INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72355, 2005, 'bio', 'Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation', 'Zwir, Igor|Huang, Henry|Groisman, Eduardo A', 'Department of Molecular Microbiology, Howard Hughes Medical Institute, Washington University School of Medicine, St Louis, MO 63110, USA.|Department of Molecular Microbiology, Howard Hughes Medical Institute, Washington University School of Medicine, St Louis, MO 63110, USA.|Department of Molecular Microbiology, Howard Hughes Medical Institute, Washington University School of Medicine, St Louis, MO 63110, USA.', 'United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Databases, Genetic|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation|Gene Expression Regulation, Bacterial|Genome|Genomics|Promoter Regions, Genetic|Response Elements|Salmonella enterica|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti672', '16159917', 'A critical challenge of the post-genomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis regulatory features and map such features into expression patterns to classify genes into distinct networks. However, these methods are not focused on distinguishing between differentially regulated genes within a given network. Here we describe an unsupervised machine learning method, termed GPS for gene promoter scan, that discriminates among co-regulated promoters by simultaneously considering both cis-acting regulatory features and gene expression. GPS is particularly useful for knowledge discovery in environments with reduced datasets and high levels of uncertainty.'),
(72356, 2005, 'bio', 'Using information theory to search for co-evolving residues in proteins', 'Martin, L C|Gloor, G B|Dunn, S D|Wahl, L M', 'Department of Applied Mathematics, University of Western Ontario, London, Canada.|Department of Applied Mathematics, University of Western Ontario, London, Canada.|Department of Applied Mathematics, University of Western Ontario, London, Canada.|Department of Applied Mathematics, University of Western Ontario, London, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Amino Acid Sequence|Computational Biology|Computer Simulation|Databases, Genetic|Databases, Protein|Evolution, Molecular|Genetic Linkage|Information Theory|Models, Statistical|Models, Theoretical|Molecular Sequence Data|Mutation|Phylogeny|Protein Folding|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Triose-Phosphate Isomerase', 'Journal Article', '10.1093/bioinformatics/bti671', '16159918', 'Some functionally important protein residues are easily detected since they correspond to conserved columns in a multiple sequence alignment (MSA). However important residues may also mutate, with compensatory mutations occurring elsewhere in the protein, which serve to preserve or restore functionality. It is difficult to distinguish these co-evolving sites from other non-conserved sites.'),
(72357, 2005, 'bio', 'Analysis of mass spectral serum profiles for biomarker selection', 'Ressom, Habtom W|Varghese, Rency S|Abdel-Hamid, Mohamed|Eissa, Sohair Abdel-Latif|Saha, Daniel|Goldman, Lenka|Petricoin, Emanuel F|Conrads, Thomas P|Veenstra, Timothy D|Loffredo, Christopher A|Goldman, Radoslav', 'Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu|Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. hwr@georgetown.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Blood Chemical Analysis|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Liver Neoplasms|Neoplasm Proteins|Peptide Mapping|Reproducibility of Results|Sensitivity and Specificity|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/bti670', '16159919', 'Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection.'),
(72358, 2005, 'bio', 'Energy landscape of k-point mutants of an RNA molecule', 'Clote, P|Waldispühl, J|Behzadi, B|Steyaert, J-M', 'Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. clote@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. clote@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. clote@bc.edu|Department of Biology, Boston College, Chestnut Hill, MA 02467, USA. clote@bc.edu', 'United States|United States|United States|United States', 14, 'Algorithms|Bacteria|Base Sequence|Computational Biology|Genes, Bacterial|Models, Genetic|Models, Statistical|Molecular Sequence Data|Mutation|Nucleic Acid Conformation|Point Mutation|RNA|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/bti669', '16159920', 'A k-point mutant of a given RNA sequence s = s(1), ..., s(n) is an RNA sequence s = s(1),..., s(n) obtained by mutating exactly k-positions in s, i.e. Hamming distance between s and s equals k. To understand the effect of pointwise mutation in RNA, we consider the distribution of energies of all secondary structures of k-point mutants of a given RNA sequence.'),
(72359, 2005, 'bio', 'Oligonucleotide arrays: information from replication and spatial structure', 'Upton, Graham J G|Lloyd, Julie C', 'Department of Mathematical Sciences, University of Essex, Colchester, UK. gupton@essex.ac.uk|Department of Mathematical Sciences, University of Essex, Colchester, UK. gupton@essex.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Arabidopsis|DNA Replication|Data Interpretation, Statistical|Gene Expression|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Oligonucleotides|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti668', '16159921', 'The introduction of oligonucleotide DNA arrays has resulted in much debate concerning appropriate models for the measurement of gene expression. By contrast, little account has been taken of the possibility of identifying the physical imperfections in the raw data.'),
(72360, 2005, 'bio', 'A method for detection of differential gene expression in the presence of inter-individual variability in response', 'Rocke, David M|Goldberg, Zelanna|Schweitert, Chad|Santana, Alison', 'Division of Biostatistics, University of California-Davis, Davis, CA, USA. dmrocke@ucdavis.edu|Division of Biostatistics, University of California-Davis, Davis, CA, USA. dmrocke@ucdavis.edu|Division of Biostatistics, University of California-Davis, Davis, CA, USA. dmrocke@ucdavis.edu|Division of Biostatistics, University of California-Davis, Davis, CA, USA. dmrocke@ucdavis.edu', 'United States|United States|United States|United States', 16, 'Algorithms|Computer Simulation|Dose-Response Relationship, Radiation|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Humans|Male|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Radiation Dosage|Skin|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti667', '16159922', 'Many stimuli to biological systems result in transcriptional responses that vary across the individual organism either in type or in timing. This creates substantial difficulties in detecting these responses. This is especially the case when the data for any one individual are limited and when the number of genes, probes or probe sets is large.'),
(72361, 2005, 'bio', 'Prediction of glycan structures from gene expression data based on glycosyltransferase reactions', 'Kawano, Shin|Hashimoto, Kosuke|Miyama, Takashi|Goto, Susumu|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan', 62, 'Algorithms|Binding Sites|Biomarkers, Tumor|Factor Analysis, Statistical|Gene Expression Profiling|Glycosyltransferases|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Polysaccharides|Protein Binding|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti666', '16159923', 'Glycan chains are synthesized by a combination of several kinds of glycosyltransferases (GTs). Thus, once we know the repertoire of GTs in the genome, in the transcriptome or in the proteome, it should in principle be possible to predict the repertoire of possible glycan structures in an organism or at a specific stage of the cell. Here, we show that a repertoire of glycan structures can be predicted from the set of GTs in the transcriptome. That is, using knowledge about glycan structure characteristics, we can predict glycan structures from incomplete or noisy data such as DNA microarray data.'),
(72362, 2005, 'bio', 'An expression index for Affymetrix GeneChips based on the generalized logarithm', 'Zhou, Lei|Rocke, David M', 'Department of Biostatistics, Amgen, Inc. USA.|Department of Biostatistics, Amgen, Inc. USA.', 'United States|United States', 24, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Genetic|Models, Statistical|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/bti665', '16159924', 'Affymetrix GeneChip high-density oligonucleotide arrays interrogate a single transcript using multiple short 25mer probes. Usually, a necessary step in the analysis of experiments using these GeneChips is to summarize each of these probe sets into a single expression index that can then be used for determining differential expression, for classification, for clustering, and for other analyses. In this paper, we propose a new expression index that is competitive with the best existing methods, and superior in many cases. We call this expression index method GLA, for GLog Average, since after normalization at the probe level, we take the mean generalized logarithm of perfect match probes.'),
(72363, 2005, 'bio', 'Non-transcriptional pathway features reconstructed from secondary effects of RNA interference', 'Markowetz, Florian|Bloch, Jacques|Spang, Rainer', 'Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, 14195 Berlin, Germany. florian.markowetz@molgen.mpg.de|Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, 14195 Berlin, Germany. florian.markowetz@molgen.mpg.de|Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, 14195 Berlin, Germany. florian.markowetz@molgen.mpg.de', 'Germany|Germany|Germany', 39, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Pattern Recognition, Automated|Proteome|RNA Interference|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti662', '16159925', 'Cellular signaling pathways, which are not modulated on a transcriptional level, cannot be directly deduced from expression profiling experiments. The situation changes, when external interventions such as RNA interference or gene knock-outs come into play. Even if the expression of the signaling genes is not changed, secondary effects in downstream genes shed light on the pathway, and allow partial reconstruction of its topology.'),
(72364, 2005, 'bio', 'Markov model recognition and classification of DNA/protein sequences within large text databases', 'Wren, Jonathan D|Hildebrand, William H|Chandrasekaran, Sreedevi|Melcher, Ulrich', 'Advanced Center for Genome Technology, Stephenson Research and Technology Center, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd., Rm 2025, Norman, OK 73019, USA. Jonathan.Wren@OU.edu|Advanced Center for Genome Technology, Stephenson Research and Technology Center, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd., Rm 2025, Norman, OK 73019, USA. Jonathan.Wren@OU.edu|Advanced Center for Genome Technology, Stephenson Research and Technology Center, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd., Rm 2025, Norman, OK 73019, USA. Jonathan.Wren@OU.edu|Advanced Center for Genome Technology, Stephenson Research and Technology Center, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd., Rm 2025, Norman, OK 73019, USA. Jonathan.Wren@OU.edu', 'United States|United States|United States|United States', 9, 'Abstracting and Indexing as Topic|Artificial Intelligence|Computer Simulation|Database Management Systems|Databases, Bibliographic|Databases, Factual|Markov Chains|Models, Statistical|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/bti657', '16159926', 'Short sequence patterns frequently define regions of biological interest (binding sites, immune epitopes, primers, etc.), yet a large fraction of this information exists only within the scientific literature and is thus difficult to locate via conventional means (e.g. keyword queries or manual searches). We describe herein a system to accurately identify and classify sequence patterns from within large corpora using an n-gram Markov model (MM).'),
(72365, 2005, 'bio', 'RSIR: regularized sliced inverse regression for motif discovery', 'Zhong, Wenxuan|Zeng, Peng|Ma, Ping|Liu, Jun S|Zhu, Yu', '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', 52, 'Algorithms|Amino Acid Motifs|Cell Cycle|Computational Biology|Computer Simulation|DNA|False Positive Reactions|Fungal Proteins|Gene Expression Regulation, Fungal|Genes, Fungal|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Regression Analysis|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti680', '16166098', 'Identification of transcription factor binding motifs (TFBMs) is a crucial first step towards the understanding of regulatory circuitries controlling the expression of genes. In this paper, we propose a novel procedure called regularized sliced inverse regression (RSIR) for identifying TFBMs. RSIR follows a recent trend to combine information contained in both gene expression measurements and genes promoter sequences. Compared with existing methods, RSIR is efficient in computation, very stable for data with high dimensionality and high collinearity, and improves motif detection sensitivities and specificities by avoiding inappropriate model specification.'),
(72366, 2005, 'bio', 'ChemDB: a public database of small molecules and related chemoinformatics resources', 'Chen, Jonathan|Swamidass, S Joshua|Dou, Yimeng|Bruand, Jocelyne|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, University of California, Irvine, USA.|Institute for Genomics and Bioinformatics, University of California, Irvine, USA.', 'United States|United States|United States|United States|United States', 0, 'Access to Information|Chemistry|Chemistry, Organic|Computational Biology|Databases, Factual|Databases, Genetic|Genomics|Health Resources|Information Storage and Retrieval|Internet|Models, Chemical|Models, Statistical|Natural Language Processing|Sequence Alignment|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti683', '16174682', 'The development of chemoinformatics has been hampered by the lack of large, publicly available, comprehensive repositories of molecules, in particular of small molecules. Small molecules play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes in chemical genomics and systems biology, and for the screening and discovery of new drugs and other useful compounds.'),
(72367, 2005, 'bio', 'Classification of microarrays to nearest centroids', 'Dabney, Alan R', 'Department of Biostatistics, University of Washington, Seattle, 98195, USA. adabney@u.washington.edu', 'United States', 91, 'Algorithms|Cluster Analysis|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Leukemia|Lymphoma|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/bti681', '16174683', 'Classification of biological samples by microarrays is a topic of much interest. A number of methods have been proposed and successfully applied to this problem. It has recently been shown that classification by nearest centroids provides an accurate predictor that may outperform much more complicated methods. The Prediction Analysis of Microarrays (PAM) approach is one such example, which the authors strongly motivate by its simplicity and interpretability. In this spirit, I seek to assess the performance of classifiers simpler than even PAM.'),
(72368, 2005, 'bio', 'Prediction of the coupling specificity of GPCRs to four families of G-proteins using hidden Markov models and artificial neural networks', 'Sgourakis, Nikolaos G|Bagos, Pantelis G|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece.|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece.|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Greece.', 'Greece|Greece|Greece', 37, 'Animals|Computational Biology|Computer Simulation|Computers|Databases, Genetic|False Positive Reactions|GTP-Binding Protein alpha Subunits, G12-G13|GTP-Binding Protein alpha Subunits, Gi-Go|GTP-Binding Protein alpha Subunits, Gq-G11|GTP-Binding Protein alpha Subunits, Gs|GTP-Binding Proteins|Gene Library|Genes, Plant|Humans|Ligands|Markov Chains|Models, Biological|Neural Networks (Computer)|Protein Binding|Receptors, G-Protein-Coupled|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/bti679', '16174684', 'G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). To date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups G(i/o), G(q/11) and G(s), not including G(12/13)-coupled or other promiscuous receptors.'),
(72369, 2005, 'bio', 'Doelan: a solution for quality control monitoring of microarray production', 'Jourdren, Laurent|Le Crom, Stéphane', 'IFR36, Plate-forme Transcriptome, Paris, France.|IFR36, Plate-forme Transcriptome, Paris, France.', 'France|France', 1, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Programming Languages|Quality Control|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti686', '16179358', 'Doelan is an automated tool designed to monitor the quality of DNA microarray production. The software executes a series of quality control tests on hybridizations to validate batches of chips. The reports generated by Doelan should help microarray platforms aiming at quality labels, such as ISO 9001 certification. The Doelan application is written in Java and works with a plug-in system that allows everyone to add custom validation tests.'),
(72370, 2005, 'bio', 'Noise and rank-dependent geometrical filter improves sensitivity of highly specific discovery by microarrays', 'Fathallah-Shaykh, Hassan M', 'Department of Neurological Sciences, Section of Neuro-Oncology, Rush University Medical Center 1725 West Harrison Street, Chicago, IL 60612, USA. hfathall@rush.edu', 'United States', 0, 'Algorithms|Arabidopsis|Brain|Computational Biology|Computer Graphics|DNA, Complementary|Fluorescent Dyes|Gene Expression Profiling|Gene Expression Regulation|Genomics|Humans|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Pattern Recognition, Automated|RNA|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti684', '16179359', 'MASH is a mathematical algorithm that discovers highly specific states of expression from genomic profiling by microarrays. The goal at the outset of this analysis was to improve the sensitivity of MASH. The geometrical representations of microarray datasets in the 3D space are rank-dependent and unique to each dataset. The first filter (F1) of MASH defines a zone of instability whose F1-sensitive ratios have large variations. A new filter (Fs) constructs in the 3D space rank-dependent lower and upper-bound contour surfaces, which are modeled based on the geometry of the unique noise intrinsic to each dataset. As compared with MASH, Fs increases sensitivity significantly without lowering the high specificity of discovery. Fs facilitates studies in functional genomics and systems biology.'),
(72371, 2005, 'bio', 'SNPselector: a web tool for selecting SNPs for genetic association studies', 'Xu, Hong|Gregory, Simon G|Hauser, Elizabeth R|Stenger, Judith E|Pericak-Vance, Margaret A|Vance, Jeffery M|Züchner, Stephan|Hauser, Michael A', 'The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.|The Duke Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 89, 'Algorithms|Alleles|Animals|Cardiovascular Diseases|Chromosome Mapping|Databases, Genetic|Genetic Linkage|Genetic Predisposition to Disease|Genetics, Population|Genome, Human|Genotype|Humans|Internet|Models, Genetic|Peptides|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti682', '16179360', 'Single nucleotide polymorphisms (SNPs) are commonly used for association studies to find genes responsible for complex genetic diseases. With the recent advance of SNP technology, researchers are able to assay thousands of SNPs in a single experiment. But the process of manually choosing thousands of genotyping SNPs for tens or hundreds of genes is time consuming. We have developed a web-based program, SNPselector, to automate the process. SNPselector takes a list of gene names or a list of genomic regions as input and searches the Ensembl genes or genomic regions for available SNPs. It prioritizes these SNPs on their tagging for linkage disequilibrium, SNP allele frequencies and source, function, regulatory potential and repeat status. SNPselector outputs result in compressed Excel spreadsheet files for review by the user.'),
(72372, 2005, 'bio', 'Medusa: a simple tool for interaction graph analysis', 'Hooper, Sean D|Bork, Peer', 'European Molecular Biology Laboratory Heidelberg, Germany. hooper@embl.de|European Molecular Biology Laboratory Heidelberg, Germany. hooper@embl.de', 'Germany|Germany', 140, 'Algorithms|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Databases, Protein|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bti696', '16188923', 'Medusa is a Java application for visualizing and manipulating graphs of interaction, such as data from the STRING database. It features an intuitive user interface developed with the help of biologists. Medusa is optimized for accessing protein interaction data from STRING, but can be used for any type of graph from any scientific field.'),
(72373, 2005, 'bio', 'Mapping PDB chains to UniProtKB entries', 'Martin, Andrew C R', 'Department of Biochemistry and Molecular Biology, University College London Gower Street, London WC1E 6BT, UK. andrew@bioinf.org.uk', 'United Kingdom', 0, 'Amino Acid Sequence|Automation|Computational Biology|Database Management Systems|Databases, Factual|Databases, Protein|Electronic Data Processing|Information Storage and Retrieval|Internet|Maps as Topic|Microtubule-Associated Proteins|Models, Theoretical|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti694', '16188924', 'UniProtKB/SwissProt is the main resource for detailed annotations of protein sequences. This database provides a jumping-off point to many other resources through the links it provides. Among others, these include other primary databases, secondary databases, the Gene Ontology and OMIM. While a large number of links are provided to Protein Data Bank (PDB) files, obtaining a regularly updated mapping between UniProtKB entries and PDB entries at the chain or residue level is not straightforward. In particular, there is no regularly updated resource which allows a UniProtKB/SwissProt entry to be identified for a given residue of a PDB file.'),
(72374, 2005, 'bio', 'MACiE: a database of enzyme reaction mechanisms', 'Holliday, Gemma L|Bartlett, Gail J|Almonacid, Daniel E|OBoyle, Noel M|Murray-Rust, Peter|Thornton, Janet M|Mitchell, John B O', 'Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge Lensfield Road, Cambridge, CB2 1EW, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 37, 'Catalysis|Computational Biology|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Protein|Enzymes|Gene Expression|Information Storage and Retrieval|Internet|Models, Chemical|Models, Statistical|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bti693', '16188925', 'MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction.'),
(72375, 2005, 'bio', 'Bioinformatics analyses of circular dichroism protein reference databases', 'Janes, Robert W', 'School of Biological and Chemical Sciences, Queen Mary, University of London Mile End Road, E1 4NS, UK. r.w.janes@qmul.ac.uk', 'United Kingdom', 25, 'Circular Dichroism|Computational Biology|Computer Graphics|Crystallography, X-Ray|Data Interpretation, Statistical|Databases, Protein|Humans|Models, Theoretical|Protein Conformation|Protein Denaturation|Protein Folding|Protein Structure, Secondary|Reference Standards|X-Rays', 'Journal Article', '10.1093/bioinformatics/bti690', '16188926', 'Circular dichroism (CD) spectroscopy has become established as a key method for determining the secondary structure contents of proteins which has had a significant impact on molecular biology. Many excellent mathematical protocols have been developed for this purpose and their quality is above question. However, reference database sets of proteins, with CD spectra matched to secondary structure components derived from X-ray structures, provide the key resource for this task. These databases were created many years ago, before most CD spectrophotometers became standardized and before it was commonplace to validate X-ray structures prior to publication. The analyses presented here were undertaken to investigate the overall quality of these reference databases in light of their extensive usage in determining protein secondary structure content from CD spectra.'),
(72376, 2005, 'bio', 'HapSim: a simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients', 'Montana, Giovanni', 'Department of Mathematics, Imperial College London, UK. g.montana@imperial.ac.uk', 'United Kingdom', 44, 'Algorithms|Alleles|Chromosome Mapping|Computational Biology|Computer Graphics|Computer Simulation|DNA|Gene Frequency|Genetic Linkage|Genetic Markers|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Peptidyl-Dipeptidase A|Polymorphism, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti689', '16188927', 'We have developed a simulation tool HapSim for the generation of haplotype data. The simulated haplotypes are such that their allele frequencies and linkage disequilibrium coefficients match exactly those estimated in a real sample.'),
(72377, 2005, 'bio', 'Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues', 'Wachi, Shinichiro|Yoneda, Ken|Wu, Reen', 'Center for Comparative Respiratory Biology and Medicine and Division of Pulmonary/Critical Care Medicine, University of California Davis, CA 95616, USA. swachi@ucdavis.edu|Center for Comparative Respiratory Biology and Medicine and Division of Pulmonary/Critical Care Medicine, University of California Davis, CA 95616, USA. swachi@ucdavis.edu|Center for Comparative Respiratory Biology and Medicine and Division of Pulmonary/Critical Care Medicine, University of California Davis, CA 95616, USA. swachi@ucdavis.edu', 'United States|United States|United States', 285, 'Algorithms|Cluster Analysis|Computational Biology|Computers|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genome, Human|Genomics|Humans|Lung|Lung Neoplasms|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|RNA, Messenger|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti688', '16188928', 'Global protein interaction network (interactome) analysis provides an effective way to understand the relationships between genes. Through this approach, it was demonstrated that the essential genes in yeast tend to be highly connected as well as connected to other highly connected genes. This is in contrast to the genes that are not essential, which share neither of these properties. Using a similar interactome-transcriptome approach, the topological features in the interactome of differentially expressed genes in lung squamous cancer tissues are assessed.'),
(72378, 2005, 'bio', 'Profile-based direct kernels for remote homology detection and fold recognition', 'Rangwala, Huzefa|Karypis, George', 'Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN 55455, USA.|Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN 55455, USA.', 'United States|United States', 140, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Computing Methodologies|Databases, Protein|Electronic Data Processing|Models, Statistical|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti687', '16188929', 'Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.'),
(72379, 2005, 'bio', 'A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data', 'Xie, Yang|Pan, Wei|Khodursky, Arkady B', 'Division of Biostatistics, School of Public Health, University of Minnesota Minneapolis, MN 55455, USA. yangxie@biostat.umn.ed|Division of Biostatistics, School of Public Health, University of Minnesota Minneapolis, MN 55455, USA. yangxie@biostat.umn.ed|Division of Biostatistics, School of Public Health, University of Minnesota Minneapolis, MN 55455, USA. yangxie@biostat.umn.ed', 'United States|United States|United States', 73, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computational Biology|Computer Simulation|DNA, Complementary|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/bti685', '16188930', 'False discovery rate (FDR) is defined as the expected percentage of false positives among all the claimed positives. In practice, with the true FDR unknown, an estimated FDR can serve as a criterion to evaluate the performance of various statistical methods under the condition that the estimated FDR approximates the true FDR well, or at least, it does not improperly favor or disfavor any particular method. Permutation methods have become popular to estimate FDR in genomic studies. The purpose of this paper is 2-fold. First, we investigate theoretically and empirically whether the standard permutation-based FDR estimator is biased, and if so, whether the bias inappropriately favors or disfavors any method. Second, we propose a simple modification of the standard permutation to yield a better FDR estimator, which can in turn serve as a more fair criterion to evaluate various statistical methods.'),
(72380, 2005, 'bio', 'Functional stoichiometric analysis of metabolic networks', 'Urbanczik, R|Wagner, C', 'Institute of Pharmacology, University of Bern, Switzerland. robert.urbanczik@pki.unibe.ch|Institute of Pharmacology, University of Bern, Switzerland. robert.urbanczik@pki.unibe.ch', 'Switzerland|Switzerland', 27, 'Algorithms|Computational Biology|Computer Simulation|Fungal Proteins|Gene Expression Regulation, Fungal|Genes, Fungal|Genetic Vectors|Metabolism|Protein Interaction Mapping|Saccharomyces cerevisiae|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/bti674', '16188931', 'An important tool in Systems Biology is the stoichiometric modeling of metabolic networks, where the stationary states of the network are described by a high-dimensional polyhedral cone, the so-called flux cone. Exhaustive descriptions of the metabolism can be obtained by computing the elementary vectors of this cone but, owing to a combinatorial explosion of the number of elementary vectors, this approach becomes computationally intractable for genome scale networks.'),
(72381, 2005, 'bio', 'A friendly statistics package for microarray analysis', 'Sykacek, P|Furlong, R A|Micklem, G', 'Department of Genetics, University of Cambridge, Cambridge, UK. peter@sykacek.net|Department of Genetics, University of Cambridge, Cambridge, UK. peter@sykacek.net|Department of Genetics, University of Cambridge, Cambridge, UK. peter@sykacek.net', 'United Kingdom|United Kingdom|United Kingdom', 10, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/bti663', '16188932', 'The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platform-independent R-package that allows efficient exploration of microarray data without the need for computer programming. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spike-based normalization. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis results from quantified microarray data. In addition to providing analysis without programming, the definition file also serves as exact documentation of all the analysis steps.'),
(72382, 2005, 'bio', 'Do you do text?', 'Blaschke, Christian|Yeh, Alexander|Camon, Evelyn|Colosimo, Marc|Apweiler, Rolf|Hirschman, Lynette|Valencia, Alfonso', 'Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.|Centro Nacional de Biotecnología, CNB-CSIC Cantoblanco, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 6, 'Algorithms|Animals|Artificial Intelligence|Bayes Theorem|Computational Biology|Computers|Data Interpretation, Statistical|Databases, Factual|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Mice|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/bti695', '16195360', '-'),
(72383, 2005, 'bio', 'Computational discovery of transcriptional regulatory rules', 'Pham, Tho Hoan|Clemente, José Carlos|Satou, Kenji|Ho, Tu Bao', 'Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan. h-pham@jaist.ac.jp|Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan. h-pham@jaist.ac.jp|Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan. h-pham@jaist.ac.jp|Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan. h-pham@jaist.ac.jp', 'Japan|Japan|Japan|Japan', 19, 'Base Sequence|Binding Sites|Chromosome Mapping|Gene Expression Profiling|Gene Expression Regulation|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti1117', '16204087', 'Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. However, although location data obtained from experimental methods are noisy owing to imperfections in the measuring methods, computational approaches suffer from over-prediction problems owing to the short length of the sequence motifs bound by the transcription factors. Also, these interactions are usually environment-dependent: many regulators only bind to the promoter region of genes under specific environmental conditions. Even more, the presence of regulators at a promoter region indicates binding but not necessarily function: the regulator may act positively, negatively or not act at all. Therefore, identifying true and functional interactions between transcription factors and genes in specific environment conditions and describing the relationship between them are still open problems.'),
(72384, 2005, 'bio', 'Data-adaptive test statistics for microarray data', 'Mukherjee, Sach|Roberts, Stephen J|van der Laan, Mark J', 'Department of Engineering Science, University of Oxford, UK. sach@robots.ox.ac.uk|Department of Engineering Science, University of Oxford, UK. sach@robots.ox.ac.uk|Department of Engineering Science, University of Oxford, UK. sach@robots.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 22, 'Algorithms|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/bti1119', '16204088', 'An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution.'),
(72385, 2005, 'bio', 'Identifying active transcription factors and kinases from expression data using pathway queries', 'Sohler, Florian|Zimmer, Ralf', 'Department of Informatics, Ludwig-Maximilians-Universität, München, Germany. florian.sohler@bio.ifi.lmu.de.|Department of Informatics, Ludwig-Maximilians-Universität, München, Germany. florian.sohler@bio.ifi.lmu.de.', 'Germany|Germany', 15, 'Algorithms|Computer Simulation|Fungal Proteins|Gene Expression Profiling|Models, Biological|Phosphotransferases|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti1120', '16204089', 'Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data.'),
(72386, 2005, 'bio', 'Analyzing microarray data using quantitative association rules', 'Georgii, Elisabeth|Richter, Lothar|Rückert, Ulrich|Kramer, Stefan', 'Technische Universität München, Institut für Informatik/I12, Garching bei München, Germany.|Technische Universität München, Institut für Informatik/I12, Garching bei München, Germany.|Technische Universität München, Institut für Informatik/I12, Garching bei München, Germany.|Technische Universität München, Institut für Informatik/I12, Garching bei München, Germany.', 'Germany|Germany|Germany|Germany', 66, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti1121', '16204090', 'We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72387, 2005, 'bio', 'A framework for computational and experimental methods: identifying dimerization residues in CCR chemokine receptors', 'de Juan, David|Mellado, Mario|Rodríguez-Frade, José Miguel|Hernanz-Falcón, Patricia|Serrano, Antonio|del Sol, Antonio|Valencia, Alfonso|Martínez-A, Carlos|Rojas, Ana María', 'Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.|Protein Design Group, National Center of Biotechnology (CNB-CSIC) Cantoblanco, Madrid, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 13, 'Amino Acid Sequence|Amino Acids|Binding Sites|Dimerization|Models, Chemical|Models, Immunological|Molecular Sequence Data|Protein Binding|Receptors, CCR5|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti1101', '16204091', 'Solving relevant biological problems requires answering complex questions. Addressing such questions traditionally implied the design of time-consuming experimental procedures which most of the time are not accessible to average-sized laboratories. The current trend is to move towards a multidisciplinary approach integrating both theoretical knowledge and experimental work. This combination creates a powerful tool for shedding light on biological problems. To illustrate this concept, we present here a descriptive example of where computational methods were shown to be a key aspect in detecting crucial players in an important biological problem: the dimerization of chemokine receptors. Using evolutionary based sequence analysis in combination with structural predictions two CCR5 residues were selected as important for dimerization and further validated experimentally. The experimental validation of computational procedures demonstrated here provides a wealth of valuable information not obtainable by any of the individual approaches alone.'),
(72388, 2005, 'bio', 'A fully Bayesian model to cluster gene-expression profiles', 'Vogl, C|Sanchez-Cabo, F|Stocker, G|Hubbard, S|Wolkenhauer, O|Trajanoski, Z', 'Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at|Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at|Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at|Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at|Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at|Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at', 'Austria|Austria|Austria|Austria|Austria|Austria', 7, 'Algorithms|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Genetic|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1093/bioinformatics/bti1122', '16204092', 'With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance, results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data.'),
(72389, 2005, 'bio', 'Fusing microarray experiments with multivariate regression', 'Gilks, Walter R|Tom, Brian D M|Brazma, Alvis', 'MRC Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 18, 'Algorithms|Cell Cycle|Cell Cycle Proteins|Data Interpretation, Statistical|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Regression Analysis|Schizosaccharomyces|Systems Integration', 'Journal Article', '10.1093/bioinformatics/bti1123', '16204093', 'It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should distil important aspects of the data while suppressing unwanted sources of variation and be amenable to further informal and formal methods of analysis. Also, the variability in the quality of experimentation should be taken into account.'),
(72390, 2005, 'bio', 'Lineage-specific gene loss following mitochondrial endosymbiosis and its potential for function prediction in eukaryotes', 'Gabaldón, Toni|Huynen, Martijn A', 'Center for Molecular and Biomolecular Informatics and Nijmegen Center for Molecular Life Sciences, Radboud University of Nijmegen Medical Center, Nijmegen, The Netherlands. T.Gabaldon@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics and Nijmegen Center for Molecular Life Sciences, Radboud University of Nijmegen Medical Center, Nijmegen, The Netherlands. T.Gabaldon@cmbi.ru.nl', 'Netherlands|Netherlands', 26, 'Animals|Chromosome Mapping|DNA, Mitochondrial|Evolution, Molecular|Gene Deletion|Genetic Variation|Humans|Linkage Disequilibrium|Mitochondrial Proteins|Sequence Homology, Nucleic Acid|Species Specificity|Symbiosis', 'Journal Article', '10.1093/bioinformatics/bti1124', '16204094', 'The endosymbiotic origin of mitochondria has resulted in a massive horizontal transfer of genetic material from an alpha-proteobacterium to the early eukaryotes. Using large-scale phylogenetic analysis we have previously identified 630 orthologous groups of proteins derived from this event. Here we show that this proto-mitochondrial protein set has undergone extensive lineage-specific gene loss in the eukaryotes, with an average of three losses per orthologous group in a phylogeny of nine species. This gene loss has resulted in a high variability of the alphaproteobacterial-derived gene content of present-day eukaryotic genomes that might reflect functional adaptation to different environments. Proteins functioning in the same biochemical pathway tend to have a similar history of gene loss events, and we use this property to predict functional interactions among proteins in our set.'),
(72391, 2005, 'bio', 'A gamma mixture model better accounts for among site rate heterogeneity', 'Mayrose, Itay|Friedman, Nir|Pupko, Tal', 'Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel.|Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel.', 'Israel|Israel|Israel', 80, 'Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Models, Genetic|Models, Statistical|Phylogeny|Sequence Analysis, DNA|Statistical Distributions|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/bti1125', '16204095', 'Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for this rate heterogeneity usually use a gamma density function to model the rate distribution across sites. This density function, however, may not fit real datasets, especially when there is a multimodal distribution of rates. Here, we present a novel evolutionary model based on a mixture of gamma density functions. This model better describes the among-site rate variation characteristic of molecular sequence evolution. The use of this model may improve the accuracy of various phylogenetic methods, such as reconstructing phylogenetic trees, dating divergence events, inferring ancestral sequences and detecting conserved sites in proteins.'),
(72392, 2005, 'bio', 'Computing recombination networks from binary sequences', 'Huson, Daniel H|Kloepper, Tobias H', 'Center for Bioinformatics, Tübingen University Sand 14, Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics, Tübingen University Sand 14, Tübingen, Germany. huson@informatik.uni-tuebingen.de', 'Germany|Germany', 44, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Models, Genetic|Phylogeny|Recombination, Genetic|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Signal Transduction|Software|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/bti1126', '16204096', 'Phylogenetic networks are becoming an important tool in molecular evolution, as the evolutionary role of reticulate events, such as hybridization, horizontal gene transfer and recombination, is becoming more evident, and as the available data is dramatically increasing in quantity and quality.'),
(72393, 2005, 'bio', 'Discriminating between rate heterogeneity and interspecific recombination in DNA sequence alignments with phylogenetic factorial hidden Markov models', 'Husmeier, Dirk', 'Biomathematics and Statistics, Scotland, Edinburgh, UK. dirk@bioss.ac.uk', 'United Kingdom', 25, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Discriminant Analysis|Evolution, Molecular|Genetic Variation|Markov Chains|Models, Genetic|Phylogeny|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/bti1127', '16204097', 'A recently proposed method for detecting recombination in DNA sequence alignments is based on the combination of hidden Markov models (HMMs) with phylogenetic trees. Although this method was found to detect breakpoints of recombinant regions more accurately than most existing techniques, it inherently fails to distinguish between recombination and rate variation. In the present paper, we propose to marry the phylogenetic tree to a factorial HMM (FHMM). The states of the first hidden chain represent tree topologies, whereas the states of the second independent hidden chain represent different global scaling factors of the branch lengths. Inference is done in terms of a hierarchical Bayesian model, where parameters and hidden states are sampled from the posterior distribution with Gibbs sampling.'),
(72394, 2005, 'bio', 'Recovering haplotype structure through recombination and gene conversion', 'Lajoie, Mathieu|El-Mabrouk, Nadia', 'Département dInformatique et de Recherche Opérationnelle, Université de Montréal, Succursale Centre-ville, Montréal, QC, Canada.|Département dInformatique et de Recherche Opérationnelle, Université de Montréal, Succursale Centre-ville, Montréal, QC, Canada.', 'Canada|Canada', 3, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Gene Conversion|Genetic Variation|Haplotypes|Models, Genetic|Phylogeny|Recombination, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti1128', '16204098', 'Understanding haplotype evolution subject to mutation, recombination and gene conversion is fundamental to understand genetic specificities of human populations and hereditary bases of complex disorders. The goal of this project is to develop new algorithmic tools assisting the reconstruction of historical relationships between haplotypes and the inference of haplotypes from genotypes.'),
(72395, 2005, 'bio', 'Association Cluster Detector: a tool for heuristic detection of significance clusters in whole-genome scans', 'Marquès-Bonet, Tomàs|Lao, Oscar|Goertsches, Robert|Comabella, Manuel|Montalban, Xavier|Navarro, Arcadi', 'Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 5, 'Algorithms|Chromosome Mapping|Cluster Analysis|DNA Mutational Analysis|Multigene Family|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti1118', '16204099', 'Whole genome scans analyze large sets of genetic markers, mainly single nucleotide polymorphisms, over the entire genome in order to find variants and regions associated with complex traits so these can be further investigated. Analyzing the results of such scans becomes difficult due to multiple testing problems and to the genomic distributions of recombination, linkage disequilibrium and true associations, which generate an extremely complex network of dependences between markers. Here we present Association Cluster Detector (ACD), a simple tool aiming to ease the analysis of the results of whole genome scans. ACD facilitates correction for multiple tests using several standard procedures and implements a sliding-window heuristic method that helps in detecting potentially interesting candidate regions by exploiting the property of non-random distribution of significantly associated markers.'),
(72396, 2005, 'bio', 'Model-P: a basecalling method for resequencing microarrays of diploid samples', 'Zhan, Yiping|Kulp, David', 'Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA.|Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA.', 'United States|United States', 6, 'AT Rich Sequence|Algorithms|Base Pair Mismatch|Computer Simulation|DNA Probes|Diploidy|Gene Targeting|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sample Size|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti1129', '16204100', 'Basecalling is a critical step of the analysis of DNA resequencing microarray data for single nucleotide polymorphism discovery and genotyping. For microarrays hybridized with DNA derived from diploid organisms, basecalling with high accuracy at high call rates is a challenging task. Current methods sometimes do not produce satisfactory results.'),
(72397, 2005, 'bio', 'Predicting post-synaptic activity in proteins with data mining', 'Pappa, Gisele L|Baines, Anthony J|Freitas, Alex A', 'Computing Laboratory, University of Kent, Canterbury, UK.|Computing Laboratory, University of Kent, Canterbury, UK.|Computing Laboratory, University of Kent, Canterbury, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Database Management Systems|Databases, Protein|Excitatory Postsynaptic Potentials|Information Storage and Retrieval|Nerve Endings|Nerve Tissue Proteins|Neurotransmitter Agents|Sequence Analysis, Protein|Structure-Activity Relationship|Synapses', 'Journal Article', '10.1093/bioinformatics/bti1102', '16204101', 'The bioinformatics problem being addressed in this paper is to predict whether or not a protein has post-synaptic activity. This problem is of great intrinsic interest because proteins with post-synaptic activities are connected with functioning of the nervous system. Indeed, many proteins having post-synaptic activity have been functionally characterized by biochemical, immunological and proteomic exercises. They represent a wide variety of proteins with functions in extracellular signal reception and propagation through intracellular apparatuses, cell adhesion molecules and scaffolding proteins that link them in a web. The challenge is to automatically discover features of the primary sequences of proteins that typically occur in proteins with post-synaptic activity but rarely (or never) occur in proteins without post-synaptic activity, and vice-versa. In this context, we used data mining to automatically discover classification rules that predict whether or not a protein has post-synaptic activity. The discovered rules were analysed with respect to their predictive accuracy (generalization ability) and with respect to their interestingness to biologists (in the sense of representing novel, unexpected knowledge).'),
(72398, 2005, 'bio', 'Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in Ecoli', 'Simão, E|Remy, E|Thieffry, D|Chaouiya, C', 'Institut de Biologie du Développement de Marseille Campus de Luminy, Marseille cedex 9, France.|Institut de Biologie du Développement de Marseille Campus de Luminy, Marseille cedex 9, France.|Institut de Biologie du Développement de Marseille Campus de Luminy, Marseille cedex 9, France.|Institut de Biologie du Développement de Marseille Campus de Luminy, Marseille cedex 9, France.', 'France|France|France|France', 63, 'Computer Simulation|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation|Models, Biological|Neural Networks (Computer)|Signal Transduction|Tryptophan', 'Journal Article', '10.1093/bioinformatics/bti1130', '16204102', 'The integrated dynamical modelling of mixed metabolic/genetic networks constitutes one of the challenges of systems biology. Furthermore, as most of available data about genetic and metabolic regulations are qualitative, there is a pressing need for rigorous qualitative mathematical approaches.'),
(72399, 2005, 'bio', 'Genome-wide decoding of hierarchical modular structure of transcriptional regulation by cis-element and expression clustering', 'Leyfer, Dmitriy|Weng, Zhiping', 'Bioinformatics Program, Boston University, Boston, MA 02215, USA. dleyfer@gnsbiotech.com|Bioinformatics Program, Boston University, Boston, MA 02215, USA. dleyfer@gnsbiotech.com', 'United States|United States', 7, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Genetic Code|Models, Genetic|Multigene Family|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti1131', '16204103', 'A holistic approach to the study of cellular processes is identifying both gene-expression changes and regulatory elements promoting such changes. Cellular regulatory processes can be viewed as transcriptional modules (TMs), groups of coexpressed genes regulated by groups of transcription factors (TFs). We set out to devise a method that would identify TMs while avoiding arbitrary thresholds on TM sizes and number.'),
(72400, 2005, 'bio', 'A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes', 'Schwartz, Jean-Marc|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto, Japan. jean@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto, Japan. jean@kuicr.kyoto-u.ac.jp', 'Japan|Japan', 35, 'Algorithms|Computer Simulation|Energy Metabolism|Fungal Proteins|Gene Expression Profiling|Glycolysis|Kinetics|Models, Biological|Programming, Linear|Signal Transduction|Yeasts', 'Journal Article', '10.1093/bioinformatics/bti1132', '16204104', 'It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions.'),
(72401, 2005, 'bio', 'Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data', 'Nariai, Naoki|Tamada, Yoshinori|Imoto, Seiya|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo, Japan. nariai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo, Japan. nariai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo, Japan. nariai@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Shirokanedai, Minato-ku, Tokyo, Japan. nariai@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan', 35, 'Algorithms|Chromosome Mapping|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti1133', '16204105', 'Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein-protein interaction networks simultaneously from DNA microarray data, protein-protein interaction data and other genome-wide data.'),
(72402, 2005, 'bio', 'Knowledge-based framework for hypothesis formation in biochemical networks', 'Tran, Nam|Baral, Chitta|Nagaraj, Vinay J|Joshi, Lokesh', 'Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ 85281, USA. namtran@asu.edu|Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ 85281, USA. namtran@asu.edu|Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ 85281, USA. namtran@asu.edu|Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ 85281, USA. namtran@asu.edu', 'United States|United States|United States|United States', 11, 'Algorithms|Artificial Intelligence|Biochemistry|Computer Simulation|Models, Biological|Models, Chemical|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti1134', '16204106', 'The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding pattern in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalisms they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about biochemical networks.'),
(72403, 2005, 'bio', 'Decomposing protein networks into domain-domain interactions', 'Albrecht, Mario|Huthmacher, Carola|Tosatto, Silvio C E|Lengauer, Thomas', 'Max Planck Institute for Informatics, Saarbrücken, Germany. mario.albrecht@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. mario.albrecht@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. mario.albrecht@mpi-inf.mpg.de|Max Planck Institute for Informatics, Saarbrücken, Germany. mario.albrecht@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany', 18, 'Algorithms|Amino Acid Sequence|Binding Sites|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/bti1135', '16204107', 'The application of novel experimental techniques has generated large networks of protein-protein interactions. Frequently, important information on the structure and cellular function of protein-protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain-domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure.'),
(72404, 2005, 'bio', 'Horizontal gene transfer depends on gene content of the host', 'Pál, Csaba|Papp, Balázs|Lercher, Martin J', 'European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.|European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany|Germany|Germany', 37, 'Base Composition|Chromosome Mapping|Escherichia coli|Escherichia coli Proteins|Gene Transfer, Horizontal|Genetic Variation|Genome, Bacterial|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti1136', '16204108', 'Horizontal gene transfer is a major contributor to the evolution of bacterial genomes. We examine this process through a combination of comparative genomics and in silico analysis of the Escherichia coli metabolic network. We validate our horizontal transfer estimates by confirming the predicted gradual amelioration of GC content over time. We find that the chance of acquiring a gene by horizontal transfer is up to six times higher if an enzyme that catalyses a coupled metabolite flux is already encoded in the host genome.'),
(72405, 2005, 'bio', 'Growing Bayesian network models of gene networks from seed genes', 'Peña, J M|Björkegren, J|Tegnér, J', 'Computational Biology, Department of Physics and Measurement Technology, Linköping University, Linköping, Sweden. jmp@ifm.liu.se|Computational Biology, Department of Physics and Measurement Technology, Linköping University, Linköping, Sweden. jmp@ifm.liu.se|Computational Biology, Department of Physics and Measurement Technology, Linköping University, Linköping, Sweden. jmp@ifm.liu.se', 'Sweden|Sweden|Sweden', 54, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Genetic|Neural Networks (Computer)|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti1137', '16204109', 'For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem.'),
(72406, 2005, 'bio', 'Reconsidering complete search algorithms for protein backbone NMR assignment', 'Vitek, Olga|Bailey-Kellogg, Chris|Craig, Bruce|Kuliniewicz, Paul|Vitek, Jan', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. ovitek@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. ovitek@stat.purdue.edu', 'United States|United States|United States|United States|United States', 14, 'Algorithms|Amino Acid Sequence|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Magnetic Resonance Spectroscopy|Models, Chemical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti1138', '16204110', 'Nuclear magnetic resonance (NMR) spectroscopy is widely used to determine and analyze protein structures. An essential step in NMR studies is determining the backbone resonance assignment, which maps individual atoms to experimentally measured resonance frequencies. Performing assignment is challenging owing to the noise and ambiguity in NMR spectra. Although automated procedures have been investigated, by-and-large they are still struggling to gain acceptance because of inherent limits in scalability and/or unacceptable levels of assignment error. To have confidence in the results, an algorithm should be complete, i.e. able to identify all solutions consistent with the data, including all arbitrary configurations of extra and missing peaks. The ensuing combinatorial explosion in the space of possible assignments has led to the perception that complete search is hopelessly inefficient and cannot scale to realistic datasets.'),
(72407, 2005, 'bio', 'RNA secondary structural alignment with conditional random fields', 'Sato, Kengo|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, Hiyoshi Kohoku-ku, Yokohama, Japan. satoken@bio.keio.ac.jp|Department of Biosciences and Informatics, Keio University, Hiyoshi Kohoku-ku, Yokohama, Japan. satoken@bio.keio.ac.jp', 'Japan|Japan', 53, 'Base Sequence|Chromosome Mapping|Computer Simulation|Effect Modifier, Epidemiologic|Models, Genetic|Models, Statistical|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/bti1139', '16204111', 'The computational identification of non-coding RNA regions on the genome is currently receiving much attention. However, it is essentially harder than gene-finding problems for protein-coding regions because non-coding RNA sequences do not have strong statistical signals. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignment of RNA sequences. Several methods have been proposed to accomplish the structural alignment tasks for RNA sequences, and we found that one of the most important points is to estimate an accurate score matrix for calculating structural alignments.'),
(72408, 2005, 'bio', 'Fast maximum-likelihood refinement of electron microscopy images', 'Scheres, Sjors H W|Valle, Mikel|Carazo, José-María', 'Centro Nacional de Biotecnología-CSIC, Campus Universidad Autónoma, Madrid, Spain.|Centro Nacional de Biotecnología-CSIC, Campus Universidad Autónoma, Madrid, Spain.|Centro Nacional de Biotecnología-CSIC, Campus Universidad Autónoma, Madrid, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Cryoelectron Microscopy|Image Enhancement|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Likelihood Functions|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti1140', '16204112', 'Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization.'),
(72409, 2005, 'bio', 'A probabilistic model for mining implicit chemical compound-gene relations from literature', 'Zhu, Shanfeng|Okuno, Yasushi|Tsujimoto, Gozoh|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Japan.', 'Japan|Japan|Japan|Japan', 71, 'Artificial Intelligence|Biopolymers|Computer Simulation|Genes|Information Storage and Retrieval|Models, Statistical|Natural Language Processing|Periodicals as Topic|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti1141', '16204113', 'The importance of chemical compounds has been emphasized more in molecular biology, and chemical genomics has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit chemical compound and gene relations from the co-occurrence in the literature.'),
(72410, 2005, 'bio', 'Implementing the iHOP concept for navigation of biomedical literature', 'Hoffmann, Robert|Valencia, Alfonso', 'National Center of Biotechnology, CNB-CSIC Campus de la UAM. Madrid, Spain. hoffmann@cbio.mskcc.org|National Center of Biotechnology, CNB-CSIC Campus de la UAM. Madrid, Spain. hoffmann@cbio.mskcc.org', 'Spain|Spain', 176, 'Biomedical Engineering|Computational Biology|Database Management Systems|Genes|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1142', '16204114', 'The World Wide Web has profoundly changed the way in which we access information. Searching the internet is easy and fast, but more importantly, the interconnection of related contents makes it intuitive and closer to the associative organization of human memory. However, the information retrieval tools currently available to researchers in biology and medicine lag far behind the possibilities that the layman has come to expect from the internet.'),
(72411, 2005, 'bio', 'Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts', 'Küffner, Robert|Fundel, Katrin|Zimmer, Ralf', 'Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. Robert.Kueffner@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. Robert.Kueffner@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. Robert.Kueffner@bio.ifi.lmu.de', 'Germany|Germany|Germany', 29, 'Expert Systems|Gene Expression|Gene Expression Profiling|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Proteome|Systems Integration', 'Journal Article', '10.1093/bioinformatics/bti1143', '16204115', 'The interpretation of expression data without appropriate expert knowledge is difficult and usually limited to exploratory data analysis, such as clustering and detecting differentially regulated genes. However, comparing experimental results against manually compiled knowledge resources might limit or bias the perspective on the data. Thus, manual analysis by experts is required to obtain confident predictions about involved processes.'),
(72412, 2005, 'bio', 'DPDB: a database for the storage, representation and analysis of polymorphism in the Drosophila genus', 'Casillas, Sònia|Petit, Natalia|Barbadilla, Antonio', 'Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain.|Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain.|Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain.', 'Spain|Spain|Spain', 12, 'Animals|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Drosophila|Genetic Variation|Information Storage and Retrieval|Molecular Sequence Data|Polymorphism, Genetic', 'Evaluation Studies', '10.1093/bioinformatics/bti1103', '16204116', 'Polymorphism studies are one of the main research areas of this genomic era. To date, however, no comprehensive secondary databases have been designed to provide searchable collections of polymorphic sequences with their associated diversity measures.'),
(72413, 2005, 'bio', 'Web servicing the biological office', 'Szugat, Martin|Güttler, Daniel|Fundel, Katrin|Sohler, Florian|Zimmer, Ralf', 'Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. prothesaurus@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. prothesaurus@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. prothesaurus@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. prothesaurus@bio.ifi.lmu.de|Department of Informatics, Ludwig-Maximilians-Universität München, München, Germany. prothesaurus@bio.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany', 2, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Natural Language Processing|Terminology as Topic|User-Computer Interface|Vocabulary, Controlled|Word Processing', 'Journal Article', '10.1093/bioinformatics/bti1144', '16204117', 'Biologists routinely use Microsoft Office applications for standard analysis tasks. Despite ubiquitous internet resources, information needed for everyday work is often not directly and seamlessly available. Here we describe a very simple and easily extendable mechanism using Web Services to enrich standard MS Office applications with internet resources. We demonstrate its capabilities by providing a Web-based thesaurus for biological objects, which maps names to database identifiers and vice versa via an appropriate synonym list. The client application ProTag makes these features available in MS Office applications using Smart Tags and Add-Ins.'),
(72414, 2005, 'bio', 'Design of a description language for generating wrapper to collect biological data', 'Lim, Myungeun|Chung, Myunggeun|Bae, Myungnam|Park, Sunhee', 'Bioinformatics Research Team, Electronics and Telecommunications Research Institute 161, Gajeong-dong, Daejeon, Republic of Korea. melim@etri.re.kr|Bioinformatics Research Team, Electronics and Telecommunications Research Institute 161, Gajeong-dong, Daejeon, Republic of Korea. melim@etri.re.kr|Bioinformatics Research Team, Electronics and Telecommunications Research Institute 161, Gajeong-dong, Daejeon, Republic of Korea. melim@etri.re.kr|Bioinformatics Research Team, Electronics and Telecommunications Research Institute 161, Gajeong-dong, Daejeon, Republic of Korea. melim@etri.re.kr', '-|-|-|-', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Programming Languages|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1104', '16204120', 'The biological data are scattered in various areas with various formats and they are changing continuously. Therefore, data integration becomes an important issue to provide researcher a dynamic access of data. In the data integration process, the method of extracting heterogeneous data dynamically from the data source is an essential part. Data extraction method using wrapper can provide flexibility and extensibility to an integration system.'),
(72415, 2005, 'bio', 'A query language for biological networks', 'Leser, Ulf', 'Department for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany. leser@informatik.hu-berlin.de', 'Germany', 51, 'Computational Biology|Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti1105', '16204121', 'Many areas of modern biology are concerned with the management, storage, visualization, comparison and analysis of networks, but no appropriate query language for such complex data structures yet exists.'),
(72416, 2005, 'bio', 'Adding some SPICE to DAS', 'Prlixc4x87, Andreas|Down, Thomas A|Hubbard, Tim J P', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge, UK. ap3@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge, UK. ap3@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge, UK. ap3@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 39, 'Algorithms|Amino Acid Sequence|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1106', '16204122', 'The distributed annotation system (DAS) defines a communication protocol used to exchange biological annotations. It is motivated by the idea that annotations should not be provided by single centralized databases but instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is carried out by DAS clients. The original DAS protocol was designed to serve annotation of genomic sequences. We have extended the protocol to be applicable to macromolecular structures. Here we present SPICE, a new DAS client that can be used to visualize protein sequence and structure annotations.'),
(72417, 2005, 'bio', 'SIMAP--the similarity matrix of proteins', 'Arnold, Roland|Rattei, Thomas|Tischler, Patrick|Truong, Minh-Duc|Stümpflen, Volker|Mewes, Werner', 'Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health Ingolstädter, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 31, 'Algorithms|Amino Acid Sequence|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti1107', '16204123', 'Sequence similarity searches are of great importance in bioinformatics. Exhaustive searches for homologous proteins in databases are computationally expensive and can be replaced by a database of pre-calculated homologies in many cases. Retrieving similarities from an incrementally updated database instead of repeatedly recalculating them should provide homologs much faster and frees computational resources for other purposes.'),
(72418, 2005, 'bio', 'ARTS: alignment of RNA tertiary structures', 'Dror, Oranit|Nussinov, Ruth|Wolfson, Haim', 'School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. oranit@post.tau.ac.il|School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. oranit@post.tau.ac.il|School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel. oranit@post.tau.ac.il', 'Israel|Israel|Israel', 56, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bti1108', '16204124', 'A fast growing number of non-coding RNAs have recently been discovered to play essential roles in many cellular processes. Similar to proteins, understanding the functions of these active RNAs requires methods for analyzing their tertiary structures. However, in contrast to the wide range of structure-based approaches available for proteins, there is still a lack of methods for studying RNA structures.'),
(72419, 2005, 'bio', 'Predicting protein stability changes from sequences using support vector machines', 'Capriotti, Emidio|Fariselli, Piero|Calabrese, Remo|Casadio, Rita', 'Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna Bologna, Italy.', 'Italy|Italy|Italy|Italy', 109, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Artificial Intelligence|Computer Simulation|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Denaturation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/bti1109', '16204125', 'The prediction of protein stability change upon mutations is key to understanding protein folding and misfolding. At present, methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are, however, necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases.'),
(72420, 2005, 'bio', 'Fast protein classification with multiple networks', 'Tsuda, Koji|Shin, HyunJung|Schölkopf, Bernhard', 'Max Planck Institute for Biological Cybernetics, Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, Tübingen, Germany.', 'Germany|Germany|Germany', 150, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression|Models, Biological|Protein Interaction Mapping|Proteins|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/bti1110', '16204126', 'Support vector machines (SVMs) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced. In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n(3)), and produces a dense matrix of size n x n.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72421, 2005, 'bio', 'Less is more: towards an optimal universal description of protein folds', 'Szustakowski, Joseph D|Kasif, Simon|Weng, Zhiping', 'Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.|Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.', 'United States|United States|United States', 19, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Databases, Protein|Dictionaries as Topic|Molecular Sequence Data|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti1111', '16204127', 'Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as protein parts to describe fold-sized structures.'),
(72422, 2005, 'bio', 'Augmented cell-graphs for automated cancer diagnosis', 'Demir, Cigdem|Gultekin, S Humayun|Yener, Bülent', 'Department of Computer Science, Rensselaer Polytechnic Institute Troy, NY 12180, USA. demir@cs.rpi.edu|Department of Computer Science, Rensselaer Polytechnic Institute Troy, NY 12180, USA. demir@cs.rpi.edu|Department of Computer Science, Rensselaer Polytechnic Institute Troy, NY 12180, USA. demir@cs.rpi.edu', 'United States|United States|United States', 39, 'Algorithms|Artificial Intelligence|Brain Neoplasms|Glioma|Humans|Image Interpretation, Computer-Assisted|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/bti1100', '16204128', 'This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.'),
(72423, 2005, 'bio', 'Evaluating the usefulness of protein structure models for molecular replacement', 'Giorgetti, Alejandro|Raimondo, Domenico|Miele, Adriana Erica|Tramontano, Anna', 'Department of Biochemical Sciences, University of Rome La Sapienza, Rome, Italy.|Department of Biochemical Sciences, University of Rome La Sapienza, Rome, Italy.|Department of Biochemical Sciences, University of Rome La Sapienza, Rome, Italy.|Department of Biochemical Sciences, University of Rome La Sapienza, Rome, Italy.', 'Italy|Italy|Italy|Italy', 45, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Computer Simulation|Crystallography, X-Ray|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti1112', '16204129', 'We investigate the relationship between the quality of models of protein structure and their usefulness as search models in molecular replacement, a widely used method to experimentally determine protein structures by X-ray crystallography.'),
(72424, 2005, 'bio', 'Non-coding RNAs in Ciona intestinalis', 'Missal, Kristin|Rose, Dominic|Stadler, Peter F', 'Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany. kristin@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany. kristin@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany. kristin@bioinf.uni-leipzig.de', 'Germany|Germany|Germany', 43, 'Animals|Base Sequence|Chromosome Mapping|Ciona intestinalis|Conserved Sequence|Evolution, Molecular|Genetic Variation|Molecular Sequence Data|RNA, Untranslated|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/bti1113', '16204130', 'The analysis of animal genomes showed that only a minute part of their DNA codes for proteins. Recent experimental results agree, however, that a large fraction of these genomes are transcribed and hence are probably functional at the RNA level. A computational survey of vertebrate genomes has predicted thousands of previously unknown ncRNAs with evolutionarily conserved secondary structures. Extending these comparative studies beyond vertebrates is difficult, however, since most ncRNAs evolve quickly at the sequence level while conserving their characteristic secondarystructures.'),
(72425, 2005, 'bio', 'The fragment assembly string graph', 'Myers, Eugene W', 'Department of Computer Science, University of California Berkeley, CA, USA. gene@eecs.berkeley.edu', 'United States', 218, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Fragmentation|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1114', '16204131', 'We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes.'),
(72426, 2005, 'bio', 'Finding exact optimal motifs in matrix representation by partitioning', 'Leung, Henry C M|Chin, Francis Y L', 'Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Hong Kong. cmleung2@cs.hku.hk', 'Hong Kong|Hong Kong', 13, 'Algorithms|Amino Acid Motifs|Base Sequence|Molecular Sequence Data|Multigene Family|Proteins|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1115', '16204132', 'Finding common patterns, or motifs, in the promoter regions of co-expressed genes is an important problem in bioinformatics. A common representation of the motif is by probability matrix or PSSM (position specific scoring matrix). However, even for a motif of length six or seven, there is no algorithm that can guarantee finding the exact optimal matrix from an infinite number of possible matrices.'),
(72427, 2005, 'bio', 'Prediction of regulatory modules comprising microRNAs and target genes', 'Yoon, Sungroh|De Micheli, Giovanni', 'Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sryoon@stanford.edu|Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sryoon@stanford.edu', 'United States|United States', 0, 'Algorithms|Base Sequence|Gene Expression Regulation|Gene Targeting|MicroRNAs|Molecular Sequence Data|Proteins|RNA Processing, Post-Transcriptional|Regulatory Elements, Transcriptional|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/bti1116', '16204133', 'MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation.'),
(72428, 2005, 'bio', 'Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions', 'Lapinsh, Maris|Prusis, Peteris|Uhlén, Staffan|Wikberg, Jarl E S', 'Department of Pharmaceutical Biosciences, Uppsala University Box 591 BMC, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University Box 591 BMC, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University Box 591 BMC, SE-751 24 Uppsala, Sweden.|Department of Pharmaceutical Biosciences, Uppsala University Box 591 BMC, SE-751 24 Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 70, 'Amines|Binding Sites|Chemistry, Organic|Cluster Analysis|Databases, Factual|Drug Interactions|Hydrogen-Ion Concentration|Least-Squares Analysis|Ligands|Models, Biological|Models, Chemical|Models, Molecular|Models, Statistical|Models, Theoretical|Mutagenesis|Pharmacology|Protein Binding|Proteomics|Receptors, G-Protein-Coupled', 'Journal Article', '10.1093/bioinformatics/bti703', '16204343', 'Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs).'),
(72429, 2005, 'bio', 'Pcons5: combining consensus, structural evaluation and fold recognition scores', 'Wallner, Björn|Elofsson, Arne', 'Stockholm Bioinformatics Center, Stockholm University SE-106 91 Stockholm, Sweden. bjorn@sbc.su.se|Stockholm Bioinformatics Center, Stockholm University SE-106 91 Stockholm, Sweden. bjorn@sbc.su.se', 'Sweden|Sweden', 81, 'Algorithms|Computational Biology|Computer Simulation|Databases, Protein|Models, Statistical|Neural Networks (Computer)|Protein Conformation|Protein Folding|Protein Structure, Secondary|Proteins|ROC Curve|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti702', '16204344', 'The success of the consensus approach to the protein structure prediction problem has led to development of several different consensus methods. Most of them only rely on a structural comparison of a number of different models. However, there are other types of information that might be useful such as the score from the server and structural evaluation.'),
(72430, 2005, 'bio', 'An attempt to define allergen-specific molecular surface features: a bioinformatic approach', 'Furmonaviciene, Ruta|Sutton, Brian J|Glaser, Fabian|Laughton, Charlie A|Jones, Nick|Sewell, Herb F|Shakib, Farouk', 'Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.|Allergy Research Group, Institute of Infection, Immunity and Inflammation, The University of Nottingham Nottingham, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Allergens|Amino Acid Motifs|Amino Acid Sequence|Cell Membrane|Computational Biology|Crystallography, X-Ray|Cysteine Endopeptidases|Desensitization, Immunologic|Humans|Hypersensitivity|Immune System|Immunoglobulin E|Molecular Sequence Data|Plant Proteins|Plants|Profilins|Software|T-Lymphocytes|Th2 Cells', 'Journal Article', '10.1093/bioinformatics/bti700', '16204345', 'Allergens are proteins that elicit T helper lymphocyte type 2 (Th2) responses culminating in IgE antibody production and allergic disease. However, we have no answer to the fundamental question of why certain proteins are allergens, while others are not. We hypothesized that analysis of the surface of diverse allergens may reveal common structural features which might enable them to be recognized as Th2-inducing antigens by cells of the innate immune system. We have therefore used the ConSurf server to search for allergen-specific motifs. This has enabled us to identify residue conservation patterns in the homologues of Ara t 8 (plant profilin), Act c 1 (actinidin), Bet v 1 (plant pathogenesis-related protein) and Ves v 5 (venom allergen). The results demonstrate the presence of allergen-specific patches consisting of an unusually high proportion of surface-exposed hydrophobic residues. The patches that have been identified may represent molecular patterns recognizable by cells of the innate immune system.'),
(72431, 2005, 'bio', 'Sample size determination for the false discovery rate', 'Pounds, Stan|Cheng, Cheng', 'Department of Biostatistics, St Jude Childrens Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA. stanley.pounds@stjude.org|Department of Biostatistics, St Jude Childrens Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA. stanley.pounds@stjude.org', 'United States|United States', 41, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|False Positive Reactions|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/bti699', '16204346', 'There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR).'),
(72432, 2005, 'bio', 'OxfordGrid: a web interface for pairwise comparative map views', 'Yang, Hongyu|Gingle, Alan R', 'Center for Applied Genetic Technologies, University of Georgia 111 Riverbend Road, Athens, GA 30602, USA.|Center for Applied Genetic Technologies, University of Georgia 111 Riverbend Road, Athens, GA 30602, USA.', 'Georgia|Georgia', 1, 'Algorithms|Chromosome Mapping|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Genetic Linkage|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/bti698', '16204347', 'OxfordGrid is a web application and database schema for storing and interactively displaying genetic map data in a comparative, dot-plot, fashion. Its display is composed of a matrix of cells, each representing a pairwise comparison of mapped probe data for two linkage groups or chromosomes. These are arranged along the axes with one forming grid columns and the other grid rows with the degree and pattern of synteny/colinearity between the two linkage groups manifested in the cells dot density and structure. A mouse click over the selected grid cell launches an image map-based display for the selected cell. Both individual and linear groups of mapped probes can be selected and displayed. Also, configurable links can be used to access other web resources for mapped probe information.'),
(72433, 2005, 'bio', 'Discrimination of outer membrane proteins using support vector machines', 'Park, Keun-Joon|Gromiha, M Michael|Horton, Paul|Suwa, Makiko', 'Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan.|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan.', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Artificial Intelligence|Bacterial Outer Membrane Proteins|Computational Biology|Databases, Protein|Genetic Vectors|Genome|Humans|Internet|Membrane Proteins|Models, Statistical|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Protein Structure, Secondary|Protein Structure, Tertiary', 'Journal Article', '10.1093/bioinformatics/bti697', '16204348', 'Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures.'),
(72434, 2005, 'bio', 'CREDO: a web-based tool for computational detection of conserved sequence motifs in noncoding sequences', 'Hindemitt, Tobias|Mayer, Klaus F X', 'MIPS/Institute for Bioinformatics, GSF Research Centre for Environment and Health Ingolstaedter Landstrasse 1, 85758 Neuherberg, Germany.|MIPS/Institute for Bioinformatics, GSF Research Centre for Environment and Health Ingolstaedter Landstrasse 1, 85758 Neuherberg, Germany.', 'Germany|Germany', 0, 'Algorithms|Amino Acid Motifs|Base Sequence|Computational Biology|Computer Graphics|Conserved Sequence|DNA|Databases, Protein|Internet|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti691', '16204349', 'CREDO is a user-friendly, web-based tool that integrates the analysis and results of different algorithms widely used for the computational detection of conserved sequence motifs in noncoding sequences. It enables easy comparison of the individual results. CREDO offers intuitive interfaces for easy and rapid configuration of the applied algorithms and convenient views on the results in graphical and tabular formats.'),
(72435, 2005, 'bio', 'Discovering hidden viral piracy', 'Kim, Eddo|Kliger, Yossef', 'Compugen Ltd Tel Aviv 69512, Israel.|Compugen Ltd Tel Aviv 69512, Israel.', 'Israel|Israel', 0, 'Computational Biology|DNA Virus Infections|DNA Viruses|DNA, Viral|Evolution, Molecular|Gene Expression Regulation|Gene Expression Regulation, Viral|Humans|Immune System|Protein Sorting Signals|RNA Viruses|Receptors, Cytokine|Viral Proteins|Virus Replication', 'Journal Article', '10.1093/bioinformatics/bti706', '16210423', 'Viruses and developers of anti-inflammatory therapies share a common interest in proteins that manipulate the immune response. Large double-stranded DNA viruses acquire host proteins to evade host defense mechanisms. Hence, viral pirated proteins may have a therapeutic potential. Although dozens of viral piracy events have already been identified, we hypothesized that sequence divergence impedes the discovery of many others.'),
(72436, 2005, 'bio', 'GARSA: genomic analysis resources for sequence annotation', 'Dávila, Alberto M R|Lorenzini, Daniel M|Mendes, Pablo N|Satake, Thiago S|Sousa, Gabriel R|Campos, Linair M|Mazzoni, Camila J|Wagner, Glauber|Pires, Paulo F|Grisard, Edmundo C|Cavalcanti, Maria C R|Campos, Maria Luiza M', 'DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br|DBBM, Instituto Oswaldo Cruz Fiocruz, Brazil. davila@fiocruz.br', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 17, 'Animals|Bothrops|Chromatography|Computational Biology|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Expressed Sequence Tags|Genome|Genomics|Information Storage and Retrieval|Internet|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software|Trypanosoma|Trypanosoma vivax|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti705', '16210424', 'Growth of genome data and analysis possibilities have brought new levels of difficulty for scientists to understand, integrate and deal with all this ever-increasing information. In this scenario, GARSA has been conceived aiming to facilitate the tasks of integrating, analyzing and presenting genomic information from several bioinformatics tools and genomic databases, in a flexible way. GARSA is a user-friendly web-based system designed to analyze genomic data in the context of a pipeline. EST and GGS data can be analyzed using the system since it accepts (1) chromatograms, (2) download of sequences from GenBank, (3) Fasta files stored locally or (4) a combination of all three. Quality evaluation of chromatograms, vector removing and clusterization are easily performed as part of the pipeline. A number of local and customizable Blast and CDD analyses can be performed as well as Interpro, complemented with phylogeny analyses. GARSA is being used for the analyses of Trypanosoma vivax (GSS and EST), Trypanosoma rangeli (GSS, EST and ORESTES), Bothrops jararaca (EST), Piaractus mesopotamicus (EST) and Lutzomyia longipalpis (EST).'),
(72437, 2005, 'bio', 'QUASAR--scoring and ranking of sequence-structure alignments', 'Birzele, Fabian|Gewehr, Jan E|Zimmer, Ralf', 'Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University, Amalienstrasse 17, D-80333 Munich, Germany. fabian.birzele@ifi.lmu.de|Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University, Amalienstrasse 17, D-80333 Munich, Germany. fabian.birzele@ifi.lmu.de|Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University, Amalienstrasse 17, D-80333 Munich, Germany. fabian.birzele@ifi.lmu.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Databases, Protein|Molecular Structure|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bti712', '16216828', 'Sequence-structure alignments are a common means for protein structure prediction in the fields of fold recognition and homology modeling, and there is a broad variety of programs that provide such alignments based on sequence similarity, secondary structure or contact potentials. Nevertheless, finding the best sequence-structure alignment in a pool of alignments remains a difficult problem. QUASAR (quality of sequence-structure alignments ranking) provides a unifying framework for scoring sequence-structure alignments that aids finding well-performing combinations of well-known and custom-made scoring schemes. Those scoring functions can be benchmarked against widely accepted quality scores like MaxSub, TMScore, Touch and APDB, thus enabling users to test their own alignment scores against standard-of-truth structure-based scores. Furthermore, individual score combinations can be optimized with respect to benchmark sets based on known structural relationships using QUASARs in-built optimization routines.'),
(72438, 2005, 'bio', 'Syntons, metabolons and interactons: an exact graph-theoretical approach for exploring neighbourhood between genomic and functional data', 'Boyer, Frédéric|Morgat, Anne|Labarre, Laurent|Pothier, Joël|Viari, Alain', 'INRIA Rhône-Alpes, HELIX Group 655 avenue de lEurope, 38334 Montbonnot Cedex, France.|INRIA Rhône-Alpes, HELIX Group 655 avenue de lEurope, 38334 Montbonnot Cedex, France.|INRIA Rhône-Alpes, HELIX Group 655 avenue de lEurope, 38334 Montbonnot Cedex, France.|INRIA Rhône-Alpes, HELIX Group 655 avenue de lEurope, 38334 Montbonnot Cedex, France.|INRIA Rhône-Alpes, HELIX Group 655 avenue de lEurope, 38334 Montbonnot Cedex, France.', 'France|France|France|France|France', 35, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Databases, Protein|Escherichia coli|Evolution, Molecular|Genes, Bacterial|Genome|Genome, Bacterial|Genomics|Models, Biological|Models, Genetic|Models, Statistical|Protein Interaction Mapping|RNA, Ribosomal|RNA, Transfer|Software', 'Journal Article', '10.1093/bioinformatics/bti711', '16216829', 'Modern comparative genomics does not restrict to sequence but involves the comparison of metabolic pathways or protein-protein interactions as well. Central in this approach is the concept of neighbourhood between entities (genes, proteins, chemical compounds). Therefore there is a growing need for new methods aiming at merging the connectivity information from different biological sources in order to infer functional coupling.'),
(72439, 2005, 'bio', 'The influence of missing value imputation on detection of differentially expressed genes from microarray data', 'Scheel, Ida|Aldrin, Magne|Glad, Ingrid K|Sørum, Ragnhild|Lyng, Heidi|Frigessi, Arnoldo', 'Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no|Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no|Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no|Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no|Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no|Department of Mathematics, University of Oslo PO Box 1053, Blindern, NO-0316 Oslo, Norway. idasch@math.uio.no', 'Norway|Norway|Norway|Norway|Norway|Norway', 0, 'Algorithms|Analysis of Variance|Cluster Analysis|Computational Biology|DNA, Complementary|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Likelihood Functions|Linear Models|Mathematical Computing|Models, Genetic|Models, Statistical|Models, Theoretical|Multigene Family|Normal Distribution|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti708', '16216830', 'Missing values are problematic for the analysis of microarray data. Imputation methods have been compared in terms of the similarity between imputed and true values in simulation experiments and not of their influence on the final analysis. The focus has been on missing at random, while entries are missing also not at random.'),
(72440, 2005, 'bio', 'ATID: a web-oriented database for collection of publicly available alternative translational initiation events', 'Cai, Jun|Zhang, Jing|Huang, Ying|Li, Yanda', 'MOE Key Laboratory of Bioinformatics and Department of Automation, Tsinghua University Beijing 100084, China. caijun99@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Department of Automation, Tsinghua University Beijing 100084, China. caijun99@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Department of Automation, Tsinghua University Beijing 100084, China. caijun99@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Department of Automation, Tsinghua University Beijing 100084, China. caijun99@mails.tsinghua.edu.cn', 'China|China|China|China', 6, 'Amino Acid Sequence|Animals|Base Sequence|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Mice|Models, Genetic|Molecular Sequence Data|Programming Languages|Protein Biosynthesis|Protein Isoforms|Protein Structure, Tertiary|Sequence Analysis, Protein|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti704', '16216831', 'Alternative translational initiation is an important cellular mechanism contributing to the diversity of protein products and functions. We develop a database that provides a comprehensive collection of alternative translational initiation events. The purpose of this alternative translational initiation database (ATID) is to facilitate the systematic study of alternative translational initiation of genes. The current version of database contains 300 genes from Homo sapiens, Mus musculus and other species. Each of the genes has two or more isoforms due to alternative translational initiation. Resources in ATID, including gene information, alternative products of genes and domain structures of isoforms, are provided through a user-friendly web interface.'),
(72441, 2005, 'bio', 'CoGenT++: an extensive and extensible data environment for computational genomics', 'Goldovsky, Leon|Janssen, Paul|Ahrén, Dag|Audit, Benjamin|Cases, Ildefonso|Darzentas, Nikos|Enright, Anton J|López-Bigas, Núria|Peregrin-Alvarez, José M|Smith, Mike|Tsoka, Sophia|Kunin, Victor|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.|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.|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|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Chromosome Mapping|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Genomics|Information Storage and Retrieval|Sequence Analysis|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti579', '16216832', 'CoGenT++ is a data environment for computational research in comparative and functional genomics, designed to address issues of consistency, reproducibility, scalability and accessibility.'),
(72442, 2005, 'bio', 'Identifying genes from up-down properties of microarray expression series', 'Willbrand, Karen|Radvanyi, Francois|Nadal, Jean-Pierre|Thiery, Jean-Paul|Fink, Thomas M A', 'Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.|Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.|Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.|Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.|Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France.', 'France|France|France|France|France', 21, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti549', '16221984', 'We consider any collection of microarrays that can be ordered to form a progression, for example, as a function of time, severity of disease or dose of a stimulant. By plotting the expression level of each gene as a function of time, or severity, or dose, we form an expression series, or curve, for each gene. While most of these curves will exhibit random fluctuations, some will contain a pattern, and these are the genes that are most likely associated with the quantity used to order them.'),
(72443, 2005, 'bio', 'Improving disulfide connectivity prediction with sequential distance between oxidized cysteines', 'Tsai, Chi-Hung|Chen, Bo-Juen|Chan, Chen-Hsiung|Liu, Hsuan-Liang|Kao, Cheng-Yan', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106.|Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 106.', '-|-|-|-|-', 41, 'Chymotrypsinogen|Computational Biology|Cysteine|Databases, Protein|Disulfides|Models, Molecular|Molecular Structure|Oxidation-Reduction|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/bti715', '16223789', 'Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys-cys linkages of proteins).'),
(72444, 2005, 'bio', 'Athena: a resource for rapid visualization and systematic analysis of Arabidopsis promoter sequences', 'OConnor, Timothy R|Dyreson, Curtis|Wyrick, John J', 'School of Molecular Biosciences, Washington State University, Pullman, WA, USA.|School of Molecular Biosciences, Washington State University, Pullman, WA, USA.|School of Molecular Biosciences, Washington State University, Pullman, WA, USA.', 'United States|United States|United States', 166, 'Arabidopsis|Arabidopsis Proteins|Binding Sites|Computational Biology|DNA, Plant|Databases, Nucleic Acid|Genes, Plant|Internet|Promoter Regions, Genetic|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti714', '16223790', 'To better understand the regulatory networks that control plant gene expression, tools are needed to systematically analyze and visualize promoter regulatory sequences in Arabidopsis thaliana. We have developed the Athena database, which contains 30,067 predicted Arabidopsis promoter sequences and consensus sequences for 105 previously characterized transcription factor (TF) binding sites. Athena provides four novel tools to facilitate the analysis of promoter sequences: a promoter visualization tool to enable the rapid inspection of key regulatory sequences in multiple promoters, a TF binding site enrichment tool to identify statistically over-represented TF sites occurring in a user-selected subset of promoters, a data-mining tool to rapidly select promoter sequences containing the specified combination of TF binding sites, and a tool to display the distribution of TF binding site positions in a selected set of promoter sequences.'),
(72445, 2005, 'bio', 'Suffix-tree analyser (STAN): looking for nucleotidic and peptidic patterns in chromosomes', 'Nicolas, Jacques|Durand, Patrick|Ranchy, Grégory|Tempel, Sébastien|Valin, Anne-Sophie', 'IRISA-INRIA, Campus de Beaulieu, 35402 Rennes cedex, France. stan@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35402 Rennes cedex, France. stan@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35402 Rennes cedex, France. stan@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35402 Rennes cedex, France. stan@irisa.fr|IRISA-INRIA, Campus de Beaulieu, 35402 Rennes cedex, France. stan@irisa.fr', 'France|France|France|France|France', 19, 'Amino Acid Sequence|Arabidopsis|Base Sequence|Chromosomes|Chromosomes, Plant|Computational Biology|DNA Transposable Elements|DNA, Plant|Databases, Nucleic Acid|Genomics|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/bti710', '16223791', 'We have developed STAN (suffix-tree analyser), a tool to search for nucleotidic and peptidic patterns within whole chromosomes. Pattern syntax uses a string variable grammar-like formalism which allows the description of complex patterns including ambiguities, insertions/deletions, gaps, repeats and palindromes. STAN is based on a reduction to multipart matching on a suffix-tree data structure and can handle large DNA sequences, whether assembled or not.'),
(72446, 2005, 'bio', 'Microarray phenotyping in Dictyostelium reveals a regulon of chemotaxis genes', 'Booth, Ezgi O|Van Driessche, Nancy|Zhuchenko, Olga|Kuspa, Adam|Shaulsky, Gad', 'Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.|Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 18, 'Animals|Chemotaxis|Cluster Analysis|Computational Biology|Cyclic AMP|Cytoskeleton|Dictyostelium|Gene Expression Profiling|Gene Expression Regulation|Genes, Protozoan|Genomics|Oligonucleotide Array Sequence Analysis|Phenotype|Regulon|Ribosomes|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti726', '16234315', 'Coordinate regulation of gene expression can provide information on gene function. To begin a large-scale analysis of Dictyostelium gene function, we clustered genes based on their expression in wild-type and mutant strains and analyzed their functions.'),
(72447, 2005, 'bio', 'Regularized ROC method for disease classification and biomarker selection with microarray data', 'Ma, Shuangge|Huang, Jian', 'Department of Biostatistics, University of Washington, Washington, USA.|Department of Biostatistics, University of Washington, Washington, USA.', 'United States|United States', 118, 'Algorithms|Breast Neoplasms|Colonic Neoplasms|Computational Biology|Disease|Estrogens|Female|Gene Expression Profiling|Genetic Markers|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|ROC Curve|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/bti724', '16234316', 'An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease classification. Thus there is a need for developing statistical methods that can efficiently use such high-throughput genomic data, select biomarkers with discriminant power and construct classification rules. The ROC (receiver operator characteristic) technique has been widely used in disease classification with low-dimensional biomarkers because (1) it does not assume a parametric form of the class probability as required for example in the logistic regression method, (2) it accommodates case-control designs and (3) it allows treating false positives and false negatives differently. However, due to computational difficulties, the ROC-based classification has not been used with microarray data. Moreover, the standard ROC technique does not incorporate built-in biomarker selection.'),
(72448, 2005, 'bio', 'Differential coexpression analysis using microarray data and its application to human cancer', 'Choi, Jung Kyoon|Yu, Ungsik|Yoo, Ook Joon|Kim, Sangsoo', 'National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, 52 Ueun-dong, Yuseong-gu, Daejeon, Korea.|National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, 52 Ueun-dong, Yuseong-gu, Daejeon, Korea.|National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, 52 Ueun-dong, Yuseong-gu, Daejeon, Korea.|National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, 52 Ueun-dong, Yuseong-gu, Daejeon, Korea.', '-|-|-|-', 191, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Female|Gene Expression Profiling|Humans|Male|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/bti722', '16234317', 'Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer.'),
(72449, 2005, 'bio', 'Prediction of protein-protein interactions using random decision forest framework', 'Chen, Xue-Wen|Liu, Mei', 'Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu|Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu', 'United States|United States', 281, 'Computational Biology|Databases, Factual|Decision Theory|Likelihood Functions|Multiprotein Complexes|Protein Binding|Protein Structure, Tertiary|Proteins|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/bti721', '16234318', 'Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins, therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions.'),
(72450, 2006, 'bio', 'A novel algorithm and web-based tool for comparing two alternative phylogenetic trees', 'Nye, Tom M W|Liò, Pietro|Gilks, Walter R', 'Biostatistics Unit, Medical Research Council, Cambridge, UK. thomas.nye@mrc-bsu.cam.ac.uk|Biostatistics Unit, Medical Research Council, Cambridge, UK. thomas.nye@mrc-bsu.cam.ac.uk|Biostatistics Unit, Medical Research Council, Cambridge, UK. thomas.nye@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 119, 'Algorithms|Computational Biology|Computer Graphics|HIV|Internet|Models, Genetic|Models, Statistical|Phylogeny|Programming Languages|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti720', '16234319', 'We describe an algorithm and software tool for comparing alternative phylogenetic trees. The main application of the software is to compare phylogenies obtained using different phylogenetic methods for some fixed set of species or obtained using different gene sequences from those species. The algorithm pairs up each branch in one phylogeny with a matching branch in the second phylogeny and finds the optimum 1-to-1 map between branches in the two trees in terms of a topological score. The software enables the user to explore the corresponding mapping between the phylogenies interactively, and clearly highlights those parts of the trees that differ, both in terms of topology and branch length.'),
(72451, 2005, 'bio', 'Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX', 'Strömbäck, Lena|Lambrix, Patrick', 'Department of Computer and Information Science, Linköpings universitet, S-581 83 Linköping, Sweden. lestr@ida.liu.se|Department of Computer and Information Science, Linköpings universitet, S-581 83 Linköping, Sweden. lestr@ida.liu.se', 'Sweden|Sweden', 102, 'Computational Biology|Data Interpretation, Statistical|Databases, Factual|Humans|Models, Biological|Molecular Biology|Programming Languages|Proteomics|Software', 'Comparative Study', '10.1093/bioinformatics/bti718', '16234320', 'Analysis and simulation of pathway data is of high importance in bioinformatics. Standards for representation of information about pathways are necessary for integration and analysis of data from various sources. Recently, a number of representation formats for pathway data, SBML, PSI MI and BioPAX, have been proposed.'),
(72452, 2005, 'bio', 'Effect of pooling samples on the efficiency of comparative studies using microarrays', 'Zhang, Shu-Dong|Gant, Timothy W', 'MRC Toxicology Unit Hodgkin Building, Lancaster Road University of Leicester, Leicester, UK. sdz1@le.ac.uk|MRC Toxicology Unit Hodgkin Building, Lancaster Road University of Leicester, Leicester, UK. sdz1@le.ac.uk', 'United Kingdom|United Kingdom', 56, 'Computational Biology|Costs and Cost Analysis|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size', 'Comparative Study', '10.1093/bioinformatics/bti717', '16234321', 'Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large.'),
(72453, 2005, 'bio', 'A novel strategy to design highly specific PCR primers based on the stability and uniqueness of 3-end subsequences', 'Miura, Fumihito|Uematsu, Chihiro|Sakaki, Yoshiyuki|Ito, Takashi', 'Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo Kashiwa, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo Kashiwa, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo Kashiwa, Japan.|Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo Kashiwa, Japan.', 'Japan|Japan|Japan|Japan', 20, 'Algorithms|Base Sequence|Computational Biology|DNA Primers|DNA, Fungal|Drug Design|Polymerase Chain Reaction|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/bti716', '16234322', 'In contrast with conventional PCR using a pair of specific primers, some applications utilize a single unique primer in combination with a common primer, thereby relying solely on the former for specificity. These applications include rapid amplification of cDNA ends (RACE), adaptor-tagged competitive PCR (ATAC-PCR), PCR-mediated genome walking and so forth. Since the primers designed by conventional methods often fail to work in these applications, an improved strategy is required, particularly, for a large-scale analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72454, 2005, 'bio', 'Improving the efficiency of SPR moves in phylogenetic tree search methods based on maximum likelihood', 'Hordijk, Wim|Gascuel, Olivier', 'Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM, UMR CNRS 5506, Université Montpellier 2 161 rue Ada, 34392 Montpellier, France. wim@santafe.edu|Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM, UMR CNRS 5506, Université Montpellier 2 161 rue Ada, 34392 Montpellier, France. wim@santafe.edu', 'France|France', 120, 'Algorithms|Computational Biology|Databases, Genetic|Likelihood Functions|Mathematics|Models, Genetic|Phylogeny|Software', 'Journal Article', '10.1093/bioinformatics/bti713', '16234323', 'Maximum likelihood (ML) methods have become very popular for constructing phylogenetic trees from sequence data. However, despite noticeable recent progress, with large and difficult datasets (e.g. multiple genes with conflicting signals) current ML programs still require huge computing time and can become trapped in bad local optima of the likelihood function. When this occurs, the resulting trees may still show some of the defects (e.g. long branch attraction) of starting trees obtained using fast distance or parsimony programs.'),
(72455, 2005, 'bio', 'Evaluating and improving cDNA sequence quality with cQC', 'Hayden, Celine A|Wheeler, Travis J|Jorgensen, Richard A', 'Department of Plant Sciences, University of Arizona, Tucson, AZ 85721-0036, USA.|Department of Plant Sciences, University of Arizona, Tucson, AZ 85721-0036, USA.|Department of Plant Sciences, University of Arizona, Tucson, AZ 85721-0036, USA.', 'United States|United States|United States', 3, '5 Untranslated Regions|Computational Biology|DNA, Complementary|Genomics|Internet|Open Reading Frames|Quality Control|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti709', '16234324', 'Errors are prevalent in cDNA sequences but the extent to which sequence collections differ in frequencies and types of errors has not been investigated systematically. cDNA quality control, or cQC, was developed to evaluate the quality of cDNA sequence collections and to revise those sequences that differ from a higher quality genomic sequence. After removing rRNA, vector, bacterial insertion sequence and chimeric cDNA contaminants, small-scale nucleotide discrepancies were found in 51% of cDNA sequences from one Arabidopsis cDNA collection, 89% from a second Arabidopsis collection and 75% from a rice collection. These errors created premature termination codons in 4 and 42% of cDNA sequences in the respective Arabidopsis collections and in 7% of the rice cDNA sequences.'),
(72456, 2006, 'bio', 'OligoFaktory: a visual tool for interactive oligonucleotide design', 'Schretter, Colas|Milinkovitch, Michel C', 'Unit of Evolutionary Genetics, Institute of Molecular Biology and Medicine, Free University of Brussels, Gosselies, Belgium. cschrett@ulb.ac.be|Unit of Evolutionary Genetics, Institute of Molecular Biology and Medicine, Free University of Brussels, Gosselies, Belgium. cschrett@ulb.ac.be', 'Belgium|Belgium', 17, 'Algorithms|Base Sequence|Computational Biology|Computer Graphics|DNA Primers|Genetic Techniques|Information Storage and Retrieval|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Polymerase Chain Reaction|Programming Languages|RNA, Small Interfering|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti728', '16239305', 'The OligoFaktory is a set of tools for the design, on an arbitrary number of target sequences, of high-quality long oligonucleotide for micro-array, of primer pair for PCR, of siRNA and more. The user-centered interface exists in two flavours: a web portal and a standalone software for Mac OS X Tiger. A unified presentation of results provides overviews with distribution charts and relative location bar graphs, as well as detailed features for each oligonucleotide. Input and output files conform to a common XML interchange file format to allow both automatic generation of input data, archiving, and post-processing of results. The design pipeline can use BLAST servers to evaluate specificity of selected oligonucleotides.'),
(72457, 2005, 'bio', 'ConFind: a robust tool for conserved sequence identification', 'Smagala, James A|Dawson, Erica D|Mehlmann, Martin|Townsend, Michael B|Kuchta, Robert D|Rowlen, Kathy L', 'Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.|Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.|Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.|Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.|Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.|Department of Chemistry and Biochemistry, The University of Colorado at Boulder, UCB #215, Boulder, CO 80309, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Conserved Sequence|DNA, Viral|Hemagglutinin Glycoproteins, Influenza Virus|Influenza A virus|Neuraminidase|Sequence Alignment|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/bti719', '16239306', 'ConFind (conserved region finder) identifies regions of conservation in multiple sequence alignments that can serve as diagnostic targets. Designed to work with a large number of closely related, highly variable sequences, ConFind provides robust handling of alignments containing partial sequences and ambiguous characters. Conserved regions are defined in terms of minimum region length, maximum informational entropy (variability) per position, number of exceptions allowed to the maximum entropy criterion and the minimum number of sequences that must contain a non-ambiguous character at a position to be considered for inclusion in a conserved region. Comparison of the calculated entropy for an alignment of 95 influenza A hemagglutinin sequences with random deletions results in a 98% reduction in the average error in ConFind relative to the Find Conserved Regions option in BioEdit.'),
(72458, 2005, 'bio', 'Improving molecular cancer class discovery through sparse non-negative matrix factorization', 'Gao, Yuan|Church, George', 'Department of Genetics, Harvard Medical School Boston, MA 02115, USA. g1m1c1@receptor.med.harvard.edu|Department of Genetics, Harvard Medical School Boston, MA 02115, USA. g1m1c1@receptor.med.harvard.edu', 'United States|United States', 218, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Factor Analysis, Statistical|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/bti653', '16244221', 'Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.'),
(72459, 2006, 'bio', 'PilZ domain is part of the bacterial c-di-GMP binding protein', 'Amikam, Dorit|Galperin, Michael Y', 'Department of Biotechnology and Environmental Sciences, Tel-Hai Academic College Tel-Hai, Israel.|Department of Biotechnology and Environmental Sciences, Tel-Hai Academic College Tel-Hai, Israel.', 'Israel|Israel', 326, 'Alginates|Amino Acid Sequence|Bacterial Proteins|Carrier Proteins|Computational Biology|Cyclic GMP|Escherichia coli Proteins|Gene Expression Regulation, Bacterial|Genome, Bacterial|Glucosyltransferases|Intracellular Signaling Peptides and Proteins|Molecular Sequence Data|Movement|Phylogeny|Polysaccharides|Protein Structure, Tertiary|Proteins|Pseudomonas aeruginosa|Second Messenger Systems', 'Journal Article', '10.1093/bioinformatics/bti739', '16249258', 'Recent studies identified c-di-GMP as a universal bacterial secondary messenger regulating biofilm formation, motility, production of extracellular polysaccharide and multicellular behavior in diverse bacteria. However, except for cellulose synthase, no protein has been shown to bind c-di-GMP and the targets for c-di-GMP action remain unknown. Here we report identification of the PilZ (pills) domain (Pfam domain PF07238) in the sequences of bacterial cellulose synthases, alginate biosynthesis protein Alg44, proteins of enterobacterial YcgR and firmicute YpfA families, and other proteins encoded in bacterial genomes and present evidence indicating that this domain is (part of) the long-sought c-di-GMP-binding protein. Association of the PilZ domain with a variety of other domains, including likely components of bacterial multidrug secretion system, could provide clues to multiple functions of the c-di-GMP in bacterial pathogenesis and cell development.'),
(72460, 2006, 'bio', 'Integrating time-course microarray gene expression profiles with cytotoxicity for identification of biomarkers in primary rat hepatocytes exposed to cadmium', 'Tan, Yongxi|Shi, Leming|Hussain, Saber M|Xu, Jun|Tong, Weida|Frazier, John M|Wang, Charles', 'Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.|Cedars-Sinai Research Institute, Cedars-Sinai Medical Center Los Angeles, CA 90048, USA.', 'United States|United States|United States|United States|United States|United States|United States', 25, 'Algorithms|Animals|Biomarkers|Cadmium|Computational Biology|Dose-Response Relationship, Drug|Gene Expression Profiling|Gene Expression Regulation|Hepatocytes|L-Lactate Dehydrogenase|Models, Theoretical|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Rats|Regression Analysis|Reproducibility of Results|Signal Transduction|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti737', '16249259', 'DNA microarrays can provide information about the expression levels of thousands of genes simultaneously at the transcriptomic level, while conventional cell viability and cytotoxicity measurement methods provide information about the biological functions at the cellular level. Integrating these data at different levels provides a promising approach for evaluating or predicting how cells respond to chemical exposure. It is important to investigate the multi-scale biological system in a systematic way to better understand the gene regulation networks and signal transduction pathways involved in the cellular responses to environmental factors.'),
(72461, 2006, 'bio', 'Gene selection using support vector machines with non-convex penalty', 'Zhang, Hao Helen|Ahn, Jeongyoun|Lin, Xiaodong|Park, Cheolwoo', 'Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. hzhang@stat.ncsu.edu|Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. hzhang@stat.ncsu.edu|Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. hzhang@stat.ncsu.edu|Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. hzhang@stat.ncsu.edu', 'United States|United States|United States|United States', 173, 'Algorithms|Bayes Theorem|Cluster Analysis|Databases, Genetic|Humans|Models, Genetic|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti736', '16249260', 'With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes simultaneously in one single experiment. One current difficulty in interpreting microarray data comes from their innate nature of high-dimensional low sample size. Therefore, robust and accurate gene selection methods are required to identify differentially expressed group of genes across different samples, e.g. between cancerous and normal cells. Successful gene selection will help to classify different cancer types, lead to a better understanding of genetic signatures in cancers and improve treatment strategies. Although gene selection and cancer classification are two closely related problems, most existing approaches handle them separately by selecting genes prior to classification. We provide a unified procedure for simultaneous gene selection and cancer classification, achieving high accuracy in both aspects.'),
(72462, 2006, 'bio', 'Automatic term list generation for entity tagging', 'Sandler, Ted|Schein, Andrew I|Ungar, Lyle H', 'Department of Computer and Information Science, University of Pennsylvania 3330 Walnut Street, Philadelphia, 19104, USA. tsandler@seas.upenn.edu|Department of Computer and Information Science, University of Pennsylvania 3330 Walnut Street, Philadelphia, 19104, USA. tsandler@seas.upenn.edu|Department of Computer and Information Science, University of Pennsylvania 3330 Walnut Street, Philadelphia, 19104, USA. tsandler@seas.upenn.edu', 'United States|United States|United States', 7, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Documentation|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Proteins', 'Journal Article', '10.1093/bioinformatics/bti733', '16249261', 'Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed manually. We show that distributional clustering methods which group words based on the contexts that they appear in, including neighboring words and syntactic relations extracted using a shallow parser, can be used to aid in the construction of term lists.'),
(72463, 2005, 'bio', 'Haplotype-based linkage disequilibrium mapping via direct data mining', 'Li, Jing|Jiang, Tao', 'Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@eecs.case.edu|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@eecs.case.edu', 'United States|United States', 41, 'Algorithms|Chromosome Mapping|Computational Biology|Cystic Fibrosis|Data Interpretation, Statistical|Databases, Genetic|Diabetes Mellitus, Type 1|Friedreich Ataxia|Genetic Diseases, Inborn|Genome, Human|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1093/bioinformatics/bti732', '16249262', 'With the availability of large-scale, high-density single-nucleotide polymorphism markers and information on haplotype structures and frequencies, a great challenge is how to take advantage of haplotype information in the association mapping of complex diseases in case-control studies.'),
(72464, 2005, 'bio', 'Integrated minimum-set primers and unique probe design algorithms for differential detection on symptom-related pathogens', 'Huang, Yu-Cheng|Chang, Chun-Fan|Chan, Chen-Hsiung|Yeh, Tze-Jung|Chang, Ya-Chun|Chen, Chaur-Chin|Kao, Cheng-Yan', 'Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.|Bioinformatics Laboratory, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.', '-|-|-|-|-|-|-', 16, 'Algorithms|Base Sequence|Communicable Diseases|Computational Biology|DNA Primers|DNA Probes|Drug Design|Humans|Models, Genetic|Plant Viruses|Software Design|Thermodynamics|Virulence', 'Journal Article', '10.1093/bioinformatics/bti730', '16249263', 'Differential detection on symptom-related pathogens (SRP) is critical for fast identification and accurate control against epidemic diseases. Conventional polymerase chain reaction (PCR) requires a large number of unique primers to amplify selected SRP target sequences. With multiple-use primers (mu-primers), multiple targets can be amplified and detected in one PCR experiment under standard reaction condition and reduced detection complexity. However, the time complexity of designing mu-primers with the best heuristic method available is too vast. We have formulated minimum-set mu-primer design problem as a set covering problem (SCP), and used modified compact genetic algorithm (MCGA) to solve this problem optimally and efficiently. We have also proposed new strategies of primer/probe design algorithm (PDA) on combining both minimum-set (MS) mu-primers and unique (UniQ) probes. Designed primer/probe set by PDA-MS/UniQ can amplify multiple genes simultaneously upon physical presence with minimum-set mu-primer amplification (MMA) before intended differential detection with probes-array hybridization (PAH) on the selected target set of SRP.'),
(72465, 2005, 'bio', 'ISER: selection of differentially expressed genes from DNA array data by non-linear data transformations and local fitting', 'Drummond, R D|Pinheiro, A|Rocha, C S|Menossi, M', 'Laboratório de Genoma Funcional-Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas 13083-875, PO Box 6010, Brazil.|Laboratório de Genoma Funcional-Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas 13083-875, PO Box 6010, Brazil.|Laboratório de Genoma Funcional-Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas 13083-875, PO Box 6010, Brazil.|Laboratório de Genoma Funcional-Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas 13083-875, PO Box 6010, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 7, 'Algorithms|Computational Biology|Databases, Nucleic Acid|Gene Expression Profiling|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|ROC Curve|Software', 'Journal Article', '10.1093/bioinformatics/bti729', '16249264', 'This report describes an algorithm (intensity-dependent selection of expression ratios or ISER) developed to analyse DNA array data by optimizing the selection of genes with the most significant variations in expression amongst two RNA samples. The algorithm is designed for use when little or no replication of array hybridizations is available.'),
(72466, 2005, 'bio', 'GSMA: software implementation of the genome search meta-analysis method', 'Pardi, Fabio|Levinson, Douglas F|Lewis, Cathryn M', 'Department of Medical and Molecular Genetics, Kings College London, 8th Floor Guys Tower, Guys Hospital, London SE1 9RT, United Kingdom.|Department of Medical and Molecular Genetics, Kings College London, 8th Floor Guys Tower, Guys Hospital, London SE1 9RT, United Kingdom.|Department of Medical and Molecular Genetics, Kings College London, 8th Floor Guys Tower, Guys Hospital, London SE1 9RT, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 23, 'Chromosome Mapping|Computational Biology|Genomics|Humans|Meta-Analysis as Topic|Software', 'Journal Article', '10.1093/bioinformatics/bti725', '16249265', 'Meta-analysis can be used to pool results of genome-wide linkage scans. This is of great value in complex diseases, where replication of linked regions occurs infrequently. The genome search meta-analysis (GSMA) method is widely used for this analysis, and a computer program is now available to implement the GSMA.'),
(72467, 2005, 'bio', 'Large-scale prokaryotic gene prediction and comparison to genome annotation', 'Nielsen, Pernille|Krogh, Anders', 'Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen, Denmark. pern@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen, Denmark. pern@binf.ku.dk', 'Denmark|Denmark', 0, 'Base Composition|Computational Biology|DNA|Databases, Genetic|Genomics|Markov Chains|Models, Genetic|Open Reading Frames|Prokaryotic Cells|Software', 'Comparative Study', '10.1093/bioinformatics/bti701', '16249266', 'Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame (ORF) is considered to be a gene.'),
(72468, 2006, 'bio', 'Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm', 'Grotkjaer, Thomas|Winther, Ole|Regenberg, Birgitte|Nielsen, Jens|Hansen, Lars Kai', 'Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk|Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk|Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk|Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk|Center for Microbial Biotechnology BioCentrum-DTU, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. tg@biocentrum.dtu.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression Profiling|Genes, Fungal|Genome|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Open Reading Frames|Pattern Recognition, Automated|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/bti746', '16257984', 'Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult to assess the significance of the results. We have developed a consensus clustering algorithm, where the final result is averaged over multiple clustering runs, giving a robust and reproducible clustering, capable of capturing small signal variations. The algorithm preserves valuable properties of hierarchical clustering, which is useful for visualization and interpretation of the results.'),
(72469, 2006, 'bio', 'A generic motif discovery algorithm for sequential data', 'Jensen, Kyle L|Styczynski, Mark P|Rigoutsos, Isidore|Stephanopoulos, Gregory N', 'Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.|Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Cluster Analysis|Computational Biology|Conserved Sequence|Gene Expression Profiling|Humans|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Protein Structure, Secondary|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/bti745', '16257985', 'Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems.'),
(72470, 2006, 'bio', 'Classification using functional data analysis for temporal gene expression data', 'Leng, Xiaoyan|Müller, Hans-Georg', 'Wake Forest University School of Medicine, Public Health Sciences, Section on Biostatistics Medical Center Blvd., MRI-3, Winston-Salem, NC 27157, USA. ileng@wfubmc.edu|Wake Forest University School of Medicine, Public Health Sciences, Section on Biostatistics Medical Center Blvd., MRI-3, Winston-Salem, NC 27157, USA. ileng@wfubmc.edu', 'United States|United States', 0, 'Algorithms|Animals|Cell Cycle|Computational Biology|Computer Simulation|Dictyostelium|Fungal Proteins|Gene Expression|Gene Expression Regulation|Genes, Fungal|Models, Statistical|Normal Distribution|Principal Component Analysis|Probability|Regression Analysis|Reproducibility of Results|Software|Stochastic Processes|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti742', '16257986', 'Temporal gene expression profiles provide an important characterization of gene function, as biological systems are predominantly developmental and dynamic. We propose a method of classifying collections of temporal gene expression curves in which individual expression profiles are modeled as independent realizations of a stochastic process. The method uses a recently developed functional logistic regression tool based on functional principal components, aimed at classifying gene expression curves into known gene groups. The number of eigenfunctions in the classifier can be chosen by leave-one-out cross-validation with the aim of minimizing the classification error.'),
(72471, 2006, 'bio', 'Intelligent client for integrating bioinformatics services', 'Navas-Delgado, Ismael|Rojano-Muñoz, Maria del Mar|Ramírez, Sergio|Pérez, Antonio J|Andrés León, Eduardo|Aldana-Montes, Jose F|Trelles, Oswaldo', 'Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.|Integrated Bioinformatics (GNV5) at the University of Málaga, National Institute for Bioinformatics (INB), Málaga, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 31, 'Automation|Computational Biology|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Protein|Hypermedia|Information Storage and Retrieval|Information Systems|Internet|Natural Language Processing|Programming Languages|Software|Software Design|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti740', '16257987', 'In addition to existing bioinformatics software, a lot of new tools are being developed world wide to supply services for an ever growing, widely dispersed and heterogeneous collection of biological data. The integration of these resources under a common platform is a challenging task. To this end, several groups are developing integration technologies, in which services are usually registered in some sort of catalogue to allow novel discovering and accessing mechanisms to be implemented. However, each service demands specific interfaces to accommodate their parameters and it is a complicated task linking the different service inputs and outputs to solve a biological problem.'),
(72472, 2006, 'bio', 'A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer', 'Zhang, Wensheng|Rekaya, Romdhane|Bertrand, Keith', 'Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.|Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.|Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia', 0, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Breast Neoplasms|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti738', '16267079', 'An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data.'),
(72473, 2006, 'bio', 'Sequence features of DNA binding sites reveal structural class of associated transcription factor', 'Narlikar, Leelavati|Hartemink, Alexander J', 'Duke University, Department of Computer Science Box 90129, Durham, NC 27708, USA. lee@cs.duke.edu|Duke University, Department of Computer Science Box 90129, Durham, NC 27708, USA. lee@cs.duke.edu', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Binding Sites|Conserved Sequence|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Structure-Activity Relationship|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti731', '16267080', 'A key goal in molecular biology is to understand the mechanisms by which a cell regulates the transcription of its genes. One important aspect of this transcriptional regulation is the binding of transcription factors (TFs) to their specific cis-regulatory counterparts on the DNA. TFs recognize and bind their DNA counterparts according to the structure of their DNA-binding domains (e.g. zinc finger, leucine zipper, homeodomain). The structure of these domains can be used as a basis for grouping TFs into classes. Although the structure of DNA-binding domains varies widely across TFs generally, the TFs within a particular class bind to DNA in a similar fashion, suggesting the existence of class-specific features in the DNA sequences bound by each class of TFs.'),
(72474, 2006, 'bio', 'GenoMiner: a tool for genome-wide search of coding and non-coding conserved sequence tags', 'Castrignanò, Tiziana|De Meo, Paolo DOnorio|Grillo, Giorgio|Liuni, Sabino|Mignone, Flavio|Talamo, Ivano Giuseppe|Pesole, Graziano', 'Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Universitàe Ricerca, CASPUR, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 3, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Conserved Sequence|Expressed Sequence Tags|Molecular Sequence Data|Open Reading Frames|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti754', '16267081', 'GenoMiner is a software tool that searches for regions of similarity between user-submitted genome or transcript sequences and user-specified whole genome assemblies. The program then identifies conserved sequence tags (CSTs) in these homologous regions and provides a prediction of their coding or non-coding nature. The analysis is carried out through three steps: (1) definition of sequence regions homologous to the query sequence in the selected target genomes by a fast BLAT alignment, (2) identification of CSTs by a more sensitive BLAST-like alignment between the query and the homologous regions in the target genomes and (3) assessment of the coding or non-coding nature of detected CSTs through the computation of a suitable coding potential score. GenoMiner allows the user to search the query sequence against a number of vertebrate genome assemblies in a single run providing a user-friendly graphical output.'),
(72475, 2006, 'bio', 'libSRES: a C library for stochastic ranking evolution strategy for parameter estimation', 'Ji, Xinglai|Xu, Ying', 'Computational Biology Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computational Biology Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States', 0, 'Algorithms|Biochemistry|Computational Biology|Computer Simulation|Gene Library|Kinetics|Models, Genetic|Programming Languages|Sequence Analysis, DNA|Software|Stochastic Processes|Time Factors', 'Journal Article', '10.1093/bioinformatics/bti753', '16267082', 'Estimation of kinetic parameters in a biochemical pathway or network represents a common problem in systems studies of biological processes. We have implemented a C library, named libSRES, to facilitate a fast implementation of computer software for study of non-linear biochemical pathways. This library implements a (mu, lambda)-ES evolutionary optimization algorithm that uses stochastic ranking as the constraint handling technique. Considering the amount of computing time it might require to solve a parameter-estimation problem, an MPI version of libSRES is provided for parallel implementation, as well as a simple user interface. libSRES is freely available and could be used directly in any C program as a library function. We have extensively tested the performance of libSRES on various pathway parameter-estimation problems and found its performance to be satisfactory.'),
(72476, 2006, 'bio', 'SSEP-Domain: protein domain prediction by alignment of secondary structure elements and profiles', 'Gewehr, Jan E|Zimmer, Ralf', 'Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Amalienstrasse 17, D-80333 Munich, Germany. jan.gewehr@bio.ifi.lmu.de|Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Amalienstrasse 17, D-80333 Munich, Germany. jan.gewehr@bio.ifi.lmu.de', 'Germany|Germany', 46, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti751', '16267083', 'The prediction of protein domains is a crucial task for functional classification, homology-based structure prediction and structural genomics. In this paper, we present the SSEP-Domain protein domain prediction approach, which is based on the application of secondary structure element alignment (SSEA) and profile-profile alignment (PPA) in combination with InterPro pattern searches. SSEA allows rapid screening for potential domain regions while PPA provides us with the necessary specificity for selecting significant hits. The combination with InterPro patterns allows finding domain regions without solved structural templates if sequence family definitions exist.'),
(72477, 2006, 'bio', 'Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data', 'Blangiardo, Marta|Toti, Simona|Giusti, Betti|Abbate, Rosanna|Magi, Alberto|Poggi, Filippo|Rossi, Luciana|Torricelli, Francesca|Biggeri, Annibale', 'Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk|Department of Statistics G. Parenti, University of Florence & Biostatistic Unit CSPO, Florence, Italy. m.blangiardo@imperial.ac.uk', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 7, 'Algorithms|Analysis of Variance|Bayes Theorem|Calibration|Computational Biology|Computer Simulation|Down-Regulation|Evaluation Studies as Topic|Gene Expression Regulation|Humans|Image Processing, Computer-Assisted|Leukocytes|Leukocytes, Mononuclear|Lipopolysaccharides|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Research Design|Sensitivity and Specificity|Weights and Measures', 'Journal Article', '10.1093/bioinformatics/bti750', '16267084', 'Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously. A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene. Several approaches to model gene-specific variances are proposed. We take advantage of calibration experiments, in which the probes hybridized on the two channels come from the same population (self-self experiment). In this case it is possible to estimate the gene-specific variance, to be incorporated in comparative experiments on the same tissue, cellular line or species.'),
(72478, 2006, 'bio', 'BioThesaurus: a web-based thesaurus of protein and gene names', 'Liu, Hongfang|Hu, Zhang-Zhi|Zhang, Jian|Wu, Cathy', 'Department of Information Systems, University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.|Department of Information Systems, University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.|Department of Information Systems, University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.|Department of Information Systems, University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA.', 'United States|United States|United States|United States', 89, 'Animals|Computational Biology|Databases, Factual|Databases, Genetic|Databases, Protein|Genome|Humans|Information Storage and Retrieval|Internet|Models, Genetic|Names|Proteins|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/bti749', '16267085', 'BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to protein entries in the UniProt Knowledgebase. Currently covering more than two million proteins, BioThesaurus consists of over 2.8 million names extracted from multiple molecular biological databases according to the database cross-references in iProClass. The BioThesaurus web site allows the retrieval of synonymous names of given protein entries and the identification of protein entries sharing the same names.'),
(72479, 2006, 'bio', 'Improved spliced alignment from an information theoretic approach', 'Zhang, Miao|Gish, Warren', 'Department of Genetics, School of Medicine, Washington University-St Louis, 4566 Scott Avenue, St Louis, MO 63110, USA.|Department of Genetics, School of Medicine, Washington University-St Louis, 4566 Scott Avenue, St Louis, MO 63110, USA.', 'United States|United States', 22, 'Algorithms|Alternative Splicing|Animals|Caenorhabditis elegans|Computational Biology|Exons|Expressed Sequence Tags|Humans|Mice|Models, Statistical|Oryza|Polymorphism, Genetic|Programming Languages|RNA Splicing|RNA, Messenger|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/bti748', '16267086', 'mRNA sequences and expressed sequence tags represent some of the most abundant experimental data for identifying genes and alternatively spliced products in metazoans. These transcript sequences are frequently studied by aligning them to a genomic sequence template. For existing programs, error-prone, polymorphic and cross-species data, as well as non-canonical splice sites, still present significant barriers to producing accurate, complete alignments.'),
(72480, 2006, 'bio', 'Nexplorer: phylogeny-based exploration of sequence family data', 'Gopalan, Vivek|Qiu, Wei-Gang|Chen, Michael Z|Stoltzfus, Arlin', 'Center for Advanced Research in Biotechnology, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, 9600 Gudelsky Drive, Rockville, MD 20850, USA.|Center for Advanced Research in Biotechnology, 9600 Gudelsky Drive, Rockville, MD 20850, USA.', 'United States|United States|United States|United States', 10, 'Animals|Computational Biology|Computer Graphics|Databases, Protein|Humans|Internet|Mitochondrial Proton-Translocating ATPases|Phylogeny|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti747', '16267087', 'Nexplorer is a web-based program for interactive browsing and manipulation of character data in NEXUS format, well suited for use with alignments and trees representing families of homologous genes or proteins. Users may upload a sequence family dataset, or choose from one of several thousand already available. Nexplorer provides a flexible means to develop customized views that combine a tree and a data matrix or alignment, to create subsets of data, and to output data files or publication-quality graphics.'),
(72481, 2005, 'bio', 'mILD: a tool for constructing and analyzing matrices of pairwise phylogenetic character incongruence tests', 'Planet, Paul J|Sarkar, Indra Neil', 'Division of Invertebrate Zoology, American Museum of Natural History, New York, NY, USA.|Division of Invertebrate Zoology, American Museum of Natural History, New York, NY, USA.', 'United States|United States', 16, 'Algorithms|Computational Biology|Databases, Genetic|Evolution, Molecular|Genomics|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/bti744', '16267088', 'Pairwise comparisons of disagreement in phylogenetic datasets offer a powerful tool for isolating historical incongruence for closer analysis. Statistically significant phylogenetic character incongruence may reflect important differences in evolutionary history, such as horizontal gene transfer. Such testing can also be used to specify possible combinations of datasets for further phylogenetic analysis. The process of comparing multiple datasets can be very time consuming, and it is sometimes unclear how to combine data partitions given the observed patterns of incongruence. Here we present an application that automates the process of making pairwise comparisons between large numbers of phylogenetic datasets using the Incongruence Length Difference (ILD) test. The application also implements strategies for data combination based on the patterns of incongruence observed in pairwise comparisons.'),
(72482, 2006, 'bio', 'Sequence-based heuristics for faster annotation of non-coding RNA families', 'Weinberg, Zasha|Ruzzo, Walter L', 'Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA. zasha@cs.washington.edu|Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA. zasha@cs.washington.edu', 'United States|United States', 69, 'Algorithms|Computational Biology|Genome|Humans|Markov Chains|Models, Statistical|Nucleic Acid Conformation|Protein Structure, Secondary|Proteins|RNA|RNA, Transfer|RNA, Untranslated|ROC Curve|Sensitivity and Specificity|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bti743', '16267089', 'Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CMs accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be.'),
(72483, 2006, 'bio', 'A genotype calling algorithm for affymetrix SNP arrays', 'Rabbee, Nusrat|Speed, Terence P', 'Department of Statistics, University of California-Berkeley, Berkeley, CA, USA. nrabbee@post.harvard.edu|Department of Statistics, University of California-Berkeley, Berkeley, CA, USA. nrabbee@post.harvard.edu', 'United States|United States', 239, 'Algorithms|Alleles|Alternative Splicing|Computational Biology|DNA Mutational Analysis|Gene Expression Profiling|Gene Frequency|Genotype|Haplotypes|Humans|Models, Genetic|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Regression Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti741', '16267090', 'A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72484, 2006, 'bio', 'An efficient comprehensive search algorithm for tagSNP selection using linkage disequilibrium criteria', 'Qin, Zhaohui S|Gopalakrishnan, Shyam|Abecasis, Gonçalo R', 'Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu', 'United States|United States|United States', 42, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Expressed Sequence Tags|Genetic Markers|Linkage Disequilibrium|Molecular Sequence Data|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti762', '16269414', 'Selecting SNP markers for genome-wide association studies is an important and challenging task. The goal is to minimize the number of markers selected for genotyping in a particular platform and therefore reduce genotyping cost while simultaneously maximizing the information content provided by selected markers.'),
(72485, 2006, 'bio', 'LogoBar: bar graph visualization of protein logos with gaps', 'Pérez-Bercoff, Asa|Koch, Johan|Bürglin, Thomas R', 'Department of Biosciences at Novum, and Center for Genomics and Bioinformatics, Karolinska Institutet, Alfred Nobels Allé 7, SE-141 89 Huddinge, Sweden.|Department of Biosciences at Novum, and Center for Genomics and Bioinformatics, Karolinska Institutet, Alfred Nobels Allé 7, SE-141 89 Huddinge, Sweden.|Department of Biosciences at Novum, and Center for Genomics and Bioinformatics, Karolinska Institutet, Alfred Nobels Allé 7, SE-141 89 Huddinge, Sweden.', 'Sweden|Sweden|Sweden', 28, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Amino Acids|Computational Biology|Computers|Conserved Sequence|Databases, Protein|Internet|Molecular Sequence Data|Programming Languages|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti761', '16269415', 'LogoBar is a Java application to display protein sequence logos. In our software gaps are accounted for when calculating the information content present at each residue position in a multiple alignment. The resulting logo is displayed as a graph consisting of bars, although traditional letter representation is also possible. Amino acids are displayed from the bottom up with decreasing frequencies i.e. the most abundant residue is placed at the bottom of the logo. The bars can be color-coded according to user specifications. Gaps in the alignment are also displayed, either on top or at the bottom of the logo. Furthermore, residues can either be arranged according to their relative abundance or grouped according to user criteria to emphasize the conserved nature of particular positions.'),
(72486, 2006, 'bio', 'COMPAM :visualization of combining pairwise alignments for multiple genomes', 'Lee, Dohoon|Choi, Jeong-Hyeon|Dalkilic, Mehmet M|Kim, Sun', 'School of Computer Engineering, Miryang National University Miryang, Kyungnam 627-702, Korea. dohhlee@indiana.edu|School of Computer Engineering, Miryang National University Miryang, Kyungnam 627-702, Korea. dohhlee@indiana.edu|School of Computer Engineering, Miryang National University Miryang, Kyungnam 627-702, Korea. dohhlee@indiana.edu|School of Computer Engineering, Miryang National University Miryang, Kyungnam 627-702, Korea. dohhlee@indiana.edu', '-|-|-|-', 6, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti759', '16269416', 'COMPAM is a tool for visualizing relationships among multiple whole genomes by combining all pairwise genome alignments. It displays shared conserved regions (blocks) and where these blocks occur (edges) as block relation graphs which can be explored interactively. An unannotated genome, e.g. can then be explored using information from well-annotated genomes, COG-based genome annotation and genes. COMPAM can run either as a stand-alone application or through an applet that is provided as service to PLATCOM, a toolset for whole genome comparative analysis, where a wide variety of genomes can be easily selected. Features provided by COMPAM include the ability to export genome relationship information into file formats that can be used by other existing tools.'),
(72487, 2006, 'bio', 'PRODISTIN Web Site: a tool for the functional classification of proteins from interaction networks', 'Baudot, Anaïs|Martin, David|Mouren, Pierre|Chevenet, François|Guénoche, Alain|Jacq, Bernard|Brun, Christine', 'LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.|LGPD/IBDM UMR6545 CNRS.', '-|-|-|-|-|-|-', 17, 'Algorithms|Documentation|Gene Expression Profiling|Internet|Online Systems|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti757', '16269417', 'The PRODISTIN Web Site is a web service allowing users to functionally classify genes/proteins from any type of interaction network. The resulting computation provides a classification tree in which (1) genes/proteins are clustered according to the identity of their interaction partners and (2) functional classes are delineated in the tree using the Biological Process Gene Ontology annotations.'),
(72488, 2006, 'bio', 'ClaNC: point-and-click software for classifying microarrays to nearest centroids', 'Dabney, Alan R', 'Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. adabney@u.washington.edu', 'United States', 25, 'Algorithms|Cluster Analysis|Computer Graphics|Computer Simulation|Computers|Data Interpretation, Statistical|Gene Expression Profiling|Information Storage and Retrieval|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti756', '16269418', 'ClaNC (classification to nearest centroids) is a simple and an accurate method for classifying microarrays. This document introduces a point-and-click interface to the ClaNC methodology. The software is available as an R package.'),
(72489, 2005, 'bio', 'The Bioinformatics Open Access option', 'Saxby, Claire', 'Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. adabney@u.washington.edu', 'United States', 0, 'Access to Information|Biomedical Research|Computational Biology|Databases, Bibliographic|Databases, Genetic|Databases, Protein|Information Dissemination|Information Storage and Retrieval|Internet|Molecular Biology|Programming Languages|Publishing|Software Design', 'Editorial', '10.1093/bioinformatics/bti707', '16269827', '-'),
(72490, 2006, 'bio', 'Prediction of functional specificity determinants from protein sequences using log-likelihood ratios', 'Pei, Jimin|Cai, Wei|Kinch, Lisa N|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA.|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center 5323 Harry Hines Boulevard, Dallas, TX 75390-9050, USA.', 'United States|United States|United States|United States', 45, 'Algorithms|Binding Sites|Computer Simulation|Conserved Sequence|Likelihood Functions|Models, Biological|Models, Chemical|Models, Molecular|Models, Statistical|Protein Binding|Protein Conformation|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti766', '16278237', 'A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results.'),
(72491, 2006, 'bio', 'Pathway recognition and augmentation by computational analysis of microarray expression data', 'Novak, Barbara A|Jain, Ajay N', 'UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California at San Francisco San Francisco, CA 94143-0128, USA.|UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California at San Francisco San Francisco, CA 94143-0128, USA.', 'United States|United States', 17, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Biological|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Signal Transduction|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti764', '16278238', 'We present a system, QPACA (Quantitative Pathway Analysis in Cancer) for analysis of biological data in the context of pathways. QPACA supports data visualization and both fine- and coarse-grained specifications, but, more importantly, addresses the problems of pathway recognition and pathway augmentation.'),
(72492, 2006, 'bio', 'Residue-rotamer-reduction algorithm for the protein side-chain conformation problem', 'Xie, Wei|Sahinidis, Nikolaos V', 'Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign 600 South Mathews Avenue, Urbana, IL 61801, USA.|Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign 600 South Mathews Avenue, Urbana, IL 61801, USA.', 'United States|United States', 33, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Protein Subunits|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti763', '16278239', 'The protein side-chain conformation problem is a central problem in proteomics with wide applications in protein structure prediction and design. Computational complexity results show that the problem is hard to solve. Yet, instances from realistic applications are large and demand fast and reliable algorithms.'),
(72493, 2006, 'bio', 'PCP: a program for supervised classification of gene expression profiles', 'Buturovixc4x87, Ljubomir J', 'San Francisco State University 1600 Holloway Avenue, San Francisco, CA 94132, USA. ljubomir@sfsu.edu', 'United States', 21, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti760', '16278240', 'PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns (vectors of measurements). The principal use of PCP in bioinformatics is design and evaluation of classifiers for use in clinical diagnostic tests based on measurements of gene expression. PCP implements leading pattern classification and gene selection algorithms and incorporates cross-validation estimation of classifier performance. Importantly, the implementation integrates gene selection and class prediction stages, which is vital for computing reliable performance estimates in small-sample scenarios. Additionally, the program includes automated and efficient model selection (optimization of parameters) for support vector machine (SVM) classifier. The distribution includes Linux and Windows/Cygwin binaries. The program can easily be ported to other platforms.'),
(72494, 2006, 'bio', 'Systematic intervention of transcription for identifying network response to disease and cellular phenotypes', 'Li, Huai|Zhan, Ming', 'Bioinformatics Unit, Branch of Research Resources, National Institute on Aging/NIH, Baltimore, MD 21224, USA.|Bioinformatics Unit, Branch of Research Resources, National Institute on Aging/NIH, Baltimore, MD 21224, USA.', 'United States|United States', 25, 'Aging|Algorithms|Animals|Caenorhabditis elegans|Cell Line, Tumor|Computational Biology|Computer Simulation|Gene Expression Regulation|Humans|Insulin|Longevity|Models, Biological|Models, Genetic|Models, Statistical|Phenotype|Probability|Protein Interaction Mapping|Sequence Alignment|Software|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/bti752', '16278241', 'A major challenge in post-genomic research has been to understand how physiological and pathological phenotypes arise from the networks of expressed genes. Here, we addressed this issue by developing an algorithm to mimic the behavior of regulatory networks in silico and to identify the dynamic response to disease and changing cellular conditions.'),
(72495, 2006, 'bio', 'Intervention in a family of Boolean networks', 'Choudhary, Ashish|Datta, Aniruddha|Bittner, Michael L|Dougherty, Edward R', 'Department of Electrical Engineering, Texas A and M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A and M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A and M University College Station, TX 77843, USA.|Department of Electrical Engineering, Texas A and M University College Station, TX 77843, USA.', 'United States|United States|United States|United States', 17, 'Algorithms|Cell Line, Tumor|Feedback|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Melanoma|Models, Genetic|Models, Statistical|Protein Engineering|Proto-Oncogene Proteins|Recombinant Proteins|Signal Transduction|Wnt Proteins|Wnt-5a Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti765', '16286362', 'Intervention in a gene regulatory network is used to avoid undesirable states, such as those associated with a disease. Several types of intervention have been studied in the framework of a probabilistic Boolean network (PBN), which is a collection of Boolean networks in which the gene state vector transitions according to the rules of one of the constituent networks and where network choice is governed by a selection distribution. The theory of automatic control has been applied to find optimal strategies for manipulating external control variables that affect the transition probabilities to desirably affect dynamic evolution over a finite time horizon. In this paper we treat a case in which we lack the governing probability structure for Boolean network selection, so we simply have a family of Boolean networks, but where these networks possess a common attractor structure. This corresponds to the situation in which network construction is treated as an ill-posed inverse problem in which there are many Boolean networks created from the data under the constraint that they all possess attractor structures matching the data states, which are assumed to arise from sampling the steady state of the real biological network.'),
(72496, 2006, 'bio', 'An efficient randomized algorithm for contact-based NMR backbone resonance assignment', 'Kamisetty, Hetunandan|Bailey-Kellogg, Chris|Pandurangan, Gopal', 'Department of Computer Science, Purdue University West Lafayette, IN 47907, USA.|Department of Computer Science, Purdue University West Lafayette, IN 47907, USA.|Department of Computer Science, Purdue University West Lafayette, IN 47907, USA.', 'United States|United States|United States', 13, 'Algorithms|Amino Acid Sequence|Binding Sites|Magnetic Resonance Spectroscopy|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti786', '16287932', 'Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance (NMR) spectroscopy. A minimalist approach to assignment, which we call contact-based, seeks to dramatically reduce experimental time and expense by replacing the standard suite of through-bond experiments with the through-space (nuclear Overhauser enhancement spectroscopy, NOESY) experiment. In the contact-based approach, spectral data are represented in a graph with vertices for putative residues (of unknown relation to the primary sequence) and edges for hypothesized NOESY interactions, such that observed spectral peaks could be explained if the residues were close enough. Due to experimental ambiguity, several incorrect edges can be hypothesized for each spectral peak. An assignment is derived by identifying consistent patterns of edges (e.g. for alpha-helices and beta-sheets) within a graph and by mapping the vertices to the primary sequence. The key algorithmic challenge is to be able to uncover these patterns even when they are obscured by significant noise.'),
(72497, 2006, 'bio', '2SNP: scalable phasing based on 2-SNP haplotypes', 'Brinza, Dumitru|Zelikovsky, Alexander', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.', 'Georgia|Georgia', 30, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Haplotypes|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/bti785', '16287933', '2SNP software package implements a new very fast scalable algorithm for haplotype inference based on genotype statistics collected only for pairs of SNPs. This software can be used for comparatively accurate phasing of large number of long genome sequences, e.g. obtained from DNA arrays. As an input 2SNP takes genotype matrix and outputs the corresponding haplotype matrix. On datasets across 79 regions from HapMap 2SNP is several orders of magnitude faster than GERBIL and PHASE while matching them in quality measured by the number of correctly phased genotypes, single-site and switching errors. For example, 2SNP requires 41 s on Pentium 4 2 Ghz processor to phase 30 genotypes with 1381 SNPs (ENm010.7p15:2 data from HapMap) versus GERBIL and PHASE requiring more than a week and admitting no less errors than 2SNP.'),
(72498, 2006, 'bio', 'Automatic assignment of biomedical categories: toward a generic approach', 'Ruch, Patrick', 'University Hospitals of Geneva, Medical Informatics Service CH-1201, Geneva. Patrick.Ruch@sim.hcuge.ch', '-', 98, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Documentation|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Proteins', 'Evaluation Studies', '10.1093/bioinformatics/bti783', '16287934', 'We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent.'),
(72499, 2006, 'bio', 'Equivalent binding sites reveal convergently evolved interaction motifs', 'Henschel, Andreas|Kim, Wan Kyu|Schroeder, Michael', 'Bioinformatics Group, Biotechnological Centre TU Dresden, Germany. ah@biotec.tu-dresden.de|Bioinformatics Group, Biotechnological Centre TU Dresden, Germany. ah@biotec.tu-dresden.de|Bioinformatics Group, Biotechnological Centre TU Dresden, Germany. ah@biotec.tu-dresden.de', 'Germany|Germany|Germany', 26, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Binding Sites|Evolution, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/bti782', '16287935', 'Much research has been devoted to the characterization of interaction interfaces found in complexes with known structure. In this context, the interactions of non-homologous domains at equivalent binding sites are of particular interest, as they can reveal convergently evolved interface motifs. Such motifs are an important source of information to formulate rules for interaction specificity and to design ligands based on the common features shared among diverse partners.'),
(72500, 2006, 'bio', 'Highly consistent patterns for inherited human diseases at the molecular level', 'López-Bigas, Núria|Blencowe, Benjamin J|Ouzounis, Christos A', 'Genome Bioinformatics Laboratory, Center for Genomic Regulation, Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta 37-49, E-08003, Barcelona, Spain. nuria.lopez@crg.es|Genome Bioinformatics Laboratory, Center for Genomic Regulation, Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta 37-49, E-08003, Barcelona, Spain. nuria.lopez@crg.es|Genome Bioinformatics Laboratory, Center for Genomic Regulation, Universitat Pompeu Fabra, Pg. Maritim de la Barceloneta 37-49, E-08003, Barcelona, Spain. nuria.lopez@crg.es', 'Spain|Spain|Spain', 42, 'Biomarkers|Gene Expression Profiling|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Humans|Models, Genetic|Molecular Biology|Pattern Recognition, Automated|Periodicals as Topic|Proteome|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti781', '16287936', 'Over 1600 mammalian genes are known to cause an inherited disorder, when subjected to one or more mutations. These disease genes represent a unique resource for the identification and quantification of relationships between phenotypic attributes of a disease and the molecular features of the associated disease genes, including their ascribed annotated functional classes and expression patterns. Such analyses can provide a more global perspective and a deeper understanding of the probable causes underlying human hereditary diseases. In this perspective and critical view of disease genomics, we present a comparative analysis of genes reported to cause inherited diseases in humans in terms of their causative effects on physiology, their genetics and inheritance modes, the functional processes they are involved in and their expression profiles across a wide spectrum of tissues. Our analysis reveals that there are more extensive correlations between these attributes of genetic disease genes than previously appreciated. For instance, the functional pattern of genes causing dominant and recessive diseases is markedly different. Also, the function of the genes and their expression correlate with the type of disease they cause when mutated. The results further indicate that a comparative genomics approach for the analysis of genes linked to human genetic diseases will facilitate the elucidation of the underlying molecular and cellular mechanisms.'),
(72501, 2006, 'bio', 'Elucidation of directionality for co-expressed genes: predicting intra-operon termination sites', 'Gupta, Anshuman|Maranas, Costas D|Albert, Réka', 'Academic Services and Emerging Technologies, The Pennsylvania State University University Park, PA, USA.|Academic Services and Emerging Technologies, The Pennsylvania State University University Park, PA, USA.|Academic Services and Emerging Technologies, The Pennsylvania State University University Park, PA, USA.', 'United States|United States|United States', 21, 'Algorithms|Bacillus subtilis|Bacterial Proteins|Codon, Terminator|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Genetic|Oligonucleotide Array Sequence Analysis|Operon|Signal Transduction|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti780', '16287937', 'In this paper, we present a novel framework for inferring regulatory and sequence-level information from gene co-expression networks. The key idea of our methodology is the systematic integration of network inference and network topological analysis approaches for uncovering biological insights.'),
(72502, 2006, 'bio', 'Classification of microarray data with factor mixture models', 'Martella, Francesca', 'Dipartimento di Statistica, Probabilità e Statistiche Applicate, Universitá degli Studi di Roma La Sapienza P.le A. Moro, 5-00185 Rome, Italy. francesca.martella@uniroma1.it', 'Italy', 24, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Databases, Genetic|Factor Analysis, Statistical|Gene Expression Profiling|Humans|Leukemia|Models, Genetic|Models, Statistical|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1093/bioinformatics/bti779', '16287938', 'The classification of few tissue samples on a very large number of genes represents a non-standard problem in statistics but a usual one in microarray expression data analysis. In fact, the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. We consider high-density oligonucleotide microarray data, where the expression level is associated to an absolute call, which represents a qualitative indication of whether or not a transcript is detected within a sample. The absolute call is generally not taken in consideration in analyses.'),
(72503, 2006, 'bio', 'PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization', 'Dogrusoz, U|Erson, E Z|Giral, E|Demir, E|Babur, O|Cetintas, A|Colak, R', 'Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr|Bilkent Center for Bioinformatics (BCBI), Bilkent University, Ankara 06800, Turkey. patikaweb@cs.bilkent.edu.tr', 'Turkey|Turkey|Turkey|Turkey|Turkey|Turkey|Turkey', 19, 'Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Gene Expression Regulation|Information Storage and Retrieval|Internet|Models, Biological|Online Systems|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti776', '16287939', 'Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.'),
(72504, 2006, 'bio', 'Mean curvature as a major determinant of beta-sheet propensity', 'Koh, Eunhee|Kim, Taehyo|Cho, Hyun-Soo', 'Department of Biology, Yonsei University 134, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea. ehkoh@yonsei.ac.kr|Department of Biology, Yonsei University 134, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea. ehkoh@yonsei.ac.kr|Department of Biology, Yonsei University 134, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea. ehkoh@yonsei.ac.kr', '-|-|-', 10, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti775', '16287940', 'Despite the importance of beta-sheets as building blocks in proteins and also toxic elements in the pathological disorders, ranging from Alzheimers disease to mad cow disease, the principles underlying their stability are not well understood. Non-random beta-sheet propensities of amino acids have been revealed both by their distinct statistical preferences within known protein structures and by the relative thermodynamic scales through the experimental host-guest systems. However, recent fitting analysis has proved that a native beta-sheet conforms to a minimal surface with zero mean curvature, like the physical model of soap films.'),
(72505, 2006, 'bio', 'WindowMasker: window-based masker for sequenced genomes', 'Morgulis, Aleksandr|Gertz, E Michael|Schäffer, Alejandro A|Agarwala, Richa', 'National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services Building 38A, Room 1003N, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services Building 38A, Room 1003N, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services Building 38A, Room 1003N, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Institutes of Health, Department of Health and Human Services Building 38A, Room 1003N, 8600 Rockville Pike, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Humans|Molecular Sequence Data|Pattern Recognition, Automated|Programming Languages|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti774', '16287941', 'Matches to repetitive sequences are usually undesirable in the output of DNA database searches. Repetitive sequences need not be matched to a query, if they can be masked in the database. RepeatMasker/Maskeraid (RM), currently the most widely used software for DNA sequence masking, is slow and requires a library of repetitive template sequences, such as a manually curated RepBase library, that may not exist for newly sequenced genomes.'),
(72506, 2006, 'bio', 'Improved prediction of bacterial transcription start sites', 'Gordon, J J|Towsey, M W|Hogan, J M|Mathews, S A|Timms, P', 'Faculty of Information Technology, Queensland University of Technology GPO Box 2434, Brisbane, QLD 4001, Australia.|Faculty of Information Technology, Queensland University of Technology GPO Box 2434, Brisbane, QLD 4001, Australia.|Faculty of Information Technology, Queensland University of Technology GPO Box 2434, Brisbane, QLD 4001, Australia.|Faculty of Information Technology, Queensland University of Technology GPO Box 2434, Brisbane, QLD 4001, Australia.|Faculty of Information Technology, Queensland University of Technology GPO Box 2434, Brisbane, QLD 4001, Australia.', 'Australia|Australia|Australia|Australia|Australia', 49, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Computer Simulation|DNA, Bacterial|Escherichia coli|Models, Genetic|Pattern Recognition, Automated|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA|Transcription Initiation Site', 'Evaluation Studies', '10.1093/bioinformatics/bti771', '16287942', 'Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in Escherichia coli. The accepted method for this problem is to use position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in large numbers of false positive predictions.'),
(72507, 2006, 'bio', 'Gene Time E{chi}pression Warper: a tool for alignment, template matching and visualization of gene expression time series', 'Criel, Jo|Tsiporkova, Elena', 'Devgen N.V., Science IT Technologiepark 30, B-9052 Ghent, Belgium.|Devgen N.V., Science IT Technologiepark 30, B-9052 Ghent, Belgium.', 'Belgium|Belgium', 23, 'Algorithms|Computer Graphics|Gene Expression Profiling|Sequence Alignment|Sequence Analysis, DNA|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti787', '16293669', 'An application tool for alignment, template matching and visualization of gene expression time series is presented. The core algorithm is based on dynamic time warping techniques used in the speech recognition field. These techniques allow for non-linear (elastic) alignment of temporal sequences of feature vectors and consequently enable detection of similar shapes with different phases.'),
(72508, 2006, 'bio', 'Enhanced recognition of protein transmembrane domains with prediction-based structural profiles', 'Cao, Baoqiang|Porollo, Aleksey|Adamczak, Rafal|Jarrell, Mark|Meller, Jaroslaw', 'Biomedical Informatics, Childrens Hospital Research Foundation, Cincinnati, OH 45229, USA.|Biomedical Informatics, Childrens Hospital Research Foundation, Cincinnati, OH 45229, USA.|Biomedical Informatics, Childrens Hospital Research Foundation, Cincinnati, OH 45229, USA.|Biomedical Informatics, Childrens Hospital Research Foundation, Cincinnati, OH 45229, USA.|Biomedical Informatics, Childrens Hospital Research Foundation, Cincinnati, OH 45229, USA.', 'United States|United States|United States|United States|United States', 67, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Cell Membrane|Membrane Proteins|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software|Solvents|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/bti784', '16293670', 'Membrane domain prediction has recently been re-evaluated by several groups, suggesting that the accuracy of existing methods is still rather limited. In this work, we revisit this problem and propose novel methods for prediction of alpha-helical as well as beta-sheet transmembrane (TM) domains. The new approach is based on a compact representation of an amino acid residue and its environment, which consists of predicted solvent accessibility and secondary structure of each amino acid. A recently introduced method for solvent accessibility prediction trained on a set of soluble proteins is used here to indicate segments of residues that are predicted not to be accessible to water and, therefore, may be buried in the membrane. While evolutionary profiles in the form of a multiple alignment are used to derive these simple structural profiles, they are not used explicitly for the membrane domain prediction and the overall number of parameters in the model is significantly reduced. This offers the possibility of a more reliable estimation of the free parameters in the model with a limited number of experimentally resolved membrane protein structures.'),
(72509, 2006, 'bio', 'Protein Peeling: an approach for splitting a 3D protein structure into compact fragments', 'Gelly, Jean-Christophe|de Brevern, Alexandre G|Hazout, Serge', 'INSERM U726, Equipe de Bioinformatique Génomique and Moléculaire (EBGM), Université Denis Diderot-Paris 7, case 7113, 75251 Paris Cedex 05, France.|INSERM U726, Equipe de Bioinformatique Génomique and Moléculaire (EBGM), Université Denis Diderot-Paris 7, case 7113, 75251 Paris Cedex 05, France.|INSERM U726, Equipe de Bioinformatique Génomique and Moléculaire (EBGM), Université Denis Diderot-Paris 7, case 7113, 75251 Paris Cedex 05, France.', 'France|France|France', 22, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Peptide Fragments|Peptide Mapping|Protein Conformation|Protein Folding|Protein Subunits|Proteins|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/bti773', '16301202', 'The object of this study is to propose a new method to identify small compact units that compose protein three-dimensional structures. These fragments, called protein units (PU), are a new level of description to well understand and analyze the organization of protein structures. The method only works from the contact probability matrix, i.e. the inter Calpha-distances translated into probabilities. It uses the principle of conventional hierarchical clustering, leading to a series of nested partitions of the 3D structure. Every step aims at dividing optimally a unit into 2 or 3 subunits according to a criterion called partition index assessing the structural independence of the subunits newly defined. Moreover, an entropy-derived squared correlation R is used for assessing globally the protein structure dissection. The method is compared to other splitting algorithms and shows relevant performance.'),
(72510, 2006, 'bio', 'Accurate anchoring alignment of divergent sequences', 'Huang, Weichun|Umbach, David M|Li, Leping', 'Biostatistics Branch, The National Institute of Environmental Health Sciences/NIH, Research Triangle Park, NC 27709, USA.|Biostatistics Branch, The National Institute of Environmental Health Sciences/NIH, Research Triangle Park, NC 27709, USA.|Biostatistics Branch, The National Institute of Environmental Health Sciences/NIH, Research Triangle Park, NC 27709, USA.', 'United States|United States|United States', 29, 'Algorithms|Animals|Base Sequence|Cluster Analysis|Computational Biology|Gene Deletion|Genome|Genomics|Humans|Mice|Models, Statistical|Molecular Sequence Data|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology|Software|Species Specificity|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti772', '16301203', 'Obtaining high quality alignments of divergent homologous sequences for cross-species sequence comparison remains a challenge.'),
(72511, 2006, 'bio', 'The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling', 'Arnold, Konstantin|Bordoli, Lorenza|Kopp, Jürgen|Schwede, Torsten', 'Biozentrum Basel, University of Basel Switzerland.|Biozentrum Basel, University of Basel Switzerland.|Biozentrum Basel, University of Basel Switzerland.|Biozentrum Basel, University of Basel Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Databases, Protein|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Online Systems|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti770', '16301204', 'Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases.'),
(72512, 2006, 'bio', 'Inferring functional information from domain co-evolution', 'Kim, Yohan|Koyutürk, Mehmet|Topkara, Umut|Grama, Ananth|Subramaniam, Shankar', 'Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093, USA.|Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States', 21, 'Algorithms|Bacterial Proteins|Computational Biology|Databases, Protein|Escherichia coli|Escherichia coli Proteins|Evolution, Molecular|Genome, Bacterial|Membrane Proteins|Methyl-Accepting Chemotaxis Proteins|Models, Statistical|Phylogeny|Predictive Value of Tests|Protein Binding|Protein Kinases|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/bti723', '16301205', 'Co-evolution is a powerful mechanism for understanding protein function. Prior work in this area has shown that co-evolving proteins are more likely to share the same function than those that do not because of functional constraints. Many of the efforts founded on this observation, however, are at the level of entire sequences, implicitly assuming that the complete protein sequence follows a single evolutionary trajectory. Since it is well known that a domain can exist in various contexts, this assumption is not valid for numerous multi-domain proteins. Motivated by these observations, we introduce a novel technique called Coevolutionary-Matrix that captures co-evolution between regions of two proteins. Instead of using existing domain information, the method exploits residue-level conservation to identify co-evolving regions that might correspond to domains.'),
(72513, 2006, 'bio', 'REDfly: a Regulatory Element Database for Drosophila', 'Gallo, Steven M|Li, Long|Hu, Zihua|Halfon, Marc S', 'Center for Computational Research, 140 Farber Hall, State University of New York at Buffalo, 3435 Main Street, Buffalo, NY 14214, USA.|Center for Computational Research, 140 Farber Hall, State University of New York at Buffalo, 3435 Main Street, Buffalo, NY 14214, USA.|Center for Computational Research, 140 Farber Hall, State University of New York at Buffalo, 3435 Main Street, Buffalo, NY 14214, USA.|Center for Computational Research, 140 Farber Hall, State University of New York at Buffalo, 3435 Main Street, Buffalo, NY 14214, USA.', 'United States|United States|United States|United States', 64, 'Animals|Databases, Genetic|Drosophila Proteins|Drosophila melanogaster|Internet|Online Systems|Regulatory Elements, Transcriptional', 'Journal Article', '10.1093/bioinformatics/bti794', '16303794', 'Bioinformatics studies of transcriptional regulation in the metazoa are significantly hindered by the absence of readily available data on large numbers of transcriptional cis-regulatory modules (CRMs). Even the richly annotated Drosophila melanogaster genome lacks extensive CRM information. We therefore present here a database of Drosophila CRMs curated from the literature complete with both DNA sequence and a searchable description of the gene expression pattern regulated by each CRM. This resource should greatly facilitate the development of computational approaches to CRM discovery as well as bioinformatics analyses of regulatory sequence properties and evolution.'),
(72514, 2006, 'bio', 'Describing ancient horizontal gene transfers at the nucleotide and gene levels by comparative pathogenicity island genometrics', 'Collyn, F|Guy, L|Marceau, M|Simonet, M|Roten, C-A H', 'Inserm E0364--Université de Lille II, Faculté de Médecine Henri Warembourg, Institut Pasteur de Lille 1 rue du Pr Calmette, F-59021 Lille, France.|Inserm E0364--Université de Lille II, Faculté de Médecine Henri Warembourg, Institut Pasteur de Lille 1 rue du Pr Calmette, F-59021 Lille, France.|Inserm E0364--Université de Lille II, Faculté de Médecine Henri Warembourg, Institut Pasteur de Lille 1 rue du Pr Calmette, F-59021 Lille, France.|Inserm E0364--Université de Lille II, Faculté de Médecine Henri Warembourg, Institut Pasteur de Lille 1 rue du Pr Calmette, F-59021 Lille, France.|Inserm E0364--Université de Lille II, Faculté de Médecine Henri Warembourg, Institut Pasteur de Lille 1 rue du Pr Calmette, F-59021 Lille, France.', 'France|France|France|France|France', 15, 'Chromosome Mapping|Evolution, Molecular|Gene Transfer, Horizontal|Genome, Bacterial|Genomic Islands|Nucleotides|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Virulence Factors|Yersinia', 'Journal Article', '10.1093/bioinformatics/bti793', '16303795', 'Lateral gene transfer is a major mechanism contributing to bacterial genome dynamics and pathovar emergence via pathogenicity island (PAI) spreading. However, since few of these genomic exchanges are experimentally reproducible, it is difficult to establish evolutionary scenarios for the successive PAI transmissions between bacterial genera. Methods initially developed at the gene and/or nucleotide level for genomics, i.e. comparisons of concatenated sequences, ortholog frequency, gene order or dinucleotide usage, were combined and applied here to homologous PAIs: we call this approach comparative PAI genometrics.'),
(72515, 2006, 'bio', 'A bi-dimensional regression tree approach to the modeling of gene expression regulation', 'Ruan, Jianhua|Zhang, Weixiong', 'Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO 63130, USA.|Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO 63130, USA.', 'United States|United States', 17, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Fungal|Models, Genetic|Oligonucleotide Array Sequence Analysis|Regression Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Software|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti792', '16303796', 'The transcriptional regulation of a gene depends on the binding of cis-regulatory elements on its promoter to some transcription factors and the expression levels of the transcription factors. Most existing approaches to studying transcriptional regulation model these dependencies separately, i.e. either from promoters to gene expression or from the expression levels of transcription factors to the expression levels of genes. Little effort has been devoted to a single model for integrating both dependencies.'),
(72516, 2006, 'bio', 'A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins', 'Mihalek, I|Res, I|Lichtarge, O', 'Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. imihalek@bcm.tmc.edu|Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. imihalek@bcm.tmc.edu|Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. imihalek@bcm.tmc.edu', 'United States|United States|United States', 18, 'Algorithms|Amino Acid Sequence|Binding Sites|Enzymes|Evolution, Molecular|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Protein Binding|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/bti791', '16303797', 'Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72517, 2006, 'bio', 'Statistical estimation of gene expression using multiple laser scans of microarrays', 'Khondoker, Mizanur R|Glasbey, Chris A|Worton, Bruce J', 'Biomathematics and Statistics Scotland, University of Edinburgh Kings Buildings, Edinburgh, EH9 3JZ, Scotland, UK. mizanur@bioss.ac.uk|Biomathematics and Statistics Scotland, University of Edinburgh Kings Buildings, Edinburgh, EH9 3JZ, Scotland, UK. mizanur@bioss.ac.uk|Biomathematics and Statistics Scotland, University of Edinburgh Kings Buildings, Edinburgh, EH9 3JZ, Scotland, UK. mizanur@bioss.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 25, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Confocal|Microscopy, Fluorescence|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti790', '16303798', 'We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias.'),
(72518, 2006, 'bio', 'Detecting periodic patterns in unevenly spaced gene expression time series using Lomb-Scargle periodograms', 'Glynn, Earl F|Chen, Jie|Mushegian, Arcady R', 'Stowers Institute for Medical Research, 1000 East 50th Street, Kansas City, MO 64110, USA.|Stowers Institute for Medical Research, 1000 East 50th Street, Kansas City, MO 64110, USA.|Stowers Institute for Medical Research, 1000 East 50th Street, Kansas City, MO 64110, USA.', 'United States|United States|United States', 137, 'Algorithms|Animals|Biological Clocks|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Periodicity|Plasmodium falciparum|Proteome|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti789', '16303799', 'Periodic patterns in time series resulting from biological experiments are of great interest. The commonly used Fast Fourier Transform (FFT) algorithm is applicable only when data are evenly spaced and when no values are missing, which is not always the case in high-throughput measurements. The choice of statistic to evaluate the significance of the periodic patterns for unevenly spaced gene expression time series has not been well substantiated.'),
(72519, 2006, 'bio', 'Maximum significance clustering of oligonucleotide microarrays', 'de Ridder, Dick|Staal, Frank J T|van Dongen, Jacques J M|Reinders, Marcel J T', 'Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands. D.deRidder@ewi.tudelft.nl|Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands. D.deRidder@ewi.tudelft.nl|Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands. D.deRidder@ewi.tudelft.nl|Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands. D.deRidder@ewi.tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti788', '16303800', 'Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds, any information on variability is lost. In this paper we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a criterion for clustering microarrays.'),
(72520, 2005, 'bio', 'Modeling genome evolution with a diffusion approximation of a birth-and-death process', 'Karev, Georgy P|Berezovskaya, Faina S|Koonin, Eugene V', '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', 13, 'Algorithms|Biological Evolution|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Models, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1202', '16306387', 'In our previous studies, we developed discrete-space birth, death and innovation models (BDIMs) of genome evolution. These models explain the origin of the characteristic Pareto distribution of paralogous gene family sizes in genomes, and model parameters that provide for the evolution of these distributions within a realistic time frame have been identified. However, extracting the temporal dynamics of genome evolution from discrete-space BDIM was not technically feasible. We were interested in obtaining dynamic portraits of the genome evolution process by developing a diffusion approximation of BDIM.'),
(72521, 2005, 'bio', 'Incorporation of splice site probability models for non-canonical introns improves gene structure prediction in plants', 'Sparks, Michael E|Brendel, Volker', 'Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011-3260, USA.|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011-3260, USA.', 'United States|United States', 19, 'Base Composition|Chromosome Mapping|Computer Simulation|DNA, Plant|Genome, Plant|Introns|Models, Genetic|Models, Statistical|RNA Splice Sites|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1205', '16306388', 'The vast majority of introns in protein-coding genes of higher eukaryotes have a GT dinucleotide at their 5-terminus and an AG dinucleotide at their 3 end. About 1-2% of introns are non-canonical, with the most abundant subtype of non-canonical introns being characterized by GC and AG dinucleotides at their 5- and 3-termini, respectively. Most current gene prediction software, whether based on ab initio or spliced alignment approaches, does not include explicit models for non-canonical introns or may exclude their prediction altogether. With present amounts of genome and transcript data, it is now possible to apply statistical methodology to non-canonical splice site prediction. We pursued one such approach and describe the training and implementation of GC-donor splice site models for Arabidopsis and rice, with the goal of exploring whether specific modeling of non-canonical introns can enhance gene structure prediction accuracy.'),
(72522, 2005, 'bio', 'The choice of optimal distance measure in genome-wide datasets', 'Glazko, Galina|Gordon, Alexander|Mushegian, Arcady', 'Stowers Institute for Medical Research, Kansas City, MO 64110, USA. gvg@stowers-institute.org|Stowers Institute for Medical Research, Kansas City, MO 64110, USA. gvg@stowers-institute.org|Stowers Institute for Medical Research, Kansas City, MO 64110, USA. gvg@stowers-institute.org', 'United States|United States|United States', 18, 'Algorithms|Chromosome Mapping|Computer Simulation|Databases, Genetic|Linkage Disequilibrium|Models, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1201', '16306389', 'Many types of genomic data are naturally represented as binary vectors. Numerous tasks in computational biology can be cast as analysis of relationships between these vectors, and the first step is, frequently, to compute their pairwise distance matrix. Many distance measures have been proposed in the literature, but there is no theory justifying the choice of distance measure.'),
(72523, 2005, 'bio', 'DNA assembly with gaps (Dawg): simulating sequence evolution', 'Cartwright, Reed A', 'Department of Genetics, University of Georgia, Athens, GA 30602-7223, USA. rac@uga.edu', 'Georgia', 82, 'Base Sequence|Biological Evolution|Computer Simulation|DNA|DNA Transposable Elements|Evolution, Molecular|Gene Deletion|Models, Genetic|Molecular Sequence Data|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/bti1200', '16306390', 'Relationships amongst taxa are inferred from biological data using phylogenetic methods and procedures. Very few known phylogenies exist against which to test the accuracy of our inferences. Therefore, in the absence of biological data, simulated data must be used to test the accuracy of methods which produce these inferences. Researchers have limited or non-existent options for simulations useful for studying the impact of insertions, deletions, and alignments on phylogenetic accuracy.'),
(72524, 2005, 'bio', 'Within-host evolution of CD8+-TL epitopes encoded by overlapping and non-overlapping reading frames of simian immunodeficiency virus', 'Hughes, Austin L|Piontkivska, Helen|Krebs, Kendall C|OConnor, David H|Watkins, David I', 'Department of Biological Sciences, University of South Carolina Columbia, SC 29208, USA. austin@biol.sc.edu|Department of Biological Sciences, University of South Carolina Columbia, SC 29208, USA. austin@biol.sc.edu|Department of Biological Sciences, University of South Carolina Columbia, SC 29208, USA. austin@biol.sc.edu|Department of Biological Sciences, University of South Carolina Columbia, SC 29208, USA. austin@biol.sc.edu|Department of Biological Sciences, University of South Carolina Columbia, SC 29208, USA. austin@biol.sc.edu', 'United States|United States|United States|United States|United States', 12, 'Animals|CD8-Positive T-Lymphocytes|Epitope Mapping|Epitopes, T-Lymphocyte|Evolution, Molecular|Macaca mulatta|Open Reading Frames|Selection, Genetic|Simian Immunodeficiency Virus', 'Journal Article', '10.1093/bioinformatics/bti1203', '16306391', 'In order to understand the impact of overlapping reading frames on natural selection by host CD8+ T lymphocytes (CD8(+)-TL), we analyzed the pattern of nucleotide substitution in simian immunodeficiency virus (SIV) genomes sampled from populations at time of death in 35 rhesus monkeys. Both the mean number of nonsynonymous nucleotide substitutions per nonsynonymous site (d(N)) and the mean number of synonymous nucleotide substitutions per synonymous site (d(S)) were elevated in overlap regions in comparison to non-overlap regions. Mean d(N) exceeded mean d(S) in CD8(+)-TL epitopes restricted by the hosts class I major histocompatibility complex molecules. This pattern, which is indicative of positive Darwinian selection favoring amino acid changes in these epitopes, was seen in both overlap and non-overlap regions, but mean d(N) was particularly elevated in restricted CD8(+)-TL epitopes encoded in overlap regions. Amino acid changes from the inoculum were defined as parallel if the same amino acid change occurred at the same site independently in two or more monkeys, and a surprisingly high proportion (71.9%) of observed amino acid changes throughout the SIV genome occurred in parallel in different monkeys. The proportion of parallel changes in restricted epitopes encoded by overlapping reading frames was still higher (80%), supporting the hypothesis that the interaction of positive selection and overlapping reading frames enhances the probability of convergent or parallel amino acid change.'),
(72525, 2005, 'bio', 'Four-helix bundle: a ubiquitous sensory module in prokaryotic signal transduction', 'Ulrich, Luke E|Zhulin, Igor B', 'Center for Bioinformatics and Computational Biology, School of Biology, Georgia Institute of Technology Atlanta, GA 30332-0230 USA. gtg209d@mail.gatech.edu|Center for Bioinformatics and Computational Biology, School of Biology, Georgia Institute of Technology Atlanta, GA 30332-0230 USA. gtg209d@mail.gatech.edu', 'Georgia|Georgia', 42, 'Amino Acid Sequence|Bacterial Proteins|Chemoreceptor Cells|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Receptors, Cell Surface|Sequence Analysis, Protein|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/bti1204', '16306392', 'Transmembrane chemoreceptors in Escherichia coli utilize ligand-binding domains for detecting various external signals. The structure of this domain in the E.coli aspartate receptor, Tar, is known and its signal transduction mechanism is under investigation. Current domain models for this important sensory module are inaccurate and, therefore, cannot reveal the distribution of this domain within the current genomic landscape.'),
(72526, 2005, 'bio', 'Mining sequence annotation databanks for association patterns', 'Artamonova, Irena I|Frishman, Goar|Gelfand, Mikhail S|Frishman, Dmitrij', 'Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany.|Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 29, 'Conserved Sequence|Databases, Protein|Information Storage and Retrieval|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Nucleic Acid|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/bti1206', '16306393', 'Millions of protein sequences currently being deposited to sequence databanks will never be annotated manually. Similarity-based annotation generated by automatic software pipelines unavoidably contains spurious assignments due to the imperfection of bioinformatics methods. Examples of such annotation errors include over- and underpredictions caused by the use of fixed recognition thresholds and incorrect annotations caused by transitivity based information transfer to unrelated proteins or transfer of errors already accumulated in databases. One of the most difficult and timely challenges in bioinformatics is the development of intelligent systems aimed at improving the quality of automatically generated annotation. A possible approach to this problem is to detect anomalies in annotation items based on association rule mining.'),
(72527, 2006, 'bio', 'Bioinformatic analysis of exon repetition, exon scrambling and trans-splicing in humans', 'Shao, Xiang|Shepelev, Valery|Fedorov, Alexei', 'Department of Medicine and Program in Bioinformatics and Proteomics/Genomics, Medical University of Ohio, Toledo, 43614, USA.|Department of Medicine and Program in Bioinformatics and Proteomics/Genomics, Medical University of Ohio, Toledo, 43614, USA.|Department of Medicine and Program in Bioinformatics and Proteomics/Genomics, Medical University of Ohio, Toledo, 43614, USA.', 'United States|United States|United States', 23, 'Algorithms|Chromosome Mapping|Computational Biology|DNA Mutational Analysis|Databases, Genetic|Exons|Expressed Sequence Tags|Genetic Variation|Genome, Human|Humans|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Trans-Splicing', 'Evaluation Studies', '10.1093/bioinformatics/bti795', '16308355', 'Using bioinformatic approaches we aimed to characterize poorly understood abnormalities in splicing known as exon scrambling, exon repetition and trans-splicing.'),
(72528, 2006, 'bio', 'Application of compression-based distance measures to protein sequence classification: a methodological study', 'Kocsor, András|Kertész-Farkas, Attila|Kaján, László|Pongor, Sándor', 'Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged Aradi vértanúk tere 1., H-6720 Szeged, Hungary. kocsor@inf.u-szeged.hu|Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged Aradi vértanúk tere 1., H-6720 Szeged, Hungary. kocsor@inf.u-szeged.hu|Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged Aradi vértanúk tere 1., H-6720 Szeged, Hungary. kocsor@inf.u-szeged.hu|Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged Aradi vértanúk tere 1., H-6720 Szeged, Hungary. kocsor@inf.u-szeged.hu', 'Hungary|Hungary|Hungary|Hungary', 52, 'Algorithms|Amino Acid Sequence|Data Compression|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/bti806', '16317070', 'Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences.'),
(72529, 2006, 'bio', 'SimShift: identifying structural similarities from NMR chemical shifts', 'Ginzinger, Simon W|Fischer, Johannes', 'LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München Amalienstrasse 17, D-80333 München, Germany. Simon.Ginzinger@bio.ifi.lmu.de|LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München Amalienstrasse 17, D-80333 München, Germany. Simon.Ginzinger@bio.ifi.lmu.de', 'Germany|Germany', 11, 'Algorithms|Amino Acid Sequence|Magnetic Resonance Spectroscopy|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/bti805', '16317071', 'An important quantity that arises in NMR spectroscopy experiments is the chemical shift. The interpretation of these data is mostly done by human experts, to our knowledge there are no algorithms that predict protein structure from chemical shift sequences alone. One approach to facilitate this process could be to compare two such sequences, where the structure of one protein has already been resolved. Our claim is that similarity of chemical shifts thereby found implies structural similarity of the respective proteins.'),
(72530, 2006, 'bio', 'Comparison of Bayesian and maximum-likelihood inference of population genetic parameters', 'Beerli, Peter', 'School of Computational Science and Department of Biological Sciences, Florida State University, Tallahassee, FL 32306-4120, USA. beerli@csit.fsu.edu', 'United States', 0, 'Bayes Theorem|Biological Evolution|Chromosome Mapping|Computer Simulation|Genetics, Population|Likelihood Functions|Models, Genetic|Models, Statistical|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/bti803', '16317072', 'Comparison of the performance and accuracy of different inference methods, such as maximum likelihood (ML) and Bayesian inference, is difficult because the inference methods are implemented in different programs, often written by different authors. Both methods were implemented in the program MIGRATE, that estimates population genetic parameters, such as population sizes and migration rates, using coalescence theory. Both inference methods use the same Markov chain Monte Carlo algorithm and differ from each other in only two aspects: parameter proposal distribution and maximization of the likelihood function. Using simulated datasets, the Bayesian method generally fares better than the ML approach in accuracy and coverage, although for some values the two approaches are equal in performance.'),
(72531, 2006, 'bio', 'Identification of plant microRNA homologs', 'Dezulian, Tobias|Remmert, Michael|Palatnik, Javier F|Weigel, Detlef|Huson, Daniel H', 'Center for Bioinformatics Tübingen, Tübingen University, Germany. dezulian@informatik.uni-tuebingen.de|Center for Bioinformatics Tübingen, Tübingen University, Germany. dezulian@informatik.uni-tuebingen.de|Center for Bioinformatics Tübingen, Tübingen University, Germany. dezulian@informatik.uni-tuebingen.de|Center for Bioinformatics Tübingen, Tübingen University, Germany. dezulian@informatik.uni-tuebingen.de|Center for Bioinformatics Tübingen, Tübingen University, Germany. dezulian@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany', 100, 'Algorithms|Base Sequence|Conserved Sequence|MicroRNAs|Molecular Sequence Data|Online Systems|Plants|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti802', '16317073', 'MicroRNAs (miRNAs) are a recently discovered class of non-coding RNAs that regulate gene and protein expression in plants and animals. MiRNAs have so far been identified mostly by specific cloning of small RNA molecules, complemented by computational methods. We present a computational identification approach that is able to identify candidate miRNA homologs in any set of sequences, given a query miRNA. The approach is based on a sequence similarity search step followed by a set of structural filters.'),
(72532, 2006, 'bio', 'Application of latent semantic analysis to protein remote homology detection', 'Dong, Qi-Wen|Wang, Xiao-Long|Lin, Lei', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. qwdong@insun.hit.edu.cn|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. qwdong@insun.hit.edu.cn|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. qwdong@insun.hit.edu.cn', 'China|China|China', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Conserved Sequence|Molecular Sequence Data|Natural Language Processing|Pattern Recognition, Automated|Proteins|Semantics|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/bti801', '16317074', 'Remote homology detection between protein sequences is a central problem in computational biology. The discriminative method such as the support vector machine (SVM) is one of the most effective methods. Many of the SVM-based methods focus on finding useful representations of protein sequence, using either explicit feature vector representations or kernel functions. Such representations may suffer from the peaking phenomenon in many machine-learning methods because the features are usually very large and noise data may be introduced. Based on these observations, this research focuses on feature extraction and efficient representation of protein vectors for SVM protein classification.'),
(72533, 2006, 'bio', 'Conservation analysis of large biochemical networks', 'Vallabhajosyula, Ravishankar Rao|Chickarmane, Vijay|Sauro, Herbert M', 'Keck Graduate Institute 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu|Keck Graduate Institute 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu|Keck Graduate Institute 535 Watson Drive, Claremont, CA 91711, USA. rrao@kgi.edu', 'United States|United States|United States', 0, 'Algorithms|Biochemistry|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti800', '16317075', 'Large biochemical networks pose a unique challenge from the point of view of evaluating conservation laws. The computational problem in most cases exceeds the capability of available software tools, often resulting in inaccurate computation of the number and form of conserved cycles. Such errors have profound effects on subsequent calculations, particularly in the evaluation of the Jacobian which is a critical quantity in many other calculations. The goal of this paper is to outline a new algorithm that is computationally efficient and robust at extracting the correct conservation laws for very large biochemical networks.'),
(72534, 2006, 'bio', 'Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology', 'Schmidt, Henning|Jirstrand, Mats', 'Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-41288 Göteborg, Sweden. henning@fcc.chalmers.se|Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, SE-41288 Göteborg, Sweden. henning@fcc.chalmers.se', 'Sweden|Sweden', 336, 'Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Programming Languages|Research Design|Software|Systems Biology|Systems Integration', 'Journal Article', '10.1093/bioinformatics/bti799', '16317076', 'We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.'),
(72535, 2006, 'bio', 'Paircoil2: improved prediction of coiled coils from sequence', 'McDonnell, A V|Jiang, T|Keating, A E|Berger, B', 'Mathematics Department, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Mathematics Department, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Mathematics Department, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.|Mathematics Department, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/bti797', '16317077', 'We introduce Paircoil2, a new version of the Paircoil program, which uses pairwise residue probabilities to detect coiled-coil motifs in protein sequence data. Paircoil2 achieves 98% sensitivity and 97% specificity on known coiled coils in leave-family-out cross-validation. It also shows superior performance compared with published methods in tests on proteins of known structure.'),
(72536, 2006, 'bio', 'PAGE: phase-shifted analysis of gene expression', 'Leung, Elo|Bushel, Pierre R', 'Bioinformatics and Computational Biology, School of Computational Sciences, George Mason University, Manassas, VA 20110, USA.|Bioinformatics and Computational Biology, School of Computational Sciences, George Mason University, Manassas, VA 20110, USA.', 'United States|United States', 6, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti778', '16322047', 'Grouping of gene expression patterns across biological experiments, treatments and time-series data is performed in q-intervals of measurements using phase-shifted analysis of gene expression (PAGE), a Java-based tool to find clusters of genes that share trends of expression profiles within the dataset. The patterns and genes within q-Clusters are visualized in trend plots and compared to determine biological relevance from the gene annotations.'),
(72537, 2006, 'bio', 'The ASCH superfamily: novel domains with a fold related to the PUA domain and a potential role in RNA metabolism', 'Iyer, Lakshminarayan M|Burroughs, A Maxwell|Aravind, L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 26, 'Animals|Conserved Sequence|Evolution, Molecular|Genetic Variation|Humans|Phylogeny|Protein Structure, Tertiary|RNA|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti767', '16322048', 'Several studies show that transcription coactivators are often bi-functional ribonucleoprotein complexes that also regulate pre-mRNA processing and splicing decisions. Using sensitive sequence profile searches and structural comparisons we show that the C-terminal domain of the human coactivator protein ASC-1 defines a novel superfamily, the ASC-1 homology (ASCH) domain. The approximately 110 amino acid long ASCH domains are widely represented in all the three superkingdoms of life and several prokaryotic viruses. We show that the ASCH superfamily adopts a beta-barrel fold similar to the PUA domain superfamily. Using multiple lines of evidence, we suggest that members of the ASCH superfamily are likely to function as RNA-binding domains in contexts related to coactivation, RNA-processing and possibly prokaryotic translation regulation. Structural analysis of ASCH domains reveals the presence of a potential RNA-binding cleft associated with a conserved sequence motif, which is characteristic of this superfamily. Despite their similar structure, the ASCH and PUA domains appear to occupy distinct functional niches, with the former domains typically occurring in a standalone form in polypeptides, and the latter domains showing fusions to a variety of RNA-modifying enzymes.'),
(72538, 2006, 'bio', 'apTreeshape: statistical analysis of phylogenetic tree shape', 'Bortolussi, Nicolas|Durand, Eric|Blum, Michael|François, Olivier', 'Team of Mathematical Biology (TIMB), TIMC, Faculty of Medicine, F38706 La Tronche, France.|Team of Mathematical Biology (TIMB), TIMC, Faculty of Medicine, F38706 La Tronche, France.|Team of Mathematical Biology (TIMB), TIMC, Faculty of Medicine, F38706 La Tronche, France.|Team of Mathematical Biology (TIMB), TIMC, Faculty of Medicine, F38706 La Tronche, France.', 'France|France|France|France', 0, 'Algorithms|Biological Evolution|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Genetics, Population|Models, Genetic|Models, Statistical|Pedigree|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/bti798', '16322049', 'apTreeshape is a R package dedicated to simulation and analysis of phylogenetic tree topologies using statistical imbalance measures. It is a companion library of the R package ape, which provides additional functions for reading, plotting, manipulating phylogenetic trees and for connecting to public phylogenetic tree databases. One strength of the package is to include appropriate corrections of classical shape statistics as well as new tests based on the statistical theory of likelihood ratios.'),
(72539, 2006, 'bio', 'PubMed Assistant: a biologist-friendly interface for enhanced PubMed search', 'Ding, Jing|Hughes, Laron M|Berleant, Daniel|Fulmer, Andy W|Wurtele, Eve Syrkin', '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', 0, 'Artificial Intelligence|Biology|Database Management Systems|Medical Subject Headings|Natural Language Processing|Periodicals as Topic|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti821', '16332704', 'MEDLINE is one of the most important bibliographical information sources for biologists and medical workers. Its PubMed interface supports Boolean queries, which are potentially expressive and exact. However, PubMed is also designed to support simplicity of use at the expense of query expressiveness and exactness. Many PubMed users have never tried explicit Boolean queries. We developed a Java program, PubMed Assistant, to make literature access easier in several ways. PubMed Assistant provides an interface that efficiently displays information about the citations and includes useful functions such as keyword highlighting, export to citation managers, clickable links to Google Scholar and others that are lacking in PubMed.'),
(72540, 2006, 'bio', 'Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment', 'Missal, Kristin|Cross, Michael A|Drasdo, Dirk', 'Bioinformatics Group, Department of Computer Science, University of Leipzig Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, University of Leipzig Härtelstrasse 16-18, D-04107 Leipzig, Germany.|Bioinformatics Group, Department of Computer Science, University of Leipzig Härtelstrasse 16-18, D-04107 Leipzig, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Animals|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Hematopoietic Stem Cells|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti820', '16332705', 'The topology and function of gene regulation networks are commonly inferred from time series of gene expression levels in cell populations. This strategy is usually invalid if the gene expression in different cells of the population is not synchronous. A promising, though technically more demanding alternative is therefore to measure the gene expression levels in single cells individually. The inference of a gene regulation network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, owing to experimental limitations a complete determination of the precursor state is not possible.'),
(72541, 2006, 'bio', 'Evaluating phylogenetic footprinting for human-rodent comparisons', 'Sauer, Tilman|Shelest, Ekaterina|Wingender, Edgar', 'Department of Bioinformatics, UKG, Georg-August-University of Goettingen Goldschmidtstrasse 1, 37077 Goettingen, Germany. tsa@bioinf.med.uni-goettingen.de|Department of Bioinformatics, UKG, Georg-August-University of Goettingen Goldschmidtstrasse 1, 37077 Goettingen, Germany. tsa@bioinf.med.uni-goettingen.de|Department of Bioinformatics, UKG, Georg-August-University of Goettingen Goldschmidtstrasse 1, 37077 Goettingen, Germany. tsa@bioinf.med.uni-goettingen.de', 'Germany|Germany|Germany', 0, 'Animals|Base Sequence|Binding Sites|Chromosome Mapping|Conserved Sequence|DNA Footprinting|Evolution, Molecular|Humans|Mice|Molecular Sequence Data|Phylogeny|Protein Binding|Rats|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti819', '16332706', 'Phylogenetic footprinting is a widely applied approach to identify regulatory regions and potential transcription factor binding sites (TFBSs) using alignments of non-coding orthologous regions from two or more organisms. A systematic evaluation of its validity and usability based on known TFBSs is needed to use phylogenetic footprinting most effectively in the identification of unknown TFBSs.'),
(72542, 2006, 'bio', 'BALLView: a tool for research and education in molecular modeling', 'Moll, Andreas|Hildebrandt, Andreas|Lenhof, Hans-Peter|Kohlbacher, Oliver', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany. amoll@bioinf.uni-saarland.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. amoll@bioinf.uni-saarland.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. amoll@bioinf.uni-saarland.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. amoll@bioinf.uni-saarland.de', 'Germany|Germany|Germany|Germany', 0, 'Computer Graphics|Computer Simulation|Computer-Assisted Instruction|Models, Molecular|Molecular Biology|Research Design|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti818', '16332707', 'We present BALLView, a molecular viewer and modeling tool. It combines state-of-the-art visualization capabilities with powerful modeling functionality including implementations of force field methods and continuum electrostatics models. BALLView is a versatile and extensible tool for research in structural bioinformatics and molecular modeling. Furthermore, the convenient and intuitive graphical user interface offers novice users direct access to the full functionality, rendering it ideal for teaching. Through an interface to the object-oriented scripting language Python it is easily extensible.'),
(72543, 2006, 'bio', 'Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data', 'Valentini, Giorgio', 'DSI, Dipartimento di Scienze dellInformazione, Università degli Studi di Milano, Via Comelico 39, Italy. valentini@dsi.unimi.it', 'Italy', 26, 'Artificial Intelligence|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Online Systems|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/bti817', '16332708', 'We present a new R package for the assessment of the reliability of clusters discovered in high-dimensional DNA microarray data. The package implements methods based on random projections that approximately preserve distances between examples in the projected subspaces.'),
(72544, 2006, 'bio', 'Least absolute regression network analysis of the murine osteoblast differentiation network', 'van Someren, E P|Vaes, B L T|Steegenga, W T|Sijbers, A M|Dechering, K J|Reinders, M J T', 'Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl|Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl|Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl|Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl|Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl|Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 56, 'Animals|Cell Differentiation|Cells, Cultured|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Mice|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Osteoblasts|Regression Analysis|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/bti816', '16332709', 'We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error.'),
(72545, 2006, 'bio', 'A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data', 'Macisaac, Kenzie D|Gordon, D Benjamin|Nekludova, Lena|Odom, Duncan T|Schreiber, Joerg|Gifford, David K|Young, Richard A|Fraenkel, Ernest', 'MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.|MIT Computer Science and Artificial Intelligence, Laboratory 32, Vassar Street, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Binding Sites|Chromatin Immunoprecipitation|Humans|Mice|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti815', '16332710', 'Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known.'),
(72546, 2006, 'bio', 'In silico sequence evolution with site-specific interactions along phylogenetic trees', 'Gesell, Tanja|von Haeseler, Arndt', 'Heinrich-Heine University Duesseldorf, Universitaetsstrasse 1 40225 Duesseldorf, Germany.|Heinrich-Heine University Duesseldorf, Universitaetsstrasse 1 40225 Duesseldorf, Germany.', 'Germany|Germany', 27, 'Algorithms|Base Pair Mismatch|Base Sequence|Computer Simulation|Evolution, Molecular|Models, Genetic|Models, Statistical|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/bti812', '16332711', 'A biological sequence usually has many sites whose evolution depends on other positions of the sequence, but this is not accounted for by commonly used models of sequence evolution. Here we introduce a Markov model of nucleotide sequence evolution in which the instantaneous substitution rate at a site depends on the states of other sites. Based on the concept of neighbourhood systems, our model represents a universal description of arbitrarily complex dependencies among sites.'),
(72547, 2006, 'bio', 'Lateral gene transfer of a dermonecrotic toxin between spiders and bacteria', 'Cordes, Matthew H J|Binford, Greta J', 'Department of Biochemistry and Molecular Biophysics, University of Arizona, Tucson, AZ 85721, USA. cordes@email.arizona.edu|Department of Biochemistry and Molecular Biophysics, University of Arizona, Tucson, AZ 85721, USA. cordes@email.arizona.edu', 'United States|United States', 25, 'Bacterial Toxins|Computer Simulation|Conserved Sequence|Dermotoxins|Evolution, Molecular|Gene Transfer, Horizontal|Models, Chemical|Models, Genetic|Models, Molecular|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Spider Venoms', 'Journal Article', '10.1093/bioinformatics/bti811', '16332712', 'Spiders in the genus Loxosceles, including the notoriously toxic brown recluse, cause severe necrotic skin lesions owing to the presence of a venom enzyme called sphingomyelinase D (SMaseD). This enzyme activity is unknown elsewhere in the animal kingdom but is shared with strains of pathogenic Corynebacteria that cause various illnesses in farm animals. The presence of the same toxic activity only in distantly related organisms poses an interesting and medically important question in molecular evolution.'),
(72548, 2006, 'bio', 'A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli', 'Idicula-Thomas, Susan|Kulkarni, Abhijit J|Kulkarni, Bhaskar D|Jayaraman, Valadi K|Balaji, Petety V', 'School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.|School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.|School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.|School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.|School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.', 'India|India|India|India|India', 78, 'Algorithms|Artificial Intelligence|Databases, Protein|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Inclusion Bodies|Pattern Recognition, Automated|Sequence Analysis, Protein|Solubility', 'Evaluation Studies', '10.1093/bioinformatics/bti810', '16332713', 'Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding, thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures.'),
(72549, 2006, 'bio', 'TRAP: automated classification, quantification and annotation of tandemly repeated sequences', 'Sobreira, Tiago José P|Durham, Alan M|Gruber, Arthur', 'Departamento de Parasitologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo SP, 05508-000, Brazil.|Departamento de Parasitologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo SP, 05508-000, Brazil.|Departamento de Parasitologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo SP, 05508-000, Brazil.', 'Brazil|Brazil|Brazil', 28, 'Algorithms|Artificial Intelligence|DNA|Databases, Genetic|Documentation|Pattern Recognition, Automated|Sequence Analysis, DNA|Software|Tandem Repeat Sequences|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti809', '16332714', 'TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72550, 2006, 'bio', 'DNAVis: interactive visualization of comparative genome annotations', 'Fiers, Mark W E J|van de Wetering, Huub|Peeters, Tim H J M|van Wijk, Jarke J|Nap, Jan-Peter', 'Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.|Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.|Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.|Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.|Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Algorithms|Chromosome Mapping|Computer Graphics|Documentation|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti807', '16332715', 'The software package DNAVis offers a fast, interactive and real-time visualization of DNA sequences and their comparative genome annotations. DNAVis implements advanced methods of information visualization such as linked views, perspective walls and semantic zooming, in addition to the display of heterologous data in dot plot-like matrix views.'),
(72551, 2005, 'bio', 'Beware of mis-assembled genomes', 'Salzberg, Steven L|Yorke, James A', 'Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.|Applied Bioinformatics, Plant Research International, Wageningen University and Research Centre, Wageningen, The Netherlands.', 'Netherlands|Netherlands', 108, 'Computational Biology|Databases, Nucleic Acid|Genomics|Repetitive Sequences, Nucleic Acid|Software', 'Letter', '10.1093/bioinformatics/bti769', '16332717', '-'),
(72552, 2006, 'bio', 'Multiple Alignment of protein structures and sequences for VMD', 'Eargle, John|Wright, Dan|Luthey-Schulten, Zaida', 'Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, 607 South Mathews Avenue, Urbana, IL 61801, USA.|Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, 607 South Mathews Avenue, Urbana, IL 61801, USA.|Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, 607 South Mathews Avenue, Urbana, IL 61801, USA.', 'United States|United States|United States', 85, 'Amino Acid Sequence|Computer Graphics|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti825', '16339280', 'Multiple Alignment is a new interface for performing and analyzing multiple protein structure alignments. It enables viewing levels of sequence and structure similarity on the aligned structures and performing a variety of evolutionary and bioinformatic tasks, including the construction of structure-based phylogenetic trees and minimal basis sets of structures that best represent the topology of the phylogenetic tree. It is implemented as a plugin for VMD (Visual Molecular Dynamics), which is distributed by the NIH Resource for Macromolecular Modeling and Bioinformatics at the University of Illinois.'),
(72553, 2006, 'bio', 'Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information', 'Li, Lexin', 'Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. li@stat.ncsu.edu', 'United States', 35, 'Biomarkers, Tumor|Gene Expression Profiling|Humans|Lymphoma, B-Cell|Neoplasm Proteins|Proportional Hazards Models|Reproducibility of Results|Retrospective Studies|Sensitivity and Specificity|Survival Analysis|Survival Rate', 'Evaluation Studies', '10.1093/bioinformatics/bti824', '16339281', 'It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.'),
(72554, 2006, 'bio', 'Histone acetylation and transcriptional regulation in the genome of Saccharomyces cerevisiae', 'Guo, Xiang|Tatsuoka, Kay|Liu, Rongxiang', 'GlaxoSmithKline, Bioinformatics Division 709 Swedeland Road, King of Prussia, PA 19406, USA.|GlaxoSmithKline, Bioinformatics Division 709 Swedeland Road, King of Prussia, PA 19406, USA.|GlaxoSmithKline, Bioinformatics Division 709 Swedeland Road, King of Prussia, PA 19406, USA.', 'United States|United States|United States', 16, 'Acetylation|Binding Sites|Chromosome Mapping|Gene Expression Regulation, Fungal|Genome, Fungal|Histones|Protein Binding|Saccharomyces cerevisiae|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/bti823', '16339282', 'In eukaryotic genomes, histone acetylation and thereafter departure from the chromatin are essential for gene transcription initiation. Because gene transcription is tightly regulated by transcription factors, there are some speculations on the cooperation of histone acetylation and transcription factor binding. However, systematic statistical analyses of this relationship on a genomic scale have not been reported.'),
(72555, 2006, 'bio', 'SubLoc: a server/client suite for protein subcellular location based on SOAP', 'Chen, Hu|Huang, Ni|Sun, Zhirong', 'Institute of Bioinformatics and Systems Biology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China.|Institute of Bioinformatics and Systems Biology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China.|Institute of Bioinformatics and Systems Biology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China.', 'China|China|China', 37, 'Database Management Systems|Databases, Protein|Information Storage and Retrieval|Online Systems|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/bti822', '16339283', 'Based on SOAP(Simple Object Access Protocol) technology, the SubLoc server/client suite offers a user-friendly interface for searching and predicting protein subcellular location.'),
(72556, 2006, 'bio', 'VIS-O-BAC: exploratory visualization of functional genome studies from bacteria', 'Dieterich, Guido|Kärst, Uwe|Wehland, Jürgen|Jänsch, Lothar', 'Department of Cell Biology, Research Centre for Biotechnology (GBF), Mascheroder Weg 1, 38124 Braunschweig, Germany.|Department of Cell Biology, Research Centre for Biotechnology (GBF), Mascheroder Weg 1, 38124 Braunschweig, Germany.|Department of Cell Biology, Research Centre for Biotechnology (GBF), Mascheroder Weg 1, 38124 Braunschweig, Germany.|Department of Cell Biology, Research Centre for Biotechnology (GBF), Mascheroder Weg 1, 38124 Braunschweig, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Genome, Bacterial|Internet|Online Systems|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/bti814', '16339284', 'The visualization-aided exploration of complex datasets will allow the research community to formulate novel functional hypotheses leading to a better understanding of biological processes at all levels. Therefore, we have developed a web resource termed VIS-O-BAC designed for the functional investigation of expression data for model systems, such as bacterial pathogens based on a graphical display. Genome-scale datasets derived from typical omic approaches can directly be explored with respect to three biologically relevant aspects, the genome structure (operon organization), the organization of genes in pathways (KEGG) and the gene function with Gene Ontology (GO) terms. The integrated viewers can be used in parallel and combine expression data and functional annotations from different external data repositories. The graphical visualizations evidently accelerate both the validation of regulatory information and the detection of affected biological processes.'),
(72557, 2006, 'bio', 'An extensible automated protein annotation tool: standardizing input and output using validated XML', 'Deevi, S Vishnu V|Martin, Andrew C R', 'School of Animal and Microbial Sciences, The University of Reading, P.O. Box 228, Whiteknights, Reading RG6 6AJ, UK.|School of Animal and Microbial Sciences, The University of Reading, P.O. Box 228, Whiteknights, Reading RG6 6AJ, UK.', 'United Kingdom|United Kingdom', 1, 'Amino Acid Sequence|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Molecular Sequence Data|Programming Languages|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/bti808', '16339285', 'There is a frequent need to apply a large range of local or remote prediction and annotation tools to one or more sequences. We have created a tool able to dispatch one or more sequences to assorted services by defining a consistent XML format for data and annotations.'),
(72558, 2006, 'bio', 'Gene sequence signatures revealed by mining the UniGene affiliation network', 'Zhang, Jiexin|Zhang, Li|Coombes, Kevin R', 'Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Box 447, Houston, TX 77030-4009, USA.|Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Box 447, Houston, TX 77030-4009, USA.|Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Box 447, Houston, TX 77030-4009, USA.', 'United States|United States|United States', 4, 'Animals|Chromosome Mapping|Database Management Systems|Databases, Genetic|Humans|Information Storage and Retrieval|Mice|Multigene Family|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/bti796', '16339286', 'In the post-genomic era, developing tools to decode biological information from genomic sequences is important. Inspired by affiliation network theory, we investigated gene sequences of two kinds of UniGene clusters (UCs): narrowly expressed transcripts (NETs), whose expression is confined to a few tissues, and prevalently expressed transcripts (PETs) that are expressed in many tissues.'),
(72559, 2006, 'bio', 'Quality classification of tandem mass spectrometry data', 'Salmi, Jussi|Moulder, Robert|Filén, Jan-Jonas|Nevalainen, Olli S|Nyman, Tuula A|Lahesmaa, Riitta|Aittokallio, Tero', 'Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi|Department of Information Technology and Turku Centre for Computer Science, University of Turku, Finland. jussi.salmi@it.utu.fi', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Cluster Analysis|Complex Mixtures|Mass Spectrometry|Pattern Recognition, Automated|Peptide Mapping|Proteome|Proteomics|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/bti829', '16352652', 'Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase.'),
(72560, 2006, 'bio', 'Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions', 'Huang, Yao-Ming|Bystroff, Christopher', 'Center for Bioinformatics, Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.|Center for Bioinformatics, Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.', 'United States|United States', 38, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Conserved Sequence|Evolution, Molecular|Molecular Sequence Data|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/bti828', '16352653', 'In recent years, advances have been made in the ability of computational methods to discriminate between homologous and non-homologous proteins in the twilight zone of sequence similarity, where the percent sequence identity is a poor indicator of homology. To make these predictions more valuable to the protein modeler, they must be accompanied by accurate alignments. Pairwise sequence alignments are inferences of orthologous relationships between sequence positions. Evolutionary distance is traditionally modeled using global amino acid substitution matrices. But real differences in the likelihood of substitutions may exist for different structural contexts within proteins, since structural context contributes to the selective pressure.'),
(72561, 2006, 'bio', 'Differential gene expression detection and sample classification using penalized linear regression models', 'Wu, Baolin', 'Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA. baolin@biostat.umn.edu', 'United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Linear Models|Models, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Regression Analysis|Sample Size', 'Evaluation Studies', '10.1093/bioinformatics/bti827', '16352654', 'Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the large p small n is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the nearest shrunken centroid proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.'),
(72562, 2006, 'bio', 'A simple method to predict protein-binding from aligned sequences--application to MHC superfamily and beta2-microglobulin', 'Duprat, Elodie|Lefranc, Marie-Paule|Gascuel, Olivier', 'Laboratoire dImmunoGénétique Moléculaire IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France.|Laboratoire dImmunoGénétique Moléculaire IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France.|Laboratoire dImmunoGénétique Moléculaire IGH (UPR CNRS 1142), 141 rue de la Cardonille, 34396 Montpellier Cedex 5, France.', 'France|France|France', 16, 'Algorithms|Animals|Bayes Theorem|Binding Sites|Humans|Major Histocompatibility Complex|Pattern Recognition, Automated|Protein Binding|Receptors, Antigen, T-Cell|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship|beta 2-Microglobulin', 'Journal Article', '10.1093/bioinformatics/bti826', '16352655', 'The MHC superfamily (MhcSF) consists of immune system MHC class I (MHC-I) proteins, along with proteins with a MHC-I-like structure that are involved in a large variety of biological processes. beta2-Microglobulin (B2M) non-covalent binding to MHC-I proteins is required for their surface expression and function, whereas MHC-I-like proteins interact, or not, with B2M. This study was designed to predict B2M binding (or non-binding) of newly identified MhcSF proteins, in order to decipher their function, understand the molecular recognition mechanisms and identify deleterious mutations. IMGT standardization of MhcSF protein domains provides a unique numbering of the multiple alignment positions, and conditions to develop such predictive tool.'),
(72563, 2006, 'bio', 'RNAshapes: an integrated RNA analysis package based on abstract shapes', 'Steffen, Peter|Voss, Björn|Rehmsmeier, Marc|Reeder, Jens|Giegerich, Robert', 'Faculty of Technology and, Bielefeld University, 33594 Bielefeld, Germany. psteffen@techfak.uni-bielefeld.de|Faculty of Technology and, Bielefeld University, 33594 Bielefeld, Germany. psteffen@techfak.uni-bielefeld.de|Faculty of Technology and, Bielefeld University, 33594 Bielefeld, Germany. psteffen@techfak.uni-bielefeld.de|Faculty of Technology and, Bielefeld University, 33594 Bielefeld, Germany. psteffen@techfak.uni-bielefeld.de|Faculty of Technology and, Bielefeld University, 33594 Bielefeld, Germany. psteffen@techfak.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Graphics|Computer Simulation|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|Pattern Recognition, Automated|RNA|Sequence Analysis, RNA|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk010', '16357029', 'We introduce RNAshapes, a new software package that integrates three RNA analysis tools based on the abstract shapes approach: the analysis of shape representatives, the calculation of shape probabilities and the consensus shapes approach. This new package is completely reimplemented in C and outruns the original implementations significantly in runtime and memory requirements. Additionally, we added a number of useful features like suboptimal folding with correct dangling energies, structure graph output, shape matching and a sliding window approach.'),
(72564, 2006, 'bio', 'CMfinder--a covariance model based RNA motif finding algorithm', 'Yao, Zizhen|Weinberg, Zasha|Ruzzo, Walter L', 'Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. yzizhen@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. yzizhen@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA. yzizhen@cs.washington.edu', 'United States|United States|United States', 215, 'Algorithms|Base Sequence|Computer Simulation|Conserved Sequence|Models, Genetic|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btk008', '16357030', 'The recent discoveries of large numbers of non-coding RNAs and computational advances in genome-scale RNA search create a need for tools for automatic, high quality identification and characterization of conserved RNA motifs that can be readily used for database search. Previous tools fall short of this goal.'),
(72565, 2006, 'bio', 'eQTL Explorer: integrated mining of combined genetic linkage and expression experiments', 'Mueller, Michael|Goel, Anuj|Thimma, Manjula|Dickens, Nicholas J|Aitman, Timothy J|Mangion, Jonathan', 'MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.|MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.|MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.|MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.|MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.|MRC Clinical Sciences Centre, Imperial College London, W12 0NN, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Gene Expression Profiling|Genetic Linkage|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk007', '16357031', 'The development of computational resources to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. eQTL Explorer stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer provides a new and powerful tool to interrogate these very large and complex datasets.'),
(72566, 2006, 'bio', 'SEAN: SNP prediction and display program utilizing EST sequence clusters', 'Huntley, Derek|Baldo, Angela|Johri, Saurabh|Sergot, Marek', 'Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ, UK. d.huntley@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 27, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Graphics|DNA Mutational Analysis|Expressed Sequence Tags|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk006', '16357032', 'SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.'),
(72567, 2006, 'bio', 'EDGE: extraction and analysis of differential gene expression', 'Leek, Jeffrey T|Monsen, Eva|Dabney, Alan R|Storey, John D', 'Department of Biostatistics, University of Washington, Seattle 98195, USA. jtleek@u.washington.edu|Department of Biostatistics, University of Washington, Seattle 98195, USA. jtleek@u.washington.edu|Department of Biostatistics, University of Washington, Seattle 98195, USA. jtleek@u.washington.edu|Department of Biostatistics, University of Washington, Seattle 98195, USA. jtleek@u.washington.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk005', '16357033', 'EDGE (Extraction of Differential Gene Expression) is an open source, point-and-click software program for the significance analysis of DNA microarray experiments. EDGE can perform both standard and time course differential expression analysis. The functions are based on newly developed statistical theory and methods. This document introduces the EDGE software package.'),
(72568, 2006, 'bio', 'SoDA: implementation of a 3D alignment algorithm for inference of antigen receptor recombinations', 'Volpe, Joseph M|Cowell, Lindsay G|Kepler, Thomas B', 'Computational Biology and Bioinformatics Program, Duke University Medical Center Box 90090 Duke University, Durham, NC 27708-0090, USA.|Computational Biology and Bioinformatics Program, Duke University Medical Center Box 90090 Duke University, Durham, NC 27708-0090, USA.|Computational Biology and Bioinformatics Program, Duke University Medical Center Box 90090 Duke University, Durham, NC 27708-0090, USA.', 'United States|United States|United States', 94, 'Algorithms|Base Sequence|Humans|Immunoglobulins|Molecular Sequence Data|Online Systems|Receptors, Antigen|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btk004', '16357034', 'The antigen receptors of adaptive immunity-T-cell receptors and immunoglobulins-are encoded by genes assembled stochastically from combinatorial libraries of gene segments. Immunoglobulin genes then experience further diversification through hypermutation. Analysis of the somatic genetics of the immune response depends explicitly on inference of the details of the recombinatorial process giving rise to each of the participating antigen receptor genes. We have developed a dynamic programming algorithm to perform this reconstruction and have implemented it as web-accessible software called SoDA (Somatic Diversification Analysis).'),
(72569, 2006, 'bio', 'HideNseek, a post-genome approach to locate transgenes exemplified in Arabidopsis thaliana', 'Yang, Guojun|Hall, Timothy C', 'Institute of Developmental and Molecular Biology, Department of Biology, Texas A&M University, College Station, TX 77843, USA.|Institute of Developmental and Molecular Biology, Department of Biology, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 1, 'Algorithms|Arabidopsis|Chromosome Mapping|Genome, Plant|Genomics|Restriction Mapping|Sequence Analysis, DNA|Software|Transgenes', 'Evaluation Studies', '10.1093/bioinformatics/btk003', '16357035', 'Determination of transgene location is essential for investigating the effects of position on transgene expression levels and facilitates cloning of the resident gene affected by insertion. Currently used PCR-based approaches for determination of transgene location are relatively complicated and often fail when the transgene is duplicated, rearranged or fragmented. HideNseek is a new bioinformatics tool that allows computation of transgene locations, provided that a suitable genomic restriction enzyme digestion profile is available. Since the new approach is not based on the terminal sequences of the transgene insert, it is less sensitive to transgene duplication, rearrangement or fragmentation. HideNseek has been tested experimentally and by in silico simulation. The experimental example provided here shows that this simple approach is feasible, permitting rapid location of transgenes with little bench work.'),
(72570, 2006, 'bio', 'Ridom TraceEdit: a DNA trace editor and viewer', 'Rothgänger, Jörg|Weniger, Markus|Weniger, Thomas|Mellmann, Alexander|Harmsen, Dag', 'Ridom GmbH, 97082 Würzburg, Germany.|Ridom GmbH, 97082 Würzburg, Germany.|Ridom GmbH, 97082 Würzburg, Germany.|Ridom GmbH, 97082 Würzburg, Germany.|Ridom GmbH, 97082 Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Online Systems|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk002', '16357036', 'Ridom TraceEdit is a cross-platform graphical DNA trace viewer and editor. TraceEdit displays the chromatogram files from Applied Biosystems automated sequencers and files in the Staden SCF format. Incorrect base calls can be edited and saved.'),
(72571, 2006, 'bio', 'Bayesian sparse hidden components analysis for transcription regulation networks', 'Sabatti, Chiara|James, Gareth M', 'Department of Human Genetics, UCLA, Los Angeles, CA 90095-7088, USA. csabatti@mednet.ucla.edu|Department of Human Genetics, UCLA, Los Angeles, CA 90095-7088, USA. csabatti@mednet.ucla.edu', 'United States|United States', 0, 'Algorithms|Animals|Bayes Theorem|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btk017', '16368767', 'In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert.'),
(72572, 2006, 'bio', 'BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature', 'Kim, Jung-Jae|Zhang, Zhuo|Park, Jong C|Ng, See-Kiong', 'Computer Science Division & AITrc, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, South Korea.|Computer Science Division & AITrc, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, South Korea.|Computer Science Division & AITrc, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, South Korea.|Computer Science Division & AITrc, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, South Korea.', '-|-|-|-', 22, 'Artificial Intelligence|Biomedical Research|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btk016', '16368768', 'Contrasts are useful conceptual vehicles for learning processes and exploratory research of the unknown. For example, contrastive information between proteins can reveal what similarities, divergences and relations there are of the two proteins, leading to invaluable insights for better understanding about the proteins. Such contrastive information are found to be reported in the biomedical literature. However, there have been no reported attempts in current biomedical text mining work that systematically extract and present such useful contrastive information from the literature for exploitation.'),
(72573, 2006, 'bio', 'Local RNA base pairing probabilities in large sequences', 'Bernhart, Stephan H|Hofacker, Ivo L|Stadler, Peter F', 'Institut für Theoretische Chemie, Universität Wien, Währingerstr. 17, A-1090 Wien, Austria.|Institut für Theoretische Chemie, Universität Wien, Währingerstr. 17, A-1090 Wien, Austria.|Institut für Theoretische Chemie, Universität Wien, Währingerstr. 17, A-1090 Wien, Austria.', 'Austria|Austria|Austria', 120, 'Algorithms|Base Pairing|Chromosome Mapping|Computer Simulation|Models, Genetic|Models, Statistical|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btk014', '16368769', 'The genome-wide search for non-coding RNAs requires efficient methods to compute and compare local secondary structures. Since the exact boundaries of such putative transcripts are typically unknown, arbitrary sequence windows have to be used in practice. Here we present a method for robustly computing the probabilities of local base pairs from long RNA sequences independent of the exact positions of the sequence window.'),
(72574, 2006, 'bio', 'Multidimensional local false discovery rate for microarray studies', 'Ploner, Alexander|Calza, Stefano|Gusnanto, Arief|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden. alexander.ploner@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden. alexander.ploner@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden. alexander.ploner@meb.ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden. alexander.ploner@meb.ki.se', 'Sweden|Sweden|Sweden|Sweden', 27, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btk013', '16368770', 'The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability.'),
(72575, 2006, 'bio', 'FLOSS: flexible ordered subset analysis for linkage mapping of complex traits', 'Browning, B L', 'Genetic Data Sciences, GlaxoSmithKline Five Moore Drive, P.O. Box 13398, Research Triangle Park, NC 27709, USA. brian_browning1@yahoo.com', 'United States', 14, 'Algorithms|Chromosome Mapping|Genetics, Population|Quantitative Trait Loci|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk012', '16368771', 'The FLOSS software package is a flexible framework for ordered subset analysis. FLOSS is specifically designed for use with the Merlin linkage analysis package, but FLOSS can be used with any linkage analysis software package that reports NPL Z-scores for each locus and family. When FLOSS is used with the Merlin linkage analysis package, one can use either non-parametric Z-scores or Kong and Cox linear allele sharing model LOD scores. Monte Carlo P-values are calculated using a permutation test with an efficient Besag-Clifford sequential stopping rule. FLOSS also has a flexible tool for assigning family covariate scores from Merlin input files. FLOSS includes user documentation and is written in Java for easy portability. The FLOSS source code is documented and designed to be extensible.'),
(72576, 2006, 'bio', 'Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization', 'Rabinowitz, Matthew|Myers, Lance|Banjevic, Milena|Chan, Albert|Sweetkind-Singer, Joshua|Haberer, Jessica|McCann, Kelly|Wolkowicz, Roland', 'Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net|Gene Security Network, Palo Alto, CA, USA. mrabinowitz@genesecurity.net', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Amino Acid Sequence|Anti-HIV Agents|Binding Sites|Computer Simulation|Enzyme Inhibitors|HIV Protease|HIV Reverse Transcriptase|HIV-1|Models, Chemical|Models, Statistical|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btk011', '16368772', 'Genotype-phenotype modeling problems are often overcomplete, or ill-posed, since the number of potential predictors-genes, proteins, mutations and their interactions-is large relative to the number of measured outcomes. Such datasets can still be used to train sparse parameter models that generalize accurately, by exerting a principle similar to Occams Razor: When many possible theories can explain the observations, the most simple is most likely to be correct. We apply this philosophy to modeling the drug response of Type-1 Human Immunodeficiency Virus (HIV-1). Owing to the decreasing expense of genetic sequencing relative to in vitro phenotype testing, a statistical model that reliably predicts viral drug response from genetic data is an important tool in the selection of antiretroviral therapy (ART). The optimization techniques described will have application to many genotype-phenotype modeling problems for the purpose of enhancing clinical decisions.'),
(72577, 2006, 'bio', 'MiGenes: a searchable interspecies database of mitochondrial proteins curated using gene ontology annotation', 'Basu, Siddhartha|Bremer, Erich|Zhou, Chun|Bogenhagen, Daniel F', 'Department of Pharmacological Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-8651, USA.|Department of Pharmacological Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-8651, USA.|Department of Pharmacological Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-8651, USA.|Department of Pharmacological Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-8651, USA.', 'United States|United States|United States|United States', 17, 'Animals|Database Management Systems|Databases, Protein|Documentation|Humans|Information Storage and Retrieval|Mitochondrial Proteins|Natural Language Processing|Species Specificity|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btk009', '16368773', 'There has been an explosion of interest in the role of mitochondria in programmed cell death and other fundamental pathological processes underlying the development of human diseases. Nevertheless, the inventory of mitochondrial proteins encoded in the nuclear genome remains incomplete, providing an impediment to mitochondrial research at the interface with systems biology. We created the MiGenes database to further define the scope of the mitochondrial proteome in humans and model organisms including mice, rats, flies and worms as well as budding and fission yeasts. MiGenes is intended to stimulate mitochondrial research using model organisms.'),
(72578, 2006, 'bio', 'ChroCoLoc: an application for calculating the probability of co-localization of microarray gene expression', 'Blake, Jonathon|Schwager, Christian|Kapushesky, Misha|Brazma, Alvis', 'EMBL Meyerhofstr 1 D-69117 Heidelberg, Germany. blake@ebi.ac.uk|EMBL Meyerhofstr 1 D-69117 Heidelberg, Germany. blake@ebi.ac.uk|EMBL Meyerhofstr 1 D-69117 Heidelberg, Germany. blake@ebi.ac.uk|EMBL Meyerhofstr 1 D-69117 Heidelberg, Germany. blake@ebi.ac.uk', 'Germany|Germany|Germany|Germany', 9, 'Algorithms|Chromosome Mapping|Chromosomes, Human|Computer Simulation|Gene Expression Profiling|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk022', '16377611', 'With the production of whole genome microarray chips the ability arises to investigate whether the regulation of particular groups of genes may be influenced by their chromosomal localization. Chromosome Co-Localization probability calculator (ChroCoLoc) is a publicly available web-based tool for the analysis of co-localization of co-expressed genes identified by microarray experiments.'),
(72579, 2006, 'bio', 'GenomeDiagram: a python package for the visualization of large-scale genomic data', 'Pritchard, Leighton|White, Jennifer A|Birch, Paul R J|Toth, Ian K', 'Plant Pathogen Programme, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK. lpritc@scri.ac.uk|Plant Pathogen Programme, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK. lpritc@scri.ac.uk|Plant Pathogen Programme, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK. lpritc@scri.ac.uk|Plant Pathogen Programme, Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, UK. lpritc@scri.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 50, 'Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk021', '16377612', 'We present GenomeDiagram, a flexible, open-source Python module for the visualization of large-scale genomic, comparative genomic and other data with reference to a single chromosome or other biological sequence. GenomeDiagram may be used to generate publication-quality vector graphics, rastered images and in-line streamed graphics for webpages. The package integrates with datatypes from the BioPython project, and is available for Windows, Linux and Mac OS X systems.'),
(72580, 2006, 'bio', 'Improving missing value estimation in microarray data with gene ontology', 'Tuikkala, Johannes|Elo, Laura|Nevalainen, Olli S|Aittokallio, Tero', 'Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland. jotatu@utu.fi|Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland. jotatu@utu.fi|Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland. jotatu@utu.fi|Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland. jotatu@utu.fi', 'Finland|Finland|Finland|Finland', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btk019', '16377613', 'Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation.'),
(72581, 2006, 'bio', 'ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation', 'Montgomery, S B|Griffith, O L|Sleumer, M C|Bergman, C M|Bilenky, M|Pleasance, E D|Prychyna, Y|Zhang, X|Jones, S J M', 'Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.|Canadas Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4E6.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 92, 'Binding Sites|Database Management Systems|Databases, Genetic|Documentation|Gene Expression Regulation|Genetic Variation|Internet|Natural Language Processing|Periodicals as Topic|Promoter Regions, Genetic|Protein Binding|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btk027', '16397004', 'Our understanding of gene regulation is currently limited by our ability to collectively synthesize and catalogue transcriptional regulatory elements stored in scientific literature. Over the past decade, this task has become increasingly challenging as the accrual of biologically validated regulatory sequences has accelerated. To meet this challenge, novel community-based approaches to regulatory element annotation are required.'),
(72582, 2006, 'bio', 'A multi-step approach to time series analysis and gene expression clustering', 'Amato, R|Ciaramella, A|Deniskina, N|Del Mondo, C|di Bernardo, D|Donalek, C|Longo, G|Mangano, G|Miele, G|Raiconi, G|Staiano, A|Tagliaferri, R', 'Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.|Dipartimento di Scienze Fisiche, University of Naples Federico II, Naples, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 41, 'Artificial Intelligence|Cluster Analysis|Computer Graphics|Computer Simulation|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Oligonucleotide Array Sequence Analysis|Proteins|Software|Time Factors|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btk026', '16397005', 'The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation.'),
(72583, 2006, 'bio', 'BuchneraBASE: a post-genomic resource for Buchnera sp APS', 'Prickett, M Dennis|Page, Matt|Douglas, Angela E|Thomas, Gavin H', 'Department of Biology, University of York, York YO10 5YW, UK.|Department of Biology, University of York, York YO10 5YW, UK.|Department of Biology, University of York, York YO10 5YW, UK.|Department of Biology, University of York, York YO10 5YW, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Buchnera|Database Management Systems|Databases, Genetic|Genome, Bacterial|Genomics|Information Storage and Retrieval|Internet|Online Systems', 'Journal Article', '10.1093/bioinformatics/btk024', '16397006', 'BuchneraBASE is a bioinformatic research tool for the genome of the symbiotic bacterium Buchnera sp. APS that includes an improved genome annotation, comparative information about related insect symbiont genomes and a complete mapping of metabolic reactions to an Escherichia coli in silico model. The database is designed to accommodate genome-wide post-genomic datasets that are becoming available for this organism.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72584, 2006, 'bio', 'OPM: orientations of proteins in membranes database', 'Lomize, Mikhail A|Lomize, Andrei L|Pogozheva, Irina D|Mosberg, Henry I', 'College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI 48109-1065, USA.|College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI 48109-1065, USA.|College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI 48109-1065, USA.|College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI 48109-1065, USA.', 'United States|United States|United States|United States', 0, 'Computer Graphics|Computer Simulation|Databases, Protein|Membrane Proteins|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk023', '16397007', 'The Orientations of Proteins in Membranes (OPM) database provides a collection of transmembrane, monotopic and peripheral proteins from the Protein Data Bank whose spatial arrangements in the lipid bilayer have been calculated theoretically and compared with experimental data. The database allows analysis, sorting and searching of membrane proteins based on their structural classification, species, destination membrane, numbers of transmembrane segments and subunits, numbers of secondary structures and the calculated hydrophobic thickness or tilt angle with respect to the bilayer normal. All coordinate files with the calculated membrane boundaries are available for downloading.'),
(72585, 2006, 'bio', 'ANDY: a general, fault-tolerant tool for database searching on computer clusters', 'Smith, Andrew|Chandonia, John-Marc|Brenner, Steven E', 'Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720-3102, USA.|Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720-3102, USA.|Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720-3102, USA.', 'United States|United States|United States', 0, 'Computing Methodologies|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Online Systems|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btk020', '16397008', 'ANDY (seArch coordination aND analYsis) is a set of Perl programs and modules for distributing large biological database searches, and in general any sequence of commands, across the nodes of a Linux computer cluster. ANDY is compatible with several commonly used distributed resource management (DRM) systems, and it can be easily extended to new DRMs. A distinctive feature of ANDY is the choice of either dedicated or fair-use operation: ANDY is almost as efficient as single-purpose tools that require a dedicated cluster, but it runs on a general-purpose cluster along with any other jobs scheduled by a DRM. Other features include communication through named pipes for performance, flexible customizable routines for error-checking and summarizing results, and multiple fault-tolerance mechanisms.'),
(72586, 2006, 'bio', 'A non-linear optimization procedure to estimate distances and instantaneous substitution rate matrices under the GTR model', 'Catanzaro, Daniele|Pesenti, Raffaele|Milinkovitch, Michel C', 'Laboratory of Evolutionary Genetics, Institute for Molecular Biology and Medicine, IBMM, Université Libre de Bruxelles CP300, Rue Jeener et Brachet 12, B-6041, Gosselies, Belgium.|Laboratory of Evolutionary Genetics, Institute for Molecular Biology and Medicine, IBMM, Université Libre de Bruxelles CP300, Rue Jeener et Brachet 12, B-6041, Gosselies, Belgium.|Laboratory of Evolutionary Genetics, Institute for Molecular Biology and Medicine, IBMM, Université Libre de Bruxelles CP300, Rue Jeener et Brachet 12, B-6041, Gosselies, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Base Pair Mismatch|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genetic Variation|Linkage Disequilibrium|Models, Genetic|Nonlinear Dynamics|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btk001', '16397009', 'The general-time-reversible (GTR) model is one of the most popular models of nucleotide substitution because it constitutes a good trade-off between mathematical tractability and biological reality. However, when it is applied for inferring evolutionary distances and/or instantaneous rate matrices, the GTR model seems more prone to inapplicability than more restrictive time-reversible models. Although it has been previously noted that the causes for intractability are caused by the impossibility of computing the logarithm of a matrix characterised by negative eigenvalues, the issue has not been investigated further.'),
(72587, 2006, 'bio', 'A regularized discriminative model for the prediction of protein-peptide interactions', 'Lehrach, Wolfgang P|Husmeier, Dirk|Williams, Christopher K I', 'University of Edinburgh, Edinburgh EH1 2QL, UK. wlehrach@ed.ac.uk|University of Edinburgh, Edinburgh EH1 2QL, UK. wlehrach@ed.ac.uk|University of Edinburgh, Edinburgh EH1 2QL, UK. wlehrach@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 19, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Discriminant Analysis|Models, Chemical|Models, Statistical|Molecular Sequence Data|Peptide Mapping|Peptides|Protein Binding|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/bti804', '16397010', 'Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. Since high-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, it would be desirable to more specifically target or partially bypass them with complementary in silico approaches. In the present paper, we present a probabilistic discriminative approach to predicting PRM-mediated protein-protein interactions from sequence data. The model is motivated by the discriminative model of Segal and Sharan as an alternative to the generative approach of Reiss and Schwikowski. In our evaluation, we focus on predicting the interaction network. As proposed by Williams, we overcome the problem of susceptibility to over-fitting by adopting a Bayesian a posteriori approach based on a Laplacian prior in parameter space.'),
(72588, 2006, 'bio', 'Haloferax volcanii PitA: an example of functional interaction between the Pfam chlorite dismutase and antibiotic biosynthesis monooxygenase families?', 'Bab-Dinitz, Elizabeta|Shmuely, Hagay|Maupin-Furlow, Julie|Eichler, Jerry|Shaanan, Boaz', 'Department of Life Sciences, Ben Gurion University Beersheva 84105, Israel.|Department of Life Sciences, Ben Gurion University Beersheva 84105, Israel.|Department of Life Sciences, Ben Gurion University Beersheva 84105, Israel.|Department of Life Sciences, Ben Gurion University Beersheva 84105, Israel.|Department of Life Sciences, Ben Gurion University Beersheva 84105, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Anti-Bacterial Agents|Binding Sites|Computer Simulation|Databases, Protein|Enzyme Activation|Haloferax volcanii|Mixed Function Oxygenases|Models, Biological|Models, Chemical|Models, Molecular|Multigene Family|Oxidoreductases|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btk043', '16403788', 'A curious fusion between chlorite dismutase-like and antibiotic biosynthesis monooxygenase-like domains within a single open reading frame has been revealed by both sequence homology and structural modeling in Haloferax volcanii PitA and its homologues in other halophilic archaea. While this fusion may reflect an environmental adaptation to life in hypersaline environments and hence one specific to haloarchaea, PitA and its homologues may represent a paradigm of biologically-relevant interplay between these two distinct activities in accordance with the Rosetta Stone approach.'),
(72589, 2006, 'bio', 'GUUGle: a utility for fast exact matching under RNA complementary rules including G-U base pairing', 'Gerlach, Wolfgang|Giegerich, Robert', 'Faculty of Technology, Bielefeld University 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University 33615 Bielefeld, Germany.', 'Germany|Germany', 35, 'Algorithms|Base Pairing|Base Sequence|Dinucleoside Phosphates|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btk041', '16403789', 'RNA secondary structure analysis often requires searching for potential helices in large sequence data.'),
(72590, 2006, 'bio', 'MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data', 'Katajamaa, Mikko|Miettinen, Jarkko|Oresic, Matej', 'Turku Centre for Biotechnology Turku, Finland.|Turku Centre for Biotechnology Turku, Finland.|Turku Centre for Biotechnology Turku, Finland.', 'Finland|Finland|Finland', 0, 'Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Mass Spectrometry|Peptide Mapping|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk039', '16403790', 'New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammons mapping and curvilinear distance analysis for data visualization and exploratory analysis.'),
(72591, 2006, 'bio', 'Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data', 'Wang, Zuyi|Wang, Yue|Xuan, Jianhua|Dong, Yibin|Bakay, Marina|Feng, Yuanjian|Clarke, Robert|Hoffman, Eric P', 'Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.|Center for Genetic Medicine, Childrens National Medical Center Washington, DC 20010, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Biomarkers, Tumor|Chromosome Mapping|Diagnosis, Computer-Assisted|Genetic Markers|Humans|Neoplasms|Neural Networks (Computer)|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btk036', '16403791', 'Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy.'),
(72592, 2006, 'bio', 'A greedier approach for finding tag SNPs', 'Chang, Chia-Jung|Huang, Yao-Ting|Chao, Kun-Mao', 'Department of Computer Science and Information Engineering, National Taiwan University Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University Taipei, Taiwan.|Department of Computer Science and Information Engineering, National Taiwan University Taipei, Taiwan.', '-|-|-', 0, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Expressed Sequence Tags|Genetic Variation|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btk035', '16403792', 'Recent studies have shown that a small subset of Single Nucleotide Polymorphisms (SNPs) (called tag SNPs) is sufficient to capture the haplotype patterns in a high linkage disequilibrium region. To find the minimum set of tag SNPs, exact algorithms for finding the optimal solution could take exponential time. On the other hand, approximation algorithms are more efficient but may fail to find the optimal solution.'),
(72593, 2006, 'bio', 'Using a state-space model with hidden variables to infer transcription factor activities', 'Li, Zheng|Shaw, Stephen M|Yedwabnick, Matthew J|Chan, Christina', 'Department of Chemical Engineering and Material Science, Michigan State University East Lansing, 48824, USA.|Department of Chemical Engineering and Material Science, Michigan State University East Lansing, 48824, USA.|Department of Chemical Engineering and Material Science, Michigan State University East Lansing, 48824, USA.|Department of Chemical Engineering and Material Science, Michigan State University East Lansing, 48824, USA.', 'United States|United States|United States|United States', 36, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btk034', '16403793', 'In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feedback loops and auto-regulation, making SSM a useful complement to existing models.'),
(72594, 2006, 'bio', 'IWoCS: analyzing ribosomal intergenic transcribed spacers configuration and taxonomic relationships', 'DAuria, Giuseppe|Pushker, Ravindra|Rodriguez-Valera, Francisco', 'Evolutionary Genomics Group and Division de Microbiologia, Universidad Miguel Hernandez Campus de San Juan, 03550 San Juan de Alicante, Spain. gdauria@umh.es|Evolutionary Genomics Group and Division de Microbiologia, Universidad Miguel Hernandez Campus de San Juan, 03550 San Juan de Alicante, Spain. gdauria@umh.es|Evolutionary Genomics Group and Division de Microbiologia, Universidad Miguel Hernandez Campus de San Juan, 03550 San Juan de Alicante, Spain. gdauria@umh.es', 'Spain|Spain|Spain', 0, 'Algorithms|Conserved Sequence|DNA, Ribosomal Spacer|Databases, Genetic|Expressed Sequence Tags|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S|RNA, Ribosomal, 23S|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Species Specificity|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btk033', '16403794', 'Lately the use of 16S-23S Intergenic Transcribed Spacer (ITS) sequences for bacterial typing purposes has increased. The presence of conserved regions like tRNA genes or boxes together with hypervariable regions allows performing intraspecific discrimination of very close bacterial strains. On the other hand this mosaic of variability makes the ITS a sequence difficult to analyze and compare.'),
(72595, 2006, 'bio', 'Short fuzzy tandem repeats in genomic sequences, identification, and possible role in regulation of gene expression', 'Boeva, Valentina|Regnier, Mireille|Papatsenko, Dmitri|Makeev, Vsevolod', 'Department of Bioengineering and Bioinformatics, Moscow State University Moscow, Russia. valeyo@imb.ac.ru|Department of Bioengineering and Bioinformatics, Moscow State University Moscow, Russia. valeyo@imb.ac.ru|Department of Bioengineering and Bioinformatics, Moscow State University Moscow, Russia. valeyo@imb.ac.ru|Department of Bioengineering and Bioinformatics, Moscow State University Moscow, Russia. valeyo@imb.ac.ru', '-|-|-|-', 60, 'Algorithms|Animals|Chromosome Mapping|Drosophila|Fuzzy Logic|Gene Expression Regulation|Sequence Alignment|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btk032', '16403795', 'Genomic sequences are highly redundant and contain many types of repetitive DNA. Fuzzy tandem repeats (FTRs) are of particular interest. They are found in regulatory regions of eukaryotic genes and are reported to interact with transcription factors. However, accurate assessment of FTR occurrences in different genome segments requires specific algorithm for efficient FTR identification and classification.'),
(72596, 2006, 'bio', 'Copying nodes versus editing links: the source of the difference between genetic regulatory networks and the WWW', 'Louzoun, Yoram|Muchnik, Lev|Solomon, Sorin', 'Department of mathematics, Bar Ilan University, Ramat Gan 52900, Israel. louzouy@math.biu.ac.il|Department of mathematics, Bar Ilan University, Ramat Gan 52900, Israel. louzouy@math.biu.ac.il|Department of mathematics, Bar Ilan University, Ramat Gan 52900, Israel. louzouy@math.biu.ac.il', 'Israel|Israel|Israel', 0, 'Animals|Biomimetics|Cell Physiological Phenomena|Gene Expression Regulation|Humans|Information Storage and Retrieval|Internet|Models, Biological|Signal Transduction|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btk030', '16403796', 'We study two kinds of networks: genetic regulatory networks and the World Wide Web. We systematically test microscopic mechanisms to find the set of such mechanisms that optimally explain each networks specific properties. In the first case we formulate a model including mainly random unbiased gene duplications and mutations. In the second case, the basic moves are website generation and rapid surf-induced link creation (/destruction). The different types of mechanisms reproduce the appropriate observed network properties. We use those to show that different kinds of networks have strongly system-dependent macroscopic experimental features. The diverging properties result from dissimilar node and link basic dynamics. The main non-uniform properties include the clustering coefficient, small-scale motifs frequency, time correlations, centrality and the connectivity of outgoing links. Some other features are generic such as the large-scale connectivity distribution of incoming links (scale-free) and the network diameter (small-worlds). The common properties are just the general hallmark of autocatalysis (self-enhancing processes), while the specific properties hinge on the specific elementary mechanisms.'),
(72597, 2006, 'bio', 'Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures', 'Dalevi, Daniel|Dubhashi, Devdatt|Hermansson, Malte', 'Department of Computing Science, Chalmers University, SE 412 96 Göteborg, Sweden. dalevi@cs.chalmers.se|Department of Computing Science, Chalmers University, SE 412 96 Göteborg, Sweden. dalevi@cs.chalmers.se|Department of Computing Science, Chalmers University, SE 412 96 Göteborg, Sweden. dalevi@cs.chalmers.se', 'Sweden|Sweden|Sweden', 0, 'Artificial Intelligence|Bayes Theorem|Chromosome Mapping|DNA Fingerprinting|DNA, Bacterial|Gene Transfer, Horizontal|Genome, Bacterial|Markov Chains|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btk029', '16403797', 'Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in Neisseria meningitis. In this study we extend the classifier approach using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk).'),
(72598, 2006, 'bio', 'Inferring global levels of alternative splicing isoforms using a generative model of microarray data', 'Shai, Ofer|Morris, Quaid D|Blencowe, Benjamin J|Frey, Brendan J', 'Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8. ofer@psi.toronto.edu|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8. ofer@psi.toronto.edu|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8. ofer@psi.toronto.edu|Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada M5S 3G8. ofer@psi.toronto.edu', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Alternative Splicing|Artificial Intelligence|Computer Simulation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btk028', '16403798', 'Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organisms proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues.'),
(72599, 2006, 'bio', 'BioShell--a package of tools for structural biology computations', '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', 45, 'Computational Biology|Computer Simulation|Databases, Protein|Models, Chemical|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btk037', '16407320', 'BioShell is a suite of programs performing common tasks accompanying protein structure modeling. BioShell design is based on UNIX shell flexibility and should be used as its extension. Using BioShell various molecular modeling procedures can be integrated in a single pipeline.'),
(72600, 2006, 'bio', 'LAMARC 20: maximum likelihood and Bayesian estimation of population parameters', 'Kuhner, Mary K', 'Department of Genome Sciences Box 357730 University of Washington Seattle, 98195-7730, USA. lamarc@gs.washington.edu', 'United States', 510, 'Bayes Theorem|Biological Evolution|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Genetic Variation|Genetics, Population|Likelihood Functions|Models, Genetic|Models, Statistical|Software', 'Journal Article', '10.1093/bioinformatics/btk051', '16410317', 'We present a Markov chain Monte Carlo coalescent genealogy sampler, LAMARC 2.0, which estimates population genetic parameters from genetic data. LAMARC can co-estimate subpopulation Theta = 4N(e)mu, immigration rates, subpopulation exponential growth rates and overall recombination rate, or a user-specified subset of these parameters. It can perform either maximum-likelihood or Bayesian analysis, and accomodates nucleotide sequence, SNP, microsatellite or elecrophoretic data, with resolved or unresolved haplotypes. It is available as portable source code and executables for all three major platforms.'),
(72601, 2006, 'bio', 'Maximum likelihood estimates of allele frequencies and error rates from samples of related individuals by gene counting', 'Thomas, Alun|Camp, Nicola J', 'Department of Medical Informatics, University of Utah, 391 Chipeta Way, Suite D, Salt Lake City, 84108, USA. alun@genepi.med.utah.edu|Department of Medical Informatics, University of Utah, 391 Chipeta Way, Suite D, Salt Lake City, 84108, USA. alun@genepi.med.utah.edu', 'United States|United States', 6, 'Biological Evolution|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Gene Frequency|Genetic Variation|Genetics, Population|Likelihood Functions|Models, Genetic|Models, Statistical|Pedigree|Polymorphism, Single Nucleotide|Programming Languages|Research Design|Software', 'Journal Article', '10.1093/bioinformatics/btk049', '16410318', 'Graphical modeling is used to extend the gene counting method to compute maximum likelihood estimates of allele frequencies for samples of individuals related in extended pedigrees. Genotypes may be missing or partially observed, and error rates can be simultaneously estimated.'),
(72602, 2006, 'bio', 'Hierarchical multi-label prediction of gene function', 'Barutcuoglu, Zafer|Schapire, Robert E|Troyanskaya, Olga G', 'Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA.', 'United States|United States|United States', 55, 'Algorithms|Bayes Theorem|Computational Biology|Databases, Protein|Mitosis|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btk048', '16410319', 'Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions.'),
(72603, 2006, 'bio', 'Comparison of Affymetrix GeneChip expression measures', 'Irizarry, Rafael A|Wu, Zhijin|Jaffee, Harris A', 'Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA. rafa@jhu.edu|Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA. rafa@jhu.edu|Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA. rafa@jhu.edu', 'United States|United States|United States', 143, 'Algorithms|Benchmarking|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software', 'Comparative Study', '10.1093/bioinformatics/btk046', '16410320', 'In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted.'),
(72604, 2006, 'bio', 'Chemical effects in biological systems (CEBS) object model for toxicology data, SysTox-OM: design and application', 'Xirasagar, Sandhya|Gustafson, Scott F|Huang, Cheng-Cheng|Pan, Qinyan|Fostel, Jennifer|Boyer, Paul|Merrick, B Alex|Tomer, Kenneth B|Chan, Denny D|Yost, Kenneth J|Choi, Danielle|Xiao, Nianqing|Stasiewicz, Stanley|Bushel, Pierre|Waters, Michael D', 'Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com|Science Applications International Corporation, 20201 Century Boulevard, 3rd Floor, Germantown, MD 20874, USA. xirasagars@saic.com', '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', 20, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Models, Biological|Programming Languages|Proteomics|Software Design|Toxicogenetics', 'Journal Article', '10.1093/bioinformatics/btk045', '16410321', 'The CEBS data repository is being developed to promote a systems biology approach to understand the biological effects of environmental stressors. CEBS will house data from multiple gene expression platforms (transcriptomics), protein expression and protein-protein interaction (proteomics), and changes in low molecular weight metabolite levels (metabolomics) aligned by their detailed toxicological context. The system will accommodate extensive complex querying in a user-friendly manner. CEBS will store toxicological contexts including the study design details, treatment protocols, animal characteristics and conventional toxicological endpoints such as histopathology findings and clinical chemistry measures. All of these data types can be integrated in a seamless fashion to enable data query and analysis in a biologically meaningful manner.'),
(72605, 2006, 'bio', 'LSAT: learning about alternative transcripts in MEDLINE', 'Shah, Parantu K|Bork, Peer', 'European Molecular Biology Laboratory, Heidelberg, Germany. shah@embl.de|European Molecular Biology Laboratory, Heidelberg, Germany. shah@embl.de', 'Germany|Germany', 0, 'Benchmarking|Computational Biology|Database Management Systems|Information Storage and Retrieval|MEDLINE|Models, Biological|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk044', '16410322', 'Generation of alternative transcripts from the same gene is an important biological event due to their contribution in creating functional diversity in eukaryotes. In this work, we choose the task of extracting information around this complex topic using a two-step procedure involving machine learning and information extraction.'),
(72606, 2006, 'bio', 'Tools for the SBML Community', 'Gillespie, Colin S|Wilkinson, Darren J|Proctor, Carole J|Shanley, Daryl P|Boys, Richard J|Kirkwood, Thomas B L', 'School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk|School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk|School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk|School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk|School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk|School of Mathematics and Statistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK. c.gillespie@ncl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Biochemistry|Computer Simulation|Database Management Systems|Databases, Factual|Models, Biological|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk042', '16410323', 'SBML is quickly becoming the standard format to exchange biochemical models. The tools presented in this paper are loosely-coupled, and are intended to be incorporated into SBML aware applications. The rationale for this is to reduce the amount of repeated work carried out within the community and to create tools that offer a greater number of features to the end-user.'),
(72607, 2006, 'bio', 'OrthologID: automation of genome-scale ortholog identification within a parsimony framework', 'Chiu, Joanna C|Lee, Ernest K|Egan, Mary G|Sarkar, Indra Neil|Coruzzi, Gloria M|DeSalle, Rob', 'Department of Biology, New York University, New York, NY 10003, USA.|Department of Biology, New York University, New York, NY 10003, USA.|Department of Biology, New York University, New York, NY 10003, USA.|Department of Biology, New York University, New York, NY 10003, USA.|Department of Biology, New York University, New York, NY 10003, USA.|Department of Biology, New York University, New York, NY 10003, USA.', 'United States|United States|United States|United States|United States|United States', 71, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Conserved Sequence|Databases, Genetic|Pattern Recognition, Automated|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btk040', '16410324', 'The determination of gene orthology is a prerequisite for mining and utilizing the rapidly increasing amount of sequence data for genome-scale phylogenetics and comparative genomic studies. Until now, most researchers use pairwise distance comparisons algorithms, such as BLAST, COG, RBH, RSD and INPARANOID, to determine gene orthology. In contrast, orthology determination within a character-based phylogenetic framework has not been utilized on a genomic scale owing to the lack of efficiency and automation.'),
(72608, 2006, 'bio', 'Automated discovery of 3D motifs for protein function annotation', 'Polacco, Benjamin J|Babbitt, Patricia C', 'Department of Biopharmaceutical Sciences, University of California, San Francisco, 94143-2250, USA.|Department of Biopharmaceutical Sciences, University of California, San Francisco, 94143-2250, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btk038', '16410325', 'Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3-10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function.'),
(72609, 2006, 'bio', 'Multiple association analysis via simulated annealing (MASSA)', 'Pérez-Enciso, M', 'Institut Catalá de Reçerca i Estudis Avançats, Pg Lluis Companys 23, 08010 Barcelona, Spain. miguel.perez@uab.es', 'Spain', 4, 'Algorithms|Animals|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Genetics, Population|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btk050', '16414961', 'Genome-wide association studies are now technically feasible and likely to become a fundamental tool in unraveling the ultimate genetic basis of complex traits. However, new statistical and computational methods need to be developed to extract the maximum information in a realistic computing time. Here we propose a new method for multiple association analysis via simulated annealing that allows for epistasis and any number of markers. It consists of finding the model with lowest Bayesian information criterion using simulated annealing. The data are described by means of a mixed model and new alternative models are proposed using a set of rules, e.g. new sites can be added (or deleted), or new epistatic interactions can be included between existing genetic factors. The method is illustrated with simulated and real data.'),
(72610, 2006, 'bio', 'Panta rhei (QAlign2): an open graphical environment for sequence analysis', 'Sammeth, Michael|Griebel, Thasso|Tille, Felix|Stoye, Jens', 'Genome Informatics, Technical faculty, Bielefeld University, PO Box 10 01 31, 33501 Bielefeld, Germany.|Genome Informatics, Technical faculty, Bielefeld University, PO Box 10 01 31, 33501 Bielefeld, Germany.|Genome Informatics, Technical faculty, Bielefeld University, PO Box 10 01 31, 33501 Bielefeld, Germany.|Genome Informatics, Technical faculty, Bielefeld University, PO Box 10 01 31, 33501 Bielefeld, Germany.', 'Germany|Germany|Germany|Germany', 4, 'Algorithms|Computer Graphics|Database Management Systems|Information Storage and Retrieval|Phylogeny|Sequence Alignment|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl007', '16418234', 'The first version of the graphical multiple sequence alignment environment QAlign was published in 2003. Heavy response from the molecular-biological user community clearly demonstrated the need for such a platform.'),
(72611, 2006, 'bio', 'Inference of gene regulatory networks and compound mode of action from time course gene expression profiles', 'Bansal, Mukesh|Della Gatta, Giusy|di Bernardo, Diego', 'Telethon Institute of Genetics and Medicine, Via P. Castellino 111, 80131 Naples, Italy.|Telethon Institute of Genetics and Medicine, Via P. Castellino 111, 80131 Naples, Italy.|Telethon Institute of Genetics and Medicine, Via P. Castellino 111, 80131 Naples, Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Bayes Theorem|Cell Physiological Phenomena|Computer Simulation|Escherichia coli|Gene Expression Profiling|Models, Biological|Models, Statistical|Time Factors', 'Comparative Study', '10.1093/bioinformatics/btl003', '16418235', 'Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. Here we developed an algorithm that can infer the local network of gene-gene interactions surrounding a gene of interest. This is achieved by a perturbation of the gene of interest and subsequently measuring the gene expression profiles at multiple time points. We applied this algorithm to computer simulated data and to experimental data on a nine gene network in Escherichia coli.'),
(72612, 2006, 'bio', 'An interactive power analysis tool for microarray hypothesis testing and generation', 'Seo, Jinwook|Gordish-Dressman, Heather|Hoffman, Eric P', 'Research Center for Genetic Medicine, Childrens National Medical Center, Washington, DC 20010, USA. jseo@cnmcresearch.org|Research Center for Genetic Medicine, Childrens National Medical Center, Washington, DC 20010, USA. jseo@cnmcresearch.org|Research Center for Genetic Medicine, Childrens National Medical Center, Washington, DC 20010, USA. jseo@cnmcresearch.org', 'United States|United States|United States', 88, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk052', '16418236', 'Human clinical projects typically require a priori statistical power analyses. Towards this end, we sought to build a flexible and interactive power analysis tool for microarray studies integrated into our public domain HCE 3.5 software package. We then sought to determine if probe set algorithms or organism type strongly influenced power analysis results.'),
(72613, 2006, 'bio', 'Combining signals from spotted cDNA microarrays obtained at different scanning intensities', 'Piepho, H P|Keller, B|Hoecker, N|Hochholdinger, F', 'Bioinformatics Unit, Institute for Crop Production and Grassland Research, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. piepho@uni-hohenheim.de|Bioinformatics Unit, Institute for Crop Production and Grassland Research, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. piepho@uni-hohenheim.de|Bioinformatics Unit, Institute for Crop Production and Grassland Research, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. piepho@uni-hohenheim.de|Bioinformatics Unit, Institute for Crop Production and Grassland Research, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. piepho@uni-hohenheim.de', 'Germany|Germany|Germany|Germany', 10, 'DNA, Complementary|Data Interpretation, Statistical|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Regression Analysis|Zea mays', 'Journal Article', '10.1093/bioinformatics/btk047', '16418237', 'The analysis of spotted cDNA microarrays involves scanning of color signals from fluorescent dyes. A common problem is that a given scanning intensity is not usually optimal for all spotted cDNAs. Specifically, some spots may be at the saturation limit, resulting in poor separation of signals from different tissues or conditions. The problem may be addressed by multiple scans with varying scanning intensities. Multiple scanning intensities raise the question of how to combine different signals from the same spot, particularly when measurement error is not negligible.'),
(72614, 2006, 'bio', 'TAMAL: an integrated approach to choosing SNPs for genetic studies of human complex traits', 'Hemminger, Bradley M|Saelim, Billy|Sullivan, Patrick F', 'School of Information and Library Science, University of North Carolina at Chapel Hill, NC, USA.|School of Information and Library Science, University of North Carolina at Chapel Hill, NC, USA.|School of Information and Library Science, University of North Carolina at Chapel Hill, NC, USA.', 'United States|United States|United States', 60, 'Chromosome Mapping|DNA Mutational Analysis|Genetics, Population|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk025', '16418238', 'Investigators conducting studies of the molecular genetics of complex traits in humans often need rationally to select a set of single nucleotide polymorphisms (SNPs) from the hundreds or thousands available for a candidate gene. Accomplishing this requires integration of genomic data from distributed databases and is both time-consuming and error-prone. We developed the TAMAL (Technology And Money Are Limiting) web site to help identify promising SNPs for further investigation. For a given list of genes, TAMAL identifies SNPs that meet user-specified criteria (e.g. haplotype tagging SNPs or SNP predicted to lead to amino acid changes) from current versions of online resources (i.e. HapMap, Perlegen, Affymetrix, dbSNP and the UCSC genome browser).'),
(72615, 2006, 'bio', 'REMORA: a pilot in the ocean of BioMoby web-services', 'Carrere, Sébastien|Gouzy, Jérôme', 'INRA-CNRS Laboratoire des Interactions Plantes Micro-organismes (LIPM) BP 52627, 31326 Castanet Tolosan Cedex, France.|INRA-CNRS Laboratoire des Interactions Plantes Micro-organismes (LIPM) BP 52627, 31326 Castanet Tolosan Cedex, France.', 'France|France', 32, 'Computer Graphics|Database Management Systems|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl001', '16423924', 'Emerging web-services technology allows interoperability between multiple distributed architectures. Here, we present REMORA, a web server implemented according to the BioMoby web-service specifications, providing life science researchers with an easy-to-use workflow generator and launcher, a repository of predefined workflows and a survey system.'),
(72616, 2006, 'bio', 'SUSPECTS: enabling fast and effective prioritization of positional candidates', 'Adie, E A|Adams, R R|Evans, K L|Porteous, D J|Pickard, B S', 'Medical Genetics Section, School of Molecular and Clinical Medicine, University of Edinburgh EH4 2XU UK. euan.adie@ed.ac.uk|Medical Genetics Section, School of Molecular and Clinical Medicine, University of Edinburgh EH4 2XU UK. euan.adie@ed.ac.uk|Medical Genetics Section, School of Molecular and Clinical Medicine, University of Edinburgh EH4 2XU UK. euan.adie@ed.ac.uk|Medical Genetics Section, School of Molecular and Clinical Medicine, University of Edinburgh EH4 2XU UK. euan.adie@ed.ac.uk|Medical Genetics Section, School of Molecular and Clinical Medicine, University of Edinburgh EH4 2XU UK. euan.adie@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 191, 'Chromosome Mapping|Genetic Markers|Genetic Predisposition to Disease|Genetic Testing|Humans|Internet|Online Systems|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk031', '16423925', 'SUSPECTS is a web-based server which combines annotation and sequence-based approaches to prioritize disease candidate genes in large regions of interest. It uses multiple lines of evidence to rank genes quickly and effectively while limiting the effect of annotation bias to significantly improve performance.'),
(72617, 2006, 'bio', 'Genetic test bed for feature selection', 'Choudhary, Ashish|Brun, Marcel|Hua, Jianping|Lowey, James|Suh, Ed|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States|United States|United States', 18, 'Algorithms|Breast Neoplasms|Computer Simulation|Data Collection|Databases, Genetic|Female|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl008', '16428263', 'Given a large set of potential features, such as the set of all gene-expression values from a microarray, it is necessary to find a small subset with which to classify. The task of finding an optimal feature set of a given size is inherently combinatoric because to assure optimality all feature sets of a given size must be checked. Thus, numerous suboptimal feature-selection algorithms have been proposed. There are strong impediments to evaluate feature-selection algorithms using real data when data are limited, a common situation in genetic classification. The difficulty is compound. First, there are no class-conditional distributions from which to draw data points, only a single small labeled sample. Second, there are no test data with which to estimate the feature-set errors, and one must depend on a training-data-based error estimator. Finally, there is no optimal feature set with which to compare the feature sets found by the algorithms.');
