeca@gmail.com|Bioinformatics and Genomics Unit, Center for Genomic Regulation, C/ Dr.Aiguader 88, E08003 Barcelona, Spain. paolo.ribeca@gmail.com', 'Spain|Spain', 20, 'Amino Acid Motifs|Computational Biology|Genome, Human|Genomics|Humans|Markov Chains|Probability|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn525', '18845582', 'The computation of the statistical properties of motif occurrences has an obviously relevant application: patterns that are significantly over- or under-represented in genomes or proteins are interesting candidates for biological roles. However, the problem is computationally hard, as a result, virtually all the existing motif finders use fast but approximate scoring functions, in spite of the fact that they have been shown to produce systematically incorrect results. A few interesting exact approaches are known, but they are very slow and hence not practical in the case of realistic sequences.'),
(74413, 2008, 'bio', 'A better block partition and ligation strategy for individual haplotyping', 'Zhao, Yuzhong|Xu, Yun|Wang, Zhihao|Zhang, Hong|Chen, Guoliang', 'Department of Computer Science, University of Science and Technology of China and Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application, Hefei, Anhui 230027, P.R. China.|Department of Computer Science, University of Science and Technology of China and Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application, Hefei, Anhui 230027, P.R. China.|Department of Computer Science, University of Science and Technology of China and Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application, Hefei, Anhui 230027, P.R. China.|Department of Computer Science, University of Science and Technology of China and Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application, Hefei, Anhui 230027, P.R. China.|Department of Computer Science, University of Science and Technology of China and Anhui Province-MOST Co-Key Laboratory of High Performance Computing and Its Application, Hefei, Anhui 230027, P.R. China.', 'China|China|China|China|China', 0, 'Algorithms|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/btn519', '18845583', 'Haplotype played an important role in the association studies of disease gene and drug responsivity over the past years, but the low throughput of expensive biological experiments largely limited its application. Alternatively, some efficient statistical methods were developed to deduce haplotypes from genotypes directly. Because these algorithms usually needed to estimate the frequencies of numerous possible haplotypes, the partition and ligation strategy was widely adopted to reduce the time complexity. The haplotypes were usually partitioned uniformly in the past, but recent studies showed that the haplotypes had their own block structure, which may be not uniform. More reasonable block partition and ligation strategy according to the haplotype structure may further improve the accuracy of individual haplotyping.'),
(74414, 2008, 'bio', 'Profile Comparer: a program for scoring and aligning profile hidden Markov models', 'Madera, Martin', 'Department of Computer Science, University of Bristol, Bristol, UK. martin.madera@gmail.com', 'United Kingdom', 0, 'Algorithms|Computational Biology|Markov Chains|Models, Biological|Software', 'Comparative Study', '10.1093/bioinformatics/btn504', '18845584', 'Profile Comparer (PRC) is a stand-alone program for scoring and aligning profile hidden Markov models (HMMs) of protein families. PRC can read models produced by SAM and HMMER, two popular profile HMM packages, as well as PSI-BLAST checkpoint files. This application note provides a brief description of the profile-profile algorithm used by PRC.'),
(74415, 2008, 'bio', 'A bioinformatics analysis of the cell line nomenclature', 'Sarntivijai, Sirarat|Ade, Alexander S|Athey, Brian D|States, David J', 'National Center for Integrative Biomedical Informatics and the Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics and the Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics and the Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.|National Center for Integrative Biomedical Informatics and the Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States', 23, 'Cell Line|Computational Biology|Databases, Factual|MEDLINE|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btn502', '18849319', 'Cell lines are used extensively in biomedical research, but the nomenclature describing cell lines has not been standardized. The problems are both linguistic and experimental. Many ambiguous cell line names appear in the published literature. Users of the same cell line may refer to it in different ways, and cell lines may mutate or become contaminated without the knowledge of the user. As a first step towards rationalizing this nomenclature, we created a cell line knowledgebase (CLKB) with a well-structured collection of names and descriptive data for cell lines cultured in vitro. The objectives of this work are: (i) to assist users in extracting useful information from biomedical text and (ii) to highlight the importance of standardizing cell line names in biomedical research. This CLKB contains a broad collection of cell line names compiled from ATCC, Hyper CLDB and MeSH. In addition to names, the knowledgebase specifies relationships between cell lines. We analyze the use of cell line names in biomedical text. Issues include ambiguous names, polymorphisms in the use of names and the fact that some cell line names are also common English words. Linguistic patterns associated with the occurrence of cell line names are analyzed. Applying these patterns to find additional cell line names in the literature identifies only a small number of additional names. Annotation of microarray gene expression studies is used as a test case. The CLKB facilitates data exploration and comparison of different cell lines in support of clinical and experimental research.'),
(74416, 2008, 'bio', 'POPE--a tool to aid high-throughput phylogenetic analysis', 'Juliusdottir, Thorhildur|Pettersson, Fredrik|Copley, Richard R', 'Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK. tota@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK. tota@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford, OX3 7BN, UK. tota@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Genetic|Evolution, Molecular|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn533', '18849569', 'POPE (Phylogeny, Ortholog and Paralog Extractor) provides an integrated platform for automatic ortholog identification. Intermediate steps can be visualized, modified and analyzed in order to assess and improve the underlying quality of orthology and paralogy assignments.'),
(74417, 2008, 'bio', 'Position-dependent motif characterization using non-negative matrix factorization', 'Hutchins, Lucie N|Murphy, Sean M|Singh, Priyam|Graber, Joel H', 'Center for Genome Dynamics, The Jackson Laboratory, Bar Harbor, ME 04609, USA.|Center for Genome Dynamics, The Jackson Laboratory, Bar Harbor, ME 04609, USA.|Center for Genome Dynamics, The Jackson Laboratory, Bar Harbor, ME 04609, USA.|Center for Genome Dynamics, The Jackson Laboratory, Bar Harbor, ME 04609, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|RNA, Messenger|Regulatory Sequences, Ribonucleic Acid|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btn526', '18852176', 'Cis-acting regulatory elements are frequently constrained by both sequence content and positioning relative to a functional site, such as a splice or polyadenylation site. We describe an approach to regulatory motif analysis based on non-negative matrix factorization (NMF). Whereas existing pattern recognition algorithms commonly focus primarily on sequence content, our method simultaneously characterizes both positioning and sequence content of putative motifs.'),
(74418, 2008, 'bio', 'GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies', 'Holden, Marit|Deng, Shiwei|Wojnowski, Leszek|Kulle, Bettina', 'Norwegian Computing Center, Oslo, Norway, Department of Pharmacology, University of Mainz, Mainz, Germany.|Norwegian Computing Center, Oslo, Norway, Department of Pharmacology, University of Mainz, Mainz, Germany.|Norwegian Computing Center, Oslo, Norway, Department of Pharmacology, University of Mainz, Mainz, Germany.|Norwegian Computing Center, Oslo, Norway, Department of Pharmacology, University of Mainz, Mainz, Germany.', 'Germany|Germany|Germany|Germany', 130, 'Genome|Genome-Wide Association Study|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btn516', '18854360', 'The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.'),
(74419, 2008, 'bio', 'Osiris: an integrated promoter database for Oryza sativa L', 'Morris, Robert T|OConnor, Timothy R|Wyrick, John J', 'School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA.|School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA.|School of Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, WA, USA.', 'United States|United States|United States', 35, 'Binding Sites|Databases, Genetic|Genome, Plant|Internet|Oryza|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn537', '18922805', 'Rice (Oryza sativa L.) is an important model monocot and cereal crop. While the rice genome sequence has been published and annotated, relatively little is known about the transcriptional networks that regulate rice gene expression. For this reason, we have developed Osiris, a database containing promoter sequences, predicted transcription factor (TF) binding sites, gene ontology annotation and microarray expression data for 24 209 genes in the rice genome. These tools are seamlessly integrated in the Osiris web site, allowing the user to visualize TF binding sites in multiple promoters, analyze the statistical significance of enriched TF binding sites, query for genes containing similar promoter regulatory logic or gene function and visualize the microarray expression patterns of queried or selected gene sets.'),
(74420, 2008, 'bio', 'BioCaster: detecting public health rumors with a Web-based text mining system', 'Collier, Nigel|Doan, Son|Kawazoe, Ai|Goodwin, Reiko Matsuda|Conway, Mike|Tateno, Yoshio|Ngo, Quoc-Hung|Dien, Dinh|Kawtrakul, Asanee|Takeuchi, Koichi|Shigematsu, Mika|Taniguchi, Kiyosu', 'National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 150, 'Humans|Information Storage and Retrieval|Internet|Population Surveillance|Public Health|Software', 'Journal Article', '10.1093/bioinformatics/btn534', '18922806', 'BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Laymans terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles.'),
(74421, 2009, 'bio', 'Comment on A congruence index for testing topological similarity between trees', 'Kupczok, Anne|von Haeseler, Arndt', 'National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp|National Institute of Informatics, ROIS, PRESTO, Japan. collier@nii.ac.jp', 'Japan|Japan', 7, 'Computer Simulation|Phylogeny|Software', 'Comment', '10.1093/bioinformatics/btn539', '18922807', '-'),
(74422, 2008, 'bio', 'Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model', 'Shen, Ronglai|Taylor, Jeremy M G|Ghosh, Debashis', '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', 5, 'Bayes Theorem|Biomarkers|Humans|Markov Chains|Monte Carlo Method|Neoplasm Proteins|Neoplasms|Protein Array Analysis|Proteomics|Tissue Array Analysis', 'Journal Article', '10.1093/bioinformatics/btn536', '18922808', 'Tissue microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immuno-histochemical staining assays. Standard analysis approach estimates a sample mean expression in the tumor, ignoring the complex tissue-specific staining patterns observed on tissue arrays.'),
(74423, 2008, 'bio', 'mspire: mass spectrometry proteomics in Ruby', 'Prince, John T|Marcotte, Edward M', 'Institute for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA.|Institute for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA.', 'United States|United States', 0, 'Databases, Protein|Mass Spectrometry|Programming Languages|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn513', '18930952', 'Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages. The software library mspire-developed in the Ruby programming language-offers quick and memory-efficient readers for standard xml proteomics formats, converters for intermediate file types in typical proteomics spectral-identification work flows (including the Bioworks .srf format), and modules for the calculation of peptide false identification rates.'),
(74424, 2008, 'bio', 'WAVECLOCK: wavelet analysis of circadian oscillation', 'Price, Tom S|Baggs, Julie E|Curtis, Anne M|Fitzgerald, Garret A|Hogenesch, John B', 'Institute of Psychiatry, Kings College London. thomas.price@iop.kcl.ac.uk|Institute of Psychiatry, Kings College London. thomas.price@iop.kcl.ac.uk|Institute of Psychiatry, Kings College London. thomas.price@iop.kcl.ac.uk|Institute of Psychiatry, Kings College London. thomas.price@iop.kcl.ac.uk|Institute of Psychiatry, Kings College London. thomas.price@iop.kcl.ac.uk', '-|-|-|-|-', 30, 'Algorithms|Animals|Circadian Rhythm|Computational Biology|Humans|RNA, Messenger|RNA, Small Interfering', 'Journal Article', '10.1093/bioinformatics/btn521', '18931366', 'Oscillations in mRNA and protein of circadian clock components can be continuously monitored in vitro using synchronized cell lines. These rhythms can be highly variable due to culture conditions and are non-stationary due to baseline trends, damping and drift in period length. We present a technique for characterizing the modal frequencies of oscillation using continuous wavelet decomposition to non-parametrically model changes in amplitude and period while removing baseline effects and noise.'),
(74425, 2008, 'bio', 'The Protein Feature Ontology: a tool for the unification of protein feature annotations', 'Reeves, Gabrielle A|Eilbeck, Karen|Magrane, Michele|ODonovan, Claire|Montecchi-Palazzi, Luisa|Harris, Midori A|Orchard, Sandra|Jimenez, Rafael C|Prlic, Andreas|Hubbard, Tim J P|Hermjakob, Henning|Thornton, Janet M', 'EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk|EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. gabby@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Computational Biology|Databases, Protein|Internet|Proteins|Proteome|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn528', '18936051', 'The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of uncharacterized protein structures and sequences. Consequently, many computational tools have been developed to help elucidate protein function. However, such services are spread throughout the world, often with standalone web pages. Integration of these methods is needed and so far this has not been possible as there was no common vocabulary available that could be used as a standard language.'),
(74426, 2008, 'bio', 'REJECTOR: software for population history inference from genetic data via a rejection algorithm', 'Jobin, Matthew J|Mountain, Joanna L', 'Department of Anthropology, Stanford University, Stanford, CA 94305, USA. mjobin@stanford.edu|Department of Anthropology, Stanford University, Stanford, CA 94305, USA. mjobin@stanford.edu', 'United States|United States', 0, 'Algorithms|Animals|Evolution, Molecular|Gene Expression Profiling|Genetics, Population|Humans|Microsatellite Repeats|Software', 'Journal Article', '10.1093/bioinformatics/btn540', '18936052', 'We introduce REJECTOR, software for parameter estimation and comparison of alternate models of population history from genetic data via a rejection algorithm. Through coalescent simulation, REJECTOR generates numerous gene genealogies, and hence simulated data, under a model of population history specified by the user. Summary statistics derived from such simulated data are compared with observed statistics, leading to acceptance or rejection of a given set of parameter values. We performed tests of the software using known parameter values in order to assess the inferential power provided by each summary statistic. The tests demonstrate the precision and accuracy of estimation made possible using this approach.'),
(74427, 2008, 'bio', 'IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions', 'Busch, Anke|Richter, Andreas S|Backofen, Rolf', 'Bioinformatics Group, Albert-Ludwigs-University Freiburg, Georges-Koehler-Allee 106, Freiburg D-79110, Germany. IntaRNA@informatik.uni-freiburg.de|Bioinformatics Group, Albert-Ludwigs-University Freiburg, Georges-Koehler-Allee 106, Freiburg D-79110, Germany. IntaRNA@informatik.uni-freiburg.de|Bioinformatics Group, Albert-Ludwigs-University Freiburg, Georges-Koehler-Allee 106, Freiburg D-79110, Germany. IntaRNA@informatik.uni-freiburg.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Escherichia coli|RNA, Bacterial|RNA, Messenger|RNA, Untranslated|Software', 'Journal Article', '10.1093/bioinformatics/btn544', '18940824', 'During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches.'),
(74428, 2008, 'bio', 'Predicting small ligand binding sites in proteins using backbone structure', 'Bordner, Andrew J', 'Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu', 'United States', 0, 'Artificial Intelligence|Binding Sites|Databases, Protein|Ligands|Protein Conformation|Proteins|Software|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/btn543', '18940825', 'Specific non-covalent binding of metal ions and ligands, such as nucleotides and cofactors, is essential for the function of many proteins. Computational methods are useful for predicting the location of such binding sites when experimental information is lacking. Methods that use structural information, when available, are particularly promising since they can potentially identify non-contiguous binding motifs that cannot be found using only the amino acid sequence. Furthermore, a prediction method that can utilize low-resolution models is advantageous because high-resolution structures are available for only a relatively small fraction of proteins.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74429, 2008, 'bio', 'QGene 40, an extensible Java QTL-analysis platform', 'Joehanes, Roby|Nelson, James C', 'Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA.|Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA.', 'United States|United States', 0, 'Computational Biology|Crosses, Genetic|Inbreeding|Programming Languages|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btn523', '18940826', 'Of many statistical methods developed to date for quantitative trait locus (QTL) analysis, only a limited subset are available in public software allowing their exploration, comparison and practical application by researchers. We have developed QGene 4.0, a plug-in platform that allows execution and comparison of a variety of modern QTL-mapping methods and supports third-party addition of new ones. The software accommodates line-cross mating designs consisting of any arbitrary sequence of selfing, backcrossing, intercrossing and haploid-doubling steps that includes map, population, and trait simulators, and is scriptable.'),
(74430, 2008, 'bio', 'Effective cluster-based seed design for cross-species sequence comparisons', 'Zhou, Leming|Mihai, Ingrid|Florea, Liliana', 'Department of Computer Science, George Washington University, Washington, DC 20052, USA. lmzhou@gwmail.gwu.edu|Department of Computer Science, George Washington University, Washington, DC 20052, USA. lmzhou@gwmail.gwu.edu|Department of Computer Science, George Washington University, Washington, DC 20052, USA. lmzhou@gwmail.gwu.edu', 'United States|United States|United States', 2, 'Algorithms|Animals|Base Sequence|Cluster Analysis|Genome|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/btn547', '18940827', 'To annotate newly sequenced organisms, cross-species sequence comparison algorithms can be applied to align gene sequences to the genome of a related species. To improve the accuracy of alignment, spaced seeds must be optimized for each comparison. As the number and diversity of genomes increase, an efficient alternative is to cluster pairwise comparisons into groups and identify seeds for groups instead of individual comparisons. Here we investigate a measure of comparison closeness and identify classes of comparisons that show similar seed behavior and therefore can employ the same seed.'),
(74431, 2008, 'bio', 'Prediction of kinase-specific phosphorylation sites using conditional random fields', 'Dang, Thanh Hai|Van Leemput, Koenraad|Verschoren, Alain|Laukens, Kris', 'Intelligent Systems Laboratory and Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium.|Intelligent Systems Laboratory and Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium.|Intelligent Systems Laboratory and Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium.|Intelligent Systems Laboratory and Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Databases, Protein|Phosphorylation|Protein Kinases|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btn546', '18940828', 'Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion.'),
(74432, 2008, 'bio', 'SPOCTOPUS: a combined predictor of signal peptides and membrane protein topology', 'Viklund, Håkan|Bernsel, Andreas|Skwark, Marcin|Elofsson, Arne', 'Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.|Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.|Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.|Department of Biochemistry and Biophysics, Center for Biomembrane Research, Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 142, 'Algorithms|Escherichia coli|Genome, Bacterial|Genome, Fungal|Membrane Proteins|Neural Networks (Computer)|Protein Sorting Signals|Saccharomyces cerevisiae|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btn550', '18945683', 'SPOCTOPUS is a method for combined prediction of signal peptides and membrane protein topology, suitable for genome-scale studies. Its objective is to minimize false predictions of transmembrane regions as signal peptides and vice versa. We provide a description of the SPOCTOPUS algorithm together with a performance evaluation where SPOCTOPUS compares favorably with state-of-the-art methods for signal peptide and topology predictions.'),
(74433, 2008, 'bio', 'Integrated search and alignment of protein structures', 'Sacan, Ahmet|Toroslu, I Hakki|Ferhatosmanoglu, Hakan', 'Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu|Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu', 'Turkey|Turkey|Turkey', 0, 'Algorithms|Databases, Protein|Models, Molecular|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Structural Homology, Protein', 'Comparative Study', '10.1093/bioinformatics/btn545', '18945684', 'Identification and comparison of similar three-dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here, we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The contact hits obtained from a distance-based indexing method are extended to obtain high-scoring segment pairs, which are then used to generate structural alignments.'),
(74434, 2008, 'bio', 'CARPET: a web-based package for the analysis of ChIP-chip and expression tiling data', 'Cesaroni, Matteo|Cittaro, Davide|Brozzi, Alessandro|Pelicci, Pier Giuseppe|Luzi, Lucilla', 'Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. matteo.cesaroni@ifom-ieo-campus.it|Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. matteo.cesaroni@ifom-ieo-campus.it|Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. matteo.cesaroni@ifom-ieo-campus.it|Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. matteo.cesaroni@ifom-ieo-campus.it|Department of Experimental Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milano, Italy. matteo.cesaroni@ifom-ieo-campus.it', 'Italy|Italy|Italy|Italy|Italy', 23, 'Chromatin Immunoprecipitation|Databases, Genetic|Gene Expression Profiling|Genome|Internet|Oligonucleotide Array Sequence Analysis|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn542', '18945685', 'CARPET (collection of automated routine programs for easy tiling) is a set of Perl, Python and R scripts, integrated on the Galaxy2 web-based platform, for the analysis of ChIP-chip and expression tiling data, both for standard and custom chip designs. CARPET allows rapid experimental data entry, simple quality control, normalization, easy identification and annotation of enriched ChIP-chip regions, detection of the absolute or relative transcriptional status of genes assessed by expression tiling experiments and, more importantly, it allows the integration of ChIP-chip and expression data. Results can be visualized instantly in a genomic context within the UCSC genome browser as graph-based custom tracks through Galaxy2. All generated and uploaded data can be stored within sessions and are easily shared with other users.'),
(74435, 2008, 'bio', 'Aggressive assembly of pyrosequencing reads with mates', 'Miller, Jason R|Delcher, Arthur L|Koren, Sergey|Venter, Eli|Walenz, Brian P|Brownley, Anushka|Johnson, Justin|Li, Kelvin|Mobarry, Clark|Sutton, Granger', 'The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. jmiller@jcvi.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 344, 'Computational Biology|Genome|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn548', '18952627', 'DNA sequence reads from Sanger and pyrosequencing platforms differ in cost, accuracy, typical coverage, average read length and the variety of available paired-end protocols. Both read types can complement one another in a hybrid approach to whole-genome shotgun sequencing projects, but assembly software must be modified to accommodate their different characteristics. This is true even of pyrosequencing mated and unmated read combinations. Without special modifications, assemblers tuned for homogeneous sequence data may perform poorly on hybrid data.'),
(74436, 2009, 'bio', 'MS-specific noise model reveals the potential of iTRAQ in quantitative proteomics', 'Hundertmark, C|Fischer, R|Reinl, T|May, S|Klawonn, F|Jänsch, L', 'Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.|Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.|Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.|Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.|Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.|Helmholtz Centre for Infection Research, Department for Cell Biology, Braunschweig, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Databases, Protein|Mass Spectrometry|Peptides|Proteome|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn551', '18952628', 'Mass spectrometry (MS) data are impaired by noise similar to many other analytical methods. Therefore, proteomics requires statistical approaches to determine the reliability of regulatory information if protein quantification is based on ion intensities observed in MS.'),
(74437, 2008, 'bio', 'BASH: a tool for managing BeadArray spatial artefacts', 'Cairns, J M|Dunning, M J|Ritchie, M E|Russell, R|Lynch, A G', 'Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB20RE, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 51, 'Artifacts|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn557', '18953044', 'With their many replicates and their random layouts, Illumina BeadArrays provide greater scope fordetecting spatial artefacts than do other microarray technologies. They are also robust to artefact exclusion, yet there is a lack of tools that can perform these tasks for Illumina. We present BASH, a tool for this purpose. BASH adopts the concepts of Harshlight, but implements them in a manner that utilizes the unique characteristics of the Illumina technology. Using bead-level data, spatial artefacts of various kinds can thus be identified and excluded from further analyses.'),
(74438, 2008, 'bio', 'AUGIST: inferring species trees while accommodating gene tree uncertainty', 'Oliver, Jeffrey C', 'Interdisciplinary Program in Insect Science, University of Arizona, Tucson, AZ 85721, USA. jeffrey.oliver@yale.edu', 'United States', 0, 'Algorithms|Animals|Evolution, Molecular|Genes|Genetic Variation|Humans|Phylogeny|Software|Species Specificity|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btn556', '18953045', 'AUGIST (accomodating uncertainty in genealogies while inferring species tress) is a new software package for inferring species trees while accommodating uncertainty in gene genealogies. It is written for the Mesquite software system and provides sampling procedures to incorporate uncertainty in gene tree reconstruction while providing confidence estimates for inferred species trees.'),
(74439, 2008, 'bio', 'Modularity of cellular networks shows general center-periphery polarization', 'Vinogradov, Alexander E', 'Institute of Cytology, Russian Academy of Sciences, St. Petersburg 194064, Russia. aevin@mail.cytspb.rssi.ru', '-', 0, 'Computational Biology|Escherichia coli|Gene Regulatory Networks|Metabolic Networks and Pathways|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btn555', '18953046', 'The modular biology is supposed to be a bridge from the molecular to the systems biology. Using a new approach, it is shown here that the protein interaction networks of yeast Saccharomyces cerevisiae and bacteria Escherichia coli consist of two large-scale modularity layers, central and peripheral, separated by a zone of depressed modularity. This finding based on the analysis of network topology is further supported by the discovery that there are many more Gene Ontology categories (terms) and KEGG biochemical pathways that are overrepresented in the central and peripheral layers than in the intermediate zone. The categories of the central layer are mostly related to nuclear information processing, regulation and cell cycle, whereas the peripheral layer is dealing with various metabolic and energetic processes, transport and cell communication. A similar center-periphery polarization of modularity is found in the protein domain networks (built-in interactome) and in a powergrid (as a non-biological example). These data suggest a polarized modularity model of cellular networks where the central layer seems to be regulatory and to use information storage of the nucleus, whereas the peripheral layer seems devoted to more specialized tasks and environmental interactions, with a complex bus between the layers.'),
(74440, 2008, 'bio', 'Poisson approximation for significance in genome-wide ChIP-chip tiling arrays', 'Zhang, Yu', 'Department of Statistics, the Pennsylvania State University, 325 Thomas Bldg, State College, PA, USA. yuzhang@stat.psu.edu', 'United States', 9, 'Algorithms|Chromatin Immunoprecipitation|Genome|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn549', '18953047', 'A genome-wide ChIP-chip tiling array study requires millions of simultaneous comparisons of hybridization for significance. Controlling the false positive rate in genome-wide tiling array studies is very important, because the number of computationally identified regions can easily go beyond the capability of experimental verification. No accurate and efficient method exists for evaluating statistical significance in tiling arrays. The Bonferroni method is overly conservative and the permutation test is time consuming for genome-wide studies.'),
(74441, 2008, 'bio', 'Identifying molecular markers associated with classification of genotypes by External Logistic Biplots', 'Demey, J R|Vicente-Villardón, J L|Galindo-Villardón, M P|Zambrano, A Y', 'Centro de Biotecnología, Instituto de Estudios Avanzados (IDEA), Caracas, Venezuela. jhonny.demey@gmail.com|Centro de Biotecnología, Instituto de Estudios Avanzados (IDEA), Caracas, Venezuela. jhonny.demey@gmail.com|Centro de Biotecnología, Instituto de Estudios Avanzados (IDEA), Caracas, Venezuela. jhonny.demey@gmail.com|Centro de Biotecnología, Instituto de Estudios Avanzados (IDEA), Caracas, Venezuela. jhonny.demey@gmail.com', '-|-|-|-', 0, 'Africa|Algorithms|Asia|Cluster Analysis|Computational Biology|Europe|Genetic Markers|Genetic Variation|Genotype|Humans|Logistic Models', 'Journal Article', '10.1093/bioinformatics/btn552', '18974073', 'For characterization of genetic diversity in genotypes several molecular techniques, usually resulting in a binary data matrix, have been used. Despite the fact that in Cluster Analysis (CA) and Principal Coordinates Analysis (PCoA) the interpretation of the variables responsible for grouping is not straightforward, these methods are commonly used to classify genotypes using DNA molecular markers. In this article, we present a novel algorithm that uses a combination of PCoA, CA and Logistic Regression (LR), as a better way to interpret the variables (alleles or bands) associated to the classification of genotypes. The combination of three standard techniques with some new ideas about the geometry of the procedures, allows constructing an External Logistic Biplot (ELB) that helps in the interpretation of the variables responsible for the classification or ordination. An application of the method to study the genetic diversity of four populations from Africa, Asia and Europe, using the HapMap data is included.'),
(74442, 2009, 'bio', 'Conditional random pattern algorithm for LOH inference and segmentation', 'Wu, Ling-Yun|Zhou, Xiaobo|Li, Fuhai|Yang, Xiaorong|Chang, Chung-Che|Wong, Stephen T C', 'Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cell Line, Tumor|Computational Biology|Computer Simulation|Databases, Genetic|Humans|Loss of Heterozygosity|Markov Chains|Models, Genetic|Myelodysplastic Syndromes|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|ROC Curve', 'Journal Article', '10.1093/bioinformatics/btn561', '18974074', 'Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs.'),
(74443, 2008, 'bio', 'Protease substrate site predictors derived from machine learning on multilevel substrate phage display data', 'Chen, Ching-Tai|Yang, Ei-Wen|Hsu, Hung-Ju|Sun, Yi-Kun|Hsu, Wen-Lian|Yang, An-Suei', 'Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.|Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.', '-|-|-|-|-|-', 0, 'Algorithms|Animals|Artificial Intelligence|Binding Sites|Computational Biology|Computer Simulation|Databases, Protein|Humans|Kinetics|Models, Biological|Peptide Hydrolases|Peptide Library|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btn538', '18974075', 'Regulatory proteases modulate proteomic dynamics with a spectrum of specificities against substrate proteins. Predictions of the substrate sites in a proteome for the proteases would facilitate understanding the biological functions of the proteases. High-throughput experiments could generate suitable datasets for machine learning to grasp complex relationships between the substrate sequences and the enzymatic specificities. But the capability in predicting protease substrate sites by integrating the machine learning algorithms with the experimental methodology has yet to be demonstrated.'),
(74444, 2008, 'bio', 'Small RNA gene identification and mRNA target predictions in bacteria', 'Pichon, Christophe|Felden, Brice', 'Unité Pathogénie Bactérienne des Muqueuses, Institut Pasteur, 25-28 Rue du Docteur Roux, 75724 Paris, France.|Unité Pathogénie Bactérienne des Muqueuses, Institut Pasteur, 25-28 Rue du Docteur Roux, 75724 Paris, France.', 'France|France', 51, 'Bacteria|Computational Biology|Genome, Bacterial|Genomics|Models, Biological|Nucleic Acid Conformation|RNA, Bacterial|RNA, Messenger|RNA, Untranslated', 'Journal Article', '10.1093/bioinformatics/btn560', '18974076', 'Bacterial small ribonucleic acids (sRNAs) that are not ribosomal and transfer or messenger RNAs were initially identified in the sixties, whereas their molecular functions are still under active investigation today. It is now widely accepted that most play central roles in gene expression regulation in response to environmental changes. Interestingly, some are also implicated in bacterial virulence. Functional studies revealed that a large subset of these sRNAs act by an antisense mechanism thanks to pairing interactions with dedicated mRNA targets, usually around their translation start sites, to modulate gene expression at the posttranscriptional level. Some sRNAs modulate protein activity or mimic the structure of other macromolecules. In the last few years, in silico methods have been developed to detect more bacterial sRNAs. Among these, computational analyses of the bacterial genomes by comparative genomics have predicted the existence of a plethora of sRNAs, some that were confirmed to be expressed in vivo. The prediction accuracy of these computational tools is highly variable and can be perfectible. Here we review the computational studies that have contributed to detecting the sRNA gene and mRNA targets in bacteria and the methods for their experimental testing. In addition, the remaining challenges are discussed.'),
(74445, 2008, 'bio', 'iMapper: a web application for the automated analysis and mapping of insertional mutagenesis sequence data against Ensembl genomes', 'Kong, Jun|Zhu, Fei|Stalker, Jim|Adams, David J', 'Experimental Cancer Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambs CB101HH, UK.|Experimental Cancer Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambs CB101HH, UK.|Experimental Cancer Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambs CB101HH, UK.|Experimental Cancer Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambs CB101HH, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Base Sequence|Genome|Humans|Internet|Mutagenesis, Insertional|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn541', '18974167', 'Insertional mutagenesis is a powerful method for gene discovery. To identify the location of insertion sites in the genome linker based polymerase chain reaction (PCR) methods (such as splinkerette-PCR) may be employed. We have developed a web application called iMapper (Insertional Mutagenesis Mapping and Analysis Tool) for the efficient analysis of insertion site sequence reads against vertebrate and invertebrate Ensembl genomes. Taking linker based sequences as input, iMapper scans and trims the sequence to remove the linker and sequences derived from the insertional mutagen. The software then identifies and removes contaminating sequences derived from chimeric genomic fragments, vector or the transposon concatamer and then presents the clipped sequence reads to a sequence mapping server which aligns them to an Ensembl genome. Insertion sites can then be navigated in Ensembl in the context of genomic features such as gene structures. iMapper also generates test-based format for nucleic acid or protein sequences (FASTA) and generic file format (GFF) files of the clipped sequence reads and provides a graphical overview of the mapped insertion sites against a karyotype. iMapper is designed for high-throughput applications and can efficiently process thousands of DNA sequence reads.'),
(74446, 2009, 'bio', 'Bayesian robust analysis for genetic architecture of quantitative traits', 'Yang, Runqing|Wang, Xin|Li, Jian|Deng, Hongwen', 'School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, PR China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, PR China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, PR China. runqingyang@sjtu.edu.cn|School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, PR China. runqingyang@sjtu.edu.cn', 'China|China|China|China', 9, 'Bayes Theorem|Computer Simulation|Crosses, Genetic|Databases, Genetic|Oryza|Phenotype|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btn558', '18974168', 'In most quantitative trait locus (QTL) mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may affect the accuracy of QTL detection and lead to detection of spurious QTLs. To improve the robustness of QTL mapping methods, we replaced the normal distribution for residuals in multiple interacting QTL models with the normal/independent distributions that are a class of symmetric and long-tailed distributions and are able to accommodate residual outliers. Subsequently, we developed a Bayesian robust analysis strategy for dissecting genetic architecture of quantitative traits and for mapping genome-wide interacting QTLs in line crosses.'),
(74447, 2008, 'bio', 'Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models', 'Rogers, Simon|Girolami, Mark|Kolch, Walter|Waters, Katrina M|Liu, Tao|Thrall, Brian|Wiley, H Steven', 'Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Cluster Analysis|Databases, Nucleic Acid|Databases, Protein|Gene Expression Profiling|Humans|Proteins|Proteome|Proteomics|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btn553', '18974169', 'Modern transcriptomics and proteomics enable us to survey the expression of RNAs and proteins at large scales. While these data are usually generated and analyzed separately, there is an increasing interest in comparing and co-analyzing transcriptome and proteome expression data. A major open question is whether transcriptome and proteome expression is linked and how it is coordinated.'),
(74448, 2009, 'bio', 'Slider--maximum use of probability information for alignment of short sequence reads and SNP detection', 'Malhis, Nawar|Butterfield, Yaron S N|Ester, Martin|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Agency, Vancouver and School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Genome Sciences Centre, BC Cancer Agency, Vancouver and School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Genome Sciences Centre, BC Cancer Agency, Vancouver and School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.|Genome Sciences Centre, BC Cancer Agency, Vancouver and School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Base Pair Mismatch|Base Sequence|Computational Biology|Databases, Nucleic Acid|Humans|Polymorphism, Single Nucleotide|Probability|Sequence Alignment|Time Factors', 'Journal Article', '10.1093/bioinformatics/btn565', '18974170', 'A plethora of alignment tools have been created that are designed to best fit different types of alignment conditions. While some of these are made for aligning Illumina Sequence Analyzer reads, none of these are fully utilizing its probability (prb) output. In this article, we will introduce a new alignment approach (Slider) that reduces the alignment problem space by utilizing each read bases probabilities given in the prb files.'),
(74449, 2008, 'bio', 'SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap', 'Johnson, Andrew D|Handsaker, Robert E|Pulit, Sara L|Nizzari, Marcia M|ODonnell, Christopher J|de Bakker, Paul I W', 'The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.|The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.|The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.|The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.|The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.|The Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Genome, Human|Genome-Wide Association Study|Genotype|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btn564', '18974171', 'The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies).'),
(74450, 2008, 'bio', 'Reconstruction of transcriptional dynamics from gene reporter data using differential equations', 'Finkenstädt, Bärbel|Heron, Elizabeth A|Komorowski, Michal|Edwards, Kieron|Tang, Sanyi|Harper, Claire V|Davis, Julian R E|White, Michael R H|Millar, Andrew J|Rand, David A', 'Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry CV47AL, UK. b.f.finkenstadt@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Arabidopsis|Computer Simulation|Genes, Plant|Genes, Reporter|Humans|Markov Chains|Monte Carlo Method|Rats|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn562', '18974172', 'Promoter-driven reporter genes, notably luciferase and green fluorescent protein, provide a tool for the generation of a vast array of time-course data sets from living cells and organisms. The aim of this study is to introduce a modeling framework based on stochastic differential equations (SDEs) and ordinary differential equations (ODEs) that addresses the problem of reconstructing transcription time-course profiles and associated degradation rates. The dynamical model is embedded into a Bayesian framework and inference is performed using Markov chain Monte Carlo algorithms.'),
(74451, 2009, 'bio', 'Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays', 'Furusawa, Chikara|Ono, Naoaki|Suzuki, Shingo|Agata, Tomoharu|Shimizu, Hiroshi|Yomo, Tetsuya', 'Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp|Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp|Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp|Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp|Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp|Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Osaka 565-0871, Japan. furusawa@ist.osaka-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Artifacts|Base Pair Mismatch|Computational Biology|DNA Probes|Humans|Models, Biological|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn570', '18977779', 'High-density DNA microarrays provide us with useful tools for analyzing DNA and RNA comprehensively. However, the background signal caused by the non-specific binding (NSB) between probe and target makes it difficult to obtain accurate measurements. To remove the background signal, there is a set of background probes on Affymetrix Exon arrays to represent the amount of non-specific signals, and an accurate estimation of non-specific signals using these background probes is desirable for improvement of microarray analyses.'),
(74452, 2008, 'bio', 'PConPy--a Python module for generating 2D protein maps', 'Ho, Hui Kian|Kuiper, Michael J|Kotagiri, Ramamohanarao', 'Department of Computer Science & Software Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia. hohkhkh1@csse.unimelb.edu.au|Department of Computer Science & Software Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia. hohkhkh1@csse.unimelb.edu.au|Department of Computer Science & Software Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia. hohkhkh1@csse.unimelb.edu.au', 'Australia|Australia|Australia', 8, 'Hydrogen Bonding|Internet|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn566', '18977780', 'PConPy is an open-source Python module for generating protein contact maps, distance maps and hydrogen bond plots. These maps can be generated in a number of publication-quality vector and raster image formats. Contact maps can be annotated with secondary structure and hydrogen bond assignments. PConPy offers a more flexible choice of contact definition parameters than existing toolkits, most notably a greater choice of inter-residue distance metrics. PConPy can be used as a stand-alone application or imported into existing source code. A web-interface to PConPy is also available for use.'),
(74453, 2009, 'bio', 'Stochastic modelling of genotypic drug-resistance for human immunodeficiency virus towards long-term combination therapy optimization', 'Prosperi, Mattia C F|DAutilia, Roberto|Incardona, Francesca|De Luca, Andrea|Zazzi, Maurizio|Ulivi, Giovanni', 'Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it|Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it|Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it|Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it|Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it|Department of Computer Science and Automation, University of Roma TRE, Informa Contract Research Organisation, Infectious Disease Clinic, Catholic University of Sacred Heart, Rome, Italy. prosperi@dia.uniroma3.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Drug Resistance, Viral|Drug Therapy, Combination|Genotype|HIV|HIV Infections|Humans|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btn568', '18977781', 'Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios.'),
(74454, 2009, 'bio', 'Shortest path analysis using partial correlations for classifying gene functions from gene expression data', 'Fitch, A Marie|Jones, M Beatrix', 'Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand. m.fitch@massey.ac.nz|Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand. m.fitch@massey.ac.nz', 'New Zealand|New Zealand', 2, 'Cell Compartmentation|Computational Biology|Cytoplasm|Gene Expression Regulation, Fungal|Genes, Fungal|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btn574', '18984597', 'Gaussian graphical models (GGMs) are a popular tool for representing gene association structures. We propose using estimated partial correlations from these models to attach lengths to the edges of the GGM, where the length of an edge is inversely related to the partial correlation between the gene pair. Graphical lasso is used to fit the GGMs and obtain partial correlations. The shortest paths between pairs of genes are found. Where terminal genes have the same biological function intermediate genes on the path are classified as having the same function. We validate the method using genes of known function using the Rosetta Compendium of yeast (Saccharomyces Cerevisiae) gene expression profiles. We also compare our results with those obtained using a graph constructed using correlations.'),
(74455, 2008, 'bio', 'Cross-hybridization modeling on Affymetrix exon arrays', 'Kapur, Karen|Jiang, Hui|Xing, Yi|Wong, Wing Hung', 'Department of Statistics, Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.|Department of Statistics, Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.|Department of Statistics, Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.|Department of Statistics, Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.', 'United States|United States|United States|United States', 0, 'Animals|Base Sequence|Computational Biology|Databases, Genetic|Exons|Gene Expression Profiling|Humans|Mice|Molecular Probes|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1093/bioinformatics/btn571', '18984598', 'Microarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts.'),
(74456, 2009, 'bio', 'TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops', 'Milne, Iain|Lindner, Dominik|Bayer, Micha|Husmeier, Dirk|McGuire, Gráinne|Marshall, David F|Wright, Frank', 'Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk|Scottish Crop Research Institute, Invergowrie, Dundee, UK. iain.milne@scri.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 461, 'Codon|Computer Graphics|Computers|Evolution, Molecular|Internet|Phylogeny|Sequence Alignment|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn575', '18984599', 'TOPALi v2 simplifies and automates the use of several methods for the evolutionary analysis of multiple sequence alignments. Jobs are submitted from a Java graphical user interface as TOPALi web services to either run remotely on high-performance computing clusters or locally (with multiple cores supported). Methods available include model selection and phylogenetic tree estimation using the Bayesian inference and maximum likelihood (ML) approaches, in addition to recombination detection methods. The optimal substitution model can be selected for protein or nucleic acid (standard, or protein-coding using a codon position model) data using accurate statistical criteria derived from ML co-estimation of the tree and the substitution model. Phylogenetic software available includes PhyML, RAxML and MrBayes.'),
(74457, 2009, 'bio', 'How frugal is Mother Nature with haplotypes?', 'Climer, Sharlee|Jäger, Gerold|Templeton, Alan R|Zhang, Weixiong', 'Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA.|Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA.|Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA.|Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA.', 'United States|United States|United States|United States', 0, 'Databases, Genetic|Haplotypes|Heterozygote|Humans|Nature', 'Journal Article', '10.1093/bioinformatics/btn572', '18987010', 'Inference of haplotypes from genotype data is crucial and challenging for many vitally important studies. The first, and most critical step, is the ascertainment of a biologically sound model to be optimized. Many models that have been proposed rely partially or entirely on reducing the number of unique haplotypes in the solution.');
