INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70197, 2002, 'bio', 'Distribution patterns of over-represented k-mers in non-coding yeast DNA', 'Hampson, Steven|Kibler, Dennis|Baldi, Pierre', 'Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA. hampson@ics.uci.edu|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA. hampson@ics.uci.edu|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697-3425, USA. hampson@ics.uci.edu', 'United States|United States|United States', 36, 'Amino Acid Motifs|Base Sequence|Cluster Analysis|Conserved Sequence|Databases, Nucleic Acid|Gene Expression Regulation|Genome, Fungal|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Yeasts', 'Journal Article', '10.1093/bioinformatics/18.4.513', '12016049', 'Over-represented k-mers in genomic DNA regions are often of particular biological interest. For example, over-represented k-mers in co-regulated families of genes are associated with the DNA binding sites of transcription factors. To measure over-representation, we introduce a statistical background model based on single-mismatches, and apply it to the pooled 500 bp ORF Upstream Regions (USRs) of yeast. More importantly, we investigate the context and spatial distribution of over-represented k-mers in yeast USRs.'),
(70198, 2002, 'bio', 'Construction of optimal quality control for oligo arrays', 'Colbourn, Charles J|Ling, Alan C H|Tompa, Martin', 'Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-5406, USA.|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-5406, USA.|Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-5406, USA.', 'United States|United States|United States', 7, 'Algorithms|Computer Simulation|Equipment Design|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.4.529', '12016050', 'Oligo arrays are important experimental tools for the high throughput measurement of gene expression levels. During production of oligo arrays, it is important to identify any faulty manufacturing step.'),
(70199, 2002, 'bio', 'Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees', 'Xu, Ying|Olman, Victor|Xu, Dong', 'Protein Informatics Group, Life Sciences Division, Oak Ridge National Laboratory, MS 6480, Oak Ridge, TN 27831-6480, USA. xyn@ornl.gov|Protein Informatics Group, Life Sciences Division, Oak Ridge National Laboratory, MS 6480, Oak Ridge, TN 27831-6480, USA. xyn@ornl.gov|Protein Informatics Group, Life Sciences Division, Oak Ridge National Laboratory, MS 6480, Oak Ridge, TN 27831-6480, USA. xyn@ornl.gov', 'United States|United States|United States', 187, 'Algorithms|Arabidopsis|Cluster Analysis|Decision Trees|Fibroblasts|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Saccharomyces cerevisiae|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/18.4.536', '12016051', 'Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem.'),
(70200, 2002, 'bio', 'A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments', 'Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo, MMC 303, 420 Delaware Street SE, Minneapolis, MN 55455-0378, USA. weip@biostat.umn.edu', 'United States', 396, 'Acute Disease|Algorithms|Bayes Theorem|Gene Expression|Gene Expression Profiling|Humans|Leukemia, Myeloid|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Regression Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/18.4.546', '12016052', 'A common task in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Recently several statistical methods have been proposed to accomplish this goal when there are replicated samples under each condition. However, it may not be clear how these methods compare with each other. Our main goal here is to compare three methods, the t-test, a regression modeling approach (Thomas et al., Genome Res., 11, 1227-1236, 2001) and a mixture model approach (Pan et al., http://www.biostat.umn.edu/cgi-bin/rrs?print+2001,2001a,b) with particular attention to their different modeling assumptions.'),
(70201, 2002, 'bio', 'Binary analysis and optimization-based normalization of gene expression data', 'Shmulevich, Ilya|Zhang, Wei', 'Cancer Genomics Laboratory, Department of Pathology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, Department of Pathology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org', 'United States|United States', 134, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Glioma|Humans|Leiomyosarcoma|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Statistics as Topic|Statistics, Nonparametric', 'Journal Article', '10.1093/bioinformatics/18.4.555', '12016053', 'Most approaches to gene expression analysis use real-valued expression data, produced by high-throughput screening technologies, such as microarrays. Often, some measure of similarity must be computed in order to extract meaningful information from the observed data. The choice of this similarity measure frequently has a profound effect on the results of the analysis, yet no standards exist to guide the researcher.'),
(70202, 2002, 'bio', 'Application of Bayesian decomposition for analysing microarray data', 'Moloshok, T D|Klevecz, R R|Grant, J D|Manion, F J|Speier, W F|Ochs, M F', 'Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu|Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu|Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu|Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu|Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu|Bioinformatics Working Group, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. td_moloshok@fccc.edu', 'United States|United States|United States|United States|United States|United States', 55, 'Algorithms|Bayes Theorem|Cell Cycle|Databases, Genetic|Gene Expression Regulation|Genome, Fungal|Markov Chains|Models, Genetic|Models, Statistical|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Periodicity|Reproducibility of Results|Saccharomyces cerevisiae|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/18.4.566', '12016054', 'Microarray and gene chip technology provide high throughput tools for measuring gene expression levels in a variety of circumstances, including cellular response to drug treatment, cellular growth and development, tumorigenesis, among many other processes. In order to interpret the large data sets generated in experiments, data analysis techniques that consider biological knowledge during analysis will be extremely useful. We present here results showing the application of such a tool to expression data from yeast cell cycle experiments.'),
(70203, 2002, 'bio', 'Making sense of microarray data distributions', 'Hoyle, David C|Rattray, Magnus|Jupp, Ray|Brass, Andrew', 'School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK. david.c.hoyle@man.ac.uk|School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK. david.c.hoyle@man.ac.uk|School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK. david.c.hoyle@man.ac.uk|School of Biological Sciences, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK. david.c.hoyle@man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 104, 'Algorithms|Analysis of Variance|Animals|Chi-Square Distribution|Databases, Genetic|Genome|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|RNA, Messenger|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/18.4.576', '12016055', 'Typical analysis of microarray data has focused on spot by spot comparisons within a single organism. Less analysis has been done on the comparison of the entire distribution of spot intensities between experiments and between organisms.'),
(70204, 2002, 'bio', 'Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts', 'Greenbaum, Dov|Jansen, Ronald|Gerstein, Mark', 'Department Genetics, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.|Department Genetics, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.|Department Genetics, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.', 'United States|United States|United States', 125, 'Algorithms|Database Management Systems|Databases, Nucleic Acid|Databases, Protein|Gene Expression|Gene Expression Profiling|Genome|Information Storage and Retrieval|Open Reading Frames|Proteins|Reference Values|Sensitivity and Specificity|Transcription, Genetic|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.4.585', '12016056', 'Protein abundance is related to mRNA expression through many different cellular processes. Up to now, there have been conflicting results on how correlated the levels of these two quantities are. Given that expression and abundance data are significantly more complex and noisy than the underlying genomic sequence information, it is reasonable to simplify and average them in terms of broad proteomic categories and features (e.g. functions or secondary structures), for understanding their relationship. Furthermore, it will be essential to integrate, within a common framework, the results of many varied experiments by different investigators. This will allow one to survey the characteristics of highly expressed genes and proteins.'),
(70205, 2002, 'bio', 'TranScout: prediction of gene expression regulatory proteins from their sequences', 'Aguilar, Daniel|Oliva, Baldomero|Aviles, Francesc X|Querol, Enrique', 'Institut de Biotecnologia i de Biomedicina and Departament de Bioquimica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.|Institut de Biotecnologia i de Biomedicina and Departament de Bioquimica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.|Institut de Biotecnologia i de Biomedicina and Departament de Bioquimica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.|Institut de Biotecnologia i de Biomedicina and Departament de Bioquimica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.', 'Spain|Spain|Spain|Spain', 9, 'Algorithms|Amino Acid Motifs|Conserved Sequence|DNA-Binding Proteins|Database Management Systems|Databases, Nucleic Acid|Databases, Protein|Gene Expression Regulation|Genome, Bacterial|Information Storage and Retrieval|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/18.4.597', '12016057', 'The advent of genomics yields thousands of reading frames in search of function. Identification of conserved functional motifs in protein sequences can be helpful for function prediction.'),
(70206, 2002, 'bio', 'Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces', 'Mitaku, Shigeki|Hirokawa, Takatsugu|Tsuji, Toshiyuki', 'Tokyo University of Agriculture and Technology, Faculty of Technology, Department of Biotechnology, Nakacho, Koganei, Tokyo 184-8588, Japan. mitaku@cc.tuat.ac.jp|Tokyo University of Agriculture and Technology, Faculty of Technology, Department of Biotechnology, Nakacho, Koganei, Tokyo 184-8588, Japan. mitaku@cc.tuat.ac.jp|Tokyo University of Agriculture and Technology, Faculty of Technology, Department of Biotechnology, Nakacho, Koganei, Tokyo 184-8588, Japan. mitaku@cc.tuat.ac.jp', 'Japan|Japan|Japan', 137, 'Amino Acid Sequence|Amino Acids|Databases, Protein|Membrane Proteins|Models, Chemical|Molecular Sequence Data|Open Reading Frames|Protein Conformation|Protein Folding|Reproducibility of Results|Sensitivity and Specificity|Water', 'Journal Article', '10.1093/bioinformatics/18.4.608', '12016058', 'An amphiphilicity index of amino acid residues was developed for improving the method of transmembrane helix prediction.'),
(70207, 2002, 'bio', 'An HMM model for coiled-coil domains and a comparison with PSSM-based predictions', 'Delorenzi, Mauro|Speed, Terry', 'Genetics and Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria 3050, Australia. delorenzi@wehi.edu.au|Genetics and Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria 3050, Australia. delorenzi@wehi.edu.au', 'Australia|Australia', 309, 'Algorithms|Amino Acid Sequence|Databases, Protein|Genome|Information Storage and Retrieval|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/18.4.617', '12016059', 'Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a window-less Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile.'),
(70208, 2002, 'bio', 'The SWIB and the MDM2 domains are homologous and share a common fold', 'Bennett-Lovsey, Riccardo|Hart, Sarah E|Shirai, Hiroki|Mizuguchi, Kenji', 'Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, 80 Tennis Court Road, Cambridge CB2 1GA, UK.|Department of Biochemistry, University of Cambridge, Old Addenbrookes Site, 80 Tennis Court Road, Cambridge CB2 1GA, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Amino Acid Sequence|Bacterial Outer Membrane Proteins|Chlamydia|Chromosomal Proteins, Non-Histone|Databases, Protein|Genes, p53|Genome, Bacterial|Humans|Models, Molecular|Molecular Sequence Data|National Library of Medicine (U.S.)|Nuclear Proteins|Protein Conformation|Protein Folding|Proto-Oncogene Proteins|Proto-Oncogene Proteins c-mdm2|Sequence Alignment|Sequence Homology|Transcription Factors|Tumor Suppressor Protein p53|United States', 'Comparative Study', '10.1093/bioinformatics/18.4.626', '12016060', 'Using a novel algorithm for protein sequence/structure analysis, we propose a probable homology between the SWIB domain (the conserved domain of the 60 kda subunit of the SWIB complex involved in chromatin remodelling) and the p53-binding domain of the MDM2 oncoprotein. The homology suggests that the SWIB domain would adopt a structure similar to that of the MDM2 domain and that these two families of proteins may share a similar functional mechanism.'),
(70209, 2002, 'bio', 'CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences', 'Ponger, Loïc|Mouchiroud, Dominique', 'Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558 - UniversitéClaude Bernard, 43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. ponger@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558 - UniversitéClaude Bernard, 43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. ponger@biomserv.univ-lyon1.fr', 'France|France', 127, 'Animals|CpG Islands|Databases, Protein|Exons|Genome, Human|Humans|Information Storage and Retrieval|Introns|Mice|Molecular Sequence Data|Pattern Recognition, Automated|Sensitivity and Specificity|Sequence Analysis, Protein|Software|Transcription Initiation Site', 'Comparative Study', '10.1093/bioinformatics/18.4.631', '12016061', 'CpGProD is an application for identifying mammalian promoter regions associated with CpG islands in large genomic sequences. Although it is strictly dedicated to this particular promoter class corresponding to approximately 50% of the genes, CpGProD exhibits a higher sensitivity and specificity than other tools used for promoter prediction. Notably, CpGProD uses different parameters according to species (human, mouse) studied. Moreover, CpGProD predicts the promoter orientation on the DNA strand.'),
(70210, 2002, 'bio', 'TROLL--tandem repeat occurrence locator', 'Castelo, Adalberto T|Martins, Wellington|Gao, Guang R', 'Department of Electrical and Computer Engineering, University of Delaware, 140 Evans Hall, NW 19716, USA. castelo@capsl.udel.edu|Department of Electrical and Computer Engineering, University of Delaware, 140 Evans Hall, NW 19716, USA. castelo@capsl.udel.edu|Department of Electrical and Computer Engineering, University of Delaware, 140 Evans Hall, NW 19716, USA. castelo@capsl.udel.edu', 'United States|United States|United States', 150, 'Algorithms|Arabidopsis|Genome, Plant|Microsatellite Repeats|Pattern Recognition, Automated|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Tandem Repeat Sequences|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.4.634', '12016062', 'Tandem Repeat Occurrence Locator (TROLL), is a light-weight Simple Sequence Repeat (SSR) finder based on a slight modification of the Aho-Corasick algorithm. It is fast and only requires a standard Personal Computer (PC) to operate. We report running times of 127 s to find all SSRs of length 20 bp or more on the complete Arabdopsis genome--approx. 130 Mbases divided in five chromosomes--using a PC Athlon 650 MHz with 256 MB of RAM.'),
(70211, 2002, 'bio', 'Advanced pairwise structure alignments of proteins and analysis of conformational changes', 'Ochagavia, Maria Elena|Richelle, Jean|Wodak, Shoshana J', 'Center for Genetic Engineering and Biotechnology, Ave. 31 between Avenues 158 and 190, Cubanacán, City of Havana, 10600 Cuba.|Center for Genetic Engineering and Biotechnology, Ave. 31 between Avenues 158 and 190, Cubanacán, City of Havana, 10600 Cuba.|Center for Genetic Engineering and Biotechnology, Ave. 31 between Avenues 158 and 190, Cubanacán, City of Havana, 10600 Cuba.', 'Cuba|Cuba|Cuba', 6, 'Algorithms|Carrier Proteins|Cluster Analysis|Imaging, Three-Dimensional|Internet|Models, Molecular|Pattern Recognition, Automated|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/18.4.637', '12016063', 'Comparing the 3D structures of two proteins or analyzing the structural changes undergone by a protein upon ligand binding or when it crystallizes under different conditions, can be both tricky and tedious, especially when the two proteins are distantly related, or when the structural changes are complex. Readily accessible tools for performing these tasks automatically and reliably should therefore be welcome.'),
(70212, 2002, 'bio', 'DOLOP--database of bacterial lipoproteins', 'Madan Babu, M|Sankaran, K', 'Centre for Biotechnology, Anna University, Chennai-600025, India. madanm@mrc-lmb.cam.ac.uk|Centre for Biotechnology, Anna University, Chennai-600025, India. madanm@mrc-lmb.cam.ac.uk', 'India|India', 100, 'Algorithms|Bacteria|Database Management Systems|Databases, Protein|Genome, Bacterial|Information Storage and Retrieval|Lipoproteins|Protein Sorting Signals|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/18.4.641', '12016064', 'Bacterial lipoproteins and lipid modification are gaining importance owing to their essential nature, roles in pathogenesis and interesting commercial applications. We have created an exclusive knowledge base for bacterial lipoproteins by processing information from 510 entries to provide a list of 199 distinct lipoproteins with relevant links to molecular details. Features include functional classification, predictive algorithm for query sequences, primary sequence analysis and lists of predicted lipoproteins from 43 completed bacterial genomes along with interactive information exchange facility.'),
(70213, 2002, 'bio', 'Desktop prediction/analysis of mass spectrometric data in proteomic projects by using massXpert', 'Rusconi, Filippo|Belghazi, Maya', 'Institut Européen de Chimie et Biologie-Ecole polytechnique, 16, avenue Pey Berland, F-33607 Pessac Cedex, France. frusconi@lptc.u-bordeaux.fr|Institut Européen de Chimie et Biologie-Ecole polytechnique, 16, avenue Pey Berland, F-33607 Pessac Cedex, France. frusconi@lptc.u-bordeaux.fr', 'France|France', 18, 'Data Display|Mass Spectrometry|Protein Conformation|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.4.644', '12016065', 'With the ever-increasing demand for biopolymer mass spectrometry, specialized software is needed to both predict and process mass spectrometric data. With massXpert, we present a proteomics-oriented program mainly concerned with protein physico-chemistry and protein/peptide mass spectra simulations.'),
(70214, 2002, 'bio', 'PATH: a task for the inference of phylogenies', 'del Val, Coral|Ernst, Peter|Bräuning, Rüdiger|Glatting, Karl-Heinz|Suhai, Sandor', 'Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.|Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.|Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.|Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.|Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 4, 'Databases, Nucleic Acid|Databases, Protein|Evolution, Molecular|Internet|Models, Genetic|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/18.4.646', '12016066', 'Phylogenetic Analysis Task in Husar (PATH) is a task for the inference of phylogenies. It executes three phylogenetic methods and automatically chooses the evolutionary model for each set of data. The output of the tasks shows the consensus trees together with full results obtained from all executed methods.'),
(70215, 2002, 'bio', 'Information concept in biology', 'Nishikawa, Ken', 'Department of Molecular Biophysics, DKFZ, INF280, D-69120 Heidelberg, Germany.', 'Germany', 5, 'Cluster Analysis|Computational Biology|Information Systems|Sequence Analysis, Protein', 'Editorial', '10.1093/bioinformatics/18.5.649', '12050059', '-'),
(70216, 2002, 'bio', 'A heuristic managing errors for DNA sequencing', 'Bxc5x82azewicz, Jacek|Formanowicz, Piotr|Guinand, Frederic|Kasprzak, Marta', 'Institute of Computing Science, Poznaxc5x84 University of Technology, 60-965 Poznaxc5x84, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, 60-965 Poznaxc5x84, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, 60-965 Poznaxc5x84, Poland. blazewic@sol.put.poznan.pl|Institute of Computing Science, Poznaxc5x84 University of Technology, 60-965 Poznaxc5x84, Poland. blazewic@sol.put.poznan.pl', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Base Sequence|Computer Simulation|False Positive Reactions|Models, Genetic|Molecular Sequence Data|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Reproducibility of Results|Research Design|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.5.652', '12050060', 'A new heuristic algorithm for solving DNA sequencing by hybridization problem with positive and negative errors.'),
(70217, 2002, 'bio', 'PGAAS: a prokaryotic genome assembly assistant system', 'Yu, Zhou|Li, Tao|Zhao, Jindong|Luo, Jingchu', 'College of Life Sciences, The National Key Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.|College of Life Sciences, The National Key Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.|College of Life Sciences, The National Key Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.|College of Life Sciences, The National Key Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China.', 'China|China|China|China', 6, 'Algorithms|Contig Mapping|Cyanobacteria|Database Management Systems|Databases, Protein|Genome|National Library of Medicine (U.S.)|Prokaryotic Cells|Sensitivity and Specificity|Sequence Alignment|Software|United States', 'Journal Article', '10.1093/bioinformatics/18.5.661', '12050061', 'In order to accelerate the finishing phase of genome assembly, especially for the whole genome shotgun approach of prokaryotic species, we have developed a software package designated prokaryotic genome assembly assistant system (PGAAS). The approach upon which PGAAS is based is to confirm the order of contigs and fill gaps between contigs through peptide links obtained by searching each contig end with BLASTX against protein databases.'),
(70218, 2002, 'bio', 'The generation of long telomere overhangs in human cells: a model and its implication', 'Hao, Yu-hua|Tan, Zheng', 'College of Life Sciences, Wuhan University, Wuhan 430072, Peoples Republic of China.|College of Life Sciences, Wuhan University, Wuhan 430072, Peoples Republic of China.', 'China|China', 7, 'Chromosomes, Human|Computer Simulation|DNA Primers|DNA Replication|DNA, Single-Stranded|Humans|Models, Genetic|Models, Statistical|RNA|Repetitive Sequences, Nucleic Acid|Sensitivity and Specificity|Stochastic Processes|Telomere', 'Comparative Study', '10.1093/bioinformatics/18.5.666', '12050062', 'Linear chromosomes carry on both ends repetitive DNA sequences called telomere. In the conventional model of semi-conservative DNA replication, the 3-end of a linear DNA strand cannot be fully replicated, resulting in a single-stranded 3 overhang at one end of the double-stranded DNA product. In this model, the length of the overhang is expected to be about the size of an RNA primer (about nine nucleotides for human cells). However, it has been found that the telomere overhangs in human cells can be as long as several hundred nucleotides. At present, the opinion regarding how such long overhangs are produced is controversial.'),
(70219, 2002, 'bio', 'Detecting cryptically simple protein sequences using the SIMPLE algorithm', 'Albà, M Mar|Laskowski, Roman A|Hancock, John M', 'Grup de Recerca en Informatica Biomèdica, Universitat Pompeu Fabra, Dr. Aiguader 80, 08003 Barcelona, Spain.|Grup de Recerca en Informatica Biomèdica, Universitat Pompeu Fabra, Dr. Aiguader 80, 08003 Barcelona, Spain.|Grup de Recerca en Informatica Biomèdica, Universitat Pompeu Fabra, Dr. Aiguader 80, 08003 Barcelona, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Amino Acid Sequence|Databases, Protein|Genetic Variation|Internet|Minisatellite Repeats|Models, Genetic|Models, Statistical|Molecular Sequence Data|Proteins|Repetitive Sequences, Amino Acid|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/18.5.672', '12050063', 'Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function.'),
(70220, 2002, 'bio', 'Sequence complexity profiles of prokaryotic genomic sequences: a fast algorithm for calculating linguistic complexity', 'Troyanskaya, Olga G|Arbell, Ora|Koren, Yair|Landau, Gad M|Bolshoy, Alexander', 'Genome Diversity Center, Institute of Evolution, University of Haifa, Haifa, Israel.|Genome Diversity Center, Institute of Evolution, University of Haifa, Haifa, Israel.|Genome Diversity Center, Institute of Evolution, University of Haifa, Haifa, Israel.|Genome Diversity Center, Institute of Evolution, University of Haifa, Haifa, Israel.|Genome Diversity Center, Institute of Evolution, University of Haifa, Haifa, Israel.', 'Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Base Sequence|Computing Methodologies|Databases, Genetic|Decision Trees|Feasibility Studies|Genome|Haemophilus influenzae|Models, Genetic|Molecular Sequence Data|Prokaryotic Cells|Quality Control|Repetitive Sequences, Nucleic Acid|Sensitivity and Specificity|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1093/bioinformatics/18.5.679', '12050064', 'One of the major features of genomic DNA sequences, distinguishing them from texts in most spoken or artificial languages, is their high repetitiveness. Variation in the repetitiveness of genomic texts reflects the presence and density of different biologically important messages. Thus, deviation from an expected number of repeats in both directions indicates a possible presence of a biological signal. Linguistic complexity corresponds to repetitiveness of a genomic text, and potential regulatory sites may be discovered through construction of typical patterns of complexity distribution.'),
(70221, 2002, 'bio', 'Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions', 'Zavaljevski, Nela|Stevens, Fred J|Reifman, Jaques', 'Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA. nelaz@ra.anl.gov|Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA. nelaz@ra.anl.gov|Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, USA. nelaz@ra.anl.gov', 'United States|United States|United States', 86, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Protein|Humans|Immunoglobulin kappa-Chains|Models, Theoretical|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.5.689', '12050065', 'Data that characterize primary and tertiary structures of proteins are now accumulating at a rapid and accelerating rate and require automated computational tools to extract critical information relating amino acid changes with the spectrum of functionally attributes exhibited by a protein. We propose that immunoglobulin-type beta-domains, which are found in approximate 400 functionally distinct forms in humans alone, provide the immense genetic variation within limited conformational changes that might facilitate the development of new computational tools. As an initial step, we describe here an approach based on Support Vector Machine (SVM) technology to identify amino acid variations that contribute to the functional attribute of pathological self-assembly by some human antibody light chains produced during plasma cell diseases.'),
(70222, 2002, 'bio', 'Characterization of the folding degree of proteins', 'Estrada, Ernesto', 'Faculty of Pharmacy, Department of Organic Chemistry, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain. estrada66@yahoo.com', 'Spain', 171, 'Amino Acid Sequence|Databases, Protein|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Folding|Proteins|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.5.697', '12050066', 'The characterization of the folding degree of chains is central to the elucidation of structure--function relationships in proteins. Here we present a new index for characterizing the folding degree of a (protein) chain. This index shows a range of features that are desirable for the study of the relation between structure and function in proteins.'),
(70223, 2002, 'bio', 'Extraction of knowledge on protein-protein interaction by association rule discovery', 'Oyama, T|Kitano, K|Satou, K|Ito, T', 'INTEC Web and Genome Informatics Corporation, 3-23 Shimoshin-machi, Toyama 930-0804, Japan. oyama@isl.intec.co.jp|INTEC Web and Genome Informatics Corporation, 3-23 Shimoshin-machi, Toyama 930-0804, Japan. oyama@isl.intec.co.jp|INTEC Web and Genome Informatics Corporation, 3-23 Shimoshin-machi, Toyama 930-0804, Japan. oyama@isl.intec.co.jp|INTEC Web and Genome Informatics Corporation, 3-23 Shimoshin-machi, Toyama 930-0804, Japan. oyama@isl.intec.co.jp', 'Japan|Japan|Japan|Japan', 68, 'Algorithms|Artificial Intelligence|Databases, Protein|Evaluation Studies as Topic|Information Storage and Retrieval|Protein Binding|Proteins|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/18.5.705', '12050067', 'Protein-protein interactions are systematically examined using the yeast two-hybrid method. Consequently, a lot of protein-protein interaction data are currently being accumulated. Nevertheless, general information or knowledge on protein-protein interactions is poorly extracted from these data. Thus we have been trying to extract the knowledge from the protein-protein interaction data using data mining.'),
(70224, 2002, 'bio', 'Evaluation of computational metabolic-pathway predictions for Helicobacter pylori', 'Paley, Suzanne M|Karp, Peter D', 'Bioinformatics Research Group, SRI International, EK207, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. paley@ai.sri.com|Bioinformatics Research Group, SRI International, EK207, 333 Ravenswood Ave, Menlo Park, CA 94025, USA. paley@ai.sri.com', 'United States|United States', 69, 'Algorithms|Database Management Systems|Databases, Factual|Databases, Nucleic Acid|Enzymes|Evaluation Studies as Topic|False Positive Reactions|Genome, Bacterial|Helicobacter pylori|Software', 'Comparative Study', '10.1093/bioinformatics/18.5.715', '12050068', 'We seek to determine the accuracy of computational methods for predicting metabolic pathways in sequenced genomes, and to understand the contributions of both the prediction algorithms, and the reference pathway databases used by those algorithms, to the prediction accuracy.'),
(70225, 2002, 'bio', 'Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns', 'Li, Jinyan|Wong, Limsoon', 'Kent Ridge Digital Labs, 21 Heng Mui Keng Terrace, Singapore 119613. jinyan@krdl.org.sg|Kent Ridge Digital Labs, 21 Heng Mui Keng Terrace, Singapore 119613. jinyan@krdl.org.sg', 'Singapore|Singapore', 0, 'Algorithms|Colonic Neoplasms|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Therapy|Humans|Internet|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/18.5.725', '12050069', 'Gene groups that are significantly related to a disease can be detected by conducting a series of gene expression experiments. This work is aimed at discovering special types of gene groups that satisfy the following property. In each group, its member genes are found to be one-to-one contained in pre-determined intervals of gene expression level with a large frequency in one class of cells but are never found unanimously in these intervals in the other class of cells. We call these gene groups emerging patterns, to emphasize the patterns frequency changes between two classes of cells. We use effective discretization and gene selection methods to obtain the most discriminatory genes. We also use efficient algorithms to derive the patterns from these genes. According to our studies on the ALL/AML dataset and the colon tumor dataset, some patterns, which consist of one or more genes, can reach a high frequency of 90%, or even 100%. In other words, they nearly or fully dominate one class of cells, even though they rarely occur in the other class. The discovered patterns are used to classify new cells with a higher accuracy than other reported methods. Based on these patterns, we also conjecture the possibility of a personalized treatment plan which converts colon tumor cells into normal cells by modulating the expression levels of a few genes.'),
(70226, 2002, 'bio', 'Adaptive quality-based clustering of gene expression profiles', 'De Smet, Frank|Mathys, Janick|Marchal, Kathleen|Thijs, Gert|De Moor, Bart|Moreau, Yves', 'ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be|ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be|ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be|ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be|ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be|ESAT-SCD (SISTA), K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. frank.desmet@esat.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 140, 'Algorithms|Cluster Analysis|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Genome, Fungal|Mitosis|Models, Genetic|Models, Statistical|Saccharomyces cerevisiae|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.5.735', '12050070', 'Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters, they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks.'),
(70227, 2002, 'bio', 'Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering', 'Bozinov, Daniel|Rahnenführer, Jörg', 'Center for Human Molecular Genetics, University of Nebraska Medical Center, Omaha, NE 68198, USA. dbozinov@unmc.edu|Center for Human Molecular Genetics, University of Nebraska Medical Center, Omaha, NE 68198, USA. dbozinov@unmc.edu', 'United States|United States', 91, 'Algorithms|Cluster Analysis|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Image Processing, Computer-Assisted|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/18.5.747', '12050071', 'Microarray images challenge existing analytical methods in many ways given that gene spots are often comprised of characteristic imperfections. Irregular contours, donut shapes, artifacts, and low or heterogeneous expression impair corresponding values for red and green intensities as well as their ratio R/G. New approaches are needed to ensure accurate data extraction from these images.'),
(70228, 2002, 'bio', 'Xdigitise: visualization of hybridization experiments', 'Wruck, Wasco|Griffiths, Huw|Steinfath, Matthias|Lehrach, Hans|Radelof, Uwe|OBrien, John', 'Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de|Max-Planck-Institute for Molecular Genetics Berlin-Dahlem, Ihnestrasse 73, 14195 Berlin, Germany. wruck_w@molgen.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Algorithms|DNA Fingerprinting|Gene Expression Profiling|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Software', 'Comparative Study', '10.1093/bioinformatics/18.5.757', '12050072', 'Xdigitise is a software system for visualization of hybridization experiments giving the user facilities to analyze the corresponding images manually or automatically. Images of the high-density DNA arrays are displayed as well as the results of an external image analysis bundled with Xdigitise, e.g. the spot locations are marked and the duplicate correlations are shown by a color scale.'),
(70229, 2002, 'bio', 'Gene structure identification with MyGV using cDNA evidence and protein homologs to improve ab initio predictions', 'Zhu, Wei|Brendel, Volker', 'Department of Zoology and Genetics, Iowa State University, Ames, IA 50011, USA.|Department of Zoology and Genetics, Iowa State University, Ames, IA 50011, USA.', 'United States|United States', 3, 'Animals|Base Sequence|Data Display|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Drosophila|Genome|Information Storage and Retrieval|Internet|Molecular Sequence Data|Sequence Analysis, DNA|Sequence Homology|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.5.761', '12050073', 'MyGV is an application to visualize (potentially genome-scale) gene structure annotation and prediction. The output of any external gene prediction program can be easily converted to a generalized format for input into MyGV. The application displays all input simultaneously in graphical representation, with a toggle option for a text-based view. Zooming capabilities allow detailed comparisons for specific genome locations. The tool is particularly helpful for refinement of ab initio predicted gene structures by spliced alignment with cDNA or protein homologs.'),
(70230, 2002, 'bio', 'EMBL-Align: a new public nucleotide and amino acid multiple sequence alignment database', 'Lombard, V|Camon, E B|Parkinson, H E|Hingamp, P|Stoesser, G|Redaschi, N', 'EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk|EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk|EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk|EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk|EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk|EMBL Outstation The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. align@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 30, 'Amino Acid Sequence|Base Sequence|Database Management Systems|Databases, Nucleic Acid|Databases, Protein|Genome|Information Storage and Retrieval|Internet|Molecular Sequence Data|National Library of Medicine (U.S.)|Sequence Alignment|United States', 'Journal Article', '10.1093/bioinformatics/18.5.763', '12050074', 'The submission of multiple sequence alignment data to EMBL has grown 30-fold in the past 10 years, creating a problem of archiving them. The EBI has developed a new public database of multiple sequence alignments called EMBL-Align. It has a dedicated web-based submission tool, Webin-Align. Together they represent a comprehensive data management solution for alignment data. Webin-Align accepts all the common alignment formats and can display data in CLUSTALW format as well as a new standard EMBL-Align flat file format. The alignments are stored in the EMBL-Align database and can be queried from the EBI SRS (Sequence Retrieval System) server.'),
(70231, 2002, 'bio', 'BeoBLAST: distributed BLAST and PSI-BLAST on a Beowulf cluster', 'Grant, J D|Dunbrack, R L|Manion, F J|Ochs, M F', 'Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. jd_grant@fccc.edu|Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. jd_grant@fccc.edu|Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. jd_grant@fccc.edu|Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. jd_grant@fccc.edu', 'United States|United States|United States|United States', 36, 'Computer Communication Networks|Computing Methodologies|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|National Library of Medicine (U.S.)|Sequence Analysis|United States', 'Journal Article', '10.1093/bioinformatics/18.5.765', '12050075', 'BeoBLAST is an integrated software package that handles user requests and distributes BLAST and PSI-BLAST searches to nodes of a Beowulf cluster, thus providing a simple way to implement a scalable BLAST system on top of relatively inexpensive computer clusters. Additionally, BeoBLAST offers a number of novel search features through its web interface, including the ability to perform simultaneous searches of multiple databases with multiple queries, and the ability to start a search using the PSSM generated from a previous PSI-BLAST search on a different database. The underlying system can also handle automated querying for high throughput work.'),
(70232, 2002, 'bio', 'ENDscript: a workflow to display sequence and structure information', 'Gouet, Patrice|Courcelle, Emmanuel', 'Laboratoire de Bio-Cristallographie, IBCP-CNRS UMR 5086, 7 passage du Vercors, 69367 Lyon Cedex 07, France. p.gouet@ibcp.fr|Laboratoire de Bio-Cristallographie, IBCP-CNRS UMR 5086, 7 passage du Vercors, 69367 Lyon Cedex 07, France. p.gouet@ibcp.fr', 'France|France', 0, 'Amino Acid Sequence|Data Display|Database Management Systems|Databases, Factual|Databases, Protein|Information Storage and Retrieval|Internet|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/18.5.767', '12050076', 'ENDscript is a web server grouping popular programs such as BLAST, Multalin and DSSP. It uses as query the co-ordinates file of a protein in Protein Data Bank format and generates PostScript and png figures showing: residues conserved after a multiple alignment against homologous sequences, secondary structure elements, accessibility, hydropathy and intermolecular contacts. Thus, the user can relate quickly 1D, 2D and 3D information of a protein of known structure.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70233, 2002, 'bio', 'The Sulfinator: predicting tyrosine sulfation sites in protein sequences', 'Monigatti, Flavio|Gasteiger, Elisabeth|Bairoch, Amos|Jung, Eva', 'SWISS-PROT Group, Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|SWISS-PROT Group, Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|SWISS-PROT Group, Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.|SWISS-PROT Group, Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 206, 'Amino Acid Sequence|Animals|Caenorhabditis elegans|Database Management Systems|Databases, Protein|Drosophila|Humans|Molecular Sequence Data|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software|Sulfates|Tyrosine', 'Journal Article', '10.1093/bioinformatics/18.5.769', '12050077', 'Protein tyrosine sulfation is an important post-translational modification of proteins that go through the secretory pathway. No clear-cut acceptor motif can be defined that allows the prediction of tyrosine sulfation sites in polypeptide chains. The Sulfinator is a software tool that can be used to predict tyrosine sulfation sites in protein sequences with an overall accuracy of 98%. Four different Hidden Markov Models were constructed, each of them specialized to recognize sulfated tyrosine residues depending on their location within the sequence: near the N-terminus, near the C-terminus, in the center of a window with a size of at least 25 amino acids, as well as in windows containing several tyrosine residues.'),
(70234, 2002, 'bio', 'GP3: GenePix post-processing program for automated analysis of raw microarray data', 'Fielden, M R|Halgren, R G|Dere, E|Zacharewski, T R', 'Department of Biochemistry & Molecular Biology, National Food Safety & Toxicology Center, Institute for Environmental Toxicology, Michigan State University, East Lansing, MI 48824, USA.|Department of Biochemistry & Molecular Biology, National Food Safety & Toxicology Center, Institute for Environmental Toxicology, Michigan State University, East Lansing, MI 48824, USA.|Department of Biochemistry & Molecular Biology, National Food Safety & Toxicology Center, Institute for Environmental Toxicology, Michigan State University, East Lansing, MI 48824, USA.|Department of Biochemistry & Molecular Biology, National Food Safety & Toxicology Center, Institute for Environmental Toxicology, Michigan State University, East Lansing, MI 48824, USA.', 'United States|United States|United States|United States', 36, 'Algorithms|Image Processing, Computer-Assisted|Information Storage and Retrieval|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/18.5.771', '12050078', 'Here we describe an automated and customizable program to correct, filter and normalize raw microarray data captured using GenePix, a commonly used microarray image analysis application. Files can be processed individually or in batch mode for increased throughput. User defined inputs specify the stringency of data filtering and the method and conditions of normalization. The output includes gene summaries for replicate spots and descriptive statistics for each experiment. The source code (Perl) can also be adapted to handle raw data output from other image analysis applications.'),
(70235, 2002, 'bio', 'A bioinformatics tool to select sequences for microarray studies of mouse models of oncogenesis', 'Edgerton, Mary E|Taylor, Ronald|Powell, John I|Hunter, Lawrence|Simon, Richard|Liu, Edison T', 'Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.|Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Animals|Database Management Systems|Databases, Genetic|Genome|Humans|Information Storage and Retrieval|Internet|Mice|Models, Animal|Oligonucleotide Array Sequence Analysis|Oncogenes|Sequence Homology', 'Journal Article', '10.1093/bioinformatics/18.5.774', '12050079', 'One of the challenges to the effective utilization of cDNA microarray analysis in mouse models of oncogenesis is the choice of a critical set of probes that are informative for human disease. Given the thousands of genes with a potential role in human oncogenesis and the hundreds of thousands of mouse sequences available for use as probes, selection of an informative set of mouse probes can be an overwhelming task. We have developed a web based sequence mining tool using DataBase Independent (DBI) Perl to annotate publicly available sequences. The Mouse Oncochip Design Tool uses the Mouse Genome Database (MGD) developed and maintained by the Jackson Laboratories for mouse DNA sequences. There are over 380 000 sequences in their database. The output list has been ordered to present the genes more likely to be informative in a mouse model of human cancer using a candidate set of oncogenes to order the list. Mouse sequences that represent genes that are homologous with a member of a human oncogene set are listed first. In addition it provides a set of links for information on clone source gene function.'),
(70236, 2002, 'bio', 'Exon discovery by genomic sequence alignment', 'Morgenstern, Burkhard|Rinner, Oliver|Abdeddaïm, Saïd|Haase, Dirk|Mayer, Klaus F X|Dress, Andreas W M|Mewes, Hans-Werner', 'GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE|GSF Research Center, MIPS/Institute of Bioinformatics, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. burkhard@TechFak.Uni-Bielefeld.DE', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 38, 'Animals|Computational Biology|Evolution, Molecular|Exons|Genomics|Humans|Mice|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/18.6.777', '12075013', 'During evolution, functional regions in genomic sequences tend to be more highly conserved than randomly mutating junk DNA so local sequence similarity often indicates biological functionality. This fact can be used to identify functional elements in large eukaryotic DNA sequences by cross-species sequence comparison. In recent years, several gene-prediction methods have been proposed that work by comparing anonymous genomic sequences, for example from human and mouse. The main advantage of these methods is that they are based on simple and generally applicable measures of (local) sequence similarity, unlike standard gene-finding approaches they do not depend on species-specific training data or on the presence of cognate genes in data bases. As all comparative sequence-analysis methods, the new comparative gene-finding approaches critically rely on the quality of the underlying sequence alignments.'),
(70237, 2002, 'bio', 'A Bayesian network model for protein fold and remote homologue recognition', 'Raval, A|Ghahramani, Z|Wild, D L', 'Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA.|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA.|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA.', 'United States|United States|United States', 24, 'Amino Acid Sequence|Bayes Theorem|Computational Biology|Markov Chains|Models, Molecular|Probability Theory|Protein Folding|Protein Structure, Secondary', 'Comparative Study', '10.1093/bioinformatics/18.6.788', '12075014', 'The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification.'),
(70238, 2002, 'bio', 'Confidence measures for protein fold recognition', 'Sommer, Ingolf|Zien, Alexander|von Ohsen, Niklas|Zimmer, Ralf|Lengauer, Thomas', 'Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de|Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de|Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de|Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de|Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. ingolf.sommer@gmd.de', 'Germany|Germany|Germany|Germany|Germany', 14, 'Amino Acid Sequence|Computational Biology|Confidence Intervals|Protein Folding|Protein Structure, Tertiary|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/18.6.802', '12075015', 'We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures.'),
(70239, 2002, 'bio', 'Visualizing metabolic activity on a genome-wide scale', 'Luyf, A C M|de Gast, J|van Kampen, A H C', 'Bioinformatics Laboratory, Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.|Bioinformatics Laboratory, Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.|Bioinformatics Laboratory, Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 9, 'Computational Biology|Databases, Genetic|Enzymes|Gene Expression Profiling|Genome|Metabolism|Software', 'Journal Article', '10.1093/bioinformatics/18.6.813', '12075016', 'To enhance the exploration of gene expression data in a metabolic context, one requires an application that allows the integration of this data and which represents this data in a (genome-wide) metabolic map. The layout of this metabolic map must be highly flexible to enable discoveries of biological phenomena. Moreover, it must allow the simultaneous representation of additional information about genes and enzymes. Since the layout and properties of existing maps did not fulfill our requirements, we developed a new way of representing gene expression data in metabolic charts.'),
(70240, 2002, 'bio', 'NETASA: neural network based prediction of solvent accessibility', 'Ahmad, Shandar|Gromiha, M Michael', 'Institute of Multimedia and Software, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia. shandar@jamia.net|Institute of Multimedia and Software, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia. shandar@jamia.net', 'Malaysia|Malaysia', 89, 'Algorithms|Amino Acid Sequence|Computational Biology|Humans|Molecular Sequence Data|Neural Networks (Computer)|Protein Structure, Tertiary|Proteins|Software|Solvents|Thioredoxins', 'Comparative Study', '10.1093/bioinformatics/18.6.819', '12075017', 'Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction.'),
(70241, 2002, 'bio', 'Modelling biological processes using workflow and Petri Net models', 'Peleg, Mor|Yeh, Iwei|Altman, Russ B', 'Stanford Medical Informatics, Stanford University, CA 94305, USA. peleg@smi.stanford.edu|Stanford Medical Informatics, Stanford University, CA 94305, USA. peleg@smi.stanford.edu|Stanford Medical Informatics, Stanford University, CA 94305, USA. peleg@smi.stanford.edu', 'United States|United States|United States', 108, 'Animals|Computational Biology|Computer Simulation|Erythrocytes|Humans|Models, Biological|Plasmodium|Software Design', 'Journal Article', '10.1093/bioinformatics/18.6.825', '12075018', 'Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning.'),
(70242, 2002, 'bio', 'Adaptive algorithm of automated annotation', 'Leontovich, A M|Brodsky, L I|Drachev, V A|Nikolaev, V K', 'Belozersky Institute of Physico-Chemical Biology, Moscow State University, Russia.|Belozersky Institute of Physico-Chemical Biology, Moscow State University, Russia.|Belozersky Institute of Physico-Chemical Biology, Moscow State University, Russia.|Belozersky Institute of Physico-Chemical Biology, Moscow State University, Russia.', '-|-|-|-', 8, 'Algorithms|Computational Biology|Computer Simulation|Databases, Protein|Software', 'Journal Article', '10.1093/bioinformatics/18.6.838', '12075019', 'It is common knowledge that the avalanche of data arriving from the sequencing projects cannot be annotated either experimentally or manually by experts. The need for a reliable and convenient tool for automated sequence annotation is broadly recognized.'),
(70243, 2002, 'bio', 'A novel approach to local reliability of sequence alignments', 'Schlosshauer, Maximilian|Ohlsson, Mattias', 'Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, Sweden.|Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, Sweden.', 'Sweden|Sweden', 22, 'Algorithms|Amino Acid Sequence|Computational Biology|Cytochrome c Group|Cytochromes c2|Databases, Protein|Fuzzy Logic|Molecular Sequence Data|Probability|Reproducibility of Results|Sequence Alignment', 'Evaluation Studies', '10.1093/bioinformatics/18.6.847', '12075020', 'The pairwise alignment of biological sequences obtained from an algorithm will in general contain both correct and incorrect parts. Hence, to allow for a valid interpretation of the alignment, the local trustworthiness of the alignment has to be quantified.'),
(70244, 2002, 'bio', 'The chemical organization of signaling interactions', 'Bhalla, Upinder S', 'Neurobiology, National Centre for Biological Sciences, GKVK Campus, Bangalore 560065, India. bhalla@ncbs.res.in', 'India', 16, 'Computational Biology|Computer Simulation|Databases, Factual|Enzymes|Kinetics|Models, Biological|Models, Chemical|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/18.6.855', '12075021', 'Cellular chemical signaling pathways form complex networks that are beginning to be studied at the level of chemical kinetics and databases of reactions. Chemical reaction details are traditionally represented as lists of reactions and rates. This does not map readily to the block diagram representation familiar to biologists, and obscures the functional organization of signaling networks. This study examines motifs in signaling chemistry and reports common features that may help to formalize such a mapping between pathway block diagrams and the chemistry. The same motifs may facilitate data representation and provide functional abstraction of the chemistry.'),
(70245, 2002, 'bio', 'Hybrid alignment: high-performance with universal statistics', 'Yu, Yi-Kuo|Bundschuh, Ralf|Hwa, Terence', 'Department of Physics, Florida Atlantic University, 777 Glades Road, Boca Raton 33431-0991, USA. yyu@fau.edu|Department of Physics, Florida Atlantic University, 777 Glades Road, Boca Raton 33431-0991, USA. yyu@fau.edu|Department of Physics, Florida Atlantic University, 777 Glades Road, Boca Raton 33431-0991, USA. yyu@fau.edu', 'United States|United States|United States', 17, 'Algorithms|Computational Biology|Computer Simulation|Databases, Protein|ROC Curve|Sequence Alignment', 'Evaluation Studies', '10.1093/bioinformatics/18.6.864', '12075022', 'The score statistics of a recently introduced hybrid alignment algorithm is studied in detail numerically. An extensive survey across the 2216 models of protein domains contained in the Pfam v5.4 database (Bateman et al., Nucleic Acids Res., 28, 263-266, 2000) verifies the theoretical predictions: For the position-specific scoring functions used in the Pfam models, the score statistics of hybrid alignment obey the Gumbel distribution, with the key Gumbel parameter lambda taking on the asymptotic value 1 universally for all models. Thus, the use of hybrid alignment eliminates the time-consuming computer simulations normally needed to assign p-values to alignment scores, freeing the users to experiment with different scoring parameters and functions. The performance of the hybrid algorithm in detecting sequence homology is also studied. For protein sequences from the SCOP database (Murzin et al., J. Mol. Biol., 247, 536-540, 1995) using uniform scoring functions, the performance is found to be comparable to the best of the existing methods. Preliminary results using the PfamA database suggest that the hybrid algorithm achieves similar performance as existing methods for position-specific scoring systems as well. Hybrid alignment is thereby established as a high performance alignment algorithm with well-characterized, universal statistics.'),
(70246, 2002, 'bio', 'SST: an algorithm for finding near-exact sequence matches in time proportional to the logarithm of the database size', 'Giladi, Eldar|Walker, Michael G|Wang, James Z|Volkmuth, Wayne', 'Incyte Pharmaceuticals, 3174 Porter Drive, Palo Alto, CA 94304, USA. egiladi@incyte.com|Incyte Pharmaceuticals, 3174 Porter Drive, Palo Alto, CA 94304, USA. egiladi@incyte.com|Incyte Pharmaceuticals, 3174 Porter Drive, Palo Alto, CA 94304, USA. egiladi@incyte.com|Incyte Pharmaceuticals, 3174 Porter Drive, Palo Alto, CA 94304, USA. egiladi@incyte.com', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Nucleic Acid|Sequence Alignment', 'Comparative Study', '10.1093/bioinformatics/18.6.873', '12075023', 'Searches for near exact sequence matches are performed frequently in large-scale sequencing projects and in comparative genomics. The time and cost of performing these large-scale sequence-similarity searches is prohibitive using even the fastest of the extant algorithms. Faster algorithms are desired.'),
(70247, 2002, 'bio', 'Global analysis of tandem aromatic octapeptide repeats: the significance of the aromatic-glycine motif', 'Gazit, Ehud', 'Department of Molecular Microbiology and Biotechnology, Tel-Aviv University, Israel. ehudg@post.tau.ac.il', 'Israel', 55, 'Amino Acid Motifs|Amino Acid Sequence|Amino Acids, Aromatic|Chemical Phenomena|Chemistry, Physical|Computational Biology|Glycine|Oligopeptides|Prions|Repetitive Sequences, Amino Acid', 'Journal Article', '10.1093/bioinformatics/18.6.880', '12075024', 'Tandem peptide repeats play a key role in self-assembly and aggregation processes. A notable example is the occurrence of tandem peptide repeats in prionic proteins and their role in the aggregation process that leads to the formation of the prion. One of the structural characteristics that is evident from the comparison of mammalian and yeast prion proteins is the presence of aromatic residues in their tandem repeats. These residues are accompanied by glycine residues before and/or after the aromatic amino acid. Such aromatic-glycine conjugates are also present in the tandem repeats of the large family of the bacterial ice nucleation proteins. To study the significance of such aromatic-glycine occurrences, a global analysis of all the aromatic octapeptide repeats in the Swiss-Prot and TrEMBL databases was conducted. The search pattern was formulated to compare the number of conjugates of each of the 20 natural amino acids before or after the different aromatic residues.'),
(70248, 2002, 'bio', 'AiO, combining DNA/protein programs and oligo-management', 'Karreman, Christiaan', 'Department of Oncological Chemistry, Building 23.12.U1, University of Düsseldorf, Universitätstrasse 1, Germany. karreman@onkochemie.uni-duesseldorf.de', 'Germany', 0, 'Computational Biology|DNA|Database Management Systems|Databases, Nucleic Acid|Databases, Protein|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/18.6.884', '12075025', 'AiO (All in One) is a program for Windows, that combines typical DNA/protein features such as plasmid map drawing, finding of ORFs, translate, backtranslate and high quality printing with a number of databases. These databases allow the management of oligonucleotides, oligonucleotide-manufacturers, restriction enzymes, structural DNA and program users in a multi-user/multi-group environment.'),
(70249, 2002, 'bio', 'Synonymous-non-synonymous mutation rates between sequences containing ambiguous nucleotides (Syn-SCAN)', 'Gonzales, Matthew J|Dugan, Jonathan M|Shafer, Robert W', 'Division of Infectious Diseases, Stanford University Medical Center, 300 Pasteur Drive, Room S-156, CA 94305, USA.|Division of Infectious Diseases, Stanford University Medical Center, 300 Pasteur Drive, Room S-156, CA 94305, USA.|Division of Infectious Diseases, Stanford University Medical Center, 300 Pasteur Drive, Room S-156, CA 94305, USA.', 'United States|United States|United States', 39, 'Alleles|Codon|Computational Biology|HIV-1|Models, Genetic|Mutation|Software', 'Journal Article', '10.1093/bioinformatics/18.6.886', '12075026', 'Direct PCR sequencing on genetic material containing allelic mixtures results in sequences containing ambiguous nucleotides. Because codons exhibiting allelic mixtures present evidence of evolutionary pressure, it is important to include this information in the assessment of codon synonymy. We developed a program, Synonymous-Nonsynonymous Mutation Rates between Sequences Containing Ambiguous Nucleotides (Syn-SCAN), that calculates synonymous and non-synonymous substitution rates using a model that includes allelic mixtures.'),
(70250, 2002, 'bio', 'Arrayplot for visualization and normalization of cDNA microarray data', 'Marc, Philippe|Jacq, Claude', 'Laboratoire de Génétique Moléculaire (UMR CNRS 8541) Ecole Normale Supérieure, 46 rue dUlm, 75005, Paris, France. pmarc@biologie.ens.fr|Laboratoire de Génétique Moléculaire (UMR CNRS 8541) Ecole Normale Supérieure, 46 rue dUlm, 75005, Paris, France. pmarc@biologie.ens.fr', 'France|France', 0, 'Computational Biology|DNA, Fungal|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1093/bioinformatics/18.6.888', '12075027', 'Arrayplot is an application which allows filtering, visualization and normalization of raw cDNA microarray data.'),
(70251, 2002, 'bio', 'MOUSE (Mitochondrial and Other Useful SEquences) a compilation of population genetic markers', 'Burckhardt, Florian', 'GSF-National Research Center for Environment and Health, Institute of Epidemiology, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. florian.burckhardt@gsf.de', 'Germany', 2, 'Animals|Chromosomes, Human, X|Computational Biology|DNA, Mitochondrial|Databases, Nucleic Acid|Genetic Markers|Genetics, Population|Hominidae|Humans|Species Specificity|X Chromosome', 'Comparative Study', '10.1093/bioinformatics/18.6.890', '12075028', 'Mitochondrial and Other Useful SEquences (MOUSE) is an integrated and comprehensive compilation of mtDNA from hypervariable regions I and II and of the low recombining nuclear loci Xq13.3 from about 11 200 humans and great apes, whose geographic and if applicable, linguistic classification is stored with their aligned sequences and publication details. The goal is to provide population geneticists and genetic epidemiologists with a comprehensive and user friendly repository of sequences and population information that is usually dispersed in a variety of other sources.'),
(70252, 2002, 'bio', 'BAOBAB: a Java editor for large phylogenetic trees', 'Dutheil, J|Galtier, N', 'CNRS UMR 5000 - Génome, Populations, Interactions, Université Montpellier 2 - CC 63, Place E. Bataillon, France. julien.dutheil@ens-lyon.fr|CNRS UMR 5000 - Génome, Populations, Interactions, Université Montpellier 2 - CC 63, Place E. Bataillon, France. julien.dutheil@ens-lyon.fr', 'France|France', 8, 'Computational Biology|Phylogeny|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.6.892', '12075029', 'BAOBAB is a Java user interface dedicated to viewing and editing large phylogenetic trees. Original features include: (i) a colour-mediated overview of magnified subtrees, (ii) copy/cut/paste of (sub)trees within or between windows, (iii) compressing/ uncompressing subtrees, and (iv) managing sequence files together with tree files.'),
(70253, 2002, 'bio', 'DMLE+: Bayesian linkage disequilibrium gene mapping', 'Reeve, Jeff P|Rannala, Bruce', 'Department of Medical Genetics, University of Alberta, 8-39 Medical Sciences Building, Edmonton, Canada.|Department of Medical Genetics, University of Alberta, 8-39 Medical Sciences Building, Edmonton, Canada.', 'Canada|Canada', 92, 'Bayes Theorem|Chromosome Mapping|Computational Biology|Genetic Markers|Genome, Human|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Mutation|Probability|Software', 'Journal Article', '10.1093/bioinformatics/18.6.894', '12075030', 'The program DMLE+ allows Bayesian inference of the location of a gene carrying a mutation influencing a discrete trait (such as a disease) and/or other parameters of interest (such as mutation age) based on the observed linkage disequilibrium at multiple genetic markers. DMLE+ uses either individual marker genotypes, or haplotypes, integrates over uncertain population allele frequencies, and can incorporate prior information about gene location from an annotated human genome sequence.'),
(70254, 2002, 'bio', 'Bioinformatics in structural genomics', 'Rost, Burkhard|Honig, Barry|Valencia, Alfonso', 'Department of Medical Genetics, University of Alberta, 8-39 Medical Sciences Building, Edmonton, Canada.|Department of Medical Genetics, University of Alberta, 8-39 Medical Sciences Building, Edmonton, Canada.|Department of Medical Genetics, University of Alberta, 8-39 Medical Sciences Building, Edmonton, Canada.', 'Canada|Canada|Canada', 4, 'Computational Biology|Databases, Genetic|Genomics|Humans|Proteins|Sequence Analysis', 'Editorial', '10.1093/bioinformatics/18.7.897', '12117786', '-'),
(70255, 2002, 'bio', 'Selecting targets for structural determination by navigating in a graph of protein families', 'Portugaly, Elon|Kifer, Ilona|Linial, Michal', 'Institute of Computer Sciences Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel.|Institute of Computer Sciences Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel.|Institute of Computer Sciences Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University, Jerusalem 91904, Israel.', 'Israel|Israel|Israel', 11, 'Algorithms|Cluster Analysis|Database Management Systems|Databases, Protein|Genome|Information Storage and Retrieval|Internet|Models, Chemical|Models, Molecular|Models, Statistical|Peptide Mapping|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.7.899', '12117787', 'A major goal in structural genomics is to enrich the catalogue of proteins whose 3D structures are known. In an attempt to address this problem we mapped over 10 000 proteins with solved structures onto a graph of all Swissprot protein sequences (release 36, approximately 73 000 proteins) provided by ProtoMap, with the goal of sorting proteins according to their likelihood of belonging to new superfamilies. We hypothesized that proteins within neighbouring clusters tend to share common structural superfamilies or folds. If true, the likelihood of finding new superfamilies increases in clusters that are distal from other solved structures within the graph.'),
(70256, 2002, 'bio', 'Clustering of proximal sequence space for the identification of protein families', 'Abascal, Federico|Valencia, Alfonso', 'Protein Design Group, National Centre for Biotechnology, CNB-CSIC, Cantoblanco, Madrid E-28049, Spain.|Protein Design Group, National Centre for Biotechnology, CNB-CSIC, Cantoblanco, Madrid E-28049, Spain.', 'Spain|Spain', 48, 'Algorithms|Cluster Analysis|Database Management Systems|Databases, Protein|Genes, ras|Genome|Information Storage and Retrieval|Internet|Models, Chemical|Models, Genetic|Models, Statistical|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.7.908', '12117788', 'The study of sequence space, and the deciphering of the structure of protein families and subfamilies, has up to now been required for work in comparative genomics and for the prediction of protein function. With the emergence of structural proteomics projects, it is becoming increasingly important to be able to select protein targets for structural studies that will appropriately cover the space of protein sequences, functions and genomic distribution. These problems are the motivation for the development of methods for clustering protein sequences and building families of potentially orthologous sequences, such as those proposed here.'),
(70257, 2002, 'bio', 'Target space for structural genomics revisited', 'Liu, Jinfeng|Rost, Burkhard', 'Department of Pharmacology, Columbia University, 630 West 168th Street, New York, NY 10032, USA.|Department of Pharmacology, Columbia University, 630 West 168th Street, New York, NY 10032, USA.', 'United States|United States', 63, 'Algorithms|Cluster Analysis|Database Management Systems|Databases, Protein|Eukaryotic Cells|Genome|Humans|Information Storage and Retrieval|Internet|Models, Chemical|Models, Genetic|Models, Molecular|Models, Statistical|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.7.922', '12117789', 'Structural genomics eventually aims at determining structures for all proteins. However, in the beginning experimentalists are likely to focus on globular proteins to achieve a rapid basic coverage of protein sequence space. How many proteins will structural genomics have to target? How many proteins will be excluded since we already have structural information for these or since they are not globular? We have to answer these questions in the context of our target selection for the North-East Structural Genomics Consortium (NESG).'),
(70258, 2002, 'bio', 'About the use of protein models', 'Peitsch, Manuel C', 'Novartis Pharma AG, WKL-490.4.04, 4002 Basel, Switzerland Swiss Institute of Bioinformatics, Chemin des Boveresses 155, 1066 Epalinges, Switzerland. manuel.peitsch@pharma.novartis.com', 'Switzerland', 29, 'Databases, Protein|Information Storage and Retrieval|Internet|Membrane Proteins|Models, Chemical|Models, Genetic|Models, Molecular|Models, Statistical|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/18.7.934', '12117790', 'Protein models can be of great assistance in functional genomics, as they provide the structural insights often necessary to understand protein function. Although comparative modelling is far from yielding perfect structures, this is still the most reliable method and the quality of the predictions is now well understood. Models can be classified according to their correctness and accuracy, which will impact their applicability and usefulness in functional genomics and a variety of situations.'),
(70259, 2002, 'bio', 'Ligand-induced changes in the binding sites of proteins', 'Fradera, Xavier|De La Cruz, Xavier|Silva, Carlos H T P|Gelpí, Jose Luis|Luque, F J|Orozco, Modesto', 'Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.|Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.|Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.|Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.|Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.|Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Avgda Diagonal s/n, Barcelona 08028, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 38, 'Amino Acid Sequence|Binding Sites|Cluster Analysis|Databases, Protein|Ligands|Macromolecular Substances|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Binding|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Statistics, Nonparametric|Structure-Activity Relationship', 'Comparative Study', '10.1093/bioinformatics/18.7.939', '12117791', 'Classical molecular interaction potentials, in conjunction with other theoretical techniques, are used to analyze the dependence of the binding sites of representative proteins on the bound ligand. It is found that the ligand bound introduces in general small structural perturbations at the binding site of the protein. However, such small structural changes can lead to important alterations in the recognition pattern of the protein. The impact of these findings in docking procedures is discussed.'),
(70260, 2002, 'bio', 'A bioinformatic strategy to rapidly characterize cDNA libraries', 'Ostermeier, G Charles|Dix, David J|Krawetz, Stephen A', 'Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, and Institute for Scientific Computing, Wayne State University, USA.|Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, and Institute for Scientific Computing, Wayne State University, USA.|Department of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, and Institute for Scientific Computing, Wayne State University, USA.', 'United States|United States|United States', 11, 'Adolescent|Adult|Cluster Analysis|Databases, Nucleic Acid|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Gene Library|Humans|Male|Middle Aged|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|RNA|Testis', 'Comparative Study', '10.1093/bioinformatics/18.7.949', '12117792', 'Complementary DNA libraries can define the genetic constituents of specific cells and/or tissues. Their sequencing will illuminate the transcriptome but it is a monumental task requiring considerable resources.'),
(70261, 2002, 'bio', 'Quantitative assessment of filter-based cDNA microarrays: gene expression profiles of human T-lymphoma cell lines', 'Dodson, J M|Charles, P T|Stenger, D A|Pancrazio, J J', 'Center for Bio/Molecular Science & Engineering, Code 6900, Naval Research Laboratory, Washington, DC 20375, USA.|Center for Bio/Molecular Science & Engineering, Code 6900, Naval Research Laboratory, Washington, DC 20375, USA.|Center for Bio/Molecular Science & Engineering, Code 6900, Naval Research Laboratory, Washington, DC 20375, USA.|Center for Bio/Molecular Science & Engineering, Code 6900, Naval Research Laboratory, Washington, DC 20375, USA.', 'United States|United States|United States|United States', 7, 'False Positive Reactions|Gene Expression Profiling|Humans|Lymphoma, T-Cell|Models, Statistical|Oligonucleotide Array Sequence Analysis|Phosphorus Radioisotopes|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Tumor Cells, Cultured', 'Comparative Study', '10.1093/bioinformatics/18.7.953', '12117793', 'While the use of cDNA microarrays for functional genomic analysis has become commonplace, relatively little attention has been placed on false positives, i.e. the likelihood that a change in measured radioactive or fluorescence intensity may reflect a change in gene expression when, in fact, there is none. Since cDNA arrays are being increasingly used to rapidly distinguish biomarkers for disease detection and subsequent assay development (Wellman et al., Blood, 96, 398-404, 2000), the impact of false positives can be significant. For the use of this technology, it is necessary to develop quantitative criteria for reduction of false positives with radioactively-labeled cDNA arrays.'),
(70262, 2002, 'bio', 'Deriving quantitative conclusions from microarray expression data', 'Olshen, Adam B|Jain, Ajay N', 'Comprehensive Cancer Center, Cancer Research Institute, and Department of Laboratory Medicine, University of California, San Francisco, CA 94143-0128, USA.|Comprehensive Cancer Center, Cancer Research Institute, and Department of Laboratory Medicine, University of California, San Francisco, CA 94143-0128, USA.', 'United States|United States', 48, 'Breast Neoplasms|Cluster Analysis|Database Management Systems|Databases, Nucleic Acid|Gene Expression|Gene Expression Profiling|Humans|Information Storage and Retrieval|Internet|Leukemia|Lymphoma|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.7.961', '12117794', 'The last few years have seen the development of DNA microarray technology that allows simultaneous measurement of the expression levels of thousands of genes. While many methods have been developed to analyze such data, most have been visualization-based. Methods that yield quantitative conclusions have been diverse and complex.'),
(70263, 2002, 'bio', 'Comparative genomics study of inverted repeats in bacteria', 'Lillo, Fabrizio|Basile, Salvatore|Mantegna, Rosario N', 'Istituto Nazionale per la Fisica della Materia, Unità di Palermo, Viale delle Scienze, I-90128, Palermo, Italy.|Istituto Nazionale per la Fisica della Materia, Unità di Palermo, Viale delle Scienze, I-90128, Palermo, Italy.|Istituto Nazionale per la Fisica della Materia, Unità di Palermo, Viale delle Scienze, I-90128, Palermo, Italy.', 'Italy|Italy|Italy', 26, 'Base Sequence|DNA, Bacterial|Databases, Nucleic Acid|Genome, Bacterial|Models, Genetic|Models, Statistical|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.7.971', '12117795', 'Comparative genomics provides a powerful way to investigate regularities and differences observed at DNA level across species. Here we study the number and location of inverted repeats occurring in complete genomes of bacteria. Inverted repeats are compatible with the formation of hairpin structures in the messenger RNA. Some of these structures are known to be rho-independent intrinsic terminators.'),
(70264, 2002, 'bio', 'CX, an algorithm that identifies protruding atoms in proteins', 'Pintar, Alessandro|Carugo, Oliviero|Pongor, Sándor', 'Protein Structure and Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), AREA Science Park, Padriciano 99, 34012 Trieste, Italy. pintar@icgeb.trieste.it|Protein Structure and Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), AREA Science Park, Padriciano 99, 34012 Trieste, Italy. pintar@icgeb.trieste.it|Protein Structure and Bioinformatics Group, International Center for Genetic Engineering and Biotechnology (ICGEB), AREA Science Park, Padriciano 99, 34012 Trieste, Italy. pintar@icgeb.trieste.it', 'Italy|Italy|Italy', 65, 'Algorithms|Computer Graphics|Databases, Protein|Models, Chemical|Models, Molecular|Molecular Structure|Programming Languages|Protein Conformation|Protein Interaction Mapping|Proteins|Sensitivity and Specificity|Serpins|Software|Trypsin', 'Comparative Study', '10.1093/bioinformatics/18.7.980', '12117796', 'A simple and fast algorithm is described that calculates a measure of protrusion (cx) for atoms in protein structures, directly useable with the common molecular graphics programs.'),
(70265, 2002, 'bio', 'Calculations of protein volumes: sensitivity analysis and parameter database', 'Tsai, Jerry|Gerstein, Mark', 'Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843-2128, USA. JerryTsai@TAMU.edu|Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843-2128, USA. JerryTsai@TAMU.edu', 'United States|United States', 0, 'Algorithms|Databases, Protein|Internet|Models, Molecular|Molecular Structure|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Software|Structure-Activity Relationship', 'Comparative Study', '10.1093/bioinformatics/18.7.985', '12117797', 'The precise sizes of protein atoms in terms of occupied packing volume are of great importance. We have previously presented standard volumes for protein residues based on calculations with Voronoi-like polyhedra. To understand the applicability and limitations of our set, we investigated, in detail, the sensitivity of the volume calculations to a number of factors: (i) the van der Waals radii set, (ii) the criteria for including buried atoms in the calculations or atom selection, (iii) the method of positioning the dividing plane in polyhedra construction, and (iv) the set of structures used in the averaging.'),
(70266, 2002, 'bio', 'PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways', 'Demir, E|Babur, O|Dogrusoz, U|Gursoy, A|Nisanci, G|Cetin-Atalay, R|Ozturk, M', 'Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.|Department of Molecular Biology and Genetics Computer Engineering Department Center for Bioinformatics, Bilkent University, Ankara 06533, Turkey.', 'Turkey|Turkey|Turkey|Turkey|Turkey|Turkey|Turkey', 0, 'Cell Physiological Phenomena|Computer Graphics|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software Design|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.7.996', '12117798', 'Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively.'),
(70267, 2002, 'bio', 'Determining a unique defining DNA sequence for yeast species using hashing techniques', 'Wesselink, Jan-Jaap|De La Iglesia, Beatriz|James, Stephen A|Dicks, Jo L|Roberts, Ian N|Rayward-Smith, Vic J', 'School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk|School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk|School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk|School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk|School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk|School of Information Systems, University of East Anglia, Norwich NR4 7TJ, UK. jjw@sys.uea.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|DNA, Fungal|Databases, Nucleic Acid|Genes, rRNA|Models, Genetic|Models, Statistical|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Sequence Tagged Sites|Software|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.7.1004', '12117799', 'Yeasts are often still identified with physiological growth tests, which are both time consuming and unsuitable for detection of a mixture of organisms. Hence, there is a need for molecular methods to identify yeast species.'),
(70268, 2002, 'bio', 'TangleSolve: topological analysis of site-specific recombination', 'Saka, Yuki|Vázquez, Mariel', 'Mathematics Department, U.C. Berkeley, Berkeley, CA 94720-3840, USA.|Mathematics Department, U.C. Berkeley, Berkeley, CA 94720-3840, USA.', 'United States|United States', 0, 'Algorithms|Binding Sites|Computer Graphics|DNA, Recombinant|Internet|Models, Chemical|Models, Genetic|Recombination, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.7.1011', '12117800', 'TangleSolve is a program for analysing site-specific recombination using the tangle model. The program offers an easy-to-use graphical user interface and a visualization tool. Biologists working in topological enzymology can use this program to compute and visualize site-specific recombination mechanisms that accommodate their experimental data. TangleSolve can also prove useful as a teaching aid for mathematical biology and computational molecular biology courses.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70269, 2002, 'bio', 'CaGE: cardiac gene expression knowledgebase', 'Bober, Michael|Wiehe, Kevin|Yung, Christina|Onal Suzek, Tugba|Lin, Matthew|Baumgartner, William|Winslow, Raimond', 'Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net|Whitaker Biomedical Engineering Institute, Center for Computational Medicine & Biology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. mbbober@pol.net', 'United States|United States|United States|United States|United States|United States|United States', 12, 'Artificial Intelligence|Database Management Systems|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Information Storage and Retrieval|Myocardium|National Library of Medicine (U.S.)|United States', 'Journal Article', '10.1093/bioinformatics/18.7.1013', '12117801', 'CaGE is a Cardiac Gene Expression knowledgebase we have developed to facilitate the analysis of genes important to human cardiac function. CaGE integrates the functionality of the LocusLink database with data from several human cardiac expression libraries, phenotypic data from OMIM and data from large-scale microarray gene expression studies to create a knowledgebase of gene expression in human cardiac tissue. The knowledgebase is fully searchable via the web using several intuitive query interfaces. Results can be displayed in several concise easy to navigate formats.'),
(70270, 2002, 'bio', 'CS-PSeq-Gen: simulating the evolution of protein sequence under constraints', 'Tufféry, P', 'Equipe de Bioinformatique Génomique et Moléculaire, INSERM U436, Université Paris 7, case 7113, 2, place Jussieu, 75251 Paris cedex 05, France. tuffery@urbb.jussieu.fr', 'France', 0, 'Amino Acid Sequence|Cluster Analysis|Computer Simulation|Databases, Protein|Models, Genetic|Models, Molecular|Models, Statistical|Molecular Sequence Data|Normal Distribution|Phylogeny|Proteins|Sensitivity and Specificity|Sequence Analysis, Protein|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/18.7.1015', '12117802', 'CS-PSeq-Gen is a program derived from PSeq-Gen, designed to perform simulations of the evolution of protein sequences under the constraints of a reconstructed phylogeny. It also provides a basis for the investigation of the correlated evolution of sites.'),
(70271, 2002, 'bio', 'SNAPper: gene order predicts gene function', 'Kolesov, Grigory|Mewes, Hans-Werner|Frishman, Dmitrij', 'Institute for Bioinformatics, GSF-National Research Center for Environment and Heath, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. g.kolesov@gsf.de|Institute for Bioinformatics, GSF-National Research Center for Environment and Heath, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. g.kolesov@gsf.de|Institute for Bioinformatics, GSF-National Research Center for Environment and Heath, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. g.kolesov@gsf.de', 'Germany|Germany|Germany', 16, 'Algorithms|Database Management Systems|Databases, Protein|Expressed Sequence Tags|Genes|Internet|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/18.7.1017', '12117803', 'SNAPper is a network service for predicting gene function based on the conservation of gene order.'),
(70272, 2002, 'bio', 'Ismb 2002 Proceedings of the 10th International Conference on Intelligent Systems for Molecular Biology Edmonton, Canada, August 3-7, 2002', '-', '-', '-', 16, 'Computational Biology', 'Congress', '10.1093/bioinformatics/18.7.1017', '12169523', '-'),
(70273, 2002, 'bio', 'Mining viral protease data to extract cleavage knowledge', 'Narayanan, Ajit|Wu, Xikun|Yang, Z Rong', 'School of Engineering and Computer Sciences (Bioinformatics Laboratory), Old Library, University of Exeter, Exeter EX4 4PT, UK. a.narayanan@ex.ac.uk|School of Engineering and Computer Sciences (Bioinformatics Laboratory), Old Library, University of Exeter, Exeter EX4 4PT, UK. a.narayanan@ex.ac.uk|School of Engineering and Computer Sciences (Bioinformatics Laboratory), Old Library, University of Exeter, Exeter EX4 4PT, UK. a.narayanan@ex.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 61, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Databases, Protein|Endopeptidases|Enzyme Stability|HIV|Hepacivirus|Information Storage and Retrieval|Molecular Sequence Data|Nerve Net|Protein Binding|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship|Substrate Specificity|Viral Proteins', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s5', '12169525', 'The motivation is to identify, through machine learning techniques, specific patterns in HIV and HCV viral polyprotein amino acid residues where viral protease cleaves the polyprotein as it leaves the ribosome. An understanding of viral protease specificity may help the development of future anti-viral drugs involving protease inhibitors by identifying specific features of protease activity for further experimental investigation. While viral sequence information is growing at a fast rate, there is still comparatively little understanding of how viral polyproteins are cut into their functional unit lengths. The aim of the work reported here is to investigate whether it is possible to generalise from known cleavage sites to unknown cleavage sites for two specific viruses-HIV and HCV. An understanding of proteolytic activity for specific viruses will contribute to our understanding of viral protease function in general, thereby leading to a greater understanding of protease families and their substrate characteristics.'),
(70274, 2002, 'bio', 'The metric space of proteins-comparative study of clustering algorithms', 'Sasson, Ori|Linial, Nathan|Linial, Michal', 'School of Computer Science and Engineering Department of Biological Chemistry, Institute of Life Sciences, Hebrew University, Jerusalem 91904, Israel. ori@cs.huji.ac.il|School of Computer Science and Engineering Department of Biological Chemistry, Institute of Life Sciences, Hebrew University, Jerusalem 91904, Israel. ori@cs.huji.ac.il|School of Computer Science and Engineering Department of Biological Chemistry, Institute of Life Sciences, Hebrew University, Jerusalem 91904, Israel. ori@cs.huji.ac.il', 'Israel|Israel|Israel', 36, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Databases, Protein|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s14', '12169526', 'A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation.'),
(70275, 2002, 'bio', 'DNA sequence and structure: direct and indirect recognition in protein-DNA binding', 'Steffen, N R|Murphy, S D|Tolleri, L|Hatfield, G W|Lathrop, R H', 'Information and Computer Science, University of California, Irvine, Irvine, CA, 92697-3425, USA. nsteffen@uci.edu|Information and Computer Science, University of California, Irvine, Irvine, CA, 92697-3425, USA. nsteffen@uci.edu|Information and Computer Science, University of California, Irvine, Irvine, CA, 92697-3425, USA. nsteffen@uci.edu|Information and Computer Science, University of California, Irvine, Irvine, CA, 92697-3425, USA. nsteffen@uci.edu|Information and Computer Science, University of California, Irvine, Irvine, CA, 92697-3425, USA. nsteffen@uci.edu', 'United States|United States|United States|United States|United States', 56, 'Algorithms|Amino Acid Sequence|Binding Sites|DNA, Bacterial|DNA-Binding Proteins|Escherichia coli Proteins|Integration Host Factors|Macromolecular Substances|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/18.suppl_1.s22', '12169527', 'Direct recognition, or direct readout, of DNA bases by a DNA-binding protein involves amino acids that interact directly with features specific to each base. Experimental evidence also shows that in many cases the protein achieves partial sequence specificity by indirect recognition, i.e., by recognizing structural properties of the DNA. (1) Could threading a DNA sequence onto a crystal structure of bound DNA help explain the indirect recognition component of sequence specificity? (2) Might the resulting pure-structure computational motif manifest itself in familiar sequence-based computational motifs?'),
(70276, 2002, 'bio', 'Beyond tandem repeats: complex pattern structures and distant regions of similarity', 'Hauth, Amy M|Joseph, Deborah A', 'Department of Computer Sciences, University of Wisconsin - Madison, 1210 W. Dayton Ave., Madison, WI 53706, USA.|Department of Computer Sciences, University of Wisconsin - Madison, 1210 W. Dayton Ave., Madison, WI 53706, USA.', 'United States|United States', 34, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Gene Expression Profiling|Humans|Molecular Sequence Data|Pattern Recognition, Automated|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Tandem Repeat Sequences', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s31', '12169528', 'Tandem repeats (TRs) are associated with human disease, play a role in evolution and are important in regulatory processes. Despite their importance, locating and characterizing these patterns within anonymous DNA sequences remains a challenge. In part, the difficulty is due to imperfect conservation of patterns and complex pattern structures. We study recognition algorithms for two complex pattern structures: variable length tandem repeats (VLTRs) and multi-period tandem repeats (MPTRs).'),
(70277, 2002, 'bio', 'The POPPs: clustering and searching using peptide probability profiles', 'Wise, Michael J', 'Department of Genetics, Cambridge, CB2 3EH, England. mw263@cam.ac.uk', '-', 20, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Peptides|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s38', '12169529', 'The POPPs is a suite of inter-related software tools which allow the user to discover what is statistically unusual in the composition of an unknown protein, or to automatically cluster proteins into families based on peptide composition. Finally, the user can search for related proteins based on peptide composition. Statistically based peptide composition provides a view of proteins that is, to some extent, orthogonal to that provided by sequence. In a test study, the POPP suite is able to regroup into their families sets of approximately 100 randomised Pfam protein domains. The POPPs suite is used to explore the diverse set of late embryogenesis abundant (LEA) proteins.'),
(70278, 2002, 'bio', 'A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins', 'Martelli, Pier Luigi|Fariselli, Piero|Krogh, Anders|Casadio, Rita', 'Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 159, 'Algorithms|Markov Chains|Membrane Proteins|Models, Chemical|Models, Molecular|Models, Statistical|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s46', '12169530', 'Membrane proteins are an abundant and functionally relevant subset of proteins that putatively include from about 15 up to 30% of the proteome of organisms fully sequenced. These estimates are mainly computed on the basis of sequence comparison and membrane protein prediction. It is therefore urgent to develop methods capable of selecting membrane proteins especially in the case of outer membrane proteins, barely taken into consideration when proteome wide analysis is performed. This will also help protein annotation when no homologous sequence is found in the database. Outer membrane proteins solved so far at atomic resolution interact with the external membrane of bacteria with a characteristic beta barrel structure comprising different even numbers of beta strands (beta barrel membrane proteins). In this they differ from the membrane proteins of the cytoplasmic membrane endowed with alpha helix bundles (all alpha membrane proteins) and need specialised predictors.'),
(70279, 2002, 'bio', 'Fully automated ab initio protein structure prediction using I-SITES, HMMSTR and ROSETTA', 'Bystroff, Christopher|Shao, Yu', 'Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. bystrc@rpi.edu|Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. bystrc@rpi.edu', 'United States|United States', 101, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Caspases|Caspases, Initiator|Databases, Protein|Information Storage and Retrieval|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Systems Integration', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s54', '12169531', 'The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm.'),
(70280, 2002, 'bio', 'Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners', 'Pollastri, G|Baldi, P', 'Institute for Genomics and Bioinformatics, Department of Information and Computer Science, University of California, Irvine, Irvine, CA 92697-3425, USA. gpollast@ics.uci.edu|Institute for Genomics and Bioinformatics, Department of Information and Computer Science, University of California, Irvine, Irvine, CA 92697-3425, USA. gpollast@ics.uci.edu', 'United States|United States', 124, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Feedback|Markov Chains|Models, Molecular|Models, Statistical|Molecular Sequence Data|Neural Networks (Computer)|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s62', '12169532', 'Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.'),
(70281, 2002, 'bio', 'Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues', 'Pupko, Tal|Bell, Rachel E|Mayrose, Itay|Glaser, Fabian|Ben-Tal, Nir', 'The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 397, 'Algorithms|Amino Acid Sequence|Amino Acids|Cluster Analysis|Conserved Sequence|Evolution, Molecular|Gene Expression Profiling|Models, Molecular|Molecular Sequence Data|Phylogeny|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Structure-Activity Relationship|src Homology Domains', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s71', '12169533', 'A number of proteins of known three-dimensional (3D) structure exist, with yet unknown function. In light of the recent progress in structure determination methodology, this number is likely to increase rapidly. A novel method is presented here: Rate4Site, which maps the rate of evolution among homologous proteins onto the molecular surface of one of the homologues whose 3D-structure is known. Functionally important regions often correspond to surface patches of slowly evolving residues.'),
(70282, 2002, 'bio', 'Inferring sub-cellular localization through automated lexical analysis', 'Nair, Rajesh|Rost, Burkhard', 'CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. nair@cubic.bioc.coplumbia.edu|CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. nair@cubic.bioc.coplumbia.edu', 'United States|United States', 79, 'Abstracting and Indexing as Topic|Algorithms|Animals|Cellular Structures|Databases, Protein|Humans|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Proteins|Sequence Analysis, Protein|Tissue Distribution|Vocabulary, Controlled', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s78', '12169534', 'The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available.'),
(70283, 2002, 'bio', 'Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns', 'Myasnikova, E|Samsonova, A|Samsonova, M|Reinitz, J', 'St.Petersburg State Technical University, 29 Polytechnitcheskaya ul., St.Petersburg, 195251, Russia.|St.Petersburg State Technical University, 29 Polytechnitcheskaya ul., St.Petersburg, 195251, Russia.|St.Petersburg State Technical University, 29 Polytechnitcheskaya ul., St.Petersburg, 195251, Russia.|St.Petersburg State Technical University, 29 Polytechnitcheskaya ul., St.Petersburg, 195251, Russia.', '-|-|-|-', 25, 'Aging|Algorithms|Animals|Artificial Intelligence|Cluster Analysis|Computing Methodologies|Drosophila|Gene Expression Profiling|Gene Expression Regulation, Developmental|Genes, Insect|Pattern Recognition, Automated|Regression Analysis', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s87', '12169535', 'In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila.'),
(70284, 2002, 'bio', 'Variance stabilization applied to microarray data calibration and to the quantification of differential expression', 'Huber, Wolfgang|von Heydebreck, Anja|Sültmann, Holger|Poustka, Annemarie|Vingron, Martin', 'Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de|Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de|Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de|Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de|Department of Molecular Genome Analysis, German Cancer Research Center, INF 280, Heidelberg, 69120, Germany. w.huber@dkfz.de', 'Germany|Germany|Germany|Germany|Germany', 1398, 'Algorithms|Analysis of Variance|Calibration|Data Interpretation, Statistical|Gene Expression Profiling|Likelihood Functions|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reference Standards|Reproducibility of Results|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s96', '12169536', 'We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.'),
(70285, 2002, 'bio', 'A variance-stabilizing transformation for gene-expression microarray data', 'Durbin, B P|Hardin, J S|Hawkins, D M|Rocke, D M', 'Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@wald.ucdavis.edu|Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@wald.ucdavis.edu|Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@wald.ucdavis.edu|Department of Statistics, UC Davis, Davis, CA 95616, USA. bpdurbin@wald.ucdavis.edu', 'United States|United States|United States|United States', 282, 'Algorithms|Analysis of Variance|Calibration|Data Interpretation, Statistical|Gene Expression Profiling|Likelihood Functions|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reference Standards|Reproducibility of Results|Sensitivity and Specificity|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s105', '12169537', 'Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background.'),
(70286, 2002, 'bio', 'Binary tree-structured vector quantization approach to clustering and visualizing microarray data', 'Sultan, M|Wigle, D A|Cumbaa, C A|Maziarz, M|Glasgow, J|Tsao, M S|Jurisica, I', 'Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.|Division of Cancer Informatics, Ontario Cancer Institute, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 61, 'Algorithms|Carcinoma, Non-Small-Cell Lung|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Humans|Lung Neoplasms|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s111', '12169538', 'With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into meaningful groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified.'),
(70287, 2002, 'bio', 'Linking gene expression data with patient survival times using partial least squares', 'Park, Peter J|Tian, Lu|Kohane, Isaac S', 'Childrens Hospital Informatics Program and Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA. peter_park@harvard.edu|Childrens Hospital Informatics Program and Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA. peter_park@harvard.edu|Childrens Hospital Informatics Program and Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA. peter_park@harvard.edu', 'United States|United States|United States', 76, 'Algorithms|Gene Expression Profiling|Genetic Predisposition to Disease|Genetic Testing|Humans|Least-Squares Analysis|Lung Neoplasms|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proportional Hazards Models|Reproducibility of Results|Risk Assessment|Risk Factors|Sensitivity and Specificity|Survival|Survival Analysis|United States', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s120', '12169539', 'There is an increasing need to link the large amount of genotypic data, gathered using microarrays for example, with various phenotypic data from patients. The classification problem in which gene expression data serve as predictors and a class label phenotype as the binary outcome variable has been examined extensively, but there has been less emphasis in dealing with other types of phenotypic data. In particular, patient survival times with censoring are often not used directly as a response variable due to the complications that arise from censoring. We show that the issues involving censored data can be circumvented by reformulating the problem as a standard Poisson regression problem. The procedure for solving the transformed problem is a combination of two approaches: partial least squares, a regression technique that is especially effective when there is severe collinearity due to a large number of predictors, and generalized linear regression, which extends standard linear regression to deal with various types of response variables. The linear combinations of the original variables identified by the method are highly correlated with the patient survival times and at the same time account for the variability in the covariates. The algorithm is fast, as it does not involve any matrix decompositions in the iterations. We apply our method to data sets from lung carcinoma and diffuse large B-cell lymphoma studies to verify its effectiveness.'),
(70288, 2002, 'bio', 'Microarray synthesis through multiple-use PCR primer design', 'Fernandes, Rohan J|Skiena, Steven S', 'Applied Algorithm Laboratory, Department of Computer Science, State University of New York Stony Brook, NY, 11794-4400, USA.|Applied Algorithm Laboratory, Department of Computer Science, State University of New York Stony Brook, NY, 11794-4400, USA.', 'United States|United States', 0, 'Algorithms|Base Sequence|Computer-Aided Design|DNA Primers|Equipment Design|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s128', '12169540', 'A substantial percentage of the expense in constructing full-genome spotted microarrays comes from the cost of synthesizing the PCR primers to amplify the desired DNA. We propose a computationally-based method to substantially reduce this cost. Historically, PCR primers are designed so that each primer occurs uniquely in the genome. This condition is unnecessarily strong for selective amplification, since only the primer pair associated with each amplification need be unique. We demonstrate that careful design in a genome-level amplification project permits us to save the cost of several thousand primers over conventional approaches.'),
(70289, 2002, 'bio', 'Discovering statistically significant biclusters in gene expression data', 'Tanay, Amos|Sharan, Roded|Shamir, Ron', 'School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, 69978, Israel. amos@tau.ac.il|School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, 69978, Israel. amos@tau.ac.il|School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, 69978, Israel. amos@tau.ac.il', 'Israel|Israel|Israel', 615, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Humans|Lymphoma|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s136', '12169541', 'In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).'),
(70290, 2002, 'bio', 'Co-clustering of biological networks and gene expression data', 'Hanisch, Daniel|Zien, Alexander|Zimmer, Ralf|Lengauer, Thomas', 'Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Daniel.Hanisch@scai.fhg.de|Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Daniel.Hanisch@scai.fhg.de|Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Daniel.Hanisch@scai.fhg.de|Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer Gesellschaft, Schloss Birlinghoven, Sankt Augustin, 53754, Germany. Daniel.Hanisch@scai.fhg.de', 'Germany|Germany|Germany|Germany', 49, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation|Metabolism|Models, Biological|Models, Genetic|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s145', '12169542', 'Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.'),
(70291, 2002, 'bio', 'Statistical process control for large scale microarray experiments', 'Model, Fabian|König, Thomas|Piepenbrock, Christian|Adorján, Péter', 'Epigenomics AG, Kastanienallee 24, Berlin, D-10435, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, Berlin, D-10435, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, Berlin, D-10435, Germany. Fabian.Model@epigenomics.com|Epigenomics AG, Kastanienallee 24, Berlin, D-10435, Germany. Fabian.Model@epigenomics.com', 'Germany|Germany|Germany|Germany', 48, 'Algorithms|Artifacts|DNA Methylation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Quality Assurance, Health Care|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s155', '12169543', 'Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible.'),
(70292, 2002, 'bio', 'Evaluating machine learning approaches for aiding probe selection for gene-expression arrays', 'Tobler, J B|Molla, M N|Nuwaysir, E F|Green, R D|Shavlik, J W', 'Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.|Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.|Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.|Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.|Department of Computer Science, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|DNA Probes|Gene Expression Profiling|Models, Genetic|Models, Statistical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s164', '12169544', 'Microarrays are a fast and cost-effective method of performing thousands of DNA hybridization experiments simultaneously. DNA probes are typically used to measure the expression level of specific genes. Because probes greatly vary in the quality of their hybridizations, choosing good probes is a difficult task. If one could accurately choose probes that are likely to hybridize well, then fewer probes would be needed to represent each gene in a gene-expression microarray, and, hence, more genes could be placed on an array of a given physical size. Our goal is to empirically evaluate how successfully three standard machine-learning algorithms-naïve Bayes, decision trees, and artificial neural networks-can be applied to the task of predicting good probes. Fortunately it is relatively easy to get training examples for such a learning task: place various probes on a gene chip, add a sample where the corresponding genes are highly expressed, and then record how well each probe measures the presence of its corresponding gene. With such training examples, it is possible that an accurate predictor of probe quality can be learned.'),
(70293, 2002, 'bio', 'The degenerate primer design problem', 'Linhart, Chaim|Shamir, Ron', 'School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv 69978, Israel. chaiml@tau.ac.il|School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv 69978, Israel. chaiml@tau.ac.il', 'Israel|Israel', 70, 'Algorithms|Combinatorial Chemistry Techniques|DNA Mutational Analysis|DNA Primers|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Multigene Family|Polymerase Chain Reaction|Receptors, Odorant|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s172', '12169545', 'A PCR primer sequence is called degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program called HYDEN for designing highly-degenerate primers for a set of genomic sequences. We report on the success of the program in an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, HYDEN was used to design primers with degeneracies up to 10(10) that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.'),
(70294, 2002, 'bio', 'Splicing graphs and EST assembly problem', 'Heber, Steffen|Alekseyev, Max|Sze, Sing-Hoi|Tang, Haixu|Pevzner, Pavel A', 'Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093-0114, USA. sheber@ucsd.edu|Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093-0114, USA. sheber@ucsd.edu|Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093-0114, USA. sheber@ucsd.edu|Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093-0114, USA. sheber@ucsd.edu|Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093-0114, USA. sheber@ucsd.edu', 'United States|United States|United States|United States|United States', 0, 'Adenylosuccinate Lyase|Algorithms|Alternative Splicing|Consensus Sequence|DNA, Recombinant|Expressed Sequence Tags|Gene Expression Profiling|Genetic Variation|Humans|Sequence Alignment|Sequence Analysis, DNA|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s181', '12169546', 'The traditional approach to annotate alternative splicing is to investigate every splicing variant of the gene in a case-by-case fashion. This approach, while useful, has some serious shortcomings. Recent studies indicate that alternative splicing is more frequent than previously thought and some genes may produce tens of thousands of different transcripts. A list of alternatively spliced variants for such genes would be difficult to build and hard to analyse. Moreover, such a list does not show the relationships between different transcripts and does not show the overall structure of all transcripts. A better approach would be to represent all splicing variants for a given gene in a way that captures the relationships between different splicing variants.'),
(70295, 2002, 'bio', 'Exact genetic linkage computations for general pedigrees', 'Fishelson, M|Geiger, D', 'Computer Science Department, Technion, Haifa 32000, Israel. fmaayan@cs.technion.ac.il|Computer Science Department, Technion, Haifa 32000, Israel. fmaayan@cs.technion.ac.il', 'Israel|Israel', 203, 'Algorithms|Bayes Theorem|Chromosome Mapping|Coronary Artery Disease|Gene Expression Profiling|Genetic Linkage|Genetic Predisposition to Disease|Genetic Testing|Humans|Models, Genetic|Models, Statistical|Pedigree|Phylogeny|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s189', '12169547', 'Genetic linkage analysis is a useful statistical tool for mapping disease genes and for associating functionality of genes with their location on the chromosome. There is a need for a program that computes multipoint likelihood on general pedigrees with many markers that also deals with two-locus disease models.'),
(70296, 2002, 'bio', 'Assigning probes into a small number of pools separable by electrophoresis', 'Kivioja, Teemu|Arvas, Mikko|Kataja, Kari|Penttilä, Merja|Söderlund, Hans|Ukkonen, Esko', 'Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI|Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI|Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI|Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI|Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI|Department of Computer Science, University of Helsinki, Teollisuuskatu 23, Helsinki, PO Box 26, FIN-00014, Finland. Teemu.Kivioja@cs.Helsinki.FI', 'Finland|Finland|Finland|Finland|Finland|Finland', 18, 'Algorithms|DNA|DNA Probes|Electrophoresis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s199', '12169548', 'Measuring transcriptional expression levels (transcriptional profiling) has become one of the most important methods in functional genomics. Still, new measuring methods are needed to obtain more reliable, quantitative data about transcription on a genomic scale. In this paper we concentrate on certain computational optimization problems arising in the design of one such novel method. From a computational point of view the key feature of the new method is that the hybridized probes are distinguished from each other based on their different size. Therefore the probes have to be assigned into pools such that the probes in the same pool have unique sizes different enough from each other. Identification of expressed RNA is given by probe pool and probe size while quantification is given by the label of the probe, e.g. fluorescence intensity.'),
(70297, 2002, 'bio', 'Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models', 'Rubin, Daniel L|Shafa, Farhad|Oliver, Diane E|Hewett, Micheal|Altman, Russ B', 'Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu|Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu|Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu|Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu|Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Hypermedia|Information Storage and Retrieval|Models, Genetic|Pharmacogenetics|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s207', '12169549', 'The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change.'),
(70298, 2002, 'bio', 'Evaluating functional network inference using simulations of complex biological systems', 'Smith, V Anne|Jarvis, Erich D|Hartemink, Alexander J', 'Department of Neurobiology, Duke University Medical Center, Box 3209, Durham, NC 27710, USA. asmith@neuro.duke.edu|Department of Neurobiology, Duke University Medical Center, Box 3209, Durham, NC 27710, USA. asmith@neuro.duke.edu|Department of Neurobiology, Duke University Medical Center, Box 3209, Durham, NC 27710, USA. asmith@neuro.duke.edu', 'United States|United States|United States', 0, 'Adaptation, Physiological|Algorithms|Animals|Brain|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Neurological|Signal Transduction|Songbirds|Vocalization, Animal', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s216', '12169550', 'Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data.'),
(70299, 2002, 'bio', 'The Pathway Tools software', 'Karp, Peter D|Paley, Suzanne|Romero, Pedro', 'Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA. pkarp@ai.sri.com|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA. pkarp@ai.sri.com|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA. pkarp@ai.sri.com', 'United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Animals|Database Management Systems|Databases, Genetic|Gene Expression Regulation|Humans|Information Storage and Retrieval|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s225', '12169551', 'Bioinformatics requires reusable software tools for creating model-organism databases (MODs).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70300, 2002, 'bio', 'Discovering regulatory and signalling circuits in molecular interaction networks', 'Ideker, Trey|Ozier, Owen|Schwikowski, Benno|Siegel, Andrew F', 'Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu|Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu|Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu|Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu', 'United States|United States|United States|United States', 845, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction|Yeasts', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s233', '12169552', 'In model organisms such as yeast, large databases of protein-protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score.'),
(70301, 2002, 'bio', 'Modelling regulatory pathways in E coli from time series expression profiles', 'Ong, Irene M|Glasner, Jeremy D|Page, David', 'Department of Computer Sciences, Department of Biostatistics & Medical Informatics Department of Genetics, University of Wisconsin, Madison 53706, USA. ong@cs.wisc.edu|Department of Computer Sciences, Department of Biostatistics & Medical Informatics Department of Genetics, University of Wisconsin, Madison 53706, USA. ong@cs.wisc.edu|Department of Computer Sciences, Department of Biostatistics & Medical Informatics Department of Genetics, University of Wisconsin, Madison 53706, USA. ong@cs.wisc.edu', 'United States|United States|United States', 164, 'Adaptation, Physiological|Bayes Theorem|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Models, Statistical|Signal Transduction|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s241', '12169553', 'Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data.'),
(70302, 2002, 'bio', 'Of truth and pathways: chasing bits of information through myriads of articles', 'Krauthammer, Michael|Kra, Pauline|Iossifov, Ivan|Gomez, Shawn M|Hripcsak, George|Hatzivassiloglou, Vasileios|Friedman, Carol|Rzhetsky, Andrey', 'Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.|Department of Medical Informatics, Columbia University, New York, NY 10032, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Models, Statistical|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/18.suppl_1.s249', '12169554', 'Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.'),
(70303, 2002, 'bio', 'Minreg: inferring an active regulator set', 'Peer, Dana|Regev, Aviv|Tanay, Amos', 'School of Computer Science & Engineering, Hebrew University of Jerusalem.|School of Computer Science & Engineering, Hebrew University of Jerusalem.|School of Computer Science & Engineering, Hebrew University of Jerusalem.', '-|-|-', 51, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcriptional Activation', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s258', '12169555', 'Regulatory relations between genes are an important component of molecular pathways. Here, we devise a novel global method that uses a set of gene expression profiles to find a small set of relevant active regulators, identify the genes that they regulate, and automatically annotate them. We show that our algorithm is capable of handling a large number of genes in a short time and is robust to a wide range of parameters. We apply our method to a combined dataset of S. cerevisiae expression profiles, and validate the resulting model of regulation by cross-validation and extensive biological analysis of the selected regulators and their derived annotations.'),
(70304, 2002, 'bio', 'Marginalized kernels for biological sequences', 'Tsuda, Koji|Kin, Taishin|Asai, Kiyoshi', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi Koto-ku, Tokyo 135-0064, Japan. koji.tsuda@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi Koto-ku, Tokyo 135-0064, Japan. koji.tsuda@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6 Aomi Koto-ku, Tokyo 135-0064, Japan. koji.tsuda@aist.go.jp', 'Japan|Japan|Japan', 99, 'Algorithms|Amino Acid Sequence|DNA Gyrase|Markov Chains|Models, Chemical|Models, Statistical|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s268', '12169556', 'Kernel methods such as support vector machines require a kernel function between objects to be defined a priori. Several works have been done to derive kernels from probability distributions, e.g., the Fisher kernel. However, a general methodology to design a kernel is not fully developed.'),
(70305, 2002, 'bio', 'A tree kernel to analyse phylogenetic profiles', 'Vert, Jean-Philippe', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan. Jean-Philippe.Vert@mines.org', 'Japan', 94, 'Algorithms|Artificial Intelligence|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Phylogeny|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s276', '12169557', 'The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often similar or at least related to each other. The question we address in this paper is the following: how to measure the similarity between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods?'),
(70306, 2002, 'bio', 'Statistically based postprocessing of phylogenetic analysis by clustering', 'Stockham, Cara|Wang, Li-San|Warnow, Tandy', 'Texas Institute for Computational and Applied Mathematics, University of Texas, ACES 6.412, Austin TX 78712, USA.|Texas Institute for Computational and Applied Mathematics, University of Texas, ACES 6.412, Austin TX 78712, USA.|Texas Institute for Computational and Applied Mathematics, University of Texas, ACES 6.412, Austin TX 78712, USA.', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Consensus Sequence|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Phylogeny|Sequence Analysis', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s285', '12169558', 'Phylogenetic analyses often produce thousands of candidate trees. Biologists resolve the conflict by computing the consensus of these trees. Single-tree consensus as postprocessing methods can be unsatisfactory due to their inherent limitations.'),
(70307, 2002, 'bio', 'Efficiently detecting polymorphisms during the fragment assembly process', 'Fasulo, Daniel|Halpern, Aaron|Dew, Ian|Mobarry, Clark', 'Informatics Research, Celera Genomics, 45 W. Gude Dr., Rockville MD 20850, USA. daniel.fasulo@celera.com|Informatics Research, Celera Genomics, 45 W. Gude Dr., Rockville MD 20850, USA. daniel.fasulo@celera.com|Informatics Research, Celera Genomics, 45 W. Gude Dr., Rockville MD 20850, USA. daniel.fasulo@celera.com|Informatics Research, Celera Genomics, 45 W. Gude Dr., Rockville MD 20850, USA. daniel.fasulo@celera.com', 'United States|United States|United States|United States', 18, 'Algorithms|Base Sequence|Consensus Sequence|DNA Fragmentation|Gene Expression Profiling|Genetic Variation|Molecular Sequence Data|Polymorphism, Genetic|Polymorphism, Restriction Fragment Length|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s294', '12169559', 'Current genomic sequence assemblers assume that the input data is derived from a single, homogeneous source. However, recent whole-genome shotgun sequencing projects have violated this assumption, resulting in input fragments covering the same region of the genome whose sequences differ due to polymorphic variation in the population. While single-nucleotide polymorphisms (SNPs) do not pose a significant problem to state-of-the-art assembly methods, these methods do not handle insertion/deletion (indel) polymorphisms of more than a few bases.'),
(70308, 2002, 'bio', 'Multiple genome rearrangement: a general approach via the evolutionary genome graph', 'Korkin, Dmitry|Goldfarb, Lev', 'Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. dkorkin@unb.ca|Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. dkorkin@unb.ca', 'Canada|Canada', 6, 'Algorithms|Chromosome Mapping|Evolution, Molecular|Gene Expression Profiling|Phylogeny|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s303', '12169560', 'In spite of a well-known fact that genome rearrangements are supposed to be viewed in the light of the evolutionary relationships within and between the species involved, no formal underlying framework based on the evolutionary considerations for treating the questions arising in the area has been proposed. If such an underlying framework is provided, all the basic questions in the area can be posed in a biologically more appropriate and useful form: e.g., the similarity between two genomes can then be computed via the nearest ancestor, rather than directly, ignoring the evolutionary connections.'),
(70309, 2002, 'bio', 'Efficient multiple genome alignment', 'Höhl, Michael|Kurtz, Stefan|Ohlebusch, Enno', 'Faculty of Technology, University of Bielefeld, PO Box 10 01 31, Bielefeld D-33501, Germany.|Faculty of Technology, University of Bielefeld, PO Box 10 01 31, Bielefeld D-33501, Germany.|Faculty of Technology, University of Bielefeld, PO Box 10 01 31, Bielefeld D-33501, Germany.', 'Germany|Germany|Germany', 120, 'Algorithms|Animals|Base Sequence|Chromosome Mapping|Evolution, Molecular|Gene Expression Profiling|Humans|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s312', '12169561', 'To allow a direct comparison of the genomic DNA sequences of sufficiently similar organisms, there is an urgent need for software tools that can align more than two genomic sequences.'),
(70310, 2002, 'bio', 'PseudoViewer: automatic visualization of RNA pseudoknots', 'Han, Kyungsook|Lee, Yujin|Kim, Wootaek', 'Department of Computer Science and Engineering, Inha University, Inchon 402-751, South Korea. khan@inha.ac.kr|Department of Computer Science and Engineering, Inha University, Inchon 402-751, South Korea. khan@inha.ac.kr|Department of Computer Science and Engineering, Inha University, Inchon 402-751, South Korea. khan@inha.ac.kr', '-|-|-', 25, 'Algorithms|Base Pairing|Base Sequence|Computer Graphics|Hypermedia|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Programming Languages|RNA|Sequence Analysis, RNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_1.s321', '12169562', 'Several algorithms have been developed for drawing RNA secondary structures, however none of these can be used to draw RNA pseudoknot structures. In the sense of graph theory, a drawing of RNA secondary structures is a tree, whereas a drawing of RNA pseudoknots is a graph with inner cycles within a pseudoknot as well as possible outer cycles formed between a pseudoknot and other structural elements. Thus, RNA pseudoknots are more difficult to visualize than RNA secondary structures. Since no automatic method for drawing RNA pseudoknots exists, visualizing RNA pseudoknots relies on significant amount of manual work and does not yield satisfactory results. The task of visualizing RNA pseudoknots by hand becomes more challenging as the size and complexity of the RNA pseudoknots increase.'),
(70311, 2002, 'bio', 'A powerful non-homology method for the prediction of operons in prokaryotes', 'Moreno-Hagelsieb, Gabriel|Collado-Vides, Julio', 'Program of Computational Genomics, CIFN, UNAM, A.P. 565-A, Cuernavaca, Morelos 62100, Mexico. moreno@cifn.unam.mx|Program of Computational Genomics, CIFN, UNAM, A.P. 565-A, Cuernavaca, Morelos 62100, Mexico. moreno@cifn.unam.mx', 'Mexico|Mexico', 158, 'Algorithms|Bacteria|Base Sequence|Chromosome Mapping|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genome, Bacterial|Molecular Sequence Data|Operon|Prokaryotic Cells|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s329', '12169563', 'The prediction of the transcription unit organization of genomes is an important clue in the inference of functional relationships of genes, the interpretation and evaluation of transcriptome experiments, and the overall inference of the regulatory networks governing the expression of genes in response to the environment. Though several methods have been devised to predict operons, most need a high characterization of the genome analysed. Log-likelihoods derived from inter-genic distance distributions work surprisingly well to predict operons in Escherichia coli and are available for any genome as soon as the gene sets are predicted.'),
(70312, 2002, 'bio', 'Identifying operons and untranslated regions of transcripts using Escherichia coli RNA expression analysis', 'Tjaden, Brian|Haynor, David R|Stolyar, Sergey|Rosenow, Carsten|Kolker, Eugene', 'Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, USA. tjaden@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, USA. tjaden@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, USA. tjaden@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, USA. tjaden@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, USA. tjaden@cs.washington.edu', 'United States|United States|United States|United States|United States', 36, '5 Untranslated Regions|Algorithms|Base Sequence|Chromosome Mapping|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genome, Bacterial|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Operon|Prokaryotic Cells|RNA, Bacterial|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s337', '12169564', 'Microarrays traditionally have been used to assay the transcript expression of coding regions of genes. Here, we use Escherichia coli oligonucleotide microarrays to assay transcript expression of both open reading frames (ORFs) and intergenic regions. We then use hidden Markov models to analyse this expression data and estimate transcription boundaries of genes. This approach allows us to identify 5 untranslated regions (5 UTRs) of transcripts as well as genes that are likely to be operon members. The operon elements we identify correspond to documented operons with 99% specificity and 63% sensitivity. Similarly we find that our 5 UTR results accurately coincide with experimentally verified promoter regions for most genes.'),
(70313, 2002, 'bio', 'Detecting recombination with MCMC', 'Husmeier, Dirk|McGuire, Gráinne', 'Biomathematics and Statistics Scotland (BioSS), JCMB, The Kings Buildings, Edinburgh EH9 3JZ, UK. dirk@bioss.ac.uk|Biomathematics and Statistics Scotland (BioSS), JCMB, The Kings Buildings, Edinburgh EH9 3JZ, UK. dirk@bioss.ac.uk', 'United Kingdom|United Kingdom', 12, 'Algorithms|Base Sequence|Gene Expression Profiling|Hepatitis B virus|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Neisseria|Phylogeny|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software|Zea mays', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s345', '12169565', 'We present a statistical method for detecting recombination, whose objective is to accurately locate the recombinant breakpoints in DNA sequence alignments of small numbers of taxa (4 or 5). Our approach explicitly models the sequence of phylogenetic tree topologies along a multiple sequence alignment. Inference under this model is done in a Bayesian way, using Markov chain Monte Carlo (MCMC). The algorithm returns the site-dependent posterior probability of each tree topology, which is used for detecting recombinant regions and locating their breakpoints.'),
(70314, 2002, 'bio', 'Finding composite regulatory patterns in DNA sequences', 'Eskin, Eleazar|Pevzner, Pavel A', 'Department of Computer Science, Columbia University, New York, 10027 NY, USA. eeskin@cs.columbia.edu|Department of Computer Science, Columbia University, New York, 10027 NY, USA. eeskin@cs.columbia.edu', 'United States|United States', 240, 'Algorithms|Base Pairing|Base Sequence|Chromosome Mapping|Gene Expression Profiling|Molecular Sequence Data|Pattern Recognition, Automated|Phylogeny|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_1.s354', '12169566', 'Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in finding regulatory signals. Current approaches to pattern discovery focus on monad patterns that correspond to relatively short contiguous strings. However, many of the actual regulatory signals are composite patterns that are groups of monad patterns that occur near each other. A difficulty in discovering composite patterns is that one or both of the component monad patterns in the group may be too weak. Since the traditional monad-based motif finding algorithms usually output one (or a few) high scoring patterns, they often fail to find composite regulatory signals consisting of weak monad parts. In this paper, we present a MITRA (MIsmatch TRee Algorithm) approach for discovering composite signals. We demonstrate that MITRA performs well for both monad and composite patterns by presenting experiments over biological and synthetic data.'),
(70315, 2002, 'bio', 'Why are complementary DNA strands symmetric?', 'Baisnée, Pierre-François|Hampson, Steve|Baldi, Pierre', 'Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine 92697-3425, USA. baisnee@ics.uci.edu|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine 92697-3425, USA. baisnee@ics.uci.edu|Department of Information and Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine 92697-3425, USA. baisnee@ics.uci.edu', 'United States|United States|United States', 74, 'Base Composition|Base Sequence|Chromosomes|Computer Simulation|DNA, Complementary|Databases, Genetic|Genome|Humans|Markov Chains|Models, Genetic|Models, Statistical|Modems|Molecular Sequence Data|RNA|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis|Sequence Homology, Nucleic Acid|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/18.8.1021', '12176825', 'Over sufficiently long windows, complementary strands of DNA tend to have the same base composition. A few reports have indicated that this first-order parity rule extends at higher orders to oligonucleotide composition, at least in some organisms or taxa. However, the scientific literature falls short of providing a comprehensive study of reverse-complement symmetry at multiple orders and across the kingdom of life. It also lacks a characterization of this symmetry and a convincing explanation or clarification of its origin.'),
(70316, 2002, 'bio', 'Improving gene recognition accuracy by combining predictions from two gene-finding programs', 'Rogic, Sanja|Ouellette, B F Francis|Mackworth, Alan K', 'Computer Science Department, The University of California at Santa Cruz, Baskin Engineering, 95064, USA. rogic@cse.ucsc.edu|Computer Science Department, The University of California at Santa Cruz, Baskin Engineering, 95064, USA. rogic@cse.ucsc.edu|Computer Science Department, The University of California at Santa Cruz, Baskin Engineering, 95064, USA. rogic@cse.ucsc.edu', 'United States|United States|United States', 39, 'Algorithms|Animals|Computing Methodologies|DNA|Database Management Systems|Databases, Genetic|Drosophilidae|Exons|False Positive Reactions|Humans|Information Storage and Retrieval|Mice|Rats|Reading Frames|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis', 'Comparative Study', '10.1093/bioinformatics/18.8.1034', '12176826', 'Despite constant improvements in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with desired correctness. The current programs can identify up to 75% of exons correctly and less than 50% of predicted gene structures correspond to actual genes. New approaches to computational gene-finding are clearly needed.'),
(70317, 2002, 'bio', 'Computational identification of putative programmed translational frameshift sites', 'Shah, Atul A|Giddings, Michael C|Parvaz, Jasmin B|Gesteland, Raymond F|Atkins, John F|Ivanov, Ivaylo P', 'Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.|Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.|Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.|Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.|Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.|Department of Human Genetics, University of Utah, SLC, 84112-5330, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Composition|Base Sequence|DNA|Databases, Genetic|Frameshift Mutation|Gene Expression Regulation|Models, Genetic|Models, Statistical|Molecular Sequence Data|Protein Biosynthesis|Reproducibility of Results|Saccharomyces|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.8.1046', '12176827', 'In an effort to identify potential programmed frameshift sites by statistical analysis, we explore the hypothesis that selective pressure would have rendered such sites underabundant and underrepresented in protein-coding sequences. We developed a computer program to compare the frequencies of k-length subsequences of nucleotides with the frequencies predicted by a zero order Markov chain determined by the codon bias of the same set of sequences. The program was used to calculate and evaluate the distribution of 7-base oligonucleotides in the 6000+ putative protein-coding sequences of S. cerevisiae preliminary to the laboratory testing of the most highly underrepresented oligos for frameshifting efficiency.'),
(70318, 2002, 'bio', 'Mapping physiological states from microarray expression measurements', 'Stephanopoulos, Gregory|Hwang, Daehee|Schmitt, William A|Misra, Jatin|Stephanopoulos, George', 'Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge 02139, USA. gregstep@mit.edu|Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge 02139, USA. gregstep@mit.edu|Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge 02139, USA. gregstep@mit.edu|Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge 02139, USA. gregstep@mit.edu|Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge 02139, USA. gregstep@mit.edu', 'United States|United States|United States|United States|United States', 48, 'Algorithms|Cluster Analysis|Cyanobacteria|DNA|Databases, Genetic|Discriminant Analysis|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Models, Statistical|Neoplasms, Glandular and Epithelial|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.8.1054', '12176828', 'The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability.'),
(70319, 2002, 'bio', 'A conditional density error model for the statistical analysis of microarray data', 'Love, Brad|Rank, David R|Penn, Sharron G|Jenkins, David A|Thomas, Russell S', 'Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com|Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com|Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com|Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com|Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com', 'United States|United States|United States|United States|United States', 0, 'Computer Simulation|DNA|DNA Replication|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/18.8.1064', '12176829', 'In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors.'),
(70320, 2002, 'bio', 'Analysis of expression profile using fuzzy adaptive resonance theory', 'Tomida, Shuta|Hanai, Taizo|Honda, Hiroyuki|Kobayashi, Takeshi', 'Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Japan.|Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Japan.|Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Japan.|Department of Biotechnology, School of Engineering Nagoya University, Furo-cho, Chikusa-ku, Japan.', 'Japan|Japan|Japan|Japan', 54, 'Algorithms|Artificial Intelligence|Cluster Analysis|Databases, Genetic|Fuzzy Logic|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|RNA, Messenger|Reproducibility of Results|Saccharomyces cerevisiae|Sensitivity and Specificity|Spores, Fungal|Stochastic Processes|Time Factors', 'Comparative Study', '10.1093/bioinformatics/18.8.1073', '12176830', 'It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report.'),
(70321, 2002, 'bio', 'Protein motif extraction with neuro-fuzzy optimization', 'Chang, Bill C H|Halgamuge, Saman K', 'Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, VIC 3010, Australia. bcch@mame.mu.oz.au|Mechatronics Research Group, Mechanical and Manufacturing Engineering, University of Melbourne, VIC 3010, Australia. bcch@mame.mu.oz.au', 'Australia|Australia', 41, 'Algorithms|Amino Acid Motifs|Databases, Protein|Epidermal Growth Factor|False Positive Reactions|Fuzzy Logic|Neural Networks (Computer)|Pattern Recognition, Automated|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/18.8.1084', '12176831', 'It is attempted to improve the speed and flexibility of protein motif identification. The proposed algorithm is able to extract both rigid and flexible protein motifs.'),
(70322, 2002, 'bio', 'Probabilistic alignment of motifs with sequences', 'Gonnet, Pedro|Lisacek, Frédérique', 'GeneBio S.A., 25. Av. de Champel, 1206 Geneva, Switzerland. pedro.gonnet@genebio.com|GeneBio S.A., 25. Av. de Champel, 1206 Geneva, Switzerland. pedro.gonnet@genebio.com', 'Switzerland|Switzerland', 20, 'Amino Acid Motifs|Amino Acid Sequence|Artificial Intelligence|Bacillus subtilis|Databases, Protein|Feedback|Lipoproteins|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Probability|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.8.1091', '12176832', 'Motif detection is an important component of the classification and annotation of protein sequences. A method for aligning motifs with an amino acid sequence is introduced. The motifs can be described by the secondary (i.e. functional, biophysical, etc.) characteristics of a signal or pattern to be detected. The results produced are based on the statistical relevance of the alignment. The method was targeted to avoid the problems (i.e. over-fitting, biological interpretation and mathematical soundness) encountered in other methods currently available.'),
(70323, 2002, 'bio', 'Simplifying amino acid alphabets by means of a branch and bound algorithm and substitution matrices', 'Cannata, Nicola|Toppo, Stefano|Romualdi, Chiara|Valle, Giorgio', 'CRIBI Biotechnology Centre, Universitá di Padova, via Ugo Bassi 58/B, Italy.|CRIBI Biotechnology Centre, Universitá di Padova, via Ugo Bassi 58/B, Italy.|CRIBI Biotechnology Centre, Universitá di Padova, via Ugo Bassi 58/B, Italy.|CRIBI Biotechnology Centre, Universitá di Padova, via Ugo Bassi 58/B, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Decision Trees|Evaluation Studies as Topic|Information Storage and Retrieval|Internet|Models, Statistical|Molecular Sequence Data|Programming Languages|Sensitivity and Specificity|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software Design', 'Journal Article', '10.1093/bioinformatics/18.8.1102', '12176833', 'Protein and DNA are generally represented by sequences of letters. In a number of circumstances simplified alphabets (where one or more letters would be represented by the same symbol) have proved their potential utility in several fields of bioinformatics including searching for patterns occurring at an unexpected rate, studying protein folding and finding consensus sequences in multiple alignments. The main issue addressed in this paper is the possibility of finding a general approach that would allow an exhaustive analysis of all the possible simplified alphabets, using substitution matrices like PAM and BLOSUM as a measure for scoring.'),
(70324, 2002, 'bio', 'Prediction of Golgi Type II membrane proteins based on their transmembrane domains', 'Yuan, Zheng|Teasdale, Rohan D', 'Institute for Molecular Bioscience and Special Research Centre for Functional and Applied Genomics, University of Queensland, St Lucia, 4072, Australia.|Institute for Molecular Bioscience and Special Research Centre for Functional and Applied Genomics, University of Queensland, St Lucia, 4072, Australia.', 'Australia|Australia', 47, 'Algorithms|Amino Acid Sequence|Databases, Protein|Discriminant Analysis|Evaluation Studies as Topic|False Positive Reactions|Golgi Apparatus|Hydrophobic and Hydrophilic Interactions|Membrane Proteins|Molecular Sequence Data|Protein Transport|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/18.8.1109', '12176834', 'A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches.'),
(70325, 2002, 'bio', 'A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families', 'Pupko, Tal|Peer, Itsik|Hasegawa, Masami|Graur, Dan|Friedman, Nir', 'The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp|The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan. tal@ism.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 45, 'Algorithms|Amino Acid Sequence|Databases, Genetic|Electron Transport Complex IV|Evolution, Molecular|Genetic Variation|Likelihood Functions|Models, Genetic|Models, Statistical|Molecular Sequence Data|Muramidase|Mutagenesis|Phylogeny|Reproducibility of Results|Rod Opsins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/18.8.1116', '12176835', 'We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of the ancestral sequences, assuming that the rate is gamma distributed among sites. Our algorithm probably finds the global maximum. The use of joint reconstruction is motivated by studies that use the sequences at all the internal nodes in a phylogenetic tree, such as, for instance, the inference of patterns of amino-acid replacement, or tracing the biochemical changes that occurred during the evolution of a given protein family.'),
(70326, 2002, 'bio', 'Tagging gene and protein names in biomedical text', 'Tanabe, Lorraine|Wilbur, W John', 'National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, USA. tanabe@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, USA. tanabe@ncbi.nlm.nih.gov', 'United States|United States', 201, 'Abbreviations as Topic|Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|DNA|False Negative Reactions|False Positive Reactions|Genes|Information Storage and Retrieval|MEDLINE|Models, Statistical|National Library of Medicine (U.S.)|Pattern Recognition, Automated|Proteins|Reproducibility of Results|Sensitivity and Specificity|Terminology as Topic|United States', 'Comparative Study', '10.1093/bioinformatics/18.8.1124', '12176836', 'The MEDLINE database of biomedical abstracts contains scientific knowledge about thousands of interacting genes and proteins. Automated text processing can aid in the comprehension and synthesis of this valuable information. The fundamental task of identifying gene and protein names is a necessary first step towards making full use of the information encoded in biomedical text. This remains a challenging task due to the irregularities and ambiguities in gene and protein nomenclature. We propose to approach the detection of gene and protein names in scientific abstracts as part-of-speech tagging, the most basic form of linguistic corpus annotation.'),
(70327, 2002, 'bio', 'Neural network predicts sequence of TP53 gene based on DNA chip', 'Spicker, Jeppe S|Wikman, Friedrik|Lu, Ming-Lan|Cordon-Cardo, Carlos|Workman, Christopher|ØRntoft, Torben F|Brunak, Søren|Knudsen, Steen', 'Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.|Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Lyngby, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 0, 'Base Sequence|Genes, p53|Humans|In Situ Hybridization, Fluorescence|Molecular Sequence Data|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.8.1133', '12176837', 'We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence.'),
(70328, 2002, 'bio', 'TFBS: Computational framework for transcription factor binding site analysis', 'Lenhard, Boris|Wasserman, Wyeth W', 'Bioinformatics Unit, Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden. Boris.Lenhard@cgb.ki.se|Bioinformatics Unit, Center for Genomics and Bioinformatics, Karolinska Institutet, Stockholm, Sweden. Boris.Lenhard@cgb.ki.se', 'Sweden|Sweden', 0, 'Binding Sites|DNA|Databases, Nucleic Acid|Internet|Pattern Recognition, Automated|Programming Languages|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/18.8.1135', '12176838', 'TFBS is a set of integrated, object-oriented Perl modules for transcription factor binding site detection and analysis. It implements objects representing specificity profile matrices, binding sites and sets thereof, pattern generators, and pattern database interfaces. The modules are interoperable with the BioPerl open source system.'),
(70329, 2002, 'bio', 'ShiftDetector: detection of shift mutations', 'Seroussi, Eyal|Ron, Micha|Kedra, Darek', 'Institute of Animal Science, Volcani Center, Bet-Dagan 50250, Israel. seroussi@agri.huji.ac.il|Institute of Animal Science, Volcani Center, Bet-Dagan 50250, Israel. seroussi@agri.huji.ac.il|Institute of Animal Science, Volcani Center, Bet-Dagan 50250, Israel. seroussi@agri.huji.ac.il', 'Israel|Israel|Israel', 15, 'Alleles|Base Sequence|DNA Mutational Analysis|Databases, Genetic|Gene Expression|Genetic Carrier Screening|Internet|Models, Statistical|Molecular Sequence Data|Mutagenesis, Insertional|National Library of Medicine (U.S.)|Polymerase Chain Reaction|Programming Languages|Sensitivity and Specificity|Software|United States', 'Journal Article', '10.1093/bioinformatics/18.8.1137', '12176839', 'Sequencing of a bi-allelic PCR product, which contains an allele with a deletion/insertion mutation results in a superimposed tracefile following the site of this shift mutation. A trace file of this type hampers the use of current computer programs for base calling. ShiftDetector analyses a sequencing trace file in order to discover if it is a superimposed sequence of two molecules that differ in a shift mutation of 1 to 25 bases. The program calculates a probability score for the existence of such a shift and reconstructs the sequence of the original molecule.'),
(70330, 2002, 'bio', 'MArray: analysing single, replicated or reversed microarray experiments', 'Wang, Junbai|Nygaard, Vigdis|Smith-Sørensen, Birgitte|Hovig, Eivind|Myklebost, Ola', 'Department of Tumour Biology, Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no|Department of Tumour Biology, Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no|Department of Tumour Biology, Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no|Department of Tumour Biology, Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no|Department of Tumour Biology, Norwegian Radium Hospital, N0310 Oslo, Norway. junbaiw@radium.uio.no', 'Norway|Norway|Norway|Norway|Norway', 0, 'Computer Graphics|DNA|Database Management Systems|Databases, Nucleic Acid|Image Processing, Computer-Assisted|Information Storage and Retrieval|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.8.1139', '12176840', 'MArray is a Matlab toolbox with a graphical user interface that allows the user to analyse single or paired microarray datasets by direct input of the raw data output file from image analysis packages, such as QuantArray or GenePiX. The application provides simple procedures to manually evaluate the quality of each measurement, multiple approaches to both ratio normalization (simple normalization, intensity dependent normalization) and evaluation of the reproducibility of paired experiments (using the techniques simple statistical method and quality control ellipse and significance analysis of microarrays). Specifically, interactive spot evaluation functions are available in MArray and an online gene information database (NCBI UniGene) is linked. The application may provide a valuable aid in selecting and optimizing experimental procedures, as well as serving as an analytical tool for two-state biological comparisons, such as a study of single-dose activation. It is entirely platform independent, and only requires Matlab installed.'),
(70331, 2002, 'bio', 'Domain fishing: a first step in protein comparative modelling', 'Contreras-Moreira, B|Bates, P A', 'Biomolecular Modelling Laboratory, Cancer Research, UK. b.contreras-moreira@cancer.org.uk|Biomolecular Modelling Laboratory, Cancer Research, UK. b.contreras-moreira@cancer.org.uk', 'United Kingdom|United Kingdom', 143, 'Algorithms|Databases, Protein|Information Storage and Retrieval|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/18.8.1141', '12176841', 'To optimize the search for structural templates in protein comparative modelling, the query sequence is split into domains. The initial list of templates for each domain, extracted from PFAM plus PDB and SCOP, is then ranked according to sequence identity (%ID), coverage and resolution. If %ID is less than 30, secondary structure matching is used to filter out false templates.'),
(70332, 2002, 'bio', 'DiffTool: building, visualizing and querying protein clusters', 'Chetouani, Farid|Glaser, Philippe|Kunst, Frank', 'Laboratoire Génomique des Microorganismes Pathogénes, Institut Pasteur, Département de Biologie Moléculaire, 25 rue du Dr Roux, 75724 Paris Cedex 15, France. fchetou@infobiogen.fr|Laboratoire Génomique des Microorganismes Pathogénes, Institut Pasteur, Département de Biologie Moléculaire, 25 rue du Dr Roux, 75724 Paris Cedex 15, France. fchetou@infobiogen.fr|Laboratoire Génomique des Microorganismes Pathogénes, Institut Pasteur, Département de Biologie Moléculaire, 25 rue du Dr Roux, 75724 Paris Cedex 15, France. fchetou@infobiogen.fr', 'France|France|France', 0, 'Cluster Analysis|Computer Graphics|Database Management Systems|Databases, Protein|Genome|Information Storage and Retrieval|Internet|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology|Software', 'Journal Article', '10.1093/bioinformatics/18.8.1143', '12176842', 'DiffTool is a resource to build and visualize protein clusters computed from a sequence database. The package provides a clustering tool to construct protein families according to sequence similarities and a web interface to query the corresponding clusters. A subtractive genome analysis tool selects protein families specific for a genome or a group of genomes. For each protein cluster, DiffTool includes access to sequences, coloured multiple alignments and phylogenetic trees.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70333, 2002, 'bio', 'The NCTU BioInfo Archive of biological data sets for bioinformatics research and experimentation', 'Hu, Yuh-Jyh', 'Computer and Information Science Department, National Chiao Tung University, Taiwan, ROC. yhu@cis.nctu.edu.tw', '-', 3, 'Computational Biology|Computing Methodologies|Database Management Systems|Databases, Factual|Databases, Genetic|Information Storage and Retrieval|Internet|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/18.8.1145', '12176843', 'Most of the current bioinformatics literature lacks an explicit and clear description of the data used in experiments when introducing or evaluating computational tools. Without the exact data set that was fed into computational tools in experiments, any mistaken preparation of the data for later experiments may lead to discrepancy in conclusion. The NCTU BioInfo Archive is a new web-based bioinformatic data archive. It serves as a test bed for evaluating computational tools, as a bridge to link other research communities with bioinformatics, and also as an environment full of motivations and possibilities to encourage more exploratory research in bioinformatics.'),
(70334, 2002, 'bio', 'XEMBL: distributing EMBL data in XML format', 'Wang, Lichun|Riethoven, Jean-Jack|Robinson, Alan', 'European Bioinformatics Institute, EMBL Outstation - Hinxton, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. lcwang@ebi.ac.uk|European Bioinformatics Institute, EMBL Outstation - Hinxton, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. lcwang@ebi.ac.uk|European Bioinformatics Institute, EMBL Outstation - Hinxton, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. lcwang@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 32, 'Computer Communication Networks|Computing Methodologies|Database Management Systems|Databases, Nucleic Acid|Information Storage and Retrieval|Internet|Programming Languages|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/18.8.1147', '12176844', 'Data in the EMBL Nucleotide Sequence Database is traditionally available in a flat file format that has a number of known shortcomings. With XML rapidly emerging as a standard data exchange format that can address some problems of flat file formats by defining data structure and syntax, there is now a demand to distribute EMBL data in an XML format. XEMBL is a service tool that employs CORBA servers to access EMBL data, and distributes the data in XML format via a number of mechanisms.'),
(70335, 2002, 'bio', 'The EBI SRS server-new features', 'Zdobnov, Evgeny M|Lopez, Rodrigo|Apweiler, Rolf|Etzold, Thure', 'EMBL Outstation - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|EMBL Outstation - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|EMBL Outstation - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|EMBL Outstation - European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Communication Networks|Computing Methodologies|Database Management Systems|Databases, Genetic|Internet|Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.8.1149', '12176845', 'Here we report on recent developments at the EBI SRS server (http://srs.ebi.ac.uk). SRS has become an integration system for both data retrieval and sequence analysis applications. The EBI SRS server is a primary gateway to major databases in the field of molecular biology produced and supported at EBI as well as European public access point to the MEDLINE database provided by US National Library of Medicine (NLM). It is a reference server for latest developments in data and application integration. The new additions include: concept of virtual databases, integration of XML databases like the Integrated Resource of Protein Domains and Functional Sites (InterPro), Gene Ontology (GO), MEDLINE, Metabolic pathways, etc., user friendly data representation in Nice views, SRSQuickSearch bookmarklets.'),
(70336, 2002, 'bio', 'Genome structures embossed by oligonucleotide-stickiness', 'Nishigaki, Koichi|Saito, Ayumu', 'Department of Functional Materials Science, Saitama University, 255 Shimo-Okubo, Saitama, Saitama 338-8570, Japan. koichi@fms.saitama-u.ac.jp|Department of Functional Materials Science, Saitama University, 255 Shimo-Okubo, Saitama, Saitama 338-8570, Japan. koichi@fms.saitama-u.ac.jp', 'Japan|Japan', 1, 'Algorithms|Base Sequence|Binding Sites|Chromosomes|Databases, Nucleic Acid|Genome|Genome, Bacterial|Genome, Human|Humans|Models, Chemical|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Sensitivity and Specificity|Sequence Analysis, DNA|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/18.9.1153', '12217906', 'An unmanageably large amount of data on genome sequences is accumulating, prompting researchers to develop new methods to analyze them. We have devised a novel method designated oligostickiness, a measure roughly proportional to the binding affinity of an oligonucleotide to a DNA of interest, in order to analyze genome sequences as a whole.'),
(70337, 2002, 'bio', 'Pooled library tissue tags for EST-based gene discovery', 'Gavin, A J|Scheetz, T E|Roberts, C A|OLeary, B|Braun, T A|Sheffield, V C|Soares, M B|Robinson, J P|Casavant, T L', 'Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.|Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Database Management Systems|Expressed Sequence Tags|Gene Library|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Molecular Sequence Data|Rats|Reproducibility of Results|Sequence Analysis, DNA|Sequence Tagged Sites|Software', 'Journal Article', '10.1093/bioinformatics/18.9.1162', '12217907', 'In gene discovery projects based on EST sequencing, effective post-sequencing identification methods are important in determining tissue sources of ESTs within pooled cDNA libraries. In the past, such identification efforts have been characterized by higher than necessary failure rates due to the presence of errors within the subsequence containing the oligo tag intended to define the tissue source for each EST.'),
(70338, 2002, 'bio', 'Identification of regulatory elements using a feature selection method', 'Kelexc5x9f, Sxc3xbcndxc3xbcz|van der Laan, Mark|Eisen, Michael B', 'Division of Biostatistics, U. of California, Berkeley, CA 94720, USA. keles@stat.berkeley.edu|Division of Biostatistics, U. of California, Berkeley, CA 94720, USA. keles@stat.berkeley.edu|Division of Biostatistics, U. of California, Berkeley, CA 94720, USA. keles@stat.berkeley.edu', 'United States|United States|United States', 0, 'Amino Acid Motifs|Base Sequence|Gene Expression Regulation|Mitosis|Models, Genetic|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.9.1167', '12217908', 'Many methods have been described to identify regulatory motifs in the transcription control regions of genes that exhibit similar patterns of gene expression across a variety of experimental conditions. Here we focus on a single experimental condition, and utilize gene expression data to identify sequence motifs associated with genes that are activated under this experimental condition. We use a linear model with two-way interactions to model gene expression as a function of sequence features (words) present in presumptive transcription control regions. The most relevant features are selected by a feature selection method called stepwise selection with monte carlo cross validation. We apply this method to a publicly available dataset of the yeast Saccharomyces cerevisiae, focussing on the 800 basepairs immediately upstream of each genes translation start site (the upstream control region (UCR)).'),
(70339, 2002, 'bio', 'Geometry of gene expression dynamics', 'Rifkin, S A|Kim, J', 'Department of Ecology and Evolutionary Biology, PO Box 208106, Yale University, New Haven, CT 06520, USA.|Department of Ecology and Evolutionary Biology, PO Box 208106, Yale University, New Haven, CT 06520, USA.', 'United States|United States', 20, 'Algorithms|Cell Cycle|Computer Graphics|DNA|Fourier Analysis|Gene Expression|Gene Expression Regulation|Genome, Bacterial|Models, Genetic|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Periodicity|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Analysis, DNA|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.9.1176', '12217909', 'A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure.'),
(70340, 2002, 'bio', 'Determination of minimum sample size and discriminatory expression patterns in microarray data', 'Hwang, Daehee|Schmitt, William A|Stephanopoulos, George|Stephanopoulos, Gregory', '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', 26, 'Acute Disease|Algorithms|Databases, Nucleic Acid|Discriminant Analysis|Gene Expression Profiling|Humans|Leukemia, Myeloid|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Predictive Value of Tests|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Sequence Analysis, DNA|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.9.1184', '12217910', 'Transcriptional profiling using microarrays can reveal important information about cellular and tissue expression phenotypes, but these measurements are costly and time consuming. Additionally, tissue sample availability poses further constraints on the number of arrays that can be analyzed in connection with a particular disease or state of interest. It is therefore important to provide a method for the determination of the minimum number of microarrays required to separate, with statistical reliability, distinct disease states or other physiological differences.'),
(70341, 2002, 'bio', 'Bayesian infinite mixture model based clustering of gene expression profiles', 'Medvedovic, Mario|Sivaganesan, Siva', 'Center for Genome Information, Department of Environmental Health, University of Cincinnati Medical Center, 3223 Eden Av. ML 56, Cincinnati, OH 45267-0056, USA. medvedm@email.uc.edu|Center for Genome Information, Department of Environmental Health, University of Cincinnati Medical Center, 3223 Eden Av. ML 56, Cincinnati, OH 45267-0056, USA. medvedm@email.uc.edu', 'United States|United States', 0, 'Bayes Theorem|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Fungal|Mitosis|Models, Genetic|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Analysis, DNA|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/18.9.1194', '12217911', 'The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data.'),
(70342, 2002, 'bio', 'Ratio statistics of gene expression levels and applications to microarray data analysis', 'Chen, Yidong|Kamat, Vishnu|Dougherty, Edward R|Bittner, Michael L|Meltzer, Paul S|Trent, Jeffery M', 'Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.|Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.|Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.|Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.|Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.|Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Building 50, Room 5154, 50 South Drive, MSC 8000, Bethesda, MD 20892, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Cell Line|Computer Simulation|Gene Expression|Gene Expression Regulation|Image Enhancement|Melanoma|Models, Statistical|Myeloid Cells|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.9.1207', '12217912', 'Expression-based analysis for large families of genes has recently become possible owing to the development of cDNA microarrays, which allow simultaneous measurement of transcript levels for thousands of genes. For each spot on a microarray, signals in two channels must be extracted from their backgrounds. This requires algorithms to extract signals arising from tagged mRNA hybridized to arrayed cDNA locations and algorithms to determine the significance of signal ratios.'),
(70343, 2002, 'bio', 'Multi-class cancer classification via partial least squares with gene expression profiles', 'Nguyen, Danh V|Rocke, David M', 'Department of Statistics, Texas A&M University, College Station, TX 77843, USA. dnguyen@stat.tamu.edu|Department of Statistics, Texas A&M University, College Station, TX 77843, USA. dnguyen@stat.tamu.edu', 'United States|United States', 198, 'Algorithms|Breast Neoplasms|Databases, Nucleic Acid|Discriminant Analysis|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Least-Squares Analysis|Leukemia|Lymphoma|Models, Genetic|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Tumor Cells, Cultured', 'Comparative Study', '10.1093/bioinformatics/18.9.1216', '12217913', 'Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously.'),
(70344, 2002, 'bio', 'Optimal enhancement of immune response', 'Stengel, Robert F|Ghigliazza, Raffaele M|Kulkarni, Nilesh V', 'School of Engineering and Applied Science, Princeton University, Princeton, NJ 08540, USA. stengel@princeton.edu|School of Engineering and Applied Science, Princeton University, Princeton, NJ 08540, USA. stengel@princeton.edu|School of Engineering and Applied Science, Princeton University, Princeton, NJ 08540, USA. stengel@princeton.edu', 'United States|United States|United States', 0, 'Antibodies|Antigens|Combined Modality Therapy|Computer Simulation|Feedback, Physiological|Humans|Infection|Models, Immunological|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.9.1227', '12217914', 'Therapeutic enhancement of innate immune response to microbial attack is addressed as the optimal control of a dynamic system. Interactions between an invading pathogen and the innate immune system are characterized by four non-linear, ordinary differential equations that describe rates of change of pathogen, plasma cell, and antibody concentrations, and of an indicator of organic health. Without therapy, the dynamic model evidences sub-clinical or clinical decay, chronic stabilization, or unrestrained lethal growth of the pathogen, the response pattern depends on the initial concentration of pathogens in the simulated attack. In the model, immune response can be augmented by therapeutic agents that kill the pathogen directly, that stimulate the production of plasma cells or antibodies, or that enhance organ health. A previous paper demonstrated open-loop optimal control solutions that defeat the pathogen and preserve organ health, given initial conditions that otherwise would be lethal (Stengel et al. (2002)). Therapies based on separate and combined application of the agents were derived by minimizing a quadratic cost function that weighted both system response and control usage, providing implicit control over harmful side effects.'),
(70345, 2002, 'bio', 'The correlation error and finite-size correction in an ungapped sequence alignment', 'Park, Yonil|Spouge, John L', 'National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, Bethesda, MD 20894, USA.', 'United States|United States', 9, 'Database Management Systems|Databases, Genetic|False Positive Reactions|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Monte Carlo Method|National Library of Medicine (U.S.)|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis|Sequence Homology|Statistics as Topic|United States', 'Comparative Study', '10.1093/bioinformatics/18.9.1236', '12217915', 'The BLAST program for comparing two sequences assumes independent sequences in its random model. The resulting random alignment matrices have correlations across their diagonals. Analytic formulas for the BLAST p-value essentially neglect these correlations and are equivalent to a random model with independent diagonals. Progress on the independent diagonals model has been surprisingly rapid, but the practical magnitude of the correlations it neglects remains unknown. In addition, BLAST uses a finite-size correction that is particularly important when either of the sequences being compared is short. Several formulas for the finite-size correction have now been given, but the corresponding errors in the BLAST p-values have not been quantified. As the lengths of compared sequences tend to infinity, it is also theoretically unknown whether the neglected correlations vanish faster than the finite-size correction.'),
(70346, 2002, 'bio', 'The use of structure information to increase alignment accuracy does not aid homologue detection with profile HMMs', 'Griffiths-Jones, Sam|Bateman, Alex', 'The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK. sgj@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK. sgj@sanger.ac.uk', 'United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Database Management Systems|Databases, Genetic|Evaluation Studies as Topic|Gene Expression Profiling|Information Storage and Retrieval|Internet|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/18.9.1243', '12217916', 'The best quality multiple sequence alignments are generally considered to derive from structural superposition. However, no previous work has studied the relative performance of profile hidden Markov models (HMMs) derived from such alignments. Therefore several alignment methods have been used to generate multiple sequence alignments from 348 structurally aligned families in the HOMSTRAD database. The performance of profile HMMs derived from the structural and sequence-based alignments has been assessed for homologue detection.'),
(70347, 2002, 'bio', 'ESyPred3D: Prediction of proteins 3D structures', 'Lambert, Christophe|Léonard, Nadia|De Bolle, Xavier|Depiereux, Eric', 'Facultés Universitaires Notre-Dame de la Paix, Unité de Recherche en Biologie Moléculaire, Rue de Bruxelles, 61, B-5000 Namur, Belgium. christophe.lambert@fundp.ac.be|Facultés Universitaires Notre-Dame de la Paix, Unité de Recherche en Biologie Moléculaire, Rue de Bruxelles, 61, B-5000 Namur, Belgium. christophe.lambert@fundp.ac.be|Facultés Universitaires Notre-Dame de la Paix, Unité de Recherche en Biologie Moléculaire, Rue de Bruxelles, 61, B-5000 Namur, Belgium. christophe.lambert@fundp.ac.be|Facultés Universitaires Notre-Dame de la Paix, Unité de Recherche en Biologie Moléculaire, Rue de Bruxelles, 61, B-5000 Namur, Belgium. christophe.lambert@fundp.ac.be', 'Belgium|Belgium|Belgium|Belgium', 513, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Internet|Models, Genetic|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/18.9.1250', '12217917', 'Homology or comparative modeling is currently the most accurate method to predict the three-dimensional structure of proteins. It generally consists in four steps: (1) databanks searching to identify the structural homolog, (2) target-template alignment, (3) model building and optimization, and (4) model evaluation. The target-template alignment step is generally accepted as the most critical step in homology modeling.'),
(70348, 2002, 'bio', 'A dissimilarity matrix between protein atom classes based on Gaussian mixtures', 'Rantanen, Ville-Veikko|Gyllenberg, Mats|Koski, Timo|Johnson, Mark S', 'Department of Mathematics, University of Turku, FIN-20014 Turku, Finland.|Department of Mathematics, University of Turku, FIN-20014 Turku, Finland.|Department of Mathematics, University of Turku, FIN-20014 Turku, Finland.|Department of Mathematics, University of Turku, FIN-20014 Turku, Finland.', 'Finland|Finland|Finland|Finland', 4, 'Cluster Analysis|Databases, Protein|Ligands|Models, Molecular|Models, Statistical|Normal Distribution|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/18.9.1257', '12217918', 'Previously, Rantanen et al. (2001, J. Mol. Biol., 313, 197-214) constructed a protein atom-ligand fragment interaction library embodying experimentally solved, high-resolution three-dimensional (3D) structural data from the Protein Data Bank (PDB). The spatial locations of protein atoms that surround ligand fragments were modeled with Gaussian mixture models, the parameters of which were estimated with the expectation-maximization (EM) algorithm. In the validation analysis of this library, there was strong indication that the protein atom classification, 24 classes, was too large and that a reduction in the classes would lead to improved predictions.'),
(70349, 2002, 'bio', 'VIZARD: analysis of Affymetrix Arabidopsis GeneChip data', 'Moseyko, Nick|Feldman, Lewis J', 'Department of Plant and Microbial Biology, University of California at Berkeley, 111 Koshland Hall, Berkeley, CA 94720-3102, USA. moseyko@uclink4.berkeley.edu|Department of Plant and Microbial Biology, University of California at Berkeley, 111 Koshland Hall, Berkeley, CA 94720-3102, USA. moseyko@uclink4.berkeley.edu', 'United States|United States', 0, 'Arabidopsis|Computer Graphics|Data Display|Database Management Systems|Databases, Nucleic Acid|Gene Expression|Genome, Plant|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.9.1264', '12217919', 'The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array.'),
(70350, 2002, 'bio', 'SEGE: A database on intron less/single exonic genes from eukaryotes', 'Sakharkar, Meena K|Kangueane, Pandjassarame|Petrov, Dmitri A|Kolaskar, A S|Subbiah, S', 'Department of Applied Physics, Stanford University, CA 94305, USA. mmeena@ntu.edu.sg|Department of Applied Physics, Stanford University, CA 94305, USA. mmeena@ntu.edu.sg|Department of Applied Physics, Stanford University, CA 94305, USA. mmeena@ntu.edu.sg|Department of Applied Physics, Stanford University, CA 94305, USA. mmeena@ntu.edu.sg|Department of Applied Physics, Stanford University, CA 94305, USA. mmeena@ntu.edu.sg', 'United States|United States|United States|United States|United States', 0, 'Animals|Arabidopsis|Caenorhabditis elegans|Databases, Nucleic Acid|Drosophila|Exons|Genome|Genome, Fungal|Genome, Human|Genome, Plant|Humans|Information Storage and Retrieval|Introns|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.9.1266', '12217920', 'Eukaryotes have both intron containing and intron less genes. Several databases are available for intron containing genes in eukaryotes. In this note, we describe a database for intron less genes from eukaryotes. Intron less eukaryotic genes having prokaryotic architecture will help to understand gene evolution in a much simpler way unlike intron containing genes.'),
(70351, 2002, 'bio', 'UniBLAST: a system to filter, cluster, and display BLAST results and assign unique gene annotation', 'Zhou, Yan|Huang, Guyang Matthew|Wei, Liping', 'Institute of Genetics, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, 220 Handan Road, Shanghai 200433, Peoples Republic of China.|Institute of Genetics, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, 220 Handan Road, Shanghai 200433, Peoples Republic of China.|Institute of Genetics, State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, 220 Handan Road, Shanghai 200433, Peoples Republic of China.', 'China|China|China', 0, 'Cluster Analysis|Computer Graphics|Data Display|Database Management Systems|Databases, Nucleic Acid|Genome|Genome, Human|Genome, Plant|Humans|Information Storage and Retrieval|Internet|National Library of Medicine (U.S.)|Oryza|Sensitivity and Specificity|Sequence Analysis, DNA|United States|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.9.1268', '12217921', 'More and more often, a gene is epitomized by a large number of sequences in GenBank. This high redundancy makes it very difficult to identify a unique best match for a query sequence from its BLAST results. We developed a novel program UniBLAST that filters out uninformative hits, clusters the redundant hits, groups the hits by LocusLink, and graphically displays the results. We also implemented a scoring function in UniBLAST to assign a unique gene name to a query sequence. UniBLAST significantly increases the efficiency of gene annotation.'),
(70352, 2002, 'bio', 'Parallelized multiple alignment', 'Kleinjung, Jens|Douglas, Nigel|Heringa, Jaap', 'Division of Mathematical Biology, National Institute for Medical Research, Mill Hill, London NW7 1AA, UK.|Division of Mathematical Biology, National Institute for Medical Research, Mill Hill, London NW7 1AA, UK.|Division of Mathematical Biology, National Institute for Medical Research, Mill Hill, London NW7 1AA, UK.', 'United Kingdom|United Kingdom|United Kingdom', 29, 'Algorithms|Computing Methodologies|Databases, Genetic|Internet|Sensitivity and Specificity|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/18.9.1270', '12217922', 'Multiple sequence alignment is a frequently used technique for analyzing sequence relationships. Compilation of large alignments is computationally expensive, but processing time can be considerably reduced when the computational load is distributed over many processors. Parallel processing functionality in the form of single-instruction multiple-data (SIMD) technology was implemented into the multiple alignment program Praline by using message passing interface (MPI) routines. Over the alignments tested here, the parallelized program performed up to ten times faster on 25 processors compared to the single processor version.'),
(70353, 2002, 'bio', 'OrthoGUI: graphical presentation of Orthostrapper results', 'Hollich, Volker|Storm, Christian E V|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 0, 'ATP-Binding Cassette Transporters|Adenosine Triphosphate|Algorithms|Computer Graphics|Data Display|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Protein Binding|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology|Species Specificity|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/18.9.1272', '12217923', 'Orthostrapper is a program that calculates orthology support values for pairs of sequences in a multiple alignment (Storm and Sonnhammer, Bioinformatics, 18, 92-99, 2002). Here we present OrthoGUI, a web interface and display tool for Orthostrapper analysis. OrthoGUI visualizes the Orthostrapper output in both tabular and tree representations, and can also apply a clustering algorithm to identify groups of multiple orthologs, which are indicated by colour coding.'),
(70354, 2002, 'bio', 'A kinase sequence database: sequence alignments and family assignment', 'Buzko, Oleksandr|Shokat, Kevan M', 'Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 513 Parnassus, San Francisco, CA 94143-0450, USA. buzko@cmp.ucsf.edu|Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 513 Parnassus, San Francisco, CA 94143-0450, USA. buzko@cmp.ucsf.edu', 'United States|United States', 39, 'Cluster Analysis|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Protein Kinases|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology', 'Journal Article', '10.1093/bioinformatics/18.9.1274', '12217924', 'The Kinase Sequence Database (KSD) located at http://kinase.ucsf.edu/ksd contains information on 290 protein kinase families derived by profile-based clustering of the non-redundant list of sequences obtained from a GenBank-wide search. Included in the database are a total of 5,041 protein kinases from over 100 organisms. Clustering into families is based on the extent of homology within the kinase catalytic domain (250-300 residues in length). Alignments of the families are viewed by interactive Excel-based sequence spreadsheets. In addition, KSD features evolutionary trees derived for each family and detailed information on each sequence as well as links to the corresponding GenBank entries. Sequence manipulation tools, such as evolutionary tree generation, novel sequence assignment, and statistical analysis, are also provided.'),
(70355, 2002, 'bio', 'PeCoP: automatic determination of persistently conserved positions in protein families', 'Friedberg, Iddo|Margalit, Hanah', 'Department of Molecular Genetics and Biotechnology, The Hebrew University--Hadassah Medical School, 91120, Jerusalem, Israel. idoerg@burnham.org|Department of Molecular Genetics and Biotechnology, The Hebrew University--Hadassah Medical School, 91120, Jerusalem, Israel. idoerg@burnham.org', 'Israel|Israel', 0, 'Amino Acid Sequence|Conserved Sequence|Databases, Protein|Internet|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/18.9.1276', '12217925', 'PeCoP is a WWW-based service which accepts a protein sequence, and reports positions that are conserved in close and distant sequence family members. The collation of family members is performed using iterative PSI-BLAST runs. Examining positional conservation in close and distant family members enables a better selection of positions that may play a role in determining the proteins structure and function.'),
(70356, 2002, 'bio', 'Applications of Tree-Maps to hierarchical biological data', 'McConnell, Patrick|Johnson, Kimberly|Lin, Simon', 'Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham NC 27710, USA. mccon012@mc.duke.edu|Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham NC 27710, USA. mccon012@mc.duke.edu|Duke Bioinformatics Shared Resource, Duke University Medical Center, Durham NC 27710, USA. mccon012@mc.duke.edu', 'United States|United States|United States', 15, 'Algorithms|Cluster Analysis|Computer Graphics|Databases, Genetic|Genomics|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Phylogeny|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/18.9.1278', '12217926', 'A brief overview of Tree-Maps provides the basis for understanding two new implementations of Tree-Map methods. TreeMapClusterView provides a new way to view microarray gene expression data, and GenePlacer provides a view of gene ontology annotation data. We also discuss the benefits of Tree-Maps to visualize complex hierarchies in functional genomics.'),
(70357, 2002, 'bio', 'An ontology driven architecture for derived representations of macromolecular structure', 'Greer, Douglas S|Westbrook, John D|Bourne, Philip E', 'The Research Collaboratory for Structural Bioinformatics at the San Diego Supercomputer Center, 9500 Gilman Drive, University of California, San Diego, La Jolla CA 92093-0505, USA. dsg@sdsc.edu|The Research Collaboratory for Structural Bioinformatics at the San Diego Supercomputer Center, 9500 Gilman Drive, University of California, San Diego, La Jolla CA 92093-0505, USA. dsg@sdsc.edu|The Research Collaboratory for Structural Bioinformatics at the San Diego Supercomputer Center, 9500 Gilman Drive, University of California, San Diego, La Jolla CA 92093-0505, USA. dsg@sdsc.edu', 'United States|United States|United States', 0, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Local Area Networks|Macromolecular Substances|Molecular Structure', 'Journal Article', '10.1093/bioinformatics/18.9.1280', '12217927', 'An object metamodel based on a standard scientific ontology has been developed and used to generate a CORBA interface, an SQL schema and an XML representation for macromolecular structure (MMS) data. In addition to the interface and schema definitions, the metamodel was also used to generate the core elements of a CORBA reference server and a JDBC database loader. The Java source code which implements this metamodel, the CORBA server, database loader and XML converter along with detailed documentation and code examples are available as part of the OpenMMS toolkit.'),
(70358, 2002, 'bio', 'An intelligent biological information management system', 'Palakal, Mathew|Mukhopadhyay, Snehasis|Mostafa, Javed|Raje, Rajeev|NCho, Mathias|Mishra, Santosh', 'Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu|Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu|Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu|Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu|Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu|Computer Science Department, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA. mpalakal@cs.iupui.edu', 'India|India|India|India|India|India', 6, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Databases, Factual|Feasibility Studies|Humans|Information Storage and Retrieval|Internet|Lithotripsy|Pilot Projects|Polymorphism, Genetic|PubMed|User-Computer Interface|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/18.10.1283', '12376371', 'As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area.'),
(70359, 2002, 'bio', 'Condition specific transcription factor binding site characterization in Saccharomyces cerevisiae', 'Harrison, Rhonda|DeLisi, Charles', 'Bioinformatics Department, Boston University, 44 Cummington Street, Boston, MA 02215, USA. rhonda@bu.edu|Bioinformatics Department, Boston University, 44 Cummington Street, Boston, MA 02215, USA. rhonda@bu.edu', 'United States|United States', 0, 'Amino Acid Motifs|Base Sequence|Binding Sites|Consensus Sequence|DNA-Binding Proteins|Database Management Systems|Databases, Genetic|Databases, Protein|Fungal Proteins|Gene Expression Regulation, Fungal|Genome, Fungal|Information Storage and Retrieval|Internet|Molecular Sequence Data|Open Reading Frames|Protein Binding|Quality Control|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/18.10.1289', '12376372', 'We demonstrate a computational process by which transcription factor binding sites can be elucidated using genome-wide expression and binding profiles. The profiles direct us to the intergenic locations likely to contain the promoter regions for a given factor. These sequences are multiply and locally aligned to give an anchor motif from which further characterization can take place.'),
(70360, 2002, 'bio', 'Optimal algorithms for local vertex quartet cleaning', 'Della Vedova, Gianluca|Wareham, H Todd', 'Dipartimento di Statistica, Universitá degli Studi di Milano-Bicocca, via Bicocca degli Arcimboldi 8, 20126 Milano, Italy. gianluca.dellavedova@unimib.it|Dipartimento di Statistica, Universitá degli Studi di Milano-Bicocca, via Bicocca degli Arcimboldi 8, 20126 Milano, Italy. gianluca.dellavedova@unimib.it', 'Italy|Italy', 4, 'Algorithms|Evolution, Molecular|Genetic Variation|Models, Genetic|Models, Statistical|Phylogeny|Quality Control|Sequence Alignment|Sequence Analysis, DNA|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/18.10.1297', '12376373', 'Reconstructing evolutionary trees is an important problem in biology. A response to the computational intractability of most of the traditional criteria for inferring evolutionary trees has been a focus on new criteria, particularly quartet-based methods that seek to merge trees derived on subsets of four species from a given species-set into a tree for that entire set. Unfortunately, most of these methods are very sensitive to errors in the reconstruction of the trees for individual quartets of species. A recently developed technique called quartet cleaning can alleviate this difficulty in certain cases by using redundant information in the complete set of quartet topologies for a given species-set to correct such errors.'),
(70361, 2002, 'bio', 'Short inversions and conserved gene cluster', 'Sankoff, David', 'Centre de recherches mathématiques, Université de Montréal, CP 6128 succursale Centre-Ville, Montréal, Québec, Canada H3C 3J7. sankoff@poste.umontreal.ca', 'Canada', 0, 'Base Sequence|Chromosome Inversion|Conserved Sequence|DNA, Circular|Evolution, Molecular|Gene Conversion|Gene Expression Profiling|Gene Expression Regulation|Gene Rearrangement|Genomics|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/18.10.1305', '12376374', 'Two independent sets of recent observations on newly sequenced microbial genomes pertain to the prevalence of short inversion as a gene order rearrangement process and to the lack of conservation of gene order within conserved gene clusters. We propose a model of inversion where the key parameter is the length of the inverted fragment.'),
(70362, 2002, 'bio', 'Comparative ab initio prediction of gene structures using pair HMMs', 'Meyer, Irmtraud M|Durbin, Richard', 'The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. im1@sanger.ac.uk|The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. im1@sanger.ac.uk', 'United Kingdom|United Kingdom', 85, 'Algorithms|Animals|Databases, Nucleic Acid|Exons|Gene Expression Regulation|Humans|Markov Chains|Mice|Models, Genetic|Proteomics|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/18.10.1309', '12376375', 'We present a novel comparative method for the ab initio prediction of protein coding genes in eukaryotic genomes. The method simultaneously predicts the gene structures of two un-annotated input DNA sequences which are homologous to each other and retrieves the subsequences which are conserved between the two DNA sequences. It is capable of predicting partial, complete and multiple genes and can align pairs of genes which differ by events of exon-fusion or exon-splitting. The method employs a probabilistic pair hidden Markov model. We generate annotations using our model with two different algorithms: the Viterbi algorithm in its linear memory implementation and a new heuristic algorithm, called the stepping stone, for which both memory and time requirements scale linearly with the sequence length. We have implemented the model in a computer program called DOUBLESCAN. In this article, we introduce the method and confirm the validity of the approach on a test set of 80 pairs of orthologous DNA sequences from mouse and human. More information can be found at: http://www.sanger.ac.uk/Software/analysis/doublescan/'),
(70363, 2002, 'bio', 'Gene perturbation and intervention in probabilistic Boolean networks', 'Shmulevich, Ilya|Dougherty, Edward R|Zhang, Wei', 'Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org|Cancer Genomics Laboratory, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 85, Houston, TX 77030, USA. is@ieee.org', 'United States|United States|United States', 0, 'Chromosome Mapping|Computer Simulation|Gene Expression Regulation|Genetic Engineering|Human Genome Project|Models, Genetic|Models, Statistical|Mutagenesis|Mutation|Reproducibility of Results|Sensitivity and Specificity|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/18.10.1319', '12376376', 'A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior.'),
(70364, 2002, 'bio', 'Bayesian automatic relevance determination algorithms for classifying gene expression data', 'Li, Yi|Campbell, Colin|Tipping, Michael', 'Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK.|Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK.|Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK Microsoft Research, 7 J J Thomson Avenue, Cambridge, CB3 0FD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 126, 'Algorithms|Bayes Theorem|Cluster Analysis|Colonic Neoplasms|Databases, Nucleic Acid|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Markers|Humans|Leukemia, Myeloid, Acute|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Pattern Recognition, Automated|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.10.1332', '12376377', 'We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays.'),
(70365, 2002, 'bio', 'Selecting signature oligonucleotides to identify organisms using DNA arrays', 'Kaderali, Lars|Schliep, Alexander', 'Center for Applied Computer Sciences Cologne (ZAIK), University of Cologne, Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Center for Applied Computer Sciences Cologne (ZAIK), University of Cologne, Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de', 'Germany|Germany', 88, 'Algorithms|Base Sequence|Computer Simulation|Computer-Aided Design|DNA Probes|Databases, Nucleic Acid|Equipment Design|Gene Targeting|HIV-1|Humans|Microbiological Techniques|Models, Chemical|Models, Genetic|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Quality Control|RNA, Ribosomal, 28S|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Temperature', 'Evaluation Studies', '10.1093/bioinformatics/18.10.1340', '12376378', 'DNA arrays are a very useful tool to quickly identify biological agents present in some given sample, e.g. to identify viruses causing disease, for quality control in the food industry, or to determine bacteria contaminating drinking water. The selection of specific oligos to attach to the array surface is a relevant problem in the experiment design process. Given a set S of genomic sequences (the target sequences), the task is to find at least one oligonucleotide, called probe, for each sequence in S. This probe will be attached to the array surface, and must be chosen in a way that it will not hybridize to any other sequence but the intended target. Furthermore, all probes on the array must hybridize to their intended targets under the same reaction conditions, most importantly at the temperature T at which the experiment is conducted.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70366, 2002, 'bio', 'Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility', 'Lin, Kuang|May, Alex C W|Taylor, William R', 'Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill NW7 1AA, UK.|Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill NW7 1AA, UK.|Division of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill NW7 1AA, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Databases, Protein|Models, Genetic|Models, Molecular|Models, Statistical|Molecular Sequence Data|Neural Networks (Computer)|Protein Conformation|Protein Folding|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.10.1350', '12376379', 'Fold recognition programs align a probe protein sequence onto protein three-dimensional (3D) structure templates. The alignment between the probe sequence and the most suitable template can be used to predict the 3D structure and often biological function of the probe. Here we present a new threading scoring function of protein sequence-structure compatibility. An artificial neural network model is trained to predict compatibility of amino acid side-chains with structural environments. Log-odds scores of predicted probabilities from this model can then be used to construct protein sequence-structure alignments.'),
(70367, 2002, 'bio', 'Data mining of sequences and 3D structures of allergenic proteins', 'Ivanciuc, Ovidiu|Schein, Catherine H|Braun, Werner', 'Sealy Center for Structural Biology, Department of Human Biological Chemistry and Genetics, University of Texas Medical Branch, Galveston, TX 77555-1157, USA.|Sealy Center for Structural Biology, Department of Human Biological Chemistry and Genetics, University of Texas Medical Branch, Galveston, TX 77555-1157, USA.|Sealy Center for Structural Biology, Department of Human Biological Chemistry and Genetics, University of Texas Medical Branch, Galveston, TX 77555-1157, USA.', 'United States|United States|United States', 67, 'Allergens|Amino Acid Sequence|Cross Reactions|Database Management Systems|Databases, Protein|Immunoglobulin E|Information Storage and Retrieval|Internet|Molecular Sequence Data|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/18.10.1358', '12376380', 'Many sequences, and in some cases structures, of proteins that induce an allergic response in atopic individuals have been determined in recent years. This data indicates that allergens, regardless of source, fall into discreet protein families. Similarities in the sequence may explain clinically observed cross-reactivities between different biological triggers. However, previously available allergy databases group allergens according to their biological sources, or observed clinical cross-reactivities, without providing data about the proteins. A computer-aided data mining system is needed to compare the sequential and structural details of known allergens. This information will aid in predicting allergenic cross-responses and eventually in determining possible common characteristics of IgE recognition.'),
(70368, 2002, 'bio', 'Quantification of protein surfaces, volumes and atom-atom contacts using a constrained Voronoi procedure', 'McConkey, B J|Sobolev, V|Edelman, M', 'Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel. brendan.mcconkey@weizmann.ac.il|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel. brendan.mcconkey@weizmann.ac.il|Department of Plant Sciences, Weizmann Institute of Science, Rehovot 76100, Israel. brendan.mcconkey@weizmann.ac.il', 'Israel|Israel|Israel', 70, 'Aldehyde Reductase|Algorithms|Computer Simulation|Databases, Protein|Glycoside Hydrolases|Immunoglobulin Fab Fragments|Macromolecular Substances|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Solvents|Surface Properties', 'Comparative Study', '10.1093/bioinformatics/18.10.1365', '12376381', 'Geometric representations of proteins and ligands, including atom volumes, atom-atom contacts and solvent accessible surfaces, can be used to characterize interactions between and within proteins, ligands and solvent. Voronoi algorithms permit quantification of these properties by dividing structures into cells with a one-to-one correspondence with constituent atoms. As there is no generally accepted measure of atom-atom contacts, a continuous analytical representation of inter-atomic contacts will be useful. Improved geometric algorithms will also be helpful in increasing the speed and accuracy of iterative modeling algorithms.'),
(70369, 2002, 'bio', 'Finding motifs in the twilight zone', 'Keich, U|Pevzner, P A', 'Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. keich@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. keich@cs.ucsd.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Base Sequence|Benchmarking|Consensus Sequence|DNA|DNA-Binding Proteins|Escherichia coli|Molecular Sequence Data|Promoter Regions, Genetic|Quality Control|Regulatory Sequences, Nucleic Acid|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.10.1374', '12376382', 'Gene activity is often affected by binding transcription factors to short fragments in DNA sequences called motifs. Identification of subtle regulatory motifs in a DNA sequence is a difficult pattern recognition problem. In this paper we design a new motif finding algorithm that can detect very subtle motifs.'),
(70370, 2002, 'bio', 'Subtle motifs: defining the limits of motif finding algorithms', 'Keich, U|Pevzner, P A', 'Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA. keich@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA. keich@cs.ucsd.edu', 'United States|United States', 56, 'Algorithms|Amino Acid Motifs|Base Sequence|Benchmarking|Consensus Sequence|DNA|DNA Mutational Analysis|Models, Genetic|Models, Statistical|Molecular Sequence Data|Quality Control|Regulatory Sequences, Nucleic Acid|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.10.1382', '12376383', 'What constitutes a subtle motif? Intuitively, it is a motif that is almost indistinguishable, in the statistical sense, from random motifs. This question has important practical consequences: consider, for example, a biologist that is generating a sample of upstream regulatory sequences with the goal of finding a regulatory pattern that is shared by these sequences. If the sequences are too short then one risks losing some of the regulatory patterns that are located further upstream. Conversely, if the sequences are too long, the motif becomes too subtle and one is then likely to encounter random motifs which are at least as significant statistically as the regulatory pattern itself. In practical terms one would like to recognize the sequence length threshold, or the twilight zone, beyond which the motifs are in some sense too subtle.'),
(70371, 2002, 'bio', 'Fold-recognition detects an error in the Protein Data Bank', 'Bujnicki, Janusz|Rychlewski, Leszek|Fischer, Daniel', 'Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, ks. Trojdena, 4 02-109, Warsaw, Poland .|Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, ks. Trojdena, 4 02-109, Warsaw, Poland .|Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, ks. Trojdena, 4 02-109, Warsaw, Poland .', 'Poland|Poland|Poland', 19, 'Amino Acid Sequence|Bacterial Proteins|Computer Simulation|Conserved Sequence|Crystallography|Databases, Protein|Deinococcus|Internet|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Folding|Proteins|Quality Control|Reproducibility of Results|Ribosomal Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.10.1391', '12376384', '-'),
(70372, 2002, 'bio', 'GENOTRACE: cDNA-based local GENOme assembly from TRACE archives', 'Berezikov, Eugene|Plasterk, Ronald H A|Cuppen, Edwin', 'Hubrecht Laboratory, Netherlands Institute for Developmental Biology, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.|Hubrecht Laboratory, Netherlands Institute for Developmental Biology, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.|Hubrecht Laboratory, Netherlands Institute for Developmental Biology, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, 'Archives|Chromosome Mapping|DNA, Complementary|Database Management Systems|Databases, Nucleic Acid|Hypermedia|Information Storage and Retrieval|Internet|National Library of Medicine (U.S.)|Sequence Analysis, DNA|Software|Software Design|United States', 'Journal Article', '10.1093/bioinformatics/18.10.1396', '12376385', 'GENOTRACE identifies the genomic organization for a cDNA using raw data from genome sequencing projects in progress (trace archives). Local genomic contigs are generated, allowing for example the design of PCR primers in intronic sequences to amplify coding regions of a gene, needed for example for mutation or SNP detection.'),
(70373, 2002, 'bio', 'Genquire: genome annotation browser/editor', 'Wilkinson, M D|Block, D|Crosby, W L', 'Plant Biotechnology Institute, National Research Council Canada, Saskatoon, Saskatchwan, S7N 0W9, Canada. mwilkinson@gene.pbi.nrc.ca|Plant Biotechnology Institute, National Research Council Canada, Saskatoon, Saskatchwan, S7N 0W9, Canada. mwilkinson@gene.pbi.nrc.ca|Plant Biotechnology Institute, National Research Council Canada, Saskatoon, Saskatchwan, S7N 0W9, Canada. mwilkinson@gene.pbi.nrc.ca', 'Canada|Canada|Canada', 8, 'Chromosome Mapping|Contig Mapping|Database Management Systems|Databases, Nucleic Acid|Documentation|Hypermedia|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface|Word Processing', 'Journal Article', '10.1093/bioinformatics/18.10.1398', '12376386', 'We present a software package, Genquire, that allows visualization, querying, hand editing, and de novo markup of complete or partially annotated genomes. The system is written in Perl/Tk and uses, where possible, existing BioPerl data models and methods for representation and manipulation of the sequence and annotation objects. An adaptor API is provided to allow Genquire to display a wide range of databases and flat files, and a plugins API provides an interface to other sequence analysis software.'),
(70374, 2002, 'bio', 'Genexp--a genetic network simulation environment', 'Vu, Tra Thi|Vohradsky, Jiri', 'Institute of Microbiology, CAS, Videnska 1083, 142 20 Prague, Czech Republic.|Institute of Microbiology, CAS, Videnska 1083, 142 20 Prague, Czech Republic.', '-|-', 16, 'Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Genes|Genetic Engineering|Genome|Genomics|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/18.10.1400', '12376387', 'An environment for simulation of dynamics of genetic regulatory networks is presented. The model is based on the recurrent neural network principle and allows to interactively simulate various genetic regulatory interactions under different features of the system. The results are displayed graphically.'),
(70375, 2002, 'bio', 'CINEMA-MX: a modular multiple alignment editor', 'Lord, P W|Selley, J N|Attwood, T K', 'School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK. p.lord@russet.org.uk|School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK. p.lord@russet.org.uk|School of Biological Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK. p.lord@russet.org.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Graphics|Database Management Systems|Databases, Nucleic Acid|Information Storage and Retrieval|Sequence Alignment|Software|User-Computer Interface|Word Processing', 'Journal Article', '10.1093/bioinformatics/18.10.1402', '12376388', 'Analyzing and visualizing multiple sequence alignments is a common task in many areas of molecular biology and bioinformatics. Many tools exist for this purpose, but are not easily customizable for specific in-house uses. Here we report the development of an editor, CINEMA-MX, that addresses these issues. CINEMA-MX is highly modular and configurable, and we present examples to illustrate its extensibility.'),
(70376, 2002, 'bio', 'GENIE: estimating demographic history from molecular phylogenies', 'Pybus, O G|Rambaut, A', 'Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. oliver.pybus@zoo.ox.ac.uk|Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK. oliver.pybus@zoo.ox.ac.uk', 'United Kingdom|United Kingdom', 112, 'Computer Simulation|Database Management Systems|Demography|Evolution, Molecular|Genetics, Population|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Pedigree|Phylogeny|Population Dynamics|Software', 'Journal Article', '10.1093/bioinformatics/18.10.1404', '12376389', 'GENIE implements a statistical framework for inferring the demographic history of a population from phylogenies that have been reconstructed from sampled DNA sequences. The methods are based on population genetic models known collectively as coalescent theory.'),
(70377, 2002, 'bio', 'ECCB 2002 Proceedings of the European Conference on Computational Biology, October 6-9, 2002, Saarbrucken, Germany', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1093/bioinformatics/18.10.1404', '12385976', '-'),
(70378, 2002, 'bio', 'Multiple sequence alignment with arbitrary gap costs: computing an optimal solution using polyhedral combinatorics', 'Althaus, Ernst|Caprara, Alberto|Lenhof, Hans-Peter|Reinert, Knut', 'International Computer Science Institute, Berkeley, CA 94704-1198, USA. althaus@icsi.berkeley.edu|International Computer Science Institute, Berkeley, CA 94704-1198, USA. althaus@icsi.berkeley.edu|International Computer Science Institute, Berkeley, CA 94704-1198, USA. althaus@icsi.berkeley.edu|International Computer Science Institute, Berkeley, CA 94704-1198, USA. althaus@icsi.berkeley.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Conserved Sequence|Models, Biological|Models, Chemical|Models, Statistical|Numerical Analysis, Computer-Assisted|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s4', '12385977', 'Multiple sequence alignment is one of the dominant problems in computational molecular biology. Numerous scoring functions and methods have been proposed, most of which result in NP-hard problems. In this paper we propose for the first time a general formulation for multiple alignment with arbitrary gap-costs based on an integer linear program (ILP). In addition we describe a branch-and-cut algorithm to effectively solve the ILP to optimality. We evaluate the performances of our approach in terms of running time and quality of the alignments using the BAliBase database of reference alignments. The results show that our implementation ranks amongst the best programs developed so far.'),
(70379, 2002, 'bio', 'Comparative genomics of microbial pathogens and symbionts', 'Andersson, Siv G E|Alsmark, Cecilia|Canbäck, Björn|Davids, Wagied|Frank, Carolin|Karlberg, Olof|Klasson, Lisa|Antoine-Legault, Boris|Mira, Alex|Tamas, Ivica', 'Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.|Department of Molecular Evolution, Evolutionary Biology Center, University of Uppsala, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 20, 'Alphaproteobacteria|Chromosome Mapping|Evolution, Molecular|Gammaproteobacteria|Gene Transfer, Horizontal|Genetic Variation|Genome, Bacterial|Genomic Instability|Genomics|Models, Genetic|Mutation|Phylogeny|Proteobacteria|Sequence Analysis, DNA|Species Specificity|Symbiosis', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s17', '12385978', 'We are interested in quantifying the contribution of gene acquisition, loss, expansion and rearrangements to the evolution of microbial genomes. Here, we discuss factors influencing microbial genome divergence based on pair-wise genome comparisons of closely related strains and species with different lifestyles. A particular focus is on intracellular pathogens and symbionts of the genera Rickettsia, Bartonella and BUCHNERA: Extensive gene loss and restricted access to phage and plasmid pools may provide an explanation for why single host pathogens are normally less successful than multihost pathogens. We note that species-specific genes tend to be shorter than orthologous genes, suggesting that a fraction of these may represent fossil-orfs, as also supported by multiple sequence alignments among species. The results of our genome comparisons are placed in the context of phylogenomic analyses of alpha and gamma proteobacteria. We highlight artefacts caused by different rates and patterns of mutations, suggesting that atypical phylogenetic placements can not a priori be taken as evidence for horizontal gene transfer events. The flexibility in genome structure among free-living microbes contrasts with the extreme stability observed for the small genomes of aphid endosymbionts, in which no rearrangements or inflow of genetic material have occurred during the past 50 millions years (1). Taken together, the results suggest that genomic stability correlate with the content of repeated sequences and mobile genetic elements, and thereby indirectly with bacterial lifestyles.'),
(70380, 2002, 'bio', 'Stochastic roadmap simulation for the study of ligand-protein interactions', 'Apaydin, Mehmet Serkan|Guestrin, Carlos E|Varma, Chris|Brutlag, Douglas L|Latombe, Jean-Claude', 'Department of Computer Science Department of Biochemistry, Stanford University, CA 94305, USA. apaydin@cs.stanford.edu|Department of Computer Science Department of Biochemistry, Stanford University, CA 94305, USA. apaydin@cs.stanford.edu|Department of Computer Science Department of Biochemistry, Stanford University, CA 94305, USA. apaydin@cs.stanford.edu|Department of Computer Science Department of Biochemistry, Stanford University, CA 94305, USA. apaydin@cs.stanford.edu|Department of Computer Science Department of Biochemistry, Stanford University, CA 94305, USA. apaydin@cs.stanford.edu', 'United States|United States|United States|United States|United States', 20, 'Amino Acid Sequence|Amino Acids|Binding Sites|Computer Simulation|Ligands|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Multiprotein Complexes|Mutagenesis, Site-Directed|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Stochastic Processes|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s18', '12385979', 'Understanding the dynamics of ligand-protein interactions is indispensable in the design of novel therapeutic agents. In this paper, we establish the use of Stochastic Roadmap Simulation (SRS) for the study of ligand-protein interactions through two studies. In our first study, we measure the effects of mutations on the catalytic site of a protein, a process called computational mutagenesis. In our second study, we focus on distinguishing the catalytic site from other putative binding sites. SRS compactly represents many Monte Carlo (MC) simulation paths in a compact graph structure, or roadmap. Furthermore, SRS allows us to analyze all the paths in this roadmap simultaneously. In our application of SRS to the domain of ligand-protein interactions, we consider a new parameter called escape time, the expected number of MC simulation steps required for the ligand to escape from the funnel of attraction of the binding site, as a metric for analyzing such interactions. Although computing escape times would probably be infeasible with MC simulation, these computations can be performed very efficiently with SRS. Our results for six mutant complexes for the first study and seven ligand-protein complexes for the second study, are very promising: In particular, the first results agree well with the biological interpretation of the mutations, while the second results show that escape time is a good metric to distinguish the catalytic site for five out of seven complexes.'),
(70381, 2002, 'bio', 'Modeling splicing sites with pairwise correlations', 'Arita, Masanori|Tsuda, Koji|Asai, Kiyoshi', 'Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. m-arita@aist.go.jp|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. m-arita@aist.go.jp|Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. m-arita@aist.go.jp', 'Japan|Japan|Japan', 18, 'Algorithms|Base Pairing|Chromosome Mapping|Computer Simulation|Genome, Human|Humans|Models, Genetic|Models, Statistical|RNA Splice Sites|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Statistics as Topic', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s27', '12385980', 'A new method for finding subtle patterns in sequences is introduced. It approximates the multiple correlations among residuals with pair-wise correlations, with the learning cost O(m(2)n) where n is the number of training sequences, each of length m. The method suits to model splicing sites in human DNA, which are reported to have higher-order dependencies.'),
(70382, 2002, 'bio', 'Automated annotation of keywords for proteins related to mycoplasmataceae using machine learning techniques', 'Bazzan, Ana L C|Engel, Paulo M|Schroeder, Luciana F|da Silva, Sérgio C', 'Instituto de Informática, Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil. bazzan@inf.ufrgs.br|Instituto de Informática, Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil. bazzan@inf.ufrgs.br|Instituto de Informática, Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil. bazzan@inf.ufrgs.br|Instituto de Informática, Univ. Fed. do Rio Grande do Sul, Porto Alegre, Brazil. bazzan@inf.ufrgs.br', 'Brazil|Brazil|Brazil|Brazil', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Bacterial Proteins|Database Management Systems|Databases, Protein|Documentation|Gene Expression Profiling|Information Storage and Retrieval|Mycoplasmataceae|Natural Language Processing|Terminology as Topic|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s35', '12385981', 'With the increase in submission of sequences to public databases, the curators of these are not able to cope with the amount of information. The motivation of this work is to generate a system for automated annotation of data we are particularly interested in, namely proteins related to the Mycoplasmataceae family. Following previous works on automatic annotation using symbolic machine learning techniques, the present work proposes a method of automatic annotation of keywords (a part of the SWISS-PROT annotation procedure), and the validation, by an expert, of the annotation rules generated. The aim of this procedure is twofold: to complete the annotation of keywords of those proteins which is far from adequate, and to produce a prototype of the validation environment, which is aimed at an expert who does not have a deep knowledge of the structure of the current databases containing the necessary information s/he needs.'),
(70383, 2002, 'bio', 'A new distance measure for comparing sequence profiles based on path lengths along an entropy surface', 'Benson, Gary', 'Department of Biomathematical Sciences, Mount Sinai School of Medicine, New York, USA.', 'United States', 0, 'Algorithms|Animals|Caenorhabditis elegans|Chromosome Mapping|Consensus Sequence|Entropy|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Tandem Repeat Sequences', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s44', '12385982', 'We describe a new distance measure for comparing DNA sequence profiles. For this measure, columns in a multiple alignment are treated as character frequency vectors (sum of the frequencies equal to one). The distance between two vectors is based on minimum path length along an entropy surface. Path length is estimated using a random graph generated on the entropy surface and Dijkstras algorithm for all shortest paths to a source. We use the new distance measure to analyze similarities within familes of tandem repeats in the C. elegans genome and show that this new measure gives more accurate refinement of family relationships than a method based on comparing consensus sequences.'),
(70384, 2002, 'bio', 'Common intervals and sorting by reversals: a marriage of necessity', 'Bergeron, A|Heber, S|Stoye, J', 'LaCIM, Université du Québec à Montréal, Canada.|LaCIM, Université du Québec à Montréal, Canada.|LaCIM, Université du Québec à Montréal, Canada.', 'Canada|Canada|Canada', 29, 'Algorithms|Chromosome Mapping|Computer Simulation|Gene Rearrangement|Models, Genetic|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s54', '12385983', 'This paper revisits the problem of sorting by reversals with tools developed in the context of detecting common intervals. Mixing the two approaches yields new definitions and algorithms for the reversal distance computations, that apply directly on the original permutation. Traditional constructions such as recasting the signed permutation as a positive permutation, or traversing the overlap graph to analyze its connected components, are replaced by elementary definitions in terms of intervals of the permutation. This yields simple linear time algorithms that identify the essential features in a single pass over the permutation and use only simple data structures like arrays and stacks.'),
(70385, 2002, 'bio', 'Comparative analysis of protein interaction networks', 'Bork, Peer', 'European Molecular Biology Laboratory, Heidelberg, Germany.', 'Germany', 2, 'Animals|Computer Simulation|Databases, Protein|Gene Expression Regulation|Humans|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s64', '12385984', 'Recent advances in proteomics and computational biology have lead to a flood of protein interaction data and resulting interaction networks (e.g. (Gavin et al., 2002)). Here I first analyse the status and quality of parts lists (genes and proteins), then comparatively assess large-scale protein interaction data (von Mering et al., 2002) and finally try to identify biological meaningful units (e.g. pathways, cellular processes) within interaction networks that are derived from the conservation of gene neighborhood (Snel et al., 2002). Possible extensions of gene neighborhood analysis to eukaryotes (von Mering and Bork, 2002) will be discussed.'),
(70386, 2002, 'bio', 'Theoretical analysis of alternative splice forms using computational methods', 'Boué, Stéphanie|Vingron, Martin|Kriventseva, Evgenia|Koch, Ina', 'Max-Planck-Institute for Molecular Genetics, Department Computational Molecular Biology, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Department Computational Molecular Biology, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Department Computational Molecular Biology, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Department Computational Molecular Biology, Berlin, Germany.', 'Germany|Germany|Germany|Germany', 15, 'Algorithms|Alternative Splicing|Computer Simulation|Exons|Expressed Sequence Tags|Humans|Models, Genetic|Models, Molecular|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Sequence Analysis, RNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s65', '12385985', 'Nowadays understanding alternative splicing is one of the greatest challenges in biology, because it is a genetic process much more important than thought at the time of its discovery. In this paper, we explain the approach of using the different available databases and software tools to start a large scale investigation of alternative splice forms. To collect information about alternative splicing we investigated known data in the databases using different computational methods. The investigations proceeded from the genomic sequence data to structural protein data. Then, we interpreted those data to find the relationship between alternative splice forms and protein function and structure. We found some interesting features of alternative splicing which are presented here. We discuss the results of one chosen example. They concern the coverage quality of the protein sequence of a known structure, an EST analysis, the validation of splice variants, the determination of the alternative splice type, and finally the link between alternative splicing and disease.'),
(70387, 2002, 'bio', 'Using robotics to fold proteins and dock ligands', 'Brutlag, Douglas|Apaydin, Serkan|Guestrin, Carlos|Hsu, David|Varma, Chris|Singh, Amit|Latombe, Jean-Claude', 'Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.|Department of Biochemistry, Stanford University, California, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Binding Sites|Computer Simulation|Ligands|Models, Chemical|Models, Molecular|Motion|Protein Binding|Protein Conformation|Protein Folding|Proteins|Robotics', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s74', '12385986', 'The problems of protein folding and ligand docking have been explored largely using molecular dynamics or Monte Carlo methods. These methods are very compute intensive because they often explore a much wider range of energies, conformations and time than necessary. In addition, Monte Carlo methods often get trapped in local minima. We initially showed that robotic motion planning permitted one to determine the energy of binding and dissociation of ligands from protein binding sites (Singh et al., 1999). The robotic motion planning method maps complicated three-dimensional conformational states into a much simpler, but higher dimensional space in which conformational rearrangements can be represented as linear paths. The dimensionality of the conformation space is of the same order as the number of degrees of conformational freedom in three-dimensional space. We were able to determine the relative energy of association and dissociation of a ligand to a protein by calculating the energetics of interaction for a few thousand conformational states in the vicinity of the protein and choosing the best path from the roadmap. More recently, we have applied roadmap planning to the problem of protein folding (Apaydin et al., 2002a). We represented multiple conformations of a protein as nodes in a compact graph with the edges representing the probability of moving between neighboring states. Instead of using Monte Carlo simulation to simulate thousands of possible paths through various conformational states, we were able to use Markov methods to calculate the steady state occupancy of each conformation, needing to calculate the energy of each conformation only once. We referred to this Markov method of representing multiple conformations and transitions as stochastic roadmap simulation or SRS. We demonstrated that the distribution of conformational states calculated with exhaustive Monte Carlo simulations asymptotically approached the Markov steady state if the same Boltzman energy distribution was used in both methods. SRS permits one to calculate contributions from all possible paths simultaneously with far fewer energy calculations than Monte Carlo or molecular dynamics methods. The SRS method also permits one to represent multiple unfolded starting states and multiple, near-native, folded states and all possible paths between them simultaneously. The SRS method is also independent of the function used to calculate the energy of the various conformational states. In a paper to be presented at this conference (Apaydin et al., 2002b) we have also applied SRS to ligand docking in which we calculate the dynamics of ligand-protein association and dissociation in the region of various binding sites on a number of proteins. SRS permits us to determine the relative times of association to and dissociation from various catalytic and non-catalytic binding sites on protein surfaces. Instead of just following the best path in a roadmap, we can calculate the contribution of all the possible binding or dissociation paths and their relative probabilities and energies simultaneously.'),
(70388, 2002, 'bio', 'Feature subset selection for splice site prediction', 'Degroeve, Sven|De Baets, Bernard|Van de Peer, Yves|Rouzé, Pierre', 'Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Gent, Belgium. svgro@gengenp.rug.ac.be|Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Gent, Belgium. svgro@gengenp.rug.ac.be|Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Gent, Belgium. svgro@gengenp.rug.ac.be|Department of Plant Systems Biology, Flanders Interuniversity Institute for Biotechnology (VIB), Gent, Belgium. svgro@gengenp.rug.ac.be', 'Belgium|Belgium|Belgium|Belgium', 0, 'Arabidopsis|Artificial Intelligence|Base Sequence|Chromosome Mapping|Computer Simulation|DNA, Plant|Genome, Plant|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|RNA Splice Sites|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s75', '12385987', 'The large amount of available annotated Arabidopsis thaliana sequences allows the induction of splice site prediction models with supervised learning algorithms (see Haussler (1998) for a review and references). These algorithms need information sources or features from which the models can be computed. For splice site prediction, the features we consider in this study are the presence or absence of certain nucleotides in close proximity to the splice site. Since it is not known how many and which nucleotides are relevant for splice site prediction, the set of features is chosen large enough such that the probability that all relevant information sources are in the set is very high. Using only those features that are relevant for constructing a splice site prediction system might improve the system and might also provide us with useful biological knowledge. Using fewer features will of course also improve the prediction speed of the system.'),
(70389, 2002, 'bio', 'Annotating regulatory DNA based on man-mouse genomic comparison', 'Dieterich, Christoph|Cusack, Brian|Wang, Haiyan|Rateitschak, Katja|Krause, Antje|Vingron, Martin', 'Max-Planck-Institute for Molecular Genetics, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Berlin, Germany.|Max-Planck-Institute for Molecular Genetics, Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 25, 'Algorithms|Animals|Chromosome Mapping|Conserved Sequence|DNA|Databases, Nucleic Acid|Genes, Regulator|Genome, Human|Humans|Mice|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s84', '12385988', 'Non-coding DNA segments that are conserved between the human and mouse genomic sequence are good indicators of possible regulatory sequences. Here we report on a systematic approach to delineate such conserved elements from upstream regions of orthologous gene pairs from man and mouse. We focus on orthologous genes in order to maximize our chances to find functionally similar regulatory elements. The identification of conserved elements is effected using the Waterman-Eggert local suboptimal alignment algorithm. We have modified an implementation of this algorithm such that it integrates the determination of statistical significance for the local suboptimal alignments. This has the effect of outputting a dynamically determined number of suboptimal alignments that are deemed statistically significant. Comparison with experimentally determined annotation shows a striking enrichement of regulatory sites among the conserved regions. Furthermore, the conserved regions tend to cover the promotor region described in the EPD database.'),
(70390, 2002, 'bio', 'Detecting genomic features under weak selective pressure: the example of codon usage in animals and plants', 'Duret, Laurent', 'Laboratoire de Biométrie et Biologie Evolutive, Lyon, France.', 'France', 3, 'Animals|Biological Evolution|Chromosome Mapping|Codon|DNA Mutational Analysis|DNA, Plant|Evolution, Molecular|Genetic Drift|Genetic Variation|Genome, Plant|Phenotype|Selection, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s91', '12385989', 'Large scale experiments of gene inactivation in yeast have shown that 50% of genes have no detectable impact on the phenotype, and similar observations have been made in other model organisms. This apparent paradox is probably due to the fact that many genes only have a marginal contribution to the fitness of organisms. Because of the size of populations and the number of generations that can be studied in laboratories, experimental approaches only permit to detect functional elements that have a strong phenotypic impact. Comparative sequence analysis can help to solve this problem: the analysis of sequences evolution permits to detect the action of selection, and hence to reveal functional features of genomes. This approach will be illustrated by the study of synonymous codon usage in animals and plants.'),
(70391, 2002, 'bio', 'An efficient and accurate distance based algorithm to reconstruct tandem duplication trees', 'Elemento, Olivier|Gascuel, Olivier', 'Département dInformatique Fondamentale et Applications, LIRMM, Montpellier, France.|Département dInformatique Fondamentale et Applications, LIRMM, Montpellier, France.', 'France|France', 0, 'Chromosome Mapping|Computer Simulation|Models, Genetic|Sequence Alignment|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s92', '12385990', 'The problem of reconstructing the duplication tree of a set of tandemly repeated sequences which are supposed to have arisen through unequal recombination, was first introduced by Fitch (1977, Genetics, 86, 93-104), and has recently received a lot of attention. In this paper, we describe DTSCORE, a fast distance based algorithm to reconstruct tandem duplication trees, which is statistically consistent. As a cousin of the ADDTREE algorithm (Sattath and Tversky, 1977, Psychometrika, 42, 319-345), the raw DTSCORE has a time complexity in O(n(5)), where n is the number of observed repeated sequences. Through a series of algorithmic refinements, we improve its complexity to O(n(4)) in the worst case, but stress that the refined DTSCORE algorithm should perform faster with real data. We assess the topological accuracy of DTSCORE using simulated data sets, and compare it to existing reconstruction methods. The results clearly show that DTSCORE is more accurate than all the other methods we studied. Finally, we report the results of DTSCORE on a real dataset.'),
(70392, 2002, 'bio', 'Identifying transcription factor binding sites through Markov chain optimization', 'Ellrott, Kyle|Yang, Chuhu|Sladek, Frances M|Jiang, Tao', 'Department of Computer Science, University of California, Riverside, 92521, USA.|Department of Computer Science, University of California, Riverside, 92521, USA.|Department of Computer Science, University of California, Riverside, 92521, USA.|Department of Computer Science, University of California, Riverside, 92521, USA.', 'United States|United States|United States|United States', 70, 'Algorithms|Binding Sites|Chromosome Mapping|Computer Simulation|DNA|DNA-Binding Proteins|Hepatocyte Nuclear Factor 4|Humans|Markov Chains|Models, Biological|Models, Chemical|Phosphoproteins|Protein Binding|Sequence Analysis, DNA|Sequence Analysis, Protein|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s100', '12385991', 'Even though every cell in an organism contains the same genetic material, each cell does not express the same cohort of genes. Therefore, one of the major problems facing genomic research today is to determine not only which genes are differentially expressed and under what conditions, but also how the expression of those genes is regulated. The first step in determining differential gene expression is the binding of sequence-specific DNA binding proteins (i.e. transcription factors) to regulatory regions of the genes (i.e. promoters and enhancers). An important aspect to understanding how a given transcription factor functions is to know the entire gamut of binding sites and subsequently potential target genes that the factor may bind/regulate. In this study, we have developed a computer algorithm to scan genomic databases for transcription factor binding sites, based on a novel Markov chain optimization method, and used it to scan the human genome for sites that bind to hepatocyte nuclear factor 4 alpha (HNF4alpha). A list of 71 known HNF4alpha binding sites from the literature were used to train our Markov chain model. By looking at the window of 600 nucleotides around the transcription start site of each confirmed gene on the human genome, we identified 849 sites with varying binding potential and experimentally tested 109 of those sites for binding to HNF4alpha. Our results show that the program was very successful in identifying 77 new HNF4alpha binding sites with varying binding affinities (i.e. a 71% success rate). Therefore, this computational method for searching genomic databases for potential transcription factor binding sites is a powerful tool for investigating mechanisms of differential gene regulation.'),
(70393, 2002, 'bio', 'A similarity-based method for genome-wide prediction of disease-relevant human genes', 'Freudenberg, J|Propping, P', 'Institute of Human Genetics, Bonn University Hospital, Germany. jan.freudenberg@uni-bonn.de|Institute of Human Genetics, Bonn University Hospital, Germany. jan.freudenberg@uni-bonn.de', 'Germany|Germany', 174, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Databases, Genetic|Diagnosis, Computer-Assisted|Gene Expression Profiling|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genetic Testing|Genome, Human|Humans|Phenotype', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s110', '12385992', 'A method for prediction of disease relevant human genes from the phenotypic appearance of a query disease is presented. Diseases of known genetic origin are clustered according to their phenotypic similarity. Each cluster entry consists of a disease and its underlying disease gene. Potential disease genes from the human genome are scored by their functional similarity to known disease genes in these clusters, which are phenotypically similar to the query disease.'),
(70394, 2002, 'bio', 'Contextual alignment of biological sequences (Extended abstract)', 'Gambin, Anna|Lasota, Sxc5x82awomir|Szklarczyk, Radosxc5x82aw|Tiuryn, Jerzy|Tyszkiewicz, Jerzy', 'Institute of Informatics, Warsaw University, Poland.|Institute of Informatics, Warsaw University, Poland.|Institute of Informatics, Warsaw University, Poland.|Institute of Informatics, Warsaw University, Poland.|Institute of Informatics, Warsaw University, Poland.', 'Poland|Poland|Poland|Poland|Poland', 11, 'Algorithms|Conserved Sequence|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|Sequence Homology', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s116', '12385993', 'We present a model of contextual alignment of biological sequences. It is an extension of the classical alignment, in which we assume that the cost of a substitution depends on the surrounding symbols. In this model the cost of transforming one sequence into another depends on the order of editing operations. We present efficient algorithms for calculating this cost, as well as reconstructing (the representation of) all the orders of operations which yield this optimal cost. A precise characterization of the families of linear orders which can emerge this way is given.'),
(70395, 2002, 'bio', 'Breakpoint medians and breakpoint phylogenies: a fixed-parameter approach', 'Gramm, Jens|Niedermeier, Rolf', 'Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Federic Republic of Germany.|Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Federic Republic of Germany.', 'Germany|Germany', 15, 'Algorithms|Biological Evolution|Chromosome Mapping|Computer Simulation|Gene Rearrangement|Models, Genetic|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s128', '12385994', 'With breakpoint distance, the genome rearrangement field delivered one of the currently most popular measures in phylogenetic studies for related species. Here, BREAKPOINT MEDIAN, which is NP-complete already for three given species (whose genomes are represented as signed orderings), is the core basic problem. For the important special case of three species, approximation (ratio 7/6) and exact heuristic algorithms were developed. Here, we provide an exact, fixed-parameter algorithm with provable performance bounds. For instance, a breakpoint median for three signed orderings over nelements that causes at most d breakpoints can be computed in time O((2.15)(d).n). We show the algorithms practical usefulness through experimental studies. In particular, we demonstrate that a simple implementation of our algorithm combined with a new tree construction heuristic allows for a new approach to breakpoint phylogeny, yielding evolutionary trees that are competitive in comparison with known results developed in a recent series of papers that use clever algorithm engineering methods.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70396, 2002, 'bio', 'Biological information: making it accessible and integrated (and trying to make sense of it)', 'Hubbard, Tim', 'Sanger Institute, Cambridgeshire, UK.', 'United Kingdom', 13, 'Chromosome Mapping|Computational Biology|Database Management Systems|Databases, Factual|Information Dissemination|Information Storage and Retrieval|Models, Biological|Systems Biology|Systems Integration', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s140', '12385995', 'The availability of the genome sequences of human and mouse, human sequence variation data and other large genetic data sets will lead to a revolution in understanding of the human machine and the treatment of its diseases. The success of the international genome sequencing consortiums shows what can be achieved by well coordinated large scale public domain projects and the benefits of data access to all. It is already clear that the availability of this sequence is having a huge impact on research worldwide. Complete genome sequences provide a framework to pull all biological data together such that each piece has the potential to say something about biology as a whole. Biology is too complex for any organisation to have a monopoly of ideas or data, so the collection, analysis and access to this data can be contributed to by research institutes around the world. However, although it is possible for all this data to be accessible to all through the internet, the more organisations provide data or analysis separately, the harder it becomes for anyone to collect and integrate the results. To address these problems of intergration of data, open standards for biological data exchange, such as the Distributed Annotation System (DAS) are being developed and bioinformatics (Dowell et al., 2001) as a whole is now being strongly driven by the open source software (OSS) model for collaborative software development (Hubbard and Birney, 1999). The leading provider of human genome annotation, the Ensembl project (http://www.ensembl.org), is entirely an OSS project and has been widely adopted by academic and commerical organisations alike (Hubbard et al., 2002). Accurate automatic annotation of features such as genes in vertebrate genomes currently relies on supporting evidence in the form of homologies to mRNAs, ESTs or protein. However, it appears that sufficient high quality experimentally curated annotation now exists to be used as a substrate for machine learning algorithms to create effective models of biological signal sequences (Down and Hubbard, 2002). Is there hope for ab initio prediction methods after all?'),
(70397, 2002, 'bio', 'An automatic block and spot indexing with k-nearest neighbors graph for microarray image analysis', 'Jung, Ho-Youl|Cho, Hwan-Gue', 'Department of Computer Science, Pusan National University, Jangjeon-dong, Keumjeong-gu, Korea. hyjung@pearl.cs.pusan.ac.kr|Department of Computer Science, Pusan National University, Jangjeon-dong, Keumjeong-gu, Korea. hyjung@pearl.cs.pusan.ac.kr', '-|-', 0, 'Algorithms|Artificial Intelligence|DNA|Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s141', '12385996', 'In this paper, we propose a fully automatic block and spot indexing algorithm for microarray image analysis. A microarray is a device which enables a parallel experiment of ten to hundreds of thousands of test genes in order to measure gene expression. Due to this huge size of experimental data, automated image analysis is gaining importance in microarray image processing systems. Currently, most of the automated microarray image processing systems require manual block indexing and, in some cases, spot indexing. If the microarray image is large and contains a lot of noise, it is very troublesome work. In this paper, we show it is possible to locate the addresses of blocks and spots by applying the Nearest Neighbors Graph Model. Also, we propose an analytic model for the feasibility of block addressing. Our analytic model is validated by a large body of experimental results.'),
(70398, 2002, 'bio', 'Estonian Genome Project--before the take-off and take-off', 'Metspalu, Andres', 'University of Tartu/ Estonian Biocentre and EGI. Inc.', 'Estonia', 9, 'Chromosome Mapping|Estonia|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genetic Testing|Genetics, Population|Humans', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s152', '12385997', 'I will give an overview of the preparations of the EGP including all aspects of the project and current satus of the pilot phase, describe the informatics part of the project together with other aspects (legal, ethical, logistics, financial etc.) and where we stand in terms of population genetics and genotyping technology in order to start using the sample collection for case--control studies for revealing the genetic component of the common diseases.'),
(70399, 2002, 'bio', 'Stochastic pairwise alignments', 'Mückstein, U|Hofacker, I L|Stadler, P F', 'Institut für Theoretische Chemie und Molekulare Strukturbiologie Universität Wien, Vienna, Austria.|Institut für Theoretische Chemie und Molekulare Strukturbiologie Universität Wien, Vienna, Austria.|Institut für Theoretische Chemie und Molekulare Strukturbiologie Universität Wien, Vienna, Austria.', 'Austria|Austria|Austria', 19, 'Algorithms|Computer Simulation|Models, Chemical|Models, Genetic|Models, Statistical|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Stochastic Processes', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s153', '12385998', 'The level of sequence conservation between related nucleic acids or proteins often varies considerably along the sequence. Both regions with high variability (mutational hot-spots) and regions of almost perfect sequence identity may occur in the same pair of molecules. The reliability of an alignment therefore strongly depends on the level of local sequence similarity. Especially in regions of high variability, many alignments of almost equal quality exist, and the optimal alignment is highly arbitrary.'),
(70400, 2002, 'bio', 'Proteome analysis based on motif statistics', 'Nicodème, P|Doerks, T|Vingron, M', 'Laboratory Statistics and Genomes, CNRS - Génopole Evry, F EMBL-Heidelberg, Germany. nicodeme@genopole.cnrs.fr|Laboratory Statistics and Genomes, CNRS - Génopole Evry, F EMBL-Heidelberg, Germany. nicodeme@genopole.cnrs.fr|Laboratory Statistics and Genomes, CNRS - Génopole Evry, F EMBL-Heidelberg, Germany. nicodeme@genopole.cnrs.fr', 'Germany|Germany|Germany', 11, 'Amino Acid Motifs|Amino Acid Sequence|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|Models, Chemical|Models, Genetic|Models, Statistical|Molecular Sequence Data|Proteome|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s161', '12385999', 'Even for the amino acid motifs collected in the Prosite database there may be chance occurences as opposed to those occurences where the motif is involved in fold or function of a protein. With recent mathematical advances in assessing the significance of observing such a motif a particular number of times, we can now study the over- or under-representation of particular motifs in a complete genome and attempt to make functional deductions.'),
(70401, 2002, 'bio', 'Correlating gene promoters and expression in gene disruption experiments', 'Palin, Kimmo|Ukkonen, Esko|Brazma, Alvis|Vilo, Jaak', 'Department of Computer Science, University of Helsinki, Finland. kimmo.palin@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Finland. kimmo.palin@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Finland. kimmo.palin@cs.helsinki.fi|Department of Computer Science, University of Helsinki, Finland. kimmo.palin@cs.helsinki.fi', 'Finland|Finland|Finland|Finland', 13, 'Algorithms|Binding Sites|DNA, Fungal|Gene Expression Profiling|Gene Expression Regulation|Gene Silencing|Promoter Regions, Genetic|Protein Binding|Research|Saccharomyces cerevisiae|Statistics as Topic|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s172', '12386000', 'Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts).'),
(70402, 2002, 'bio', 'Selecting targets for therapeutic validation through differential protein expression using chromatography-mass spectrometry', 'Patterson, Scott', 'Celera, Rockville, USA.', 'United States', 0, 'Biomarkers, Tumor|Chromatography|Drug Delivery Systems|Drug Design|Gene Expression Profiling|Gene Targeting|Humans|Mass Spectrometry|Neoplasm Proteins|Pancreatic Neoplasms', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s181', '12386001', 'The identification of potential targets for therapeutic intervention can be accomplished on a systematic basis by a variety of techniques that include quantitative analysis of gene-specific mRNA levels and expressed proteins in normal and diseased cells. Differences in the expression levels of nucleic acid and protein gene products could suggest protein drug targets that are directly causative of disease, or reveal biochemical pathways that could be modulated by therapeutic molecules. Any effort based on mRNA or protein expression level comparisons could be confounded by a number of factors: level in steady-state may not be correlated with actual encoded protein levels, differentially expressed protein levels might be a result of disease process, and not causative of the process, and therapeutic intervention based on such a difference will be unproductive and the differential expression of mRNA or protein may be the result of biological variation unrelated to the disease process under study. In order to address these possibly confounding factors, it is necessary to validate potential targets by establishing their firm association with disease, and their minimal distribution in non-diseased tissues of any type. This requirement suggests that emphasis on true and reproducible quantitation of protein expression levels in a variety of samples will be an effective and highly efficient method of generating drug targets with a high degree of utility. To achieve this aim, we have established an industrial-scale proteomics-based discovery platform consisting of cell biology, protein chemistry, and mass spectrometry technical groups together with bioinformatics groups. The analytical method used for quantitation employs isotope labeling for differential analysis (ICATTM, Applied Biosystems, Inc.). With this technique, tryptic peptides are generated from labeled proteins that have been specifically captured from various subcellular locations or protein families. The resulting peptides are identified and quantified by mass spectrometry. To evaluate this approach on a large-scale, we have applied it to a study of continuous cell lines derived from human pancreatic adenocarcinomas. We have been able to establish processes for target discovery for small molecule drug targets as well as therapeutic antibody target identification for cell surface proteins. In addition, we have developed a process for identification of serum markers of this disease based upon standardized fractionation procedures. The results of these analyses will be presented together with the some of the issues from both the wet and dry (computational) lab that need to be addressed in such an undertaking.'),
(70403, 2002, 'bio', 'ProClust: improved clustering of protein sequences with an extended graph-based approach', 'Pipenbacher, P|Schliep, A|Schneckener, S|Schönhuth, A|Schomburg, D|Schrader, R', 'ZAIK/ZPR, Universität zu Köln, Germany.|ZAIK/ZPR, Universität zu Köln, Germany.|ZAIK/ZPR, Universität zu Köln, Germany.|ZAIK/ZPR, Universität zu Köln, Germany.|ZAIK/ZPR, Universität zu Köln, Germany.|ZAIK/ZPR, Universität zu Köln, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 78, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computer Graphics|Molecular Sequence Data|Pattern Recognition, Automated|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s182', '12386002', 'The problem of finding remote homologues of a given protein sequence via alignment methods is not fully solved. In fact, the task seems to become more difficult with more data. As the size of the database increases, so does the noise level, the highest alignment scores due to random similarities increase and can be higher than the alignment score between true homologues. Comparing two sequences with an arbitrary alignment method yields a similarity value which may indicate an evolutionary relationship between them. A threshold value is usually chosen to distinguish between true homologue relationships and random similarities. To compensate for the higher probability of spurious hits in larger databases, this threshold is increased. Increasing specificity however leads to decreased sensitivity as a matter of principle. Sensitivity can be recovered by utilizing refined protocols. A number of approaches to this challenge have made use of the fact that proteins are often members of some larger protein family. This can be exploited by using position-specific substitution matrices or profiles, or by making use of transitivity of homology. Transitivity refers to the concept of concluding homology between proteins A and C based on homology between A and a third protein B and between B and C. It has been demonstrated that transitivity can lead to substantial improvement in recognition of remote homologues particularly in cases where the alignment score of A and C is below the noise level. A natural limit to the use of transitivity is imposed by domains. Domains, compact independent sub-units of proteins, are often shared between otherwise distinct proteins, and can cause substantial problems by incorrectly linking otherwise unrelated proteins.'),
(70404, 2002, 'bio', 'Building an automated classification of DNA-binding protein domains', 'Ponomarenko, Julia V|Bourne, Philip E|Shindyalov, Ilya N', 'Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk.|Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk.', '-|-|-', 23, 'Artificial Intelligence|Binding Sites|Computer Simulation|DNA|DNA-Binding Proteins|Databases, Protein|Models, Chemical|Models, Molecular|Protein Binding|Protein Structure, Tertiary|Sequence Analysis|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s192', '12386003', 'Intensive growth in 3D structure data on DNA-protein complexes as reflected in the Protein Data Bank (PDB) demands new approaches to the annotation and characterization of these data and will lead to a new understanding of critical biological processes involving these data. These data and those from other protein structure classifications will become increasingly important for the modeling of complete proteomes. We propose a fully automated classification of DNA-binding protein domains based on existing 3D-structures from the PDB. The classification, by domain, relies on the Protein Domain Parser (PDP) and the Combinatorial Extension (CE) algorithm for structural alignment. The approach involves the analysis of 3D-interaction patterns in DNA-protein interfaces, assignment of structural domains interacting with DNA, clustering of domains based on structural similarity and DNA-interacting patterns. Comparison with existing resources on describing structural and functional classifications of DNA-binding proteins was used to validate and improve the approach proposed here. In the course of our study we defined a set of criteria and heuristics allowing us to automatically build a biologically meaningful classification and define classes of functionally related protein domains. It was shown that taking into consideration interactions between protein domains and DNA considerably improves the classification accuracy. Our approach provides a high-throughput and up-to-date annotation of DNA-binding protein families which can be found at http://spdc.sdsc.edu.'),
(70405, 2002, 'bio', 'Building and analysing genome-wide gene disruption networks', 'Rung, J|Schlitt, T|Brazma, A|Freivalds, K|Vilo, J', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Chromosome Mapping|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Silencing|Genome, Fungal|Models, Biological|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s202', '12386004', 'Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure.'),
(70406, 2002, 'bio', 'Genome segmentation using piecewise constant intensity models and reversible jump MCMC', 'Salmenkivi, Marko|Kere, Juha|Mannila, Heikki', 'HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland.|HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland.|HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland.', 'Finland|Finland|Finland', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Genetic Variation|Markov Chains|Models, Genetic|Models, Statistical|Monte Carlo Method|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s211', '12386005', 'The existence of whole genome sequences makes it possible to search for global structure in the genome. We consider modeling the occurrence frequencies of discrete patterns (such as starting points of ORFs or other interesting phenomena) along the genome. We use piecewise constant intensity models with varying number of pieces, and show how a reversible jump Markov Chain Monte Carlo (RJMCMC) method can be used to obtain a posteriori distribution on the intensity of the patterns along the genome. We apply the method to modeling the occurrence of ORFs in the human genome. The results show that the chromosomes consist of 5-35 clearly distinct segments, and that the posteriori number and length of the segments shows significant variation. On the other hand, for the yeast genome the intensity of ORFs is nearly constant.'),
(70407, 2002, 'bio', 'BioMiner--modeling, analyzing, and visualizing biochemical pathways and networks', 'Sirava, M|Schäfer, T|Eiglsperger, M|Kaufmann, M|Kohlbacher, O|Bornberg-Bauer, E|Lenhof, H P', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.|Center for Bioinformatics, Saarland University, Saarbrücken, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 33, 'Biochemistry|Computer Graphics|Computer Simulation|Energy Metabolism|Gene Expression Regulation|Models, Biological|Multienzyme Complexes|Signal Transduction|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s219', '12386006', 'Understanding the biochemistry of a newly sequenced organism is an essential task for post-genomic analysis. Since, however, genome and array data grow much faster than biochemical information, it is necessary to infer reactions by comparative analysis. No integrated and easy to use software tool for this purpose exists as yet.'),
(70408, 2002, 'bio', 'The mutual information: detecting and evaluating dependencies between variables', 'Steuer, R|Kurths, J|Daub, C O|Weise, J|Selbig, J', 'University Potsdam, Nonlinear Dynamics Group, Germany. steuer@agnld.uni-potsdam.de|University Potsdam, Nonlinear Dynamics Group, Germany. steuer@agnld.uni-potsdam.de|University Potsdam, Nonlinear Dynamics Group, Germany. steuer@agnld.uni-potsdam.de|University Potsdam, Nonlinear Dynamics Group, Germany. steuer@agnld.uni-potsdam.de|University Potsdam, Nonlinear Dynamics Group, Germany. steuer@agnld.uni-potsdam.de', 'Germany|Germany|Germany|Germany|Germany', 459, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Comparative Study', '10.1093/bioinformatics/18.suppl_2.s231', '12386007', 'Clustering co-expressed genes usually requires the definition of distance or similarity between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data.'),
(70409, 2002, 'bio', 'Application of metabolomics to plant genotype discrimination using statistics and machine learning', 'Taylor, Janet|King, Ross D|Altmann, Thomas|Fiehn, Oliver', 'Department of Computer Science, University of Wales, Aberystwyth, Penglais, UK. jat@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, UK. jat@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, UK. jat@aber.ac.uk|Department of Computer Science, University of Wales, Aberystwyth, Penglais, UK. jat@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Computer Simulation|Gas Chromatography-Mass Spectrometry|Gene Expression Profiling|Gene Expression Regulation, Plant|Genotype|Models, Biological|Models, Statistical|Proteome|Signal Transduction|Species Specificity|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s241', '12386008', 'Metabolomics is a post genomic technology which seeks to provide a comprehensive profile of all the metabolites present in a biological sample. This complements the mRNA profiles provided by microarrays, and the protein profiles provided by proteomics. To test the power of metabolome analysis we selected the problem of discrimating between related genotypes of Arabidopsis. Specifically, the problem tackled was to discrimate between two background genotypes (Col0 and C24) and, more significantly, the offspring produced by the crossbreeding of these two lines, the progeny (whose genotypes would differ only in their maternally inherited mitichondia and chloroplasts).'),
(70410, 2002, 'bio', 'Principles of protein-protein interactions', 'Teichmann, Sarah A', 'MRC Laboratory of Molecular Biology, Cambridge, UK.', 'United Kingdom', 20, 'Binding Sites|Computer Simulation|Evolution, Molecular|Models, Chemical|Models, Genetic|Models, Molecular|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/18.suppl_2.s249', '12386009', 'In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. Thus experimental investigation of protein-protein interactions has been extensive, including recent large-scale screens using mass spectrometry. The role of computational research on protein-protein interactions encompasses not only prediction, but also understanding the nature of the interactions and their three-dimensional structures. I will discuss properties such as sequence conservation and co-regulation of genes and proteins involved in different types of physical interactions. Given that all proteins consist of their evolutionary units, the domains, all interactions occur between these domains. The interactions between domains belonging to different protein families will be the second topic of my talk.'),
(70411, 2002, 'bio', 'An approximate matching algorithm for finding (sub-)optimal sequences in S-attributed grammars', 'Waldispühl, J|Behzadi, B|Steyaert, J-M', 'Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr', 'France|France|France', 0, 'Algorithms|Natural Language Processing|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Evaluation Studies', '10.1093/bioinformatics/18.suppl_2.s250', '12386010', 'S-attributed grammars (a generalization of classical Context-Free grammars) provide a versatile formalism for sequence analysis which allows to express long range constraints: the RNA folding problem is a typical example of application. Efficient algorithms have been developed to solve problems expressed with these tools, which generally compute the optimal attribute of the sequence w.r.t. the grammar. However, it is often more meaningful and/or interesting from the biological point of view to consider almost optimal attributes as well as approximate sequences, we thus need more flexible and powerful algorithms able to perform these generalized analyses.'),
(70412, 2002, 'bio', 'The underlying principles of scientific publication', 'Ball, Catherine A|Sherlock, Gavin|Parkinson, Helen|Rocca-Sera, Philippe|Brooksbank, Catherine|Causton, Helen C|Cavalieri, Duccio|Gaasterland, Terry|Hingamp, Pascal|Holstege, Frank|Ringwald, Martin|Spellman, Paul|Stoeckert, Christian J|Stewart, Jason E|Taylor, Ronald|Brazma, Alvis|Quackenbush, John|Microarray Gene Expression Data', 'Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr|Ecole Polytechnique, Palaiseau, France. waldispu@lix.polytechnique.fr', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 0, 'Computational Biology|Cooperative Behavior|Databases, Nucleic Acid|Gene Expression Profiling|Guidelines as Topic|Internet|Oligonucleotide Array Sequence Analysis|Periodicals as Topic|Publishing|Research|United States', 'Consensus Development Conference', '10.1093/bioinformatics/18.11.1409', '12424109', '-'),
(70413, 2002, 'bio', 'ARROGANT: an application to manipulate large gene collections', 'Kulkarni, Amit V|Williams, Noelle Sevilir|Lian, Yun|Wren, Jonathan D|Mittelman, David|Pertsemlidis, Alexander|Garner, Harold R', 'Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.|Program in Biomedical Engineering, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390, USA.', 'United States|United States|United States|United States|United States|United States|United States', 8, 'Base Sequence|Cloning, Molecular|Database Management Systems|Databases, Nucleic Acid|Documentation|Gene Expression Regulation|Heart Diseases|Humans|Information Storage and Retrieval|Molecular Sequence Data|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1410', '12424110', 'ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.'),
(70414, 2002, 'bio', 'Identification of mixups among DNA sequencing plates', 'Stojanovic, Nikola|Chang, Jean L|Lehoczky, Jessica|Zody, Michael C|Dewar, Ken', 'Whitehead Institute, Center for Genome Research, 320 Charles Street, Cambridge MA 02141, USA. nick@genome.wi.mit.edu|Whitehead Institute, Center for Genome Research, 320 Charles Street, Cambridge MA 02141, USA. nick@genome.wi.mit.edu|Whitehead Institute, Center for Genome Research, 320 Charles Street, Cambridge MA 02141, USA. nick@genome.wi.mit.edu|Whitehead Institute, Center for Genome Research, 320 Charles Street, Cambridge MA 02141, USA. nick@genome.wi.mit.edu|Whitehead Institute, Center for Genome Research, 320 Charles Street, Cambridge MA 02141, USA. nick@genome.wi.mit.edu', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Artifacts|Computer Simulation|Contig Mapping|Documentation|Equipment Failure Analysis|Human Genome Project|Humans|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Research Design|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/18.11.1418', '12424111', 'During the process of high-throughput genome sequencing there are opportunities for mixups of reagents and data associated with particular projects. The sequencing templates or sequence data generated for an assembly may become contaminated with reagents or sequences from another project, resulting in poorer quality and inaccurate assemblies.'),
(70415, 2002, 'bio', 'MethPrimer: designing primers for methylation PCRs', 'Li, Long-Cheng|Dahiya, Rajvir', 'Department of Urology, Veterans Affairs Medical Center, and University of California San Francisco, San Francisco, CA 94121, USA. longli@itsa.ucsf.edu|Department of Urology, Veterans Affairs Medical Center, and University of California San Francisco, San Francisco, CA 94121, USA. longli@itsa.ucsf.edu', 'United States|United States', 1665, 'Base Sequence|Computer-Aided Design|CpG Islands|DNA Methylation|DNA Primers|Equipment Design|Estrogen Receptor beta|Humans|Internet|Molecular Sequence Data|Multimedia|Pilot Projects|Polymerase Chain Reaction|Quality Control|Receptors, Estrogen|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1427', '12424112', 'DNA methylation is an epigenetic mechanism of gene regulation. Bisulfite- conversion-based PCR methods, such as bisulfite sequencing PCR (BSP) and methylation specific PCR (MSP), remain the most commonly used techniques for methylation mapping. Existing primer design programs developed for standard PCR cannot handle primer design for bisulfite-conversion-based PCRs due to changes in DNA sequence context caused by bisulfite treatment and many special constraints both on the primers and the region to be amplified for such experiments. Therefore, the present study was designed to develop a program for such applications.'),
(70416, 2002, 'bio', 'PRIMEGENS: robust and efficient design of gene-specific probes for microarray analysis', 'Xu, Dong|Li, Guangshan|Wu, Liyou|Zhou, Jizhong|Xu, Ying', 'Protein Informatics Group, Life Sciences Division Environmental Sciences Division, Oak Ridge, TN 37831, USA. xud@ornl.gov|Protein Informatics Group, Life Sciences Division Environmental Sciences Division, Oak Ridge, TN 37831, USA. xud@ornl.gov|Protein Informatics Group, Life Sciences Division Environmental Sciences Division, Oak Ridge, TN 37831, USA. xud@ornl.gov|Protein Informatics Group, Life Sciences Division Environmental Sciences Division, Oak Ridge, TN 37831, USA. xud@ornl.gov|Protein Informatics Group, Life Sciences Division Environmental Sciences Division, Oak Ridge, TN 37831, USA. xud@ornl.gov', 'United States|United States|United States|United States|United States', 76, 'Algorithms|Computer-Aided Design|DNA Primers|DNA Probes|DNA, Bacterial|Database Management Systems|Deinococcus|Equipment Design|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Shewanella|Software|Species Specificity', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1432', '12424113', 'DNA microarray is a powerful high-throughput tool for studying gene function and regulatory networks. Due to the problem of potential cross hybridization, using full-length genes for microarray construction is not appropriate in some situations. A bioinformatic tool, PRIMEGENS, has recently been developed for the automatic design of PCR primers using DNA fragments that are specific to individual open reading frames (ORFs).'),
(70417, 2002, 'bio', 'Comparison of microarray designs for class comparison and class discovery', 'Dobbin, K|Simon, R', 'National Cancer Institute, EPN Mailstop 7434, 6130 Executive Blvd, Bethesda, MD 20892, USA. dobbinke@mail.nih.gov|National Cancer Institute, EPN Mailstop 7434, 6130 Executive Blvd, Bethesda, MD 20892, USA. dobbinke@mail.nih.gov', 'United States|United States', 98, 'Algorithms|Cluster Analysis|Computer Simulation|Equipment Failure Analysis|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Quality Control|RNA|Reference Standards|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/18.11.1438', '12424114', 'Two-color microarray experiments in which an aliquot derived from a common RNA sample is placed on each array are called reference designs. Traditionally, microarray experiments have used reference designs, but designs without a reference have recently been proposed as alternatives.'),
(70418, 2002, 'bio', 'Gene expression data analysis with a dynamically extended self-organized map that exploits class information', 'Mavroudi, Seferina|Papadimitriou, Stergios|Bezerianos, Anastasios', 'Department of Medical Physics, School of Medicine, University of Patras, 26500 Patras, Greece. severina@heart.med.upatras.gr|Department of Medical Physics, School of Medicine, University of Patras, 26500 Patras, Greece. severina@heart.med.upatras.gr|Department of Medical Physics, School of Medicine, University of Patras, 26500 Patras, Greece. severina@heart.med.upatras.gr', 'Greece|Greece|Greece', 19, 'Algorithms|DNA|Databases, Nucleic Acid|Feedback|Gene Expression Profiling|Gene Expression Regulation|Genome, Fungal|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1446', '12424115', 'Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling.'),
(70419, 2002, 'bio', 'Nonparametric methods for identifying differentially expressed genes in microarray data', 'Troyanskaya, Olga G|Garber, Mitchell E|Brown, Patrick O|Botstein, David|Altman, Russ B', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.', 'United States|United States|United States|United States|United States', 231, 'Carcinoma, Squamous Cell|Computer Simulation|False Positive Reactions|Gene Expression|Gene Expression Profiling|Humans|Lung Neoplasms|Lymphoma, B-Cell|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reference Values|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Statistics, Nonparametric', 'Comparative Study', '10.1093/bioinformatics/18.11.1454', '12424116', 'Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene (ideal discriminator method). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs.'),
(70420, 2002, 'bio', 'Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data', 'McShane, Lisa M|Radmacher, Michael D|Freidlin, Boris|Yu, Ren|Li, Ming-Chung|Simon, Richard', 'National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov|National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov|National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov|National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov|National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov|National Cancer Institute, Biometric Research Branch, DCTD, NIH, Bethesda, MD 20892-7434, USA. lm5h@nih.gov', 'United States|United States|United States|United States|United States|United States', 162, 'Cluster Analysis|DNA|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Male|Melanoma|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Prostatic Hyperplasia|Prostatic Neoplasms|Quality Control|Reference Values|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Stochastic Processes', 'Comparative Study', '10.1093/bioinformatics/18.11.1462', '12424117', 'Recent technological advances such as cDNA microarray technology have made it possible to simultaneously interrogate thousands of genes in a biological specimen. A cDNA microarray experiment produces a gene expression profile. Often interest lies in discovering novel subgroupings, or clusters, of specimens based on their profiles, for example identification of new tumor taxonomies. Cluster analysis techniques such as hierarchical clustering and self-organizing maps have frequently been used for investigating structure in microarray data. However, clustering algorithms always detect clusters, even on random data, and it is easy to misinterpret the results without some objective measure of the reproducibility of the clusters.'),
(70421, 2002, 'bio', 'Theoretical and experimental comparisons of gene expression indexes for oligonucleotide arrays', 'Lemon, William J|Palatini, Jeffrey J T|Krahe, Ralf|Wright, Fred A', 'Division of Human Cancer Genetics, The Ohio State University, Columbus, Ohio, USA.|Division of Human Cancer Genetics, The Ohio State University, Columbus, Ohio, USA.|Division of Human Cancer Genetics, The Ohio State University, Columbus, Ohio, USA.|Division of Human Cancer Genetics, The Ohio State University, Columbus, Ohio, USA.', 'United States|United States|United States|United States', 45, 'Artifacts|Cells, Cultured|Cluster Analysis|DNA Probes|Fibroblasts|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|RNA|Reference Standards|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Statistics as Topic', 'Comparative Study', '10.1093/bioinformatics/18.11.1470', '12424118', 'Oligonucleotide expression arrays exhibit systematic and reproducible variation produced by the multiple distinct probes used to represent a gene. Recently, a gene expression index has been proposed that explicitly models probe effects, and provides improved fits of hybridization intensity for arrays containing perfect match (PM) and mismatch (MM) probe pairs.'),
(70422, 2002, 'bio', 'Statistical analysis of a small set of time-ordered gene expression data using linear splines', 'De Hoon, M J L|Imoto, S|Miyano, S', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. mdehoon@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. mdehoon@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. mdehoon@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan', 44, 'Algorithms|Cluster Analysis|Cyanobacteria|DNA, Bacterial|Gene Expression Profiling|Gene Expression Regulation|Likelihood Functions|Linear Models|Models, Genetic|Models, Statistical|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Species Specificity|Stochastic Processes|Time Factors', 'Comparative Study', '10.1093/bioinformatics/18.11.1477', '12424119', 'Recently, the temporal response of genes to changes in their environment has been investigated using cDNA microarray technology by measuring the gene expression levels at a small number of time points. Conventional techniques for time series analysis are not suitable for such a short series of time-ordered data. The analysis of gene expression data has therefore usually been limited to a fold-change analysis, instead of a systematic statistical approach.'),
(70423, 2002, 'bio', 'A duplication growth model of gene expression networks', 'Bhan, Ashish|Galas, David J|Dewey, T Gregory', 'Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. Ashish_Bhan@kgi.edu|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. Ashish_Bhan@kgi.edu|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. Ashish_Bhan@kgi.edu', 'United States|United States|United States', 144, 'Algorithms|Cell Cycle|Cluster Analysis|Computer Simulation|Gene Duplication|Gene Expression Profiling|Gene Expression Regulation|Genes|Genes, Duplicate|Linear Models|Markov Chains|Models, Genetic|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.11.1486', '12424120', 'There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70424, 2002, 'bio', 'A simulated annealing algorithm for finding consensus sequences', 'Keith, Jonathan M|Adams, Peter|Bryant, Darryn|Kroese, Dirk P|Mitchelson, Keith R|Cochran, Duncan A E|Lala, Gita H', 'Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.|Department of Mathematics, The University of Queensland, Qld 4072, Australia. jonathan@maths.uq.edu.au.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 29, 'Algorithms|Computer Simulation|Consensus Sequence|Markov Chains|Models, Genetic|Models, Statistical|Monte Carlo Method|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1494', '12424121', 'A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules.'),
(70425, 2002, 'bio', 'Empirical determination of effective gap penalties for sequence comparison', 'Reese, J T|Pearson, W R', 'Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States', 52, 'Amino Acid Sequence|Computer Simulation|Databases, Protein|Evolution, Molecular|Genetic Variation|Molecular Sequence Data|Point Mutation|Quality Control|Sequence Alignment|Sequence Analysis, Protein|Sequence Deletion|Sequence Homology, Amino Acid', 'Comparative Study', '10.1093/bioinformatics/18.11.1500', '12424122', 'No general theory guides the selection of gap penalties for local sequence alignment. We empirically determined the most effective gap penalties for protein sequence similarity searches with substitution matrices over a range of target evolutionary distances from 20 to 200 Point Accepted Mutations (PAMs).'),
(70426, 2002, 'bio', 'An evaluation of beta-turn prediction methods', 'Kaur, Harpreet|Raghava, G P S', 'Institute of Microbial Technology, Chandigarh, India.|Institute of Microbial Technology, Chandigarh, India.', 'India|India', 46, 'Algorithms|Amino Acid Sequence|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Neural Networks (Computer)|Protein Structure, Secondary|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.11.1508', '12424123', 'beta-turn is an important element of protein structure. In the past three decades, numerous beta-turn prediction methods have been developed based on various strategies. For a detailed discussion about the importance of beta-turns and a systematic introduction of the existing prediction algorithms for beta-turns and their types, please see a recent review (Chou, Analytical Biochemistry, 286, 1-16, 2000). However at present, it is still difficult to say which method is better than the other. This is because of the fact that these methods were developed on different sets of data. Thus, it is important to evaluate the performance of beta-turn prediction methods.'),
(70427, 2002, 'bio', 'Finding relevant references to genes and proteins in Medline using a Bayesian approach', 'Leonard, Julie E|Colombe, Jeffrey B|Levy, Joshua L', 'Incellico Inc, 2327 Englert Dr, Durham, NC 27713, USA. jleonard@incellico.com|Incellico Inc, 2327 Englert Dr, Durham, NC 27713, USA. jleonard@incellico.com|Incellico Inc, 2327 Englert Dr, Durham, NC 27713, USA. jleonard@incellico.com', 'United States|United States|United States', 9, 'Abstracting and Indexing as Topic|Algorithms|Bayes Theorem|Database Management Systems|Dictionaries as Topic|False Negative Reactions|False Positive Reactions|Genes|Information Storage and Retrieval|MEDLINE|Models, Statistical|National Library of Medicine (U.S.)|Natural Language Processing|Pattern Recognition, Automated|Proteins|Subject Headings|United States', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1515', '12424124', 'Mining the biomedical literature for references to genes and proteins always involves a tradeoff between high precision with false negatives, and high recall with false positives. Having a reliable method for assessing the relevance of literature mining results is crucial to finding ways to balance precision and recall, and for subsequently building automated systems to analyze these results. We hypothesize that abstracts and titles that discuss the same gene or protein use similar words. To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for references to genes and proteins, and used a Bayesian metric for scoring the relevance of each reference assignment.'),
(70428, 2002, 'bio', 'Euclidian space and grouping of biological objects', 'Grishin, Vyacheslav N|Grishin, Nick V', 'Department of Biochemistry Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA. grishin@chop.swmed.edu|Department of Biochemistry Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA. grishin@chop.swmed.edu', 'United States|United States', 15, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Databases, Protein|Evolution, Molecular|Genetic Variation|Glutamate-Ammonia Ligase|Glutamate-Cysteine Ligase|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Ribonucleoproteins, Small Nuclear|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/18.11.1523', '12424125', 'Biological objects tend to cluster into discrete groups. Objects within a group typically possess similar properties. It is important to have fast and efficient tools for grouping objects that result in biologically meaningful clusters. Protein sequences reflect biological diversity and offer an extraordinary variety of objects for polishing clustering strategies. Grouping of sequences should reflect their evolutionary history and their functional properties. Visualization of relationships between sequences is of no less importance. Tree-building methods are typically used for such visualization. An alternative concept to visualization is a multidimensional sequence space. In this space, proteins are defined as points and distances between the points reflect the relationships between the proteins. Such a space can also be a basis for model-based clustering strategies that typically produce results correlating better with biological properties of proteins.'),
(70429, 2002, 'bio', 'A motif of a microbial starch-binding domain found in human genethonin', 'Janecek, Stefan', 'Institute of Molecular Biology, Slovak Academy of Sciences, Dúbravská cesta 21, SK-84251 Bratislava, Slovakia. stefan.janecek@savba.sk', 'Slovakia', 32, 'Amino Acid Motifs|Amino Acid Sequence|Aspergillus niger|Conserved Sequence|Databases, Protein|Glucan 1,4-alpha-Glucosidase|Glucosyltransferases|Humans|Membrane Proteins|Models, Molecular|Molecular Sequence Data|Muscle Proteins|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Species Specificity|Starch', 'Comparative Study', '10.1093/bioinformatics/18.11.1534', '12424126', 'The sequence of the starch-binding domain present in 10% of amylolytic enzymes of microbial origin and classified as the carbohydrate-binding module family 20, was identified in the equivalent part of sequence of human genethonin, a skeletal muscle protein of unknown function. The sequence identity between the starch-binding domain from Bacillus sp. strain 1011 cyclodextrin glucanotransferase and the corresponding segment of human genethonin was higher than 28%. The amino acid residues known to be involved in the raw starch binding were found to be conserved in the genethonin sequence. The three-dimensional structure of the genethonin starch-binding domain was modelled and its eventual function briefly discussed.'),
(70430, 2002, 'bio', 'AcePrimer: automation of PCR primer design based on gene structure', 'McKay, Sheldon J|Jones, Steven J M', 'Genome Sciences Centre, British Columbia Cancer Agency, 600 W 10th Avenue, Vancouver, BC, Canada V5Z 4E6.|Genome Sciences Centre, British Columbia Cancer Agency, 600 W 10th Avenue, Vancouver, BC, Canada V5Z 4E6.', 'Canada|Canada', 12, 'Alleles|Animals|Base Sequence|Caenorhabditis elegans|Computer Simulation|DNA Primers|Database Management Systems|Databases, Nucleic Acid|Gene Deletion|Internet|Molecular Sequence Data|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.11.1538', '12424127', 'AcePrimer is an internet-accessed application based on CGI/Perl programming that designs PCR primers to search for deletion alleles in Caenorhabditis elegans gene knockout experiments and uses electronic PCR to search the entire genomic DNA sequence for potential false priming or multiple PCR amplification targets. Features such as the ability to target specific exons with the poison primer approach and evaluation of primers with electronic PCR provide a flexible, web-based approach to design effective primers whilst minimizing the need for empirical optimization of PCR experiments.'),
(70431, 2002, 'bio', 'SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis', 'Colantuoni, Carlo|Henry, George|Zeger, Scott|Pevsner, Jonathan', 'Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA.|Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA.|Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA.|Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA.', 'United States|United States|United States|United States', 100, 'Algorithms|Computer Communication Networks|Database Management Systems|Databases, Nucleic Acid|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Internet|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.11.1540', '12424128', 'SNOMAD is a collection of algorithms for the normalization and standardization of gene expression datasets derived from diverse biological and technological sources. In addition to conventional transformations and visualization tools, SNOMAD includes two non-linear transformations which correct for bias and variance which are non-uniformly distributed across the range of microarray element signal intensities: (1). Local mean normalization, and (2). Local variance correction (Z-score generation using a locally calculated standard deviation).'),
(70432, 2002, 'bio', 'GeneCards 2002: towards a complete, object-oriented, human gene compendium', 'Safran, Marilyn|Solomon, Irina|Shmueli, Orit|Lapidot, Michal|Shen-Orr, Shai|Adato, Avital|Ben-Dor, Uri|Esterman, Nir|Rosen, Naomi|Peter, Inga|Olender, Tsviya|Chalifa-Caspi, Vered|Lancet, Doron', 'Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il|Departments of Molecular Genetics Biological Services (Bioinformatics Unit) Molecular Cell Biology, The Weizmann Institute of Science, Rehovot, Israel. marilyn.safran@weizmann.ac.il', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 128, 'Algorithms|Chromosome Mapping|Computer Communication Networks|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Genome, Human|Humans|Information Storage and Retrieval|Internet|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/18.11.1542', '12424129', 'In the post-genomic era, functional analysis of genes requires a sophisticated interdisciplinary arsenal. Comprehensive resources are challenged to provide consistently improving, state-of-the-art tools.'),
(70433, 2002, 'bio', 'Poxvirus Orthologous Clusters (POCs)', 'Ehlers, Angelika|Osborne, John|Slack, Stephanie|Roper, Rachel L|Upton, Chris', 'Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8W 3P6, Canada.|Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8W 3P6, Canada.|Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8W 3P6, Canada.|Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8W 3P6, Canada.|Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8W 3P6, Canada.', 'Canada|Canada|Canada|Canada|Canada', 47, 'Cluster Analysis|DNA, Viral|Database Management Systems|Databases, Genetic|Documentation|Genome, Viral|Internet|Multimedia|Poxviridae|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.11.1544', '12424130', 'Poxvirus Orthologous Clusters (POCs) is a JAVA client-server application which accesses an updated database containing all complete poxvirus genomes, it automatically groups orthologous genes into families based on BLASTP scores for assessment by a human database curator. POCs has a user-friendly interface permitting complex SQL queries to retrieve interesting groups of DNA and protein sequences as well as gene families for subsequent interrogation by a variety of integrated tools: BLASTP, BLASTX, TBLASTN, Jalview (multiple alignment), Dotlet (Dotplot), Laj (local alignment), and NAP (nucleotide to amino acid alignment).'),
(70434, 2002, 'bio', 'QuickTree: building huge Neighbour-Joining trees of protein sequences', 'Howe, Kevin|Bateman, Alex|Durbin, Richard', 'The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. klh@sanger.ac.uk|The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. klh@sanger.ac.uk|The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK. klh@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 192, 'Algorithms|Database Management Systems|Databases, Protein|Evolution, Molecular|HIV Envelope Protein gp120|Humans|Phylogeny|Quality Control|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/18.11.1546', '12424131', 'We have written a fast implementation of the popular Neighbor-Joining tree building algorithm. QuickTree allows the reconstruction of phylogenies for very large protein families (including the largest Pfam alignment containing 27000 HIV GP120 glycoprotein sequences) that would be infeasible using other popular methods.'),
(70435, 2002, 'bio', 'Ramachandran plot on the web', 'Sheik, S S|Sundararajan, P|Hussain, A S Z|Sekar, K', 'Bioinformatics Centre, Supercomputer Education & Research Centre, Indian Institute of Science, Bangalore 560012, India.|Bioinformatics Centre, Supercomputer Education & Research Centre, Indian Institute of Science, Bangalore 560012, India.|Bioinformatics Centre, Supercomputer Education & Research Centre, Indian Institute of Science, Bangalore 560012, India.|Bioinformatics Centre, Supercomputer Education & Research Centre, Indian Institute of Science, Bangalore 560012, India.', 'India|India|India|India', 41, 'Amino Acid Motifs|Amino Acids|Computer Graphics|Computer Simulation|Data Display|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Models, Molecular|Protein Structure, Secondary|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.11.1548', '12424132', 'A graphics package has been developed to display the main chain torsion angles phi, psi (phi, Psi), (Ramachandran angles) in a protein of known structure. In addition, the package calculates the Ramachandran angles at the central residue in the stretch of three amino acids having specified the flanking residue types. The package displays the Ramachandran angles along with a detailed analysis output. This software is incorporated with all the protein structures available in the Protein Databank.'),
(70436, 2002, 'bio', 'Bioinformatics: biology by other means', 'Valencia, Alfonso', 'Bioinformatics Centre, Supercomputer Education & Research Centre, Indian Institute of Science, Bangalore 560012, India.', 'India', 8, 'Biology|Biomedical Research|Biotechnology|Computational Biology|Europe|Genomics|Molecular Biology|Proteomics', 'Editorial', '10.1093/bioinformatics/18.12.1551', '12490437', '-'),
(70437, 2002, 'bio', 'Accomplishments and challenges in literature data mining for biology', 'Hirschman, Lynette|Park, Jong C|Tsujii, Junichi|Wong, Limsoon|Wu, Cathy H', 'The MITRE Corporation, USA. lynette@mitre.org|The MITRE Corporation, USA. lynette@mitre.org|The MITRE Corporation, USA. lynette@mitre.org|The MITRE Corporation, USA. lynette@mitre.org|The MITRE Corporation, USA. lynette@mitre.org', 'United States|United States|United States|United States|United States', 0, 'Benchmarking|Biology|Computational Biology|Database Management Systems|Databases, Bibliographic|Evaluation Studies as Topic|Information Storage and Retrieval|Natural Language Processing|Online Systems|Quality Control', 'Journal Article', '10.1093/bioinformatics/18.12.1553', '12490438', 'We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.'),
(70438, 2002, 'bio', 'The presence of signal peptide significantly affects transmembrane topology prediction', 'Lao, Demelo M|Arai, Masafumi|Ikeda, Masami|Shimizu, Toshio', 'Department of Electronic and Information System Engineering, Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8561, Japan.|Department of Electronic and Information System Engineering, Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8561, Japan.|Department of Electronic and Information System Engineering, Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8561, Japan.|Department of Electronic and Information System Engineering, Faculty of Science and Technology, Hirosaki University, Hirosaki 036-8561, Japan.', 'Japan|Japan|Japan|Japan', 41, 'Amino Acid Sequence|Cell Membrane|Membrane Proteins|Molecular Sequence Data|Protein Sorting Signals|Protein Structure, Secondary|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.12.1562', '12490439', '-'),
(70439, 2002, 'bio', 'Computational antisense oligo prediction with a neural network model', 'Chalk, Alistair M|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden. alistair.chalk@cgb.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden. alistair.chalk@cgb.ki.se', 'Sweden|Sweden', 0, 'Algorithms|Animals|Cricetinae|Database Management Systems|Databases, Nucleic Acid|Gene Expression Regulation|Humans|Information Storage and Retrieval|Models, Genetic|Neural Networks (Computer)|Oligoribonucleotides, Antisense|Proteins|RNA, Messenger|Rats|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA', 'Evaluation Studies', '10.1093/bioinformatics/18.12.1567', '12490440', 'The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model.'),
(70440, 2002, 'bio', 'Comparing expression profiles of genes with similar promoter regions', 'Park, Peter J|Butte, Atul J|Kohane, Isaac S', 'Informatics Program and Division of Endocrinology, Childrens Hospital, 300 Longwood Avenue, Boston, MA 02115, USA. peter_park@harvard.edu|Informatics Program and Division of Endocrinology, Childrens Hospital, 300 Longwood Avenue, Boston, MA 02115, USA. peter_park@harvard.edu|Informatics Program and Division of Endocrinology, Childrens Hospital, 300 Longwood Avenue, Boston, MA 02115, USA. peter_park@harvard.edu', 'United States|United States|United States', 0, 'Databases, Nucleic Acid|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Reproducibility of Results|Saccharomyces cerevisiae|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Statistics as Topic|Transcription Factors|Transcription, Genetic', 'Comparative Study', '10.1093/bioinformatics/18.12.1576', '12490441', 'Gene regulatory elements are often predicted by seeking common sequences in the promoter regions of genes that are clustered together based on their expression profiles. We consider the problem in the opposite direction: we seek to find the genes that have similar promoter regions and determine the extent to which these genes have similar expression profiles.'),
(70441, 2002, 'bio', 'Robust estimators for expression analysis', 'Hubbell, Earl|Liu, Wei-Min|Mei, Rui', 'Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051, USA. earl_hubbell@affymetrix.com|Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051, USA. earl_hubbell@affymetrix.com|Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051, USA. earl_hubbell@affymetrix.com', 'United States|United States|United States', 476, 'Algorithms|DNA Probes|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Stochastic Processes|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.12.1585', '12490442', 'We consider the problem of estimating values associated with gene expression from oligonucleotide arrays. Such estimates should linearly track concentration, yield non-negative results, have statistical guarantees of robustness against outliers, and allow estimates of significance and variance.'),
(70442, 2002, 'bio', 'Analysis of high density expression microarrays with signed-rank call algorithms', 'Liu, W-m|Mei, R|Di, X|Ryder, T B|Hubbell, E|Dee, S|Webster, T A|Harrington, C A|Ho, M-h|Baid, J|Smeekens, S P', 'Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com|Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Statistics, Nonparametric|Transcription, Genetic|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.12.1593', '12490443', 'We consider the detection of expressed genes and the comparison of them in different experiments with the high-density oligonucleotide microarrays. The results are summarized as the detection calls and comparison calls, and they should be robust against data outliers over a wide target concentration range. It is also helpful to provide parameters that can be adjusted by the user to balance specificity and sensitivity under various experimental conditions.'),
(70443, 2002, 'bio', 'Between-group analysis of microarray data', 'Culhane, Aedín C|Perrière, Guy|Considine, Elizabeth C|Cotter, Thomas G|Higgins, Desmond G', 'Department of Biochemistry, University College Cork, Cork, Ireland. A.Culhane@ucc.ie|Department of Biochemistry, University College Cork, Cork, Ireland. A.Culhane@ucc.ie|Department of Biochemistry, University College Cork, Cork, Ireland. A.Culhane@ucc.ie|Department of Biochemistry, University College Cork, Cork, Ireland. A.Culhane@ucc.ie|Department of Biochemistry, University College Cork, Cork, Ireland. A.Culhane@ucc.ie', 'Ireland|Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Bone Neoplasms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Leukemia, Myeloid, Acute|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Software|Transcription, Genetic', 'Comparative Study', '10.1093/bioinformatics/18.12.1600', '12490444', 'Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data.'),
(70444, 2002, 'bio', 'Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments', 'Black, Michael A|Doerge, R W', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.blackma@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.blackma@stat.purdue.edu', 'United States|United States', 0, 'Analysis of Variance|Databases, Nucleic Acid|Escherichia coli|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Spores, Fungal|Yeasts', 'Comparative Study', '10.1093/bioinformatics/18.12.1609', '12490445', 'We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques.'),
(70445, 2002, 'bio', 'A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer', 'Martoglio, Ann-Marie|Miskin, James W|Smith, Stephen K|MacKay, David J C', 'Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK. amm53@cam.ac.uk|Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK. amm53@cam.ac.uk|Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK. amm53@cam.ac.uk|Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK. amm53@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 16, 'Algorithms|Cluster Analysis|Cystadenoma|Female|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Observer Variation|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Quality Control|Reference Values|Reproducibility of Results|Sensitivity and Specificity|Transcription, Genetic', 'Comparative Study', '10.1093/bioinformatics/18.12.1617', '12490446', 'A number of algorithms and analytical models have been employed to reduce the multidimensional complexity of DNA array data and attempt to extract some meaningful interpretation of the results. These include clustering, principal components analysis, self-organizing maps, and support vector machine analysis. Each method assumes an implicit model for the data, many of which separate genes into distinct clusters defined by similar expression profiles in the samples tested. A point of concern is that many genes may be involved in a number of distinct behaviours, and should therefore be modelled to fit into as many separate clusters as detected in the multidimensional gene expression space. The analysis of gene expression data using a decomposition model that is independent of the observer involved would be highly beneficial to improve standard and reproducible classification of clinical and research samples.'),
(70446, 2002, 'bio', 'Partial least squares proportional hazard regression for application to DNA microarray survival data', 'Nguyen, Danh V|Rocke, David M', 'Department of Statistics, Texas A&M University, College Station, TX 77843, USA. dnguyen@stat.tamu.edu|Department of Statistics, Texas A&M University, College Station, TX 77843, USA. dnguyen@stat.tamu.edu', 'United States|United States', 110, 'Breast Neoplasms|Gene Expression|Gene Expression Regulation, Neoplastic|Humans|Least-Squares Analysis|Lymphoma, B-Cell|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Proportional Hazards Models|Regression Analysis|Reproducibility of Results|Sensitivity and Specificity|Survival Analysis', 'Evaluation Studies', '10.1093/bioinformatics/18.12.1625', '12490447', 'Microarrays are increasingly used in cancer research. When gene transcription data from microarray experiments also contains patient survival information, it is often of interest to predict the survival times based on the gene expression. In this paper we consider the well-known proportional hazard (PH) regression model for survival analysis. Ordinarily, the PH model is used with a few covariates and many observations (subjects). We consider here the case that the number of covariates, p, exceeds the number of samples, N, a setting typical of gene expression data from DNA microarrays.'),
(70447, 2002, 'bio', 'Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model', 'Sásik, R|Calvo, E|Corbeil, J', 'School of Medicine, University of California San Diego, La Jolla, CA 92093-0679, USA. sasik@corgon.ucsd.edu|School of Medicine, University of California San Diego, La Jolla, CA 92093-0679, USA. sasik@corgon.ucsd.edu|School of Medicine, University of California San Diego, La Jolla, CA 92093-0679, USA. sasik@corgon.ucsd.edu', 'United States|United States|United States', 69, 'Algorithms|Animals|Gene Expression Profiling|Gene Expression Regulation|Mice|Models, Genetic|Models, Statistical|Nonlinear Dynamics|Normal Distribution|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Stochastic Processes|T-Lymphocytes', 'Comparative Study', '10.1093/bioinformatics/18.12.1633', '12490448', 'High-density oligonucleotide arrays (GeneChip, Affymetrix, Santa Clara, CA) have become a standard research tool in many areas of biomedical research. They quantitatively monitor the expression of thousands of genes simultaneously by measuring fluorescence from gene-specific targets or probes. The relationship between signal intensities and transcript abundance as well as normalization issues have been the focus of much recent attention (Hill et al., 2001, Chudin et al., 2002, Naef et al., 2002a). It is desirable that a researcher has the best possible analytical tools to make the most of the information that this powerful technology has to offer. At present there are three analytical methods available: the newly released Affymetrix Microarray Suite 5.0 (AMS) software that accompanies the GeneChip product, the method of Li and Wong (LW, Li and Wong, 2001), and the method of Naef et al. (FN, Naef et al., 2001). The AMS method is tailored for analysis of a single microarray, and can therefore be used with any experimental design. The LW method on the other hand depends on a large number of microarrays in an experiment and cannot be used for an isolated microarray, and the FN method is particular to paired microarrays, such as resulting from an experiment in which each treatment sample has a corresponding control sample. Our focus is on analysis of experiments in which there is a series of samples. In this case only the AMS, LW, and the method described in this paper can be used. The present method is model-based, like the LW method, but assumes multiplicative not additive noise, and employs elimination of statistically significant outliers for improved results. Unlike LW and AMS, we do not assume probe-specific background (measured by the so-called mismatch probes). Rather, we assume uniform background, whose level is estimated using both the mismatch and perfect match probe intensities.'),
(70448, 2002, 'bio', 'Modeling the percolation of annotation errors in a database of protein sequences', 'Gilks, Walter R|Audit, Benjamin|De Angelis, Daniela|Tsoka, Sophia|Ouzounis, Christos A', 'Medical Research Council Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|Medical Research Council Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|Medical Research Council Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|Medical Research Council Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk|Medical Research Council Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Databases, Protein|Documentation|False Positive Reactions|Humans|Information Storage and Retrieval|Models, Genetic|Models, Molecular|Models, Statistical|Proteins|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/18.12.1641', '12490449', 'Public sequence databases contain information on the sequence, structure and function of proteins. Genome sequencing projects have led to a rapid increase in protein sequence information, but reliable, experimentally verified, information on protein function lags a long way behind. To address this deficit, functional annotation in protein databases is often inferred by sequence similarity to homologous, annotated proteins, with the attendant possibility of error. Now, the functional annotation in these homologous proteins may itself have been acquired through sequence similarity to yet other proteins, and it is generally not possible to determine how the functional annotation of any given protein has been acquired. Thus the possibility of chains of misannotation arises, a process we term error percolation. With some simple assumptions, we develop a dynamical probabilistic model for these misannotation chains. By exploring the consequences of the model for annotation quality it is evident that this iterative approach leads to a systematic deterioration of database quality.'),
(70449, 2002, 'bio', 'Local structure-based sequence profile database for local and global protein structure predictions', 'Yang, An-Suei|Wang, Lu-Yong', 'Department of Pharmacology and Columbia Genome Center, Columbia University, 630 West 168th street, PH 7 W Room 318, New York, NY 10032, USA. ay1@columbia.edu|Department of Pharmacology and Columbia Genome Center, Columbia University, 630 West 168th street, PH 7 W Room 318, New York, NY 10032, USA. ay1@columbia.edu', 'United States|United States', 19, 'Algorithms|Amino Acid Sequence|Animals|Database Management Systems|Databases, Protein|False Negative Reactions|False Positive Reactions|Humans|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/18.12.1650', '12490450', 'A large body of evidence suggests that protein structural information is frequently encoded in local sequences-sequence-structure relationships derived from local structure/sequence analyses could significantly enhance the capacities of protein structure prediction methods. In this paper, the prediction capacity of a database (LSBSP2) that organizes local sequence-structure relationships encoded in local structures with two consecutive secondary structure elements is tested with two computational procedures for protein structure prediction. The goal is twofold: to test the folding hypothesis that local structures are determined by local sequences, and to enhance our capacity in predicting protein structures from their amino acid sequences.'),
(70450, 2002, 'bio', 'Structure-dependent sequence alignment for remotely related proteins', 'Yang, An-Suei', 'Department of Pharmacology and Columbia Genome Center, Columbia University, 630 West 168th street, PH 7 W Room 318, New York, NY 10032, USA. ay1@columbia.edu', 'United States', 0, 'Algorithms|Databases, Protein|Models, Molecular|Protein Conformation|Proteins|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/18.12.1658', '12490451', 'The quality of a model structure derived from a comparative modeling procedure is dictated by the accuracy of the predicted sequence-template alignment. As the sequence-template pairs are increasingly remote in sequence relationship, the prediction of the sequence-template alignments becomes increasingly problematic with sequence alignment methods. Structural information of the template, used in connection with the sequence relationship of the sequence-template pair, could significantly improve the accuracy of the sequence-template alignment. In this paper, we describe a sequence-template alignment method that integrates sequence and structural information to enhance the accuracy of sequence-template alignments for distantly related protein pairs.'),
(70451, 2002, 'bio', 'PFDB: a generic protein family database integrating the CATH domain structure database with sequence based protein family resources', 'Shepherd, Adrian J|Martin, Nigel J|Johnson, Roger G|Kellam, Paul|Orengo, Christine A', 'Department of Biochemistry and Molecular Biology, University College, London, Gower Street, London WC1E 6BT.|Department of Biochemistry and Molecular Biology, University College, London, Gower Street, London WC1E 6BT.|Department of Biochemistry and Molecular Biology, University College, London, Gower Street, London WC1E 6BT.|Department of Biochemistry and Molecular Biology, University College, London, Gower Street, London WC1E 6BT.|Department of Biochemistry and Molecular Biology, University College, London, Gower Street, London WC1E 6BT.', '-|-|-|-|-', 8, 'Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Internet|Proteins|Sequence Analysis, Protein|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/18.12.1666', '12490452', 'The PFDB (Protein Family Database) is a new database designed to integrate protein family-related data with relevant functional and genomic data. It currently manages biological data for three projects-the CATH protein domain database (Orengo et al., 1997, Pearl et al., 2001), the VIDA virus domains database (Albà et al., 2001) and the Gene3D database (Buchan et al., 2001). The PFDB has been designed to accommodate protein families identified by a variety of sequence based or structure based protocols and provides a generic resource for biological research by enabling mapping between different protein families and diverse biochemical and genetic data, including complete genomes.'),
(70452, 2002, 'bio', 'A hierarchical approach to aligning collinear regions of genomes', 'Roytberg, Mikhail A|Ogurtsov, Aleksey Y|Shabalina, Svetlana A|Kondrashov, Alexey S', 'Institute of Mathematical Problems in Biology, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems in Biology, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems in Biology, Pushchino, Moscow Region 142290, Russia.|Institute of Mathematical Problems in Biology, Pushchino, Moscow Region 142290, Russia.', '-|-|-|-', 10, 'Algorithms|Animals|Base Sequence|Chickens|Conserved Sequence|Evolution, Molecular|Fractals|Genome|Humans|Mice|Molecular Sequence Data|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Takifugu', 'Comparative Study', '10.1093/bioinformatics/18.12.1673', '12490453', 'As a first approximation, similarity between two long orthologous regions of genomes can be represented by a chain of local similarities. Within such a chain, pairs of successive similarities are collinear (non-conflicting), i.e. segments involved in the nth similarity precede in both sequences segments involved in the (n+1)th similarity. However, when all similarities between two long sequences are considered, usually there are many conflicts between them. Although some conflicts can be avoided by masking transposons or low-complexity sequences, selecting only those similarities that reflect orthology and, thus, belong to the evolutionarily true chain is not trivial.'),
(70453, 2002, 'bio', 'SNPper: retrieval and analysis of human SNPs', 'Riva, A|Kohane, I S', 'Childrens Hospital Informatics Program, Childrens Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA. Alberto.Riva@TCH.Harvard.edu|Childrens Hospital Informatics Program, Childrens Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA. Alberto.Riva@TCH.Harvard.edu', 'United States|United States', 0, 'Database Management Systems|Databases, Nucleic Acid|Genetic Variation|Genetics, Population|Genome, Human|Humans|Information Storage and Retrieval|Internet|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.12.1681', '12490454', 'Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for the study of the human genome. SNPs can be used as markers to create high-density genetic maps, as causal candidates for diseases, or to reconstruct the history of our genome. SNP-based studies rely on the availability of large numbers of validated, high-frequency SNPs whose position on the chromosomes is known with precision. Although large collections of SNPs exist in public databases, researchers need tools to effectively retrieve and manipulate them.'),
(70454, 2002, 'bio', 'Oligodb--interactive design of oligo DNA for transcription profiling of human genes', 'Mrowka, Ralf|Schuchhardt, Johannes|Gille, Christoph', 'Johannes-Müller-Institut für Physiologie, Charité, Humboldt-Universität zu Berlin, Germany. ralf.mrowka@charite.de|Johannes-Müller-Institut für Physiologie, Charité, Humboldt-Universität zu Berlin, Germany. ralf.mrowka@charite.de|Johannes-Müller-Institut für Physiologie, Charité, Humboldt-Universität zu Berlin, Germany. ralf.mrowka@charite.de', 'Germany|Germany|Germany', 22, 'Algorithms|Computer-Aided Design|DNA|Databases, Nucleic Acid|Gene Expression Profiling|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Online Systems|Software|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.12.1686', '12490455', 'The increasing use of customized oligo-DNA-microarrays has created a demand for convenient design of oligo nucleotides. The oligo-nucleotides have to serve as specific templates in the hybridization reaction.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(70455, 2002, 'bio', 'PCR designer for restriction analysis of various types of sequence mutation', 'Ke, Xiayi|Collins, Andrew|Ye, Shu', 'Wellcome Trust Centre For Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. xiayi@well.ox.ac.uk|Wellcome Trust Centre For Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. xiayi@well.ox.ac.uk|Wellcome Trust Centre For Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK. xiayi@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 6, 'Amidohydrolases|Base Pair Mismatch|Base Sequence|Biotinidase|Computer-Aided Design|DNA Primers|DNA Restriction Enzymes|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Molecular Sequence Data|Mutation|Point Mutation|Polymerase Chain Reaction|Restriction Mapping|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.12.1688', '12490456', 'Restriction analysis is widely used to detect gene mutations such as insertions, deletions and single nucleotide polymorphisms (SNPs). Although such mutation sites sometimes present some natural restriction sites to differentiate the wild-type and mutant sequences, mismatches are often needed in order to create artificial restriction fragment length polymorphisms (RFLPs). In this report, a computer program is described that screens for suitable restriction enzymes, introducing mismatches where appropriate and when necessary, designs primers using the information of the selected restriction enzymes, their recognition sequence and locations as well as the information about the mismatches if any. The program, supported by a WWW web interface, is intended to be used online.'),
(70456, 2002, 'bio', 'Design of endonuclease restriction sites into primers for PCR cloning', 'Hou, Jianghui', 'Molecular Medicine Center, Edinburgh University, Edinburgh EH4 2XU, UK. jianghui.hou@ed.ac.uk', 'United Kingdom', 2, 'Algorithms|Animals|Artificial Gene Fusion|Cloning, Molecular|Computer-Aided Design|DNA Primers|DNA Restriction Enzymes|Epithelium|Mice|Polymerase Chain Reaction|Sodium Channels|Software|Temperature', 'Journal Article', '10.1093/bioinformatics/18.12.1690', '12490457', 'I present a software system PCRCLNG that facilitates the design of endonuclease restriction sites into the 5-end of PCR primers. The product amplified using these primers can be directly cloned into vectors. The program estimates the annealing temperature for each primer and selects the primer pairs with comparable annealing temperature. Finally the software determines whether the PCR product can be cloned into the vector to generate in-frame gene fusion.'),
(70457, 2002, 'bio', 'RED: the analysis, management and dissemination of expressed sequence tags', 'Everitt, R|Minnema, S E|Wride, M A|Koster, C S|Hance, J E|Mansergh, F C|Rancourt, D E', 'Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, Canada T2N 4N1. reveritt@ucalgary.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 5, 'Animals|Base Sequence|Database Management Systems|Databases, Nucleic Acid|Expressed Sequence Tags|Humans|Information Storage and Retrieval|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.12.1692', '12490458', 'The Rancourt EST Database (RED) is a web-based system for the analysis, management, and dissemination of expressed sequence tags (ESTs). RED represents a flexible template DNA sequence database that can be easily manipulated to suit the needs of other laboratories undertaking mid-size sequencing projects.'),
(70458, 2002, 'bio', 'GENECOUNTING: haplotype analysis with missing genotypes', 'Zhao, Jing Hua|Lissarrague, Sebastien|Essioux, Laurent|Sham, Pak Chung', 'Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. j.zhao@public-health.ucl.ac.uk|Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. j.zhao@public-health.ucl.ac.uk|Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. j.zhao@public-health.ucl.ac.uk|Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK. j.zhao@public-health.ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 107, 'Algorithms|Alleles|Gene Frequency|Genetic Markers|Genotype|Haplotypes|Likelihood Functions|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/18.12.1694', '12490459', 'A general algorithm is described for haplotype analysis of unrelated individuals with missing genotypes. It can handle problems involving multiple polymorphic markers with missing data.'),
(70459, 2002, 'bio', 'DNACompress: fast and effective DNA sequence compression', 'Chen, Xin|Li, Ming|Ma, Bin|Tromp, John', 'Computer Science Department, University of California, Santa Barbara, CA 99106, USA. chxin@cs.ucsb.edu|Computer Science Department, University of California, Santa Barbara, CA 99106, USA. chxin@cs.ucsb.edu|Computer Science Department, University of California, Santa Barbara, CA 99106, USA. chxin@cs.ucsb.edu|Computer Science Department, University of California, Santa Barbara, CA 99106, USA. chxin@cs.ucsb.edu', 'United States|United States|United States|United States', 157, 'Algorithms|Computing Methodologies|Database Management Systems|Databases, Nucleic Acid|Humans|Information Storage and Retrieval|Quality Control|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/18.12.1696', '12490460', 'While achieving the best compression ratios for DNA sequences, our new DNACompress program significantly improves the running time of all previous DNA compression programs.'),
(70460, 2002, 'bio', 'ADME-AP: a database of ADME associated proteins', 'Sun, L Z|Ji, Z L|Chen, X|Wang, J F|Chen, Y Z', 'Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.|Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.|Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.|Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.|Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 27, 'Databases, Protein|Information Storage and Retrieval|Pharmaceutical Preparations|Pharmacogenetics|Proteins|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.12.1699', '12490461', 'Drug absorption, distribution, metabolism and excretion (ADME) often involve interaction of a drug with specific proteins. Knowledge about these ADME-associated proteins is important in facilitating the study of the molecular mechanism of disposition and individual response as well as therapeutic action of drugs. It is also useful in the development and testing of pharmacokinetics prediction tools. Several databases describing specific classes of ADME-associated proteins have appeared. A new database, ADME-associated proteins (ADME-AP), is introduced to provide comprehensive information about all classes of ADME-associated proteins described in the literature including physiological function of each protein, pharmacokinetic effect, ADME classification, direction and driving force of disposition, location and tissue distribution, substrates, synonyms, gene name and protein availability in other species. Cross-links to other databases are also provided to facilitate the access of information about the sequence, 3D structure, function, polymorphisms, genetic disorders, nomenclature, ligand binding properties and related literatures of each protein. ADME-AP currently contains entries for 321 proteins and 964 substrates.'),
(70461, 2002, 'bio', 'PoPMuSiC, rationally designing point mutations in protein structures', 'Kwasigroch, J M|Gilis, D|Dehouck, Y|Rooman, M', 'Ingénierie Biomoléculaire, CP 165/64, Université Libre de Bruxelles, avenue Roosevelt 50, 1050 Bruxelles, Belgium. kwasigroch@ulb.ac.be|Ingénierie Biomoléculaire, CP 165/64, Université Libre de Bruxelles, avenue Roosevelt 50, 1050 Bruxelles, Belgium. kwasigroch@ulb.ac.be|Ingénierie Biomoléculaire, CP 165/64, Université Libre de Bruxelles, avenue Roosevelt 50, 1050 Bruxelles, Belgium. kwasigroch@ulb.ac.be|Ingénierie Biomoléculaire, CP 165/64, Université Libre de Bruxelles, avenue Roosevelt 50, 1050 Bruxelles, Belgium. kwasigroch@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium', 58, 'Computer-Aided Design|Databases, Protein|Electronic Mail|Information Storage and Retrieval|Point Mutation|Protein Conformation|Protein Engineering|Protein Folding|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.12.1701', '12490462', 'PoPMuSiC is an efficient tool for rational computer-aided design of single-site mutations in proteins and peptides. Two types of queries can be submitted. The first option allows to estimate the changes in folding free energy for specific point mutations given by the user. In the second option, all possible point mutations in a given protein or protein region are performed and the most stabilizing or destabilizing mutations, or the neutral mutations with respect to thermodynamic stability, are selected. For each sequence position or secondary structure the deviation from the most stable sequence is moreover evaluated, which helps to identify the most suitable sites for the introduction of mutations.'),
(70462, 2002, 'bio', 'OWEN: aligning long collinear regions of genomes', 'Ogurtsov, Aleksey Y|Roytberg, Mikhail A|Shabalina, Svetlana A|Kondrashov, Alexey S', 'National Center for Biotechnology Information, NIH, 45 Center Drive, Bethesda, MD 20892-6510, USA. ogurtsov@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NIH, 45 Center Drive, Bethesda, MD 20892-6510, USA. ogurtsov@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NIH, 45 Center Drive, Bethesda, MD 20892-6510, USA. ogurtsov@ncbi.nlm.nih.gov|National Center for Biotechnology Information, NIH, 45 Center Drive, Bethesda, MD 20892-6510, USA. ogurtsov@ncbi.nlm.nih.gov', 'United States|United States|United States|United States', 46, 'Algorithms|Base Sequence|Chromosomes, Human, 6-12 and X|Conserved Sequence|Databases, Nucleic Acid|Evolution, Molecular|Genome|Humans|Information Storage and Retrieval|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/18.12.1703', '12490463', 'OWEN is an interactive tool for aligning two long DNA sequences that represents similarity between them by a chain of collinear local similarities. OWEN employs several methods for constructing and editing local similarities and for resolving conflicts between them. Alignments of sequences of lengths over 10(6) can often be produced in minutes. OWEN requires memory below 20 L, where L is the sum of lengths of the compared sequences.'),
(70463, 2002, 'bio', 'The GeneAround GO viewer', 'Tanoue, Junko|Yoshikawa, Masatoshi|Uemura, Shunsuke', 'Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan.', 'Japan|Japan|Japan', 9, 'Computer Graphics|Database Management Systems|Databases, Nucleic Acid|Documentation|Evolution, Molecular|Information Storage and Retrieval|Natural Language Processing|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/18.12.1705', '12490464', 'We have developed a system for visualizing the Gene Ontology((TM)) hierarchy. The graphical browser interactively displays diagrams of the inheritance relationship for each term to help understand the meanings of terms when handling gene annotation data.'),
(70464, 2003, 'bio', 'Wavelets in bioinformatics and computational biology: state of art and perspectives', 'Liò, Pietro', 'Department of Zoology, University of Cambridge, UK. plio@hgmp.mrc.ac.uk', 'United Kingdom', 169, 'Algorithms|Computational Biology|Computer Simulation|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteins|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/19.1.2', '12499286', 'At a recent meeting, the wavelet transform was depicted as a small child kicking back at its father, the Fourier transform. Wavelets are more efficient and faster than Fourier methods in capturing the essence of data. Nowadays there is a growing interest in using wavelets in the analysis of biological sequences and molecular biology-related signals.'),
(70465, 2003, 'bio', 'Novel organelle-targeting signals in viral proteins', 'Mohan, K V Krishna|Atreya, C D', 'Section of Viral Pathogenesis and Vaccine Adverse Reactions, Laboratory of Pediatric and Respiratory Viral Diseases, Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD 20892, USA.|Section of Viral Pathogenesis and Vaccine Adverse Reactions, Laboratory of Pediatric and Respiratory Viral Diseases, Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD 20892, USA.', 'United States|United States', 8, 'Conserved Sequence|Databases, Protein|Humans|Peroxisomal Targeting Signal 2 Receptor|Peroxisome-Targeting Signal 1 Receptor|Peroxisomes|Protein Sorting Signals|Receptors, Cytoplasmic and Nuclear|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/19.1.10', '12499287', '-'),
(70466, 2003, 'bio', 'Reconstruction of DNA sequencing by hybridization', 'Zhang, Ji-Hong|Wu, Ling-Yun|Zhang, Xiang-Sun', 'Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China. zhijihong@amss.ac.cn|Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China. zhijihong@amss.ac.cn|Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China. zhijihong@amss.ac.cn', 'China|China|China', 19, 'Algorithms|False Negative Reactions|False Positive Reactions|Humans|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Proteins|Quality Control|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/19.1.14', '12499288', 'It is widely recognized that the hybridization process is prone to errors and that the future of DNA sequencing by hybridization is predicated on the ability to successfully cope with such errors. However, the occurrence of hybridization errors results in the computational difficulty of the reconstruction of DNA sequencing by hybridization. The reconstruction problem of DNA sequencing by hybridization with errors is a strongly NP-hard problem. So far the problem has not been solved well.'),
(70467, 2003, 'bio', 'A divide-and-conquer approach to fragment assembly', 'Otu, Hasan H|Sayood, Khalid', 'University of Nebraska-Lincoln, Department of Electrical Engineering, 209N WSEC, 68503, USA. otu@eecomm.unl.edu|University of Nebraska-Lincoln, Department of Electrical Engineering, 209N WSEC, 68503, USA. otu@eecomm.unl.edu', 'United States|United States', 17, 'Algorithms|Animals|Chromosomes, Mammalian|Cluster Analysis|Computer Simulation|Consensus Sequence|DNA|DNA Fragmentation|Mice|Polynucleotides|Quality Control|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/19.1.22', '12499289', 'One of the major problems in DNA sequencing is assembling the fragments obtained by shotgun sequencing. Most existing fragment assembly techniques follow the overlap-layout-consensus approach. This framework requires extensive computation in each phase and becomes inefficient with increasing number of fragments.'),
(70468, 2003, 'bio', 'Representation of DNA sequences with virtual potentials and their processing by (SEQREP) Kohonen self-organizing maps', 'Aires-de-Sousa, João|Aires-de-Sousa, Luisa', 'Departamento de Química, CQFB, campus Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Monte de Caparica Clínica, Portugal. jas@fct.unl.pt|Departamento de Química, CQFB, campus Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Monte de Caparica Clínica, Portugal. jas@fct.unl.pt', 'Portugal|Portugal', 0, 'Algorithms|Alu Elements|Base Sequence|DNA|Databases, Nucleic Acid|Gene Products, env|HIV Envelope Protein gp120|HIV-1|Humans|Information Storage and Retrieval|Molecular Sequence Data|Neural Networks (Computer)|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Evaluation Studies', '10.1093/bioinformatics/19.1.30', '12499290', 'We propose representing individual positions in DNA sequences by virtual potentials generated by other bases of the same sequence. This is a compact representation of the neighbourhood of a base. The distribution of the virtual potentials over the whole sequence can be used as a representation of the entire sequence (SEQREP code). It is a flexible code, with a length independent of the sequence size, does not require previous alignment, and is convenient for processing by neural networks or statistical techniques.'),
(70469, 2003, 'bio', 'Genetic algorithms applied to multi-class prediction for the analysis of gene expression data', 'Ooi, C H|Tan, Patrick', 'Nanyang Technological University, School of Mechanical and Production Engineering, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore.|Nanyang Technological University, School of Mechanical and Production Engineering, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore.', 'Singapore|Singapore', 0, 'Algorithms|Chromosomes|DNA|Databases, Nucleic Acid|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/19.1.37', '12499291', 'An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently noisy, and the development of new methodologies that can enhance the successful classification of these complex datasets.'),
(70470, 2003, 'bio', 'Evolutionary algorithms for finding optimal gene sets in microarray prediction', 'Deutsch, J M', 'University of California, Santa Cruz, USA. josh@physics.ucsc.edu', 'United States', 105, 'Algorithms|DNA|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Markers|Humans|Leukemia|Male|Models, Genetic|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Quality Control|Sample Size|Sequence Alignment|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/19.1.45', '12499292', 'Microarray data has been shown recently to be efficacious in distinguishing closely related cell types that often appear in different forms of cancer, but is not yet practical clinically. However, the data might be used to construct a minimal set of marker genes that could then be used clinically by making antibody assays to diagnose a specific type of cancer. Here a replication algorithm is used for this purpose. It evolves an ensemble of predictors, all using different combinations of genes to generate a set of optimal predictors.'),
(70471, 2003, 'bio', 'Analysis of whole-genome microarray replicates using mixed models', 'Wernisch, Lorenz|Kendall, Sharon L|Soneji, Shamit|Wietzorrek, Andreas|Parish, Tanya|Hinds, Jason|Butcher, Philip D|Stoker, Neil G', 'School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk|School of Crystallography, Birkbeck College, London WC1E 7HX, UK. l.wernisch@bbk.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 66, 'Algorithms|Analysis of Variance|Cells, Cultured|DNA, Bacterial|Drosophila Proteins|Gene Expression Regulation, Bacterial|Genome, Bacterial|Humans|Models, Genetic|Models, Statistical|Mutagenesis, Site-Directed|Mycobacterium tuberculosis|Oligonucleotide Array Sequence Analysis|Protein-Serine-Threonine Kinases|Quality Control|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1093/bioinformatics/19.1.53', '12499293', 'Microarray experiments are inherently noisy. Replication is the key to estimating realistic fold-changes despite such noise. In the analysis of the various sources of noise the dependency structure of the replication needs to be taken into account.'),
(70472, 2003, 'bio', 'Prediction of biologically significant components from microarray data: Independently Consistent Expression Discriminator (ICED)', 'Bijlani, Rahul|Cheng, Yinhe|Pearce, David A|Brooks, Andrew I|Ogihara, Mitsunori', 'Department of Computer Science, University of Rochester School of Medicine and Dentistry, NY 14642, USA.|Department of Computer Science, University of Rochester School of Medicine and Dentistry, NY 14642, USA.|Department of Computer Science, University of Rochester School of Medicine and Dentistry, NY 14642, USA.|Department of Computer Science, University of Rochester School of Medicine and Dentistry, NY 14642, USA.|Department of Computer Science, University of Rochester School of Medicine and Dentistry, NY 14642, USA.', 'United States|United States|United States|United States|United States', 26, 'Algorithms|Animals|DNA|Gene Expression Profiling|Genetic Markers|Humans|Male|Mice|Neoplasm Proteins|Neuronal Ceroid-Lipofuscinoses|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reference Values|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1093/bioinformatics/19.1.62', '12499294', 'Class distinction is a supervised learning approach that has been successfully employed in the analysis of high-throughput gene expression data. Identification of a set of genes that predicts differential biological states allows for the development of basic and clinical scientific approaches to the diagnosis of disease. The Independent Consistent Expression Discriminator (ICED) was designed to provide a more biologically relevant search criterion during predictor selection by embracing the inherent variability of gene expression in any biological state. The four components of ICED include (i) normalization of raw data, (ii) assignment of weights to genes from both classes, (iii) counting of votes to determine optimal number of predictor genes for class distinction, (iv) calculation of prediction strengths for classification results. The search criteria employed by ICED is designed to identify not only genes that are consistently expressed at one level in one class and at a consistently different level in another class but identify genes that are variable in one class and consistent in another. The result is a novel approach to accurately select biologically relevant predictors of differential disease states from a small number of microarray samples.'),
(70473, 2003, 'bio', 'Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients', 'Li, Jinyan|Liu, Huiqing|Downing, James R|Yeoh, Allen Eng-Juh|Wong, Limsoon', 'Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. jinyan@lit.a-star.edu.sg|Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. jinyan@lit.a-star.edu.sg|Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. jinyan@lit.a-star.edu.sg|Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. jinyan@lit.a-star.edu.sg|Laboratories for Information Technology, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. jinyan@lit.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 90, 'Algorithms|Biomarkers, Tumor|Cluster Analysis|DNA, Neoplasm|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Markers|Humans|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Precursor Cell Lymphoblastic Leukemia-Lymphoma', 'Comparative Study', '10.1093/bioinformatics/19.1.71', '12499295', 'For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method.'),
(70474, 2003, 'bio', 'Mining gene expression databases for association rules', 'Creighton, Chad|Hanash, Samir', 'Bioinformatics Program Pediatrics and Communicable Diseases, University of Michigan, Ann Arbor 48109, USA. ccreight@umich.edu|Bioinformatics Program Pediatrics and Communicable Diseases, University of Michigan, Ann Arbor 48109, USA. ccreight@umich.edu', 'United States|United States', 40, 'Algorithms|Cluster Analysis|Database Management Systems|Databases, Nucleic Acid|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Information Storage and Retrieval|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/19.1.79', '12499296', 'Global gene expression profiling, both at the transcript level and at the protein level, can be a valuable tool in the understanding of genes, biological networks, and cellular states. As larger and larger gene expression data sets become available, data mining techniques can be applied to identify patterns of interest in the data. Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS --> RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained).'),
(70475, 2003, 'bio', 'MADGE: scalable distributed data management software for cDNA microarrays', 'McIndoe, Richard A|Lanzen, Aaron|Hurtz, Kimberly', 'Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville 32610, USA. rmcindoe@mail.mcg.edu|Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville 32610, USA. rmcindoe@mail.mcg.edu|Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville 32610, USA. rmcindoe@mail.mcg.edu', 'United States|United States|United States', 2, 'Database Management Systems|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/19.1.87', '12499297', 'The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments.'),
(70476, 2003, 'bio', 'Gene selection: a Bayesian variable selection approach', 'Lee, Kyeong Eun|Sha, Naijun|Dougherty, Edward R|Vannucci, Marina|Mallick, Bani K', 'Department of Statistics, Texas A&M University, College Station 77843-3143, USA.|Department of Statistics, Texas A&M University, College Station 77843-3143, USA.|Department of Statistics, Texas A&M University, College Station 77843-3143, USA.|Department of Statistics, Texas A&M University, College Station 77843-3143, USA.|Department of Statistics, Texas A&M University, College Station 77843-3143, USA.', 'United States|United States|United States|United States|United States', 277, 'Algorithms|Bayes Theorem|Breast Neoplasms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes|Genes, BRCA1|Genes, BRCA2|Genetic Markers|Genetic Predisposition to Disease|Humans|Leukemia, Myeloid|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Sample Size', 'Evaluation Studies', '10.1093/bioinformatics/19.1.90', '12499298', 'Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data.'),
(70477, 2003, 'bio', 'Predicting HIV drug resistance with neural networks', 'Drxc4x83ghici, Sorin|Potter, R Brian', 'Department of Computer Science, 431 State Hall, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu|Department of Computer Science, 431 State Hall, Wayne State University, Detroit, MI 48202, USA. sod@cs.wayne.edu', 'United States|United States', 97, 'Algorithms|Amino Acid Substitution|Anti-HIV Agents|Databases, Genetic|Databases, Protein|Drug Resistance, Viral|HIV|HIV Protease|HIV Protease Inhibitors|Indinavir|Information Storage and Retrieval|Models, Biological|Models, Molecular|Mutation|Neural Networks (Computer)|Protein Binding|Protein Conformation|Saquinavir|Sequence Analysis, Protein|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/19.1.98', '12499299', 'Drug resistance is a very important factor influencing the failure of current HIV therapies. The ability to predict the drug resistance of HIV protease mutants may be useful in developing more effective and longer lasting treatment regimens.'),
(70478, 2003, 'bio', 'Finding the common structure shared by two homologous RNAs', 'Perriquet, O|Touzet, H|Dauchet, M', 'LIFL, UPRESA CNRS 8022, Bâtiment M3, Université des Sciences et Technologies de Lille, 59655 Villeneuve dAscq Cedex, France. perrique@lifl.fr|LIFL, UPRESA CNRS 8022, Bâtiment M3, Université des Sciences et Technologies de Lille, 59655 Villeneuve dAscq Cedex, France. perrique@lifl.fr|LIFL, UPRESA CNRS 8022, Bâtiment M3, Université des Sciences et Technologies de Lille, 59655 Villeneuve dAscq Cedex, France. perrique@lifl.fr', 'France|France|France', 52, 'Algorithms|Cytochromes|Databases, Nucleic Acid|Endoribonucleases|Enterovirus|Models, Genetic|Models, Statistical|RNA|RNA, Catalytic|RNA, Messenger|RNA, Ribosomal, 16S|RNA, Viral|Ribonuclease P|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1093/bioinformatics/19.1.108', '12499300', 'CARNAC is a new method for pairwise folding of RNA sequences. The program takes into account local similarity, stem energy, and covariations to produce the common folding. It can handle all RNA types, and has also been adapted to align a new homologous sequence along a reference structured sequence.'),
(70479, 2003, 'bio', 'Search for structural similarity in proteins', 'Leluk, Jacek|Konieczny, Leszek|Roterman, Irena', 'Institute of Biochemistry and Molecular Biology University of Wrocxc5x82aw, Tamka 2, Poland. lulu@bf.uni.wroc.pl|Institute of Biochemistry and Molecular Biology University of Wrocxc5x82aw, Tamka 2, Poland. lulu@bf.uni.wroc.pl|Institute of Biochemistry and Molecular Biology University of Wrocxc5x82aw, Tamka 2, Poland. lulu@bf.uni.wroc.pl', 'Poland|Poland|Poland', 22, 'Algorithms|Amino Acid Sequence|Amino Acids|Animals|Cattle|Consensus Sequence|Humans|Information Storage and Retrieval|Models, Molecular|Molecular Sequence Data|Peptide Fragments|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Serpins', 'Comparative Study', '10.1093/bioinformatics/19.1.117', '12499301', 'The expanding protein sequence and structure databases await methods allowing rapid similarity search. Geometric parameters-dihedral angle between two sequential peptide bond planes (V) and radius of curvature (R) as they appear in pentapeptide fragments in polypeptide chains-are proposed for use in evaluating structural similarity in proteins (VeaR). The parabolic (empirical) function expressing the radius of curvatures dependence on the V-angle in model polypeptides is altered in real proteins in a form characteristic for a particular protein. This can be used as a criterion for judging similarity.'),
(70480, 2003, 'bio', 'Whole-proteome interaction mining', 'Bock, Joel R|Gough, David A', 'Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla 92093-0412, USA.|Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla 92093-0412, USA.', 'United States|United States', 118, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Bacterial Proteins|Campylobacter jejuni|Chemical Phenomena|Chemistry, Physical|Cluster Analysis|Databases, Factual|Information Storage and Retrieval|Iron|Macromolecular Substances|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Proteome|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/19.1.125', '12499302', 'A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms.'),
(70481, 2003, 'bio', 'Protein structures and information extraction from biological texts: the PASTA system', 'Gaizauskas, R|Demetriou, G|Artymiuk, P J|Willett, P', 'Department of Computer Science, University of Sheffield, Western Bank, UK.|Department of Computer Science, University of Sheffield, Western Bank, UK.|Department of Computer Science, University of Sheffield, Western Bank, UK.|Department of Computer Science, University of Sheffield, Western Bank, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 94, 'Abstracting and Indexing as Topic|Algorithms|Databases, Bibliographic|Databases, Protein|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Protein Conformation|Proteins|Publications|Sequence Alignment|Structure-Activity Relationship', 'Evaluation Studies', '10.1093/bioinformatics/19.1.135', '12499303', 'The rapid increase in volume of protein structure literature means useful information may be hidden or lost in the published literature and the process of finding relevant material, sometimes the rate-determining factor in new research, may be arduous and slow.'),
(70482, 2003, 'bio', 'Simulating complex traits influenced by genes with fuzzy-valued effects in pedigreed populations', 'Carleos, C|Rodriguez, F|Lamelas, H|Baro, J A', 'Departamento Estadistica, Universidad de Oviedo, 33007 Asturias, Spain. carleos@pinon.ccu.uniovi.es|Departamento Estadistica, Universidad de Oviedo, 33007 Asturias, Spain. carleos@pinon.ccu.uniovi.es|Departamento Estadistica, Universidad de Oviedo, 33007 Asturias, Spain. carleos@pinon.ccu.uniovi.es|Departamento Estadistica, Universidad de Oviedo, 33007 Asturias, Spain. carleos@pinon.ccu.uniovi.es', 'Spain|Spain|Spain|Spain', 8, 'Algorithms|Chromosome Mapping|Computer Simulation|Fuzzy Logic|Genetic Linkage|Inheritance Patterns|Models, Genetic|Pedigree|Phenotype|Programming Languages|Software|Software Design|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/19.1.144', '12499304', 'Methods involving fuzzy theory have been rarely applied to genetics. We present an open platform for experimentation with fuzzy numbers as a tool to represent imprecise phenotypes in genetic modeling.'),
(70483, 2003, 'bio', 'Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits', 'Purcell, S|Cherny, S S|Sham, P C', 'Social, Genetics and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, De Crespigny Park, UK. s.purcell@iop.kcl.ac.uk|Social, Genetics and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, De Crespigny Park, UK. s.purcell@iop.kcl.ac.uk|Social, Genetics and Developmental Psychiatry Research Centre, Institute of Psychiatry, Kings College London, De Crespigny Park, UK. s.purcell@iop.kcl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 1702, 'Chromosome Mapping|Gene Frequency|Genetic Linkage|Genetic Markers|Genetic Predisposition to Disease|Internet|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Pedigree|Quantitative Trait, Heritable|Sample Size|Software', 'Journal Article', '10.1093/bioinformatics/19.1.149', '12499305', 'A website for performing power calculations for the design of linkage and association genetic mapping studies of complex traits.'),
(70484, 2003, 'bio', 'MAVG: locating non-overlapping maximum average segments in a given sequence', 'Lin, Yaw-Ling|Huang, Xiaoqiu|Jiang, Tao|Chao, Kun-Mao', 'Department of Computer Science and Information Management, Providence University, Shalu 433, Taiwan.|Department of Computer Science and Information Management, Providence University, Shalu 433, Taiwan.|Department of Computer Science and Information Management, Providence University, Shalu 433, Taiwan.|Department of Computer Science and Information Management, Providence University, Shalu 433, Taiwan.', '-|-|-|-', 32, 'Algorithms|CpG Islands|Genes, Overlapping|Genome, Human|Humans|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/19.1.151', '12499306', 'MAVG is a software tool for finding k non-overlapping maximum-average segments that are sufficiently long in a given sequence of real numbers, for any k > 0. It has applications in several areas of biomolecular sequence analysis including locating GC-rich regions and CpG islands in a genomic sequence, and annotating multiple sequence alignments.'),
(70485, 2003, 'bio', 'An integrated genetic data environment (GDE)-based LINUX interface for analysis of HIV-1 and other microbial sequences', 'De Oliveira, T|Miller, R|Tarin, M|Cassol, S', 'Molecular Virology and Bioinformatics, Africa Centre/University of Natal, Durban, South Africa. toliveira@mrc.ac.za|Molecular Virology and Bioinformatics, Africa Centre/University of Natal, Durban, South Africa. toliveira@mrc.ac.za|Molecular Virology and Bioinformatics, Africa Centre/University of Natal, Durban, South Africa. toliveira@mrc.ac.za|Molecular Virology and Bioinformatics, Africa Centre/University of Natal, Durban, South Africa. toliveira@mrc.ac.za', 'South Africa|South Africa|South Africa|South Africa', 8, 'Database Management Systems|Databases, Genetic|Genetics, Microbial|HIV-1|Humans|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/19.1.153', '12499307', 'Sequence databases encode a wealth of information needed to develop improved vaccination and treatment strategies for the control of HIV and other important pathogens. To facilitate effective utilization of these datasets, we developed a user-friendly GDE-based LINUX interface that reduces input/output file formatting.'),
(70486, 2003, 'bio', 'An extensible application for assembling annotation for genomic data', 'Zhang, Jianhua|Carey, Vincent|Gentleman, Robert', 'Department of Biostatistical Science, Dana-Farber Cancer Institute, Harvard School of Public Health, and Channing Laboratory, Boston, MA, USA. jzhang@jimmy.harvard.edu|Department of Biostatistical Science, Dana-Farber Cancer Institute, Harvard School of Public Health, and Channing Laboratory, Boston, MA, USA. jzhang@jimmy.harvard.edu|Department of Biostatistical Science, Dana-Farber Cancer Institute, Harvard School of Public Health, and Channing Laboratory, Boston, MA, USA. jzhang@jimmy.harvard.edu', 'United States|United States|United States', 38, 'Database Management Systems|Databases, Genetic|Documentation|Genome|Hypermedia|Information Storage and Retrieval|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/19.1.155', '12499308', 'AnnBuilder is an R package for assembling genomic annotation data. The system currently provides parsers to process annotation data from LocusLink, Gene Ontology Consortium, and Human Gene Project and can be extended to new data sources via user defined parsers. AnnBuilder differs from other existing systems in that it provides users with unlimited ability to assemble data from user selected sources. The products of AnnBuilder are files in XML format that can be easily used by different systems.'),
(70487, 2003, 'bio', 'Microarray data assembler', 'Anbazhagan, Ramswamy', 'Department of Pathology, Johns Hopkins University School of Medicine, Baltimore MD 21231, USA. anba@jhmi.edu', 'United States', 3, 'Database Management Systems|Databases, Nucleic Acid|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/19.1.157', '12499309', 'Large volumes of microarray data are generated and deposited in public databases. Most of this data is in the form of tab-delimited text files or Excel spreadsheets. Combining data from several of these files to reanalyze these data sets is time consuming. Microarray Data Assembler is specifically designed to simplify this task. The program can list files and data sources, convert selected text files into Excel files and assemble data across multiple Excel worksheets and workbooks. This program thus makes data assembling easy, saves time and helps avoid manual error.'),
(70488, 2003, 'bio', 'XdomView: protein domain and exon position visualization', 'Vivek, Gopalan|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Biochemistry, National University of Singapore, Singapore 119260.|Department of Biochemistry, National University of Singapore, Singapore 119260.|Department of Biochemistry, National University of Singapore, Singapore 119260.', 'Singapore|Singapore|Singapore', 8, 'Database Management Systems|Databases, Protein|Exons|Imaging, Three-Dimensional|Information Storage and Retrieval|Internet|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/19.1.159', '12499310', 'The relationship between intron distribution in the eukaryotic gene and protein structural elements is essential for understanding the origin and evolution of genes. XdomView is a web-based viewer mapping protein structural domains and intron positions in eukaryotic homologues to its tertiary structure. The association of sequence signals to 3D structure in XdomView provides a valuable visualization environment for eukaryotic gene organization, gene evolution, protein folding and protein structure classification.');
