INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72070, 2005, 'bio', 'Convergent Island Statistics: a fast method for determining local alignment score significance', 'Poleksic, Aleksandar|Danzer, Joseph F|Hambly, Kevin|Debe, Derek A', 'Eidogen-Sertanty Inc., 9381 Judicial Dr., San Diego, CA 92121, USA. aleksandar@eidogen.com|Eidogen-Sertanty Inc., 9381 Judicial Dr., San Diego, CA 92121, USA. aleksandar@eidogen.com|Eidogen-Sertanty Inc., 9381 Judicial Dr., San Diego, CA 92121, USA. aleksandar@eidogen.com|Eidogen-Sertanty Inc., 9381 Judicial Dr., San Diego, CA 92121, USA. aleksandar@eidogen.com', 'United States|United States|United States|United States', 8, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/bti433', '15817690', 'Background distribution statistics for profile-based sequence alignment algorithms cannot be calculated analytically, and hence such algorithms must resort to measuring the significance of an alignment score by assessing its location among a distribution of background alignment scores. The Gumbel parameters that describe this background distribution are usually pre-computed for a limited number of scoring systems, gap schemes, and sequence lengths and compositions. The use of such look-ups is known to introduce errors, which compromise the significance assessment of a remote homology relationship. One solution is to estimate the background distribution for each pair of interest by generating a large number of sequence shuffles and use the distribution of their scores to approximate the parameters of the underlying extreme value distribution. This is computationally very expensive, as a large number of shuffles are needed to precisely estimate the score statistics.'),
(72071, 2005, 'bio', 'Use of multiple profiles corresponding to a sequence alignment enables effective detection of remote homologues', 'Anand, B|Gowri, V S|Srinivasan, N', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India.', 'India|India|India', 22, 'Algorithms|Amino Acid Sequence|Computer Simulation|Gene Expression Profiling|Models, Molecular|Molecular Sequence Data|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/bti432', '15817691', 'Position specific scoring matrices (PSSMs) corresponding to aligned sequences of homologous proteins are commonly used in homology detection. A PSSM is generated on the basis of one of the homologues as a reference sequence, which is the query in the case of PSI-BLAST searches. The reference sequence is chosen arbitrarily while generating PSSMs for reverse BLAST searches. In this work we demonstrate that the use of multiple PSSMs corresponding to a given alignment and variable reference sequences is more effective than using traditional single PSSMs and hidden Markov models.'),
(72072, 2005, 'bio', 'Stochastic reaction-diffusion simulation with MesoRD', 'Hattne, Johan|Fange, David|Elf, Johan', 'Department of Cell and Molecular Biology, BMC, Uppsala University, 75124 Uppsala, Sweden. johan.hattne@embl-hamburg.de|Department of Cell and Molecular Biology, BMC, Uppsala University, 75124 Uppsala, Sweden. johan.hattne@embl-hamburg.de|Department of Cell and Molecular Biology, BMC, Uppsala University, 75124 Uppsala, Sweden. johan.hattne@embl-hamburg.de', 'Sweden|Sweden|Sweden', 233, 'Algorithms|Computer Simulation|Diffusion|Models, Chemical|Models, Statistical|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/bti431', '15817692', 'MesoRD is a tool for stochastic simulation of chemical reactions and diffusion. In particular, it is an implementation of the next subvolume method, which is an exact method to simulate the Markov process corresponding to the reaction-diffusion master equation.'),
(72073, 2005, 'bio', 'Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary', 'Mao, Xizeng|Cai, Tao|Olyarchuk, John G|Wei, Liping', 'Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, PR China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, PR China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, PR China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, PR China.', 'China|China|China|China', 0, 'Artificial Intelligence|Chromosome Mapping|Database Management Systems|Documentation|Information Storage and Retrieval|Natural Language Processing|Proteome|Sequence Analysis|Signal Transduction|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/bti430', '15817693', 'High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways.'),
(72074, 2005, 'bio', 'A protocol for building and evaluating predictors of disease state based on microarray data', 'Wessels, Lodewyk F A|Reinders, Marcel J T|Hart, Augustinus A M|Veenman, Cor J|Dai, Hongyue|He, Yudong D|vant Veer, Laura J', 'Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Animals|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Feedback|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software Validation', 'Comparative Study', '10.1093/bioinformatics/bti429', '15817694', 'Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no standard computational approach has emerged which enables robust outcome prediction.'),
(72075, 2005, 'bio', 'Comment on Evaluation of the gene-specific dye bias in cDNA microarray experiments', 'Dobbin, Kevin K|Shih, Joanna H|Simon, Richard M', 'Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl|Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands. l.f.a.wessels@ewi.tudelft.nl', 'Netherlands|Netherlands|Netherlands', 8, 'Algorithms|Artifacts|Fluorescent Dyes|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity', 'Comment', '10.1093/bioinformatics/bti428', '15817695', '-'),
(72076, 2005, 'bio', 'NodMutDB: a database for genes and mutants involved in symbiosis', 'Mao, Chunhong|Qiu, Jing|Wang, Chunxia|Charles, Trevor C|Sobral, Bruno W S', 'Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cmao@vbi.vt.edu|Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cmao@vbi.vt.edu|Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cmao@vbi.vt.edu|Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cmao@vbi.vt.edu|Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cmao@vbi.vt.edu', 'United States|United States|United States|United States|United States', 18, 'Bacterial Proteins|Database Management Systems|Databases, Genetic|Fabaceae|Information Storage and Retrieval|Internet|Mutation|Plant Proteins|Rhizobium|Symbiosis', 'Journal Article', '10.1093/bioinformatics/bti427', '15817696', 'Functional genomics research is producing large amounts of data on the functions of individual genes related to symbiosis. We have developed a relational database, NodMutDB (Nodulation Mutant Database), to provide a comprehensive resource for depositing, organizing and retrieving information on symbiosis-related genes, mutants and published literature. NodMutDB brings together new studies and existing mutant-based literature to facilitate our understanding of how genes function in symbiotic processes in both Rhizobia and their host plants.'),
(72077, 2005, 'bio', 'Modeling and simulation of cancer immunoprevention vaccine', 'Pappalardo, F|Lollini, P-L|Castiglione, F|Motta, S', 'Faculty of Pharmacy, University of Catania, Italy. francesco@dmi.unict.it|Faculty of Pharmacy, University of Catania, Italy. francesco@dmi.unict.it|Faculty of Pharmacy, University of Catania, Italy. francesco@dmi.unict.it|Faculty of Pharmacy, University of Catania, Italy. francesco@dmi.unict.it', 'Italy|Italy|Italy|Italy', 86, 'Animals|Cancer Vaccines|Computer Simulation|Dose-Response Relationship, Drug|Drug Therapy, Computer-Assisted|Mammary Neoplasms, Experimental|Mice|Models, Biological|Treatment Outcome|Vaccination', 'Comparative Study', '10.1093/bioinformatics/bti426', '15817697', 'We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results.'),
(72078, 2005, 'bio', 'A boosting approach for motif modeling using ChIP-chip data', 'Hong, Pengyu|Liu, X Shirley|Zhou, Qing|Lu, Xin|Liu, Jun S|Wong, Wing H', 'Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Binding Sites|DNA|Databases, Nucleic Acid|Oligonucleotide Array Sequence Analysis|Protein Binding|Saccharomyces cerevisiae Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/bti402', '15817698', 'Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step toward understanding gene regulation.'),
(72079, 2005, 'bio', 'STARS: statistics on inter-atomic distances and torsion angles in protein secondary structures', 'Zheng, Yu|Yang, Daiwen', 'Department of Biological Sciences and Department of Chemistry, National University of Singapore, 14 Science Drive 4, Singapore 117543.|Department of Biological Sciences and Department of Chemistry, National University of Singapore, 14 Science Drive 4, Singapore 117543.', 'Singapore|Singapore', 0, 'Computer Graphics|Computer Simulation|Crystallography|Models, Chemical|Models, Molecular|Models, Statistical|Protein Structure, Secondary|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti437', '15827078', 'A graphics package has been developed for performing statistics on interatomic distances and torsion angles in protein secondary structures (STARS) from a protein crystal structure database. It allows one to obtain both the graphical view and the text format of distributions of the distances and angles for atoms located in 10 types of protein secondary structures. STARS will facilitate assignment of ambiguous NOESY peaks, structure determination by nuclear magnetic resonance, structure validation and comparison of protein folds.'),
(72080, 2005, 'bio', 'Visualizing profile-profile alignment: pairwise HMM logos', 'Schuster-Böckler, Benjamin|Bateman, Alex', 'The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. bsb@sanger.ac.uk|The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. bsb@sanger.ac.uk', 'United Kingdom|United Kingdom', 29, 'Computer Graphics|Computer Simulation|Gene Expression Profiling|Markov Chains|Models, Chemical|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti434', '15827079', 'The availability of advanced profile-profile comparison tools, such as PRC or HHsearch demands sophisticated visualization tools not presently available. We introduce an approach built upon the concept of HMM logos. The method illustrates the similarities of pairs of protein family profiles in an intuitive way. Two HMM logos, one for each profile, are drawn one upon the other. The aligned states are then highlighted and connected.'),
(72081, 2005, 'bio', 'Three-dimensional computation of atom depth in complex molecular structures', 'Varrazzo, Daniele|Bernini, Andrea|Spiga, Ottavia|Ciutti, Arianna|Chiellini, Stefano|Venditti, Vincenzo|Bracci, Luisa|Niccolai, Neri', 'Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.|Biomolecular Structure Research Center and Department of Molecular Biology, Università di Siena, I-53100 Siena, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computer Simulation|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein|Solvents', 'Evaluation Studies', '10.1093/bioinformatics/bti444', '15827080', 'For a complex molecular system the delineation of atom-atom contacts, exposed surface and binding sites represents a fundamental step to predict its interaction with solvent, ligands and other molecules. Recently, atom depth has been also considered as an additional structural descriptor to correlate protein structure with folding and functional properties. The distance between an atom and the nearest water molecule or the closest surface dot has been proposed as a measure of the atom depth, but, in both cases, the 3D character of depth is largely lost. In the present study, a new approach is proposed to calculate atom depths in a way that the molecular shape can be taken into account.'),
(72082, 2005, 'bio', 'LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources', 'Karchin, Rachel|Diekhans, Mark|Kelly, Libusha|Thomas, Daryl J|Pieper, Ursula|Eswar, Narayanan|Haussler, David|Sali, Andrej', 'Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org|Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143, USA. rachelk@salilab.org', 'United States|United States|United States|United States|United States|United States|United States|United States', 178, 'Algorithms|Chromosome Mapping|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Open Reading Frames|Polymorphism, Single Nucleotide|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/bti442', '15827081', 'The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity.'),
(72083, 2005, 'bio', 'EMPReSS: European mouse phenotyping resource for standardized screens', 'Green, Eain C J|Gkoutos, Georgios V|Lad, Heena V|Blake, Andrew|Weekes, Joseph|Hancock, John M', 'MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk|MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk|MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk|MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk|MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk|MRC Mammalian Genetics Unit, Harwell, Oxfordshire OX11 0RD, UK. e.green@har.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Database Management Systems|Databases, Genetic|Europe|Genomics|Internet|Mice|Phenotype|Reference Standards|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti441', '15827082', 'Standardized phenotyping protocols are essential for the characterization of phenotypes so that results are comparable between different laboratories and phenotypic data can be related to ontological descriptions in an automated manner. We describe a web-based resource for the visualization, searching and downloading of standard operating procedures and other documents, the European Mouse Phenotyping Resource for Standardized Screens-EMPReSS.'),
(72084, 2005, 'bio', 'A web-based three-dimensional protein retrieval system by matching visual similarity', 'Yeh, Jeng-Sheng|Chen, Ding-Yun|Chen, Bing-Yu|Ouhyoung, Ming', 'Department of Computer Science and Information Engineering, National Taiwan University Taipei 106, Taiwan. jsyeh@cmlab.csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University Taipei 106, Taiwan. jsyeh@cmlab.csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University Taipei 106, Taiwan. jsyeh@cmlab.csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University Taipei 106, Taiwan. jsyeh@cmlab.csie.ntu.edu.tw', '-|-|-|-', 0, 'Algorithms|Computer Graphics|Computer Simulation|Databases, Protein|Internet|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti458', '15840701', 'A web-based three-dimensional (3D) protein retrieval system is available for protein structure data including all PDB and FSSP dataset. In this system, we use a visual-based matching method to compare the protein structure from multiple viewpoints. It takes less than three seconds for each query with 90% accuracy on an average.'),
(72085, 2005, 'bio', 'Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information', 'Al-Shahrour, Fátima|Díaz-Uriarte, Ramón|Dopazo, Joaquín', 'Department of Bioinformatics, and Functional Genomics Node (INB), Centro de Investigación Príncipe Felipe Autopista del Saler 16, 46013 Valencia, Spain.|Department of Bioinformatics, and Functional Genomics Node (INB), Centro de Investigación Príncipe Felipe Autopista del Saler 16, 46013 Valencia, Spain.|Department of Bioinformatics, and Functional Genomics Node (INB), Centro de Investigación Príncipe Felipe Autopista del Saler 16, 46013 Valencia, Spain.', 'Spain|Spain|Spain', 83, 'Biomarkers, Tumor|Breast Neoplasms|Computer Simulation|Database Management Systems|Databases, Protein|Documentation|Gene Expression Profiling|Humans|Models, Biological|Neoplasm Proteins|Phenotype|Software|Structure-Activity Relationship|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/bti457', '15840702', 'The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested.'),
(72086, 2005, 'bio', 'Segmentation of cDNA microarray spots using markov random field modeling', 'Demirkaya, Omer|Asyali, Musa H|Shoukri, Mohamed M', 'Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia. demirkaya@ieee.org|Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia. demirkaya@ieee.org|Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia. demirkaya@ieee.org', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti455', '15840703', 'Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background. In this study, we have proposed a new approach based on Markov random field (MRF) modeling and tested its performance on simulated and real microarray images against a widely used segmentation method based on Mann-Whitney test adopted by QuantArray software (Boston, MA). Spot addressing was performed using QuantArray. We have also devised a simulation method to generate microarray images with realistic features. Such images can be used as gold standards for the purposes of testing and comparing different segmentation methods, and optimizing segmentation parameters.'),
(72087, 2005, 'bio', 'S-SPatt: simple statistics for patterns on Markov chains', 'Nuel, Grégory', 'Laboratoire Statistique et Génome 523 place des terrasses de lAgora, 91000 Evry, France. spatt@genopole.cnrs.fr', 'France', 9, 'Algorithms|Computer Simulation|Markov Chains|Models, Statistical|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bti451', '15840704', 'S-SPatt allows the counting of patterns occurrences in text files and, assuming these texts are generated from a random Markovian source, the computation of the P-value of a given observation using a simple binomial approximation.'),
(72088, 2005, 'bio', 'HCPM--program for hierarchical clustering of protein models', 'Gront, Dominik|Kolinski, Andrzej', 'Faculty of Chemistry, Warsaw University Pasteura 1, 02-093 Warsaw, Poland. dgront@chem.uw.edu.pl|Faculty of Chemistry, Warsaw University Pasteura 1, 02-093 Warsaw, Poland. dgront@chem.uw.edu.pl', 'Poland|Poland', 27, 'Algorithms|Cluster Analysis|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti450', '15840705', 'HCPM is a tool for clustering protein structures from comparative modeling, ab initio structure prediction, etc. A hierarchical clustering algorithm is designed and tested, and a heuristic is provided for an optimal cluster selection. The method has been successfully tested during the CASP6 experiment.'),
(72089, 2005, 'bio', 'SNPLINK: multipoint linkage analysis of densely distributed SNP data incorporating automated linkage disequilibrium removal', 'Webb, Emily L|Sellick, Gabrielle S|Houlston, Richard S', 'Section of Cancer Genetics, Institute of Cancer Research 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK.|Section of Cancer Genetics, Institute of Cancer Research 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK.|Section of Cancer Genetics, Institute of Cancer Research 15 Cotswold Road, Sutton, Surrey SM2 5NG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Software|Statistical Distributions|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti449', '15840706', 'SNPLINK is a Perl script that performs full genome linkage analysis of high-density single nucleotide polymorphism (SNP) marker sets. The presence of linkage disequilibrium (LD) between closely spaced SNP markers can falsely inflate linkage statistics. SNPLINK removes LD from the marker sets in an automated fashion before carrying out linkage analysis. SNPLINK can compute both parametric and non-parametric statistics, utilizing the freely available Allegro and Merlin software. Graphical outputs of whole genome multipoint linkage statistics are provided allowing comparison of results before and after the removal of LD.'),
(72090, 2005, 'bio', 'False discovery rate, sensitivity and sample size for microarray studies', 'Pawitan, Yudi|Michiels, Stefan|Koscielny, Serge|Gusnanto, Arief|Ploner, Alexander', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 308, 'Algorithms|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/bti448', '15840707', 'In microarray data studies most researchers are keenly aware of the potentially high rate of false positives and the need to control it. One key statistical shift is the move away from the well-known P-value to false discovery rate (FDR). Less discussion perhaps has been spent on the sensitivity or the associated false negative rate (FNR). The purpose of this paper is to explain in simple ways why the shift from P-value to FDR for statistical assessment of microarray data is necessary, to elucidate the determining factors of FDR and, for a two-sample comparative study, to discuss its control via sample size at the design stage.'),
(72091, 2005, 'bio', 'Meta-DP: domain prediction meta-server', 'Saini, Harpreet Kaur|Fischer, Daniel', 'Center of Excellence in Bioinformatics and Department of Computer Science and Engineering, University at Buffalo, 901 Washington Street, Suite 300, Buffalo, NY 14203, USA. hkaur@bioinformatics.buffalo.edu|Center of Excellence in Bioinformatics and Department of Computer Science and Engineering, University at Buffalo, 901 Washington Street, Suite 300, Buffalo, NY 14203, USA. hkaur@bioinformatics.buffalo.edu', 'United States|United States', 43, 'Algorithms|Computer Graphics|Computer Simulation|Internet|Models, Molecular|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti445', '15840708', 'Meta-DP, a domain prediction meta-server provides a simple interface to predict domains in a given protein sequence using a number of domain prediction methods. The Meta-DP is a convenient resource because through accessing a single site, users automatically obtain the results of the various domain prediction methods along with a consensus prediction. The Meta-DP is currently coupled to 10 domain prediction servers and can be extended to include any number of methods. Meta-DP can thus become a centralized repository of available methods. Meta-DP was also used to evaluate the performance of 13 domain prediction methods in the context of CAFASP-DP.'),
(72092, 2005, 'bio', 'Differential network expression during drug and stress response', 'Cabusora, Lawrence|Sutton, Electra|Fulmer, Andy|Forst, Christian V', 'Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA.', 'United States|United States|United States|United States', 87, 'Bacterial Proteins|Computer Simulation|DNA-Binding Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Mycobacterium tuberculosis|Oligonucleotide Array Sequence Analysis|Oxidative Stress|Pharmaceutical Preparations|STAT1 Transcription Factor|Trans-Activators', 'Journal Article', '10.1093/bioinformatics/bti440', '15840709', 'The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organisms ability to react to its environment.'),
(72093, 2005, 'bio', 'Sample size for FDR-control in microarray data analysis', 'Jung, Sin-Ho', 'Department of Biostatistics and Bioinformatics, CALGB Statistical Center Hock Plaza, Suite 802,2424 Erwin Road Duke University Durham, NC 27705, USA. jung005@mc.duke.edu', 'United States', 83, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size', 'Evaluation Studies', '10.1093/bioinformatics/bti456', '15845654', 'We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size calculation include the allocation proportion in each group, the number of genes in each array, the number of differentially expressing genes and the effect sizes among the differentially expressing genes. We have a closed-form sample size formula if the projected effect sizes are equal among differentially expressing genes. Otherwise, our method requires a numerical method to solve an equation. Simulation studies are conducted to show that the calculated sample sizes are accurate in practical settings. The proposed method is demonstrated with a real study.'),
(72094, 2005, 'bio', 'Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses', 'Jampachaisri, Katechan|Valinsky, Lea|Borneman, James|Press, S James', 'Department of Statistics, University of California Riverside, CA 92521, USA.|Department of Statistics, University of California Riverside, CA 92521, USA.|Department of Statistics, University of California Riverside, CA 92521, USA.|Department of Statistics, University of California Riverside, CA 92521, USA.', 'United States|United States|United States|United States', 9, 'Algorithms|Bacteria|Bacterial Physiological Phenomena|Cluster Analysis|DNA Fingerprinting|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|RNA, Ribosomal', 'Journal Article', '10.1093/bioinformatics/bti452', '15845655', 'Oligonucleotide fingerprinting of ribosomal RNA genes (OFRG) is a procedure that sorts rRNA gene (rDNA) clones into taxonomic groups through a series of hybridization experiments. The hybridization signals are classified into three discrete values 0, 1 and N, where 0 and 1, respectively, specify negative and positive hybridization events and N designates an uncertain assignment. This study examined various approaches for classifying the values including Bayesian classification with normally distributed signal data, Bayesian classification with the exponentially distributed data, and with gamma distributed data, along with tree-based classification. All classification data were clustered using the unweighted pair group method with arithmetic mean.'),
(72095, 2005, 'bio', 'Donuts, scratches and blanks: robust model-based segmentation of microarray images', 'Li, Qunhua|Fraley, Chris|Bumgarner, Roger E|Yeung, Ka Yee|Raftery, Adrian E', 'Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA.|Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA.|Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA.|Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA.|Department of Statistics, Box 354322 University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States|United States|United States', 79, 'Algorithms|Artifacts|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Models, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Comparative Study', '10.1093/bioinformatics/bti447', '15845656', 'Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of the histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three.'),
(72096, 2005, 'bio', 'MollDE: a homology modeling framework you can click with', 'Canutescu, Adrian A|Dunbrack, Roland L', 'Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA.|Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA.', 'United States|United States', 43, 'Algorithms|Computer Graphics|Computer Simulation|Models, Molecular|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti438', '15845657', 'Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDEs user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE.'),
(72097, 2005, 'bio', 'Dynamite extended: two new services to simplify protein dynamic analysis', 'Barrett, C Paul|Noble, Martin E M', 'Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford Oxford OX1 3QU, UK.|Laboratory of Molecular Biophysics, Department of Biochemistry, University of Oxford Oxford OX1 3QU, UK.', 'United Kingdom|United Kingdom', 14, 'Algorithms|Binding Sites|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti464', '15855246', 'We describe two additional services now available as part of the previously described Dynamite protein dynamics web service. Dynatraj provides principle component analysis and visualization of modes of motion for a users own ensemble of protein structures, e.g. from Molecular Dynamics, NMR or experimental ensembles. Dynapocket predicts probable configurations of a protein pocket from a single known structure. Both have been provided in response to requests from users for additional functionality from the Dynamite server. Like Dynamite, both are available free of charge to all users.'),
(72098, 2005, 'bio', 'jPHYDIT: a JAVA-based integrated environment for molecular phylogeny of ribosomal RNA sequences', 'Jeon, Yoon-Seong|Chung, Hwanwon|Park, Sunyoung|Hur, Inae|Lee, Jae-Hak|Chun, Jongsik', 'Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.|Interdisciplinary Program in Bioinformatics, Seoul National University Shilim-dong, Kwanak-gu, Seoul, Republic of Korea.', '-|-|-|-|-|-', 150, 'Algorithms|Evolution, Molecular|Phylogeny|Programming Languages|RNA, Ribosomal|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti463', '15855247', 'jPHYDIT is a Java application designed to furnish a visual and integrated environment for molecular phylogeny. The program can be used to visualize intra-strand base-pairing information in secondary and tertiary structures of ribosomal RNA (rRNA) sequences. A function for the semi-automated alignment was included to facilitate handling of the database containing a large number of multiple-aligned rRNA sequences. Integration of nucleotide sequence editing, pairwise alignment, multiple alignment and phylogenetic treeing functions provide an easy and efficient way of analyzing rRNA sequences for molecular evolution, systematics, epidemiology and ecology.'),
(72099, 2005, 'bio', 'TFBScluster: a resource for the characterization of transcriptional regulatory networks', 'Donaldson, Ian John|Chapman, Michael|Göttgens, Berthold', 'Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge Hills Road, Cambridge CB2 2XY, UK.|Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge Hills Road, Cambridge CB2 2XY, UK.|Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge Hills Road, Cambridge CB2 2XY, UK.', 'United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Base Sequence|Binding Sites|Chromosome Mapping|Cluster Analysis|Conserved Sequence|Gene Expression Regulation|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti461', '15855248', 'One major challenge of the post-sequencing era of the human genome project will be the functional annotation of the non-coding portion of the genome, in particular gene regulatory sequences. We have developed a new web-based tool, TFBScluster, which performs genome-wide identification of transcription factor binding site clusters that are conserved in multiple mammalian genomes. Clusters representing candidate gene regulatory elements can be filtered further, based on the presence or absence of additional user-defined DNA sequence motifs or by constraining the orientation or order of binding sites. Comprehensive results files, returned by email, are designed to facilitate experimental validation of computationally identified candidate gene regulatory sequences. TFBScluster, therefore, has the potential to contribute to deciphering transcriptional networks that regulate a wide range of mammalian developmental processes.'),
(72100, 2005, 'bio', 'Deltarho-web, an online tool to assess composition similarity of individual nucleic acid sequences', 'van Passel, M W J|Luyf, A C M|van Kampen, A H C|Bart, A|van der Ende, A', 'Department of Medical Microbiology, Academic Medical Center 1100 DE Amsterdam, The Netherlands.|Department of Medical Microbiology, Academic Medical Center 1100 DE Amsterdam, The Netherlands.|Department of Medical Microbiology, Academic Medical Center 1100 DE Amsterdam, The Netherlands.|Department of Medical Microbiology, Academic Medical Center 1100 DE Amsterdam, The Netherlands.|Department of Medical Microbiology, Academic Medical Center 1100 DE Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 41, 'Algorithms|Chromosome Mapping|Conserved Sequence|Internet|Online Systems|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti460', '15855249', 'Although whole-genome sequences have been analysed for the presence of anomalous DNA, no dedicated application is currently available to analyse the composition of individual sequence entries, for instance those derived by experimental techniques, such as subtractive hybridization. Since genomic dinucleotide frequency values are conserved between related species, a representative genome sequence can often be found to score for anomalous sequence composition for many of these putative horizontally transferred sequences. We developed the application deltarho-web, which enables the determination of the differences between the dinucleotide composition of an input sequence and that of a selected genome in a size-dependent manner. A feature allowing batch comparisons is included as well. In addition, deltarho-web allows the analysis of the dinucleotide composition of complete genomes. This provides complementary information for the identification of large anomalous gene clusters.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72101, 2005, 'bio', 'Association mapping and fine mapping with TreeLD', 'Zöllner, Sebastian|Wen, Xiaoquan|Pritchard, Jonathan K', 'Department of Human Genetics, The University of Chicago 920 East 58th Street-CLSC 507, Chicago, IL 60637, USA. szoellne@genetics.bsd.uchicago.edu|Department of Human Genetics, The University of Chicago 920 East 58th Street-CLSC 507, Chicago, IL 60637, USA. szoellne@genetics.bsd.uchicago.edu|Department of Human Genetics, The University of Chicago 920 East 58th Street-CLSC 507, Chicago, IL 60637, USA. szoellne@genetics.bsd.uchicago.edu', 'United States|United States|United States', 5, 'Algorithms|Chromosome Mapping|Computer Graphics|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti453', '15855250', 'The program package TreeLD implements a unified approach to association mapping and fine mapping of complex trait loci and a novel approach to visualizing association data, based on an inferred ancestry of the sample. Fundamentally, the TreeLD approach is based on the idea that the evidence for association at a particular position is contained in the ancestral tree relating the sampled chromosomes at that position. TreeLD provides an easy-to-use interface and can be applied to case-control, TDT trio and quantitative trait data.'),
(72102, 2005, 'bio', 'Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces', 'Aytuna, A Selim|Gursoy, Attila|Keskin, Ozlem', 'Koc University, Center for Computational Biology and Bioinformatics, College of Engineering, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.|Koc University, Center for Computational Biology and Bioinformatics, College of Engineering, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.|Koc University, Center for Computational Biology and Bioinformatics, College of Engineering, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 153, 'Algorithms|Amino Acid Sequence|Computer Simulation|Conserved Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti443', '15855251', 'Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces.'),
(72103, 2005, 'bio', 'ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text', 'Settles, Burr', 'Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison Madison, WI 52706, USA. bsettles@cs.wisc.edu', 'United States', 317, 'Algorithms|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Genes|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Programming Languages|Proteins|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti475', '15860559', 'ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.'),
(72104, 2005, 'bio', 'MatInspector and beyond: promoter analysis based on transcription factor binding sites', 'Cartharius, K|Frech, K|Grote, K|Klocke, B|Haltmeier, M|Klingenhoff, A|Frisch, M|Bayerlein, M|Werner, T', 'Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de|Genomatix Software GmbH Landsberger Strasse. 6, 80339 München, Germany. cartharius@genomatix.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 1543, 'Algorithms|Base Sequence|Binding Sites|Conserved Sequence|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Transcription Factors|Transcription, Genetic', 'Evaluation Studies', '10.1093/bioinformatics/bti473', '15860560', 'Promoter analysis is an essential step on the way to identify regulatory networks. A prerequisite for successful promoter analysis is the prediction of potential transcription factor binding sites (TFBS) with reasonable accuracy. The next steps in promoter analysis can be tackled only with reliable predictions, e.g. finding phylogenetically conserved patterns or identifying higher order combinations of sites in promoters of co-regulated genes.'),
(72105, 2005, 'bio', 'FSSA: a novel method for identifying functional signatures from structural alignments', 'Wang, Kai|Samudrala, Ram', 'Computational Genomics Group, Department of Microbiology, University of Washington Seattle, WA 98195, USA.|Computational Genomics Group, Department of Microbiology, University of Washington Seattle, WA 98195, USA.', 'United States|United States', 24, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti471', '15860561', 'It is commonly believed that sequence determines structure, which in turn determines function. However, the presence of many proteins with the same structural fold but different functions suggests that global structure and function do not always correlate well.'),
(72106, 2005, 'bio', 'iGNM: a database of protein functional motions based on Gaussian Network Model', 'Yang, Lee-Wei|Liu, Xiong|Jursa, Christopher J|Holliman, Mark|Rader, A J|Karimi, Hassan A|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh Pittsburgh, PA 15213, USA. lwy1@pitt.edu', 'United States|United States|United States|United States|United States|United States|United States', 105, 'Algorithms|Binding Sites|Computer Simulation|Databases, Protein|Internet|Models, Chemical|Models, Molecular|Normal Distribution|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti469', '15860562', 'The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics. The recent success of analytical methods based on elastic network models, and in particular the Gaussian Network Model (GNM), permits us to perform a high-throughput analysis of the collective dynamics of proteins.'),
(72107, 2005, 'bio', 'SWIFT (sequence-wide investigation with Fourier transform): a software tool for identifying proteins of a given class from the unannotated genome sequence', 'DAvenio, G|Grigioni, M|Orefici, G|Creti, R', 'Department of Technologies and Health, Parasitic and Immune-Mediated Diseases Istituto Superiore di Sanità, Rome, Italy. davenio@iss.it|Department of Technologies and Health, Parasitic and Immune-Mediated Diseases Istituto Superiore di Sanità, Rome, Italy. davenio@iss.it|Department of Technologies and Health, Parasitic and Immune-Mediated Diseases Istituto Superiore di Sanità, Rome, Italy. davenio@iss.it|Department of Technologies and Health, Parasitic and Immune-Mediated Diseases Istituto Superiore di Sanità, Rome, Italy. davenio@iss.it', 'Italy|Italy|Italy|Italy', 8, 'Algorithms|Bacterial Proteins|Chromosome Mapping|Fourier Analysis|Genome, Bacterial|Gram-Positive Bacteria|Membrane Proteins|Open Reading Frames|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/bti468', '15860563', 'The ever increasing number of sequenced genomes calls for new analysis techniques, which can benefit from the methodologies developed in the field of signal processing.'),
(72108, 2005, 'bio', 'A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data', 'Teschendorff, Andrew E|Wang, Yanzhong|Barbosa-Morais, Nuno L|Brenton, James D|Caldas, Carlos', 'Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 54, 'Algorithms|Bayes Theorem|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sample Size|Software', 'Comparative Study', '10.1093/bioinformatics/bti466', '15860564', 'Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem.'),
(72109, 2005, 'bio', 'INTERALIGN: interactive alignment editor for distantly related protein sequences', 'Pible, Olivier|Imbert, Gilles|Pellequer, Jean-Luc', 'Commissariat à IEnergie Atomique, CEA VALRTTO, DSV-DIEP-SBTN, Service de Biochimie post-génomique & Toxicologie Nucliaire Bagnols-sur-Cèze, France. olivier.pible@cea.fr|Commissariat à IEnergie Atomique, CEA VALRTTO, DSV-DIEP-SBTN, Service de Biochimie post-génomique & Toxicologie Nucliaire Bagnols-sur-Cèze, France. olivier.pible@cea.fr|Commissariat à IEnergie Atomique, CEA VALRTTO, DSV-DIEP-SBTN, Service de Biochimie post-génomique & Toxicologie Nucliaire Bagnols-sur-Cèze, France. olivier.pible@cea.fr', 'France|France|France', 10, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti474', '15870166', 'Improving and ascertaining the quality of a multiple sequence alignment is a very challenging step in protein sequence analysis. This is particularly the case when dealing with sequences in the twilight zone, i.e. sharing < 30% identity. Here we describe INTERALIGN, a dedicated user-friendly alignment editor including a view of secondary structures and a synchronized display of carbon alpha traces of corresponding protein structures. Profile alignment, using CLUSTALW, is implemented to improve the alignment of a sequence of unknown structure with the visually optimized structural alignment as compared with a standard multiple sequence alignment. Tree-based ordering further helps in identifying the structure closest to a given sequence.'),
(72110, 2005, 'bio', 'DIG--a system for gene annotation and functional discovery', 'Delong, Mark|Yao, Guang|Wang, Quanli|Dobra, Adrian|Black, Esther P|Chang, Jeffrey T|Bild, Andrea|West, Mike|Nevins, Joseph R|Dressman, Holly', 'Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu|Duke Institute for Genome Sciences and Policy, Duke University Medical Center Durham, NC 27710, USA. mdelong@cgt.duhs.duke.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Chromosome Mapping|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Internet|MEDLINE|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteins|Signal Transduction|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti467', '15870167', 'We describe a database and information discovery system named DIG (Duke Integrated Genomics) designed to facilitate the process of gene annotation and the discovery of functional context. The DIG system collects and organizes gene annotation and functional information, and includes tools that support an understanding of genes in a functional context by providing a framework for integrating and visualizing gene expression, protein interaction and literature-based interaction networks.'),
(72111, 2005, 'bio', 'Enhanced statistics for local alignment of multiple alignments improves prediction of protein function and structure', 'Frenkel-Morgenstern, Milana|Voet, Hillary|Pietrokovski, Shmuel', 'Department of Molecular Genetics, Weizmann Institute of Science Rehovot 76100, Israel.|Department of Molecular Genetics, Weizmann Institute of Science Rehovot 76100, Israel.|Department of Molecular Genetics, Weizmann Institute of Science Rehovot 76100, Israel.', 'Israel|Israel|Israel', 9, 'Algorithms|Data Interpretation, Statistical|Databases, Protein|Models, Chemical|Models, Statistical|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Comparative Study', '10.1093/bioinformatics/bti462', '15870168', 'Improved comparisons of multiple sequence alignments (profiles) with other profiles can identify subtle relationships between protein families and motifs significantly beyond the resolution of sequence-based comparisons.'),
(72112, 2005, 'bio', 'CGHMultiArray: exact P-values for multi-array comparative genomic hybridization data', 'van de Wiel, Mark A|Smeets, Serge J|Brakenhoff, Ruud H|Ylstra, Bauke', 'Department of Mathematics and Computer Science, Technische Universiteit Eindhoven PO Box 513, 5600 MB, Eindhoven, The Netherlands. m.a.v.d.wiel@tue.nl|Department of Mathematics and Computer Science, Technische Universiteit Eindhoven PO Box 513, 5600 MB, Eindhoven, The Netherlands. m.a.v.d.wiel@tue.nl|Department of Mathematics and Computer Science, Technische Universiteit Eindhoven PO Box 513, 5600 MB, Eindhoven, The Netherlands. m.a.v.d.wiel@tue.nl|Department of Mathematics and Computer Science, Technische Universiteit Eindhoven PO Box 513, 5600 MB, Eindhoven, The Netherlands. m.a.v.d.wiel@tue.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 31, 'Algorithms|Chromosome Mapping|Models, Genetic|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software|Statistics, Nonparametric|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti489', '15879450', 'We compute P-values, based on the Wilcoxon test with ties, to compare two conditions with array comparative genomic hybridization data, and we provide a simple interface to export and plot these P-values.'),
(72113, 2005, 'bio', 'Fast parsers for Entrez Gene', 'Liu, Mingyi|Grigoriev, Andrei', 'GPC Biotech AG Fraunhoferstrasse 20, 82152 Martinsried, Germany.|GPC Biotech AG Fraunhoferstrasse 20, 82152 Martinsried, Germany.', 'Germany|Germany', 5, 'Algorithms|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Natural Language Processing|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti488', '15879451', 'NCBI completed the transition of its main genome annotation database from Locuslink to Entrez Gene in Spring 2005. However, to this date few parsers exist for the Entrez Gene annotation file. Owing to the widespread use of Locuslink and the popularity of Perl programming language in bioinformatics, a publicly available high performance Entrez Gene parser in Perl is urgently needed. We present four such parsers that were developed using several parsing approaches (Parse::RecDescent, Parse::Yapp, Perl-byacc and Perl 5 regular expressions) and provide the first in-depth comparison of these sophisticated Perl tools. Our fastest parser processes the entire human Entrez Gene annotation file in under 12 min on one Intel Xeon 2.4 GHz CPU and can be of help to the bioinformatics community during and after the transition from Locuslink to Entrez Gene.'),
(72114, 2005, 'bio', 'A Bayesian regression approach to the inference of regulatory networks from gene expression data', 'Rogers, Simon|Girolami, Mark', 'Department of Computing Science, Bioinformatics Research Centre, University of Glasgow Glasgow, UK. srogers@dcs.gla.ac.uk|Department of Computing Science, Bioinformatics Research Centre, University of Glasgow Glasgow, UK. srogers@dcs.gla.ac.uk', 'United Kingdom|United Kingdom', 69, 'Algorithms|Bayes Theorem|Gene Expression Profiling|Gene Expression Regulation|Likelihood Functions|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Regression Analysis|Signal Transduction|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti487', '15879452', 'There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections.'),
(72115, 2005, 'bio', 'PMUT: a web-based tool for the annotation of pathological mutations on proteins', 'Ferrer-Costa, Carles|Gelpí, Josep Lluis|Zamakola, Leire|Parraga, Ivan|de la Cruz, Xavier|Orozco, Modesto', 'Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.|Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.|Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.|Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.|Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.|Molecular Modeling and Bioinformatics Unit, Institut de Recerca Biomédica, Parc Científic de Barcelona Josep Samitier 1-5, Barcelona 08028, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 374, 'Amino Acid Substitution|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Mutagenesis, Site-Directed|Neural Networks (Computer)|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti486', '15879453', 'PMUT allows the fast and accurate prediction (approximately 80% success rate in humans) of the pathological character of single point amino acidic mutations based on the use of neural networks. The program also allows the fast scanning of mutational hot spots, which are obtained by three procedures: (1) alanine scanning, (2) massive mutation and (3) genetically accessible mutations. A graphical interface for Protein Data Bank (PDB) structures, when available, and a database containing hot spot profiles for all non-redundant PDB structures are also accessible from the PMUT server.'),
(72116, 2005, 'bio', 'GMCheck: Bayesian error checking for pedigree genotypes and phenotypes', 'Thomas, Alun', 'Department of Medical Informatics and Center for High Performance Computing, University of Utah 391 Chipeta Way Suite D, Salt Lake City, UT 84108, USA. alun@genepi.med.utah.edu', 'United States', 10, 'Algorithms|Bayes Theorem|Chromosome Mapping|DNA Mutational Analysis|Decision Support Techniques|Genetics, Population|Genotype|Models, Genetic|Models, Statistical|Pedigree|Phenotype|Software', 'Journal Article', '10.1093/bioinformatics/bti485', '15879454', 'GMCheck uses graphical modeling to find the posterior probabilities of data errors given genotypes or phenotypes in a specified pedigree structure.'),
(72117, 2005, 'bio', 'Ensemble dependence model for classification and prediction of cancer and normal gene expression data', 'Qiu, Peng|Wang, Z Jane|Liu, K J Ray', 'Department of Electrical and Computer Engineering, University of Maryland College Park, MD 20742, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland College Park, MD 20742, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland College Park, MD 20742, USA. qiupeng@umd.edu', 'United States|United States|United States', 33, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Computer Simulation|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Models, Genetic|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti483', '15879455', 'DNA microarray technologies make it possible to simultaneously monitor thousands of genes expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subjects health status, and thus is promising in predicting the early development of cancer.'),
(72118, 2005, 'bio', 'Algorithms for alignment of mass spectrometry proteomic data', 'Jeffries, Neal', 'National Institute of Neurological Disorders and Stroke, National Institutes of Health Bethesda, MD 20892, USA. neal.jeffries@nih.gov', 'United States', 0, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Peptide Mapping|Proteome|Sequence Alignment|Sequence Analysis, Protein|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Comparative Study', '10.1093/bioinformatics/bti482', '15879456', 'The analysis of biological samples with high-throughput mass spectrometers has increased greatly in recent years. As larger datasets are processed, it is important that the spectra are aligned to ensure that the same protein intensities are correctly identified in each sample. Without such an alignment procedure it is possible to make errors in identifying the signals from peptides with similar molecular weight. Two algorithms are provided that can improve the alignment among samples. One algorithm is designed to work with SELDI data produced from a Ciphergen instrument, and the other can be used with data in a more general format.'),
(72119, 2005, 'bio', 'SDMinP: a program to control the family wise error rate using step-down minP adjusted P-values', 'Obreiter, M|Fischer, C|Chang-Claude, J|Beckmann, L', 'German Cancer Research Center DKFZ, Department of Clinical Epidemiology Im Neuenheimer Feld 280,69120 Heidelberg, Germany.|German Cancer Research Center DKFZ, Department of Clinical Epidemiology Im Neuenheimer Feld 280,69120 Heidelberg, Germany.|German Cancer Research Center DKFZ, Department of Clinical Epidemiology Im Neuenheimer Feld 280,69120 Heidelberg, Germany.|German Cancer Research Center DKFZ, Department of Clinical Epidemiology Im Neuenheimer Feld 280,69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/bti480', '15879457', 'SDMinP is an easy-to-use program for fast calculation of empirical and adjusted P-values for correlated and uncorrelated hypotheses in multiple testing experiments. It is based on the Free Step-Down Resampling Method for controlling the family wise error rate, and implements a variation of an efficient algorithm, which reduces the originally required re-sampling effort considerably and makes the method computationally feasible. The program is independent of the underlying test statistic and works with provided observed and permutation test statistics.'),
(72120, 2005, 'bio', 'MEPD: a resource for medaka gene expression patterns', 'Henrich, Thorsten|Ramialison, Mirana|Wittbrodt, Beate|Assouline, Beatrice|Bourrat, Franck|Berger, Anja|Himmelbauer, Heinz|Sasaki, Takashi|Shimizu, Nobuyoshi|Westerfield, Monte|Kondoh, Hisato|Wittbrodt, Joachim', 'EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de|EMBL Heidelberg Meyerhofstrasse 1, D-69117 Heidelberg, Germany. henrich@embl.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Animals|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Internet|Oryzias|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti478', '15879458', 'The Medaka Expression Pattern Database (MEPD) is a database for gene expression patterns determined by in situ hybridization in the small freshwater fish medaka (Oryzias latipes). Data have been collected from various research groups and MEPD is developing into a central expression pattern depository within the medaka community. Gene expression patterns are described by images and terms of a detailed medaka anatomy ontology of over 4000 terms, which we have developed for this purpose and submitted to Open Biological Ontologies. Sequences have been annotated via BLAST match results and using Gene Ontology terms. These new features will facilitate data analyses using bioinformatics approaches and allow cross-species comparisons of gene expression patterns. Presently, MEPD has 19,757 entries, for 1024 of them the expression pattern has been determined.'),
(72121, 2005, 'bio', 'Knowledge-based computational search for genes associated with the metabolic syndrome', 'Matsunaga, Tsutomu|Muramatsu, Masa-Aki', 'Research and Development Headquarters, NTT DATA Corporation Tokyo, 104-0033 Japan. matsunagat@nttdata.co.jp|Research and Development Headquarters, NTT DATA Corporation Tokyo, 104-0033 Japan. matsunagat@nttdata.co.jp', 'Japan|Japan', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Biomarkers|Gene Expression Profiling|Genetic Predisposition to Disease|Humans|MEDLINE|Metabolic Syndrome|Natural Language Processing|Periodicals as Topic|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/bti484', '15886278', 'A methodology to search for genes associated with multifactorial diseases by integrating the large amount of accumulated knowledge is seriously needed. A comprehensive understanding derived from a holistic view of gene relationship structures can be gained from our proposed analysis called the cross-subspace analysis (CSA). In this analysis, gene objects are generated by machine learning using their term occurrence patterns in MEDLINE abstracts and the degree of relationship between gene objects is quantified by matching these patterns.'),
(72122, 2005, 'bio', 'SCHIP: statistics for chromosome interphase positioning based on interchange data', 'Vives, Sergi|Loucas, Bradford|Vazquez, Mariel|Brenner, David J|Sachs, Rainer K|Hlatky, Lynn|Cornforth, Michael|Arsuaga, Javier', 'Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.|Departmento de Estadistica, Universidad de Barcelona Barcelona 08028, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Chromosome Aberrations|Chromosome Mapping|Data Interpretation, Statistical|Interphase|Models, Genetic|Models, Statistical|Sister Chromatid Exchange|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti470', '15886279', 'The position of chromosomes in the interphase nucleus is believed to be associated with a number of biological processes. Here, we present a web-based application that helps analyze the relative position of chromosomes during interphase in human cells, based on observed radiogenic chromosome aberrations. The inputs of the program are a table of yields of pairwise chromosome interchanges and a proposed chromosome geometric cluster. Each can either be uploaded or selected from provided datasets. The main outputs are P-values for the proposed chromosome clusters. SCHIP is designed to be used by a number of scientific communities interested in nuclear architecture, including cancer and cell biologists, radiation biologists and mathematical/computational biologists.'),
(72123, 2005, 'bio', 'Differential and trajectory methods for time course gene expression data', 'Liang, Yulan|Tayo, Bamidele|Cai, Xueya|Kelemen, Arpad', 'Department of Biostatistics, University at Buffalo Buffalo, NY 14226, USA. yliang@buffalo.edu|Department of Biostatistics, University at Buffalo Buffalo, NY 14226, USA. yliang@buffalo.edu|Department of Biostatistics, University at Buffalo Buffalo, NY 14226, USA. yliang@buffalo.edu|Department of Biostatistics, University at Buffalo Buffalo, NY 14226, USA. yliang@buffalo.edu', 'United States|United States|United States|United States', 17, 'Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Software|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti465', '15886280', 'The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differentiation approaches can reduce the dimensions of data, help to remove redundant genes and noises, and highlight the most relevant genes that are major players in the development of certain diseases or the effect of drug treatment. The purpose of this study is to investigate the efficiency of parametric (including Bayesian and non-Bayesian, linear and non-linear), non-parametric and semi-parametric gene filtering methods through the application of time course microarray data from multiple sclerosis patients being treated with interferon-beta-1a. The analysis of variance with bootstrapping (parametric), class dispersion (semi-parametric) and Pareto (non-parametric) with permutation methods are presented and compared for filtering and finding differentially expressed genes. The Bayesian linear correlated model, the Bayesian non-linear model the and non-Bayesian mixed effects model with bootstrap were also developed to characterize the differential expression patterns. Furthermore, trajectory-clustering approaches were developed in order to investigate the dynamic patterns and inter-dependency of drug treatment effects on gene expression.'),
(72124, 2005, 'bio', 'M@CBETH: a microarray classification benchmarking tool', 'Pochet, Nathalie L M M|Janssens, Frizo A L|De Smet, Frank|Marchal, Kathleen|Suykens, Johan A K|De Moor, Bart L R', 'K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be|K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be|K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be|K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be|K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be|K. U. Leuven, ESAT-SCD, Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium. Nathalie.Pochet@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Benchmarking|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti495', '15890742', 'Microarray classification can be useful to support clinical management decisions for individual patients in, for example, oncology. However, comparing classifiers and selecting the best for each microarray dataset can be a tedious and non-straightforward task. The M@CBETH (a MicroArray Classification BEnchmarking Tool on a Host server) web service offers the microarray community a simple tool for making optimal two-class predictions. M@CBETH aims at finding the best prediction among different classification methods by using randomizations of the benchmarking dataset. The M@CBETH web service intends to introduce an optimal use of clinical microarray data classification.'),
(72125, 2005, 'bio', 'Proteomic mass spectra classification using decision tree based ensemble methods', 'Geurts, Pierre|Fillet, Marianne|de Seny, Dominique|Meuwis, Marie-Alice|Malaise, Michel|Merville, Marie-Paule|Wehenkel, Louis', 'Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be|Department of Electrical Engineering and Computer Science, University of Liège 4000 Liège, Belgium. p.geurts@ulg.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 105, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Databases, Protein|Decision Trees|Mass Spectrometry|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Proteome|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti494', '15890743', 'Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples.'),
(72126, 2005, 'bio', 'Discovering patterns to extract protein-protein interactions from the literature: Part II', 'Hao, Yu|Zhu, Xiaoyan|Huang, Minlie|Li, Ming', 'State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.', 'China|China|China|China', 71, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Database Management Systems|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Protein Interaction Mapping|Software|User-Computer Interface|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/bti493', '15890744', 'An enormous number of protein-protein interaction relationships are buried in millions of research articles published over the years, and the number is growing. Rediscovering them automatically is a challenging bioinformatics task. Solutions to this problem also reach far beyond bioinformatics.'),
(72127, 2005, 'bio', 'A criticality-based framework for task composition in multi-agent bioinformatics integration systems', 'Karasavvas, Konstantinos A|Baldock, Richard|Burger, Albert', 'School of Mathematical and Computer Sciences, Heriot-Watt University Edinburgh EH14 4AS, UK. ceekk@macs.hw.ac.uk|School of Mathematical and Computer Sciences, Heriot-Watt University Edinburgh EH14 4AS, UK. ceekk@macs.hw.ac.uk|School of Mathematical and Computer Sciences, Heriot-Watt University Edinburgh EH14 4AS, UK. ceekk@macs.hw.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Artificial Intelligence|Computational Biology|Decision Support Techniques|Systems Integration|Task Performance and Analysis|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti491', '15890745', 'During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. In this paper, we propose an approach that assesses the importance of such decisions with respect to the overall result. We present a way of measuring decision criticality and describe its potential use.'),
(72128, 2005, 'bio', 'Cis-regulatory element based targeted gene finding: genome-wide identification of abscisic acid- and abiotic stress-responsive genes in Arabidopsis thaliana', 'Zhang, Weixiong|Ruan, Jianhua|Ho, Tuan-Hua David|You, Youngsook|Yu, Taotao|Quatrano, Ralph S', 'Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu|Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu|Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu|Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu|Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu|Department of Computer Science and Engineering, Washington University in Saint Louis Saint Louis, MO 63130, USA. zhang@cse.wustl.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Abscisic Acid|Arabidopsis|Arabidopsis Proteins|Chromosome Mapping|Enhancer Elements, Genetic|Gene Expression Profiling|Gene Targeting|Genes, Regulator|Genome, Plant|Open Reading Frames|Oxidative Stress|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/bti490', '15890746', 'A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding target genes.'),
(72129, 2005, 'bio', 'ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data', 'Smilde, Age K|Jansen, Jeroen J|Hoefsloot, Huub C J|Lamers, Robert-Jan A N|van der Greef, Jan|Timmerman, Marieke E', 'Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl|Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl|Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl|Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl|Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl|Biosystems Data Analysis, Faculty of Sciences, University of Amsterdam Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. asmilde@science.uva.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Analysis of Variance|Animals|Ascorbic Acid|Biomarkers|Computer Simulation|Dose-Response Relationship, Drug|Energy Metabolism|Gene Expression Profiling|Guinea Pigs|Male|Models, Biological|Models, Statistical|Multivariate Analysis|Osteoarthritis|Proteome|Software|Treatment Outcome', 'Evaluation Studies', '10.1093/bioinformatics/bti476', '15890747', 'Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets.'),
(72130, 2005, 'bio', 'PROFcon: novel prediction of long-range contacts', 'Punta, Marco|Rost, Burkhard', 'CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University 650 West 168th Street BB217, New York, NY 10032, USA. punta@cubic.bioc.columbia.edu|CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University 650 West 168th Street BB217, New York, NY 10032, USA. punta@cubic.bioc.columbia.edu', 'United States|United States', 119, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Neural Networks (Computer)|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/bti454', '15890748', 'Despite the continuing advance in the experimental determination of protein structures, the gap between the number of known protein sequences and structures continues to increase. Prediction methods can bridge this sequence-structure gap only partially. Better predictions of non-local contacts between residues could improve comparative modeling, fold recognition and could assist in the experimental structure determination.'),
(72131, 2005, 'bio', 'Exon-domain correlation and its corollaries', 'Liu, Mingyi|Wu, Shaoping|Walch, Heiko|Grigoriev, Andrei', 'GPC Biotech AG, Fraunhoferstrasse 20, 82152 Martinsried, Germany.|GPC Biotech AG, Fraunhoferstrasse 20, 82152 Martinsried, Germany.|GPC Biotech AG, Fraunhoferstrasse 20, 82152 Martinsried, Germany.|GPC Biotech AG, Fraunhoferstrasse 20, 82152 Martinsried, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Animals|Chromosome Mapping|Exons|Gene Expression Regulation|Humans|Models, Genetic|Proteome|Sequence Analysis, DNA|Signal Transduction|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti509', '15905272', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72132, 2005, 'bio', 'Dasty and UniProt DAS: a perfect pair for protein feature visualization', 'Jones, Philip|Vinod, Nisha|Down, Thomas|Hackmann, Andre|Kahari, Andreas|Kretschmann, Ernst|Quinn, Antony|Wieser, Daniela|Hermjakob, Henning|Apweiler, Rolf', 'EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk|EMBL Outstation-European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD, UK. pjones@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Algorithms|Computer Graphics|Computer Simulation|Database Management Systems|Internet|Models, Chemical|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti506', '15905273', 'In this study, we present two freely available and complementary Distributed Annotation System (DAS) resources: a DAS reference server that provides up-to-date sequence and annotation from UniProt, with additional feature links and database cross-references from InterPro and a DAS client implemented using Java and Macromedia Flash that is optimized for the display of protein features.'),
(72133, 2005, 'bio', 'Sequence to Structure (S2S): display, manipulate and interconnect RNA data from sequence to structure', 'Jossinet, Fabrice|Westhof, Eric', 'Institut de Biologie Moléculaire et Cellulaire du CNRS, UPR9002, Université Louis Pasteur, F-67084 Strasbourg, France. f.jossinet@ibmc.u-strasbg.fr|Institut de Biologie Moléculaire et Cellulaire du CNRS, UPR9002, Université Louis Pasteur, F-67084 Strasbourg, France. f.jossinet@ibmc.u-strasbg.fr', 'France|France', 0, 'Algorithms|Computer Graphics|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti504', '15905274', 'Efficient RNA sequence manipulations (such as multiple alignments) need to be constrained by rules of RNA structure folding. The structural knowledge has increased dramatically in the last years with the accumulation of several large RNA structures similar to those of the bacterial ribosome subunits. However, no tool in the RNA community provides an easy way to link and integrate progress made at the sequence level using the available three-dimensional information. Sequence to Structure (S2S) proposes a framework in which an user can easily display, manipulate and interconnect heterogeneous RNA data, such as multiple sequence alignments, secondary and tertiary structures. S2S has been implemented using the Java language and has been developed and tested under UNIX systems, such as Linux and MacOSX.'),
(72134, 2005, 'bio', 'MFAML: a standard data structure for representing and exchanging metabolic flux models', 'Yun, Hongseok|Lee, Dong-Yup|Jeong, Joonwoo|Lee, Seunghyun|Lee, Sang Yup', 'Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Bioinformatics Research Center, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.', '-|-|-|-|-', 11, 'Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Metabolism|Models, Biological|Proteome|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti502', '15905275', 'MFAML is a standard data structure designed for the formal representation and effective exchange of metabolic flux models. It allows for the explicit description of stationary states of a metabolic system by defining environmental/genetic conditions of the system, e.g. flux measurements, balancing constraints and physiological objectives as well as basic information on metabolites and reactions. In addition, a library of MFAML comprising a model parser and a converter provides an open framework for establishing the pipeline from metabolic modeling to metabolic flux analysis.'),
(72135, 2005, 'bio', 'CGHAnalyzer: a stand-alone software package for cancer genome analysis using array-based DNA copy number data', 'Margolin, Adam A|Greshock, Joel|Naylor, Tara L|Mosse, Yael|Maris, John M|Bignell, Graham|Saeed, Alexander I|Quackenbush, John|Weber, Barbara L', 'Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.|Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 25, 'Algorithms|Animals|Chromosome Mapping|Computer Graphics|DNA Mutational Analysis|Gene Dosage|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti500', '15905276', 'This synopsis provides an overview of array-based comparative genomic hybridization data display, abstraction and analysis using CGHAnalyzer, a software suite, designed specifically for this purpose. CGHAnalyzer can be used to simultaneously load copy number data from multiple platforms, query and describe large, heterogeneous datasets and export results. Additionally, CGHAnalyzer employs a host of algorithms for microarray analysis that include hierarchical clustering and class differentiation.'),
(72136, 2005, 'bio', 'Prediction error estimation: a comparison of resampling methods', 'Molinaro, Annette M|Simon, Richard|Pfeiffer, Ruth M', 'Biostatistics Branch, Division of Cancer Epidemiology and Genetics, NCI, NIH, Rockville, MD 20852, USA. annette.molinaro@yale.edu|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, NCI, NIH, Rockville, MD 20852, USA. annette.molinaro@yale.edu|Biostatistics Branch, Division of Cancer Epidemiology and Genetics, NCI, NIH, Rockville, MD 20852, USA. annette.molinaro@yale.edu', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti499', '15905277', 'In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the true prediction error of a prediction model in the presence of feature selection.'),
(72137, 2005, 'bio', 'Computational approaches for identification of conserved/unique binding pockets in the A chain of ricin', 'Zhou, Carol L Ecale|Zemla, Adam T|Roe, Diana|Young, Malin|Lam, Marisa|Schoeniger, Joseph S|Balhorn, Rod', 'Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov|Lawrence Livermore National Laboratory 7000 E. Avenue, Livermore, CA 94550, USA. zhou4@llnl.gov', 'United States|United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Conserved Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Ricin|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/bti498', '15905278', 'Specific and sensitive ligand-based protein detection assays that employ antibodies or small molecules such as peptides, aptamers or other small molecules require that the corresponding surface region of the protein be accessible and that there be minimal cross-reactivity with non-target proteins. To reduce the time and cost of laboratory screening efforts for diagnostic reagents, we developed new methods for evaluating and selecting protein surface regions for ligand targeting.'),
(72138, 2005, 'bio', 'Semi-supervised protein classification using cluster kernels', 'Weston, Jason|Leslie, Christina|Ie, Eugene|Zhou, Dengyong|Elisseeff, Andre|Noble, William Stafford', 'NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com|NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com|NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com|NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com|NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com|NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. jasonw@nec-labs.com', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti497', '15905279', 'Building an accurate protein classification system depends critically upon choosing a good representation of the input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-the-art classification performance. However, such representations are based only on labeled data--examples with known 3D structures, organized into structural classes--whereas in practice, unlabeled data are far more plentiful.'),
(72139, 2005, 'bio', 'Hotellings T2 multivariate profiling for detecting differential expression in microarrays', 'Lu, Yan|Liu, Peng-Yuan|Xiao, Peng|Deng, Hong-Wen', 'Osteoporosis Research Center, Creighton University 601 N. 30th Street, Suite 6787, Omaha, NE 68131, USA.|Osteoporosis Research Center, Creighton University 601 N. 30th Street, Suite 6787, Omaha, NE 68131, USA.|Osteoporosis Research Center, Creighton University 601 N. 30th Street, Suite 6787, Omaha, NE 68131, USA.|Osteoporosis Research Center, Creighton University 601 N. 30th Street, Suite 6787, Omaha, NE 68131, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Sample Size', 'Evaluation Studies', '10.1093/bioinformatics/bti496', '15905280', 'The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T(2) statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.'),
(72140, 2005, 'bio', 'Inferring protein-protein interactions through high-throughput interaction data from diverse organisms', 'Liu, Yin|Liu, Nianjun|Zhao, Hongyu', 'Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.', 'United States|United States|United States', 0, 'Binding Sites|Biodiversity|Caenorhabditis elegans Proteins|Computer Simulation|Drosophila Proteins|Models, Biological|Models, Chemical|Protein Binding|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein|Signal Transduction|Species Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti492', '15905281', 'Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities.'),
(72141, 2005, 'bio', 'TAMO: a flexible, object-oriented framework for analyzing transcriptional regulation using DNA-sequence motifs', 'Gordon, D Benjamin|Nekludova, Lena|McCallum, Scott|Fraenkel, Ernest', 'Whitehead Institute for Biomedical Research, Nine Cambridge Center Cambridge, MA 02142, USA.|Whitehead Institute for Biomedical Research, Nine Cambridge Center Cambridge, MA 02142, USA.|Whitehead Institute for Biomedical Research, Nine Cambridge Center Cambridge, MA 02142, USA.|Whitehead Institute for Biomedical Research, Nine Cambridge Center Cambridge, MA 02142, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Databases, Nucleic Acid|Gene Expression Regulation|Sequence Alignment|Sequence Analysis, DNA|Software|Systems Integration|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti481', '15905282', 'TAMO (Tools for Analysis of MOtifs) is an object-oriented computational framework for interpreting transcriptional regulation using DNA-sequence motifs. To simplify the application of multiple motif discovery programs to genome-wide data, TAMO provides a sophisticated motif object with interfaces to several popular programs. In addition, TAMO provides modules for integrating motif analysis with diverse data sources including genomic sequences, microarrays and various databases. Finally, TAMO includes tools for sequence analysis, algorithms for scoring, comparing and clustering motifs, and several useful statistical tests. Recently, we have applied these tools to analyze tens of thousands of motifs derived from hundreds of microarray experiments.'),
(72142, 2005, 'bio', 'A multiple-feature framework for modelling and predicting transcription factor binding sites', 'Pudimat, Rainer|Schukat-Talamazzini, Ernst-Günter|Backofen, Rolf', 'Institut für Informatik, Friedrich-Schiller-Universität Ernst-Abbe-Platz 3, D-07743 Jena, Germany.|Institut für Informatik, Friedrich-Schiller-Universität Ernst-Abbe-Platz 3, D-07743 Jena, Germany.|Institut für Informatik, Friedrich-Schiller-Universität Ernst-Abbe-Platz 3, D-07743 Jena, Germany.', 'Germany|Germany|Germany', 26, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Genetic|Models, Statistical|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti477', '15905283', 'The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcriptional regulation of genes. For analysing transcriptional regulation, computational approaches for predicting putative binding sites are applied. Commonly used stochastic models for binding sites are position-specific score matrices, which show weak predictive power.'),
(72143, 2005, 'bio', 'Computational cluster validation in post-genomic data analysis', 'Handl, Julia|Knowles, Joshua|Kell, Douglas B', 'School of Chemistry, University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester M60 1QD, UK. J.Handl@postgrad.manchester.ac.uk|School of Chemistry, University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester M60 1QD, UK. J.Handl@postgrad.manchester.ac.uk|School of Chemistry, University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester M60 1QD, UK. J.Handl@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/bti517', '15914541', 'The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics.'),
(72144, 2005, 'bio', 'Efficient recognition of folds in protein 3D structures by the improved PRIDE algorithm', 'Gáspári, Zoltán|Vlahovicek, Kristian|Pongor, Sándor', 'Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt., 62, Szeged, Hungary.|Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt., 62, Szeged, Hungary.|Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt., 62, Szeged, Hungary.', 'Hungary|Hungary|Hungary', 27, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti513', '15914542', 'An improved version of the PRIDE (PRobaility of IDEntity) fold prediction algorithm has been developed, based on more solid statistical basis, fast search capabilities and efficient input structure processing. The new algorithm is effective in identifying protein structures at the H level of the CATH hierarchy.'),
(72145, 2005, 'bio', 'Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map)', 'Park, Daeui|Lee, Semin|Bolser, Dan|Schroeder, Michael|Lappe, Michael|Oh, Donghoon|Bhak, Jong', 'Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Object Interaction Technologies Inc., CMS Building 705, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.', '-|-|-|-|-|-|-', 0, 'Algorithms|Chromosome Mapping|Protein Interaction Mapping|Proteome|Sequence Alignment|Sequence Analysis, Protein|Signal Transduction|Software|Structure-Activity Relationship', 'Comparative Study', '10.1093/bioinformatics/bti512', '15914543', 'Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes.'),
(72146, 2005, 'bio', 'Resmap: automated representation of macromolecular interfaces as two-dimensional networks', 'Swint-Kruse, Liskin|Brown, Curties S', 'Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, KS 66160, USA. lswint-kruse@kumc.edu|Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, KS 66160, USA. lswint-kruse@kumc.edu', 'United States|United States', 0, 'Artificial Intelligence|Binding Sites|Computer Simulation|DNA|Database Management Systems|Databases, Protein|Macromolecular Substances|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bti511', '15914544', 'To aid detailed comparison of a large number of macromolecular structures, Resmap imports Protein Data Bank files and represents subunit/domain interfaces as two-dimensional networks.'),
(72147, 2005, 'bio', 'Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels)', 'Rodin, Andrei S|Boerwinkle, Eric', 'Human Genetics Center, School of Public Health, University of Texas Health Science Center Houston, TX 77030, USA. arodin@uth.tmc.edu|Human Genetics Center, School of Public Health, University of Texas Health Science Center Houston, TX 77030, USA. arodin@uth.tmc.edu', 'United States|United States', 23, 'Apolipoproteins E|Bayes Theorem|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Epidemiologic Methods|Genotype|Humans|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1093/bioinformatics/bti505', '15914545', 'The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this paper, we introduce the method of Belief (Bayesian) networks to the domain of genotype-to-phenotype analyses and provide an example application.'),
(72148, 2005, 'bio', 'Dual multiple change-point model leads to more accurate recombination detection', 'Minin, Vladimir N|Dorman, Karin S|Fang, Fang|Suchard, Marc A', 'Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, CA 90095-1766, USA.|Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, CA 90095-1766, USA.|Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, CA 90095-1766, USA.|Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, CA 90095-1766, USA.', 'United States|United States|United States|United States', 105, 'Algorithms|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|DNA, Mitochondrial|Evolution, Molecular|Models, Genetic|Models, Statistical|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti459', '15914546', 'We introduce a dual multiple change-point (MCP) model for recombination detection among aligned nucleotide sequences. The dual MCP model is an extension of the model introduced previously by Suchard and co-workers. In the original single MCP model, one change-point process is used to model spatial phylogenetic variation. Here, we show that using two change-point processes, one for spatial variation of tree topologies and the other for spatial variation of substitution process parameters, increases recombination detection accuracy. Statistical analysis is done in a Bayesian framework using reversible jump Markov chain Monte Carlo sampling to approximate the joint posterior distribution of all model parameters.'),
(72149, 2005, 'bio', 'A latent variable model for chemogenomic profiling', 'Flaherty, Patrick|Giaever, Guri|Kumm, Jochen|Jordan, Michael I|Arkin, Adam P', 'Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu|Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu|Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu|Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu|Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. flaherty@berkeley.edu', 'United States|United States|United States|United States|United States', 49, 'Computer Simulation|Gene Deletion|Gene Expression Profiling|Gene Expression Regulation, Fungal|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pharmaceutical Preparations|Pharmacogenetics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Evaluation Studies', '10.1093/bioinformatics/bti515', '15919724', 'In haploinsufficiency profiling data, pleiotropic genes are often misclassified by clustering algorithms that impose the constraint that a gene or experiment belong to only one cluster. We have developed a general probabilistic model that clusters genes and experiments without requiring that a given gene or drug only appear in one cluster. The model also incorporates the functional annotation of known genes to guide the clustering procedure.'),
(72150, 2005, 'bio', 'Functional annotation from predicted protein interaction networks', 'McDermott, Jason|Bumgarner, Roger|Samudrala, Ram', 'Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.|Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.', 'United States|United States|United States', 0, 'Chromosome Mapping|Computer Simulation|Gene Expression Regulation|Models, Biological|Protein Interaction Mapping|Proteome|Sequence Analysis, Protein|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/bti514', '15919725', 'Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes.'),
(72151, 2005, 'bio', 'Using reconfigurable hardware to accelerate multiple sequence alignment with ClustalW', 'Oliver, Tim|Schmidt, Bertil|Nathan, Darran|Clemens, Ralf|Maskell, Douglas', 'School of Computer Engineering, Nanyang Technological University, Singapore. tim.oliver@pmail.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. tim.oliver@pmail.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. tim.oliver@pmail.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. tim.oliver@pmail.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. tim.oliver@pmail.ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 83, 'Computers|Databases, Protein|Equipment Design|Equipment Failure Analysis|Sequence Alignment|Sequence Analysis, Protein|Signal Processing, Computer-Assisted|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti508', '15919726', 'Aligning hundreds of sequences using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. We present a new approach to compute multiple sequence alignments in far shorter time using reconfigurable hardware. This results in an implementation of ClustalW with significant runtime savings on a standard off-the-shelf FPGA.'),
(72152, 2005, 'bio', 'SECISDesign: a server to design SECIS-elements within the coding sequence', 'Busch, Anke|Will, Sebastian|Backofen, Rolf', 'Friedrich-Schiller-University Jena, Institute of Computer Science, Ernst-Abbe-Platz 2, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, Institute of Computer Science, Ernst-Abbe-Platz 2, 07743 Jena, Germany.|Friedrich-Schiller-University Jena, Institute of Computer Science, Ernst-Abbe-Platz 2, 07743 Jena, Germany.', 'Germany|Germany|Germany', 6, 'Computer-Aided Design|DNA Transposable Elements|Escherichia coli|Humans|Internet|Protein Engineering|Proteins|Recombinant Proteins|Selenoproteins|Sequence Analysis, Protein|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti507', '15919727', 'SECISDesign is a server for the design of SECIS-elements and arbitrary RNA-elements within the coding sequence of an mRNA. The element has to satisfy both structure and sequence constraints. At the same time, a certain amino acid similarity to the original protein has to be kept. The designed sequence can be used for recombinant expression of selenoproteins in Escherichia coli.'),
(72153, 2005, 'bio', 'MeSHer: identifying biological concepts in microarray assays based on PubMed references and MeSH terms', 'Djebbari, Amira|Karamycheva, Svetlana|Howe, Eleanor|Quackenbush, John', 'The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA.|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA.|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA.|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA.', 'United States|United States|United States|United States', 18, 'Artificial Intelligence|Biology|Database Management Systems|Gene Expression Profiling|Information Storage and Retrieval|Medical Subject Headings|Models, Genetic|Models, Statistical|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|PubMed|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti503', '15919728', 'MeSHer uses a simple statistical approach to identify biological concepts in the form of Medical Subject Headings (MeSH terms) obtained from the PubMed database that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying DNA microarray platform. As a demonstration, we apply this approach to gene lists acquired from a published study of the effects of angiotensin II (Ang II) treatment on cardiac gene expression and demonstrate that this approach can aid in the interpretation of the resulting significant gene set.'),
(72154, 2005, 'bio', 'COREX/BEST server: a web browser-based program that calculates regional stability variations within protein structures', 'Vertrees, Jason|Barritt, Phillip|Whitten, Steve|Hilser, Vincent J', 'Department of Human Biological Chemistry & Genetics, Sealy Center for Structural Biology, University of Texas Medical Branch, Galveston, TX 77555, USA.|Department of Human Biological Chemistry & Genetics, Sealy Center for Structural Biology, University of Texas Medical Branch, Galveston, TX 77555, USA.|Department of Human Biological Chemistry & Genetics, Sealy Center for Structural Biology, University of Texas Medical Branch, Galveston, TX 77555, USA.|Department of Human Biological Chemistry & Genetics, Sealy Center for Structural Biology, University of Texas Medical Branch, Galveston, TX 77555, USA.', 'United States|United States|United States|United States', 37, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti520', '15923205', 'Utilizing the user-supplied coordinates of a protein structure, the COREX/BEST Server generates a structural thermodynamic ensemble. This conformational ensemble can then be used to calculate the regional variations in stability of a protein structure, and the stabilities are presented in units of energy (kcal/mol). The regional stabilities, which are calculated at the resolution of individual residues, can be mapped onto the protein structure for visual representation and downloaded from the site in the form of tab delimited text. The site provides an easy to follow summary of the theoretical and algorithmic approaches and provides links to references for more detailed descriptions.'),
(72155, 2005, 'bio', 'Quantifying optimal accuracy of local primary sequence bioinformatics methods', 'Aalberts, Daniel P|Daub, Eric G|Dill, Jesse W', 'Department of Physics, Williams College, Williamstown, MA 01267, USA. aalberts@williams.edu|Department of Physics, Williams College, Williamstown, MA 01267, USA. aalberts@williams.edu|Department of Physics, Williams College, Williamstown, MA 01267, USA. aalberts@williams.edu', 'United States|United States|United States', 2, 'Algorithms|Base Sequence|Benchmarking|Computational Biology|Computer Simulation|Models, Genetic|Models, Statistical|Molecular Sequence Data|RNA, Spliced Leader|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti521', '15923206', 'Traditional bioinformatics methods scan primary sequences for local patterns. It is important to assess how accurate local primary sequence methods can be.'),
(72156, 2005, 'bio', 'HYPROSP II--a knowledge-based hybrid method for protein secondary structure prediction based on local prediction confidence', 'Lin, Hsin-Nan|Chang, Jia-Ming|Wu, Kuen-Pin|Sung, Ting-Yi|Hsu, Wen-Lian', '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.', '-|-|-|-|-', 43, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Protein|Models, Chemical|Models, Molecular|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti524', '15932901', 'In our previous approach, we proposed a hybrid method for protein secondary structure prediction called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate, to determine whether PROSP or PSIPRED is to be used for the prediction of a target protein. HYPROSP made slight improvement of Q(3) over PSIPRED because PROSP predicted well for proteins with match rate >80%. As the portion of proteins with match rate >80% is quite small and as the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II that is based on a new quantitative measure called local match rate.'),
(72157, 2005, 'bio', 'PROVAT: a tool for Voronoi tessellation analysis of protein structures and complexes', 'Gore, Swanand P|Burke, David F|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK. swanand@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK. swanand@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK. swanand@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti523', '15932902', 'Voronoi tessellation has proved to be a useful tool in protein structure analysis. We have developed PROVAT, a versatile public domain software that enables computation and visualization of Voronoi tessellations of proteins and protein complexes. It is a set of Python scripts that integrate freely available specialized software (Qhull, Pymol etc.) into a pipeline. The calculation component of the tool computes Voronoi tessellation of a given protein system in a way described by a user-supplied XML recipe and stores resulting neighbourhood information as text files with various styles. The Python pickle file generated in the process is used by the visualization component, a Pymol plug-in, that offers a GUI to explore the tessellation visually.'),
(72158, 2005, 'bio', 'Practical FDR-based sample size calculations in microarray experiments', 'Hu, Jianhua|Zou, Fei|Wright, Fred A', 'Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, TX 77030-4009, USA. jhu@mdanderson.org|Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, TX 77030-4009, USA. jhu@mdanderson.org|Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, TX 77030-4009, USA. jhu@mdanderson.org', 'United States|United States|United States', 46, 'Algorithms|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti519', '15932903', 'Owing to the experimental cost and difficulty in obtaining biological materials, it is essential to consider appropriate sample sizes in microarray studies. With the growing use of the False Discovery Rate (FDR) in microarray analysis, an FDR-based sample size calculation is essential.'),
(72159, 2005, 'bio', 'Statistics of protein library construction', 'Firth, Andrew E|Patrick, Wayne M', 'Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand. aef@sanger.otago.ac.nz|Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand. aef@sanger.otago.ac.nz', 'New Zealand|New Zealand', 58, 'Algorithms|DNA Mutational Analysis|Evolution, Molecular|Models, Chemical|Models, Genetic|Models, Statistical|Peptide Library|Polymerase Chain Reaction|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti516', '15932904', 'We have investigated the statistics associated with constructing and sampling large protein-encoding libraries. Using fairly simple statistics we have written algorithms for estimating the diversity in libraries generated by the most commonly used protocols, including error-prone PCR, DNA shuffling, StEP PCR, oligonucleotide-directed randomization, MAX randomization, synthetic shuffling, DHR, ADO and SISDC.'),
(72160, 2005, 'bio', 'Biomarker discovery in microarray gene expression data with Gaussian processes', 'Chu, Wei|Ghahramani, Zoubin|Falciani, Francesco|Wild, David L', 'Gatsby Computational Neuroscience Unit, University College London, UK.|Gatsby Computational Neuroscience Unit, University College London, UK.|Gatsby Computational Neuroscience Unit, University College London, UK.|Gatsby Computational Neuroscience Unit, University College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 82, 'Algorithms|Biomarkers|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Neoplasm Proteins|Neoplasms|Normal Distribution|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti526', '15937031', 'In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework.'),
(72161, 2005, 'bio', 'MineBlast: a literature presentation service supporting protein annotation by data mining of BLAST results', 'Dieterich, Guido|Kärst, Uwe|Wehland, Jürgen|Jänsch, Lothar', 'Division of Cell Biology, German Research Centre for Biotechnology (GBF) Mascheroder Weg 1, D-38124 Braunschweig, Germany. gdi@gbf.de|Division of Cell Biology, German Research Centre for Biotechnology (GBF) Mascheroder Weg 1, D-38124 Braunschweig, Germany. gdi@gbf.de|Division of Cell Biology, German Research Centre for Biotechnology (GBF) Mascheroder Weg 1, D-38124 Braunschweig, Germany. gdi@gbf.de|Division of Cell Biology, German Research Centre for Biotechnology (GBF) Mascheroder Weg 1, D-38124 Braunschweig, Germany. gdi@gbf.de', 'Germany|Germany|Germany|Germany', 10, 'Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Natural Language Processing|Proteins|PubMed|Sequence Analysis, Protein|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti528', '15941742', 'MineBlast is a web service for literature search and presentation based on data-mining results received from UniProt. Users can submit a simple list of protein sequences via a web-based interface. MineBlast performs a BLASTP search in UniProt to identify names and synonyms based on homologous proteins and subsequently queries PubMed, using combined search terms inorder to find and present relevant literature.'),
(72162, 2005, 'bio', 'A new progressive-iterative algorithm for multiple structure alignment', 'Lupyan, Dmitry|Leo-Macias, Alejandra|Ortiz, Angel R', 'Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.', 'United States|United States|United States', 101, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Conserved Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti527', '15941743', 'Multiple structure alignments are becoming important tools in many aspects of structural bioinformatics. The current explosion in the number of available protein structures demands multiple structural alignment algorithms with an adequate balance of accuracy and speed, for large scale applications in structural genomics, protein structure prediction and protein classification.'),
(72163, 2005, 'bio', 'Answer to the comments of K Dobbin, J Shih and R Simon on the paper Evaluation of the gene-specific dye-bias in cDNA microarray experiments', 'Martin-Magniette, M-L|Aubert, J|Cabannes, E|Daudin, J-J', 'Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Physiology and Biophysics, Mount Sinai School of Medicine, New York, NY 10029, USA.', 'United States|United States|United States|United States', 1, 'Algorithms|Computer Simulation|Fluorescent Dyes|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Comment', '10.1093/bioinformatics/bti479', '15941744', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72164, 2005, 'bio', 'RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins', 'Yang, Zheng Rong|Thomson, Rebecca|McNeil, Philip|Esnouf, Robert M', 'School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK.|School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK.|School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK.|School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 512, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti534', '15947016', 'Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability.'),
(72165, 2005, 'bio', 'Sarment: Python modules for HMM analysis and partitioning of sequences', 'Guéguen, Laurent', 'Laboratoire Biométrie et Biologie Evolutive, (UMR 5558), (NRS), Univ Lyon 1, 43 bd 11 Nov, 69622 Villeurbanne cedex, France. gueguen@biomserv.univ-lyon1.fr', 'France', 0, 'Algorithms|Base Composition|Base Sequence|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti533', '15947017', 'Sarment is a package of Python modules for easy building and manipulation of sequence segmentations. It provides efficient implementation of usual algorithms for hidden Markov Model computation, as well as for maximal predictive partitioning. Owing to its very large variety of criteria for computing segmentations, Sarment can handle many kinds of models. Because of object-oriented programming, the results of the segmentation are very easy tomanipulate.'),
(72166, 2005, 'bio', 'Refined phylogenetic profiles method for predicting protein-protein interactions', 'Sun, Jingchun|Xu, Jinlin|Liu, Zhen|Liu, Qi|Zhao, Aimin|Shi, Tieliu|Li, Yixue', 'School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.|School of Life Sciences & Technology, Shanghai Jiaotong University, Shanghai 200240, China.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Evolution, Molecular|Phylogeny|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Signal Transduction|Software|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti532', '15947018', 'The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method.'),
(72167, 2005, 'bio', 'A quantitative determination of multi-protein interactions by the analysis of confocal images using a pixel-by-pixel assessment algorithm', 'Goucher, D R|Wincovitch, S M|Garfield, S H|Carbone, K M|Malik, T H', 'DVP/OVRR, Center for Biologics Evaluation and Research, US Food and Drug Administration, Bethesda, MD 20892, USA.|DVP/OVRR, Center for Biologics Evaluation and Research, US Food and Drug Administration, Bethesda, MD 20892, USA.|DVP/OVRR, Center for Biologics Evaluation and Research, US Food and Drug Administration, Bethesda, MD 20892, USA.|DVP/OVRR, Center for Biologics Evaluation and Research, US Food and Drug Administration, Bethesda, MD 20892, USA.|DVP/OVRR, Center for Biologics Evaluation and Research, US Food and Drug Administration, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States|United States', 10, 'Algorithms|Animals|Chemokine CCL5|Hippocampus|Image Interpretation, Computer-Assisted|Measles|Mice|Microscopy, Confocal|Neurons|Protein Interaction Mapping|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti531', '15947019', 'Recent advances in confocal microscopy have allowed scientists to assess the expression, and to some extent, the interaction/colocalization of multiple molecules within cells and tissues. In some instances, accurately quantifying the colocalization of two or more proteins may be critical. This can require the acquisition of multiple Z plane images (Z stacks) throughout a specimen and, as such, we report here the successful development of a freeware, open-source image analysis tool, IMAJIN_COLOC, developed in PERL (v. 5.8, build 806), using the PERLMagick libraries (ImageMagick). Using a pixel-by-pixel analysis algorithm, IMAJIN_COLOC can analyze images for antigen expression (any number of colors) and can measure all possible combinations of colocalization for up to three colors by analyzing a Z stack gallery acquired for each sample. The simultaneous (i.e. in a single pass) analysis of three-color colocalization, and batch analysis capabilities are distinctive features of this program.'),
(72168, 2005, 'bio', 'Thermodynamic properties of DNA sequences: characteristic values for the human genome', 'Koehler, Ryan T|Peyret, Nicolas', 'Applied Biosystems 850 Lincoln Centre Drive, Foster City, CA 94404, USA. koehlert@appliedbiosystems.com|Applied Biosystems 850 Lincoln Centre Drive, Foster City, CA 94404, USA. koehlert@appliedbiosystems.com', 'United States|United States', 0, 'Base Sequence|Chromosome Mapping|DNA|DNA Mutational Analysis|Genome, Human|Humans|In Situ Hybridization|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Thermodynamics|Transition Temperature', 'Evaluation Studies', '10.1093/bioinformatics/bti530', '15947020', 'Central to many molecular biology techniques as ubiquitous as PCR and Southern blotting is the design of oligonucleotide (oligo) probes and/or primers possessing specific thermodynamic properties. Here, we use validated theoretical methods to generate distributions of predicted thermodynamic properties for DNA oligos of various lengths. These distributions facilitate immediate appreciation of typical thermodynamic values for oligos of various lengths.'),
(72169, 2005, 'bio', 'PEDSTATS: descriptive statistics, graphics and quality assessment for gene mapping data', 'Wigginton, Janis E|Abecasis, Gonçalo R', 'Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48103, USA. wiggie@umich.edu|Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48103, USA. wiggie@umich.edu', 'United States|United States', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Data Interpretation, Statistical|Models, Genetic|Models, Statistical|Quality Control|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti529', '15947021', 'We describe a tool that produces summary statistics and basic quality assessments for gene-mapping data, accommodating either pedigree or case-control datasets. Our tool can also produce graphic output in the PDF format.'),
(72170, 2005, 'bio', 'Efficient sorting of genomic permutations by translocation, inversion and block interchange', 'Yancopoulos, Sophia|Attie, Oliver|Friedberg, Richard', 'The Feinstein Institute for Medical Research North Shore-LIJ Health System, Manhasset, NY 11030, USA. syancopo@nshs.edu|The Feinstein Institute for Medical Research North Shore-LIJ Health System, Manhasset, NY 11030, USA. syancopo@nshs.edu|The Feinstein Institute for Medical Research North Shore-LIJ Health System, Manhasset, NY 11030, USA. syancopo@nshs.edu', 'United States|United States|United States', 316, 'Algorithms|Chromosome Aberrations|Chromosome Inversion|Chromosome Mapping|DNA Mutational Analysis|Gene Rearrangement|Sequence Alignment|Sequence Analysis, DNA|Translocation, Genetic', 'Evaluation Studies', '10.1093/bioinformatics/bti535', '15951307', 'Finding genomic distance based on gene order is a classic problem in genome rearrangements. Efficient exact algorithms for genomic distances based on inversions and/or translocations have been found but are complicated by special cases, rare in simulations and empirical data. We seek a universal operation underlying a more inclusive set of evolutionary operations and yielding a tractable genomic distance with simple mathematical form.'),
(72171, 2005, 'bio', 'IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content', 'Dosztányi, Zsuzsanna|Csizmok, Veronika|Tompa, Peter|Simon, István', 'Institute of Enzymology, BRC, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu|Institute of Enzymology, BRC, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu|Institute of Enzymology, BRC, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu|Institute of Enzymology, BRC, Hungarian Academy of Sciences, PO Box 7, H-1518 Budapest, Hungary. zsuzsa@enzim.hu', 'Hungary|Hungary|Hungary|Hungary', 0, 'Algorithms|Computer Simulation|Energy Transfer|Internet|Models, Chemical|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti541', '15955779', 'Intrinsically unstructured/disordered proteins and domains (IUPs) lack a well-defined three-dimensional structure under native conditions. The IUPred server presents a novel algorithm for predicting such regions from amino acid sequences by estimating their total pairwise interresidue interaction energy, based on the assumption that IUP sequences do not fold due to their inability to form sufficient stabilizing interresidue interactions. Optional to the prediction are built-in parameter sets optimized for predicting short or long disordered regions and structured domains.'),
(72172, 2005, 'bio', 'Improving sequence-based fold recognition by using 3D model quality assessment', 'Pettitt, Chris S|McGuffin, Liam J|Jones, David T', 'Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.|Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.|Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom|United Kingdom', 64, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Conformation|Protein Folding|Proteins|Quality Control|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/bti540', '15955780', 'The ability of a simple method (MODCHECK) to determine the sequence-structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAPs) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detect native-like models.'),
(72173, 2005, 'bio', 'Statistical analysis of antigen receptor spectratype data', 'Kepler, Thomas B|He, Min|Tomfohr, John K|Devlin, Blythe H|Sarzotti, Marcella|Markert, M Louise', 'Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA. kepler@duke.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Genetic Variation|Immunoassay|Models, Genetic|Models, Immunological|Models, Statistical|Receptors, Antigen, T-Cell', 'Evaluation Studies', '10.1093/bioinformatics/bti539', '15955781', 'The effectiveness of vertebrate adaptive immunity depends crucially on the establishment and maintenance of extreme diversity in the antigen receptor repertoire. Spectratype analysis is a method used in clinical and basic immunological settings in which antigen receptor length diversity is assessed as a surrogate for functional diversity. The purpose of this paper is to describe the systematic derivation and application of statistical methods for the analysis of spectratype data.'),
(72174, 2005, 'bio', 'A semantic analysis of the annotations of the human genome', 'Khatri, Purvesh|Done, Bogdan|Rao, Archana|Done, Arina|Draghici, Sorin', 'Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA.', 'United States|United States|United States|United States|United States', 53, 'Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Genome, Human|Humans|Information Storage and Retrieval|Natural Language Processing|Proteins|Semantics|Structure-Activity Relationship|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/bti538', '15955782', 'The correct interpretation of any biological experiment depends in an essential way on the accuracy and consistency of the existing annotation databases. Such databases are ubiquitous and used by all life scientists in most experiments. However, it is well known that such databases are incomplete and many annotations may also be incorrect. In this paper we describe a technique that can be used to analyze the semantic content of such annotation databases. Our approach is able to extract implicit semantic relationships between genes and functions. This ability allows us to discover novel functions for known genes. This approach is able to identify missing and inaccurate annotations in existing annotation databases, and thus help improve their accuracy. We used our technique to analyze the current annotations of the human genome. From this body of annotations, we were able to predict 212 additional gene-function assignments. A subsequent literature search found that 138 of these gene-functions assignments are supported by existing peer-reviewed papers. An additional 23 assignments have been confirmed in the meantime by the addition of the respective annotations in later releases of the Gene Ontology database. Overall, the 161 confirmed assignments represent 75.95% of the proposed gene-function assignments. Only one of our predictions (0.4%) was contradicted by the existing literature. We could not find any relevant articles for 50 of our predictions (23.58%). The method is independent of the organism and can be used to analyze and improve the quality of the data of any public or private annotation database.'),
(72175, 2005, 'bio', 'FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded', 'Prilusky, Jaime|Felder, Clifford E|Zeev-Ben-Mordehai, Tzviya|Rydberg, Edwin H|Man, Orna|Beckmann, Jacques S|Silman, Israel|Sussman, Joel L', 'Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.|Biological Services, Weizmann Institute of Science, Rehovot 76100, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 315, 'Algorithms|Computer Graphics|Computer Simulation|Energy Transfer|Internet|Models, Chemical|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti537', '15955783', 'An easy-to-use, versatile and freely available graphic web server, FoldIndex is described: it predicts if a given protein sequence is intrinsically unfolded implementing the algorithm of Uversky and co-workers, which is based on the average residue hydrophobicity and net charge of the sequence. FoldIndex has an error rate comparable to that of more sophisticated fold prediction methods. Sliding windows permit identification of large regions within a protein that possess folding propensities different from those of the whole protein.'),
(72176, 2005, 'bio', 'HEGESMA: genome search meta-analysis and heterogeneity testing', 'Zintzaras, Elias|Ioannidis, John P A', 'Department of Biomathematics, University of Thessaly School of Medicine, Larissa 41222, Greece. zintza@med.uth.gr|Department of Biomathematics, University of Thessaly School of Medicine, Larissa 41222, Greece. zintza@med.uth.gr', 'Greece|Greece', 381, 'Chromosome Mapping|Computational Biology|Genetic Linkage|Genetic Markers|Genetic Predisposition to Disease|Genome|Genome, Human|Genotype|Humans|Linkage Disequilibrium|Lod Score|Meta-Analysis as Topic|Monte Carlo Method|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/bti536', '15955784', 'Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and weighted analysis, with the weights for each study as specified by the user. Furthermore, the program performs heterogeneity analyses restricted to the bins with similar average ranks.'),
(72177, 2005, 'bio', 'MagicMatch--cross-referencing sequence identifiers across databases', 'Smith, Mike|Kunin, Victor|Goldovsky, Leon|Enright, Anton J|Ouzounis, Christos A', 'Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK.|Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK.|Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK.|Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK.|Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti548', '15961438', 'At present, mapping of sequence identifiers across databases is a daunting, time-consuming and computationally expensive process, usually achieved by sequence similarity searches with strict threshold values.'),
(72178, 2005, 'bio', 'DNA-BAR: distinguisher selection for DNA barcoding', 'DasGupta, B|Konwar, K M|Mandoiu, I I|Shvartsman, A A', 'Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7053, USA.|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7053, USA.|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7053, USA.|Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607-7053, USA.', 'United States|United States|United States|United States', 41, 'Algorithms|Chromosome Mapping|DNA|In Situ Hybridization|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti547', '15961439', 'DNA-BAR is a software package for selecting DNA probes (henceforth referred to as distinguishers) that can be used in genomic-based identification of microorganisms. Given the genomic sequences of the microorganisms, DNA-BAR finds a near-minimum number of distinguishers yielding a distinct hybridization pattern for each microorganism. Selected distinguishers satisfy user specified bounds on length, melting temperature and GC content, as well as redundancy and cross-hybridization constraints.'),
(72179, 2005, 'bio', 'Querying and computing with BioCyc databases', 'Krummenacker, Markus|Paley, Suzanne|Mueller, Lukas|Yan, Thomas|Karp, Peter D', 'SRI International, Bioinformatics Research Group, EK207 Menlo Park, CA 94025, USA.|SRI International, Bioinformatics Research Group, EK207 Menlo Park, CA 94025, USA.|SRI International, Bioinformatics Research Group, EK207 Menlo Park, CA 94025, USA.|SRI International, Bioinformatics Research Group, EK207 Menlo Park, CA 94025, USA.|SRI International, Bioinformatics Research Group, EK207 Menlo Park, CA 94025, USA.', 'United States|United States|United States|United States|United States', 58, 'Database Management Systems|Databases, Factual|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti546', '15961440', 'We describe multiple methods for accessing and querying the complex and integrated cellular data in the BioCyc family of databases: access through multiple file formats, access through Application Program Interfaces (APIs) for LISP, Perl and Java, and SQL access through the BioWarehouse relational database.'),
(72180, 2005, 'bio', 'caBIONet--A NET wrapper to access and process genomic data stored at the National Cancer Institutes Center for Bioinformatics databases', 'Kraj, Piotr|McIndoe, Richard A', 'Institute of Molecular Medicine and Genetics, Medical College of Georgia, Augusta, GA 30912, USA.|Institute of Molecular Medicine and Genetics, Medical College of Georgia, Augusta, GA 30912, USA.', 'Georgia|Georgia', 5, 'Chromosome Mapping|Computational Biology|Database Management Systems|Databases, Genetic|Genomics|Information Storage and Retrieval|Internet|National Institutes of Health (U.S.)|Software|United States|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti545', '15961441', 'The National Cancer Institutes Center for Bioinformatics (NCICB) has developed a Java based data management and information system called caCORE. One component of this software suite is the object oriented API (caBIO) used to access the rich biological datasets collected at the NCI. This API can access the data using native Java classes, SOAP requests or HTTP calls. Non-Java based clients wanting to use this API have to use the SOAP or HTTP interfaces with the data being returned from the NCI servers as an XML data stream. Although the XML can be read and manipulated using DOM or SAX parsers, one loses the convenience and usability of an object oriented programming paradigm. caBIONet is a set of .NET wrapper classes (managers, genes, chromosomes, sequences, etc.) capable of serializing the XML data stream into local .NET objects. The software is able to search NCICB databases and provide local objects representing the data that can be manipulated and used by other .NET programs. The software was written in C# and compiled as a .NET DLL.'),
(72181, 2005, 'bio', 'Use of extreme patient samples for outcome prediction from gene expression data', 'Liu, Huiqing|Li, Jinyan|Wong, Limsoon', 'Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613. huiqing@i2r.a-star.edu.sg|Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613. huiqing@i2r.a-star.edu.sg|Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613. huiqing@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore', 29, 'Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Outcome Assessment (Health Care)|Prognosis|Risk Assessment|Risk Factors|Survival Analysis|Survival Rate', 'Evaluation Studies', '10.1093/bioinformatics/bti544', '15961442', 'Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients genotypic microarray data, predictions are made to estimate patients survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients.'),
(72182, 2005, 'bio', 'IRILmap: linkage map distance correction for intermated recombinant inbred lines/advanced recombinant inbred strains', 'Falque, M', 'INRA-UPS-CNRS-INA.PG, U.M.R. de Génétique Végétale 91190, Gif-sur-Yvette, France. falque@moulon.inra.fr', 'France', 16, 'Algorithms|Animals|Animals, Inbred Strains|Chromosome Mapping|Computer Graphics|Inbreeding|Linkage Disequilibrium|Recombination, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti543', '15961443', 'Intermated Recombinant Inbred Lines (IRILs) in plants, or Advanced Recombinant Inbred Strains in animals, are constructed by carrying out generations of intermating between F2 individuals before starting recurrent inbreeding generations by selfing or sib-mating. IRILs are powerful for high-resolution genetic mapping because they have undergone more recombination than usual Recombinant Inbred Lines (RILs). However, there is no mapping software able to generate actual centiMorgan distances from the segregation data obtained with IRILs. IRILmap software converts genetic distances computed with any linkage mapping program designed for RILs, so that IRIL-derived data can be used to get actual centiMorgan distances, directly comparable to F2, backcross or RIL-derived maps.'),
(72183, 2005, 'bio', 'The predictive power of the CluSTr database', 'Petryszak, Robert|Kretschmann, Ernst|Wieser, Daniela|Apweiler, Rolf', 'EMBL Outstation Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.|EMBL Outstation Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.|EMBL Outstation Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.|EMBL Outstation Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 33, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Databases, Factual|Databases, Genetic|Databases, Protein|Humans|Internet|Monte Carlo Method|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti542', '15961444', 'The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications.'),
(72184, 2005, 'bio', 'Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships', 'Espadaler, Jordi|Romero-Isart, Oriol|Jackson, Richard M|Oliva, Baldo', 'Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.|Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.|Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.|Grup de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona 08003, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 84, 'Algorithms|Amino Acid Sequence|Binding Sites|Conserved Sequence|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti522', '15961445', 'Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein-protein interfaces to predict putative protein interaction pairs.'),
(72185, 2005, 'bio', 'ANOSVA: a statistical method for detecting splice variation from expression data', 'Cline, Melissa S|Blume, John|Cawley, Simon|Clark, Tyson A|Hu, Jing-Shan|Lu, Gang|Salomonis, Nathan|Wang, Hui|Williams, Alan', 'Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr|Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA. cline@pasteur.fr', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 44, 'Alternative Splicing|Animals|Computational Biology|Databases, Protein|False Positive Reactions|Gene Expression Profiling|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bti1010', '15961447', 'Many or most mammalian genes undergo alternative splicing, generating a variety of transcripts from a single gene. New information on splice variation is becoming available through technology for measuring expression levels of several exons or splice junctions per gene. We have developed a statistical method, ANalysis Of Splice VAriation (ANOSVA) to detect alternative splicing from expression data. Since ANOSVA requires no transcript information, it can be applied when the level of annotation is poor. When validated against spiked clone data, it generated no false positives and few false negatives. We demonstrated ANOSVA with data from a prototype mouse alternative splicing array, run against normal adult tissues, yielding a set of genes with evidence of tissue-specific splice variation.'),
(72186, 2005, 'bio', 'A path planning approach for computing large-amplitude motions of flexible molecules', 'Cortés, J|Siméon, T|Ruiz de Angulo, V|Guieysse, D|Remaud-Siméon, M|Tran, V', 'LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.|LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.|LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.|LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.|LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.|LAAS-CNRS 7 av. du Colonel-Roche, 31077 Toulouse, France.', 'France|France|France|France|France|France', 100, 'Algorithms|Bacillus|Computational Biology|Computer Simulation|Databases, Protein|Ligands|Models, Molecular|Models, Theoretical|Molecular Conformation|Monte Carlo Method|Programming Languages|Protein Binding|Protein Conformation', 'Journal Article', '10.1093/bioinformatics/bti1017', '15961448', 'Motion is inherent in molecular interactions. Molecular flexibility must be taken into account in order to develop accurate computational techniques for predicting interactions. Energy-based methods currently used in molecular modeling (i.e. molecular dynamics, Monte Carlo algorithms) are, in practice, only able to compute local motions while accounting for molecular flexibility. However, large-amplitude motions often occur in biological processes. We investigate the application of geometric path planning algorithms to compute such large motions in flexible molecular models. Our purpose is to exploit the efficacy of a geometric conformational search as a filtering stage before subsequent energy refinements.'),
(72187, 2005, 'bio', 'Detecting coevolving amino acid sites using Bayesian mutational mapping', 'Dimmic, Matthew W|Hubisz, Melissa J|Bustamante, Carlos D|Nielsen, Rasmus', 'Department of Biological Statistics and Computational Biology, Cornell University Ithaca, NY 13101, USA. matt@dimmic.net|Department of Biological Statistics and Computational Biology, Cornell University Ithaca, NY 13101, USA. matt@dimmic.net|Department of Biological Statistics and Computational Biology, Cornell University Ithaca, NY 13101, USA. matt@dimmic.net|Department of Biological Statistics and Computational Biology, Cornell University Ithaca, NY 13101, USA. matt@dimmic.net', 'United States|United States|United States|United States', 48, 'Amino Acids|Bayes Theorem|Binding Sites|Chromosome Mapping|Computational Biology|DNA Mutational Analysis|Evolution, Molecular|Likelihood Functions|Markov Chains|Models, Statistical|Multigene Family|Mutation|Phosphoglycerate Kinase', 'Journal Article', '10.1093/bioinformatics/bti1032', '15961449', 'The evolution of protein sequences is constrained by complex interactions between amino acid residues. Because harmful substitutions may be compensated for by other substitutions at neighboring sites, residues can coevolve. We describe a Bayesian phylogenetic approach to the detection of coevolving residues in protein families. This method, Bayesian mutational mapping (BMM), assigns mutations to the branches of the evolutionary tree stochastically, and then test statistics are calculated to determine whether a coevolutionary signal exists in the mapping. Posterior predictive P-values provide an estimate of significance, and specificity is maintained by integrating over uncertainty in the estimation of the tree topology, branch lengths and substitution rates. A coevolutionary Markov model for codon substitution is also described, and this model is used as the basis of several test statistics.'),
(72188, 2005, 'bio', 'A procedure for assessing GO annotation consistency', 'Dolan, Mary E|Ni, Li|Camon, Evelyn|Blake, Judith A', 'Mouse Genome Informatics [MGI], The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA. mdolan@informatics.jax.org|Mouse Genome Informatics [MGI], The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA. mdolan@informatics.jax.org|Mouse Genome Informatics [MGI], The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA. mdolan@informatics.jax.org|Mouse Genome Informatics [MGI], The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA. mdolan@informatics.jax.org', 'United States|United States|United States|United States', 38, 'Animals|Computational Biology|Databases, Genetic|Databases, Protein|Genes|Genome|Genomics|Humans|Mice|Proteomics|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/bti1019', '15961450', 'The Gene Ontology (GO) is widely used to annotate molecular attributes of genes and gene products. Multiple groups undertaking functional annotations of genomes contribute their annotation sets to the GO database resource and these data are subsequently used in comparative functional analysis research. Although GO curators adhere to the same protocols and standards while assigning GO annotations, the specific procedure followed by each annotation group can vary. Since differences in application of annotation standards would dilute the effectiveness of comparative analysis, methods for assessing annotation consistency are essential. The development of methodologies that are broadly applicable for the assessment of GO annotation consistency is an important issue for the comparative genomics community.'),
(72189, 2005, 'bio', 'Multi-way clustering of microarray data using probabilistic sparse matrix factorization', 'Dueck, Delbert|Morris, Quaid D|Frey, Brendan J', 'Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4. delbert@psi.toronto.edu|Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4. delbert@psi.toronto.edu|Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4. delbert@psi.toronto.edu', 'Canada|Canada|Canada', 41, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Genome|Humans|Likelihood Functions|Models, Statistical|Oligonucleotide Array Sequence Analysis|Probability|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/bti1041', '15961451', 'We address the problem of multi-way clustering of microarray data using a generative model. Our algorithm, probabilistic sparse matrix factorization (PSMF), is a probabilistic extension of a previous hard-decision algorithm for this problem. PSMF allows for varying levels of sensor noise in the data, uncertainty in the hidden prototypes used to explain the data and uncertainty as to the prototypes selected to explain each data vector.'),
(72190, 2005, 'bio', 'PILER: identification and classification of genomic repeats', 'Edgar, Robert C|Myers, Eugene W', 'bob@drive5.com|bob@drive5.com', '-|-', 266, 'Algorithms|Animals|Arabidopsis|Computational Biology|Drosophila melanogaster|Gene Library|Genome|Genome, Human|Genomics|Humans|Microsatellite Repeats|Models, Genetic|Software|Terminal Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/bti1003', '15961452', 'Repeated elements such as satellites and transposons are ubiquitous in eukaryotic genomes. De novo computational identification and classification of such elements is a challenging problem. Therefore, repeat annotation of sequenced genomes has historically largely relied on sequence similarity to hand-curated libraries of known repeat families. We present a new approach to de novo repeat annotation that exploits characteristic patterns of local alignments induced by certain classes of repeats. We describe PILER, a package of efficient search algorithms for identifying such patterns. Novel repeats found using PILER are reported for Homo sapiens, Arabidopsis thalania and Drosophila melanogaster.'),
(72191, 2005, 'bio', 'Clustering short time series gene expression data', 'Ernst, Jason|Nau, Gerard J|Bar-Joseph, Ziv', 'Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. jernst@cs.cmu.edu|Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. jernst@cs.cmu.edu|Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. jernst@cs.cmu.edu', 'United States|United States|United States', 0, 'Algorithms|Cell Line, Tumor|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Helicobacter pylori|Humans|Immune System|Internet|Models, Theoretical|Neoplasms|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti1022', '15961453', 'Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clustering algorithms are unable to distinguish between real and random patterns.'),
(72192, 2005, 'bio', 'CaSPredictor: a new computer-based tool for caspase substrate prediction', 'Garay-Malpartida, H M|Occhiucci, J M|Alves, J|Belizário, J E', 'Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo São Paulo, Brazil.|Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo São Paulo, Brazil.|Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo São Paulo, Brazil.|Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo São Paulo, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 49, 'Algorithms|Amino Acid Sequence|Caspases|Computational Biology|Computer Simulation|Cysteine Endopeptidases|False Positive Reactions|Humans|Internet|Models, Statistical|Molecular Sequence Data|Protein Binding|ROC Curve|Sequence Homology, Amino Acid|Software|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/bti1034', '15961454', 'In vitro studies have shown that the most remarkable catalytic features of caspases, a family of cysteineproteases, are their stringent specificity to Asp (D) in the S1 subsite and at least four amino acids to the left of scissile bound. However, there is little information about the substrate recognition patterns in vivo. The prediction and characterization of proteolytic cleavage sites in natural substrates could be useful for uncovering these structural relationships.'),
(72193, 2005, 'bio', 'Data integration and visualization system for enabling conceptual biology', 'Gopalacharyulu, Peddinti V|Lindfors, Erno|Bounsaythip, Catherine|Kivioja, Teemu|Yetukuri, Laxman|Hollmén, Jaakko|Oresic, Matej', 'VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.|VTT Biotechnology PO Box 1500, Espoo, FIN-02044 VTT, Finland.', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Genetic|Databases, Protein|Information Storage and Retrieval|Programming Languages|Saccharomyces cerevisiae|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1015', '15961455', 'Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining.'),
(72194, 2005, 'bio', 'Identification of nine human-specific frameshift mutations by comparative analysis of the human and the chimpanzee genome sequences', 'Hahn, Yoonsoo|Lee, Byungkook', 'Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health Bethesda, MD 20892, USA.|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health Bethesda, MD 20892, USA.', 'United States|United States', 0, 'Amino Acid Sequence|Animals|Base Sequence|Computational Biology|Exons|Frameshift Mutation|Gene Deletion|Genome|Genome, Human|Genomics|Humans|Molecular Sequence Data|Mutation|Pan troglodytes|Protein Structure, Tertiary|RNA, Messenger|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/bti1000', '15961456', 'The recent release of the draft sequence of the chimpanzee genome is an invaluable resource for finding genome-wide genetic differences that might explain phenotypic differences between humans and chimpanzees.'),
(72195, 2005, 'bio', 'Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli', 'Batt, Grégory|Ropers, Delphine|de Jong, Hidde|Geiselmann, Johannes|Mateescu, Radu|Page, Michel|Schneider, Dominique', 'INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.|INRIA Rhône-Alpes Montbonnot, France.', 'France|France|France|France|France|France|France', 0, 'Bacterial Physiological Phenomena|Computational Biology|Computer Simulation|Escherichia coli|Genes, Bacterial|Models, Biological|Models, Genetic|Reproducibility of Results|Software|Systems Biology|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti1048', '15961457', 'The modeling and simulation of genetic regulatory networks have created the need for tools for model validation. The main challenges of model validation are the achievement of a match between the precision of model predictions and experimental data, as well as the efficient and reliable comparison of the predictions and observations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72196, 2005, 'bio', 'Tag SNP selection in genotype data for maximizing SNP prediction accuracy', 'Halperin, Eran|Kimmel, Gad|Shamir, Ron', 'International Computer Science Institute Berkeley, CA 94704, USA.|International Computer Science Institute Berkeley, CA 94704, USA.|International Computer Science Institute Berkeley, CA 94704, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Simulation|DNA|Genomics|Genotype|Haplotypes|Humans|Polymorphism, Single Nucleotide|Programming Languages|Reproducibility of Results|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bti1021', '15961458', 'The search for genetic regions associated with complex diseases, such as cancer or Alzheimers disease, is an important challenge that may lead to better diagnosis and treatment. The existence of millions of DNA variations, primarily single nucleotide polymorphisms (SNPs), may allow the fine dissection of such associations. However, studies seeking disease association are limited by the cost of genotyping SNPs. Therefore, it is essential to find a small subset of informative SNPs (tag SNPs) that may be used as good representatives of the rest of the SNPs.'),
(72197, 2005, 'bio', 'Enhanced position weight matrices using mixture models', 'Hannenhalli, Sridhar|Wang, Li-San', 'Department of Genetics, University of Pennsylvania Philadelphia, PA 19104, USA. sridharh@pcbi.upenn.edu|Department of Genetics, University of Pennsylvania Philadelphia, PA 19104, USA. sridharh@pcbi.upenn.edu', 'United States|United States', 34, 'Algorithms|Animals|Binding Sites|Computational Biology|Conserved Sequence|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Humans|Mice|Mutation|Promoter Regions, Genetic|Protein Binding|Protein Structure, Tertiary|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti1001', '15961459', 'Positional weight matrix (PWM) is derived from a set of experimentally determined binding sites. Here we explore whether there exist subclasses of binding sites and if the mixture of these subclass-PWMs can improve the binding site prediction. Intuitively, the subclasses correspond to either distinct binding preference of the same transcription factor in different contexts or distinct subtypes of the transcription factor.'),
(72198, 2005, 'bio', 'Mining coherent dense subgraphs across massive biological networks for functional discovery', 'Hu, Haiyan|Yan, Xifeng|Huang, Yu|Han, Jiawei|Zhou, Xianghong Jasmine', 'Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA.|Program in Molecular and Computational Biology, University of Southern California Los Angeles, CA 90089, USA.', 'United States|United States|United States|United States|United States', 55, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Fungal Proteins|Genes, Fungal|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti1049', '15961460', 'The rapid accumulation of biological network data translates into an urgent need for computational methods for graph pattern mining. One important problem is to identify recurrent patterns across multiple networks to discover biological modules. However, existing algorithms for frequent pattern mining become very costly in time and space as the pattern sizes and network numbers increase. Currently, no efficient algorithm is available for mining recurrent patterns across large collections of genome-wide networks.'),
(72199, 2005, 'bio', 'GenXHC: a probabilistic generative model for cross-hybridization compensation in high-density genome-wide microarray data', 'Huang, Jim C|Morris, Quaid D|Hughes, Timothy R|Frey, Brendan J', 'Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada M5S 3G4. jim@psi.toronto.edu|Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada M5S 3G4. jim@psi.toronto.edu|Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada M5S 3G4. jim@psi.toronto.edu|Probabilistic and Statistical Inference Group, Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada M5S 3G4. jim@psi.toronto.edu', 'Canada|Canada|Canada|Canada', 0, 'Animals|Computational Biology|DNA, Complementary|Exons|Genome|Hybridization, Genetic|Mice|Models, Statistical|Nucleic Acid Hybridization|Nucleotides|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Probability|RNA, Messenger|Software', 'Journal Article', '10.1093/bioinformatics/bti1045', '15961461', 'Microarray designs containing millions to hundreds of millions of probes that tile entire genomes are currently being released. Within the next 2 months, our group will release a microarray data set containing over 12,000,000 microarray measurements taken from 37 mouse tissues. A problem that will become increasingly significant in the upcoming era of genome-wide exon-tiling microarray experiments is the removal of cross-hybridization noise. We present a probabilistic generative model for cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation, GenXHC, that reduces cross-hybridization noise by taking into account multiple sources for each mRNA expression level measurement, as well as prior knowledge of hybridization similarities between the nucleotide sequences of microarray probes and their target cDNAs.'),
(72200, 2005, 'bio', 'Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem', 'Jönsson, Henrik|Heisler, Marcus|Reddy, G Venugopala|Agrawal, Vikas|Gor, Victoria|Shapiro, Bruce E|Mjolsness, Eric|Meyerowitz, Elliot M', 'Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.|Department of Theoretical Physics, Complex Systems Division, Lund University Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Gene Expression Regulation, Plant|Genes, Plant|Homeodomain Proteins|Meristem|Microscopy, Confocal|Plant Proteins|Protein Structure, Tertiary|Protein-Serine-Threonine Kinases|Receptor Protein-Tyrosine Kinases|Software|Up-Regulation', 'Journal Article', '10.1093/bioinformatics/bti1036', '15961462', 'The above-ground tissues of higher plants are generated from a small region of cells situated at the plant apex called the shoot apical meristem. An important genetic control circuit modulating the size of the Arabidopsis thaliana meristem is a feed-back network between the CLAVATA3 and WUSCHEL genes. Although the expression patterns for these genes do not overlap, WUSCHEL activity is both necessary and sufficient (when expressed ectopically) for the induction of CLAVATA3 expression. However, upregulation of CLAVATA3 in conjunction with the receptor kinase CLAVATA1 results in the downregulation of WUSCHEL. Despite much work, experimental data for this network are incomplete and additional hypotheses are needed to explain the spatial locations and dynamics of these expression domains. Predictive mathematical models describing the system should provide a useful tool for investigating and discriminating among possible hypotheses, by determining which hypotheses best explain observed gene expression dynamics.'),
(72201, 2005, 'bio', 'Predicting protein-protein interaction by searching evolutionary tree automorphism space', 'Jothi, Raja|Kann, Maricel G|Przytycka, Teresa M', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.', 'United States|United States|United States', 0, 'Algorithms|Bacterial Proteins|Computational Biology|Databases, Protein|Evolution, Molecular|Models, Statistical|Phylogeny|Protein Binding|Protein Interaction Mapping|Proteomics|Sequence Alignment|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti1009', '15961463', 'Uncovering the protein-protein interaction network is a fundamental step in the quest to understand the molecular machinery of a cell. This motivates the search for efficient computational methods for predicting such interactions. Among the available predictors are those that are based on the co-evolution hypothesis evolutionary trees of protein families (that are known to interact) are expected to have similar topologies. Many of these methods are limited by the fact that they can handle only a small number of protein sequences. Also, details on evolutionary tree topology are missing as they use similarity matrices in lieu of the trees.'),
(72202, 2005, 'bio', 'An HMM posterior decoder for sequence feature prediction that includes homology information', 'Käll, Lukas|Krogh, Anders|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet SE-17 177 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet SE-17 177 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet SE-17 177 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 216, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Internet|Markov Chains|Models, Statistical|Models, Theoretical|Probability|Protein Sorting Signals|Software', 'Journal Article', '10.1093/bioinformatics/bti1014', '15961464', 'When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs.'),
(72203, 2005, 'bio', 'Prediction of active sites for protein structures from computed chemical properties', 'Ko, Jaeju|Murga, Leonel F|Wei, Ying|Ondrechen, Mary Jo', 'Department of Chemistry, Indiana University of Pennsylvania 975 Oakland Avenue, Indiana, PA 15705 USA.|Department of Chemistry, Indiana University of Pennsylvania 975 Oakland Avenue, Indiana, PA 15705 USA.|Department of Chemistry, Indiana University of Pennsylvania 975 Oakland Avenue, Indiana, PA 15705 USA.|Department of Chemistry, Indiana University of Pennsylvania 975 Oakland Avenue, Indiana, PA 15705 USA.', 'India|India|India|India', 0, 'Bacterial Proteins|Binding Sites|Calibration|Catalysis|Humans|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Microscopy|Models, Statistical|Protein Conformation|Proteins|Proteomics|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bti1039', '15961465', 'Identification of functional information for a protein from its three-dimensional (3D) structure is a major challenge in genomics. The power of theoretical microscopic titration curves (THEMATICS), when coupled with a statistical analysis, provides a method for high-throughput screening for identification of catalytic sites and binding sites with high accuracy and precision. The method requires only the 3D structure of the query protein as input, but it performs as well as other methods that depend on sequence alignments and structural similarities.'),
(72204, 2005, 'bio', 'High-recall protein entity recognition using a dictionary', 'Kou, Zhenzhen|Cohen, William W|Murphy, Robert F', 'Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu|Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu|Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu', 'United States|United States|United States', 25, 'Algorithms|Computational Biology|Databases, Protein|Dictionaries as Topic|Entropy|Internet|Markov Chains|Models, Statistical|Pattern Recognition, Automated|Programming Languages|Proteomics|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti1006', '15961466', 'Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application.'),
(72205, 2005, 'bio', 'A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences', 'Li, Wei|Meyer, Clifford A|Liu, X Shirley', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health Boston, MA 02115, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health Boston, MA 02115, USA.', 'United States|United States|United States', 39, 'Amino Acid Motifs|Chromatin Immunoprecipitation|Computational Biology|CpG Islands|Genes, p53|Genome|Genome, Human|Humans|Markov Chains|Models, Statistical|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Promoter Regions, Genetic|Protein Binding|Transcription Factors|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/bti1046', '15961467', 'Transcription factors (TFs) regulate gene expression by recognizing and binding to specific regulatory regions on the genome, which in higher eukaryotes can occur far away from the regulated genes. Recently, Affymetrix developed the high-density oligonucleotide arrays that tile all the non-repetitive sequences of the human genome at 35 bp resolution. This new array platform allows for the unbiased mapping of in vivo TF binding sequences (TFBSs) using Chromatin ImmunoPrecipitation followed by microarray experiments (ChIP-chip). The massive dataset generated from these experiments pose great challenges for data analysis.'),
(72206, 2005, 'bio', 'Improved detection of DNA motifs using a self-organized clustering of familial binding profiles', 'Mahony, Shaun|Golden, Aaron|Smith, Terry J|Benos, Panayiotis V', 'National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland. shaun.mahony@nuigalway.ie|National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland. shaun.mahony@nuigalway.ie|National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland. shaun.mahony@nuigalway.ie|National Centre for Biomedical Engineering Science, NUI Galway Galway, Ireland. shaun.mahony@nuigalway.ie', 'Ireland|Ireland|Ireland|Ireland', 34, 'Algorithms|Amino Acid Motifs|Animals|Cluster Analysis|Computational Biology|DNA|Drosophila|Fungal Proteins|Internet|Models, Statistical|Nerve Net|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti1025', '15961468', 'One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be biased towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs.'),
(72207, 2005, 'bio', 'A systematic approach for comprehensive T-cell epitope discovery using peptide libraries', 'Beissbarth, Tim|Tye-Din, Jason A|Smyth, Gordon K|Speed, Terence P|Anderson, Robert P', 'Walter and Eliza Hall Institute of Medical Research 1G Royal Parade, Australia. beissbarth@wehi.edu.au|Walter and Eliza Hall Institute of Medical Research 1G Royal Parade, Australia. beissbarth@wehi.edu.au|Walter and Eliza Hall Institute of Medical Research 1G Royal Parade, Australia. beissbarth@wehi.edu.au|Walter and Eliza Hall Institute of Medical Research 1G Royal Parade, Australia. beissbarth@wehi.edu.au|Walter and Eliza Hall Institute of Medical Research 1G Royal Parade, Australia. beissbarth@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Celiac Disease|Computational Biology|Epitopes|Epitopes, T-Lymphocyte|Glutens|Humans|Models, Statistical|Molecular Sequence Data|Peptide Library|Peptides|T-Lymphocytes', 'Journal Article', '10.1093/bioinformatics/bti1013', '15961469', 'T-cell response to peptides bound on MHC Class I or Class II molecules is essential for immune recognition of pathogens. T-cells are activated by specific peptide epitopes that are determined within the antigen processing pathways and presented on the surface of other cells bound to MHC molecules. To determine which part of allergenic or pathogenic proteins can stimulate T-cells is important for the treatment of diseases. We sought to take advantage of the falling cost of synthetic, screening grade peptides, and devise a comprehensive, non-hypothesis-driven screen for T-cell epitopes. We were interested in the study of celiac disease (CD) and used the ELISPOT technique to perform a large number of T-cell assays. We therefore needed to compensate for the lack of statistical data analysis methods for ELISPOT assays.'),
(72208, 2005, 'bio', 'High-throughput inference of protein-protein interfaces from unassigned NMR data', 'Mettu, Ramgopal R|Lilien, Ryan H|Donald, Bruce Randall', 'Department of Computer Science, 6211 Sudikoff Laboratory Hanover, NH 03755, USA.|Department of Computer Science, 6211 Sudikoff Laboratory Hanover, NH 03755, USA.|Department of Computer Science, 6211 Sudikoff Laboratory Hanover, NH 03755, USA.', 'United States|United States|United States', 1, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Magnetic Resonance Spectroscopy|Protein Conformation|Protein Interaction Mapping|Proteomics|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti1005', '15961470', 'We cast the problem of identifying protein-protein interfaces, using only unassigned NMR spectra, into a geometric clustering problem. Identifying protein-protein interfaces is critical to understanding inter- and intra-cellular communication, and NMR allows the study of protein interaction in solution. However it is often the case that NMR studies of a protein complex are very time-consuming, mainly due to the bottleneck in assigning the chemical shifts, even if the apo structures of the constituent proteins are known. We study whether it is possible, in a high-throughput manner, to identify the interface region of a protein complex using only unassigned chemical shifts and residual dipolar coupling (RDC) data. We introduce a geometric optimization problem where we must cluster the cells in an arrangement on the boundary of a 3-manifold, where the arrangement is induced by a spherical quadratic form [corrected] The arrangement is induced by a spherical quadratic form, which in turn is parameterized by a SO(3)xR2. We show that this formalism derives directly from the physics of RDCs. We present an optimal algorithm for this problem that runs in O(n3 log n) time for an n-residue protein. We then use this clustering algorithm as a subroutine in a practical algorithm for identifying the interface region of a protein complex from unassigned NMR data. We present the results of our algorithm on NMR data for seven proteins from five protein complexes, and show that our approach is useful for high-throughput applications in which we seek to rapidly identify the interface region of a protein complex.'),
(72209, 2005, 'bio', 'Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps', 'Nabieva, Elena|Jim, Kam|Agarwal, Amit|Chazelle, Bernard|Singh, Mona', 'Computer Science Department, Princeton University Princeton, NJ 08544, USA.|Computer Science Department, Princeton University Princeton, NJ 08544, USA.|Computer Science Department, Princeton University Princeton, NJ 08544, USA.|Computer Science Department, Princeton University Princeton, NJ 08544, USA.|Computer Science Department, Princeton University Princeton, NJ 08544, USA.', 'United States|United States|United States|United States|United States', 341, 'Algorithms|Computational Biology|Evolution, Molecular|Fungal Proteins|Genomics|Models, Statistical|Models, Theoretical|Protein Interaction Mapping|Proteins|Proteomics', 'Journal Article', '10.1093/bioinformatics/bti1054', '15961472', 'Determining protein function is one of the most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps.'),
(72210, 2005, 'bio', 'Computing the P-value of the information content from an alignment of multiple sequences', 'Nagarajan, Niranjan|Jones, Neil|Keich, Uri', 'Computer Science Department 4130 Upson Hall Cornell University Ithaca, NY 14853, USA.|Computer Science Department 4130 Upson Hall Cornell University Ithaca, NY 14853, USA.|Computer Science Department 4130 Upson Hall Cornell University Ithaca, NY 14853, USA.', 'United States|United States|United States', 30, 'Algorithms|Amino Acid Motifs|Computational Biology|Databases, Protein|Entropy|Models, Statistical|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti1044', '15961473', 'The efficient and accurate computation of P-values is an essential requirement for motif-finding and alignment tools. We show that the approximation algorithms used in two popular motif-finding programs, MEME and Consensus, can fail to accurately compute the P-value.'),
(72211, 2005, 'bio', 'Beyond the clause: extraction of phosphorylation information from medline abstracts', 'Narayanaswamy, M|Ravikumar, K E|Vijay-Shanker, K', 'AU-KBC Research Centre, Anna University Chennai, India.|AU-KBC Research Centre, Anna University Chennai, India.|AU-KBC Research Centre, Anna University Chennai, India.', 'India|India|India', 31, 'Artificial Intelligence|Computational Biology|Database Management Systems|Databases, Protein|Humans|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Phosphorylation|Proteomics|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bti1011', '15961474', 'Phosphorylation is an important biochemical reaction that plays a critical role in signal transduction pathways and cell-cycle processes. A text mining system to extract the phosphorylation relation from the literature is reported. The focus of this paper is on the new methods developed and implemented to connect and merge pieces of information about phosphorylation mentioned in different sentences in the text. The effectiveness and accuracy of the system as a whole as well as that of the methods for extraction beyond a clause/sentence is evaluated using an independently annotated dataset, the Phospho.ELM database. The new methods developed to merge pieces of information from different sentences are shown to be effective in significantly raising the recall without much difference in precision.'),
(72212, 2005, 'bio', 'In silico identification of functional regions in proteins', 'Nimrod, Guy|Glaser, Fabian|Steinberg, David|Ben-Tal, Nir|Pupko, Tal', 'Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel.|Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel.|Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel.|Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel.|Department of Biochemistry, George S. Wise Faculty of Life Sciences, Tel Aviv University Ramat Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Binding Sites|Computational Biology|Conserved Sequence|DNA|Evolution, Molecular|Humans|Likelihood Functions|Models, Molecular|Models, Statistical|Programming Languages|Protein Conformation|Proteomics|Software|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/bti1023', '15961475', 'In silico prediction of functional regions on protein surfaces, i.e. sites of interaction with DNA, ligands, substrates and other proteins, is of utmost importance in various applications in the emerging fields of proteomics and structural genomics. When a sufficient number of homologs is found, powerful prediction schemes can be based on the observation that evolutionarily conserved regions are often functionally important, typically, only the principal functionally important region of the protein is detected, while secondary functional regions with weaker conservation signals are overlooked. Moreover, it is challenging to unambiguously identify the boundaries of the functional regions.'),
(72213, 2005, 'bio', 'Predicting the in vivo signature of human gene regulatory sequences', 'Noble, William Stafford|Kuehn, Scott|Thurman, Robert|Yu, Man|Stamatoyannopoulos, John', 'Department of Genome Sciences, University of Washington Seattle, WA, USA. noble@gs.washington.edu|Department of Genome Sciences, University of Washington Seattle, WA, USA. noble@gs.washington.edu|Department of Genome Sciences, University of Washington Seattle, WA, USA. noble@gs.washington.edu|Department of Genome Sciences, University of Washington Seattle, WA, USA. noble@gs.washington.edu|Department of Genome Sciences, University of Washington Seattle, WA, USA. noble@gs.washington.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Binding Sites|Chromatin|Computational Biology|DNA|Deoxyribonuclease I|Erythrocytes|False Positive Reactions|Genome|Genome, Human|Humans|Probability|ROC Curve|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti1047', '15961476', 'In the living cell nucleus, genomic DNA is packaged into chromatin. DNA sequences that regulate transcription and other chromosomal processes are associated with local disruptions, or openings, in chromatin structure caused by the cooperative action of regulatory proteins. Such perturbations are extremely specific for cis-regulatory elements and occur over short stretches of DNA (typically approximately 250 bp). They can be detected experimentally as DNaseI hypersensitive sites (HSs) in vivo, though the process is extremely laborious and costly. The ability to discriminate DNaseI HSs computationally would have a major impact on the annotation and utilization of the human genome.'),
(72214, 2005, 'bio', 'Statistics of local multiple alignments', 'Prakash, Amol|Tompa, Martin', 'Department of Computer Science and Engineering Box 352350 University of Washington Seattle, WA 98195-2350, USA. amol@cs.washington.edu|Department of Computer Science and Engineering Box 352350 University of Washington Seattle, WA 98195-2350, USA. amol@cs.washington.edu', 'United States|United States', 0, 'Algorithms|Animals|Base Sequence|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Models, Statistical|Molecular Sequence Data|Phylogeny|Promoter Regions, Genetic|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti1042', '15961477', 'BLAST statistics have been shown to be extremely useful for searching for significant similarity hits, for amino acid and nucleotide sequences. Although these statistics are well understood for pairwise comparisons, there has been little success developing statistical scores for multiple alignments. In particular, there is no score for multiple alignment that is well founded and treated as a standard. We extend the BLAST theory to multiple alignments. Following some simple assumptions, we present and justify a significance score for multiple segments of a local multiple alignment. We demonstrate its usefulness in distinguishing high and moderate quality multiple alignments from low quality ones, with supporting experiments on orthologous vertebrate promoter sequences.'),
(72215, 2005, 'bio', 'De novo identification of repeat families in large genomes', 'Price, Alkes L|Jones, Neil C|Pevzner, Pavel A', 'Department of Computer Science and Engineering, University of California San Diego La Jolla, CA 92093-0114, USA.|Department of Computer Science and Engineering, University of California San Diego La Jolla, CA 92093-0114, USA.|Department of Computer Science and Engineering, University of California San Diego La Jolla, CA 92093-0114, USA.', 'United States|United States|United States', 687, 'Algorithms|Animals|Caenorhabditis|Caenorhabditis elegans|Computational Biology|Genome|Internet|Mice|Models, Genetic|Models, Statistical|Rats|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1018', '15961478', 'De novo repeat family identification is a challenging algorithmic problem of great practical importance. As the number of genome sequencing projects increases, there is a pressing need to identify the repeat families present in large, newly sequenced genomes. We develop a new method for de novo identification of repeat families via extension of consensus seeds, our method enables a rigorous definition of repeat boundaries, a key issue in repeat analysis.'),
(72216, 2005, 'bio', 'Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity', 'Swamidass, S Joshua|Chen, Jonathan|Bruand, Jocelyne|Phung, Peter|Ralaivola, Liva|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Antineoplastic Agents|Computational Biology|Computer Simulation|Databases, Factual|Drug-Related Side Effects and Adverse Reactions|Female|Male|Mice|Models, Molecular|Models, Statistical|Mutagens|Neoplasms|Pattern Recognition, Automated|ROC Curve|Rats', 'Journal Article', '10.1093/bioinformatics/bti1055', '15961479', 'Small molecules play a fundamental role in organic chemistry and biology. They can be used to probe biological systems and to discover new drugs and other useful compounds. As increasing numbers of large datasets of small molecules become available, it is necessary to develop computational methods that can deal with molecules of variable size and structure and predict their physical, chemical and biological properties.'),
(72217, 2005, 'bio', 'RASE: recognition of alternatively spliced exons in Celegans', 'Rätsch, G|Sonnenburg, S|Schölkopf, B', 'Friedrich Miescher Laboratory of the Max Planck Society Max Planck, Spemannstrasse 35, Tübingen, Germany. Gunnar.Raetsch@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society Max Planck, Spemannstrasse 35, Tübingen, Germany. Gunnar.Raetsch@tuebingen.mpg.de|Friedrich Miescher Laboratory of the Max Planck Society Max Planck, Spemannstrasse 35, Tübingen, Germany. Gunnar.Raetsch@tuebingen.mpg.de', 'Germany|Germany|Germany', 95, 'Algorithms|Alternative Splicing|Animals|Base Sequence|Caenorhabditis elegans|Computational Biology|Databases, Protein|Exons|False Positive Reactions|Gene Expression Regulation|Introns|Molecular Sequence Data|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/bti1053', '15961480', 'Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human genes and at least one-third of the genes of less complex organisms, such as nematodes or flies, are alternatively spliced. In this work, we consider one major form of alternative splicing, namely the exclusion of exons from the transcript. It has been shown that alternatively spliced exons have certain properties that distinguish them from constitutively spliced exons. Although most recent computational studies on alternative splicing apply only to exons which are conserved among two species, our method only uses information that is available to the splicing machinery, i.e. the DNA sequence itself. We employ advanced machine learning techniques in order to answer the following two questions: (1) Is a certain exon alternatively spliced? (2) How can we identify yet unidentified exons within known introns?'),
(72218, 2005, 'bio', 'Families of membranous proteins can be characterized by the amino acid composition of their transmembrane domains', 'Sadka, Tali|Linial, Michal', 'School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem 91904, Israel.|School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem 91904, Israel.', 'Israel|Israel', 0, 'Algorithms|Cell Membrane|Computational Biology|Databases, Protein|Genome|Lipid Bilayers|Membrane Proteins|Models, Statistical|Normal Distribution|Protein Structure, Tertiary|Proteomics|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/bti1035', '15961481', 'In eukaryotes, membranous proteins account for 20-30% of the proteome. Most of these proteins contain one or more transmembrane (TM) domains. These are short segments that transverse the bilayer lipid membrane. Various properties of the TM domains, such as their number, their topology and their arrangement within the membrane, are closely related to the proteins cellular functions. The properties of the TM domains also determine the cellular targeting and localization of these proteins. It is not known, however, whether the information encoded by TM domains suffices for the purpose of classifying proteins into their functional families. This is the question we address here. We introduce an algorithm that creates a profile of each functional family of membranous proteins based only on the amino acid composition of their TM domains. This is complemented by a classifier program for each such family (to determine whether a given protein belongs to it or not). We find that in most instances TM domains contain enough information to allow an accurate discrimination of approximately 80% sensitivity and approximately 90% specificity among unrelated polytopic functional families with the same number of TM domains.'),
(72219, 2005, 'bio', 'Kernel methods for predicting protein-protein interactions', 'Ben-Hur, Asa|Noble, William Stafford', 'Department of Genome Sciences, University of Washington Seattle, WA, USA. asa@gs.washington.edu|Department of Genome Sciences, University of Washington Seattle, WA, USA. asa@gs.washington.edu', 'United States|United States', 384, 'Algorithms|Amino Acid Motifs|Computational Biology|Databases, Protein|False Positive Reactions|Fungal Proteins|Models, Theoretical|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/bti1016', '15961482', 'Despite advances in high-throughput methods for discovering protein-protein interactions, the interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions.'),
(72220, 2005, 'bio', 'A motif-based framework for recognizing sequence families', 'Sharan, Roded|Myers, Eugene W', 'School of Computer Science, Tel-Aviv University Tel-Aviv 69978, Israel. roded@tau.ac.il|School of Computer Science, Tel-Aviv University Tel-Aviv 69978, Israel. roded@tau.ac.il', 'Israel|Israel', 13, 'Algorithms|Alternative Splicing|Amino Acid Motifs|Animals|Automation|Binding Sites|Computational Biology|Drosophila|Fungal Proteins|Humans|Models, Statistical|Pattern Recognition, Automated|Promoter Regions, Genetic|Sequence Alignment|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti1002', '15961483', 'Many signals in biological sequences are based on the presence or absence of base signals and their spatial combinations. One of the best known examples of this is the signal identifying a core promoter--the site at which the basal transcription machinery starts the transcription of a gene. Our goal is a fully automatic pattern recognition system for a family of sequences, which simultaneously discovers the base signals, their spatial relationships and a classifier based upon them.'),
(72221, 2005, 'bio', 'Search for folding nuclei in native protein structures', 'Shmygelska, Alena', 'Department of Computer Science, University of British Columbia Vancouver, BC, Canada V6T 1Z4. oshmygel@cs.ubc.ca', 'Canada', 11, 'Algorithms|Animals|Cell Nucleus|Computational Biology|Humans|Models, Molecular|Models, Statistical|Nuclear Proteins|Polymers|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/bti1050', '15961484', 'The problem of finding folding nuclei (a set of native contacts that play an important role in folding) along with identifying folding pathways (a time-ordered sequence of folding events) of proteins is one of the most important problems in protein chemistry. Here we propose a novel and simple approach to address this problem as follows: given the topology of the native state, identify native contacts that form folding nuclei based on a graph-theoretical approach that considers effective contact order (effective loop closure) as its objective function.'),
(72222, 2005, 'bio', 'Mining ChIP-chip data for transcription factor and cofactor binding sites', 'Smith, Andrew D|Sumazin, Pavel|Das, Debopriya|Zhang, Michael Q', 'Cold Spring Harbor Laboratory 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. asmith@cshl.edu|Cold Spring Harbor Laboratory 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. asmith@cshl.edu|Cold Spring Harbor Laboratory 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. asmith@cshl.edu|Cold Spring Harbor Laboratory 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA. asmith@cshl.edu', 'United States|United States|United States|United States', 75, 'Algorithms|Amino Acid Motifs|Animals|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|Gene Expression Regulation|Humans|Liver|Models, Statistical|Multivariate Analysis|Protein Array Analysis|Protein Structure, Tertiary|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti1043', '15961485', 'Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data.'),
(72223, 2005, 'bio', 'Efficient computation of close lower and upper bounds on the minimum number of recombinations in biological sequence evolution', 'Song, Yun S|Wu, Yufeng|Gusfield, Dan', 'Department of Computer Science, University of California Davis, CA 95616, USA. yssong@cs.ucdavis.edu|Department of Computer Science, University of California Davis, CA 95616, USA. yssong@cs.ucdavis.edu|Department of Computer Science, University of California Davis, CA 95616, USA. yssong@cs.ucdavis.edu', 'United States|United States|United States', 12, 'Algorithms|Computational Biology|DNA|Databases, Protein|Evolution, Molecular|Haplotypes|Humans|Polymorphism, Single Nucleotide|Recombination, Genetic|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bti1033', '15961486', 'We are interested in studying the evolution of DNA single nucleotide polymorphism sequences which have undergone (meiotic) recombination. For a given set of sequences, computing the minimum number of recombinations needed to explain the sequences (with one mutation per site) is a standard question of interest, but it has been shown to be NP-hard, and previous algorithms that compute it exactly work either only on very small datasets or on problems with special structure.'),
(72224, 2005, 'bio', 'Robust classification modeling on microarray data using misclassification penalized posterior', 'Soukup, Mat|Cho, HyungJun|Lee, Jae K', 'Division of Biometrics III, Food and Drug Administration 9201 Corporate Blvd, Rm. N-250, Rockville, MD 20850, USA.|Division of Biometrics III, Food and Drug Administration 9201 Corporate Blvd, Rm. N-250, Rockville, MD 20850, USA.|Division of Biometrics III, Food and Drug Administration 9201 Corporate Blvd, Rm. N-250, Rockville, MD 20850, USA.', 'United States|United States|United States', 21, 'Algorithms|Computational Biology|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Internet|Leukemia|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/bti1020', '15961487', 'Genome-wide microarray data are often used in challenging classification problems of clinically relevant subtypes of human diseases. However, the identification of a parsimonious robust prediction model that performs consistently well on future independent data has not been successful due to the biased model selection from an extremely large number of candidate models during the classification model search and construction. Furthermore, common criteria of prediction model performance, such as classification error rates, do not provide a sensitive measure for evaluating performance of such astronomic competing models. Also, even though several different classification approaches have been utilized to tackle such classification problems, no direct comparison on these methods have been made.'),
(72225, 2005, 'bio', 'Automated interpretation of MS/MS spectra of oligosaccharides', 'Tang, Haixu|Mechref, Yehia|Novotny, Milos V', 'School of Informatics, Indiana University Bloomington, IN, USA. hatang@indiana.edu|School of Informatics, Indiana University Bloomington, IN, USA. hatang@indiana.edu|School of Informatics, Indiana University Bloomington, IN, USA. hatang@indiana.edu', 'India|India|India', 88, 'Algorithms|Animals|Automation|Computational Biology|Data Interpretation, Statistical|Glycoproteins|Ions|Mass Spectrometry|Models, Statistical|Monosaccharides|Oligosaccharides|Polysaccharides|Proteomics', 'Journal Article', '10.1093/bioinformatics/bti1038', '15961488', 'The emerging glycomics and glycoproteomics projects aim to characterize all forms of glycoproteins in different tissues and organisms. Tandem mass spectrometry (MS/MS) is the key experimental methodology for high-throughput glycan identification and characterization. Fragmentation of glycans from high energy collision-induced dissociation generates ions from glycosidic as well as internal cleavages. The cross-ring ions resulting from internal cleavages provide additional information that is important to reveal the type of linkage between monosaccharides. This information, however, is not incorporated into the current programs for analyzing glycan mass spectra. As a result, they can rarely distinguish from the mass spectra isomeric oligosaccharides, which have the same saccharide composition but different types of sequences, branches or linkages.'),
(72226, 2005, 'bio', 'Alignments anchored on genomic landmarks can aid in the identification of regulatory elements', 'Tharakaraman, Kannan|Mariño-Ramírez, Leonardo|Sheetlin, Sergey|Landsman, David|Spouge, John L', 'Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.|Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Building 38A, 8600 Rockville Pike, Bethesda, MD 20894-6075, USA.', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Motifs|Base Sequence|Cluster Analysis|Computational Biology|DNA|Databases, Protein|Genomics|Humans|Models, Statistical|Molecular Sequence Data|Nucleotides|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Software|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/bti1028', '15961489', 'The transcription start site (TSS) has been located for an increasing number of genes across several organisms. Statistical tests have shown that some cis-acting regulatory elements have positional preferences with respect to the TSS, but few strategies have emerged for locating elements by their positional preferences. This paper elaborates such a strategy. First, we align promoter regions without gaps, anchoring the alignment on each promoters TSS. Second, we apply a novel word-specific mask. Third, we apply a clustering test related to gapless BLAST statistics. The test examines whether any specific word is placed unusually consistently with respect to the TSS. Finally, our program A-GLAM, an extension of the GLAM program, uses significant word positions as new anchors to realign the sequences. A Gibbs sampling algorithm then locates putative cis-acting regulatory elements. Usually, Gibbs sampling requires a preliminary masking step, to avoid convergence onto a dominant but uninteresting signal from a DNA repeat. However, since the positional anchors focus A-GLAM on the motif of interest, masking DNA repeats during Gibbs sampling becomes unnecessary.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72227, 2005, 'bio', 'How old is your fold?', 'Winstanley, Henry F|Abeln, Sanne|Deane, Charlotte M', 'Department of Statistics 1 South Parks Road, Oxford OX1 3TG, UK.|Department of Statistics 1 South Parks Road, Oxford OX1 3TG, UK.|Department of Statistics 1 South Parks Road, Oxford OX1 3TG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|Evolution, Molecular|Helicobacter pylori|Humans|Phylogeny|Protein Folding|Protein Interaction Mapping|Protein Structure, Secondary|Proteins|Proteomics|Species Specificity|Time Factors|Trypanosoma', 'Journal Article', '10.1093/bioinformatics/bti1008', '15961490', 'At present there exists no age estimate for the different protein structures found in nature. It has become clear from occurrence studies that different folds arose at different points in evolutionary time. An estimation of the age of different folds would be a starting point for many investigations into protein structure evolution: how we arrived at the set of folds we see today. It would also be a powerful tool in protein structure classification allowing us to reassess the available hierarchical methods and perhaps suggest improvements.'),
(72228, 2005, 'bio', 'Experimental design for three-color and four-color gene expression microarrays', 'Woo, Yong|Krueger, Winfried|Kaur, Anupinder|Churchill, Gary', 'The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA.|The Jackson Laboratory 600 Main Street, Bar Harbor, ME 04609, USA.', 'United States|United States|United States|United States', 23, 'Algorithms|Animals|Color|Computational Biology|Data Interpretation, Statistical|Expressed Sequence Tags|Mice|Mice, Inbred C57BL|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bti1031', '15961491', 'Three-color microarrays, compared with two-color microarrays, can increase design efficiency and power to detect differential expression without additional samples and arrays. Furthermore, three-color microarray technology is currently available at a reasonable cost. Despite the potential advantages, clear guidelines for designing and analyzing three-color experiments do not exist.'),
(72229, 2005, 'bio', 'Supervised enzyme network inference from the integration of genomic data and chemical information', 'Yamanishi, Yoshihiro|Vert, Jean-Philippe|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. yoshi@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Computational Biology|Databases, Protein|Enzymes|Gene Expression Regulation, Fungal|Genes, Fungal|Genomics|Models, Chemical|Models, Genetic|Phylogeny|Protein Interaction Mapping|ROC Curve|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/bti1012', '15961492', 'The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes.'),
(72230, 2005, 'bio', 'Protein function prediction via graph kernels', 'Borgwardt, Karsten M|Ong, Cheng Soon|Schönauer, Stefan|Vishwanathan, S V N|Smola, Alex J|Kriegel, Hans-Peter', 'Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University Munich Oettingenstrasss e 67, 80538 Munich, Germany. borgwardt@dbs.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Protein|Enzymes|Models, Statistical|Protein Conformation|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti1007', '15961493', 'Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.'),
(72231, 2005, 'bio', 'Automatic detection of subsystem/pathway variants in genome analysis', 'Ye, Yuzhen|Osterman, Andrei|Overbeek, Ross|Godzik, Adam', 'Program in Bioinformatics and Systems Biology, The Burnham Institute 10901 N. Torrey Pines Road, La Jolla CA 92037, USA. yye@burnham.org|Program in Bioinformatics and Systems Biology, The Burnham Institute 10901 N. Torrey Pines Road, La Jolla CA 92037, USA. yye@burnham.org|Program in Bioinformatics and Systems Biology, The Burnham Institute 10901 N. Torrey Pines Road, La Jolla CA 92037, USA. yye@burnham.org|Program in Bioinformatics and Systems Biology, The Burnham Institute 10901 N. Torrey Pines Road, La Jolla CA 92037, USA. yye@burnham.org', 'United States|United States|United States|United States', 35, 'Animals|Automation|Computational Biology|Databases, Genetic|Genome|Genomics|Models, Biological|Models, Statistical|Models, Theoretical|Programming Languages|Protein Interaction Mapping|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bti1052', '15961494', 'Proteins work together in pathways and networks, collectively comprising the cellular machinery. A subsystem (a generalization of pathway concept) is a group of related functional roles (such as enzymes) jointly involved in a specific aspect of the cellular machinery. Subsystems provide a natural framework for comparative genome analysis and functional annotation. A subsystem may be implemented in a number of different functional variants in individual species. In order to reliably project functional assignments across multiple genomes, we have to be able to identify the variants implemented in each genome. The analysis of such variants across diverse species is an interesting problem by itself and may provide new evolutionary insights. However, no computational techniques are presently available for an automated detection and analysis of subsystem variants.'),
(72232, 2005, 'bio', 'Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data', 'Yu, Jiangsheng|Chen, Xue-Wen', 'School of Electronics Engineering and Computer Science, Peking University China.|School of Electronics Engineering and Computer Science, Peking University China.', 'China|China', 43, 'Bayes Theorem|Computational Biology|Female|Humans|Mass Spectrometry|Models, Statistical|Neural Networks (Computer)|Normal Distribution|Ovarian Neoplasms|Programming Languages|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bti1030', '15961495', 'The classification of high-dimensional data is always a challenge to statistical machine learning. We propose a novel method named shallow feature selection that assigns each feature a probability of being selected based on the structure of training data itself. Independent of particular classifiers, the high dimension of biodata can be fleetly reduced to an applicable case for consequential processing. Moreover, to improve both efficiency and performance of classification, these prior probabilities are further used to specify the distributions of top-level hyperparameters in hierarchical models of Bayesian neural network (BNN), as well as the parameters in Gaussian process models.'),
(72233, 2005, 'bio', 'Classifying noisy protein sequence data: a case study of immunoglobulin light chains', 'Yu, Chenggang|Zavaljevski, Nela|Stevens, Fred J|Yackovich, Kelly|Reifman, Jaques', 'Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org|Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org|Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org|Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org|Argonne National Laboratory 9700 S. Cass Avenue, Argonne, IL 60439, USA. cyu@bioanalysis.org', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Humans|Imaging, Three-Dimensional|Immunoglobulin Light Chains|Models, Statistical|Pattern Recognition, Automated|Protein Conformation|Proteins|ROC Curve|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti1024', '15961496', 'The classification of protein sequences obtained from patients with various immunoglobulin-related conformational diseases may provide insight into structural correlates of pathogenicity. However, clinical data are very sparse and, in the case of antibody-related proteins, the collected sequences have large variability with only a small subset of variations relevant to the protein pathogenicity (function). On this basis, these sequences represent a model system for development of strategies to recognize the small subset of function-determining variations among the much larger number of primary structure diversifications introduced during evolution. Under such conditions, most protein classification algorithms have limited accuracy. To address this problem, we propose a support vector machine (SVM)-based classifier that combines sequence and 3D structural averaging information. Each amino acid in the sequence is represented by a set of six physicochemical properties: hydrophobicity, hydrophilicity, volume, surface area, bulkiness and refractivity. Each position in the sequence is described by the properties of the amino acid at that position and the properties of its neighbors in 3D space or in the sequence. A structure template is selected to determine neighbors in 3D space and a window size is used to determine the neighbors in the sequence. The test data consist of 209 proteins of human antibody immunoglobulin light chains, each represented by aligned sequences of 120 amino acids. The methodology is applied to the classification of protein sequences collected from patients with and without amyloidosis, and indicates that the proposed modified classifiers are more robust to sequence variability than standard SVM classifiers, improving classification error between 5 and 25% and sensitivity between 9 and 17%. The classification results might also suggest possible mechanisms for the propensity of immunoglobulin light chains to amyloid formation.'),
(72234, 2005, 'bio', 'Reversal distance for partially ordered genomes', 'Zheng, Chunfang|Lenert, Aleksander|Sankoff, David', 'University of Ottawa 585 King Edward Avenue, Ottawa K1N 6N5, Canada.|University of Ottawa 585 King Edward Avenue, Ottawa K1N 6N5, Canada.|University of Ottawa 585 King Edward Avenue, Ottawa K1N 6N5, Canada.', 'Canada|Canada|Canada', 23, 'Algorithms|Chromosome Mapping|Chromosomes, Plant|Computational Biology|Databases, Genetic|Gene Order|Genes, Plant|Genome|Genome, Plant|Humans|Sequence Analysis, DNA|Sorghum|Zea mays', 'Journal Article', '10.1093/bioinformatics/bti1037', '15961497', 'The total order of the genes or markers on a chromosome inherent in its representation as a signed per-mutation must often be weakened to a partial order in the case of real data. This is due to lack of resolution (where several genes are mapped to the same chromosomal position) to missing data from some of the datasets used to compile a gene order, and to conflicts between these datasets. The available genome rearrangement algorithms, however, require total orders as input. A more general approach is needed to handle rearrangements of gene partial orders.'),
(72235, 2005, 'bio', 'ExonHunter: a comprehensive approach to gene finding', 'Brejová, Brona|Brown, Daniel G|Li, Ming|Vinar, Tomás', 'School of Computer Science, University of Waterloo 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. bbrejova@uwaterloo.ca|School of Computer Science, University of Waterloo 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. bbrejova@uwaterloo.ca|School of Computer Science, University of Waterloo 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. bbrejova@uwaterloo.ca|School of Computer Science, University of Waterloo 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. bbrejova@uwaterloo.ca', 'Canada|Canada|Canada|Canada', 33, 'Animals|Computational Biology|Databases, Genetic|Exons|Genomics|Humans|Markov Chains|Models, Genetic|Models, Statistical|Models, Theoretical|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti1040', '15961499', 'We present ExonHunter, a new and comprehensive gene finding system that outperforms existing systems and features several new ideas and approaches. Our system combines numerous sources of information (genomic sequences, expressed sequence tags and protein databases of related species) into a gene finder based on a hidden Markov model in a novel and systematic way. In our framework, various sources of information are expressed as partial probabilistic statements about positions in the sequence and their annotation. We then combine these into the final prediction via a quadratic programming method, which we show to be an extension of existing methods. Allowing only partial statements is key to our transparent handling of missing information and coping with the heterogeneous character of individual sources of information. In addition, we give a new method for modeling the length distribution of intergenic regions in hidden Markov models.'),
(72236, 2005, 'bio', 'Improving protein structure prediction with model-based search', 'Brunette, T J|Brock, Oliver', 'Bioinformatics Research Laboratory, Department of Computer Science, University of Massachusetts Amherst, MA 01003-9264, USA.|Bioinformatics Research Laboratory, Department of Computer Science, University of Massachusetts Amherst, MA 01003-9264, USA.', 'United States|United States', 7, 'Amino Acids|Computational Biology|Computer Simulation|Databases, Protein|Humans|Models, Theoretical|Monte Carlo Method|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bti1029', '15961500', 'De novo protein structure prediction can be formulated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search problems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function. This model is incrementally refined in areas of interest, whereas areas that are not of interest are excluded from further exploration. Model-based search derives its efficiency from the fact that the information obtained during the exploration of the search space is used to guide further exploration. In contrast, Monte Carlo-based techniques lack memory and exploration is performed based on random walks, ignoring the information obtained in previous steps.'),
(72237, 2005, 'bio', 'Three-stage prediction of protein beta-sheets by neural networks, alignments and graph algorithms', 'Cheng, Jianlin|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA 92697, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA 92697, USA.', 'United States|United States', 29, 'Algorithms|Computational Biology|Humans|Hydrogen Bonding|Models, Chemical|Models, Molecular|Nerve Net|Protein Conformation|Protein Structure, Secondary|Proteins|ROC Curve|X-Ray Diffraction', 'Journal Article', '10.1093/bioinformatics/bti1004', '15961501', 'Protein beta-sheets play a fundamental role in protein structure, function, evolution and bioengineering. Accurate prediction and assembly of protein beta-sheets, however, remains challenging because protein beta-sheets require formation of hydrogen bonds between linearly distant residues. Previous approaches for predicting beta-sheet topological features, such as beta-strand alignments, in general have not exploited the global covariation and constraints characteristic of beta-sheet architectures.'),
(72238, 2005, 'bio', 'YeastHub: a semantic web use case for integrating data in the life sciences domain', 'Cheung, Kei-Hoi|Yip, Kevin Y|Smith, Andrew|Deknikker, Remko|Masiar, Andy|Gerstein, Mark', 'Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University New Haven, CT 06520, USA. kei.cheung@yale.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Biology|Computational Biology|Database Management Systems|Databases as Topic|Databases, Protein|Fungal Proteins|Information Storage and Retrieval|Information Systems|Internet|Programming Languages|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1026', '15961502', 'As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies-including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository-to represent, store and query both metadata and data across life sciences datasets.'),
(72239, 2005, 'bio', 'Conservative extraction of over-represented extensible motifs', 'Apostolico, Alberto|Comin, Matteo|Parida, Laxmi', 'Department of Computer Sciences, Purdue University, Computer Sciences Building West Lafayette, IN 47907, USA. axa@dei.unipd.it|Department of Computer Sciences, Purdue University, Computer Sciences Building West Lafayette, IN 47907, USA. axa@dei.unipd.it|Department of Computer Sciences, Purdue University, Computer Sciences Building West Lafayette, IN 47907, USA. axa@dei.unipd.it', 'United States|United States|United States', 20, 'Algorithms|Amino Acid Motifs|Bacterial Proteins|Computational Biology|Internet|Models, Statistical|Probability|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/bti1051', '15961503', 'The discovery of motifs in biosequences is frequently torn between the rigidity of the model on the one hand and the abundance of candidates on the other. In particular, the variety of motifs described by strings that include dont care (dot) patterns escalates exponentially with the length of the motif, and this gets only worse if a dot is allowed to stretch up to some prescribed maximum length. This circumstance tends to generate daunting computational burdens, and often gives rise to tables that are impossible to visualize and digest. This is unfortunate, as it seems to preclude precisely those massive analyses that have become conceivable with the increasing availability of massive genomic and protein data. Although a part of the problem is endemic, another part of it seems rooted in the various characterizations offered for the notion of a motif, that are typically based either on syntax or on statistics alone. It seems worthwhile to consider alternatives that result from a prudent combination of these two aspects in the model.'),
(72240, 2005, 'bio', 'Maximum likelihood of evolutionary trees: hardness and approximation', 'Chor, Benny|Tuller, Tamir', 'School of Computer Science, Tel-Aviv University Tel-Aviv, Israel.|School of Computer Science, Tel-Aviv University Tel-Aviv, Israel.', 'Israel|Israel', 51, 'Algorithms|Animals|Biological Evolution|Computational Biology|Computer Simulation|Databases, Protein|Evolution, Molecular|Humans|Likelihood Functions|Models, Statistical|Monte Carlo Method|Phylogeny|Probability|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti1027', '15961504', 'Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees. Yet the computational complexity of ML was open for over 20 years, and only recently resolved by the authors for the Jukes-Cantor model of substitution and its generalizations. It was proved that reconstructing the ML tree is computationally intractable (NP-hard). In this work we explore three directions, which extend that result.'),
(72241, 2005, 'bio', 'BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks', 'Maere, Steven|Heymans, Karel|Kuiper, Martin', 'Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Technologiepark 927, B-9052, Ghent, Belgium.|Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Technologiepark 927, B-9052, Ghent, Belgium.|Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Ghent University, Technologiepark 927, B-9052, Ghent, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Animals|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti551', '15972284', 'The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscapes versatile visualization environment to produce an intuitive and customizable visual representation of the results.'),
(72242, 2005, 'bio', 'MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons', 'Siebert, Sven|Backofen, Rolf', 'Department of Bioinformatics, Institute of Computer Science, Friedrich-Schiller-University Jena, Ernst-Abbe Platz 2, 07743 Jena, Germany.|Department of Bioinformatics, Institute of Computer Science, Friedrich-Schiller-University Jena, Ernst-Abbe Platz 2, 07743 Jena, Germany.', 'Germany|Germany', 127, 'Algorithms|Computer Simulation|Consensus Sequence|RNA|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Structure-Activity Relationship', 'Comparative Study', '10.1093/bioinformatics/bti550', '15972285', 'Due to the importance of considering secondary structures in aligning functional RNAs, several pairwise sequence-structure alignment methods have been developed. They use extended alignment scores that evaluate secondary structure information in addition to sequence information. However, two problems for the multiple alignment step remain. First, how to combine pairwise sequence-structure alignments into a multiple alignment and second, how to generate secondary structure information for sequences whose explicit structural information is missing.'),
(72243, 2005, 'bio', 'ACT: the Artemis Comparison Tool', 'Carver, Tim J|Rutherford, Kim M|Berriman, Matthew|Rajandream, Marie-Adele|Barrell, Barclay G|Parkhill, Julian', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. artemis@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1071, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti553', '15976072', 'The Artemis Comparison Tool (ACT) allows an interactive visualisation of comparisons between complete genome sequences and associated annotations. The comparison data can be generated with several different programs, BLASTN, TBLASTX or Mummer comparisons between genomic DNA sequences, or orthologue tables generated by reciprocal FASTA comparison between protein sets. It is possible to identify regions of similarity, insertions and rearrangements at any level from the whole genome to base-pair differences. ACT uses Artemis components to display the sequences and so inherits powerful searching and analysis tools. ACT is part of the Artemis distribution and is similarly open source, written in Java and can run on any Java enabled platform, including UNIX, Macintosh and Windows.'),
(72244, 2005, 'bio', 'Design and implementation of a mosquito database through an entomological ontology', 'Koum, Guillaume|Yekel, Augustin|Ndifon, Bengyella|Simard, Frédéric', 'Laborima, Ecole Polytechnique B.P 8390 Yaounde Cameroon. g_koum@yahoo.fr|Laborima, Ecole Polytechnique B.P 8390 Yaounde Cameroon. g_koum@yahoo.fr|Laborima, Ecole Polytechnique B.P 8390 Yaounde Cameroon. g_koum@yahoo.fr|Laborima, Ecole Polytechnique B.P 8390 Yaounde Cameroon. g_koum@yahoo.fr', 'Cameroon|Cameroon|Cameroon|Cameroon', 3, 'Animals|Anopheles|Culicidae|Database Management Systems|Databases, Factual|Databases, Genetic|Documentation|Entomology|Geographic Information Systems|Geography|Humans|Information Storage and Retrieval|Insect Vectors|Malaria|User-Computer Interface', 'Corrected and Republished Article', '10.1093/bioinformatics/bti399', '15981318', 'There have been constant changes in the biology and behavior of the vector and parasite involved in the transmission of malaria. There is limited interest in developing new technologies and procedures for controlling the underlying factors of this threat, which poses an enormous challenge to health systems. To understand the various vector species and their interrelations is of prime importance in understanding the transmission mechanisms of malaria in order to react efficiently. To attain this objective, we have used an ontological approach to produce a database that we consider to be our own contribution in helping to control malarial vectors if eradication has been unsuccessful in the previous control campaign.'),
(72245, 2005, 'bio', 'PhD: a web database application for phenotype data management', 'Li, J-L|Li, M-X|Deng, H-Y|Duffy, P E|Deng, H-W', 'Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China. Jinlong.Li@sbri.org|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China. Jinlong.Li@sbri.org|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China. Jinlong.Li@sbri.org|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China. Jinlong.Li@sbri.org|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, Peoples Republic of China. Jinlong.Li@sbri.org', 'China|China|China|China|China', 13, 'Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti557', '15985495', 'A database application has been developed for phenotype data management employing the Entity-Attribute-Value (EAV) model. By applying the EAV model, this application allows users to manage arbitrary phenotypes and customize data entry forms, therefore, it is suitable for different and multi-center projects.'),
(72246, 2005, 'bio', 'Alignment of metabolic pathways', 'Pinter, Ron Y|Rokhlenko, Oleg|Yeger-Lotem, Esti|Ziv-Ukelson, Michal', 'Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. pinter@cs.technion.ac.il|Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. pinter@cs.technion.ac.il|Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. pinter@cs.technion.ac.il|Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel. pinter@cs.technion.ac.il', 'Israel|Israel|Israel|Israel', 169, 'Animals|Gene Expression Regulation|Humans|Metabolism|Models, Biological|Multienzyme Complexes|Pattern Recognition, Automated|Signal Transduction|Species Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti554', '15985496', 'Several genome-scale efforts are underway to reconstruct metabolic networks for a variety of organisms. As the resulting data accumulates, the need for analysis tools increases. A notable requirement is a pathway alignment finder that enables both the detection of conserved metabolic pathways among different species as well as divergent metabolic pathways within a species. When comparing two pathways, the tool should be powerful enough to take into account both the pathway topology as well as the nodes labels (e.g. the enzymes they denote), and allow flexibility by matching similar--rather than identical--pathways.'),
(72247, 2005, 'bio', 'A heuristic approach for detecting RNA H-type pseudoknots', 'Huang, Chun-Hsiang|Lu, Chin Lung|Chiu, Hsien-Tai', 'Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Republic of China.', 'China|China|China', 38, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bti568', '15994188', 'RNA H-type pseudoknots are ubiquitous pseudoknots that are found in almost all classes of RNA and thought to play very important roles in a variety of biological processes. Detection of these RNA H-type pseudoknots can improve our understanding of RNA structures and their associated functions. However, the currently existing programs for detecting such RNA H-type pseudoknots are still time consuming and sometimes even ineffective. Therefore, efficient and effective tools for detecting the RNA H-type pseudoknots are needed.'),
(72248, 2005, 'bio', 'Ontological analysis of gene expression data: current tools, limitations, and open problems', 'Khatri, Purvesh|Drxc4x83ghici, Sorin', 'Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu|Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu', 'United States|United States', 624, 'Algorithms|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reference Standards|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti565', '15994189', 'Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.'),
(72249, 2005, 'bio', 'The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships', 'Sato, Tetsuya|Yamanishi, Yoshihiro|Kanehisa, Minoru|Toh, Hiroyuki', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan', 95, 'Algorithms|Amino Acid Sequence|Binding Sites|Escherichia coli Proteins|Evolution, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/bti564', '15994190', 'The prediction of protein-protein interactions is currently an important issue in bioinformatics. The mirror tree method uses evolutionary information to predict protein-protein interactions. However, it has been recognized that predictions by the mirror tree method lead to many false positives. The incentive of our study was to solve this problem by improving the method of extracting the co-evolutionary information regarding the protein pairs.'),
(72250, 2005, 'bio', 'TICO: a tool for improving predictions of prokaryotic translation initiation sites', 'Tech, Maike|Pfeifer, Nico|Morgenstern, Burkhard|Meinicke, Peter', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstrasse 1, 37077 Göttingen, Germany. maike@gobics.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstrasse 1, 37077 Göttingen, Germany. maike@gobics.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstrasse 1, 37077 Göttingen, Germany. maike@gobics.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstrasse 1, 37077 Göttingen, Germany. maike@gobics.de', 'Germany|Germany|Germany|Germany', 21, 'Algorithms|Base Sequence|Codon, Initiator|Genome, Bacterial|Molecular Sequence Data|Prokaryotic Cells|Protein Biosynthesis|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/bti563', '15994191', 'We provide the tool TICO (Translation Initiation site COrrection) for improving the results of conventional gene finders for prokaryotic genomes with regard to exact localization of the translation initiation site (TIS). At the current state TICO provides an interface for direct post processing of the predictions obtained from the widely used program GLIMMER. Our program is based on a clustering algorithm for completely unsupervised scoring of potential TIS locations.'),
(72251, 2005, 'bio', 'MicroRNA identification based on sequence and structure alignment', 'Wang, Xiaowo|Zhang, Jing|Li, Fei|Gu, Jin|He, Tao|Zhang, Xuegong|Li, Yanda', 'MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.|MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China.', 'China|China|China|China|China|China|China', 223, 'Algorithms|Animals|Anopheles|Base Sequence|Chromosome Mapping|Computational Biology|Conserved Sequence|Databases, Genetic|Gene Expression Profiling|Genome|Internet|MicroRNAs|Molecular Sequence Data|RNA Precursors|Sensitivity and Specificity|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bti562', '15994192', 'MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes.'),
(72252, 2005, 'bio', 'P-cats: prediction of catalytic residues in proteins from their tertiary structures', 'Kinoshita, Kengo|Ota, Motonori', 'Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. kino@ims.u-tokyo.ac.jp|Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. kino@ims.u-tokyo.ac.jp', 'Japan|Japan', 4, 'Algorithms|Amino Acids|Computer Simulation|Models, Chemical|Models, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti561', '15994193', 'P-cats is a web server that predicts the catalytic residues in proteins from the atomic coordinates. P-cats receives a coordinate file of the tertiary structure and sends out analytical results via e-mail. The reply contains a summary and two URLs to allow the user to examine the conserved residues: one for interactive images of the prediction results and the other for a graphical view of the multiple sequence alignment.'),
(72253, 2005, 'bio', 'TO-GO: a Java-based Gene Ontology navigation environment', 'Yu, Ungsik|Choi, Yoon Jeong|Choi, Jung Kyoon|Kim, Sangsoo', 'National Genome Information Center, KRIBB, Oun 52, Yusong, Daejeon 305-333, Korea. ungsik@kribb.re.kr|National Genome Information Center, KRIBB, Oun 52, Yusong, Daejeon 305-333, Korea. ungsik@kribb.re.kr|National Genome Information Center, KRIBB, Oun 52, Yusong, Daejeon 305-333, Korea. ungsik@kribb.re.kr|National Genome Information Center, KRIBB, Oun 52, Yusong, Daejeon 305-333, Korea. ungsik@kribb.re.kr', '-|-|-|-', 4, 'Algorithms|Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Genes|Natural Language Processing|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti560', '15994194', 'TO-GO is a Gene Ontology (GO) navigation tool, which is implemented as a Java application. After the initial data downloading, the GO term tree can be interactively navigated without further network transfer. Local annotation can be incorporated. It supports querying by GO terms or associated gene product information, displaying the result as a table or a sub-tree. The result from the search for a set of external database accessions includes the number of gene products associated with each node, inclusive of sub-nodes. Search results can be further processed by set operations and these set operations can be quite useful for expression profile data analysis. A copy/paste function is also implemented in order to facilitate data exchange between applications.'),
(72254, 2005, 'bio', 'GeneInfoMiner--a web server for exploring biomedical literature using batch sequence ID', 'Xuan, Weijian|Watson, Stanley J|Meng, Fan', 'Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 4, 'Abstracting and Indexing as Topic|Database Management Systems|Documentation|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Proteins|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti559', '15994195', 'GeneInfoMiner is a web-based system for searching Medline abstracts using sequence ID lists such as GenBank accession numbers derived from high-throughput experiments. It will map query results to MeSH topics to facilitate the exploration of the biological significance of the sequence ID lists. GeneInfoMiner is based on a custom gene and protein name identification engine that can map gene and protein names to important molecular biology databases.'),
(72255, 2005, 'bio', 'SynBrowse: a synteny browser for comparative sequence analysis', 'Pan, Xiaokang|Stein, Lincoln|Brendel, Volker', 'Department of Genetics, Development and Cell Biology, Iowa State University, 2112 Molecular Biology Building, Ames, IA 50011-3260, USA.|Department of Genetics, Development and Cell Biology, Iowa State University, 2112 Molecular Biology Building, Ames, IA 50011-3260, USA.|Department of Genetics, Development and Cell Biology, Iowa State University, 2112 Molecular Biology Building, Ames, IA 50011-3260, USA.', 'United States|United States|United States', 50, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|Synteny|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti555', '15994196', 'The recent efforts of various sequence projects to sequence deeply into various phylogenies provide great resources for comparative sequence analysis. A generic and portable tool is essential for scientists to visualize and analyze sequence comparisons.'),
(72256, 2005, 'bio', 'Increased power of microarray analysis by use of an algorithm based on a multivariate procedure', 'Krohn, K|Eszlinger, M|Paschke, R|Roeder, I|Schuster, E', 'Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstrasse 22, 04103 Leipzig, Germany.|Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstrasse 22, 04103 Leipzig, Germany.|Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstrasse 22, 04103 Leipzig, Germany.|Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstrasse 22, 04103 Leipzig, Germany.|Interdisciplinary Center for Clinical Research Leipzig, University of Leipzig, Inselstrasse 22, 04103 Leipzig, Germany.', 'Germany|Germany|Germany|Germany|Germany', 3, 'Algorithms|Gene Expression Profiling|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction', 'Comparative Study', '10.1093/bioinformatics/bti570', '15998661', 'The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the multiple testing problem, increasing the probability of obtaining false positive test results. To achieve more reliable results, it is, therefore, necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate. However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach.'),
(72257, 2005, 'bio', 'Local modeling of global interactome networks', 'Scholtens, Denise|Vidal, Marc|Gentleman, Robert', 'Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. dscholtens@northwestern.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. dscholtens@northwestern.edu|Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. dscholtens@northwestern.edu', 'United States|United States|United States', 63, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti567', '15998662', 'Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput interactome data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72258, 2005, 'bio', 'A precise and scalable method for querying genes in chromosomal banding regions based on cytogenetic annotations', 'Yen, Kuo-Ho|Lee, Chiang|Liu, Hsiao-Sheng|Ho, Chung-Liang', 'Department of Computer Science and Information Engineering, National Cheng Kung University, No 1. Da-Shueh Road, Tainan, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, No 1. Da-Shueh Road, Tainan, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, No 1. Da-Shueh Road, Tainan, Taiwan.|Department of Computer Science and Information Engineering, National Cheng Kung University, No 1. Da-Shueh Road, Tainan, Taiwan.', '-|-|-|-', 2, 'Algorithms|Base Sequence|Chromosome Banding|Chromosome Mapping|Cytogenetic Analysis|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti566', '15998663', 'Staining the human metaphase chromosomes reveals characteristic banding patterns known as cytogenetic bands or cytobands. Using technologies based on metaphase chromosomes, researchers have accumulated much knowledge about the correlations between human diseases and specific cytoband aberrations, indicating the presence of disease-associated genes in those bands. With the progress of human genome project and techniques such as fluorescent in situ hybridization, many genes have been assigned to the cytobands and annotated in public databases, making it possible to find all genes in the disease-related cytobands through database queries. However, finding genes in cytobands remains an imprecise process, partly due to the insufficiency of current methods for cytoband queries, especially for those based on cytogenetic annotations.'),
(72259, 2005, 'bio', 'Discrimination of yeast genes involved in methionine and phosphate metabolism on the basis of upstream motifs', 'Gonze, Didier|Pinloche, Sylvie|Gascuel, Olivier|van Helden, Jacques', 'Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, CP 263, Campus Plaine, Blvd du Triomphe, B-1050 Bruxelles, Belgium.|Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, CP 263, Campus Plaine, Blvd du Triomphe, B-1050 Bruxelles, Belgium.|Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, CP 263, Campus Plaine, Blvd du Triomphe, B-1050 Bruxelles, Belgium.|Service de Conformation des Macromolécules Biologiques et de Bioinformatique, Université Libre de Bruxelles, CP 263, Campus Plaine, Blvd du Triomphe, B-1050 Bruxelles, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Amino Acid Sequence|Discriminant Analysis|Gene Expression Profiling|Gene Expression Regulation|Methionine|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Phosphates|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti558', '15998664', 'In yeast, methionine and phosphate metabolism are regulated by the complexes Met4p/Met28p/Cbf1p and Pho4p, respectively. The binding sites for these factors share a common core CACGTG. We evaluate our capability to discriminate phosphate- and methionine-responding genes on the basis of putative regulatory elements, despite the similarity between Met4p/Met28p/Cbf1p and Pho4p consensus.'),
(72260, 2005, 'bio', 'Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells', 'Selivanov, Vitaly A|Meshalkina, Ludmilla E|Solovjeva, Olga N|Kuchel, Philip W|Ramos-Montoya, Antonio|Kochetov, German A|Lee, Paul W N|Cascante, Marta', 'Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, Facultat de Quimica and CERQT at Parc Cientic de Barcelona, Barcelona, Catalunya, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Carbon Radioisotopes|Computer Simulation|Enzyme Activation|Gene Expression Profiling|Glucose|HT29 Cells|Humans|Isotope Labeling|Kinetics|Models, Biological|Multienzyme Complexes|Pentose Phosphate Pathway|Software|Transaldolase|Transketolase', 'Comparative Study', '10.1093/bioinformatics/bti573', '16002431', 'Addition of labeled substrates and the measurement of the subsequent distribution of the labels in isotopomers in reaction networks provide a unique method for assessing metabolic fluxes in whole cells. However, owing to insufficiency of information, attempts to quantify the fluxes often yield multiple possible sets of solutions that are consistent with a given experimental pattern of isotopomers. In the study of the pentose phosphate pathways, the need to consider isotope exchange reactions of transketolase (TK) and transaldolase (TA) (which in past analyses have often been ignored) magnifies this problem, but accounting for the interrelation between the fluxes known from biochemical studies and kinetic modeling solves it. The mathematical relationships between kinetic and equilibrium constants restrict the domain of estimated fluxes to the ones compatible not only with a given set of experimental data, but also with other biochemical information.'),
(72261, 2005, 'bio', 'A parsimonious tree-grow method for haplotype inference', 'Li, Zhenping|Zhou, Wenfeng|Zhang, Xiang-Sun|Chen, Luonan', 'Beijing Materials Institute, Beijing 101149, China.|Beijing Materials Institute, Beijing 101149, China.|Beijing Materials Institute, Beijing 101149, China.|Beijing Materials Institute, Beijing 101149, China.', 'China|China|China|China', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Haplotypes|Models, Genetic|Molecular Sequence Data|Phenotype|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti572', '16002432', 'Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, such as disease genes mapping and drug design. Parsimony haplotyping is one of haplotyping problems belonging to NP-hard class.'),
(72262, 2005, 'bio', 'Applying GIFT, a Gene Interactions Finder in Text, to fly literature', 'Domedel-Puig, Núria|Wernisch, Lorenz', 'School of Crystallography, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. n.domedel_puig@cryst.bbk.ac.uk|School of Crystallography, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK. n.domedel_puig@cryst.bbk.ac.uk', 'United Kingdom|United Kingdom', 17, 'Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Databases, Genetic|Drosophila Proteins|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping', 'Journal Article', '10.1093/bioinformatics/bti578', '16014369', 'A number of freely available text mining tools have been put together to extract highly reliable Drosophila gene interaction data from text. The system has been tested with The Interactive Fly, showing low recall (27-34%), but very high precision (93-97%).'),
(72263, 2005, 'bio', 'easyLINKAGE-Plus--automated linkage analyses using large-scale SNP data', 'Hoffmann, K|Lindner, T H', 'Institute of Medical Genetics, Charité, Humboldt University Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.|Institute of Medical Genetics, Charité, Humboldt University Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.', 'Germany|Germany', 0, 'Algorithms|Chromosome Mapping|Databases, Nucleic Acid|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti571', '16014370', 'We extended the original easyLINKAGE program by enabling linkage analyses for large-scale SNP data in addition to those of microsatellites. We implemented new modules for Allegro, Merlin, SimWalk, GeneHunter Imprinting, GeneHunter TwoLocus, SuperLink and extended FastSLink by automatic loop breaking and new outputs. We added conditional linkage analyses as well as multipoint simulation studies, and extended error test routines by checking for Mendelian/non-Mendelian genotyping errors and for deviations from Hardy-Weinberg equilibrium. Data can be analyzed in sets of markers, in defined centimorgan intervals and by using different allele frequency algorithms. The outputs consist of genome-wide as well as chromosomal postscript plots of LOD scores, NPL scores, P-values and other parameters.'),
(72264, 2005, 'bio', 'Integration of metabolic networks and gene expression in virtual reality', 'Yang, Yuting|Engin, Levent|Wurtele, Eve Syrkin|Cruz-Neira, Carolina|Dickerson, Julie A', 'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States|United States', 29, 'Algorithms|Arabidopsis|Biochemistry|Computer Graphics|Computer Simulation|Gene Expression|Gene Expression Regulation|Genes, Plant|Genome, Plant|Internet|Metabolism|Models, Biological|Multigene Family|Proteomics|Software|Software Design|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti581', '16020466', 'Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and visualize due to the amount and diverse types of information that need to be represented. For example, pathway information gives indications of interactions. Experimental data, such as transcriptomics, proteomics and metabolomics data, give snapshots of the system state. Stereoscopic virtual environments provide a true three-dimensional representation of metabolic networks, which can be intuitively manipulated, and may help to manage the data complexity.'),
(72265, 2005, 'bio', 'DEPD: a novel database for differentially expressed proteins', 'He, Quan-Yuan|Cao, Jing|Liu, Xiang-Hua|Li, Miao-Xin|Liu, Yi-Song|Xie, Jin-Yun|Liang, Song-Ping', 'College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.|College of Life Science, Hunan Normal University, Changsha 410081, Peoples Republic of China.', 'China|China|China|China|China|China|China', 0, 'Computational Biology|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Programming Languages|Proteomics|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti580', '16020467', 'The Differentially Expressed Protein Database was designed to store the output of comparative proteomics studies and provides a publicly available query and analysis platform for data mining. The database contains information about more than 3000 differentially expressed proteins (DEPs) manually extracted from the published literature, including relevant biological, experimental and methodological elements. Tools for visualization and functional analysis of DEPs are provided via a user-friendly webinterface.'),
(72266, 2005, 'bio', 'goCluster integrates statistical analysis and functional interpretation of microarray expression data', 'Wrobel, Gunnar|Chalmel, Frédéric|Primig, Michael', 'Biozentrum and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.|Biozentrum and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.|Biozentrum and Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/bti574', '16020468', 'Several tools that facilitate the interpretation of transcriptional profiles using gene annotation data are available but most of them combine a particular statistical analysis strategy with functional information. goCluster extends this concept by providing a modular framework that facilitates integration of statistical and functional microarray data analysis with data interpretation.'),
(72267, 2005, 'bio', 'simuPOP: a forward-time population genetics simulation environment', 'Peng, Bo|Kimmel, Marek', 'Department of Statistics, Rice University, Houston, TX 77005, USA. bpeng@rice.edu|Department of Statistics, Rice University, Houston, TX 77005, USA. bpeng@rice.edu', 'United States|United States', 199, 'Algorithms|Alleles|Biological Evolution|Computational Biology|Computer Graphics|Computer Simulation|Diploidy|Genetics|Genetics, Population|Genotype|Humans|Internet|Linkage Disequilibrium|Programming Languages|Software|Time|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti584', '16020469', 'simuPOP is a forward-time population genetics simulation environment. The core of simuPOP is a scripting language (Python) that provides a large number of objects and functions to manipulate populations, and a mechanism to evolve populations forward in time. Using this R/Splus-like environment, users can create, manipulate and evolve populations interactively, or write a script and run it as a batch file. Owing to its flexible and extensible design, simuPOP can simulate large and complex evolutionary processes with ease. At a more user-friendly level, simuPOP provides an increasing number of built-in scripts that perform simulations ranging from implementation of basic population genetics models to generating datasets under complex evolutionary scenarios.'),
(72268, 2005, 'bio', 'A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips', 'Liu, Xuejun|Milo, Marta|Lawrence, Neil D|Rattray, Magnus', 'School of Computer Science, University of Manchester, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Manchester M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Probability|Reproducibility of Results|Sensitivity and Specificity|Software|Statistics as Topic|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti583', '16020470', 'Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarization methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large datasets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and does not account for specific binding to the mismatch (MM) probes.'),
(72269, 2005, 'bio', 'SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures', 'Zhou, Hongyi|Zhou, Yaoqi', 'Department of Physiology and Biophysics, Howard Hughes Medical Institute Center for Single Molecule Biophysics, State University of New York, Buffalo, NY 14214, USA.|Department of Physiology and Biophysics, Howard Hughes Medical Institute Center for Single Molecule Biophysics, State University of New York, Buffalo, NY 14214, USA.', 'United States|United States', 69, 'Algorithms|Amino Acid Sequence|Computational Biology|Computing Methodologies|Databases, Protein|Evolution, Molecular|Internet|Protein Folding|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/bti582', '16020471', 'Multiple sequence alignment is an essential part of bioinformatics tools for a genome-scale study of genes and their evolution relations. However, making an accurate alignment between remote homologs is challenging. Here, we develop a method, called SPEM, that aligns multiple sequences using pre-processed sequence profiles and predicted secondary structures for pairwise alignment, consistency-based scoring for refinement of the pairwise alignment and a progressive algorithm for final multiple alignment.'),
(72270, 2005, 'bio', 'Consensus shapes: an alternative to the Sankoff algorithm for RNA consensus structure prediction', 'Reeder, Jens|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany. jreeder@TechFak.Uni-Bielefeld.DE|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany. jreeder@TechFak.Uni-Bielefeld.DE', 'Germany|Germany', 0, 'Algorithms|Base Sequence|Computer Simulation|Consensus Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti577', '16020472', 'The well-known Sankoff algorithm for simultaneous RNA sequence alignment and folding is currently considered an ideal, but computationally over-expensive method. Available tools implement this algorithm under various pragmatic restrictions. They are still expensive to use, and it is difficult to judge if the moderate quality of results is because of the underlying model or to its imperfect implementation.'),
(72271, 2005, 'bio', 'MAVisto: a tool for the exploration of network motifs', 'Schreiber, Falk|Schwöbbermeyer, Henning', 'Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany.|Leibniz Institute of Plant Genetics and Crop Plant Research, 06466 Gatersleben, Germany.', 'Germany|Germany', 140, 'Computer Simulation|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti556', '16020473', 'MAVisto is a tool for the exploration of motifs in biological networks. It provides a flexible motif search algorithm and different views for the analysis and visualization of network motifs. These views help to explore interesting motifs: the frequency of motif occurrences can be compared with randomized networks, a list of motifs along with information about structure and number of occurrences depending on the reuse of network elements shows potentially interesting motifs, a motif fingerprint reveals the overall distribution of motifs of a given size and the distribution of a particular motif in the network can be visualized using an advanced layout algorithm.'),
(72272, 2005, 'bio', 'Phylogenetic trees: visualizing, customizing and detecting incongruence', 'Trooskens, Geert|De Beule, David|Decouttere, Frederik|Van Criekinge, Wim', 'Laboratorium for Bioinformatics and Computational Genomics (BioBix), Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University Ghent, Belgium.|Laboratorium for Bioinformatics and Computational Genomics (BioBix), Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University Ghent, Belgium.|Laboratorium for Bioinformatics and Computational Genomics (BioBix), Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University Ghent, Belgium.|Laboratorium for Bioinformatics and Computational Genomics (BioBix), Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University Ghent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 36, 'Chromosome Mapping|Computer Graphics|Documentation|Models, Genetic|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti590', '16030069', 'TreeIllustrator is a user-friendly application to visualize and customize phylogenetic trees. It has a broad range of functions and capabilities, such as dragging of nodes, different tree shapes, zooming and searching capabilities, and support for large trees. It acts as a solution that integrates the specificity of visualizing phylogenetic trees and the customization options of a drawing program. It also contains a simple and effective method that compares a custom tree with the Tree of Life, by detecting incongruence.'),
(72273, 2005, 'bio', 'PACdb: PolyA Cleavage Site and 3-UTR Database', 'Brockman, J Michael|Singh, Priyam|Liu, Donglin|Quinlan, Sean|Salisbury, Jesse|Graber, Joel H', 'The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org|The Jackson Laboratory, Bar Harbor, ME 04609, USA. joel.graber@jax.org', 'United States|United States|United States|United States|United States|United States', 0, '3 Untranslated Regions|Animals|Binding Sites|Computational Biology|DNA, Complementary|Database Management Systems|Databases, Genetic|Expressed Sequence Tags|Genome|Humans|Information Storage and Retrieval|Internet|Mice|Poly A', 'Journal Article', '10.1093/bioinformatics/bti589', '16030070', 'The PolyA Cleavage Site and 3-UTR Database (PACdb) is a web-accessible database that catalogs putative 3-processing sites and 3-UTR sequences for multiple organisms. Sites have been identified primarily via expressed sequence tag-genome alignments, enabling delineation of both the specificities and heterogeneity of 3-processing events.'),
(72274, 2005, 'bio', 'DBToolkit: processing protein databases for peptide-centric proteomics', 'Martens, Lennart|Vandekerckhove, Joël|Gevaert, Kris', 'Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, A. Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, A. Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, A. Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be', 'Belgium|Belgium|Belgium', 0, 'Database Management Systems|Databases, Protein|Information Storage and Retrieval|Peptide Mapping|Peptides|Proteomics|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti588', '16030071', 'DBToolkit is a user-friendly, easily extensible tool that allows the processing of protein sequence databases to peptide-centric sequence databases. This processing is primarily aimed at enhancing the useful information content of these databases for use as optimized search spaces for efficient identification of peptide fragmentation spectra obtained by mass spectrometry. In addition, DBToolkit can be used to reliably solve a range of other typical tasks in processing sequence databases.'),
(72275, 2005, 'bio', 'GeneCruiser: a web service for the annotation of microarray data', 'Liefeld, Ted|Reich, Michael|Gould, Joshua|Zhang, Peili|Tamayo, Pablo|Mesirov, Jill P', 'Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA. liefeld@broad.mit.edu', 'United States|United States|United States|United States|United States|United States', 20, 'Animals|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Genome|Humans|Information Storage and Retrieval|Internet|Mice|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti587', '16030072', 'GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard.'),
(72276, 2005, 'bio', 'A multicellular systems biology model predicts epidermal morphology, kinetics and Ca2+ flow', 'Grabe, Niels|Neuber, Karsten', 'Center for Bioinformatics, University Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. grabe@zbh.uni-hamburg.de|Center for Bioinformatics, University Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany. grabe@zbh.uni-hamburg.de', 'Germany|Germany', 0, 'Algorithms|Calcium|Cell Differentiation|Cell Proliferation|Cells, Cultured|Computer Simulation|Epidermal Cells|Epidermis|Humans|Kinetics|Models, Biological|Systems Biology', 'Evaluation Studies', '10.1093/bioinformatics/bti585', '16030073', 'Systems biology is currently focused on integrating intracellular networks, although clinically, diseases are largely defined by their histological features. For example, no computational model can simulate today the formation of a horizontally layered epidermis. Since the epidermis is the most complex structured epithelial tissue, systems biology models could yield important insights in epithelial tissue, in which most of all human cancers arise.'),
(72277, 2005, 'bio', 'HTHquery: a method for detecting DNA-binding proteins with a helix-turn-helix structural motif', 'Ferrer-Costa, C|Shanahan, H P|Jones, S|Thornton, J M', 'Molecular Modelling and Bioinformatics, IRBB-Parc Cientific de Barcelona, UB, Josep Samitier, 1-5 08028 Barcelona, Catalonia, Spain.|Molecular Modelling and Bioinformatics, IRBB-Parc Cientific de Barcelona, UB, Josep Samitier, 1-5 08028 Barcelona, Catalonia, Spain.|Molecular Modelling and Bioinformatics, IRBB-Parc Cientific de Barcelona, UB, Josep Samitier, 1-5 08028 Barcelona, Catalonia, Spain.|Molecular Modelling and Bioinformatics, IRBB-Parc Cientific de Barcelona, UB, Josep Samitier, 1-5 08028 Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 26, 'Amino Acid Motifs|Computational Biology|DNA-Binding Proteins|Databases, Protein|False Positive Reactions|Genomics|Helix-Turn-Helix Motifs|Internet|Models, Molecular|Protein Conformation|Protein Structure, Secondary|Proteins|Software|Static Electricity|Structural Homology, Protein', 'Journal Article', '10.1093/bioinformatics/bti575', '16030074', 'HTHquery is a web-based service to determine if a protein structure has a helix-turn-helix structural motif which could bind to DNA. It is based on a similarity with a set of structural templates, the accessibility of a putative structural motif and a positive electrostatic potential in the neighbourhood of the putative motif. A set of scores are computed, based on each template, using a linear predictor. From the training set used, the predictor has a true positive rate of 83.5% and a false positive rate of 0.8%. The emphasis for the website is on providing a straightforward interface which can be easily used by a bench-based scientist.'),
(72278, 2005, 'bio', 'Resolving abbreviations to their senses in Medline', 'Gaudan, S|Kirsch, H|Rebholz-Schuhmann, D', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. gaudan@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. gaudan@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. gaudan@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Abbreviations as Topic|Abstracting and Indexing as Topic|Computational Biology|Databases, Bibliographic|Dictionaries as Topic|Information Storage and Retrieval|Internet|MEDLINE|Models, Statistical|Natural Language Processing|Software|Subject Headings|Terminology as Topic|Unified Medical Language System', 'Journal Article', '10.1093/bioinformatics/bti586', '16037121', 'Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems.'),
(72279, 2005, 'bio', 'BlastXtract--a new way of exploring translated searches', 'Claesson, Marcus J|van Sinderen, Douwe', 'Alimentary Pharmabiotic Centre and Department of Microbiology, National University of Ireland, Cork, Ireland. mclaesson@bioinfo.ucc.ie|Alimentary Pharmabiotic Centre and Department of Microbiology, National University of Ireland, Cork, Ireland. mclaesson@bioinfo.ucc.ie', 'Ireland|Ireland', 0, 'Amino Acid Sequence|Base Sequence|Bifidobacterium|Computational Biology|Computer Graphics|DNA|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Genome|Lactobacillus|Sequence Alignment|Sequence Analysis|Sequence Analysis, DNA|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti598', '16046492', 'Searches of translated, unannotated genomic DNA sequences against protein databases is a useful early-stage method for discovering protein homologues encoded by the sequence, but generates huge amounts of output data that quickly become impregnable. BlastXtract is a web-based tool for managing and visualizing results from large translated BLAST and FastA searches. It combines the speed and storage benefits of relational database management systems with an easy-to-use graphical navigation map, and greatly facilitates the early exploration of genomic sequence.'),
(72280, 2006, 'bio', 'Extraction of regulatory gene/protein networks from Medline', 'Saric, Jasmin|Jensen, Lars Juhl|Ouzounova, Rossitza|Rojas, Isabel|Bork, Peer', 'EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org|EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org|EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org|EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org|EML Research gGmbH D-69118 Heidelberg, Germany. saric@eml-r.org', 'Germany|Germany|Germany|Germany|Germany', 111, 'Abstracting and Indexing as Topic|Gene Expression Regulation|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/bti597', '16046493', 'We have previously developed a rule-based approach for extracting information on the regulation of gene expression in yeast. The biomedical literature, however, contains information on several other equally important regulatory mechanisms, in particular phosphorylation, which we now expanded for our rule-based system also to extract.'),
(72281, 2005, 'bio', 'CHORAL: a differential geometry approach to the prediction of the cores of protein structures', 'Montalvão, Rinaldo W|Smith, Richard E|Lovell, Simon C|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge Tennis Court Road, Cambridge CB2 1GA, UK. rinaldo@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge Tennis Court Road, Cambridge CB2 1GA, UK. rinaldo@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge Tennis Court Road, Cambridge CB2 1GA, UK. rinaldo@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge Tennis Court Road, Cambridge CB2 1GA, UK. rinaldo@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti595', '16046494', 'Although the cores of homologous proteins are relatively well conserved, amino acid substitutions lead to significant differences in the structures of divergent superfamilies. Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the protein cores of homologues of known structure are important for accurate comparative modelling.'),
(72282, 2005, 'bio', 'pIQPNNI: parallel reconstruction of large maximum likelihood phylogenies', 'Minh, Bui Quang|Vinh, Le Sy|von Haeseler, Arndt|Schmidt, Heiko A', 'Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Animals|Base Sequence|Computing Methodologies|Evolution, Molecular|Humans|Likelihood Functions|Models, Genetic|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti594', '16046495', 'IQPNNI is a program to infer maximum-likelihood phylogenetic trees from DNA or protein data with a large number of sequences. We present an improved and MPI-parallel implementation showing very good scaling and speed-up behavior.'),
(72283, 2005, 'bio', 'TileMap: create chromosomal map of tiling array hybridizations', 'Ji, Hongkai|Wong, Wing Hung', 'Department of Statistics, Harvard University, Cambridge, MA 02138, USA.|Department of Statistics, Harvard University, Cambridge, MA 02138, USA.', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Chromatin Immunoprecipitation|Chromosome Mapping|Computational Biology|Gene Expression Profiling|Genes, p53|Genome|Hybridization, Genetic|Internet|Markov Chains|Models, Genetic|Mutation|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity|Software|Time Factors|Transcription, Genetic|Tumor Suppressor Protein p53', 'Journal Article', '10.1093/bioinformatics/bti593', '16046496', 'Tiling array is a new type of microarray that can be used to survey genomic transcriptional activities and transcription factor binding sites at high resolution. The goal of this paper is to develop effective statistical tools to identify genomic loci that show transcriptional or protein binding patterns of interest.'),
(72284, 2005, 'bio', 'Automated Microarray Image Analysis Toolbox for MATLAB', 'White, Amanda M|Daly, Don S|Willse, Alan R|Protic, Miroslava|Chandler, Darrell P', 'Statistical Sciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory, Richland, WA 99352, USA. amanda.white@pnl.gov', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/bti576', '16046497', 'The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIAs capabilities.'),
(72285, 2005, 'bio', 'A statistical model for HIV-1 sequence classification using the subtype analyser (STAR)', 'Myers, R E|Gale, C V|Harrison, A|Takeuchi, Y|Kellam, P', 'Department of Immunology and Molecular Pathology, University College London, UK.|Department of Immunology and Molecular Pathology, University College London, UK.|Department of Immunology and Molecular Pathology, University College London, UK.|Department of Immunology and Molecular Pathology, University College London, UK.|Department of Immunology and Molecular Pathology, University College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 31, 'Algorithms|Amino Acid Sequence|Chromosome Mapping|HIV Protease|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti569', '16046498', 'HIV-1 antiretroviral drug resistance testing produces large amounts of HIV-1 protease and reverse transcriptase sequences. These provide an excellent resource to study the incidence, spread and clinical significance of HIV-1 subtypes. We have produced a program, Subtype Analyser (STAR) that rapidly and accurately subtypes HIV-1. Here we have determined a robust and statistically validated model for subtype assignment.'),
(72286, 2005, 'bio', 'SpA: web-accessible spectratype analysis: data management, statistical analysis and visualization', 'He, Min|Tomfohr, John K|Devlin, Blythe H|Sarzotti, Marcella|Markert, M Louise|Kepler, Thomas B', 'Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Computer Graphics|DNA Primers|Database Management Systems|Databases, Protein|Genes, T-Cell Receptor|Humans|Information Storage and Retrieval|Internet|Programming Languages|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti600', '16051675', 'SpA is a web-accessible system for the management, visualization and statistical analysis of T-cell receptor spectratype data. Users upload data from their spectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a secure database. The statistical engine performs several data analyses and statistical summaries. The visualization engine displays spectratype histograms in a Java applet and in an image file suitable for download. All of these results are also saved to the database and remain accessible to the user. Additional statistical tools specific to the analysis of multiple spectratypes are also available through the SpA interface.'),
(72287, 2005, 'bio', 'JIGSAW: integration of multiple sources of evidence for gene prediction', 'Allen, Jonathan E|Salzberg, Steven L', 'Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies, College Park, MD 20742, USA. jeallen@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, University of Maryland Institute for Advanced Computer Studies, College Park, MD 20742, USA. jeallen@umiacs.umd.edu', 'United States|United States', 0, 'Algorithms|Animals|Codon|Computational Biology|DNA, Complementary|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Genes, Fungal|Genes, Plant|Genome, Human|Humans|Introns|Markov Chains|Models, Genetic|Models, Statistical|Open Reading Frames|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Software Validation', 'Journal Article', '10.1093/bioinformatics/bti609', '16076884', 'Computational gene finding systems play an important role in finding new human genes, although no systems are yet accurate enough to predict all or even most protein-coding regions perfectly. Ab initio programs can be augmented by evidence such as expression data or protein sequence homology, which improves their performance. The amount of such evidence continues to grow, but computational methods continue to have difficulty predicting genes when the evidence is conflicting or incomplete. Genome annotation pipelines collect a variety of types of evidence about gene structure and synthesize the results, which can then be refined further through manual, expert curation of gene models.'),
(72288, 2005, 'bio', 'Motif-based protein ranking by network propagation', 'Kuang, Rui|Weston, Jason|Noble, William Stafford|Leslie, Christina', 'Department of Computer Science, Columbia University New York, NY 10027, USA.|Department of Computer Science, Columbia University New York, NY 10027, USA.|Department of Computer Science, Columbia University New York, NY 10027, USA.|Department of Computer Science, Columbia University New York, NY 10027, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/bti608', '16076885', 'Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets.'),
(72289, 2005, 'bio', 'An automated genotyping system for analysis of HIV-1 and other microbial sequences', 'de Oliveira, Tulio|Deforche, Koen|Cassol, Sharon|Salminen, Mika|Paraskevis, Dimitris|Seebregts, Chris|Snoeck, Joe|van Rensburg, Estrelita Janse|Wensing, Annemarie M J|van de Vijver, David A|Boucher, Charles A|Camacho, Ricardo|Vandamme, Anne-Mieke', 'Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.ac.uk|Evolution Group at the Zoology Department, University of Oxford, UK. tulio.deoliveira@zoology.oxford.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|United Kingdom', 393, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|DNA, Viral|Genotype|HIV-1|Molecular Sequence Data|Pattern Recognition, Automated|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti607', '16076886', 'Genetic analysis of HIV-1 is important not only for vaccine development, but also to guide treatment strategies, track the emergence of new viral variants and ensure that diagnostic assays are contemporary and fully optimized. However, most genotyping methods are laborious and complex, and involve the use of multiple software applications. Here, we describe the development of an automated genotyping system that can be easily applied to HIV-1 and other rapidly evolving viral pathogens.'),
(72290, 2005, 'bio', 'GenColors: accelerated comparative analysis and annotation of prokaryotic genomes at various stages of completeness', 'Romualdi, Alessandro|Siddiqui, Roman|Glöckner, Gernot|Lehmann, Rüdiger|Sühnel, Jürgen', 'Biocomputing Group, Institute of Molecular Biotechnology, 07745 Jena, Germany.|Biocomputing Group, Institute of Molecular Biotechnology, 07745 Jena, Germany.|Biocomputing Group, Institute of Molecular Biotechnology, 07745 Jena, Germany.|Biocomputing Group, Institute of Molecular Biotechnology, 07745 Jena, Germany.|Biocomputing Group, Institute of Molecular Biotechnology, 07745 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany', 10, 'Borrelia|Chromosome Mapping|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Genome|Genome, Bacterial|Genomics|Internet|Sequence Alignment|Sequence Analysis|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti606', '16076887', 'GenColors is a new web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes, considering information on related genomes and making extensive use of genome comparison. It offers a seamless integration of data from ongoing sequencing projects and annotated genomic sequences obtained from GenBank. The genome comparison tools determine, for example, best-bidirectional hits, gene conservation, syntenies and gene core sets. Swiss-Prot/TrEMBL hits allow annotations in an effective manner. To further support the annotation base-specific quality data can also be displayed if available. With GenColors dedicated genome browsers containing a group of related genomes can be easily set up and maintained. It has been efficiently used for Borrelia garinii and is currently applied to various ongoing genome projects.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72291, 2005, 'bio', 'Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis', 'Wilson, Claire L|Miller, Crispin J', 'Paterson Institute for Cancer Research, Manchester M20 4BX, UK.|Paterson Institute for Cancer Research, Manchester M20 4BX, UK.', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Gene Expression Profiling|Humans|Information Storage and Retrieval|Internet|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Quality Control|RNA|Sequence Analysis, DNA|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti605', '16076888', 'Quality Control is a fundamental aspect of successful microarray data analysis. Simpleaffy is a BioConductor package that provides access to a variety of QC metrics for assessing the quality of RNA samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls.'),
(72292, 2005, 'bio', 'Friend, an integrated analytical front-end application for bioinformatics', 'Abyzov, Alexej|Errami, Mounir|Leslin, Chesley M|Ilyin, Valentin A', 'Department of Biology, Northeastern University, Boston, MA, USA.|Department of Biology, Northeastern University, Boston, MA, USA.|Department of Biology, Northeastern University, Boston, MA, USA.|Department of Biology, Northeastern University, Boston, MA, USA.', 'United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|DNA|Database Management Systems|Databases, Genetic|Databases, Protein|Exons|Internet|Muramidase|Nucleic Acid Conformation|Phylogeny|Protein Folding|Proteins|RNA|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti602', '16076889', 'Friend is a bioinformatics application designed for simultaneous analysis and visualization of multiple structures and sequences of proteins and/or DNA/RNA. The application provides basic functionalities, such as structure visualization, with different rendering and coloring, sequence alignment and simple phylogeny analysis, along with a number of extended features to perform more complex analyses of sequence structure relationships, including structural alignment of proteins, investigation of specific interaction motifs, studies of protein-protein and protein-DNA interactions and protein super-families. It is also useful for functional annotation of proteins, protein modeling and protein folding studies. Friend provides three levels of usage: (1) an extensive GUI for a scientist with no programming experience, (2) a command line interface for scripting for a scientist with some programming experience and (3) the ability to extend Friend with user written libraries for an experienced programmer. The application is linked and communicates with local and remote sequence and structure databases.'),
(72293, 2005, 'bio', 'WebACT--an online companion for the Artemis Comparison Tool', 'Abbott, J C|Aanensen, D M|Rutherford, K|Butcher, S|Spratt, B G', 'Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. admin@webact.org|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. admin@webact.org|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. admin@webact.org|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. admin@webact.org|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. admin@webact.org', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 69, 'Computational Biology|Computer Graphics|Databases, Genetic|Genome|Genomics|Internet|Plasmids|Sequence Alignment|Sequence Analysis, DNA|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti601', '16076890', 'WebACT is an online resource which enables the rapid provision of simultaneous BLAST comparisons between up to five genomic sequences in a format amenable for visualization with the well-known Artemis Comparison Tool (ACT). Comparisons can be generated on-the-fly using sequences directly retrieved via EMBL database queries, or by entering or uploading user sequences. Furthermore, pre-computed comparisons are available between all publicly available, completed prokaryotic genomes and plasmids currently contained within the Genome Reviews database (372 sequences, representing 175 different species). The system is designed to minimize the volume of downloaded data and maximize performance. Genome sequences, annotation and pre-computed comparisons are stored in a relational database allowing flexible querying based on user-defined sequence regions, from whole genome to a defined region flanking a specified gene. Comparison and sequence files, whether computed online or retrieved from the database of pre-computed genome comparisons, can be viewed online using ACT and are available for download.'),
(72294, 2005, 'bio', 'Study of coordinative gene expression at the biological process level', 'Yu, Tianwei|Sun, Wei|Yuan, Shinsheng|Li, Ker-Chau', 'Department of Statistics, University of California Los Angeles, CA90095-1554, USA.|Department of Statistics, University of California Los Angeles, CA90095-1554, USA.|Department of Statistics, University of California Los Angeles, CA90095-1554, USA.|Department of Statistics, University of California Los Angeles, CA90095-1554, USA.', 'United States|United States|United States|United States', 11, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Environment|Fungal Proteins|Gene Expression Regulation|Gene Expression Regulation, Fungal|Genes, Fungal|Genome, Fungal|Microarray Analysis|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti599', '16076891', 'Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly.'),
(72295, 2005, 'bio', 'Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data', 'Lai, Weil R|Johnson, Mark D|Kucherlapati, Raju|Park, Peter J', 'Harvard-Partners Center for Genetics and Genomics 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Harvard-Partners Center for Genetics and Genomics 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Harvard-Partners Center for Genetics and Genomics 77 Avenue Louis Pasteur, Boston, MA 02115, USA.|Harvard-Partners Center for Genetics and Genomics 77 Avenue Louis Pasteur, Boston, MA 02115, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Gene Amplification|Gene Deletion|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1093/bioinformatics/bti611', '16081473', 'Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear.'),
(72296, 2005, 'bio', 'Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research', 'Conesa, Ana|Götz, Stefan|García-Gómez, Juan Miguel|Terol, Javier|Talón, Manuel|Robles, Montserrat', 'Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Moncada, Valencia, Spain. aconesa@ivia.es', 'Spain|Spain|Spain|Spain|Spain|Spain', 6948, 'Algorithms|Computational Biology|Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Genome|Genomics|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti610', '16081474', 'We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process.'),
(72297, 2005, 'bio', 'Bifurcation discovery tool', 'Chickarmane, Vijay|Paladugu, Sri R|Bergmann, Frank|Sauro, Herbert M', 'Keck Graduate Institute, Claremont, CA 91711, USA. vijay_chickarmane@kgi.edu|Keck Graduate Institute, Claremont, CA 91711, USA. vijay_chickarmane@kgi.edu|Keck Graduate Institute, Claremont, CA 91711, USA. vijay_chickarmane@kgi.edu|Keck Graduate Institute, Claremont, CA 91711, USA. vijay_chickarmane@kgi.edu', 'United States|United States|United States|United States', 42, 'Algorithms|Biochemistry|Computational Biology|Computer Simulation|Internet|Nonlinear Dynamics|Oscillometry|Software|Systems Biology|Time|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti603', '16081475', 'Biochemical networks often yield interesting behavior such as switching, oscillation and chaotic dynamics. This article describes a tool that is capable of searching for bifurcation points in arbitrary ODE-based reaction networks by directing the user to regions in the parameter space, where such interesting dynamical behavior can be observed.'),
(72298, 2005, 'bio', 'Analysis of dose-response effects on gene expression data with comparison of two microarray platforms', 'Hu, Jianhua|Kapoor, Mini|Zhang, Wei|Hamilton, Stanley R|Coombes, Kevin R', 'Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.|Department of Biostatistics and Applied Mathematics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Antineoplastic Agents|Cell Line, Tumor|Colorectal Neoplasms|Dose-Response Relationship, Drug|Fluorouracil|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Tumor Cells, Cultured', 'Evaluation Studies', '10.1093/bioinformatics/bti592', '16081476', 'The problems of analyzing dose effects on gene expression are gaining attention in biomedical research. A specific challenge is to detect genes with expression levels that change according to dose levels in a non-random manner, but nonetheless may be considered as potential biomarkers.'),
(72299, 2005, 'bio', 'BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis', 'Durinck, Steffen|Moreau, Yves|Kasprzyk, Arek|Davis, Sean|De Moor, Bart|Brazma, Alvis|Huber, Wolfgang', 'Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be|Department of Electronical Engineering, ESAT-SCD, K.U.Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. steffen.durinck@esat.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 670, 'Algorithms|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/bti525', '16082012', 'biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.'),
(72300, 2005, 'bio', 'Accounting for probe-level noise in principal component analysis of microarray data', 'Sanguinetti, Guido|Milo, Marta|Rattray, Magnus|Lawrence, Neil D', 'Department of Computer Science, Regent Court 211 Portobello Road, Sheffield S1 4DP, UK.|Department of Computer Science, Regent Court 211 Portobello Road, Sheffield S1 4DP, UK.|Department of Computer Science, Regent Court 211 Portobello Road, Sheffield S1 4DP, UK.|Department of Computer Science, Regent Court 211 Portobello Road, Sheffield S1 4DP, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 43, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Software|Stochastic Processes', 'Evaluation Studies', '10.1093/bioinformatics/bti617', '16091409', 'Principal Component Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of high-dimensional datasets. However, in its standard form, it does not take into account any error measures associated with the data points beyond a standard spherical noise. This indiscriminate nature provides one of its main weaknesses when applied to biological data with inherently large variability, such as expression levels measured with microarrays. Methods now exist for extracting credibility intervals from the probe-level analysis of cDNA and oligonucleotide microarray experiments. These credibility intervals are gene and experiment specific, and can be propagated through an appropriate probabilistic downstream analysis.'),
(72301, 2005, 'bio', 'A hybrid machine-learning approach for segmentation of protein localization data', 'Kasson, Peter M|Huppa, Johannes B|Davis, Mark M|Brunger, Axel T', 'Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, CA 94305, USA.|Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, CA 94305, USA.|Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, CA 94305, USA.|Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, CA 94305, USA.', 'United States|United States|United States|United States', 13, 'Animals|Artificial Intelligence|Cell Line|Gene Expression Profiling|Image Interpretation, Computer-Assisted|Lymphocyte Activation|Mice|Microscopy, Fluorescence|Pattern Recognition, Automated|Receptors, Antigen, T-Cell|Spectrometry, Fluorescence|Systems Integration|T-Lymphocytes|Tissue Distribution', 'Evaluation Studies', '10.1093/bioinformatics/bti615', '16091410', 'Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for a support vector machine.'),
(72302, 2005, 'bio', 'ProRule: a new database containing functional and structural information on PROSITE profiles', 'Sigrist, Christian J A|De Castro, Edouard|Langendijk-Genevaux, Petra S|Le Saux, Virginie|Bairoch, Amos|Hulo, Nicolas', 'Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch|Swiss Institute of Bioinformatics (SIB), 1 rue Michel Servet, CH-1211 Geneva 4, Switzerland. christian.sigrist@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 47, 'Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti614', '16091411', 'Increase the discriminatory power of PROSITE profiles to facilitate function determination and provide biologically relevant information about domains detected by profiles for the annotation of proteins.'),
(72303, 2005, 'bio', 'Assessing the application of Ka/Ks ratio test to alternatively spliced exons', 'Xing, Yi|Lee, Christopher', 'Department of Chemistry and Biochemistry, Molecular Biology Institute, Center for Genomics and Proteomics, University of California Los Angeles, Los Angeles, CA 90095-1570, USA.|Department of Chemistry and Biochemistry, Molecular Biology Institute, Center for Genomics and Proteomics, University of California Los Angeles, Los Angeles, CA 90095-1570, USA.', 'United States|United States', 20, 'Algorithms|Alternative Splicing|Chromosome Mapping|Exons|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/bti613', '16091412', 'Recently, the Ka/Ks ratio test, which assesses the protein-coding potentials of genomic regions based on their non-synonymous to synonymous divergence rates, has been proposed and successfully used in genome annotations of eukaryotes. We systematically performed the Ka/Ks ratio test on 925 transcript-confirmed alternatively spliced exons in the human genome, which we describe in this manuscript. We found that 22.3% of evolutionarily conserved alternatively spliced exons cannot pass the Ka/Ks ratio test, compared with 9.8% for constitutive exons. The false negative rate was the highest (85.7%) for exons with low frequencies of transcript inclusion. Analyses of alternatively spliced exons supported by full-length mRNA sequences yielded similar results, and nearly half of exons involved in ancestral alternative splicing events could not pass this test. Our analysis suggests a future direction to incorporate comparative genomics-based alternative splicing predictions with the Ka/Ks ratio test in higher eukaryotes with extensive RNA alternative splicing.'),
(72304, 2005, 'bio', 'Statistical challenges in the analysis of two-dimensional difference gel electrophoresis experiments using DeCyder', 'Fodor, Imola K|Nelson, David O|Alegria-Hartman, Michelle|Robbins, Kristin|Langlois, Richard G|Turteltaub, Kenneth W|Corzett, Todd H|McCutchen-Maloney, Sandra L', 'Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, CA, USA. fodor1@llnl.gov', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Blood Proteins|Computer Simulation|Data Interpretation, Statistical|Electrophoresis, Gel, Two-Dimensional|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Models, Statistical|Proteome|Smallpox Vaccine|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti612', '16091413', 'The DeCyder software (GE Healthcare) is the current state-of-the-art commercial product for the analysis of two-dimensional difference gel electrophoresis (2D DIGE) experiments. Analyses complementing DeCyder are suggested by incorporating recent advances from the microarray data analysis literature. A case study on the effect of smallpox vaccination is used to compare the results obtained from DeCyder with the results obtained by applying moderated t-tests adjusted for multiple comparisons to DeCyder output data that was additionally normalized.'),
(72305, 2005, 'bio', 'Two-stage designs for experiments with a large number of hypotheses', 'Zehetmayer, Sonja|Bauer, Peter|Posch, Martin', 'Section of Medical Statistics, Medical University of Vienna Spitalgasse 23, A-1090 Vienna, Austria.|Section of Medical Statistics, Medical University of Vienna Spitalgasse 23, A-1090 Vienna, Austria.|Section of Medical Statistics, Medical University of Vienna Spitalgasse 23, A-1090 Vienna, Austria.', 'Austria|Austria|Austria', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical', 'Evaluation Studies', '10.1093/bioinformatics/bti604', '16091414', 'When a large number of hypotheses are investigated the false discovery rate (FDR) is commonly applied in gene expression analysis or gene association studies. Conventional single-stage designs may lack power due to low sample sizes for the individual hypotheses. We propose two-stage designs where the first stage is used to screen the promising hypotheses which are further investigated at the second stage with an increased sample size. A multiple test procedure based on sequential individual P-values is proposed to control the FDR for the case of independent normal distributions with known variance.'),
(72306, 2005, 'bio', 'SPLINDID: a semi-parametric, model-based method for obtaining transcription rates and gene regulation parameters from genomic and proteomic expression profiles', 'Bhasi, Kavitha|Forrest, Alan|Ramanathan, Murali', 'Department of Pharmaceutical Sciences, State University of New York at Buffalo, 14260-1200, USA.|Department of Pharmaceutical Sciences, State University of New York at Buffalo, 14260-1200, USA.|Department of Pharmaceutical Sciences, State University of New York at Buffalo, 14260-1200, USA.', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Genomics|Models, Genetic|Proteome|RNA, Messenger|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti624', '16096347', 'To evaluate a semi-parametric, model-based approach for obtaining transcription rates from mRNA and protein expression.'),
(72307, 2005, 'bio', 'ROCR: visualizing classifier performance in R', 'Sing, Tobias|Sander, Oliver|Beerenwinkel, Niko|Lengauer, Thomas', 'Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany. tobias.sing@mpi-sb.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany. tobias.sing@mpi-sb.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany. tobias.sing@mpi-sb.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Saarbrücken, Germany. tobias.sing@mpi-sb.mpg.de', 'Germany|Germany|Germany|Germany', 1640, 'Computer Graphics|Data Interpretation, Statistical|Pattern Recognition, Automated|Programming Languages|ROC Curve|Software|Software Validation|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti623', '16096348', 'ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with Rs powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage.'),
(72308, 2005, 'bio', 'A geometric invariant-based framework for the analysis of protein conformational space', 'Tendulkar, Ashish V|Sohoni, Milind A|Ogunnaike, Babatunde|Wangikar, Pramod P', 'Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.|Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.|Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.|Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.', 'India|India|India|India', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Internet|Models, Theoretical|Molecular Conformation|Peptides|Principal Component Analysis|Probability|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti621', '16096349', 'Characterization of the restricted nature of the protein local conformational space has remained a challenge, thereby necessitating a computationally expensive conformational search in protein modeling. Moreover, owing to the lack of unilateral structural descriptors, conventional data mining techniques, such as clustering and classification, have not been applied in protein structure analysis.'),
(72309, 2005, 'bio', 'Oligonucleotide properties determination and primer designing: a critical examination of predictions', 'Chavali, Sreenivas|Mahajan, Anubha|Tabassum, Rubina|Maiti, Souvik|Bharadwaj, Dwaipayan', 'Functional Genomics Unit, Institute of Genomics and Integrative Biology, Delhi, India.|Functional Genomics Unit, Institute of Genomics and Integrative Biology, Delhi, India.|Functional Genomics Unit, Institute of Genomics and Integrative Biology, Delhi, India.|Functional Genomics Unit, Institute of Genomics and Integrative Biology, Delhi, India.|Functional Genomics Unit, Institute of Genomics and Integrative Biology, Delhi, India.', 'India|India|India|India|India', 0, 'Computer Simulation|DNA Primers|Models, Chemical|Models, Genetic|Polymerase Chain Reaction|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation|Transition Temperature', 'Comparative Study', '10.1093/bioinformatics/bti633', '16105896', 'Precise prediction of melting temperature (T(m)), secondary structures and design of oligonucleotides determine the efficiency and success of experimentation in molecular biology. Availability of a plethora of software and the users unawareness about their limitations compromises the accuracy and reliability of the predictions.'),
(72310, 2005, 'bio', 'Simple decision rules for classifying human cancers from gene expression profiles', 'Tan, Aik Choon|Naiman, Daniel Q|Xu, Lei|Winslow, Raimond L|Geman, Donald', 'Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Baltimore, MD 21218, USA. actan@jhu.edu|Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Baltimore, MD 21218, USA. actan@jhu.edu|Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Baltimore, MD 21218, USA. actan@jhu.edu|Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Baltimore, MD 21218, USA. actan@jhu.edu|Center for Cardiovascular Bioinformatics and Modeling, Whitaker Biomedical Engineering Institute, Baltimore, MD 21218, USA. actan@jhu.edu', 'United States|United States|United States|United States|United States', 276, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Cluster Analysis|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti631', '16105897', 'Various studies have shown that cancer tissue samples can be successfully detected and classified by their gene expression patterns using machine learning approaches. One of the challenges in applying these techniques for classifying gene expression data is to extract accurate, readily interpretable rules providing biological insight as to how classification is performed. Current methods generate classifiers that are accurate but difficult to interpret. This is the trade-off between credibility and comprehensibility of the classifiers. Here, we introduce a new classifier in order to address these problems. It is referred to as k-TSP (k-Top Scoring Pairs) and is based on the concept of relative expression reversals. This method generates simple and accurate decision rules that only involve a small number of gene-to-gene expression comparisons, thereby facilitating follow-up studies.'),
(72311, 2005, 'bio', 'MESHI: a new library of Java classes for molecular modeling', 'Kalisman, Nir|Levi, Ami|Maximova, Tetyana|Reshef, Dan|Zafriri-Lynn, Sharon|Gleyzer, Yan|Keasar, Chen', 'Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.|Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel.', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Computer Simulation|Models, Chemical|Models, Molecular|Programming Languages|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti630', '16105898', 'Adapting a modular and object-oriented approach in the design of molecular modeling packages may reduce the software development barrier between ideas and their programed applications. Towards this goal we developed MESHI, a new, strictly object-oriented, molecular modeling suite written in Java. MESHI provides a comprehensive library of extendable classes for all the essential components of molecular modeling: molecular and geometry elements, energy functions and optimization methods.'),
(72312, 2005, 'bio', 'Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes', 'Jensen, Shane T|Shen, Lei|Liu, Jun S', 'Department of Statistics, The Wharton School, University of Pennsylvania, USA. stjensen@wharton.upenn.edu|Department of Statistics, The Wharton School, University of Pennsylvania, USA. stjensen@wharton.upenn.edu|Department of Statistics, The Wharton School, University of Pennsylvania, USA. stjensen@wharton.upenn.edu', 'United States|United States|United States', 30, 'Algorithms|Amino Acid Motifs|Base Sequence|Cluster Analysis|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Molecular Sequence Data|Pattern Recognition, Automated|Phylogeny|Sequence Alignment|Sequence Analysis|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti628', '16105899', 'We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown.'),
(72313, 2005, 'bio', 'Statistical evaluation of pairwise protein sequence comparison with the Bayesian bootstrap', 'Price, Gavin A|Crooks, Gavin E|Green, Richard E|Brenner, Steven E', 'Department of Bioengineering, University of California, Berkeley, 94720, USA.|Department of Bioengineering, University of California, Berkeley, 94720, USA.|Department of Bioengineering, University of California, Berkeley, 94720, USA.|Department of Bioengineering, University of California, Berkeley, 94720, USA.', 'United States|United States|United States|United States', 20, 'Algorithms|Amino Acid Sequence|Bayes Theorem|Data Interpretation, Statistical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti627', '16105900', 'Protein sequence comparison methods are routinely used to infer the intricate network of evolutionary relationships found within the rapidly growing library of protein sequences, and thereby to predict the structure and function of uncharacterized proteins. In the present study, we detail an improved statistical benchmark of pairwise protein sequence comparison algorithms. We use bootstrap resampling techniques to determine standard statistical errors and to estimate the confidence of our conclusions. We show that the underlying structure within benchmark databases causes Efrons standard, non-parametric bootstrap to be biased. Consequently, the standard bootstrap underpredicts average performance when used in the context of evaluating sequence comparison methods. We have developed, as an alternative, an unbiased statistical evaluation based on the Bayesian bootstrap, a resampling method operationally similar to the standard bootstrap.'),
(72314, 2005, 'bio', 'Bias in the estimation of false discovery rate in microarray studies', 'Pawitan, Yudi|Murthy, Karuturi R Krishna|Michiels, Stefan|Ploner, Alexander', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@meb.ki.se', 'Sweden|Sweden|Sweden|Sweden', 52, 'Computer Simulation|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Selection Bias|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/bti626', '16105901', 'The false discovery rate (FDR) provides a key statistical assessment for microarray studies. Its value depends on the proportion pi(0) of non-differentially expressed (non-DE) genes. In most microarray studies, many genes have small effects not easily separable from non-DE genes. As a result, current methods often overestimate pi(0) and FDR, leading to unnecessary loss of power in the overall analysis.'),
(72315, 2005, 'bio', 'Complex networks approach to gene expression driven phenotype imaging', 'Diambra, L|da F Costa, L', 'Institute of Physics at São Carlos, University of São Paulo, Brazil. diambra@univap.br|Institute of Physics at São Carlos, University of São Paulo, Brazil. diambra@univap.br', 'Brazil|Brazil', 11, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Phenotype|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti625', '16105902', 'The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are used to measure the spatial interactions and adjacencies defined by gene expression patterns.'),
(72316, 2005, 'bio', 'Robust accurate identification of peptides (RAId): deciphering MS2 data using a structured library search with de novo based statistics', 'Alves, Gelio|Yu, Yi-Kuo', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.', 'United States|United States', 18, 'Algorithms|Amino Acid Sequence|Data Interpretation, Statistical|Databases, Protein|Mass Spectrometry|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Library|Peptide Mapping|Peptides|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti620', '16105903', 'The key to MS -based proteomics is peptide sequencing. The major challenge in peptide sequencing, whether library search or de novo, is to better infer statistical significance and better attain noise reduction. Since the noise in a spectrum depends on experimental conditions, the instrument used and many other factors, it cannot be predicted even if the peptide sequence is known. The characteristics of the noise can only be uncovered once a spectrum is given. We wish to overcome such issues.'),
(72317, 2005, 'bio', 'NdPASA: a pairwise sequence alignment server for distantly related proteins', 'Li, Wei|Wang, Junwen|Feng, Jin-An', 'Department of Chemistry, Temple University Philadelphia, PA 19122, USA.|Department of Chemistry, Temple University Philadelphia, PA 19122, USA.|Department of Chemistry, Temple University Philadelphia, PA 19122, USA.', 'United States|United States|United States', 2, 'Algorithms|Amino Acid Sequence|Internet|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/bti619', '16105904', 'NdPASA is a web server specifically designed to optimize sequence alignment between distantly related proteins. The program integrates structure information of the template sequence into a global alignment algorithm by employing neighbor-dependent propensities of amino acids as a unique parameter for alignment. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. NdPASA is most effective in aligning homologous proteins sharing low percentage of sequence identity. The server is designed to aid homologous protein structure modeling. A PSI-BLAST search engine was implemented to help users identify template candidates that are most appropriate for modeling the query sequences.'),
(72318, 2005, 'bio', 'ORIOGEN: order restricted inference for ordered gene expression data', 'Peddada, S|Harris, S|Zajd, J|Harvey, E', 'Biostatistics Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, USA. peddada@niehs.nih.gov|Biostatistics Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, USA. peddada@niehs.nih.gov|Biostatistics Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, USA. peddada@niehs.nih.gov|Biostatistics Branch, National Institute of Environmental Health Sciences, Durham, NC 27713, USA. peddada@niehs.nih.gov', 'United States|United States|United States|United States', 41, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti637', '16109745', 'ORIOGEN is a user-friendly Java-based software package for selecting and clustering genes according to their profiles across various treatment groups. In particular, ORIOGEN is useful for analyzing data obtained from time-course or dose-response type experiments.'),
(72319, 2005, 'bio', 'A statistical model providing comprehensive predictions for the mRNA differential display', 'Meintrup, David|Reisinger, Ellen', 'Institut EIT 1, Universitaet der Bundeswehr Muenchen, Neubiberg, Germany.|Institut EIT 1, Universitaet der Bundeswehr Muenchen, Neubiberg, Germany.', 'Germany|Germany', 3, 'Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/bti636', '16109746', 'Differential display (DD) or arbitrarily primed fingerprinting serves to identify differentially expressed genes, but these techniques cannot determine how many of the theoretically available genes have been uncovered. Previous mathematical models are unsatisfying as they are not suitable to analyze experimental data.'),
(72320, 2005, 'bio', 'Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes', 'Münch, Richard|Hiller, Karsten|Grote, Andreas|Scheer, Maurice|Klein, Johannes|Schobert, Max|Jahn, Dieter', 'Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.|Institute of Microbiology, Technical University Braunschweig, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 255, 'Algorithms|Bacterial Proteins|Base Sequence|Binding Sites|Computational Biology|Databases, Genetic|Databases, Protein|Genes, Bacterial|Genome|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Response Elements|Sensitivity and Specificity|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti635', '16109747', 'A new online framework for the accurate and integrative prediction of transcription factor binding sites (TFBSs) in prokaryotes was developed. The system consists of three interconnected modules: (1) The PRODORIC database as a comprehensive data source and extensive collection of TFBSs with corresponding position weight matrices. (2) The pattern matching tool Virtual Footprint for the prediction of genome based regulons and for the analysis of individual promoter regions. (3) The interactive genome browser GBPro for the visualization of TFBS search results in their genomic context and links to gene and regulator-specific information in PRODORIC. The aim of this service is to provide researchers a free and easy to use collection of interconnected tools in the field of molecular microbiology, infection and systems biology.'),
(72321, 2005, 'bio', 'Non-linear PCA: a missing data approach', 'Scholz, Matthias|Kaplan, Fatma|Guy, Charles L|Kopka, Joachim|Selbig, Joachim', 'Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.|Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.|Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.|Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.|Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.', 'Germany|Germany|Germany|Germany|Germany', 128, 'Adaptation, Physiological|Algorithms|Arabidopsis|Arabidopsis Proteins|Cold Temperature|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Plant|Models, Genetic|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti634', '16109748', 'Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values.'),
(72322, 2005, 'bio', 'Structure clustering features on the Sfold Web server', 'Chan, Chi Yu|Lawrence, Charles E|Ding, Ye', 'Bioinformatics Center, Wadsworth Center, New York State Department of Health, Albany, 12208, USA. chanc@wadsworth.org|Bioinformatics Center, Wadsworth Center, New York State Department of Health, Albany, 12208, USA. chanc@wadsworth.org|Bioinformatics Center, Wadsworth Center, New York State Department of Health, Albany, 12208, USA. chanc@wadsworth.org', 'United States|United States|United States', 50, 'Algorithms|Base Sequence|Cluster Analysis|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti632', '16109749', 'The energy landscape of RNA secondary structures is often complex, and the Boltzmann-weighted ensemble usually contains distinct clusters. Furthermore, the minimum free energy structure often lies outside of the cluster containing the structure determined by comparative sequence analysis. We have developed procedures to characterize and visualize the Boltzmann-weighted ensemble, and have made them available on the Sfold Web server. The new features on the Web server include clustering statistics, ensemble and cluster centroids, multi-dimensional scaling display and energy landscape representation of the Boltzmann-weighted ensemble.'),
(72323, 2005, 'bio', 'A status report on MAGE', 'Spellman, Paul', 'Bioinformatics Center, Wadsworth Center, New York State Department of Health, Albany, 12208, USA. chanc@wadsworth.org', 'United States', 4, 'Databases, Genetic|Documentation|Gene Expression Profiling|Information Dissemination|Oligonucleotide Array Sequence Analysis|Organizational Objectives|Societies, Scientific|Software|United States|User-Computer Interface', 'Editorial', '10.1093/bioinformatics/bti591', '16116036', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72324, 2005, 'bio', 'Modified SIMPSON O(n3) algorithm for the full sibship reconstruction problem', 'Konovalov, Dmitry A|Bajema, Nigel|Litow, Bruce', 'School of Information Technology, James Cook University Townsville, QLD, Australia. Dmitry.Konovalov@jcu.edu.au|School of Information Technology, James Cook University Townsville, QLD, Australia. Dmitry.Konovalov@jcu.edu.au|School of Information Technology, James Cook University Townsville, QLD, Australia. Dmitry.Konovalov@jcu.edu.au', 'Australia|Australia|Australia', 19, 'Algorithms|Chromosome Mapping|Genetic Markers|Genetics, Population|Humans|Numerical Analysis, Computer-Assisted|Pedigree|Siblings', 'Comparative Study', '10.1093/bioinformatics/bti642', '16118259', 'The problem of reconstructing full sibling groups from DNA marker data remains a significant challenge for computational biology. A recently published heuristic algorithm based on Mendelian exclusion rules and the Simpson index was successfully applied to the full sibship reconstruction (FSR) problem. However, the so-called SIMPSON algorithm has an unknown complexity measure, questioning its applicability range.'),
(72325, 2005, 'bio', 'Detecting single-feature polymorphisms using oligonucleotide arrays and robustified projection pursuit', 'Cui, Xinping|Xu, Jin|Asghar, Rehana|Condamine, Pascal|Svensson, Jan T|Wanamaker, Steve|Stein, Nils|Roose, Mikeal|Close, Timothy J', 'Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu|Department of Statistics, University of California, Riverside, 92521, USA. xinping.cui@ucr.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Arabidopsis|Chromosome Mapping|DNA Mutational Analysis|DNA, Plant|Genome, Plant|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1093/bioinformatics/bti640', '16118260', 'Genomic DNA was hybridized to oligonucleotide microarrays to identify single-feature polymorphisms (SFP) for Arabidopsis, which has a genome size of approximately 130 Mb. However, that method does not work well for organisms such as barley, with a much larger 5200 Mb genome. In the present study, we demonstrate SFP detection using a small number of replicate datasets and complex RNA as a surrogate for barley DNA. To identify single probes defining SFPs in the data, we developed a method using robustified projection pursuit (RPP). This method first evaluates, for each probe set, the overall differentiation of signal intensities between two genotypes and then measures the contribution of the individual probes within the probe set to the overall differentiation.'),
(72326, 2005, 'bio', 'Identification of coding and non-coding sequences using local Holder exponent formalism', 'Kulkarni, Onkar C|Vigneshwar, R|Jayaraman, Valadi K|Kulkarni, Bhaskar D', 'National Chemical Laboratory, Pune, India.|National Chemical Laboratory, Pune, India.|National Chemical Laboratory, Pune, India.|National Chemical Laboratory, Pune, India.', 'India|India|India|India', 20, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Open Reading Frames|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti639', '16118261', 'Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences.'),
(72327, 2005, 'bio', 'DNA microarray data imputation and significance analysis of differential expression', 'Jörnsten, Rebecka|Wang, Hui-Yu|Welsh, William J|Ouyang, Ming', 'Department of Statistics, Rutgers, the State University of New Jersey, New Brunswick, 08903, USA. rebecka@stat.rutgers.edu|Department of Statistics, Rutgers, the State University of New Jersey, New Brunswick, 08903, USA. rebecka@stat.rutgers.edu|Department of Statistics, Rutgers, the State University of New Jersey, New Brunswick, 08903, USA. rebecka@stat.rutgers.edu|Department of Statistics, Rutgers, the State University of New Jersey, New Brunswick, 08903, USA. rebecka@stat.rutgers.edu', 'Jersey|Jersey|Jersey|Jersey', 0, 'Algorithms|Analysis of Variance|Cluster Analysis|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation|Humans|Liver|Liver Neoplasms|Microarray Analysis|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/bti638', '16118262', 'Significance analysis of differential expression in DNA microarray data is an important task. Much of the current research is focused on developing improved tests and software tools. The task is difficult not only owing to the high dimensionality of the data (number of genes), but also because of the often non-negligible presence of missing values. There is thus a great need to reliably impute these missing values prior to the statistical analyses. Many imputation methods have been developed for DNA microarray data, but their impact on statistical analyses has not been well studied. In this work we examine how missing values and their imputation affect significance analysis of differential expression.'),
(72328, 2005, 'bio', 'Analysis of recursive gene selection approaches from microarray data', 'Li, Fan|Yang, Yiming', 'Language Technology Institute 4502 NSH Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. hustlf@cs.cmu.edu|Language Technology Institute 4502 NSH Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. hustlf@cs.cmu.edu', 'United States|United States', 0, 'Algorithms|Animals|Biomarkers, Tumor|Feedback|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software Validation', 'Comparative Study', '10.1093/bioinformatics/bti618', '16118263', 'Finding a small subset of most predictive genes from microarray for disease prediction is a challenging problem. Support vector machines (SVMs) have been found to be successful with a recursive procedure in selecting important genes for cancer prediction. However, it is not well understood how much of the success depends on the choice of the specific classifier and how much on the recursive procedure. We answer this question by examining multiple classifers [SVM, ridge regression (RR) and Rocchio] with feature selection in recursive and non-recursive settings on three DNA microarray datasets (ALL-AML Leukemia data, Breast Cancer data and GCM data).'),
(72329, 2005, 'bio', 'PAWE-3D: visualizing power for association with error in case-control genetic studies of complex traits', 'Gordon, Derek|Haynes, Chad|Blumenfeld, Jon|Finch, Stephen J', 'Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. pawe3d@linkage.rockefeller.edu|Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. pawe3d@linkage.rockefeller.edu|Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. pawe3d@linkage.rockefeller.edu|Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. pawe3d@linkage.rockefeller.edu', 'United States|United States|United States|United States', 49, 'Algorithms|Case-Control Studies|Genetic Predisposition to Disease|Genetic Testing|Genetics, Population|Humans|Imaging, Three-Dimensional|Quantitative Trait, Heritable|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti643', '16123114', 'A website that plots power and sample size calculations over a range of up to eight parameters (including diagnostic misclassification error parameters) for two commonly used statistical tests of genetic association, the linear trend test and the genotypic test of association.'),
(72330, 2005, 'bio', 'Calibrating E-values for hidden Markov models using reverse-sequence null models', 'Karplus, Kevin|Karchin, Rachel|Shackelford, George|Hughey, Richard', 'Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA. karplus@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA. karplus@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA. karplus@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, 95064, USA. karplus@soe.ucsc.edu', 'United States|United States|United States|United States', 34, 'Algorithms|Calibration|Computational Biology|Computers|Databases, Genetic|Databases, Protein|Internet|Likelihood Functions|Markov Chains|Models, Biological|Models, Genetic|Models, Statistical|Models, Theoretical|Odds Ratio|Protein Structure, Secondary|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bti629', '16123115', 'Hidden Markov models (HMMs) calculate the probability that a sequence was generated by a given model. Log-odds scoring provides a context for evaluating this probability, by considering it in relation to a null hypothesis. We have found that using a reverse-sequence null model effectively removes biases owing to sequence length and composition and reduces the number of false positives in a database search. Any scoring system is an arbitrary measure of the quality of database matches. Significance estimates of scores are essential, because they eliminate model- and method-dependent scaling factors, and because they quantify the importance of each match. Accurate computation of the significance of reverse-sequence null model scores presents a problem, because the scores do not fit the extreme-value (Gumbel) distribution commonly used to estimate HMM scores significance.'),
(72331, 2005, 'bio', 'Pairwise alignment incorporating dipeptide covariation', 'Crooks, Gavin E|Green, Richard E|Brenner, Steven E', 'Department of Plant and Microbial Biology 111 Koshland Hall #3102 University of California, Berkeley, CA 94720-3102, USA. gec@compbio.berkeley.edu|Department of Plant and Microbial Biology 111 Koshland Hall #3102 University of California, Berkeley, CA 94720-3102, USA. gec@compbio.berkeley.edu|Department of Plant and Microbial Biology 111 Koshland Hall #3102 University of California, Berkeley, CA 94720-3102, USA. gec@compbio.berkeley.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Dipeptides|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti616', '16123116', 'Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrices that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations and by assessing the ability of this algorithm to detect remote homologies.'),
(72332, 2005, 'bio', 'Negative correlation between compositional symmetries and local recombination rates', 'Chen, Liang|Zhao, Hongyu', 'Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA.|Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA.', 'United States|United States', 11, 'Animals|Base Composition|Base Sequence|Chromosome Mapping|Computer Simulation|Genetic Variation|Genome, Human|Humans|Mice|Models, Genetic|Models, Statistical|Molecular Sequence Data|Polymorphism, Single Nucleotide|Rats|Recombination, Genetic|Sequence Analysis, DNA|Species Specificity|Statistics as Topic', 'Comparative Study', '10.1093/bioinformatics/bti651', '16131519', 'Although still not much understood, the universal reverse complement symmetry in genomes may contain much information about the genome. In this article, under the hypothesis that recombination rate variations may be related to the high order DNA structure, we studied the association between local recombination rates and local symmetry levels in mouse, rat and human. We found significant negative correlations between recombination rates and reverse complement compositional symmetries in these three organisms. This negative correlation pattern also held at individual chromosome levels when data only from each individual chromosome was analyzed.'),
(72333, 2005, 'bio', 'HAPLOT: a graphical comparison of haplotype blocks, tagSNP sets and SNP variation for multiple populations', 'Gu, Sheng|Pakstis, Andrew J|Kidd, Kenneth K', 'Department of Genetics, Yale University School of Medicine, New Haven, USA. sheng.gu@yale.edu|Department of Genetics, Yale University School of Medicine, New Haven, USA. sheng.gu@yale.edu|Department of Genetics, Yale University School of Medicine, New Haven, USA. sheng.gu@yale.edu', 'United States|United States|United States', 24, 'Chromosome Mapping|Computer Graphics|DNA Mutational Analysis|Expressed Sequence Tags|Genetic Variation|Haplotypes|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti649', '16131520', 'Understanding of human variation relevant to association studies can benefit from population comparison, especially comparing populations in the same geographical region. Variations in linkage disequilibrium patterns, in tagSNP sets, and in SNP heterozygosities among populations can be used to infer the evolutionary pattern. We present here a win32 system based Perl/Tk application for visual comparisons of these variations in different populations.'),
(72334, 2005, 'bio', 'A causal inference approach for constructing transcriptional regulatory networks', 'Xing, Biao|van der Laan, Mark J', 'Genentech Inc. 1 DNA Way, South San Francisco, CA 94080, USA. xing.biao@gene.com|Genentech Inc. 1 DNA Way, South San Francisco, CA 94080, USA. xing.biao@gene.com', 'United States|United States', 28, 'Algorithms|Causality|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Pattern Recognition, Automated|Proteome|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti648', '16131521', 'Transcriptional regulatory networks specify the interactions among regulatory genes and between regulatory genes and their target genes. Discovering transcriptional regulatory networks helps us to understand the underlying mechanism of complex cellular processes and responses.'),
(72335, 2005, 'bio', 'Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data', 'Xu, Lei|Tan, Aik Choon|Naiman, Daniel Q|Geman, Donald|Winslow, Raimond L', 'The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu', 'United States|United States|United States|United States|United States', 108, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Cluster Analysis|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Male|Neoplasm Proteins|Pattern Recognition, Automated|Prostatic Neoplasms|Reproducibility of Results|Sensitivity and Specificity|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/bti647', '16131522', 'DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers.'),
(72336, 2005, 'bio', 'Detection of DNA copy number alterations using penalized least squares regression', 'Huang, Tao|Wu, Baolin|Lizardi, Paul|Zhao, Hongyu', 'Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA.|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA.|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA.|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA.', 'United States|United States|United States|United States', 46, 'Algorithms|Base Sequence|Chromosome Mapping|Gene Dosage|Genetic Variation|Least-Squares Analysis|Models, Genetic|Models, Statistical|Molecular Sequence Data|Regression Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti646', '16131523', 'Genomic DNA copy number alterations are characteristic of many human diseases including cancer. Various techniques and platforms have been proposed to allow researchers to partition the whole genome into segments where copy numbers change between contiguous segments, and subsequently to quantify DNA copy number alterations. In this paper, we incorporate the spatial dependence of DNA copy number data into a regression model and formalize the detection of DNA copy number alterations as a penalized least squares regression problem. In addition, we use a stationary bootstrap approach to estimate the statistical significance and false discovery rate.'),
(72337, 2005, 'bio', 'Network constrained clustering for gene microarray data', 'Zhu, Dongxiao|Hero, Alfred O|Cheng, Hong|Khanna, Ritu|Swaroop, Anand', 'Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu', 'United States|United States|United States|United States|United States', 45, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti655', '16141248', 'Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway.'),
(72338, 2005, 'bio', 'BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing', 'Bock, Christoph|Reither, Sabine|Mikeska, Thomas|Paulsen, Martina|Walter, Jörn|Lengauer, Thomas', 'Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de|Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de|Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de|Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de|Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de|Max-Planck-Institut für Informatik Saarbrücken, Germany. cbock@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 464, 'DNA|DNA Methylation|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti652', '16141249', 'Manual processing of DNA methylation data from bisulfite sequencing is a tedious and error-prone task. Here we present an interactive software tool that provides start-to-end support for this process. In an easy-to-use manner, the tool helps the user to import the sequence files from the sequencer, to align them, to exclude or correct critical sequences, to document the experiment, to perform basic statistics and to produce publication-quality diagrams. Emphasis is put on quality control: The program automatically assesses data quality and provides warnings and suggestions for dealing with critical sequences. The BiQ Analyzer program is implemented in the Java programming language and runs on any platform for which a recent Java virtual machine is available.'),
(72339, 2005, 'bio', 'The SuMo server: 3D search for protein functional sites', 'Jambon, Martin|Andrieu, Olivier|Combet, Christophe|Deléage, Gilbert|Delfaud, François|Geourjon, Christophe', 'Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org|Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org|Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org|Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org|Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org|Pôle BioInformatique Lyonnais, Institut de Biologie et Chimie des Protéines UMR 5086 CNRS/UCBL, IFR 128, Lyon, France. mjambon@burnham.org', 'France|France|France|France|France|France', 72, 'Amino Acid Sequence|Binding Sites|Imaging, Three-Dimensional|Internet|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti645', '16141250', 'We provide the scientific community with a web server which gives access to SuMo, a bioinformatic system for finding similarities in arbitrary 3D structures or substructures of proteins. SuMo is based on a unique representation of macromolecules using selected triplets of chemical groups having their own geometry and symmetry, regardless of the restrictive notions of main chain and lateral chains of amino acids. The heuristic for extracting similar sites was used to drive two major large-scale approaches. First, searching for ligand binding sites onto a query structure has been made possible by comparing the structure against each of the ligand binding sites found in the Protein Data Bank (PDB). Second, the reciprocal process, i.e. searching for a given 3D site of interest among the structures of the PDB is also possible and helps detect cross-reacting targets in drug design projects.'),
(72340, 2005, 'bio', 'A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings', 'Torrente, Aurora|Kapushesky, Misha|Brazma, Alvis', 'EMBL Outstation-Hinxton, European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. aurora@ebi.ac.uk|EMBL Outstation-Hinxton, European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. aurora@ebi.ac.uk|EMBL Outstation-Hinxton, European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. aurora@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti644', '16141251', 'Clustering is one of the most widely used methods in unsupervised gene expression data analysis. The use of different clustering algorithms or different parameters often produces rather different results on the same data. Biological interpretation of multiple clustering results requires understanding how different clusters relate to each other. It is particularly non-trivial to compare the results of a hierarchical and a flat, e.g. k-means, clustering.'),
(72341, 2005, 'bio', 'Optimal word sizes for dissimilarity measures and estimation of the degree of dissimilarity between DNA sequences', 'Wu, Tiee-Jian|Huang, Ying-Hsueh|Li, Lung-An', 'Department of Statistics, National Cheng-Kung University, Tainan, Taiwan. tjwu@stat.ncku.edu.tw|Department of Statistics, National Cheng-Kung University, Tainan, Taiwan. tjwu@stat.ncku.edu.tw|Department of Statistics, National Cheng-Kung University, Tainan, Taiwan. tjwu@stat.ncku.edu.tw', '-|-|-', 41, 'Algorithms|Computational Biology|Computer Simulation|Computers|DNA|Databases, Genetic|Databases, Protein|Escherichia coli|Genes, Bacterial|Genome|Humans|Lipoprotein Lipase|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Open Reading Frames|Pattern Recognition, Automated|Phylogeny|SARS Virus|Sequence Analysis, DNA|Shigella flexneri|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/bti658', '16144805', 'Several measures of DNA sequence dissimilarity have been developed. The purpose of this paper is 3-fold. Firstly, we compare the performance of several word-based or alignment-based methods. Secondly, we give a general guideline for choosing the window size and determining the optimal word sizes for several word-based measures at different window sizes. Thirdly, we use a large-scale simulation method to simulate data from the distribution of SK-LD (symmetric Kullback-Leibler discrepancy). These simulated data can be used to estimate the degree of dissimilarity beta between any pair of DNA sequences.'),
(72342, 2005, 'bio', 'Inference of transcriptional regulatory network by two-stage constrained space factor analysis', 'Yu, Tianwei|Li, Ker-Chau', 'Department of Statistics, University of California-Los Angeles, Los Angeles, CA 90095-1554, USA.|Department of Statistics, University of California-Los Angeles, Los Angeles, CA 90095-1554, USA.', 'United States|United States', 47, 'Algorithms|Computer Simulation|Factor Analysis, Statistical|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Pattern Recognition, Automated|Proteome|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti656', '16144806', 'Microarray gene expression and cross-linking chromatin immunoprecipitation data contain voluminous information that can help the identification of transcriptional regulatory networks at the full genome scale. Such high-throughput data are noisy however. In contrast, from the biomedical literature, we can find many evidenced transcription factor (TF)-target gene binding relationships that have been elucidated at the molecular level. But such sporadically generated knowledge only offers glimpses on limited patches of the network. How to incorporate this valuable knowledge resource to build more reliable network models remains a question.'),
(72343, 2005, 'bio', 'Computational methods for the design of effective therapies against drug resistant HIV strains', 'Beerenwinkel, Niko|Sing, Tobias|Lengauer, Thomas|Rahnenführer, Jörg|Roomp, Kirsten|Savenkov, Igor|Fischer, Roman|Hoffmann, Daniel|Selbig, Joachim|Korn, Klaus|Walter, Hauke|Berg, Thomas|Braun, Patrick|Fätkenheuer, Gerd|Oette, Mark|Rockstroh, Jürgen|Kupfer, Bernd|Kaiser, Rolf|Däumer, Martin', 'Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, CA, USA. niko@math.berkeley.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 72, 'Database Management Systems|Databases, Factual|Drug Design|Drug Information Services|Drug Resistance, Viral|Drug Therapy, Computer-Assisted|Genetic Therapy|HIV Infections|Humans|Viral Vaccines', 'Journal Article', '10.1093/bioinformatics/bti654', '16144807', 'The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.'),
(72344, 2005, 'bio', 'pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes', 'Guda, Chittibabu|Subramaniam, Shankar', 'Gen*NY*sis Center for Excellence in Cancer Genomics, State University of New York, One Discovery Drive, Rensselaer, NY 12144-3456, USA. cguda@albany.edu|Gen*NY*sis Center for Excellence in Cancer Genomics, State University of New York, One Discovery Drive, Rensselaer, NY 12144-3456, USA. cguda@albany.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Animals|Eukaryotic Cells|Humans|Molecular Sequence Data|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/bti650', '16144808', 'There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species.'),
(72345, 2005, 'bio', 'Shifting and scaling patterns from gene expression data', 'Aguilar-Ruiz, Jesús S', 'BIGS BioInformatics Group Seville, University of Seville, Pablo de Olavide University, Spain. aguilar@lsi.us.es', 'Spain', 110, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti641', '16144809', 'During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensionality. Also, biclustering considers overlapping, which is an interesting aspect, algorithmically and from the point of view of the result interpretation. Since the Cheng and Churchs works, the mean squared residue has turned into one of the most popular measures to search for biclusters, which ideally should discover shifting and scaling patterns.'),
(72346, 2005, 'bio', 'Generating Boolean networks with a prescribed attractor structure', 'Pal, Ranadip|Ivanov, Ivan|Datta, Aniruddha|Bittner, Michael L|Dougherty, Edward R', 'Department of Electrical Engineering, Texas A&M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A&M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A&M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A&M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A&M University College Station, TX 77843, USA.', 'United States|United States|United States|United States|United States', 123, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Pattern Recognition, Automated|Proteome|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti664', '16150807', 'Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs).'),
(72347, 2005, 'bio', 'A two-stage normalization method for partially degraded mRNA microarray data', 'Liu, Li-yu D|Wang, Naisyin|Lupton, Joanne R|Turner, Nancy D|Chapkin, Robert S|Davidson, Laurie A', 'Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.|Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.|Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.|Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.|Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.|Department of Agronomy, Biostatistic Division, National Taiwan University Taipei, Taiwan.', '-|-|-|-|-|-', 5, 'Algorithms|Animals|Artifacts|Biomarkers, Tumor|Colonic Neoplasms|Feces|Gene Expression Profiling|Neoplasm Proteins|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Rats', 'Journal Article', '10.1093/bioinformatics/bti661', '16150808', 'The goal of the study is to obtain genetic information from exfoliated colonocytes in the fecal stream rather than directly from mucosa cells within the colon. The latter is obtained through invasive procedures. The difficulties encountered by this procedure are that certain probe information may be compromised due to partially degraded mRNA. Proper normalization is essential to obtaining useful information from these fecal array data.'),
(72348, 2005, 'bio', 'Data pre-processing in liquid chromatography-mass spectrometry-based proteomics', 'Zhang, Xiang|Asara, John M|Adamec, Jiri|Ouzzani, Mourad|Elmagarmid, Ahmed K', 'Bindley Bioscience Center, Purdue University West Lafayette, IN, USA. zhang100@purdue.edu|Bindley Bioscience Center, Purdue University West Lafayette, IN, USA. zhang100@purdue.edu|Bindley Bioscience Center, Purdue University West Lafayette, IN, USA. zhang100@purdue.edu|Bindley Bioscience Center, Purdue University West Lafayette, IN, USA. zhang100@purdue.edu|Bindley Bioscience Center, Purdue University West Lafayette, IN, USA. zhang100@purdue.edu', 'United States|United States|United States|United States|United States', 64, 'Algorithms|Artificial Intelligence|Chromatography, Liquid|Database Management Systems|Databases, Protein|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Information Storage and Retrieval|Mass Spectrometry|Peptide Mapping|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti660', '16150809', 'In a liquid chromatography-mass spectrometry (LC-MS)-based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment and data quality assurance.'),
(72349, 2005, 'bio', 'DNase II is a member of the phospholipase D superfamily', 'Cymerman, Iwona A|Meiss, Gregor|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, 02-109 Warsaw, Poland. iamb@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, 02-109 Warsaw, Poland. iamb@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, 02-109 Warsaw, Poland. iamb@genesilico.pl', 'Poland|Poland|Poland', 4, 'Amino Acid Sequence|Computer Simulation|Endodeoxyribonucleases|Models, Chemical|Models, Molecular|Molecular Sequence Data|Phospholipase D|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti659', '16150810', 'DNase II is an endodeoxyribonuclease involved in apoptosis and essential for the mammalian development. Despite the understanding of biochemical properties of this enzyme, its structure and relationships to other protein families remain unknown.'),
(72350, 2005, 'bio', 'BADASP: predicting functional specificity in protein families using ancestral sequences', 'Edwards, Richard J|Shields, Denis C', 'Clinical Pharmacology, The Royal College of Surgeons in Ireland, Dublin. redwards@rcsi.ie|Clinical Pharmacology, The Royal College of Surgeons in Ireland, Dublin. redwards@rcsi.ie', 'Ireland|Ireland', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Electronic Data Processing|Models, Statistical|Multigene Family|Phylogeny|Proteins|Sensitivity and Specificity|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti678', '16159912', 'Burst After Duplication with Ancestral Sequence Predictions (BADASP) is a software package for identifying sites that may confer subfamily-specific biological functions in protein families following functional divergence of duplicated proteins. A given protein phylogeny is grouped into subfamilies based on orthology/paralogy relationships and/or user definitions. Ancestral sequences are then predicted from the sequence alignment and the functional specificity is calculated using variants of the Burst After Duplication method, which tests for radical amino acid substitutions following gene duplications that are subsequently conserved. Statistics are output along with subfamily groupings and ancestral sequences for an easy analysis with other packages.'),
(72351, 2005, 'bio', 'A comparison study: applying segmentation to array CGH data for downstream analyses', 'Willenbrock, Hanni|Fridlyand, Jane', 'Center for Biological Sequence Analysis, Department of Biotechnology, Technical University of Denmark, Kgs. Lyngby.|Center for Biological Sequence Analysis, Department of Biotechnology, Technical University of Denmark, Kgs. Lyngby.', 'Denmark|Denmark', 229, 'Algorithms|Carcinoma, Squamous Cell|Computational Biology|Computer Simulation|DNA|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Regulation, Neoplastic|Gene Library|Genetic Techniques|Genome|Humans|Internet|Mouth Neoplasms|Nucleic Acid Hybridization|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/bti677', '16159913', 'Array comparative genomic hybridization (CGH) allows detection and mapping of copy number of DNA segments. A challenge is to make inferences about the copy number structure of the genome. Several statistical methods have been proposed to determine genomic segments with different copy number levels. However, to date, no comprehensive comparison of various characteristics of these methods exists. Moreover, the segmentation results have not been utilized in downstream analyses.'),
(72352, 2005, 'bio', 'MILVA: an interactive tool for the exploration of multidimensional microarray data', 'DAlimonte, Davide|Lowe, David|Nabney, Ian T|Mersinias, Vassilis|Smith, Colin P', 'Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, UK.|Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, UK.|Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, UK.|Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, UK.|Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Cluster Analysis|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Gene Expression Regulation|Internet|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Probability|Programming Languages|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti676', '16159914', 'Clustering techniques such as k-means and hierarchical clustering are commonly used to analyze DNA microarray derived gene expression data. However, the interactions between processes underlying the cell activity suggest that the complexity of the microarray data structure may not be fully represented with discrete clustering methods.'),
(72353, 2005, 'bio', 'METIS: multiple extraction techniques for informative sentences', 'Mitchell, A L|Divoli, A|Kim, J-H|Hilario, M|Selimas, I|Attwood, T K', 'Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk|Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk|Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk|Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk|Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk|Faculty of Life Sciences and School of Computer Science, University of Manchester, UK. mitchell@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Automation|Computational Biology|DNA|Data Interpretation, Statistical|Database Management Systems|Databases, Factual|Databases, Protein|Electronic Data Processing|Information Storage and Retrieval|Internet|Language|Natural Language Processing|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti675', '16159915', 'METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems.'),
(72354, 2005, 'bio', 'Design of long oligonucleotide probes for functional gene detection in a microbial community', 'Chung, Won-Hyong|Rhee, Sung-Keun|Wan, Xiu-Feng|Bae, Jin-Woo|Quan, Zhe-Xue|Park, Yong-Ha', 'Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.|Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.|Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.|Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.|Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.|Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Yuseong-gu, Daejeon.', '-|-|-|-|-|-', 22, 'Algorithms|Cluster Analysis|Computational Biology|Conserved Sequence|DNA Primers|DNA Probes|Ecology|Gene Expression Profiling|Genes, Bacterial|Genetic Techniques|Genetic Variation|Genome, Bacterial|Internet|Models, Genetic|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/bti673', '16159916', 'Analysis of the functions of microorganisms and their dynamics in the environment is essential for understanding microbial ecology. For analysis of highly similar sequences of a functional gene family using microarrays, the previous long oligonucleotide probe design strategies have not been useful in generating probes.');
