INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92474, 2008, 'bmc', 'A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data', 'Corradi, Luca|Fato, Marco|Porro, Ivan|Scaglione, Silvia|Torterolo, Livia', 'Computer Science, Systems, and Communication Department, University of Genova, Viale Causa 12, Genova, Italy. luca.corradi@unige.it|Computer Science, Systems, and Communication Department, University of Genova, Viale Causa 12, Genova, Italy. luca.corradi@unige.it|Computer Science, Systems, and Communication Department, University of Genova, Viale Causa 12, Genova, Italy. luca.corradi@unige.it|Computer Science, Systems, and Communication Department, University of Genova, Viale Causa 12, Genova, Italy. luca.corradi@unige.it|Computer Science, Systems, and Communication Department, University of Genova, Viale Causa 12, Genova, Italy. luca.corradi@unige.it', 'Italy|Italy|Italy|Italy|Italy', 5, 'Algorithms|Computational Biology|Computer Simulation|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-480', '19014540', 'Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible for serious diseases, with a great scientific impact and a wide application area. Several standalone applications had been developed in order to analyze microarray data. Two of the most known free analysis software packages are the R-based Bioconductor and dChip. The part of dChip software concerning the calculation and the analysis of gene expression has been modified to permit its execution on both cluster environments (supercomputers) and Grid infrastructures (distributed computing).This work is not aimed at replacing existing tools, but it provides researchers with a method to analyze large datasets without any hardware or software constraints.'),
(92475, 2008, 'bmc', 'Gene set enrichment analysis for non-monotone association and multiple experimental categories', 'Lin, Rongheng|Dai, Shuangshuang|Irwin, Richard D|Heinloth, Alexandra N|Boorman, Gary A|Li, Leping', 'Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu|Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu|Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu|Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu|Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu|Biostatistics Branch, National Institute of Environmental Health Science, Research Triangle Park, NC 27713, USA. rlin@schoolph.umass.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Gene Expression|Gene Expression Profiling|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Phenotype|Proteome', 'Journal Article', '10.1186/1471-2105-9-481', '19014579', 'Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed.'),
(92476, 2008, 'bmc', 'CellProfiler Analyst: data exploration and analysis software for complex image-based screens', 'Jones, Thouis R|Kang, In Han|Wheeler, Douglas B|Lindquist, Robert A|Papallo, Adam|Sabatini, David M|Golland, Polina|Carpenter, Anne E', 'Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu|Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. thouis@broad.mit.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 278, 'Artificial Intelligence|Cells|Computational Biology|Image Processing, Computer-Assisted|Phenotype|Software', 'Journal Article', '10.1186/1471-2105-9-482', '19014601', 'Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment.'),
(92477, 2008, 'bmc', 'mtDNAmanager: a Web-based tool for the management and quality analysis of mitochondrial DNA control-region sequences', 'Lee, Hwan Young|Song, Injee|Ha, Eunho|Cho, Sung-Bae|Yang, Woo Ick|Shin, Kyoung-Jin', 'Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac|Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac|Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac|Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac|Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac|Department of Forensic Medicine, Yonsei University College of Medicine, Seoul, Korea. hylee192@yuhs.ac', '-|-|-|-|-|-', 0, 'Base Sequence|Computational Biology|DNA, Mitochondrial|Databases, Genetic|Haplotypes|Internet|Locus Control Region|Molecular Sequence Data|Phylogeny|Research Design|Software', 'Journal Article', '10.1186/1471-2105-9-483', '19014619', 'For the past few years, scientific controversy has surrounded the large number of errors in forensic and literature mitochondrial DNA (mtDNA) data. However, recent research has shown that using mtDNA phylogeny and referring to known mtDNA haplotypes can be useful for checking the quality of sequence data.'),
(92478, 2008, 'bmc', 'Genome-scale study of the importance of binding site context for transcription factor binding and gene regulation', 'Westholm, Jakub Orzechowski|Xu, Feifei|Ronne, Hans|Komorowski, Jan', 'The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. jakub@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. jakub@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. jakub@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. jakub@lcb.uu.se', 'Sweden|Sweden|Sweden|Sweden', 13, 'Amino Acid Motifs|Binding Sites|DNA|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Genome, Fungal|Models, Genetic|Open Reading Frames|Promoter Regions, Genetic|Protein Binding|RNA, Messenger|Saccharomyces cerevisiae|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-9-484', '19014636', 'The rate of mRNA transcription is controlled by transcription factors that bind to specific DNA motifs in promoter regions upstream of protein coding genes. Recent results indicate that not only the presence of a motif but also motif context (for example the orientation of a motif or its location relative to the coding sequence) is important for gene regulation.'),
(92479, 2008, 'bmc', 'CrossHybDetector: detection of cross-hybridization events in DNA microarray experiments', 'Uva, Paolo|de Rinaldis, Emanuele', 'Istituto di Ricerche di Biologia Molecolare, Merck Research Laboratories, Rome, Italy. paolo_uva@merck.com|Istituto di Ricerche di Biologia Molecolare, Merck Research Laboratories, Rome, Italy. paolo_uva@merck.com', 'Italy|Italy', 0, 'Algorithms|Computational Biology|DNA Probes|Databases, Genetic|Monte Carlo Method|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-485', '19014642', 'DNA microarrays contain thousands of different probe sequences represented on their surface. These are designed in such a way that potential cross-hybridization reactions with non-target sequences are minimized. However, given the large number of probes, the occurrence of cross hybridization events cannot be excluded. This problem can dramatically affect the data quality and cause false positive/false negative results.'),
(92480, 2008, 'bmc', 'Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification', 'Gormley, Michael|Tozeren, Aydin', 'School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA. michael.patrick.gormley@drexel.edu|School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA. michael.patrick.gormley@drexel.edu', 'United States|United States', 0, 'Algorithms|Animals|Communicable Diseases|Computational Biology|Gene Expression Profiling|Genes, Switch|Genetic Markers|Humans|Mice|Models, Genetic|Oligonucleotide Array Sequence Analysis|Phenotype', 'Journal Article', '10.1186/1471-2105-9-486', '19014681', 'Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.'),
(92481, 2008, 'bmc', 'Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable', 'Peto, Myron|Kloczkowski, Andrzej|Honavar, Vasant|Jernigan, Robert L', 'Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov|Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov', 'United States|United States|United States|United States', 3, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computational Biology|Computer Simulation|Databases, Protein|Models, Molecular|Models, Statistical|Protein Conformation|Protein Folding|Proteins|ROC Curve|Reproducibility of Results|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-487', '19014713', 'By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.'),
(92482, 2008, 'bmc', 'Identifying differential correlation in gene/pathway combinations', 'Braun, Rosemary|Cope, Leslie|Parmigiani, Giovanni', 'National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. braunr@mail.nih.gov|National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. braunr@mail.nih.gov|National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. braunr@mail.nih.gov', 'United States|United States|United States', 19, 'Algorithms|Computational Biology|Epistasis, Genetic|Gene Expression Profiling|Humans|Lung Neoplasms|Male|Models, Genetic|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Prostatic Neoplasms', 'Journal Article', '10.1186/1471-2105-9-488', '19017408', 'An important emerging trend in the analysis of microarray data is to incorporate known pathway information a priori. Expression level summaries for pathways, obtained from the expression data for the genes constituting the pathway, permit the inclusion of pathway information, reduce the high dimensionality of microarray data, and have the power to elucidate gene-interaction dependencies which are not already accounted for through known pathway identification.'),
(92483, 2008, 'bmc', 'Determining gene expression on a single pair of microarrays', 'Reid, Robert W|Fodor, Anthony A', 'Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, USA. rreid2@uncc.edu|Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223, USA. rreid2@uncc.edu', 'United States|United States', 0, 'Algorithms|Bayes Theorem|Data Interpretation, Statistical|Databases, Genetic|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|ROC Curve|Reproducibility of Results|Sensitivity and Specificity|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-9-489', '19025600', 'In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently few choices for the analysis of a pair of microarrays where N = 1 in each condition. In this paper, we demonstrate the effectiveness of a new algorithm called PINC (PINC is Not Cyber-T) that can analyze Affymetrix microarray experiments.'),
(92484, 2008, 'bmc', 'Themes in biomedical natural language processing: BioNLP08', 'Demner-Fushman, Dina|Ananiadou, Sophia|Cohen, K Bretonnel|Pestian, John|Tsujii, Junichi|Webber, Bonnie', 'US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov|US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov|US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov|US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov|US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov|US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. ddemner@mail.nih.gov', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Natural Language Processing', 'Introductory Journal Article', '10.1186/1471-2105-9-S11-S1', '19025685', '-'),
(92485, 2008, 'bmc', 'Recognizing speculative language in biomedical research articles: a linguistically motivated perspective', 'Kilicoglu, Halil|Bergler, Sabine', 'Department of Computer Science and Software Engineering, 1455 de Maisonneuve Blvd West, H3G1M8 Montréal, Québec, Canada. h_kilico@cse.concordia.ca|Department of Computer Science and Software Engineering, 1455 de Maisonneuve Blvd West, H3G1M8 Montréal, Québec, Canada. h_kilico@cse.concordia.ca', 'Canada|Canada', 50, 'Algorithms|Artificial Intelligence|Linguistics|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Semantics|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S11-S10', '19025686', 'Due to the nature of scientific methodology, research articles are rich in speculative and tentative statements, also known as hedges. We explore a linguistically motivated approach to the problem of recognizing such language in biomedical research articles. Our approach draws on prior linguistic work as well as existing lexical resources to create a dictionary of hedging cues and extends it by introducing syntactic patterns. Furthermore, recognizing that hedging cues differ in speculative strength, we assign them weights in two ways: automatically using the information gain (IG) measure and semi-automatically based on their types and centrality to hedging. Weights of hedging cues are used to determine the speculative strength of sentences.'),
(92486, 2008, 'bmc', 'Automatic inference of indexing rules for MEDLINE', 'Névéol, Aurélie|Shooshan, Sonya E|Claveau, Vincent', 'National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. neveola@nlm.nih.gov|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. neveola@nlm.nih.gov|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. neveola@nlm.nih.gov', 'United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|MEDLINE|Medical Subject Headings|Natural Language Processing|Programming Languages', 'Journal Article', '10.1186/1471-2105-9-S11-S11', '19025687', 'Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE.'),
(92487, 2008, 'bmc', 'All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning', 'Airola, Antti|Pyysalo, Sampo|Björne, Jari|Pahikkala, Tapio|Ginter, Filip|Salakoski, Tapio', 'Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi|Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi|Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi|Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi|Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi|Turku Centre for Computer Science (TUCS) and the Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. antti.airola@utu.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Databases as Topic|Natural Language Processing|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-9-S11-S2', '19025688', 'Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure.'),
(92488, 2008, 'bmc', 'Mining clinical relationships from patient narratives', 'Roberts, Angus|Gaizauskas, Robert|Hepple, Mark|Guo, Yikun', 'Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. a.roberts@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. a.roberts@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. a.roberts@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. a.roberts@dcs.shef.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 24, 'Algorithms|Humans|Information Storage and Retrieval|Medical Records Systems, Computerized|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-9-S11-S3', '19025689', 'The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships.'),
(92489, 2008, 'bmc', 'Cascaded classifiers for confidence-based chemical named entity recognition', 'Corbett, Peter|Copestake, Ann', 'Unilever Centre For Molecular Science Informatics, Chemical Laboratory, University of Cambridge, CB21EW UK. ptc24@cam.ac.uk|Unilever Centre For Molecular Science Informatics, Chemical Laboratory, University of Cambridge, CB21EW UK. ptc24@cam.ac.uk', 'United Kingdom|United Kingdom', 45, 'Algorithms|Computational Biology|Information Storage and Retrieval|Models, Chemical|Models, Statistical|Natural Language Processing|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S11-S4', '19025690', 'Chemical named entities represent an important facet of biomedical text.'),
(92490, 2008, 'bmc', 'How to make the most of NE dictionaries in statistical NER', 'Sasaki, Yutaka|Tsuruoka, Yoshimasa|McNaught, John|Ananiadou, Sophia', 'School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK. Yutaka.Sasaki@manchester.ac.uk|School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK. Yutaka.Sasaki@manchester.ac.uk|School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK. Yutaka.Sasaki@manchester.ac.uk|School of Computer Science, University of Manchester, 131 Princess Street, Manchester M17DN, UK. Yutaka.Sasaki@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Dictionaries as Topic|Models, Statistical|Online Systems|Pattern Recognition, Automated|Proteomics|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S11-S5', '19025691', 'When term ambiguity and variability are very high, dictionary-based Named Entity Recognition (NER) is not an ideal solution even though large-scale terminological resources are available. Many researches on statistical NER have tried to cope with these problems. However, it is not straightforward how to exploit existing and additional Named Entity (NE) dictionaries in statistical NER. Presumably, addition of NEs to an NE dictionary leads to better performance. However, in reality, the retraining of NER models is required to achieve this. We chose protein name recognition as a case study because it most suffers the problems related to heavy term variation and ambiguity.'),
(92491, 2008, 'bmc', 'Distinguishing the species of biomedical named entities for term identification', 'Wang, Xinglong|Matthews, Michael', 'National Centre for Text Mining, University of Manchester, 131 Princess Street, Manchester M17DN, UK. xinglong.wang@manchester.ed.ac.uk|National Centre for Text Mining, University of Manchester, 131 Princess Street, Manchester M17DN, UK. xinglong.wang@manchester.ed.ac.uk', 'United Kingdom|United Kingdom', 30, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Computational Biology|Databases as Topic|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Species Specificity|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S11-S6', '19025692', 'Term identification is the task of grounding ambiguous mentions of biomedical named entities in text to unique database identifiers. Previous work on term identification has focused on studying species-specific documents. However, full-length articles often describe entities across a number of species, in which case resolving the ambiguity of model organisms in entities is critical to achieving accurate term identification.'),
(92492, 2008, 'bmc', 'Disambiguation of biomedical text using diverse sources of information', 'Stevenson, Mark|Guo, Yikun|Gaizauskas, Robert|Martinez, David', 'Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. m.stevenson@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. m.stevenson@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. m.stevenson@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S14DP, UK. m.stevenson@dcs.shef.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Linguistics|MEDLINE|Medical Subject Headings|Natural Language Processing|Pattern Recognition, Automated|Terminology as Topic|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-9-S11-S7', '19025693', 'Like text in other domains, biomedical documents contain a range of terms with more than one possible meaning. These ambiguities form a significant obstacle to the automatic processing of biomedical texts. Previous approaches to resolving this problem have made use of various sources of information including linguistic features of the context in which the ambiguous term is used and domain-specific resources, such as UMLS.'),
(92493, 2008, 'bmc', 'Accelerating the annotation of sparse named entities by dynamic sentence selection', 'Tsuruoka, Yoshimasa|Tsujii, Junichi|Ananiadou, Sophia', 'School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester M17DN, UK. yoshimasa.tsuruoka@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S11-S8', '19025694', 'Previous studies of named entity recognition have shown that a reasonable level of recognition accuracy can be achieved by using machine learning models such as conditional random fields or support vector machines. However, the lack of training data (i.e. annotated corpora) makes it difficult for machine learning-based named entity recognizers to be used in building practical information extraction systems.'),
(92494, 2008, 'bmc', 'The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes', 'Vincze, Veronika|Szarvas, György|Farkas, Richárd|Móra, György|Csirik, János', 'University of Szeged, Department of Informatics, Human Language Technology Group, Arpád tér 2, Szeged, Hungary. vinczev@inf.u-szeged.hu|University of Szeged, Department of Informatics, Human Language Technology Group, Arpád tér 2, Szeged, Hungary. vinczev@inf.u-szeged.hu|University of Szeged, Department of Informatics, Human Language Technology Group, Arpád tér 2, Szeged, Hungary. vinczev@inf.u-szeged.hu|University of Szeged, Department of Informatics, Human Language Technology Group, Arpád tér 2, Szeged, Hungary. vinczev@inf.u-szeged.hu|University of Szeged, Department of Informatics, Human Language Technology Group, Arpád tér 2, Szeged, Hungary. vinczev@inf.u-szeged.hu', 'Hungary|Hungary|Hungary|Hungary|Hungary', 195, 'Abstracting and Indexing as Topic|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S11-S9', '19025695', 'Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus).'),
(92495, 2008, 'bmc', 'A comparison of four clustering methods for brain expression microarray data', 'Richards, Alexander L|Holmans, Peter|ODonovan, Michael C|Owen, Michael J|Jones, Lesley', 'Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk|Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk|Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk|Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk|Department of Psychological Medicine, School of Medicine, University Hospital Wales, Heath Park, Cardiff, Wales, UK. richardsal1@cardiff.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 30, 'Algorithms|Brain|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Internet|Oligonucleotide Array Sequence Analysis|Schizophrenia', 'Comparative Study', '10.1186/1471-2105-9-490', '19032745', 'DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.'),
(92496, 2008, 'bmc', 'Prediction of specificity-determining residues for small-molecule kinase inhibitors', 'Caffrey, Daniel R|Lunney, Elizabeth A|Moshinsky, Deborah J', 'Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com', 'United States|United States|United States', 0, 'Binding Sites|Databases, Genetic|Genomics|Humans|Models, Molecular|Protein Binding|Protein Kinase Inhibitors|Protein Kinases|Proteomics|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-491', '19032760', 'Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.'),
(92497, 2008, 'bmc', 'Structural descriptor database: a new tool for sequence-based functional site prediction', 'Bernardes, Juliana S|Fernandez, Jorge H|Vasconcelos, Ana Tereza R', 'Laboratório Nacional de Computação Científica LNCC/MTC, Quitandinha, Petrópolis, RJ, Brazil. julibinho@gmail.com|Laboratório Nacional de Computação Científica LNCC/MTC, Quitandinha, Petrópolis, RJ, Brazil. julibinho@gmail.com|Laboratório Nacional de Computação Científica LNCC/MTC, Quitandinha, Petrópolis, RJ, Brazil. julibinho@gmail.com', 'Brazil|Brazil|Brazil', 12, 'Algorithms|Artificial Intelligence|Binding Sites|Catalytic Domain|Databases, Protein|Information Storage and Retrieval|Internet|Pattern Recognition, Automated|Protein Conformation|Proteins|Proteomics|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-492', '19032768', 'The Structural Descriptor Database (SDDB) is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. Structural alignments and functional residues of a known protein set (defined as the training set) are used to build special Hidden Markov Models (HMM) called HMM descriptors. SDDB uses previously calculated and stored HMM descriptors for predicting active sites, binding residues, and protein function. The database integrates biologically relevant data filtered from several databases such as PDB, PDBSUM, CSA and SCOP. It accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the SCOP database.'),
(92498, 2008, 'bmc', 'EMAAS: an extensible grid-based rich internet application for microarray data analysis and management', 'Barton, G|Abbott, J|Chiba, N|Huang, D W|Huang, Y|Krznaric, M|Mack-Smith, J|Saleem, A|Sherman, B T|Tiwari, B|Tomlinson, C|Aitman, T|Darlington, J|Game, L|Sternberg, M J E|Butcher, S A', 'Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk|Division of Molecular Biosciences, Faculty of Natural Sciences, Centre for Bioinformatics, Imperial College, London, UK. g.barton@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Computational Biology|Computer Communication Networks|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-493', '19032776', 'Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management.'),
(92499, 2008, 'bmc', 'ExonMiner: Web service for analysis of GeneChip Exon array data', 'Numata, Kazuyuki|Yoshida, Ryo|Nagasaki, Masao|Saito, Ayumu|Imoto, Seiya|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. numata@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Exons|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Protein Isoforms|Software', 'Journal Article', '10.1186/1471-2105-9-494', '19036125', 'Some splicing isoform-specific transcriptional regulations are related to disease. Therefore, detection of disease specific splice variations is the first step for finding disease specific transcriptional regulations. Affymetrix Human Exon 1.0 ST Array can measure exon-level expression profiles that are suitable to find differentially expressed exons in genome-wide scale. However, exon array produces massive datasets that are more than we can handle and analyze on personal computer.'),
(92500, 2008, 'bmc', 'CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes', 'Hestand, Matthew S|van Galen, Michiel|Villerius, Michel P|van Ommen, Gert-Jan B|den Dunnen, Johan T|t Hoen, Peter A C', 'The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl|The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl|The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl|The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl|The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl|The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands. M.S.Hestand@lumc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 28, 'Algorithms|Animals|Artificial Intelligence|Base Composition|Binding Sites|Chromatin Immunoprecipitation|Databases, Genetic|Evolution, Molecular|Gene Expression Regulation|Humans|Internet|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Sequence Alignment|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-495', '19036135', 'The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments.'),
(92501, 2008, 'bmc', 'Models of deletion for visualizing bacterial variation: an application to tuberculosis spoligotypes', 'Reyes, Josephine F|Francis, Andrew R|Tanaka, Mark M', 'School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia. j.reyes@student.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia. j.reyes@student.unsw.edu.au|School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia. j.reyes@student.unsw.edu.au', 'Australia|Australia|Australia', 43, 'Evolution, Molecular|Gene Transfer, Horizontal|Genotype|Models, Genetic|Mycobacterium tuberculosis|Polymorphism, Genetic|Repetitive Sequences, Nucleic Acid', 'Comparative Study', '10.1186/1471-2105-9-496', '19036166', 'Molecular typing methods are commonly used to study genetic relationships among bacterial isolates. Many of these methods have become standardized and produce portable data. A popular approach for analyzing such data is to construct graphs, including phylogenies. Inferences from graph representations of data assist in understanding the patterns of transmission of bacterial pathogens, and basing these graph constructs on biological models of evolution of the molecular marker helps make these inferences. Spoligotyping is a widely used method for genotyping isolates of Mycobacterium tuberculosis that exploits polymorphism in the direct repeat region. Our goal was to examine a range of models describing the evolution of spoligotypes in order to develop a visualization method to represent likely relationships among M. tuberculosis isolates.'),
(92502, 2008, 'bmc', 'Clustering cancer gene expression data: a comparative study', 'de Souto, Marcilio C P|Costa, Ivan G|de Araujo, Daniel S A|Ludermir, Teresa B|Schliep, Alexander', 'Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. marcilio@dimap.ufrn.br|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. marcilio@dimap.ufrn.br|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. marcilio@dimap.ufrn.br|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. marcilio@dimap.ufrn.br|Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. marcilio@dimap.ufrn.br', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Computational Biology|DNA, Complementary|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Models, Biological|Models, Statistical|Multigene Family|Neoplasms|Normal Distribution|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-9-497', '19038021', 'The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context.'),
(92503, 2008, 'bmc', 'MOSAIC: an online database dedicated to the comparative genomics of bacterial strains at the intra-species level', 'Chiapello, Hélène|Gendrault, Annie|Caron, Christophe|Blum, Jérome|Petit, Marie-Agnès|El Karoui, Meriem', 'INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr|INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr|INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr|INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr|INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr|INRA UR1077, Unité Mathématique, Informatique & Génome, Domaine de Vilvert, 78352, Jouy-en-Josas, France. helene.chiapello@jouy.inra.fr', 'France|France|France|France|France|France', 16, 'Algorithms|Bacteria|Computational Biology|Conserved Sequence|Database Management Systems|Databases, Genetic|Genome, Bacterial|Genomics|Internet|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-498', '19038022', 'The recent availability of complete sequences for numerous closely related bacterial genomes opens up new challenges in comparative genomics. Several methods have been developed to align complete genomes at the nucleotide level but their use and the biological interpretation of results are not straightforward. It is therefore necessary to develop new resources to access, analyze, and visualize genome comparisons.'),
(92504, 2008, 'bmc', 'Natural computation meta-heuristics for the in silico optimization of microbial strains', 'Rocha, Miguel|Maia, Paulo|Mendes, Rui|Pinto, José P|Ferreira, Eugénio C|Nielsen, Jens|Patil, Kiran Raosaheb|Rocha, Isabel', 'Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt|Department of Informatics/CCTC, University of Minho, Braga, Portugal. mrocha@di.uminho.pt', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 0, 'Algorithms|Bacteria|Computational Biology|Computer Simulation|Escherichia coli|Evolution, Molecular|Gene Deletion|Genome, Bacterial|Genome, Fungal|Industrial Microbiology|Lactic Acid|Models, Biological|Saccharomyces cerevisiae|Succinic Acid', 'Journal Article', '10.1186/1471-2105-9-499', '19038030', 'One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92505, 2008, 'bmc', 'Prediction of glycosylation sites using random forests', 'Hamby, Stephen E|Hirst, Jonathan D', 'School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxsh1@nottingham.ac.uk|School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK. pcxsh1@nottingham.ac.uk', 'United Kingdom|United Kingdom', 137, 'Algorithms|Databases, Protein|Glycosylation|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1186/1471-2105-9-500', '19038042', 'Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function.'),
(92506, 2008, 'bmc', 'Genome Environment Browser (GEB): a dynamic browser for visualising high-throughput experimental data in the context of genome features', 'Huntley, Derek|Tang, Y Amy|Nesterova, Tatyana B|Butcher, Sarah|Brockdorff, Neil', 'Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. d.huntley@imperial.ac.uk|Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. d.huntley@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Genomics|Repetitive Sequences, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-501', '19038045', 'There is accumulating evidence that the milieu of repeat elements and other non-genic sequence features at a given chromosomal locus, here defined as the genome environment, can play an important role in regulating chromosomal processes such as transcription, replication and recombination. The availability of whole-genome sequences has allowed us to annotate the genome environment of any locus in detail. The development of genome wide experimental analyses of gene expression, chromatin modification and chromatin proteins means that it is now possible to identify potential links between chromosomal processes and the underlying genome environment. There is a need for novel bioinformatic tools that facilitate these studies.'),
(92507, 2008, 'bmc', 'Microarray-based gene set analysis: a comparison of current methods', 'Song, Sarah|Black, Michael A', 'Department of Biochemistry, University of Otago, Dunedin, New Zealand. qson003@stat.auckland.ac.nz|Department of Biochemistry, University of Otago, Dunedin, New Zealand. qson003@stat.auckland.ac.nz', 'New Zealand|New Zealand', 58, 'Algorithms|Computer Simulation|Gene Expression|Gene Expression Profiling|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-9-502', '19038052', 'The analysis of gene sets has become a popular topic in recent times, with researchers attempting to improve the interpretability and reproducibility of their microarray analyses through the inclusion of supplementary biological information. While a number of options for gene set analysis exist, no consensus has yet been reached regarding which methodology performs best, and under what conditions. The goal of this work was to examine the performance characteristics of a collection of existing gene set analysis methods, on both simulated and real microarray data sets. Of particular interest was the potential utility gained through the incorporation of inter-gene correlation into the analysis process.'),
(92508, 2008, 'bmc', 'ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins', 'Garg, Aarti|Raghava, Gajendra P S', 'Department of Biotechnology, Panjab University, Chandigarh, India. aarti@imtech.res.in|Department of Biotechnology, Panjab University, Chandigarh, India. aarti@imtech.res.in', 'India|India', 43, 'Animals|Artificial Intelligence|Cell Nucleus|Computational Biology|Cytoplasm|Databases, Protein|Evolution, Molecular|Fungal Proteins|Fungi|Internet|Plant Proteins|Plants|Proteins|Software', 'Journal Article', '10.1186/1471-2105-9-503', '19038062', 'The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely ESLpred. Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes, hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.'),
(92509, 2008, 'bmc', 'Highly sensitive feature detection for high resolution LC/MS', 'Tautenhahn, Ralf|Böttcher, Christoph|Neumann, Steffen', 'Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Halle, Germany. rtautenh@ipb-halle.de|Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Halle, Germany. rtautenh@ipb-halle.de|Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Halle, Germany. rtautenh@ipb-halle.de', 'Germany|Germany|Germany', 0, 'Algorithms|Chromatography, Liquid|Mass Spectrometry|Proteome|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-504', '19040729', 'Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.'),
(92510, 2008, 'bmc', 'Use of normalization methods for analysis of microarrays containing a high degree of gene effects', 'Ni, Terri T|Lemon, William J|Shyr, Yu|Zhong, Tao P', 'Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. terri.ni@vanderbilt.edu|Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. terri.ni@vanderbilt.edu|Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. terri.ni@vanderbilt.edu|Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232, USA. terri.ni@vanderbilt.edu', 'United States|United States|United States|United States', 18, 'Algorithms|Analysis of Variance|Computational Biology|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-9-505', '19040742', 'High-throughput microarrays are widely used to study gene expression across tissues and developmental stages. Analysis of gene expression data is challenging in these experiments due to the presence of significant percentages of differentially expressed genes (DEG) observed between tissues and developmental stages. Data normalization methods that are widely used today are not designed for data with a large proportion of tissue or gene effects.'),
(92511, 2008, 'bmc', 'c-REDUCE: incorporating sequence conservation to detect motifs that correlate with expression', 'Kechris, Katerina|Li, Hao', 'Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, 4200 East Ninth Avenue, B-119, Denver, CO 80262, USA. katerina.kechris@ucdenver.edu|Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, 4200 East Ninth Avenue, B-119, Denver, CO 80262, USA. katerina.kechris@ucdenver.edu', 'United States|United States', 2, 'Animals|Base Sequence|Binding Sites|Computational Biology|Conserved Sequence|Drosophila|Gene Expression|Software|Transcription Factors|Yeasts', 'Journal Article', '10.1186/1471-2105-9-506', '19040743', 'Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or motifs that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.'),
(92512, 2008, 'bmc', 'MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids', 'Xia, Jianguo|Bjorndahl, Trent C|Tang, Peter|Wishart, David S', 'Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca|Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca|Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca|Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada. jianguox@ualberta.ca', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Body Fluids|Computer Simulation|Databases, Genetic|Humans|Magnetic Resonance Spectroscopy|Metabolomics|Software|Tissue Extracts|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-507', '19040747', 'One-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomic studies involving biofluids and tissue extracts. There are several software packages that support compound identification and quantification via 1D 1H NMR by spectral fitting techniques. Because 1D 1H NMR spectra are characterized by extensive peak overlap or spectral congestion, two-dimensional (2D) NMR, with its increased spectral resolution, could potentially improve and even automate compound identification or quantification. However, the lack of dedicated software for this purpose significantly restricts the application of 2D NMR methods to most metabolomic studies.'),
(92513, 2008, 'bmc', 'Unsupervised reduction of random noise in complex data by a row-specific, sorted principal component-guided method', 'Foley, Joseph W|Katagiri, Fumiaki', 'Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, 1500 Gortner Ave., St. Paul, MN 55108, USA. jwfoley@stanford.edu|Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, 1500 Gortner Ave., St. Paul, MN 55108, USA. jwfoley@stanford.edu', 'United States|United States', 2, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-9-508', '19040754', 'Large biological data sets, such as expression profiles, benefit from reduction of random noise. Principal component (PC) analysis has been used for this purpose, but it tends to remove small features as well as random noise.'),
(92514, 2008, 'bmc', 'Position dependent mismatch discrimination on DNA microarrays - experiments and model', 'Naiser, Thomas|Kayser, Jona|Mai, Timo|Michel, Wolfgang|Ott, Albrecht', 'Experimentalphysik I, Universität Bayreuth, D-95440 Bayreuth, Germany. thomas.naiser@googlemail.com|Experimentalphysik I, Universität Bayreuth, D-95440 Bayreuth, Germany. thomas.naiser@googlemail.com|Experimentalphysik I, Universität Bayreuth, D-95440 Bayreuth, Germany. thomas.naiser@googlemail.com|Experimentalphysik I, Universität Bayreuth, D-95440 Bayreuth, Germany. thomas.naiser@googlemail.com|Experimentalphysik I, Universität Bayreuth, D-95440 Bayreuth, Germany. thomas.naiser@googlemail.com', 'Germany|Germany|Germany|Germany|Germany', 32, 'Base Pair Mismatch|DNA|Kinetics|Models, Genetic|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-509', '19046422', 'The propensity of oligonucleotide strands to form stable duplexes with complementary sequences is fundamental to a variety of biological and biotechnological processes as various as microRNA signalling, microarray hybridization and PCR. Yet our understanding of oligonucleotide hybridization, in particular in presence of surfaces, is rather limited. Here we use oligonucleotide microarrays made in-house by optically controlled DNA synthesis to produce probe sets comprising all possible single base mismatches and base bulges for each of 20 sequence motifs under study.'),
(92515, 2008, 'bmc', 'A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis', 'Liu, Bin|Wang, Xiaolong|Lin, Lei|Dong, Qiwen|Wang, Xuan', 'Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn', 'China|China|China|China|China', 93, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Genetic|Protein Folding|Proteins|Sequence Alignment|Sequence Homology', 'Comparative Study', '10.1186/1471-2105-9-510', '19046430', 'Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.'),
(92516, 2008, 'bmc', 'Comparison of methods for estimating the nucleotide substitution matrix', 'Oscamou, Maribeth|McDonald, Daniel|Yap, Von Bing|Huttley, Gavin A|Lladser, Manuel E|Knight, Rob', 'Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu|Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu|Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu|Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu|Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu|Department of Applied Mathematics, University of Colorado, Boulder, CO, USA. Maribeth.Oscamou@colorado.edu', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Computational Biology|Computer Simulation|DNA|DNA Mutational Analysis|Data Interpretation, Statistical|Evolution, Molecular|Logistic Models|Markov Chains|Models, Genetic|Nucleotides|Phylogeny|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-9-511', '19046431', 'The nucleotide substitution rate matrix is a key parameter of molecular evolution. Several methods for inferring this parameter have been proposed, with different mathematical bases. These methods include counting sequence differences and taking the log of the resulting probability matrices, methods based on Markov triples, and maximum likelihood methods that infer the substitution probabilities that lead to the most likely model of evolution. However, the speed and accuracy of these methods has not been compared.'),
(92517, 2008, 'bmc', 'A Bayesian calibration model for combining different pre-processing methods in Affymetrix chips', 'Blangiardo, Marta|Richardson, Sylvia', 'Centre for Biostatistics, Imperial College, St Marys Campus, Norfolk Place, London, UK. m.blangiardo@imperial.ac.uk|Centre for Biostatistics, Imperial College, St Marys Campus, Norfolk Place, London, UK. m.blangiardo@imperial.ac.uk', 'United Kingdom|United Kingdom', 5, 'Algorithms|Bayes Theorem|Calibration|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|RNA', 'Journal Article', '10.1186/1471-2105-9-512', '19046434', 'In gene expression studies a key role is played by the so called pre-processing, a series of steps designed to extract the signal and account for the sources of variability due to the technology used rather than to biological differences between the RNA samples. At the moment there is no commonly agreed gold standard pre-processing method and each researcher has the responsibility to choose one method, incurring the risk of false positive and false negative features arising from the particular method chosen.'),
(92518, 2008, 'bmc', 'Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions', 'Su, Shu-Yi|White, Jonathan|Balding, David J|Coin, Lachlan J M', 'Department of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK. lshu-yi.su@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK. lshu-yi.su@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK. lshu-yi.su@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK. lshu-yi.su@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Chromosomes, Human, X|Computer Simulation|Gene Dosage|Genetic Variation|Genetics, Population|Genome, Human|Genotype|Haplotypes|Humans|Male|Polymorphism, Single Nucleotide|Polyploidy|Software', 'Journal Article', '10.1186/1471-2105-9-513', '19046436', 'The power of haplotype-based methods for association studies, identification of regions under selection, and ancestral inference, is well-established for diploid organisms. For polyploids, however, the difficulty of determining phase has limited such approaches. Polyploidy is common in plants and is also observed in animals. Partial polyploidy is sometimes observed in humans (e.g. trisomy 21, Downs syndrome), and it arises more frequently in some human tissues. Local changes in ploidy, known as copy number variations (CNV), arise throughout the genome. Here we present a method, implemented in the software polyHap, for the inference of haplotype phase and missing observations from polyploid genotypes. PolyHap allows each individual to have a different ploidy, but ploidy cannot vary over the genomic region analysed. It employs a hidden Markov model (HMM) and a sampling algorithm to infer haplotypes jointly in multiple individuals and to obtain a measure of uncertainty in its inferences.'),
(92519, 2008, 'bmc', 'ElliPro: a new structure-based tool for the prediction of antibody epitopes', 'Ponomarenko, Julia|Bui, Huynh-Hoa|Li, Wei|Fusseder, Nicholas|Bourne, Philip E|Sette, Alessandro|Peters, Bjoern', 'San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. jpon@sdsc.edu', 'United States|United States|United States|United States|United States|United States|United States', 397, 'Algorithms|Binding Sites, Antibody|Cluster Analysis|Computational Biology|Epitope Mapping|Epitopes, B-Lymphocyte|Humans|Internet|Models, Immunological|Protein Conformation|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-514', '19055730', 'Reliable prediction of antibody, or B-cell, epitopes remains challenging yet highly desirable for the design of vaccines and immunodiagnostics. A correlation between antigenicity, solvent accessibility, and flexibility in proteins was demonstrated. Subsequently, Thornton and colleagues proposed a method for identifying continuous epitopes in the protein regions protruding from the proteins globular surface. The aim of this work was to implement that method as a web-tool and evaluate its performance on discontinuous epitopes known from the structures of antibody-protein complexes.'),
(92520, 2008, 'bmc', 'Improved machine learning method for analysis of gas phase chemistry of peptides', 'Gehrke, Allison|Sun, Shaojun|Kurgan, Lukasz|Ahn, Natalie|Resing, Katheryn|Kafadar, Karen|Cios, Krzysztof', 'Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu|Department of Computer Science and Engineering, University of Colorado at Denver, USA. Allison.Gehrke@email.cudenver.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Databases, Protein|Gases|Linear Models|Models, Molecular|Neural Networks (Computer)|Peptides|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-9-515', '19055745', 'Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra.'),
(92521, 2008, 'bmc', 'A fast algorithm for the multiple genome rearrangement problem with weighted reversals and transpositions', 'Bader, Martin|Abouelhoda, Mohamed I|Ohlebusch, Enno', 'Institute of Theoretical Computer Science, University of Ulm, Ulm, Germany. martin.bader@uni-ulm.de|Institute of Theoretical Computer Science, University of Ulm, Ulm, Germany. martin.bader@uni-ulm.de|Institute of Theoretical Computer Science, University of Ulm, Ulm, Germany. martin.bader@uni-ulm.de', 'Germany|Germany|Germany', 16, 'Algorithms|Analysis of Variance|Animals|Campanulaceae|Computational Biology|DNA, Chloroplast|DNA, Circular|DNA, Mitochondrial|Databases, Genetic|Evolution, Molecular|Gene Rearrangement|Genome|Genomics|Models, Genetic|Mutation|Phylogeny', 'Journal Article', '10.1186/1471-2105-9-516', '19055792', 'Due to recent progress in genome sequencing, more and more data for phylogenetic reconstruction based on rearrangement distances between genomes become available. However, this phylogenetic reconstruction is a very challenging task. For the most simple distance measures (the breakpoint distance and the reversal distance), the problem is NP-hard even if one considers only three genomes.'),
(92522, 2008, 'bmc', 'Detection of protein catalytic residues at high precision using local network properties', 'Slama, Patrick|Filippis, Ioannis|Lappe, Michael', 'Structural Bioinformatics Group, Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. slama.p@gmail.com|Structural Bioinformatics Group, Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. slama.p@gmail.com|Structural Bioinformatics Group, Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. slama.p@gmail.com', 'Germany|Germany|Germany', 14, 'Algorithms|Animals|Catalysis|Catalytic Domain|Computational Biology|Humans|Models, Biological|Models, Statistical|Molecular Conformation|Protein Conformation|Protein Folding|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Ubiquitin', 'Journal Article', '10.1186/1471-2105-9-517', '19055796', 'Identifying the active site of an enzyme is a crucial step in functional studies. While protein sequences and structures can be experimentally characterized, determining which residues build up an active site is not a straightforward process. In the present study a new method for the detection of protein active sites is introduced. This method uses local network descriptors derived from protein three-dimensional structures to determine whether a residue is part of an active site. It thus does not involve any sequence alignment or structure similarity to other proteins. A scoring function is elaborated over a set of more than 220 proteins having different structures and functions, in order to detect protein catalytic sites with a high precision, i.e. with a minimal rate of false positives.'),
(92523, 2008, 'bmc', 'Algorithm of OMA for large-scale orthology inference', 'Roth, Alexander C J|Gonnet, Gaston H|Dessimoz, Christophe', 'ETH Zurich, and Swiss Institute of Bioinformatics, Zurich, Switzerland. alexande@inf.ethz.ch|ETH Zurich, and Swiss Institute of Bioinformatics, Zurich, Switzerland. alexande@inf.ethz.ch|ETH Zurich, and Swiss Institute of Bioinformatics, Zurich, Switzerland. alexande@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland', 102, 'Algorithms|Computational Biology|Evolution, Molecular|Genomics|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-518', '19055798', 'OMA is a project that aims to identify orthologs within publicly available, complete genomes. With 657 genomes analyzed to date, OMA is one of the largest projects of its kind.'),
(92524, 2008, 'bmc', 'Integrated multi-level quality control for proteomic profiling studies using mass spectrometry', 'Cairns, David A|Perkins, David N|Stanley, Anthea J|Thompson, Douglas|Barrett, Jennifer H|Selby, Peter J|Banks, Rosamonde E', 'Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk|Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St Jamess University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Animals|Databases, Protein|Humans|Proteome|Proteomics|Quality Control|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-9-519', '19055809', 'Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.'),
(92525, 2008, 'bmc', 'Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data', 'Pelz, Carl R|Kulesz-Martin, Molly|Bagby, Grover|Sears, Rosalie C', 'Department of Molecular and Medical Genetics, Oregon Health and Sciences University, Portland, OR 97239-3098, USA. pelzc@ohsu.edu|Department of Molecular and Medical Genetics, Oregon Health and Sciences University, Portland, OR 97239-3098, USA. pelzc@ohsu.edu|Department of Molecular and Medical Genetics, Oregon Health and Sciences University, Portland, OR 97239-3098, USA. pelzc@ohsu.edu|Department of Molecular and Medical Genetics, Oregon Health and Sciences University, Portland, OR 97239-3098, USA. pelzc@ohsu.edu', 'United States|United States|United States|United States', 39, 'Analysis of Variance|Artifacts|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-520', '19055840', 'Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip.'),
(92526, 2008, 'bmc', 'An efficient genetic algorithm for structural RNA pairwise alignment and its application to non-coding RNA discovery in yeast', 'Taneda, Akito', 'Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan. taneda@eit.hirosaki-u.ac.jp', 'Japan', 0, 'Algorithms|False Positive Reactions|Gene Expression Regulation, Fungal|Genes, Fungal|Genome|Genome, Fungal|Humans|Models, Genetic|Models, Statistical|RNA|RNA, Untranslated|ROC Curve|Saccharomyces cerevisiae|Software|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-521', '19061486', 'Aligning RNA sequences with low sequence identity has been a challenging problem since such a computation essentially needs an algorithm with high complexities for taking structural conservation into account. Although many sophisticated algorithms for the purpose have been proposed to date, further improvement in efficiency is necessary to accelerate its large-scale applications including non-coding RNA (ncRNA) discovery.'),
(92527, 2008, 'bmc', 'Systematic identification of yeast cell cycle transcription factors using multiple data sources', 'Wu, Wei-Sheng|Li, Wen-Hsiung', 'Department of Evolution and Ecology, University of Chicago, Chicago, IL 60637, USA. wessonwu@gmail.com|Department of Evolution and Ecology, University of Chicago, Chicago, IL 60637, USA. wessonwu@gmail.com', 'United States|United States', 0, 'Cell Cycle|Computational Biology|Gene Expression Regulation, Fungal|Models, Genetic|Oligonucleotide Array Sequence Analysis|Transcription Factors|Yeasts', 'Journal Article', '10.1186/1471-2105-9-522', '19061501', 'Eukaryotic cell cycle is a complex process and is precisely regulated at many levels. Many genes specific to the cell cycle are regulated transcriptionally and are expressed just before they are needed. To understand the cell cycle process, it is important to identify the cell cycle transcription factors (TFs) that regulate the expression of cell cycle-regulated genes.'),
(92528, 2008, 'bmc', 'Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks', 'Nix, David A|Courdy, Samir J|Boucher, Kenneth M', 'Huntsman Cancer Institute, Department of Research Informatics, University of Utah, Salt Lake City, Utah, 84105, USA. david.nix@hci.utah.edu|Huntsman Cancer Institute, Department of Research Informatics, University of Utah, Salt Lake City, Utah, 84105, USA. david.nix@hci.utah.edu|Huntsman Cancer Institute, Department of Research Informatics, University of Utah, Salt Lake City, Utah, 84105, USA. david.nix@hci.utah.edu', 'United States|United States|United States', 152, 'Algorithms|Animals|Cell Transformation, Neoplastic|Chromatin|Chromatin Immunoprecipitation|False Positive Reactions|Gene Expression Regulation|Genome|Histones|Humans|Neurons|Poisson Distribution|Polymerase Chain Reaction|Proteins|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-523', '19061503', 'High throughput signature sequencing holds many promises, one of which is the ready identification of in vivo transcription factor binding sites, histone modifications, changes in chromatin structure and patterns of DNA methylation across entire genomes. In these experiments, chromatin immunoprecipitation is used to enrich for particular DNA sequences of interest and signature sequencing is used to map the regions to the genome (ChIP-Seq). Elucidation of these sites of DNA-protein binding/modification are proving instrumental in reconstructing networks of gene regulation and chromatin remodelling that direct development, response to cellular perturbation, and neoplastic transformation.'),
(92529, 2008, 'bmc', 'IDEA: Interactive Display for Evolutionary Analyses', 'Egan, Amy|Mahurkar, Anup|Crabtree, Jonathan|Badger, Jonathan H|Carlton, Jane M|Silva, Joana C', 'Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu|Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA. aegan@som.umaryland.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Data Interpretation, Statistical|Databases, Genetic|Evolution, Molecular|Genomics|Internet|Models, Genetic|Phylogeny|Plasmodium|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-524', '19061522', 'The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency.'),
(92530, 2008, 'bmc', 'Multi-label literature classification based on the Gene Ontology graph', 'Jin, Bo|Muller, Brian|Zhai, Chengxiang|Lu, Xinghua', 'Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. jinbo@musc.edu|Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. jinbo@musc.edu|Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. jinbo@musc.edu|Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. jinbo@musc.edu', 'United States|United States|United States|United States', 22, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computational Biology|Genomics|Information Storage and Retrieval|Internet|PubMed|Reproducibility of Results|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-525', '19063730', 'The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification.'),
(92531, 2008, 'bmc', 'SNPFile--a software library and file format for large scale association mapping and population genetics studies', 'Nielsen, Jesper|Mailund, Thomas', 'Bioinformatics Research Center, University of Aarhus, Denmark. jn@daimi.au.dk|Bioinformatics Research Center, University of Aarhus, Denmark. jn@daimi.au.dk', 'Denmark|Denmark', 0, 'Computational Biology|Databases, Genetic|Genetics, Population|Genome-Wide Association Study|Genomics|Genotype|Information Storage and Retrieval|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-9-526', '19063732', 'High-throughput genotyping technology has enabled cost effective typing of thousands of individuals in hundred of thousands of markers for use in genome wide studies. This vast improvement in data acquisition technology makes it an informatics challenge to efficiently store and manipulate the data. While spreadsheets and at text files were adequate solutions earlier, the increased data size mandates more efficient solutions.'),
(92532, 2008, 'bmc', 'Compo: composite motif discovery using discrete models', 'Sandve, Geir Kjetil|Abul, Osman|Drabløs, Finn', 'Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway. geirksa@ifi.uio.no|Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway. geirksa@ifi.uio.no|Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway. geirksa@ifi.uio.no', 'Norway|Norway|Norway', 10, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Computational Biology|Models, Genetic|Software', 'Journal Article', '10.1186/1471-2105-9-527', '19063744', 'Computational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regulatory sites in DNA. In recent years there has been increased attention towards the discovery of composite motifs, typically occurring in cis-regulatory regions of genes.'),
(92533, 2008, 'bmc', 'Gene Prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases', 'Yu, Wei|Wulf, Anja|Liu, Tiebin|Khoury, Muin J|Gwinn, Marta', 'National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. WYu@cdc.gov|National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. WYu@cdc.gov|National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. WYu@cdc.gov|National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. WYu@cdc.gov|National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. WYu@cdc.gov', 'United States|United States|United States|United States|United States', 56, 'Computational Biology|Databases, Genetic|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Humans|Internet|Polymorphism, Single Nucleotide|Risk Factors|Software', 'Journal Article', '10.1186/1471-2105-9-528', '19063745', 'Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research. We developed a Web-based application that selects and prioritizes potential disease-related genes by using a highly curated and updated literature database of genetic association studies. The application, called Gene Prospector, also provides a comprehensive set of links to additional data sources.'),
(92534, 2008, 'bmc', 'The APEX Quantitative Proteomics Tool: generating protein quantitation estimates from LC-MS/MS proteomics results', 'Braisted, John C|Kuntumalla, Srilatha|Vogel, Christine|Marcotte, Edward M|Rodrigues, Alan R|Wang, Rong|Huang, Shih-Ting|Ferlanti, Erik S|Saeed, Alexander I|Fleischmann, Robert D|Peterson, Scott N|Pieper, Rembert', 'Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org|Pathogen Functional Genomics Resource Center, J Craig Venter Institute, Rockville, MD 20850, USA. braisted@jcvi.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 119, 'Chromatography, Liquid|Databases, Protein|Proteome|Proteomics|Sequence Analysis, Protein|Software|Tandem Mass Spectrometry|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-529', '19068132', 'Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called Oi value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (Oi). This predicted spectral count is compared to the proteins observed MS total spectral count during APEX computation of protein abundances.'),
(92535, 2008, 'bmc', 'ReplicationDomain: a visualization tool and comparative database for genome-wide replication timing data', 'Weddington, Nodin|Stuy, Alexander|Hiratani, Ichiro|Ryba, Tyrone|Yokochi, Tomoki|Gilbert, David M', 'Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu|Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu|Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu|Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu|Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu|Department of Biological Sciences, Florida State University, Tallahassee, Florida 32306, USA. weddington@bio.fsu.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Computer Graphics|DNA Replication|Database Management Systems|Databases, Genetic|Genome|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-530', '19077204', 'Eukaryotic DNA replication is regulated at the level of large chromosomal domains (0.5-5 megabases in mammals) within which replicons are activated relatively synchronously. These domains replicate in a specific temporal order during S-phase and our genome-wide analyses of replication timing have demonstrated that this temporal order of domain replication is a stable property of specific cell types.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92536, 2008, 'bmc', 'Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score', 'Pandit, Shashi Bhushan|Skolnick, Jeffrey', 'Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, USA. spandit3@mail.gatech.edu|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, USA. spandit3@mail.gatech.edu', 'Georgia|Georgia', 98, 'Algorithms|Animals|Computational Biology|Computers|Databases, Protein|Humans|Models, Statistical|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-531', '19077267', 'Protein tertiary structure comparisons are employed in various fields of contemporary structural biology. Most structure comparison methods involve generation of an initial seed alignment, which is extended and/or refined to provide the best structural superposition between a pair of protein structures as assessed by a structure comparison metric. One such metric, the TM-score, was recently introduced to provide a combined structure quality measure of the coordinate root mean square deviation between a pair of structures and coverage. Using the TM-score, the TM-align structure alignment algorithm was developed that was often found to have better accuracy and coverage than the most commonly used structural alignment programs, however, there were a number of situations when this was not true.'),
(92537, 2008, 'bmc', 'Extended Newick: it is time for a standard representation of phylogenetic networks', 'Cardona, Gabriel|Rosselló, Francesc|Valiente, Gabriel', 'Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain. gabriel.cardona@uib.es', 'Spain|Spain|Spain', 0, 'Computer Graphics|Databases, Genetic|Gene Transfer, Horizontal|Hybridization, Genetic|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-9-532', '19077301', 'Phylogenetic trees resulting from molecular phylogenetic analysis are available in Newick format from specialized databases but when it comes to phylogenetic networks, which provide an explicit representation of reticulate evolutionary events such as recombination, hybridization or lateral gene transfer, the lack of a standard format for their representation has hindered the publication of explicit phylogenetic networks in the specialized literature and their incorporation in specialized databases. Two different proposals to represent phylogenetic networks exist: as a single Newick string (where each hybrid node is splitted once for each parent) or as a set of Newick strings (one for each hybrid node plus another one for the phylogenetic network).'),
(92538, 2008, 'bmc', 'GPAT: retrieval of genomic annotation from large genomic position datasets', 'Krebs, Arnaud|Frontini, Mattia|Tora, Làszlò', 'Department of Functional Genomics, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS UMR 7104, INSERM U 596, Université Louis Pasteur de Strasbourg, BP 10142-67404 ILLKIRCH Cedex, CU de Strasbourg, France. krebs@igbmc.u-strasbg.fr|Department of Functional Genomics, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS UMR 7104, INSERM U 596, Université Louis Pasteur de Strasbourg, BP 10142-67404 ILLKIRCH Cedex, CU de Strasbourg, France. krebs@igbmc.u-strasbg.fr|Department of Functional Genomics, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS UMR 7104, INSERM U 596, Université Louis Pasteur de Strasbourg, BP 10142-67404 ILLKIRCH Cedex, CU de Strasbourg, France. krebs@igbmc.u-strasbg.fr', 'France|France|France', 24, 'Chromatin Immunoprecipitation|Databases, Genetic|Genome|Genomics|Software', 'Journal Article', '10.1186/1471-2105-9-533', '19077303', 'Recent genome wide transcription factor binding site or chromatin modification mapping analysis techniques, such as chromatin immunoprecipitation (ChIP) linked to DNA microarray analysis (ChIP on chip) or ChIP coupled to high throughput sequencing (ChIP-seq), generate tremendous amounts of genomic location data in the form of one-dimensional series of signals. After pre-analysis of these data (signal pre-clearing, relevant binding site detection), biologists need to search for the biological relevance of the detected genomic positions representing transcription regulation or chromatin modification events.'),
(92539, 2008, 'bmc', 'Optimal neighborhood indexing for protein similarity search', 'Peterlongo, Pierre|Noé, Laurent|Lavenier, Dominique|Nguyen, Van Hoa|Kucherov, Gregory|Giraud, Mathieu', 'Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr|Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr|Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr|Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr|Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr|Symbiose team-project, IRISA INRIA, Rennes, France. pierre.peterlongo@irisa.fr', 'France|France|France|France|France|France', 4, 'Abstracting and Indexing as Topic|Algorithms|Computational Biology|Databases, Protein|Information Storage and Retrieval|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-534', '19087280', 'Similarity inference, one of the main bioinformatics tasks, has to face an exponential growth of the biological data. A classical approach used to cope with this data flow involves heuristics with large seed indexes. In order to speed up this technique, the index can be enhanced by storing additional information to limit the number of random memory accesses. However, this improvement leads to a larger index that may become a bottleneck. In the case of protein similarity search, we propose to decrease the index size by reducing the amino acid alphabet.'),
(92540, 2008, 'bmc', 'DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites', 'Blom, Evert-Jan|van Hijum, Sacha A F T|Hofstede, Klaas J|Silvis, Remko|Roerdink, Jos B T M|Kuipers, Oscar P', 'Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl|Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl|Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl|Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl|Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl|Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, The Netherlands. e.j.blom@rug.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Binding Sites|Cluster Analysis|DNA|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-535', '19087282', 'A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis.'),
(92541, 2008, 'bmc', 'SynteBase/SynteView: a tool to visualize gene order conservation in prokaryotic genomes', 'Lemoine, Frédéric|Labedan, Bernard|Lespinet, Olivier', 'Institut de Génétique et Microbiologie, Université Paris Sud XI, CNRS UMR 8621, Bât, 400, 91405 Orsay Cedex, France. frederic.lemoine@igmors.u-psud.fr|Institut de Génétique et Microbiologie, Université Paris Sud XI, CNRS UMR 8621, Bât, 400, 91405 Orsay Cedex, France. frederic.lemoine@igmors.u-psud.fr|Institut de Génétique et Microbiologie, Université Paris Sud XI, CNRS UMR 8621, Bât, 400, 91405 Orsay Cedex, France. frederic.lemoine@igmors.u-psud.fr', 'France|France|France', 0, 'Computational Biology|Conserved Sequence|Databases, Genetic|Evolution, Molecular|Gene Order|Genome, Archaeal|Genome, Bacterial|Genomics|Software|Synteny', 'Journal Article', '10.1186/1471-2105-9-536', '19087285', 'It has been repeatedly observed that gene order is rapidly lost in prokaryotic genomes. However, persistent synteny blocks are found when comparing more or less distant species. These genes that remain consistently adjacent are appealing candidates for the study of genome evolution and a more accurate definition of their functional role. Such studies require visualizing conserved synteny blocks in a large number of genomes at all taxonomic distances.'),
(92542, 2008, 'bmc', 'A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data', 'Mo, Fan|Hong, Xu|Gao, Feng|Du, Lin|Wang, Jun|Omenn, Gilbert S|Lin, Biaoyang', 'Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com|Systems Biology Division, Zhejiang-California Nanosystems Institute (ZCNI) of Zhejiang University, Zhejiang University Huajiachi Campus, Hangzhou, PR China. mofan.hz@gmail.com', 'China|China|China|China|China|China|China', 22, 'Alternative Splicing|Databases, Genetic|Exons|Humans|Protein Isoforms|Proteins|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-9-537', '19087293', 'Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched.'),
(92543, 2008, 'bmc', 'Pep-3D-Search: a method for B-cell epitope prediction based on mimotope analysis', 'Huang, Yan Xin|Bao, Yong Li|Guo, Shu Yan|Wang, Yan|Zhou, Chun Guang|Li, Yu Xin', 'Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com|Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com|Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com|Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com|Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com|Institute of Genetics and Cytology, Northeast Normal University, Changchun, PR China. nenu_huangyx@tom.com', 'China|China|China|China|China|China', 0, 'Algorithms|Databases, Protein|Epitope Mapping|Epitopes, B-Lymphocyte|Peptide Library|Software', 'Journal Article', '10.1186/1471-2105-9-538', '19087303', 'The prediction of conformational B-cell epitopes is one of the most important goals in immunoinformatics. The solution to this problem, even if approximate, would help in designing experiments to precisely map the residues of interaction between an antigen and an antibody. Consequently, this area of research has received considerable attention from immunologists, structural biologists and computational biologists. Phage-displayed random peptide libraries are powerful tools used to obtain mimotopes that are selected by binding to a given monoclonal antibody (mAb) in a similar way to the native epitope. These mimotopes can be considered as functional epitope mimics. Mimotope analysis based methods can predict not only linear but also conformational epitopes and this has been the focus of much research in recent years. Though some algorithms based on mimotope analysis have been proposed, the precise localization of the interaction site mimicked by the mimotopes is still a challenging task.'),
(92544, 2008, 'bmc', 'Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations', 'Corander, Jukka|Marttinen, Pekka|Sirén, Jukka|Tang, Jing', 'Department of Mathematics, Fänriksgatan 3B, Abo Akademi University, Abo, Finland. jukka.corander@abo.fi|Department of Mathematics, Fänriksgatan 3B, Abo Akademi University, Abo, Finland. jukka.corander@abo.fi|Department of Mathematics, Fänriksgatan 3B, Abo Akademi University, Abo, Finland. jukka.corander@abo.fi|Department of Mathematics, Fänriksgatan 3B, Abo Akademi University, Abo, Finland. jukka.corander@abo.fi', 'Finland|Finland|Finland|Finland', 567, 'Algorithms|Alleles|Bayes Theorem|Cluster Analysis|Computational Biology|Databases, Genetic|Genetic Linkage|Genetic Structures|Genetics, Population|Humans|Models, Genetic|Population|Sequence Analysis, DNA|Software|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-539', '19087322', 'During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions.'),
(92545, 2008, 'bmc', 'Shape-IT: new rapid and accurate algorithm for haplotype inference', 'Delaneau, Olivier|Coulonges, Cédric|Zagury, Jean-François', 'Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, 75003 Paris, France.|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, 75003 Paris, France.|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, 292 rue Saint-Martin, 75003 Paris, France.', 'France|France|France', 70, 'Algorithms|DNA Mutational Analysis|Databases, Genetic|Genotype|Haplotypes|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-9-540', '19087329', 'We have developed a new computational algorithm, Shape-IT, to infer haplotypes under the genetic model of coalescence with recombination developed by Stephens et al in Phase v2.1. It runs much faster than Phase v2.1 while exhibiting the same accuracy. The major algorithmic improvements rely on the use of binary trees to represent the sets of candidate haplotypes for each individual. These binary tree representations: (1) speed up the computations of posterior probabilities of the haplotypes by avoiding the redundant operations made in Phase v2.1, and (2) overcome the exponential aspect of the haplotypes inference problem by the smart exploration of the most plausible pathways (ie. haplotypes) in the binary trees.'),
(92546, 2008, 'bmc', 'GeNESiS: gene network evolution simulation software', 'Kratz, Anton|Tomita, Masaru|Krishnan, Arun', 'Institute for Advanced Biosciences, Keio University, 14-1, Baba-Cho, Tsuruoka, Yamagata-ken, 997-0035, Japan. anton.kratz@gmail.com|Institute for Advanced Biosciences, Keio University, 14-1, Baba-Cho, Tsuruoka, Yamagata-ken, 997-0035, Japan. anton.kratz@gmail.com|Institute for Advanced Biosciences, Keio University, 14-1, Baba-Cho, Tsuruoka, Yamagata-ken, 997-0035, Japan. anton.kratz@gmail.com', 'Japan|Japan|Japan', 6, 'Algorithms|Computer Simulation|Evolution, Molecular|Gene Regulatory Networks|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-541', '19087333', 'There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.'),
(92547, 2008, 'bmc', 'Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics', 'Brusniak, Mi-Youn|Bodenmiller, Bernd|Campbell, David|Cooke, Kelly|Eddes, James|Garbutt, Andrew|Lau, Hollis|Letarte, Simon|Mueller, Lukas N|Sharma, Vagisha|Vitek, Olga|Zhang, Ning|Aebersold, Ruedi|Watts, Julian D', 'Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. mbrusnia@systemsbiology.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Chromatography, Liquid|Computational Biology|Internet|Mass Spectrometry|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-9-542', '19087345', 'Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.'),
(92548, 2008, 'bmc', 'Emerging strengths in Asia Pacific bioinformatics', 'Ranganathan, Shoba|Hsu, Wen-Lian|Yang, Ueng-Cheng|Tan, Tin Wee', 'Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au|Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au', 'Australia|Australia|Australia|Australia', 5, 'Animals|Asia|Biomedical Research|Computational Biology|Genomics|Humans|International Cooperation|Molecular Biology|Systems Biology', 'Congress', '10.1186/1471-2105-9-S12-S1', '19091008', 'The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asias oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts.'),
(92549, 2008, 'bmc', 'Functional analysis of novel SNPs and mutations in human and mouse genomes', 'Liu, Chuan-Kun|Chen, Yan-Hau|Tang, Cheng-Yang|Chang, Shu-Chuan|Lin, Yi-Jung|Tsai, Ming-Fang|Chen, Yuan-Tsong|Yao, Adam', 'National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw|National Genotyping Center, Academia Sinica, Taipei, Taiwan 11529, R.O.C. chuankun@ibms.sinica.edu.tw', '-|-|-|-|-|-|-|-', 16, 'Animals|Automation|Computational Biology|Genetic Variation|Genome|Genome, Human|Genomics|Humans|Mice|Mutation|Polymorphism, Single Nucleotide|Programming Languages|Risk|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S10', '19091009', 'With the flood of information generated by the new generation of sequencing technologies, more efficient bioinformatics tools are needed for in-depth impact analysis of novel genomic variations. FANS (Functional Analysis of Novel SNPs) was developed to streamline comprehensive but tedious functional analysis steps into a few clicks and to offer a carefully designed presentation of results so researchers can focus more on thinking instead of typing and calculating.'),
(92550, 2008, 'bmc', 'Ortholog-based protein-protein interaction prediction and its application to inter-species interactions', 'Lee, Sheng-An|Chan, Cheng-hsiung|Tsai, Chi-Hung|Lai, Jin-Mei|Wang, Feng-Sheng|Kao, Cheng-Yan|Huang, Chi-Ying F', 'Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei 112, Taiwan. d93922005@ntu.edu.tw', '-|-|-|-|-|-|-', 0, 'Animals|Bayes Theorem|Calcium|Calmodulin|Computational Biology|Databases, Protein|Host-Pathogen Interactions|Humans|Immune System|Models, Biological|Models, Genetic|Plasmodium falciparum|Protein Interaction Mapping|Receptors, Tumor Necrosis Factor|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-S12-S11', '19091010', 'The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods.'),
(92551, 2008, 'bmc', 'Real value prediction of protein solvent accessibility using enhanced PSSM features', 'Chang, Darby Tien-Hao|Huang, Hsuan-Yu|Syu, Yu-Tang|Wu, Chih-Peng', 'Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw', '-|-|-|-', 19, 'Algorithms|Amino Acids|Chemistry, Physical|Computational Biology|Databases, Protein|Models, Statistical|Protein Conformation|Proteins|Regression Analysis|Reproducibility of Results|Sequence Analysis, Protein|Software|Solvents|Surface Properties', 'Journal Article', '10.1186/1471-2105-9-S12-S12', '19091011', 'Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).'),
(92552, 2008, 'bmc', 'WSPMaker: a web tool for calculating selection pressure in proteins and domains using window-sliding', 'Lee, Yong Seok|Kim, Tae-Hyung|Kang, Tae-Wook|Chung, Won-Hyong|Shin, Gwang-Sik', 'Korean Bioinformation Center, KRIBB, Daejeon 305-806, Korea. dolsemtl@kribb.re.kr|Korean Bioinformation Center, KRIBB, Daejeon 305-806, Korea. dolsemtl@kribb.re.kr|Korean Bioinformation Center, KRIBB, Daejeon 305-806, Korea. dolsemtl@kribb.re.kr|Korean Bioinformation Center, KRIBB, Daejeon 305-806, Korea. dolsemtl@kribb.re.kr|Korean Bioinformation Center, KRIBB, Daejeon 305-806, Korea. dolsemtl@kribb.re.kr', '-|-|-|-|-', 7, 'Amino Acids|Biological Evolution|Computational Biology|DNA|Databases, Protein|Genome|Humans|Internet|Microcephaly|Models, Genetic|Nervous System|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S13', '19091012', 'In the study of adaptive evolution, it is important to detect the protein coding sites where natural selection is acting. In general, the ratio of the rate of non-synonymous substitutions (Ka) to the rate of synonymous substitutions (Ks) is used to estimate either negative or positive selection for an entire gene region of interest. However, since each amino acid in a region has a different function and structure, the type and strength of natural selection may be different for each amino acid. Specifically, domain sites on the protein are indicative of structurally and functionally important sites. Therefore, Window-sliding tools can be used to detect evolutionary forces acting on mutation sites.'),
(92553, 2008, 'bmc', 'Molecular docking studies of dithionitrobenzoic acid and its related compounds to protein disulfide isomerase: computational screening of inhibitors to HIV-1 entry', 'Gowthaman, Uthaman|Jayakanthan, Mannu|Sundar, Durai', 'Bioinformatics Centres of BTISnet at Pondicherry University and IIT Delhi, India. gowtham_15@yahoo.com|Bioinformatics Centres of BTISnet at Pondicherry University and IIT Delhi, India. gowtham_15@yahoo.com|Bioinformatics Centres of BTISnet at Pondicherry University and IIT Delhi, India. gowtham_15@yahoo.com', 'India|India|India', 0, 'Algorithms|Anti-HIV Agents|Catalytic Domain|Chemistry, Pharmaceutical|Computational Biology|Computer Simulation|Dithionitrobenzoic Acid|Drug Design|HIV-1|Ligands|Molecular Conformation|Oxidation-Reduction|Protein Disulfide-Isomerases|Technology, Pharmaceutical', 'Journal Article', '10.1186/1471-2105-9-S12-S14', '19091013', 'Entry of HIV-1 into human lymphoid requires activities of viral envelope glycoproteins gp120 and gp41, and two host-cell proteins, the primary receptor CD4 and a chemokine co-receptor. In addition, a third cell-surface protein called protein disulfide isomerase (PDI) is found to play a major role in HIV-1 entry. PDI is capable of mediating thio-disulfide interchange reactions and could enable the reduction of gp120 disulfide bonds, which triggers the major conformational changes in gp120 and gp41 required for virus entry. In this scenario, inhibition of HIV-1 entry can be brought about by introducing agents that can block thiol-disulfide interchange reaction of cell surface PDI. There have been studies with agents that inhibit PDI activity, but the exact mode of binding remains to be elucidated, this might provide insights to develop new drugs to target PDI. This study attempts to perceive the mode of binding of dithionitrobenzoic acid (DTNB), and its structurally related compounds on PDI enzyme.'),
(92554, 2008, 'bmc', 'Flanking signal and mature peptide residues influence signal peptide cleavage', 'Choo, Khar Heng|Ranganathan, Shoba', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore. justin@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore. justin@bic.nus.edu.sg', 'Singapore|Singapore', 47, 'Bacterial Proteins|Chemistry, Physical|Computational Biology|Databases, Protein|Dimerization|Isoelectric Point|Membrane Proteins|Models, Statistical|Models, Theoretical|Peptides|Protein Sorting Signals|Protein Structure, Tertiary|Serine Endopeptidases', 'Journal Article', '10.1186/1471-2105-9-S12-S15', '19091014', 'Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria.'),
(92555, 2008, 'bmc', 'Predicted mouse peroxisome-targeted proteins and their actual subcellular locations', 'Mizuno, Yumi|Kurochkin, Igor V|Herberth, Marlis|Okazaki, Yasushi|Schönbach, Christian', 'Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan. yumimi@saitama-med.ac.jp|Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan. yumimi@saitama-med.ac.jp|Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan. yumimi@saitama-med.ac.jp|Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan. yumimi@saitama-med.ac.jp|Division of Functional Genomics and Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Hidaka, Saitama 350-1241, Japan. yumimi@saitama-med.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 13, 'Alcohol Dehydrogenase|Animals|CHO Cells|Computational Biology|Cricetinae|Cricetulus|Mice|Microscopy, Confocal|Models, Biological|PPAR alpha|Peptides|Peroxisomal Targeting Signal 2 Receptor|Peroxisomes|Protein Structure, Tertiary|Receptors, Cytoplasmic and Nuclear', 'Journal Article', '10.1186/1471-2105-9-S12-S16', '19091015', 'The import of most intraperoxisomal proteins is mediated by peroxisome targeting signals at their C-termini (PTS1) or N-terminal regions (PTS2). Both signals have been integrated in subcellular location prediction programs. However their present performance, particularly of PTS2-targeting did not seem fitting for large-scale screening of sequences.'),
(92556, 2008, 'bmc', 'Dynamic sensitivity analysis of biological systems', 'Wu, Wu Hsiung|Wang, Feng Sheng|Chang, Maw Shang', 'Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw', '-|-|-', 0, 'Algorithms|Computational Biology|Computer Simulation|Ethane|Ethanol|Fermentation|Formaldehyde|Models, Biological|Models, Theoretical|Reproducibility of Results|Saccharomyces|Sensitivity and Specificity|Systems Biology|Systems Theory', 'Journal Article', '10.1186/1471-2105-9-S12-S17', '19091016', 'A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains.'),
(92557, 2008, 'bmc', 'Semi-automatic conversion of BioProp semantic annotation to PASBio annotation', 'Tsai, Richard Tzong-Han|Dai, Hong-Jie|Huang, Chi-Hsin|Hsu, Wen-Lian', 'Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw', '-|-|-|-', 0, 'Algorithms|Artificial Intelligence|Automation|Computational Biology|Humans|Language|Models, Theoretical|Natural Language Processing|Pattern Recognition, Automated|Programming Languages|Reproducibility of Results|Semantics|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S12-S18', '19091017', 'Semantic role labeling (SRL) is an important text analysis technique. In SRL, sentences are represented by one or more predicate-argument structures (PAS). Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). PropBank is the most widely used PAS corpus and annotation format in the newswire domain. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. In this paper, we aim to build a system to convert BIOSMILEs BioProp annotation output to PASBio annotation. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator.'),
(92558, 2008, 'bmc', 'An integrated database-pipeline system for studying single nucleotide polymorphisms and diseases', 'Yang, Jin Ok|Hwang, Sohyun|Oh, Jeongsu|Bhak, Jong|Sohn, Tae-Kwon', 'Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. joy@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. joy@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. joy@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. joy@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. joy@kribb.re.kr', '-|-|-|-|-', 0, 'Automation|Computational Biology|Database Management Systems|Databases, Genetic|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Humans|Internet|Models, Biological|Polymorphism, Single Nucleotide|Unified Medical Language System|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S12-S19', '19091018', 'Studies on the relationship between disease and genetic variations such as single nucleotide polymorphisms (SNPs) are important. Genetic variations can cause disease by influencing important biological regulation processes. Despite the needs for analyzing SNP and disease correlation, most existing databases provide information only on functional variants at specific locations on the genome, or deal with only a few genes associated with disease. There is no combined resource to widely support gene-, SNP-, and disease-related information, and to capture relationships among such data. Therefore, we developed an integrated database-pipeline system for studying SNPs and diseases.'),
(92559, 2008, 'bmc', 'Using a kernel density estimation based classifier to predict species-specific microRNA precursors', 'Chang, Darby Tien-Hao|Wang, Chih-Ching|Chen, Jian-Wei', 'Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw|Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan, R.O.C. darby@ee.ncku.edu.tw', '-|-|-', 32, 'Algorithms|Artificial Intelligence|Computational Biology|Gene Expression Regulation|Humans|MicroRNAs|Models, Statistical|Pattern Recognition, Automated|RNA|Reproducibility of Results|Sequence Alignment|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-S12-S2', '19091019', 'MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches obtain more attention because that they can discover species-specific pre-miRNAs. Most ab initio approaches proposed novel features to characterize RNA molecules. However, there were fewer discussions on the associated classification mechanism in a miRNA predictor.'),
(92560, 2008, 'bmc', 'Ab-origin: an enhanced tool to identify the sourcing gene segments in germline for rearranged antibodies', 'Wang, Xiaojing|Wu, Di|Zheng, Siyuan|Sun, Jing|Tao, Lin|Li, Yixue|Cao, Zhiwei', 'Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, PR China. xjwang03@sibs.ac.cn', 'China|China|China|China|China|China|China', 24, 'Alleles|Antibodies|Computational Biology|Computer Simulation|Databases, Genetic|Gene Rearrangement|Humans|Immunoglobulin Heavy Chains|Models, Genetic|Nucleotides|ROC Curve|Reproducibility of Results|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S20', '19091020', 'In the adaptive immune system, variable regions of immunoglobulin (IG) are encoded by random recombination of variable (V), diversity (D), and joining (J) gene segments in the germline. Partitioning the functional antibody sequences to their sourcing germline gene segments is vital not only for understanding antibody maturation but also for promoting the potential engineering of the therapeutic antibodies. To date, several tools have been developed to perform such trace-back calculations. Yet, the predicting ability and processing volume of those tools vary significantly for different sets of data. Moreover, none of them give a confidence for immunoglobulin heavy diversity (IGHD) identification. Developing fast, efficient and enhanced tools is always needed with the booming of immunological data.'),
(92561, 2008, 'bmc', 'The value of position-specific scoring matrices for assessment of protein allegenicity', 'Lim, Shen Jean|Tong, Joo Chuan|Chew, Fook Tim|Tammi, Martti T', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. jean@bic.nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597. jean@bic.nus.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Allergens|Animals|Computational Biology|Databases, Factual|Databases, Protein|False Positive Reactions|Humans|Hypersensitivity, Immediate|Models, Statistical|Predictive Value of Tests|Proteins|Reproducibility of Results|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S21', '19091021', 'Bioinformatics tools are commonly used for assessing potential protein allergenicity. While these methods have achieved good accuracies for highly conserved sequences, they are less effective when the overall similarity is low. In this study, we assessed the feasibility of using position-specific scoring matrices as a basis for predicting potential allergenicity in proteins.'),
(92562, 2008, 'bmc', 'Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research', 'Lin, Hong Huang|Zhang, Guang Lan|Tongchusak, Songsak|Reinherz, Ellis L|Brusic, Vladimir', 'Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Honghuang_Lin@dfci.harvard.edu|Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Honghuang_Lin@dfci.harvard.edu|Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Honghuang_Lin@dfci.harvard.edu|Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Honghuang_Lin@dfci.harvard.edu|Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02215, USA. Honghuang_Lin@dfci.harvard.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Antigens|Binding Sites|Computational Biology|Epitope Mapping|Epitopes|Epitopes, T-Lymphocyte|False Positive Reactions|Humans|Markov Chains|Models, Theoretical|Peptides|Protein Binding|ROC Curve|Vaccines', 'Journal Article', '10.1186/1471-2105-9-S12-S22', '19091022', 'Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92563, 2008, 'bmc', 'A protein interaction based model for schizophrenia study', 'Hsu, Pei-Chun|Yang, Ueng-Cheng|Shih, Kuan-Hui|Liu, Chih-Min|Liu, Yu-Li|Hwu, Hai-Gwo', 'Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taiwan. pchsu@ym.edu.tw', '-|-|-|-|-|-', 0, 'Calcium Channels|Cluster Analysis|Computational Biology|Genetic Predisposition to Disease|Humans|Nerve Tissue Proteins|Neuregulin-1|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Protein Interaction Mapping|Risk Factors|Schizophrenia|Signal Transduction|Synapses', 'Journal Article', '10.1186/1471-2105-9-S12-S23', '19091023', 'Schizophrenia is a complex disease with multiple factors contributing to its pathogenesis. In addition to environmental factors, genetic factors may also increase susceptibility. In other words, schizophrenia is a highly heritable disease. Some candidate genes have been deduced on the basis of their known function with others found on the basis of chromosomal location. Individuals with multiple candidate genes may have increased risk. However it is not clear what kind of gene combinations may produce the disease phenotype. Their collective effect remains to be studied.'),
(92564, 2008, 'bmc', 'An integrated Korean biodiversity and genetic information retrieval system', 'Lim, Jeongheui|Bhak, Jong|Oh, Hee-Mock|Kim, Chang-Bae|Park, Yong-Ha|Paek, Woon Kee', 'Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 305-806, Korea. jhlim@kribb.re.kr', '-|-|-|-|-|-', 0, 'Animals|Biodiversity|Computational Biology|Database Management Systems|Databases, Factual|Databases, Genetic|Genes|Genome|Humans|Information Storage and Retrieval|Internet|Korea|Models, Genetic|Models, Statistical|Swine', 'Journal Article', '10.1186/1471-2105-9-S12-S24', '19091024', 'On-line biodiversity information databases are growing quickly and being integrated into general bioinformatics systems due to the advances of fast gene sequencing technologies and the Internet. These can reduce the cost and effort of performing biodiversity surveys and genetic searches, which allows scientists to spend more time researching and less time collecting and maintaining data. This will cause an increased rate of knowledge build-up and improve conservations. The biodiversity databases in Korea have been scattered among several institutes and local natural history museums with incompatible data types. Therefore, a comprehensive database and a nation wide web portal for biodiversity information is necessary in order to integrate diverse information resources, including molecular and genomic databases.'),
(92565, 2008, 'bmc', 'CMKb: a web-based prototype for integrating Australian Aboriginal customary medicinal plant knowledge', 'Gaikwad, Jitendra|Khanna, Varun|Vemulpad, Subramanyam|Jamie, Joanne|Kohen, Jim|Ranganathan, Shoba', 'Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. jgaikwad@cbms.mq.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 15, 'Australia|Biodiversity|Computational Biology|Database Management Systems|Databases, Genetic|Databases, Protein|Genomics|Humans|Information Systems|Knowledge Bases|Medicine, Traditional|Oceanic Ancestry Group|Plants, Medicinal|Risk|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S25', '19091025', 'The customary medicinal plant knowledge possessed by the Australian Aboriginal people is a significant resource. Published information on it is scattered throughout the literature, in heterogeneous data formats, and is scattered among various Aboriginal communities across Australia, due to a multiplicity of languages. This ancient knowledge is at risk due to loss of biodiversity, cultural impact and the demise of many of its custodians. We have developed the Customary Medicinal Knowledgebase (CMKb), an integrated multidisciplinary resource, to document, conserve and disseminate this knowledge.'),
(92566, 2008, 'bmc', 'Filtering of false positive microRNA candidates by a clustering-based approach', 'Leung, Wing-Sze|Lin, Marie C M|Cheung, David W|Yiu, S M', 'Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. h0328078@hkusua.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. h0328078@hkusua.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. h0328078@hkusua.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong. h0328078@hkusua.hku.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 9, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Computer Simulation|False Positive Reactions|Genome|Humans|Mice|MicroRNAs|Predictive Value of Tests|Rats|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S3', '19091026', 'MicroRNAs are small non-coding RNA gene products that play diversified roles from species to species. The explosive growth of microRNA researches in recent years proves the importance of microRNAs in the biological system and it is believed that microRNAs have valuable therapeutic potentials in human diseases. Continual efforts are therefore required to locate and verify the unknown microRNAs in various genomes. As many miRNAs are found to be arranged in clusters, meaning that they are in close proximity with their neighboring miRNAs, we are interested in utilizing the concept of microRNA clustering and applying it in microRNA computational prediction.'),
(92567, 2008, 'bmc', 'MiRTif: a support vector machine-based microRNA target interaction filter', 'Yang, Yuchen|Wang, Yu-Ping|Li, Kuo-Bin', 'Institute of Molecular and Cell Biology, 61 Biopolis Drive, 138673, Singapore. ycyang@imcb.a-star.edu.sg|Institute of Molecular and Cell Biology, 61 Biopolis Drive, 138673, Singapore. ycyang@imcb.a-star.edu.sg|Institute of Molecular and Cell Biology, 61 Biopolis Drive, 138673, Singapore. ycyang@imcb.a-star.edu.sg', 'Singapore|Singapore|Singapore', 49, '3 Untranslated Regions|Algorithms|Area Under Curve|Artificial Intelligence|Computational Biology|Computer Simulation|Computers|False Positive Reactions|Humans|MicroRNAs|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S4', '19091027', 'MicroRNAs (miRNAs) are a set of small non-coding RNAs serving as important negative gene regulators. In animals, miRNAs turn down protein translation by binding to the 3 UTR regions of target genes with imperfect complementary pairing. The identification of microRNA targets has become one of the major challenges of miRNA research. Bioinformatics investigations on miRNA target have resulted in a number of target prediction tools. Although these tools are capable of predicting hundreds of targets for a given miRNA, many of them suffer from high false positive rates, indicating the need for a post-processing filter for the predicted targets. Once trained with experimentally validated true and false targets, machine learning methods appear to be ideal approaches to distinguish the true targets from the false ones.'),
(92568, 2008, 'bmc', 'Finding microRNA regulatory modules in human genome using rule induction', 'Tran, Dang Hung|Satou, Kenji|Ho, Tu Bao', 'School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan. hungtd@jaist.ac.jp|School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan. hungtd@jaist.ac.jp|School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan. hungtd@jaist.ac.jp', 'Japan|Japan|Japan', 73, 'Computational Biology|Databases, Factual|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Genome, Human|Humans|MicroRNAs|Models, Biological|Models, Genetic|Models, Statistical|Neoplasms|RNA Processing, Post-Transcriptional|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-9-S12-S5', '19091028', 'MicroRNAs (miRNAs) are a class of small non-coding RNA molecules (20-24 nt), which are believed to participate in repression of gene expression. They play important roles in several biological processes (e.g. cell death and cell growth). Both experimental and computational approaches have been used to determine the function of miRNAs in cellular processes. Most efforts have concentrated on identification of miRNAs and their target genes. However, understanding the regulatory mechanism of miRNAs in the gene regulatory network is also essential to the discovery of functions of miRNAs in complex cellular systems. To understand the regulatory mechanism of miRNAs in complex cellular systems, we need to identify the functional modules involved in complex interactions between miRNAs and their target genes.'),
(92569, 2008, 'bmc', 'Predicting RNA-binding sites of proteins using support vector machines and evolutionary information', 'Cheng, Cheng-Wei|Su, Emily Chia-Yu|Hwang, Jenn-Kang|Sung, Ting-Yi|Hsu, Wen-Lian', 'Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan. chengwei@iis.sinica.edu.tw|Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan. chengwei@iis.sinica.edu.tw|Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan. chengwei@iis.sinica.edu.tw|Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan. chengwei@iis.sinica.edu.tw|Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan. chengwei@iis.sinica.edu.tw', '-|-|-|-|-', 86, 'Algorithms|Amino Acids|Artificial Intelligence|Binding Sites|Computational Biology|DNA|Evolution, Molecular|Neural Networks (Computer)|Protein Binding|Proteins|RNA|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S6', '19091029', 'RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities.'),
(92570, 2008, 'bmc', 'Extracting transcription factor binding sites from unaligned gene sequences with statistical models', 'Lu, Chung-Chin|Yuan, Wei-Hao|Chen, Te-Ming', 'Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. cclu@ee.nthu.edu.tw|Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. cclu@ee.nthu.edu.tw|Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. cclu@ee.nthu.edu.tw', '-|-|-', 2, 'Algorithms|Amino Acid Motifs|Bayes Theorem|Binding Sites|Computational Biology|DNA|DNA, Complementary|Genome, Fungal|Models, Statistical|Oligonucleotide Array Sequence Analysis|Probability|Sequence Alignment|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-S12-S7', '19091030', 'Transcription factor binding sites (TFBSs) are crucial in the regulation of gene transcription. Recently, chromatin immunoprecipitation followed by cDNA microarray hybridization (ChIP-chip array) has been used to identify potential regulatory sequences, but the procedure can only map the probable protein-DNA interaction loci within 1-2 kb resolution. To find out the exact binding motifs, it is necessary to build a computational method to examine the ChIP-chip array binding sequences and search for possible motifs representing the transcription factor binding sites.'),
(92571, 2008, 'bmc', 'Fast splice site detection using information content and feature reduction', 'Baten, A K M A|Halgamuge, S K|Chang, B C H', 'Biomechanical Engineering Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia. a.baten@pgrad.unimelb.edu.au|Biomechanical Engineering Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia. a.baten@pgrad.unimelb.edu.au|Biomechanical Engineering Research Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia. a.baten@pgrad.unimelb.edu.au', 'Australia|Australia|Australia', 27, 'Algorithms|Artificial Intelligence|Base Sequence|Computational Biology|DNA|Markov Chains|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|RNA Splicing|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S8', '19091031', 'Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data.'),
(92572, 2008, 'bmc', 'VarDetect: a nucleotide sequence variation exploratory tool', 'Ngamphiw, Chumpol|Kulawonganunchai, Supasak|Assawamakin, Anunchai|Jenwitheesuk, Ekachai|Tongsima, Sissades', 'Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand. chumpol.nga@biotec.or.th|Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand. chumpol.nga@biotec.or.th|Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand. chumpol.nga@biotec.or.th|Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand. chumpol.nga@biotec.or.th|Genome Institute, National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Phaholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand. chumpol.nga@biotec.or.th', 'Thailand|Thailand|Thailand|Thailand|Thailand', 15, 'Automation|Base Sequence|Chromatography|Computational Biology|Data Interpretation, Statistical|False Positive Reactions|Genetic Predisposition to Disease|Genetic Variation|Genomics|Humans|Nucleotides|Polymorphism, Single Nucleotide|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S12-S9', '19091032', 'Single nucleotide polymorphisms (SNPs) are the most commonly studied units of genetic variation. The discovery of such variation may help to identify causative gene mutations in monogenic diseases and SNPs associated with predisposing genes in complex diseases. Accurate detection of SNPs requires software that can correctly interpret chromatogram signals to nucleotides.'),
(92573, 2008, 'bmc', 'PocketMatch: a new algorithm to compare binding sites in protein structures', 'Yeturu, Kalidas|Chandra, Nagasuma', 'Bioinformatics Centre and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India. ykalidas@gmail.com|Bioinformatics Centre and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India. ykalidas@gmail.com', 'India|India', 0, 'Algorithms|Binding Sites|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1186/1471-2105-9-543', '19091072', 'Recognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.'),
(92574, 2008, 'bmc', 'AnEnPi: identification and annotation of analogous enzymes', 'Otto, Thomas D|Guimarães, Ana Carolina R|Degrave, Wim M|de Miranda, Antonio B', 'Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br', 'Brazil|Brazil|Brazil|Brazil', 24, 'Algorithms|Animals|Catalysis|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Drug Design|Enzymes|Genome|Humans|Leishmania major|Models, Biological|Protein Conformation|Software', 'Journal Article', '10.1186/1471-2105-9-544', '19091081', 'Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development.'),
(92575, 2008, 'bmc', 'Examining the significance of fingerprint-based classifiers', 'Luke, Brian T|Collins, Jack R', 'Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick, Inc, NCI-Frederick, Frederick, MD 21702, USA. lukeb@ncifcrf.gov|Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick, Inc, NCI-Frederick, Frederick, MD 21702, USA. lukeb@ncifcrf.gov', 'United States|United States', 2, 'Algorithms|Artificial Intelligence|Biomarkers|Computational Biology|Computer Simulation|Databases, Protein|Decision Trees|Gene Expression Profiling|Humans|Models, Statistical|Models, Theoretical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-9-545', '19091087', 'Experimental examinations of biofluids to measure concentrations of proteins or their fragments or metabolites are being explored as a means of early disease detection, distinguishing diseases with similar symptoms, and drug treatment efficacy. Many studies have produced classifiers with a high sensitivity and specificity, and it has been argued that accurate results necessarily imply some underlying biology-based features in the classifier. The simplest test of this conjecture is to examine datasets designed to contain no information with classifiers used in many published studies.'),
(92576, 2008, 'bmc', 'Barcodes for genomes and applications', 'Zhou, Fengfeng|Olman, Victor|Xu, Ying', 'Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA. ffzhou@csbl.bmb.uga.edu|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA. ffzhou@csbl.bmb.uga.edu|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA. ffzhou@csbl.bmb.uga.edu', 'Georgia|Georgia|Georgia', 0, 'Algorithms|Base Sequence|Computational Biology|Genome|Genomics|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-546', '19091119', 'Each genome has a stable distribution of the combined frequency for each k-mer and its reverse complement measured in sequence fragments as short as 1000 bps across the whole genome, for 1<k<6. The collection of these k-mer frequency distributions is unique to each genome and termed the genomes barcode.'),
(92577, 2008, 'bmc', 'Prediction of regulatory elements in mammalian genomes using chromatin signatures', 'Won, Kyoung-Jae|Chepelev, Iouri|Ren, Bing|Wang, Wei', 'Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA. kwon@ucsd.edu|Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA. kwon@ucsd.edu|Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA. kwon@ucsd.edu|Dept of Chemistry & Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0359, USA. kwon@ucsd.edu', 'United States|United States|United States|United States', 0, 'Animals|Chromatin|Computational Biology|Genome|Histones|Humans|Markov Chains|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-9-547', '19094206', 'Recent genomic scale survey of epigenetic states in the mammalian genomes has shown that promoters and enhancers are correlated with distinct chromatin signatures, providing a pragmatic way for systematic mapping of these regulatory elements in the genome. With rapid accumulation of chromatin modification profiles in the genome of various organisms and cell types, this chromatin based approach promises to uncover many new regulatory elements, but computational methods to effectively extract information from these datasets are still limited.'),
(92578, 2008, 'bmc', 'GeneChaser: identifying all biological and clinical conditions in which genes of interest are differentially expressed', 'Chen, Rong|Mallelwar, Rohan|Thosar, Ajit|Venkatasubrahmanyam, Shivkumar|Butte, Atul J', 'Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. rchen1@stanford.edu|Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. rchen1@stanford.edu|Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. rchen1@stanford.edu|Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. rchen1@stanford.edu|Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. rchen1@stanford.edu', 'United States|United States|United States|United States|United States', 32, 'Animals|Computational Biology|Computer Simulation|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Internet|Malaria|Male|Mice|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Spermatozoa', 'Journal Article', '10.1186/1471-2105-9-548', '19094235', 'The amount of gene expression data in the public repositories, such as NCBI Gene Expression Omnibus (GEO) has grown exponentially, and provides a gold mine for bioinformaticians, but has not been easily accessible by biologists and clinicians.'),
(92579, 2008, 'bmc', 'nGASP--the nematode genome annotation assessment project', 'Coghlan, Avril|Fiedler, Tristan J|McKay, Sheldon J|Flicek, Paul|Harris, Todd W|Blasiar, Darin|nGASP Consortium|Stein, Lincoln D', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. alc@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Caenorhabditis|Caenorhabditis elegans|Computational Biology|DNA|Databases, Genetic|Genes, Helminth|Genome, Helminth|Genomics', 'Journal Article', '10.1186/1471-2105-9-549', '19099578', 'While the C. elegans genome is extensively annotated, relatively little information is available for other Caenorhabditis species. The nematode genome annotation assessment project (nGASP) was launched to objectively assess the accuracy of protein-coding gene prediction software in C. elegans, and to apply this knowledge to the annotation of the genomes of four additional Caenorhabditis species and other nematodes. Seventeen groups worldwide participated in nGASP, and submitted 47 prediction sets across 10 Mb of the C. elegans genome. Predictions were compared to reference gene sets consisting of confirmed or manually curated gene models from WormBase.'),
(92580, 2008, 'bmc', 'Pathological rate matrices: from primates to pathogens', 'Schranz, Harold W|Yap, Von Bing|Easteal, Simon|Knight, Rob|Huttley, Gavin A', 'John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia. harold.schranz@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia. harold.schranz@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia. harold.schranz@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia. harold.schranz@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia. harold.schranz@anu.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Animals|Codon|Computational Biology|Evolution, Molecular|Humans|Markov Chains|Primates|Software', 'Journal Article', '10.1186/1471-2105-9-550', '19099591', 'Continuous-time Markov models allow flexible, parametrically succinct descriptions of sequence divergence. Non-reversible forms of these models are more biologically realistic but are challenging to develop. The instantaneous rate matrices defined for these models are typically transformed into substitution probability matrices using a matrix exponentiation algorithm that employs eigendecomposition, but this algorithm has characteristic vulnerabilities that lead to significant errors when a rate matrix possesses certain pathological properties. Here we tested whether pathological rate matrices exist in nature, and consider the suitability of different algorithms to their computation.'),
(92581, 2008, 'bmc', 'Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network', 'Brilli, Matteo|Mengoni, Alessio|Fondi, Marco|Bazzicalupo, Marco|Liò, Pietro|Fani, Renato', 'Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr|Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr|Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr|Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr|Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr|Department of Evolutionary Biology, University of Florence, via Romana 17, I-50125 Florence, Italy. brilli@biomserv.univ-lyon1.fr', 'Italy|Italy|Italy|Italy|Italy|Italy', 32, 'Evolution, Molecular|Gene Regulatory Networks|Gene Transfer, Horizontal|Genes, Bacterial|Models, Biological|Phylogeny|Plasmids|Sequence Homology, Amino Acid|Shigella|Software', 'Journal Article', '10.1186/1471-2105-9-551', '19099604', 'Phylogenetic methods are well-established bioinformatic tools for sequence analysis, allowing to describe the non-independencies of sequences because of their common ancestor. However, the evolutionary profiles of bacterial genes are often complicated by hidden paralogy and extensive and/or (multiple) horizontal gene transfer (HGT) events which make bifurcating trees often inappropriate. In this context, plasmid sequences are paradigms of network-like relationships characterizing the evolution of prokaryotes. Actually, they can be transferred among different organisms allowing the dissemination of novel functions, thus playing a pivotal role in prokaryotic evolution. However, the study of their evolutionary dynamics is complicated by the absence of universally shared genes, a prerequisite for phylogenetic analyses.'),
(92582, 2008, 'bmc', 'GeneTrailExpress: a web-based pipeline for the statistical evaluation of microarray experiments', 'Keller, Andreas|Backes, Christina|Al-Awadhi, Maher|Gerasch, Andreas|Küntzer, Jan|Kohlbacher, Oliver|Kaufmann, Michael|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Saarbrücken, Germany. ack@bioinf.uni-sb.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 66, 'Computer Graphics|Data Interpretation, Statistical|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-552', '19099609', 'High-throughput methods that allow for measuring the expression of thousands of genes or proteins simultaneously have opened new avenues for studying biochemical processes. While the noisiness of the data necessitates an extensive pre-processing of the raw data, the high dimensionality requires effective statistical analysis methods that facilitate the identification of crucial biological features and relations. For these reasons, the evaluation and interpretation of expression data is a complex, labor-intensive multi-step process. While a variety of tools for normalizing, analysing, or visualizing expression profiles has been developed in the last years, most of these tools offer only functionality for accomplishing certain steps of the evaluation pipeline.'),
(92583, 2008, 'bmc', 'Prediction of protein-protein binding site by using core interface residue and support vector machine', 'Li, Nan|Sun, Zhonghua|Jiang, Fan', 'Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, PR China. nli_2008@yahoo.com|Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, PR China. nli_2008@yahoo.com|Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, PR China. nli_2008@yahoo.com', 'China|China|China', 0, 'Amino Acid Sequence|Binding Sites|Models, Genetic|Protein Binding|Proteins|Proteomics|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-9-553', '19102736', 'The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.'),
(92584, 2008, 'bmc', 'ProfileGrids as a new visual representation of large multiple sequence alignments: a case study of the RecA protein family', 'Roca, Alberto I|Almada, Albert E|Abajian, Aaron C', 'Department of Molecular Biology and Biochemistry, 560 Steinhaus Hall, University of California, Irvine, California 92697-3900, USA. aroca@uci.edu|Department of Molecular Biology and Biochemistry, 560 Steinhaus Hall, University of California, Irvine, California 92697-3900, USA. aroca@uci.edu|Department of Molecular Biology and Biochemistry, 560 Steinhaus Hall, University of California, Irvine, California 92697-3900, USA. aroca@uci.edu', 'United States|United States|United States', 7, 'Amino Acid Sequence|Bacterial Proteins|Molecular Sequence Data|Multigene Family|Rec A Recombinases|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1186/1471-2105-9-554', '19102758', 'Multiple sequence alignments are a fundamental tool for the comparative analysis of proteins and nucleic acids. However, large data sets are no longer manageable for visualization and investigation using the traditional stacked sequence alignment representation.'),
(92585, 2008, 'bmc', 'RPPAML/RIMS: a metadata format and an information management system for reverse phase protein arrays', 'Stanislaus, Romesh|Carey, Mark|Deus, Helena F|Coombes, Kevin|Hennessy, Bryan T|Mills, Gordon B|Almeida, Jonas S', 'Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org|Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. rstanisl@mdanderson.org', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Database Management Systems|Databases, Protein|Programming Languages|Protein Array Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-555', '19102773', 'Reverse Phase Protein Arrays (RPPA) are convenient assay platforms to investigate the presence of biomarkers in tissue lysates. As with other high-throughput technologies, substantial amounts of analytical data are generated. Over 1,000 samples may be printed on a single nitrocellulose slide. Up to 100 different proteins may be assessed using immunoperoxidase or immunoflorescence techniques in order to determine relative amounts of protein expression in the samples of interest.'),
(92586, 2008, 'bmc', 'The meaning of alignment: lessons from structural diversity', 'Pirovano, Walter|Feenstra, K Anton|Heringa, Jaap', 'Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081HV Amsterdam, the Netherlands. pirovano@few.vu.nl|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081HV Amsterdam, the Netherlands. pirovano@few.vu.nl|Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081HV Amsterdam, the Netherlands. pirovano@few.vu.nl', 'Netherlands|Netherlands|Netherlands', 11, 'Algorithms|Amino Acid Sequence|Computational Biology|Computer Simulation|Crystallization|Crystallography, X-Ray|Databases, Protein|Models, Molecular|Molecular Conformation|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-9-556', '19105835', 'Protein structural alignment provides a fundamental basis for deriving principles of functional and evolutionary relationships. It is routinely used for structural classification and functional characterization of proteins and for the construction of sequence alignment benchmarks. However, the available techniques do not fully consider the implications of protein structural diversity and typically generate a single alignment between sequences.'),
(92587, 2008, 'bmc', 'SimHap GUI: an intuitive graphical user interface for genetic association analysis', 'Carter, Kim W|McCaskie, Pamela A|Palmer, Lyle J', 'Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Perth, Australia. kcarter@ichr.uwa.edu.au|Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Perth, Australia. kcarter@ichr.uwa.edu.au|Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Perth, Australia. kcarter@ichr.uwa.edu.au', 'Australia|Australia|Australia', 34, 'Algorithms|Computer Graphics|Genome-Wide Association Study|Haplotypes|Polymorphism, Single Nucleotide|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-557', '19109877', 'Researchers wishing to conduct genetic association analysis involving single nucleotide polymorphisms (SNPs) or haplotypes are often confronted with the lack of user-friendly graphical analysis tools, requiring sophisticated statistical and informatics expertise to perform relatively straightforward tasks. Tools, such as the SimHap package for the R statistics language, provide the necessary statistical operations to conduct sophisticated genetic analysis, but lacks a graphical user interface that allows anyone but a professional statistician to effectively utilise the tool.'),
(92588, 2008, 'bmc', 'SPRINT: a new parallel framework for R', 'Hill, Jon|Hambley, Matthew|Forster, Thorsten|Mewissen, Muriel|Sloan, Terence M|Scharinger, Florian|Trew, Arthur|Ghazal, Peter', 'EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk|EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computing Methodologies|Databases, Genetic|Gene Expression Profiling|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-558', '19114001', 'Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest.'),
(92589, 2008, 'bmc', 'WGCNA: an R package for weighted correlation network analysis', 'Langfelder, Peter|Horvath, Steve', 'Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA. Peter.Langfelder@gmail.com|Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA. Peter.Langfelder@gmail.com', 'United States|United States', 4739, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computing Methodologies|Databases, Genetic|Gene Expression Profiling|Humans|Mice|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-9-559', '19114008', 'Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.'),
(92590, 2009, 'bmc', 'Regularized gene selection in cancer microarray meta-analysis', 'Ma, Shuangge|Huang, Jian', 'Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States', 43, 'Algorithms|Bayes Theorem|Biomarkers, Tumor|Cluster Analysis|Computational Biology|Computer Simulation|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Liver Neoplasms|Male|Neoplasms|Oligonucleotide Array Sequence Analysis|Pancreatic Neoplasms', 'Journal Article', '10.1186/1471-2105-10-1', '19118496', 'In cancer studies, it is common that multiple microarray experiments are conducted to measure the same clinical outcome and expressions of the same set of genes. An important goal of such experiments is to identify a subset of genes that can potentially serve as predictive markers for cancer development and progression. Analyses of individual experiments may lead to unreliable gene selection results because of the small sample sizes. Meta analysis can be used to pool multiple experiments, increase statistical power, and achieve more reliable gene selection. The meta analysis of cancer microarray data is challenging because of the high dimensionality of gene expressions and the differences in experimental settings amongst different experiments.'),
(92591, 2009, 'bmc', 'GenomeGraphs: integrated genomic data visualization with R', 'Durinck, Steffen|Bullard, James|Spellman, Paul T|Dudoit, Sandrine', 'Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu|Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu|Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu|Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu', 'United States|United States|United States|United States', 49, 'Algorithms|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Exons|Genome|Genomics|Humans|Models, Statistical|Programming Languages|Protein Isoforms|Software|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-2', '19123956', 'Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92592, 2009, 'bmc', 'Thermodynamic scaling behavior in genechips', 'Ferrantini, Alessandro|Allemeersch, Joke|Van Hummelen, Paul|Carlon, Enrico', 'Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200D, B-3000 Leuven, Belgium. alessandro.ferrantini@fys.kuleuven.be|Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200D, B-3000 Leuven, Belgium. alessandro.ferrantini@fys.kuleuven.be|Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200D, B-3000 Leuven, Belgium. alessandro.ferrantini@fys.kuleuven.be|Institute for Theoretical Physics, Katholieke Universiteit Leuven, Celestijnenlaan 200D, B-3000 Leuven, Belgium. alessandro.ferrantini@fys.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium', 5, 'Algorithms|Arabidopsis|Base Pair Mismatch|DNA|Gene Expression Profiling|Humans|Models, Chemical|Nucleic Acid Hybridization|Nucleotides|Oligonucleotide Array Sequence Analysis|RNA|Temperature|Thermodynamics|Time Factors', 'Journal Article', '10.1186/1471-2105-10-3', '19123958', 'Affymetrix Genechips are characterized by probe pairs, a perfect match (PM) and a mismatch (MM) probe differing by a single nucleotide. Most of the data preprocessing algorithms neglect MM signals, as it was shown that MMs cannot be used as estimators of the non-specific hybridization as originally proposed by Affymetrix. The aim of this paper is to study in detail on a large number of experiments the behavior of the average PM/MM ratio. This is taken as an indicator of the quality of the hybridization and, when compared between different chip series, of the quality of the chip design.'),
(92593, 2009, 'bmc', 'Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis', 'Yang, Chao|He, Zengyou|Yu, Weichuan', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China. yorkey@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China. yorkey@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, PR China. yorkey@ust.hk', 'China|China|China', 161, 'Algorithms|Artificial Intelligence|Computational Biology|Data Interpretation, Statistical|Humans|Mass Spectrometry|Models, Statistical|Normal Distribution|Pattern Recognition, Automated|Peptides|Proteomics|Signal Processing, Computer-Assisted|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Statistics as Topic', 'Comparative Study', '10.1186/1471-2105-10-4', '19126200', 'In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods.'),
(92594, 2009, 'bmc', 'Bayesian optimal discovery procedure for simultaneous significance testing', 'Cao, Jing|Xie, Xian-Jin|Zhang, Song|Whitehurst, Angelique|White, Michael A', 'Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu|Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu|Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu|Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu|Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. jcao@smu.edu', 'United States|United States|United States|United States|United States', 18, 'Algorithms|Bayes Theorem|Computational Biology|Computer Simulation|Databases, Factual|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Models, Theoretical|Pattern Recognition, Automated|Programming Languages|RNA Interference|ROC Curve|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-5', '19126217', 'In high throughput screening, such as differential gene expression screening, drug sensitivity screening, and genome-wide RNAi screening, tens of thousands of tests need to be conducted simultaneously. However, the number of replicate measurements per test is extremely small, rarely exceeding 3. Several current approaches demonstrate that test statistics with shrinking variance estimates have more power over the traditional t statistic.'),
(92595, 2009, 'bmc', 'Automated alphabet reduction for protein datasets', 'Bacardit, Jaume|Stout, Michael|Hirst, Jonathan D|Valencia, Alfonso|Smith, Robert E|Krasnogor, Natalio', 'ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk|ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk|ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk|ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk|ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk|ASAP research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK. jaume.bacardit@nottingham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 45, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Computer Simulation|Databases, Protein|Humans|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-6', '19126227', 'We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques.'),
(92596, 2009, 'bmc', 'ContDist: a tool for the analysis of quantitative gene and promoter properties', 'Hackenberg, Michael|Lasso, Gorka|Matthiesen, Rune', 'Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, Spain. mlhack@gmail.com|Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, Spain. mlhack@gmail.com|Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, Spain. mlhack@gmail.com', 'Spain|Spain|Spain', 3, 'Algorithms|Animals|Chromatin|Computational Biology|CpG Islands|DNA|DNA Methylation|Epigenesis, Genetic|Genes|Humans|Mice|Models, Genetic|Promoter Regions, Genetic|Rats|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-10-7', '19128472', 'The understanding of how promoter regions regulate gene expression is complicated and far from being fully understood. It is known that histones regulation of DNA compactness, DNA methylation, transcription factor binding sites and CpG islands play a role in the transcriptional regulation of a gene. Many high-throughput techniques exist nowadays which permit the detection of epigenetic marks and regulatory elements in the promoter regions of thousands of genes. However, so far the subsequent analysis of such experiments (e.g. the resulting gene lists) have been hampered by the fact that currently no tool exists for a detailed analysis of the promoter regions.'),
(92597, 2009, 'bmc', 'Large-scale prediction of long disordered regions in proteins using random forests', 'Han, Pengfei|Zhang, Xiuzhen|Norton, Raymond S|Feng, Zhi-Ping', 'The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia. pengfei.han@rmit.edu.au|The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia. pengfei.han@rmit.edu.au|The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia. pengfei.han@rmit.edu.au|The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia. pengfei.han@rmit.edu.au', 'Australia|Australia|Australia|Australia', 34, 'Algorithms|Amino Acids|Computational Biology|Databases, Protein|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-8', '19128505', 'Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies.'),
(92598, 2009, 'bmc', 'TCP: a tool for designing chimera proteins based on the tertiary structure information', 'Yoneya, Takashi|Nishida, Reina', 'Research Planning and Administration Department, Kyowa Hakko Kirin Co., Ltd., Gunma, Japan. takashi.yoneya@kyowa-kirin.co.jp|Research Planning and Administration Department, Kyowa Hakko Kirin Co., Ltd., Gunma, Japan. takashi.yoneya@kyowa-kirin.co.jp', 'Japan|Japan', 0, 'Algorithms|Amino Acid Sequence|Animals|Antibodies, Monoclonal|Computational Biology|Epitopes|Humans|Internet|Molecular Conformation|Molecular Sequence Data|Protein Structure, Tertiary|Recombinant Fusion Proteins|Sequence Homology, Amino Acid|Software|Solvents', 'Journal Article', '10.1186/1471-2105-10-9', '19128508', 'Chimera proteins are widely used for the analysis of the protein-protein interaction region. One of the major issues is the epitope analysis of the monoclonal antibody. In the analysis, a continuous portion of an antigen is sequentially substituted into a different sequence. This method works well for an antibody recognizing a linear epitope, but not for that recognizing a discontinuous epitope. Although the designing the chimera proteins based on the tertiary structure information is required in such situations, there is no appropriate tool so far.'),
(92599, 2009, 'bmc', 'Bayesian DNA copy number analysis', 'Rancoita, Paola M V|Hutter, Marcus|Bertoni, Francesco|Kwee, Ivo', 'Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland. paola@idsia.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 27, 'Algorithms|Bayes Theorem|Chromosome Aberrations|DNA|Gene Dosage', 'Journal Article', '10.1186/1471-2105-10-10', '19133123', 'Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments.'),
(92600, 2009, 'bmc', 'Filtering for increased power for microarray data analysis', 'Hackstadt, Amber J|Hess, Ann M', 'Center for Bioinformatics and Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA. hackstad@stat.colostate.edu|Center for Bioinformatics and Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA. hackstad@stat.colostate.edu', 'United States|United States', 133, 'Animals|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Genome|Genome, Plant|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-11', '19133141', 'Due to the large number of hypothesis tests performed during the process of routine analysis of microarray data, a multiple testing adjustment is certainly warranted. However, when the number of tests is very large and the proportion of differentially expressed genes is relatively low, the use of a multiple testing adjustment can result in very low power to detect those genes which are truly differentially expressed. Filtering allows for a reduction in the number of tests and a corresponding increase in power. Common filtering methods include filtering by variance, average signal or MAS detection call (for Affymetrix arrays). We study the effects of filtering in combination with the Benjamini-Hochberg method for false discovery rate control and q-value for false discovery rate estimation.'),
(92601, 2009, 'bmc', 'Estimation of tumor heterogeneity using CGH array data', 'Wang, Kai|Li, Jian|Li, Shengting|Bolund, Lars|Wiuf, Carsten', 'Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. wangk@humgen.au.dk|Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. wangk@humgen.au.dk|Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. wangk@humgen.au.dk|Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. wangk@humgen.au.dk|Institute of Human Genetics, University of Aarhus, Aarhus, Denmark. wangk@humgen.au.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 12, 'Cell Line, Tumor|Comparative Genomic Hybridization|Computational Biology|Gene Dosage|Gene Expression Profiling|Genetic Heterogeneity|Humans|Neoplasms|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-12', '19134174', 'Array-based comparative genomic hybridization (CGH) is a commonly-used approach to detect DNA copy number variation in whole genome-wide screens. Several statistical methods have been proposed to define genomic segments with different copy numbers in cancer tumors. However, most tumors are heterogeneous and show variation in DNA copy numbers across tumor cells. The challenge is to reveal the copy number profiles of the subpopulations in a tumor and to estimate the percentage of each subpopulation.'),
(92602, 2009, 'bmc', 'MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study', 'Wan, Xiang|Yang, Can|Yang, Qiang|Xue, Hong|Tang, Nelson L S|Yu, Weichuan', 'Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, PR China. eexiangw@ust.hk', 'China|China|China|China|China|China', 48, 'Algorithms|Arthritis, Rheumatoid|Artificial Intelligence|Chi-Square Distribution|Computational Biology|Computer Simulation|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Parkinson Disease|Polymorphism, Single Nucleotide|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-13', '19134182', 'The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individuals susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.'),
(92603, 2009, 'bmc', 'MBA: a literature mining system for extracting biomedical abbreviations', 'Xu, Yun|Wang, ZhiHao|Lei, YiMing|Zhao, YuZhong|Xue, Yu', 'Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn|Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn|Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn|Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn|Department of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui, PR China. xuyun@ustc.edu.cn', 'China|China|China|China|China', 16, 'Abbreviations as Topic|Abstracting and Indexing as Topic|Algorithms|Databases, Bibliographic|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-10-14', '19134199', 'The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.'),
(92604, 2009, 'bmc', 'Robust discovery of periodically expressed genes using the laplace periodogram', 'Liang, Kuo-ching|Wang, Xiaodong|Li, Ta-Hsin', 'Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. kl2171@columbia.edu|Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. kl2171@columbia.edu|Department of Electrical Engineering, Columbia University, New York, NY 10027, USA. kl2171@columbia.edu', 'United States|United States|United States', 9, 'Algorithms|Arabidopsis|Gene Expression|Gene Expression Profiling|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-15', '19134222', 'Time-course gene expression analysis has become important in recent developments due to the increasingly available experimental data. The detection of genes that are periodically expressed is an important step which allows us to study the regulatory mechanisms associated with the cell cycle.'),
(92605, 2009, 'bmc', 'PreP+07: improvements of a user friendly tool to preprocess and analyse microarray data', 'Martin-Requena, Victoria|Muñoz-Merida, Antonio|Claros, M Gonzalo|Trelles, Oswaldo', 'Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es|Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es|Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es|Computer Architecture Department, University of Málaga, Málaga, Spain. vickymr@ac.uma.es', 'Spain|Spain|Spain|Spain', 7, 'Computational Biology|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-10-16', '19134227', 'Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way.'),
(92606, 2009, 'bmc', 'Predicting genetic interactions with random walks on biological networks', 'Chipman, Kyle C|Singh, Ambuj K', 'Biomolecular Science and Engineering Program, UC Santa Barbara, Santa Barbara, CA, USA. chipman@lifesci.ucsb.edu|Biomolecular Science and Engineering Program, UC Santa Barbara, Santa Barbara, CA, USA. chipman@lifesci.ucsb.edu', 'United States|United States', 46, 'Animals|Caenorhabditis elegans|Computational Biology|Gene Knockout Techniques|Gene Regulatory Networks|Genes, Essential|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-17', '19138426', 'Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.'),
(92607, 2009, 'bmc', 'Incorporating pathway information into boosting estimation of high-dimensional risk prediction models', 'Binder, Harald|Schumacher, Martin', 'Department of Medical Biometry and Statistics, University Medical Center Freiburg, Freiburg, Germany. binderh@fdm.uni-freiburg.de|Department of Medical Biometry and Statistics, University Medical Center Freiburg, Freiburg, Germany. binderh@fdm.uni-freiburg.de', 'Germany|Germany', 52, 'Algorithms|Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Proportional Hazards Models|Regression Analysis', 'Journal Article', '10.1186/1471-2105-10-18', '19144132', 'There are several techniques for fitting risk prediction models to high-dimensional data, arising from microarrays. However, the biological knowledge about relations between genes is only rarely taken into account. One recent approach incorporates pathway information, available, e.g., from the KEGG database, by augmenting the penalty term in Lasso estimation for continuous response models.'),
(92608, 2009, 'bmc', 'BFL: a node and edge betweenness based fast layout algorithm for large scale networks', 'Hashimoto, Tatsunori B|Nagasaki, Masao|Kojima, Kaname|Miyano, Satoru', 'Harvard College, Adams House, Cambridge, Massachusetts, MA 02138, USA. thashim@fas.harvard.edu|Harvard College, Adams House, Cambridge, Massachusetts, MA 02138, USA. thashim@fas.harvard.edu|Harvard College, Adams House, Cambridge, Massachusetts, MA 02138, USA. thashim@fas.harvard.edu|Harvard College, Adams House, Cambridge, Massachusetts, MA 02138, USA. thashim@fas.harvard.edu', 'United States|United States|United States|United States', 11, 'Algorithms|Computer Graphics|Metabolic Networks and Pathways|Pattern Recognition, Automated|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-19', '19146673', 'Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements.'),
(92609, 2009, 'bmc', 'Detecting intergene correlation changes in microarray analysis: a new approach to gene selection', 'Hu, Rui|Qiu, Xing|Glazko, Galina|Klebanov, Lev|Yakovlev, Andrei', 'Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu', 'United States|United States|United States|United States|United States', 47, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-20', '19146700', 'Microarray technology is commonly used as a simple screening tool with a focus on selecting genes that exhibit extremely large differential expressions between different phenotypes. It lacks the ability to select genes that change their relationships with other genes in different biological conditions (differentially correlated genes). We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes.'),
(92610, 2009, 'bmc', 'Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis', 'Frech, Christian|Kommenda, Michael|Dorfer, Viktoria|Kern, Thomas|Hintner, Helmut|Bauer, Johann W|Onder, Kamil', 'Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com|Upper Austria University of Applied Sciences, Hagenberg, Austria. frech.christian@gmail.com', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 6, 'Computational Biology|Databases, Protein|Evolution, Molecular|Gene Duplication|Genetic Variation|Protein Interaction Mapping|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/1471-2105-10-21', '19152684', 'Protein-protein interaction (PPI) data sets generated by high-throughput experiments are contaminated by large numbers of erroneous PPIs. Therefore, computational methods for PPI validation are necessary to improve the quality of such data sets. Against the background of the theory that most extant PPIs arose as a consequence of gene duplication, the sensitive search for homologous PPIs, i.e. for PPIs descending from a common ancestral PPI, should be a successful strategy for PPI validation.'),
(92611, 2009, 'bmc', 'Prediction of nuclear proteins using SVM and HMM models', 'Kumar, Manish|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India.', 'India|India', 28, 'Artificial Intelligence|Databases, Protein|Internet|Markov Chains|Nuclear Proteins|Protein Structure, Tertiary|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-22', '19152693', 'The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.'),
(92612, 2009, 'bmc', 'Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from ChIP-chip data', 'Datta, Debayan|Zhao, Hongyu', 'Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. debayan.datta@yale.edu|Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. debayan.datta@yale.edu', 'United States|United States', 1, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Computer Simulation|False Negative Reactions|False Positive Reactions|Genome|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-23', '19152696', 'ChIP-chip data are routinely used to identify transcription factor binding targets. However, the presence of false positives and false negatives in ChIP-chip data complicates and hinders analyses, especially when the binding targets for a specific transcription factor are compared across conditions or species.'),
(92613, 2009, 'bmc', 'An innovative approach for testing bioinformatics programs using metamorphic testing', 'Chen, Tsong Yueh|Ho, Joshua W K|Liu, Huai|Xie, Xiaoyuan', 'School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. tychen@swin.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. tychen@swin.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. tychen@swin.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. tychen@swin.edu.au', 'Australia|Australia|Australia|Australia', 89, 'Computational Biology|Computer-Assisted Instruction|Software', 'Journal Article', '10.1186/1471-2105-10-24', '19152705', 'Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguided downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.'),
(92614, 2009, 'bmc', 'Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology', 'Brown, J B|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan. jbbrown@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, 611-0011, Japan. jbbrown@kuicr.kyoto-u.ac.jp', 'Japan|Japan', 12, 'Algorithms|Amino Acid Sequence|Computational Biology|DNA Repair|Molecular Sequence Data|Nuclear Proteins|Protein Structure, Secondary|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-10-25', '19154573', 'DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic detection of DNA repair proteins in genomes via informatics techniques is desirable, however, there are many forms of DNA repair and it is not a straightforward process to identify and classify repair proteins with a single optimal method. We perform a study of the ability of homology and machine learning-based methods to identify and classify DNA repair proteins, as well as scan vertebrate genomes for the presence of novel repair proteins. Combinations of primary sequence polypeptide frequency, secondary structure, and homology information are used as feature information for input to a Support Vector Machine (SVM).'),
(92615, 2009, 'bmc', 'Identifying differential exon splicing using linear models and correlation coefficients', 'Shah, Sonia H|Pallas, Jacqueline A', 'Wolfson Institute of Biomedical Research, Department of Computer Science, University College London, Gower Street, London, UK. sonia.shah@cs.ucl.ac.uk|Wolfson Institute of Biomedical Research, Department of Computer Science, University College London, Gower Street, London, UK. sonia.shah@cs.ucl.ac.uk', 'United Kingdom|United Kingdom', 0, 'Alternative Splicing|Databases, Protein|Exons|Linear Models|RNA Splicing|Software', 'Journal Article', '10.1186/1471-2105-10-26', '19154578', 'With the availability of the Affymetrix exon arrays a number of tools have been developed to enable the analysis. These however can be expensive or have several pre-installation requirements. This led us to develop an analysis workflow for analysing differential splicing using freely available software packages that are already being widely used for gene expression analysis. The workflow uses the packages in the standard installation of R and Bioconductor (BiocLite) to identify differential splicing. We use the splice index method with the LIMMA framework. The main drawback with this approach is that it relies on accurate estimates of gene expression from the probe-level data. Methods such as RMA and PLIER may misestimate when a large proportion of exons are spliced. We therefore present the novel concept of a gene correlation coefficient calculated using only the probeset expression pattern within a gene. We show that genes with lower correlation coefficients are likely to be differentially spliced.'),
(92616, 2009, 'bmc', 'Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes', 'Maulik, Ujjwal|Mukhopadhyay, Anirban|Bandyopadhyay, Sanghamitra', 'Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. drumaulik@cse.jdvu.ac.in|Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. drumaulik@cse.jdvu.ac.in|Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. drumaulik@cse.jdvu.ac.in', 'India|India|India', 0, 'Algorithms|Animals|Cluster Analysis|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-27', '19154590', 'The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a Support Vector Machine (SVM) classifier for this purpose.'),
(92617, 2009, 'bmc', 'Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy', 'Alexopoulou, Dimitra|Andreopoulos, Bill|Dietze, Heiko|Doms, Andreas|Gandon, Fabien|Hakenberg, Jörg|Khelif, Khaled|Schroeder, Michael|Wächter, Thomas', 'Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. dimitra.alexopoulou@boitec.tu-dresden.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 22, 'Algorithms|Computational Biology|Information Storage and Retrieval|Medical Informatics|Medical Subject Headings|Pattern Recognition, Automated|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-28', '19159460', 'Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.'),
(92618, 2009, 'bmc', 'Using least median of squares for structural superposition of flexible proteins', 'Liu, Yu-Shen|Fang, Yi|Ramani, Karthik', 'Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com|Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com|Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Databases, Protein|Least-Squares Analysis|Models, Molecular|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-10-29', '19159484', 'The conventional superposition methods use an ordinary least squares (LS) fit for structural comparison of two different conformations of the same protein. The main problem of the LS fit that it is sensitive to outliers, i.e. large displacements of the original structures superimposed.'),
(92619, 2009, 'bmc', 'ArrayIDer: automated structural re-annotation pipeline for DNA microarrays', 'van den Berg, Bart Hj|Konieczka, Jay H|McCarthy, Fiona M|Burgess, Shane C', 'Department of Basic Science, PO Box 6100, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762,USA. bvandenberg@cvm.msstate.edu|Department of Basic Science, PO Box 6100, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762,USA. bvandenberg@cvm.msstate.edu|Department of Basic Science, PO Box 6100, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762,USA. bvandenberg@cvm.msstate.edu|Department of Basic Science, PO Box 6100, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762,USA. bvandenberg@cvm.msstate.edu', 'United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Expressed Sequence Tags|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-30', '19166590', 'Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (ArrayIDer) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays.'),
(92620, 2009, 'bmc', 'Genome Projector: zoomable genome map with multiple views', 'Arakawa, Kazuharu|Tamaki, Satoshi|Kono, Nobuaki|Kido, Nobuhiro|Ikegami, Keita|Ogawa, Ryu|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan. gaou@sfc.keio.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Databases, Genetic|Genome|Genomics|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-31', '19166610', 'Molecular biology data exist on diverse scales, from the level of molecules to -omics. At the same time, the data at each scale can be categorised into multiple layers, such as the genome, transcriptome, proteome, metabolome, and biochemical pathways. Due to the highly multi-layer and multi-dimensional nature of biological information, software interfaces for database browsing should provide an intuitive interface that allows for rapid migration across different views and scales. The Zoomable User Interface (ZUI) and tabbed browsing have proven successful for this purpose in other areas, especially to navigate the vast information in the World Wide Web.'),
(92621, 2009, 'bmc', 'VASCo: computation and visualization of annotated protein surface contacts', 'Steinkellner, Georg|Rader, Robert|Thallinger, Gerhard G|Kratky, Christoph|Gruber, Karl', 'Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, Graz, Austria. georg.steinkellner@uni-graz.at|Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, Graz, Austria. georg.steinkellner@uni-graz.at|Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, Graz, Austria. georg.steinkellner@uni-graz.at|Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, Graz, Austria. georg.steinkellner@uni-graz.at|Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50/3, Graz, Austria. georg.steinkellner@uni-graz.at', 'Austria|Austria|Austria|Austria|Austria', 0, 'Binding Sites|Databases, Protein|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-32', '19166624', 'Structural data from crystallographic analyses contain a vast amount of information on protein-protein contacts. Knowledge on protein-protein interactions is essential for understanding many processes in living cells. The methods to investigate these interactions range from genetics to biophysics, crystallography, bioinformatics and computer modeling. Also crystal contact information can be useful to understand biologically relevant protein oligomerisation as they rely in principle on the same physico-chemical interaction forces. Visualization of crystal and biological contact data including different surface properties can help to analyse protein-protein interactions.'),
(92622, 2009, 'bmc', 'Evaluating the impact of scoring parameters on the structure of intra-specific genetic variation using RawGeno, an R package for automating AFLP scoring', 'Arrigo, Nils|Tuszynski, Jarek W|Ehrich, Dorothee|Gerdes, Tommy|Alvarez, Nadir', 'Laboratory of Evolutionary Botany, Institute of Biology, University of Neuchâtel, 11 rue Emile-Argand, CH-2000 Neuchâtel, Switzerland. nils.arrigo@unine.ch|Laboratory of Evolutionary Botany, Institute of Biology, University of Neuchâtel, 11 rue Emile-Argand, CH-2000 Neuchâtel, Switzerland. nils.arrigo@unine.ch|Laboratory of Evolutionary Botany, Institute of Biology, University of Neuchâtel, 11 rue Emile-Argand, CH-2000 Neuchâtel, Switzerland. nils.arrigo@unine.ch|Laboratory of Evolutionary Botany, Institute of Biology, University of Neuchâtel, 11 rue Emile-Argand, CH-2000 Neuchâtel, Switzerland. nils.arrigo@unine.ch|Laboratory of Evolutionary Botany, Institute of Biology, University of Neuchâtel, 11 rue Emile-Argand, CH-2000 Neuchâtel, Switzerland. nils.arrigo@unine.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 129, 'Algorithms|Amplified Fragment Length Polymorphism Analysis|Computational Biology|Genetic Variation|Heterozygote|Software', 'Journal Article', '10.1186/1471-2105-10-33', '19171029', 'Since the transfer and application of modern sequencing technologies to the analysis of amplified fragment-length polymorphisms (AFLP), evolutionary biologists have included an increasing number of samples and markers in their studies. Although justified in this context, the use of automated scoring procedures may result in technical biases that weaken the power and reliability of further analyses.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92623, 2009, 'bmc', 'Sparse canonical methods for biological data integration: application to a cross-platform study', 'Lê Cao, Kim-Anh|Martin, Pascal G P|Robert-Granié, Christèle|Besse, Philippe', 'Station dAmélioration Génétique des Animaux UR 631, Institut National de Recherche Agronomique, F-31326 Castanet, France. k.lecao@imb.uq.edu.au|Station dAmélioration Génétique des Animaux UR 631, Institut National de Recherche Agronomique, F-31326 Castanet, France. k.lecao@imb.uq.edu.au|Station dAmélioration Génétique des Animaux UR 631, Institut National de Recherche Agronomique, F-31326 Castanet, France. k.lecao@imb.uq.edu.au|Station dAmélioration Génétique des Animaux UR 631, Institut National de Recherche Agronomique, F-31326 Castanet, France. k.lecao@imb.uq.edu.au', 'France|France|France|France', 0, 'Computational Biology|Genomics|Metabolomics|Proteomics|Systems Biology', 'Comparative Study', '10.1186/1471-2105-10-34', '19171069', 'In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.'),
(92624, 2009, 'bmc', 'The Genome Reverse Compiler: an explorative annotation tool', 'Warren, Andrew S|Setubal, João Carlos', 'Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu', 'United States|United States', 9, 'Database Management Systems|Databases, Genetic|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-35', '19173744', 'As sequencing costs have decreased, whole genome sequencing has become a viable and integral part of biological laboratory research. However, the tools with which genes can be found and functionally characterized have not been readily adapted to be part of the everyday biological sciences toolkit. Most annotation pipelines remain as a service provided by large institutions or come as an unwieldy conglomerate of independent components, each requiring their own setup and maintenance.'),
(92625, 2009, 'bmc', 'Prediction of protein-protein interaction types using association rule based classification', 'Park, Sung Hee|Reyes, José A|Gilbert, David R|Kim, Ji Woong|Kim, Sangsoo', 'Department of Bioinformatics & Life Science, Soongsil University, Seoul, Korea. shpark@ssu.ac.kr|Department of Bioinformatics & Life Science, Soongsil University, Seoul, Korea. shpark@ssu.ac.kr|Department of Bioinformatics & Life Science, Soongsil University, Seoul, Korea. shpark@ssu.ac.kr|Department of Bioinformatics & Life Science, Soongsil University, Seoul, Korea. shpark@ssu.ac.kr|Department of Bioinformatics & Life Science, Soongsil University, Seoul, Korea. shpark@ssu.ac.kr', '-|-|-|-|-', 44, 'Algorithms|Animals|Databases, Protein|Humans|Multiprotein Complexes|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-10-36', '19173748', 'Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches.'),
(92626, 2009, 'bmc', 'DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data', 'Glez-Peña, Daniel|Alvarez, Rodrigo|Díaz, Fernando|Fdez-Riverola, Florentino', 'Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain. dgpena@uvigo.es|Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain. dgpena@uvigo.es|Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain. dgpena@uvigo.es|Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain. dgpena@uvigo.es', 'Spain|Spain|Spain|Spain', 0, 'Fuzzy Logic|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-37', '19178723', 'Expression profiling assays done by using DNA microarray technology generate enormous data sets that are not amenable to simple analysis. The greatest challenge in maximizing the use of this huge amount of data is to develop algorithms to interpret and interconnect results from different genes under different conditions. In this context, fuzzy logic can provide a systematic and unbiased way to both (i) find biologically significant insights relating to meaningful genes, thereby removing the need for expert knowledge in preliminary steps of microarray data analyses and (ii) reduce the cost and complexity of later applied machine learning techniques being able to achieve interpretable models.'),
(92627, 2009, 'bmc', 'On reliable discovery of molecular signatures', 'Nilsson, Roland|Björkegren, Johan|Tegnér, Jesper', 'Computational Biology, Department of Physics, Linköping University, SE58183 Linköping, Sweden. rnilsson@broad.mit.edu|Computational Biology, Department of Physics, Linköping University, SE58183 Linköping, Sweden. rnilsson@broad.mit.edu|Computational Biology, Department of Physics, Linköping University, SE58183 Linköping, Sweden. rnilsson@broad.mit.edu', 'Sweden|Sweden|Sweden', 0, 'Algorithms|Biomarkers, Tumor|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|False Positive Reactions|Gene Expression Profiling|Genetic Predisposition to Disease|Genome|Humans|Phenotype', 'Journal Article', '10.1186/1471-2105-10-38', '19178740', 'Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR) in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability.'),
(92628, 2009, 'bmc', 'Protein domain organisation: adding order', 'Kummerfeld, Sarah K|Teichmann, Sarah A', 'Department of Developmental Biology, 279 Campus Dr, Stanford, 94305, CA, USA. sarahkk@gene.com|Department of Developmental Biology, 279 Campus Dr, Stanford, 94305, CA, USA. sarahkk@gene.com', 'United States|United States', 0, 'Algorithms|Animals|Databases, Protein|Evolution, Molecular|Genome|Humans|Phylogeny|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-39', '19178743', 'Domains are the building blocks of proteins. During evolution, they have been duplicated, fused and recombined, to produce proteins with novel structures and functions. Structural and genome-scale studies have shown that pairs or groups of domains observed together in a protein are almost always found in only one N to C terminal order and are the result of a single recombination event that has been propagated by duplication of the multi-domain unit. Previous studies of domain organisation have used graph theory to represent the co-occurrence of domains within proteins. We build on this approach by adding directionality to the graphs and connecting nodes based on their relative order in the protein. Most of the time, the linear order of domains is conserved. However, using the directed graph representation we have identified non-linear features of domain organization that are over-represented in genomes. Recognising these patterns and unravelling how they have arisen may allow us to understand the functional relationships between domains and understand how the protein repertoire has evolved.'),
(92629, 2009, 'bmc', 'NIFTI: an evolutionary approach for finding number of clusters in microarray data', 'Jonnalagadda, Sudhakar|Srinivasan, Rajagopalan', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260 Singapore. sudhakar@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, 119260 Singapore. sudhakar@nus.edu.sg', 'Singapore|Singapore', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Humans|Lymphoma|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-10-40', '19178750', 'Clustering techniques are routinely used in gene expression data analysis to organize the massive data. Clustering techniques arrange a large number of genes or assays into a few clusters while maximizing the intra-cluster similarity and inter-cluster separation. While clustering of genes facilitates learning the functions of un-characterized genes using their association with known genes, clustering of assays reveals the disease stages and subtypes. Many clustering algorithms require the user to specify the number of clusters a priori. A wrong specification of number of clusters generally leads to either failure to detect novel clusters (disease subtypes) or unnecessary splitting of natural clusters.'),
(92630, 2009, 'bmc', 'STAMP: Extensions to the STADEN sequence analysis package for high throughput interactive microsatellite marker design', 'Kraemer, Lars|Beszteri, Bánk|Gäbler-Schwarz, Steffi|Held, Christoph|Leese, Florian|Mayer, Christoph|Pöhlmann, Kevin|Frickenhaus, Stephan', 'Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de|Institut für Klinische Molekularbiologie, Universität Kiel, Arnold-Heller-Str 3, 24105 Kiel, Germany. l.kraemer@ikmb.uni-kiel.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 32, 'Animals|DNA Primers|Databases, Genetic|Drosophila melanogaster|Gastropoda|Genetic Markers|Information Storage and Retrieval|Internet|Mice|Microsatellite Repeats|Minisatellite Repeats|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-41', '19183437', 'Microsatellites (MSs) are DNA markers with high analytical power, which are widely used in population genetics, genetic mapping, and forensic studies. Currently available software solutions for high-throughput MS design (i) have shortcomings in detecting and distinguishing imperfect and perfect MSs, (ii) lack often necessary interactive design steps, and (iii) do not allow for the development of primers for multiplex amplifications. We present a set of new tools implemented as extensions to the STADEN package, which provides the backbone functionality for flexible sequence analysis workflows. The possibility to assemble overlapping reads into unique contigs (provided by the base functionality of the STADEN package) is important to avoid developing redundant markers, a feature missing from most other similar tools.'),
(92631, 2009, 'bmc', 'Selecting control genes for RT-QPCR using public microarray data', 'Popovici, Vlad|Goldstein, Darlene R|Antonov, Janine|Jaggi, Rolf|Delorenzi, Mauro|Wirapati, Pratyaksha', 'Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch|Bioinformatics Core Facility, Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. vlad.popovici@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Breast Neoplasms|Early Detection of Cancer|Female|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-10-42', '19187545', 'Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e.g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones.'),
(92632, 2009, 'bmc', 'An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters', 'Li, Qingbo|Roxas, Bryan Ap', 'Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60607, USA. qkli@uic.edu|Center for Pharmaceutical Biotechnology, College of Pharmacy, University of Illinois at Chicago, Chicago, IL 60607, USA. qkli@uic.edu', 'United States|United States', 15, 'Algorithms|Isotope Labeling|Proteome|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-10-43', '19187558', 'Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.'),
(92633, 2009, 'bmc', 'A new regularized least squares support vector regression for gene selection', 'Chen, Pei-Chun|Huang, Su-Yun|Chen, Wei J|Hsiao, Chuhsing K', '1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China. d93842005@ntu.edu.tw|1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China. d93842005@ntu.edu.tw|1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China. d93842005@ntu.edu.tw|1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China. d93842005@ntu.edu.tw', 'China|China|China|China', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Gene Expression Profiling|Genes, Neoplasm|Humans|Least-Squares Analysis|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-44', '19187562', 'Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes.'),
(92634, 2009, 'bmc', 'Comparison of small n statistical tests of differential expression applied to microarrays', 'Murie, Carl|Woody, Owen|Lee, Anna Y|Nadon, Robert', 'McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A1A4, Canada. carl.murie@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A1A4, Canada. carl.murie@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A1A4, Canada. carl.murie@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 Avenue du Docteur Penfield, Montreal, Quebec H3A1A4, Canada. carl.murie@mail.mcgill.ca', 'Canada|Canada|Canada|Canada', 50, 'Algorithms|Computational Biology|DNA, Complementary|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-10-45', '19192265', 'DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data.'),
(92635, 2009, 'bmc', 'Is searching full text more effective than searching abstracts?', 'Lin, Jimmy', 'National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, USA. jimmylin@umd.edu', 'United States', 0, 'Abstracting and Indexing as Topic|Algorithms|Information Storage and Retrieval|MEDLINE|Terminology as Topic', 'Comparative Study', '10.1186/1471-2105-10-46', '19192280', 'With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine.'),
(92636, 2009, 'bmc', 'A general modular framework for gene set enrichment analysis', 'Ackermann, Marit|Strimmer, Korbinian', 'Biotechnology Center, Technical University Dresden, 01062 Dresden, Germany.|Biotechnology Center, Technical University Dresden, 01062 Dresden, Germany.', 'Germany|Germany', 229, 'Algorithms|Animals|Computer Simulation|Databases, Genetic|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-47', '19192285', 'Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear.'),
(92637, 2009, 'bmc', 'GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists', 'Eden, Eran|Navon, Roy|Steinfeld, Israel|Lipson, Doron|Yakhini, Zohar', 'Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com|Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com|Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com|Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com|Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com', 'Israel|Israel|Israel|Israel|Israel', 0, 'Algorithms|Animals|Arabidopsis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Genes|Genomics|Humans|Information Storage and Retrieval|Internet|Models, Genetic|Models, Statistical|Software|Terminology as Topic|User-Computer Interface|Yeasts', 'Journal Article', '10.1186/1471-2105-10-48', '19192299', 'Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.'),
(92638, 2009, 'bmc', 'GenotypeColour: colour visualisation of SNPs and CNVs', 'Barlati, Sergio|Chiesa, Sergio|Magri, Chiara', 'Division of Biology and Genetics, Department of Biomedical Sciences and Biotechnology, Brescia University, Viale Europa 11, 25123 Brescia, Italy. barlati@med.unibs.it|Division of Biology and Genetics, Department of Biomedical Sciences and Biotechnology, Brescia University, Viale Europa 11, 25123 Brescia, Italy. barlati@med.unibs.it|Division of Biology and Genetics, Department of Biomedical Sciences and Biotechnology, Brescia University, Viale Europa 11, 25123 Brescia, Italy. barlati@med.unibs.it', 'Italy|Italy|Italy', 4, 'Computer Graphics|Gene Dosage|Genetic Variation|Genotype|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-49', '19193232', 'The volume of data available on genetic variations has increased considerably with the recent development of high-density, single-nucleotide polymorphism (SNP) arrays. Several software programs have been developed to assist researchers in the analysis of this huge amount of data, but few can rely upon a whole genome variability visualisation system that could help data interpretation.'),
(92639, 2009, 'bmc', 'EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data', 'Dondrup, Michael|Albaum, Stefan P|Griebel, Thasso|Henckel, Kolja|Jünemann, Sebastian|Kahlke, Tim|Kleindt, Christiane K|Küster, Helge|Linke, Burkhard|Mertens, Dominik|Mittard-Runte, Virginie|Neuweger, Heiko|Runte, Kai J|Tauch, Andreas|Tille, Felix|Pühler, Alfred|Goesmann, Alexander', 'Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology, Bielefeld University, 33594 Bielefeld, Germany. mdondrup@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 59, 'Databases, Genetic|Gene Expression Profiling|Genome|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-50', '19200358', 'Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems.'),
(92640, 2009, 'bmc', 'Optimized ancestral state reconstruction using Sankoff parsimony', 'Clemente, José C|Ikeo, Kazuho|Valiente, Gabriel|Gojobori, Takashi', 'Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima, Japan. jclement@lab.nig.ac.jp|Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima, Japan. jclement@lab.nig.ac.jp|Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima, Japan. jclement@lab.nig.ac.jp|Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima, Japan. jclement@lab.nig.ac.jp', 'Japan|Japan|Japan|Japan', 6, 'Algorithms|Computational Biology|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-51', '19200389', 'Parsimony methods are widely used in molecular evolution to estimate the most plausible phylogeny for a set of characters. Sankoff parsimony determines the minimum number of changes required in a given phylogeny when a cost is associated to transitions between character states. Although optimizations exist to reduce the computations in the number of taxa, the original algorithm takes time O(n(2)) in the number of states, making it impractical for large values of n.'),
(92641, 2009, 'bmc', 'Improving the analysis of designed studies by combining statistical modelling with study design information', 'Thissen, Uwe|Wopereis, Suzan|van den Berg, Sjoerd A A|Bobeldijk, Ivana|Kleemann, Robert|Kooistra, Teake|van Dijk, Ko Willems|van Ommen, Ben|Smilde, Age K', 'Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl|Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 0, 'Animals|Databases, Factual|Humans|Metabolomics|Models, Statistical', 'Journal Article', '10.1186/1471-2105-10-52', '19200393', 'In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.'),
(92642, 2009, 'bmc', 'Outcome prediction based on microarray analysis: a critical perspective on methods', 'Zervakis, Michalis|Blazadonakis, Michalis E|Tsiliki, Georgia|Danilatou, Vasiliki|Tsiknakis, Manolis|Kafetzopoulos, Dimitris', 'Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr|Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr|Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr|Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr|Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr|Technical University of Crete, Department of Electronic and Computer Engineering, University Campus, Chania, Crete, Greece. michalis@display.tuc.gr', 'Greece|Greece|Greece|Greece|Greece|Greece', 30, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-53', '19200394', 'Information extraction from microarrays has not yet been widely used in diagnostic or prognostic decision-support systems, due to the diversity of results produced by the available techniques, their instability on different data sets and the inability to relate statistical significance with biological relevance. Thus, there is an urgent need to address the statistical framework of microarray analysis and identify its drawbacks and limitations, which will enable us to thoroughly compare methodologies under the same experimental set-up and associate results with confidence intervals meaningful to clinicians. In this study we consider gene-selection algorithms with the aim to reveal inefficiencies in performance evaluation and address aspects that can reduce uncertainty in algorithmic validation.'),
(92643, 2009, 'bmc', 'The Seventh Asia Pacific Bioinformatics Conference (APBC2009)', 'Zhang, Michael Q|Waterman, Michael S|Zhang, Xuegong', 'Cold Spring Harbor Laboratory, NY, USA. mzhang@cshl.edu|Cold Spring Harbor Laboratory, NY, USA. mzhang@cshl.edu|Cold Spring Harbor Laboratory, NY, USA. mzhang@cshl.edu', 'United States|United States|United States', 0, 'Asia|Computational Biology|Genomics|Pacific Islands', 'Congress', '10.1186/1471-2105-10-S1-S1', '19208108', '-'),
(92644, 2009, 'bmc', 'Improved algorithms for approximate string matching (extended abstract)', 'Papamichail, Dimitris|Papamichail, Georgios', 'Department of Computer Science, University of Miami, Coral Gables, Miami, USA. dimitris@cs.miami.edu|Department of Computer Science, University of Miami, Coral Gables, Miami, USA. dimitris@cs.miami.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|Information Storage and Retrieval|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-10-S1-S10', '19208109', 'The problem of approximate string matching is important in many different areas such as computational biology, text processing and pattern recognition. A great effort has been made to design efficient algorithms addressing several variants of the problem, including comparison of two strings, approximate pattern identification in a string or calculation of the longest common subsequence that two strings share.'),
(92645, 2009, 'bmc', 'Simultaneous phylogeny reconstruction and multiple sequence alignment', 'Yue, Feng|Shi, Jian|Tang, Jijun', 'Ludwig Institute for Cancer Research, UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA. fyue@ucsd.edu|Ludwig Institute for Cancer Research, UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA. fyue@ucsd.edu|Ludwig Institute for Cancer Research, UCSD School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA. fyue@ucsd.edu', 'United States|United States|United States', 14, 'Algorithms|Databases, Genetic|Evolution, Molecular|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S1-S11', '19208110', 'A phylogeny is the evolutionary history of a group of organisms. To date, sequence data is still the most used data type for phylogenetic reconstruction. Before any sequences can be used for phylogeny reconstruction, they must be aligned, and the quality of the multiple sequence alignment has been shown to affect the quality of the inferred phylogeny. At the same time, all the current multiple sequence alignment programs use a guide tree to produce the alignment and experiments showed that good guide trees can significantly improve the multiple alignment quality.'),
(92646, 2009, 'bmc', 'Methods for comparative metagenomics', 'Huson, Daniel H|Richter, Daniel C|Mitra, Suparna|Auch, Alexander F|Schuster, Stephan C', 'Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. huson@informatik.uni-tuebingen.de|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. huson@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany', 85, 'Computational Biology|Databases, Genetic|Evolution, Molecular|Genomics|Phylogeny|Software', 'Comparative Study', '10.1186/1471-2105-10-S1-S12', '19208111', 'Metagenomics is a rapidly growing field of research that aims at studying uncultured organisms to understand the true diversity of microbes, their functions, cooperation and evolution, in environments such as soil, water, ancient remains of animals, or the digestive system of animals and humans. The recent development of ultra-high throughput sequencing technologies, which do not require cloning or PCR amplification, and can produce huge numbers of DNA reads at an affordable cost, has boosted the number and scope of metagenomic sequencing projects. Increasingly, there is a need for new ways of comparing multiple metagenomics datasets, and for fast and user-friendly implementations of such approaches.'),
(92647, 2009, 'bmc', 'DNA motif alignment by evolving a population of Markov chains', 'Bi, Chengpeng', 'Bioinformatics and Intelligent Computing Lab, Division of Clinical Pharmacology, Childrens Mercy Hospitals, Kansas City, Missouri, USA. cbi@cmh.edu', 'United States', 0, 'Algorithms|DNA|Markov Chains|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-10-S1-S13', '19208112', 'Deciphering cis-regulatory elements or de novo motif-finding in genomes still remains elusive although much algorithmic effort has been expended. The Markov chain Monte Carlo (MCMC) method such as Gibbs motif samplers has been widely employed to solve the de novo motif-finding problem through sequence local alignment. Nonetheless, the MCMC-based motif samplers still suffer from local maxima like EM. Therefore, as a prerequisite for finding good local alignments, these motif algorithms are often independently run a multitude of times, but without information exchange between different chains. Hence it would be worth a new algorithm design enabling such information exchange.'),
(92648, 2009, 'bmc', 'Parallel short sequence assembly of transcriptomes', 'Jackson, Benjamin G|Schnable, Patrick S|Aluru, Srinivas', 'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA. zbbrox@iastate.edu|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA. zbbrox@iastate.edu|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA. zbbrox@iastate.edu', 'United States|United States|United States', 27, 'Computational Biology|Gene Expression Profiling|Genome, Plant|Sequence Alignment|Sequence Analysis, DNA|Zea mays', 'Evaluation Studies', '10.1186/1471-2105-10-S1-S14', '19208113', 'The de novo assembly of genomes and transcriptomes from short sequences is a challenging problem. Because of the high coverage needed to assemble short sequences as well as the overhead of modeling the assembly problem as a graph problem, the methods for short sequence assembly are often validated using data from BACs or small sized prokaryotic genomes.'),
(92649, 2009, 'bmc', 'Finding optimal threshold for correction error reads in DNA assembling', 'Chin, Francis Y L|Leung, Henry C M|Li, Wei-Lin|Yiu, Siu-Ming', 'Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, PRChina. chin@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, PRChina. chin@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, PRChina. chin@cs.hku.hk|Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, PRChina. chin@cs.hku.hk', 'China|China|China|China', 18, 'Algorithms|Base Sequence|Computational Biology|DNA|Genome|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S1-S15', '19208114', 'DNA assembling is the problem of determining the nucleotide sequence of a genome from its substrings, called reads. In the experiments, there may be some errors on the reads which affect the performance of the DNA assembly algorithms. Existing algorithms, e.g. ECINDEL and SRCorr, correct the error reads by considering the number of times each length-k substring of the reads appear in the input. They treat those length-k substrings appear at least M times as correct substring and correct the error reads based on these substrings. However, since the threshold M is chosen without any solid theoretical analysis, these algorithms cannot guarantee their performances on error correction.'),
(92650, 2009, 'bmc', 'Crystallizing short-read assemblies around seeds', 'Hossain, Mohammad Sajjad|Azimi, Navid|Skiena, Steven', 'Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400 USA. sajjad@cs.sunysb.edu|Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400 USA. sajjad@cs.sunysb.edu|Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400 USA. sajjad@cs.sunysb.edu', 'United States|United States|United States', 0, 'Base Sequence|Computational Biology|Gene Expression Profiling|Genome, Bacterial|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S1-S16', '19208115', 'New short-read sequencing technologies produce enormous volumes of 25-30 base paired-end reads. The resulting reads have vastly different characteristics than produced by Sanger sequencing, and require different approaches than the previous generation of sequence assemblers. In this paper, we present a short-read de novo assembler particularly targeted at the new ABI SOLiD sequencing technology.'),
(92651, 2009, 'bmc', 'Short read DNA fragment anchoring algorithm', 'Wang, Wendi|Zhang, Peiheng|Liu, Xinchun', 'Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, PR China. wangwendi@ncic.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, PR China. wangwendi@ncic.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, PR China. wangwendi@ncic.ac.cn', 'China|China|China', 8, 'Algorithms|Base Sequence|Computational Biology|DNA|Databases, Genetic|Genome, Human|Humans|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S1-S17', '19208116', 'The emerging next-generation sequencing method based on PCR technology boosts genome sequencing speed considerably, the expense is also get decreased. It has been utilized to address a broad range of bioinformatics problems. Limited by reliable output sequence length of next-generation sequencing technologies, we are confined to study gene fragments with 30 - 50 bps in general and it is relatively shorter than traditional gene fragment length. Anchoring gene fragments in long reference sequence is an essential and prerequisite step for further assembly and analysis works. Due to the sheer number of fragments produced by next-generation sequencing technologies and the huge size of reference sequences, anchoring would rapidly becoming a computational bottleneck.'),
(92652, 2009, 'bmc', 'REMAS: a new regression model to identify alternative splicing events from exon array data', 'Zheng, Hao|Hang, Xingyi|Zhu, Ji|Qian, Minping|Qu, Wubin|Zhang, Chenggang|Deng, Minghua', 'LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com|LMAM, School of Mathematical Sciences and Center for Theoretical Biology, Peking University, Beijing 100871, PR China. porcupine.hao@gmail.com', 'China|China|China|China|China|China|China', 4, 'Algorithms|Alternative Splicing|Exons|Linear Models|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S1-S18', '19208117', 'Alternative splicing (AS) is an important regulatory mechanism for gene expression and protein diversity in eukaryotes. Previous studies have demonstrated that it can be causative for, or specific to splicing-related diseases. Understanding the regulation of AS will be helpful for diagnostic efforts and drug discoveries on those splicing-related diseases. As a novel exon-centric microarray platform, exon array enables a comprehensive analysis of AS by investigating the expression of known and predicted exons. Identifying of AS events from exon array has raised much attention, however, new and powerful algorithms for exon array data analysis are still absent till now.'),
(92653, 2009, 'bmc', 'A voting approach to identify a small number of highly predictive genes using multiple classifiers', 'Hassan, Md Rafiul|Hossain, M Maruf|Bailey, James|Macintyre, Geoff|Ho, Joshua W K|Ramamohanarao, Kotagiri', 'Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. mrhassan@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genes, Neoplasm|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S1-S19', '19208118', 'Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage.'),
(92654, 2009, 'bmc', 'Genome aliquoting with double cut and join', 'Warren, Robert|Sankoff, David', 'School of Information Technology and Engineering, University of Ottawa, 800 King Edward Avenue, Ottawa, Canada. rwarr059@uottawa.ca|School of Information Technology and Engineering, University of Ottawa, 800 King Edward Avenue, Ottawa, Canada. rwarr059@uottawa.ca', 'Canada|Canada', 0, 'Algorithms|Animals|Chromosome Mapping|Genome|Humans', 'Journal Article', '10.1186/1471-2105-10-S1-S2', '19208119', 'The genome aliquoting problem is, given an observed genome A with n copies of each gene, presumed to descend from an n-way polyploidization event from an ordinary diploid genome B, followed by a history of chromosomal rearrangements, to reconstruct the identity of the original genome B. The idea is to construct B, containing exactly one copy of each gene, so as to minimize the number of rearrangements d(A, B plus sign in circle B plus sign in circle ... plus sign in circle B) necessary to convert the observed genome B plus sign in circle B plus sign in circle ... plus sign in circle B into A.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92655, 2009, 'bmc', 'Knowledge driven decomposition of tumor expression profiles', 'van Vliet, Martin H|Wessels, Lodewyk F A|Reinders, Marcel J T', 'Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. M.H.vanVliet@TUDelft.nl|Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. M.H.vanVliet@TUDelft.nl|Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. M.H.vanVliet@TUDelft.nl', 'Netherlands|Netherlands|Netherlands', 3, 'Animals|Gene Expression Profiling|Genome|Humans|Linear Models|Mice|Neoplasms', 'Journal Article', '10.1186/1471-2105-10-S1-S20', '19208120', 'Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.'),
(92656, 2009, 'bmc', 'Network-based support vector machine for classification of microarray samples', 'Zhu, Yanni|Shen, Xiaotong|Pan, Wei', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. yannizhu@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. yannizhu@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. yannizhu@biostat.umn.edu', 'United States|United States|United States', 66, 'Breast Neoplasms|Databases, Genetic|Female|Gene Regulatory Networks|Genome, Human|Humans|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Parkinson Disease', 'Journal Article', '10.1186/1471-2105-10-S1-S21', '19208121', 'The importance of network-based approach to identifying biological markers for diagnostic classification and prognostic assessment in the context of microarray data has been increasingly recognized. To our knowledge, there have been few, if any, statistical tools that explicitly incorporate the prior information of gene networks into classifier building. The main idea of this paper is to take full advantage of the biological observation that neighboring genes in a network tend to function together in biological processes and to embed this information into a formal statistical framework.'),
(92657, 2009, 'bmc', 'Using random forest for reliable classification and cost-sensitive learning for medical diagnosis', 'Yang, Fan|Wang, Hua-zhen|Mi, Hong|Lin, Cheng-de|Cai, Wei-wen', 'Automation Department, Xiamen University, Xiamen, 361005, P.R.C. yang@xmu.edu.cn|Automation Department, Xiamen University, Xiamen, 361005, P.R.C. yang@xmu.edu.cn|Automation Department, Xiamen University, Xiamen, 361005, P.R.C. yang@xmu.edu.cn|Automation Department, Xiamen University, Xiamen, 361005, P.R.C. yang@xmu.edu.cn|Automation Department, Xiamen University, Xiamen, 361005, P.R.C. yang@xmu.edu.cn', '-|-|-|-|-', 50, 'Artificial Intelligence|Computational Biology|Diagnostic Techniques and Procedures|Information Storage and Retrieval|Models, Statistical|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-S1-S22', '19208122', 'Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis.'),
(92658, 2009, 'bmc', 'A statistical framework for integrating two microarray data sets in differential expression analysis', 'Lai, Yinglei|Eckenrode, Sarah E|She, Jin-Xiong', 'Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W. Washington, D.C. 20052, USA. ylai@gwu.edu|Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W. Washington, D.C. 20052, USA. ylai@gwu.edu|Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W. Washington, D.C. 20052, USA. ylai@gwu.edu', 'United States|United States|United States', 0, 'Computational Biology|Gene Expression|Gene Expression Profiling|Genome|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S1-S23', '19208123', 'Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis.'),
(92659, 2009, 'bmc', 'Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations', 'Autio, Reija|Kilpinen, Sami|Saarela, Matti|Kallioniemi, Olli|Hautaniemi, Sampsa|Astola, Jaakko', 'Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 37, 'Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Statistical Distributions', 'Comparative Study', '10.1186/1471-2105-10-S1-S24', '19208124', 'Gene expression microarray technologies are widely used across most areas of biological and medical research. Comparing and integrating microarray data from different experiments would be very useful, but is currently very challenging due to the experimental and hybridization conditions, as well as data preprocessing and normalization methods. Furthermore, even in the case of the widely-used, industry-standard Affymetrix oligonucleotide microarrays, the various array generations have different probe sets representing different genes, hindering the data integration.'),
(92660, 2009, 'bmc', 'Integrative disease classification based on cross-platform microarray data', 'Liu, Chun-Chi|Hu, Jianjun|Kalakrishnan, Mrinal|Huang, Haiyan|Zhou, Xianghong Jasmine', 'Molecular and Computational Biology, University of Southern California, CA, USA. jimliu@usc.edu|Molecular and Computational Biology, University of Southern California, CA, USA. jimliu@usc.edu|Molecular and Computational Biology, University of Southern California, CA, USA. jimliu@usc.edu|Molecular and Computational Biology, University of Southern California, CA, USA. jimliu@usc.edu|Molecular and Computational Biology, University of Southern California, CA, USA. jimliu@usc.edu', 'United States|United States|United States|United States|United States', 15, 'Disease|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-10-S1-S25', '19208125', 'Disease classification has been an important application of microarray technology. However, most microarray-based classifiers can only handle data generated within the same study, since microarray data generated by different laboratories or with different platforms can not be compared directly due to systematic variations. This issue has severely limited the practical use of microarray-based disease classification.'),
(92661, 2009, 'bmc', 'Principal component tests: applied to temporal gene expression data', 'Zhang, Wensheng|Fang, Hong-Bin|Song, Jiuzhou', 'Department of Animal and Avian Science, University of Maryland, College Park, MD 20742, USA. wzhang19@umd.edu|Department of Animal and Avian Science, University of Maryland, College Park, MD 20742, USA. wzhang19@umd.edu|Department of Animal and Avian Science, University of Maryland, College Park, MD 20742, USA. wzhang19@umd.edu', 'United States|United States|United States', 0, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Principal Component Analysis|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-S1-S26', '19208126', 'Clustering analysis is a common statistical tool for knowledge discovery. It is mainly conducted when a project still is in the exploratory phase without any priori hypotheses. However, the statistical significance testing between the clusters can be meaningful in helping the researchers to assess if the classification results from implementing a clustering algorithm need to be improved, even after the cluster number has been determined by a well-established criterion. This is important when we want to identify highly-specific patterns through classification.'),
(92662, 2009, 'bmc', 'Biclustering of gene expression data using reactive greedy randomized adaptive search procedure', 'Dharan, Smitha|Nair, Achuthsankar S', 'Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, 695 581, India. smithadharan@gmail.com|Centre for Bioinformatics, University of Kerala, Thiruvananthapuram, Kerala, 695 581, India. smithadharan@gmail.com', 'India|India', 0, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-S1-S27', '19208127', 'Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.'),
(92663, 2009, 'bmc', 'Identification of histone modifications in biomedical text for supporting epigenomic research', 'Kolárik, Corinna|Klinger, Roman|Hofmann-Apitius, Martin', 'Department of Bioinformatics, Fraunhofer Institute Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. corinna.kolarik@scai.fhg.de|Department of Bioinformatics, Fraunhofer Institute Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. corinna.kolarik@scai.fhg.de|Department of Bioinformatics, Fraunhofer Institute Algorithms and Scientific Computing (SCAI) Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. corinna.kolarik@scai.fhg.de', 'Germany|Germany|Germany', 7, 'Epigenesis, Genetic|Genome|Genomics|Histones|Information Storage and Retrieval|Protein Processing, Post-Translational|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-10-S1-S28', '19208128', 'Posttranslational modifications of histones influence the structure of chromatine and in such a way take part in the regulation of gene expression. Certain histone modification patterns, distributed over the genome, are connected to cell as well as tissue differentiation and to the adaption of organisms to their environment. Abnormal changes instead influence the development of disease states like cancer. The regulation mechanisms for modifying histones and its functionalities are the subject of epigenomics investigation and are still not completely understood. Text provides a rich resource of knowledge on epigenomics and modifications of histones in particular. It contains information about experimental studies, the conditions used, and results. To our knowledge, no approach has been published so far for identifying histone modifications in text.'),
(92664, 2009, 'bmc', 'Searching for bidirectional promoters in Arabidopsis thaliana', 'Wang, Quan|Wan, Lin|Li, Dayong|Zhu, Lihuang|Qian, Minping|Deng, Minghua', 'Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn|Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn|Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn|Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn|Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn|Center for Theoretical Biology, Peking University, Beijing100871, PR China. wangquan@ctb.pku.edu.cn', 'China|China|China|China|China|China', 0, 'Arabidopsis|Genes, Plant|Genome, Plant|Promoter Regions, Genetic|Sequence Analysis, DNA|Transcription Initiation Site|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-S1-S29', '19208129', 'A bidirectional gene pair is defined as two adjacent genes which are located on opposite strands of DNA with transcription start sites (TSSs) not more than 1000 base pairs apart and the intergenic region between two TSSs is commonly designated as a putative bidirectional promoter. Individual examples of bidirectional gene pairs have been reported for years, as well as a few genome-wide analyses have been studied in mammalian and human genomes. However, no genome-wide analysis of bidirectional genes for plants has been done. Furthermore, the exact mechanism of this gene organization is still less understood.'),
(92665, 2009, 'bmc', 'Pathogenic Bacillus anthracis in the progressive gene losses and gains in adaptive evolution', 'Yu, G X', 'Dept, of Biological Science and Dept, of Computer Science, Boise State University, Boise, Idaho 83725, USA. gongxinyu@boisestate.edu', 'United States', 7, 'Bacillus anthracis|Computational Biology|Evolution, Molecular|Genes, Bacterial|Genome, Bacterial|Multigene Family', 'Journal Article', '10.1186/1471-2105-10-S1-S3', '19208130', 'Sequence mutations represent a driving force of adaptive evolution in bacterial pathogens. It is especially evident in reductive genome evolution where bacteria underwent lifestyles shifting from a free-living to a strictly intracellular or host-depending life. It resulted in loss-of-function mutations and/or the acquisition of virulence gene clusters. Bacillus anthracis shares a common soil bacterial ancestor with its closely related bacillus species but is the only obligate, causative agent of inhalation anthrax within the genus Bacillus. The anthrax-causing Bacillus anthracis experienced the similar lifestyle changes. We thus hypothesized that the bacterial pathogen would follow a compatible evolution path.'),
(92666, 2009, 'bmc', 'ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules', 'Sun, Hong|De Bie, Tijl|Storms, Valerie|Fu, Qiang|Dhollander, Thomas|Lemmens, Karen|Verstuyf, Annemieke|De Moor, Bart|Marchal, Kathleen', 'Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be|Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. hong.sun@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Binding Sites|Databases, Genetic|Models, Genetic|Regulatory Elements, Transcriptional|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-S1-S30', '19208131', 'The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.'),
(92667, 2009, 'bmc', 'Transcriptional interaction-assisted identification of dynamic nucleosome positioning', 'Dai, Zhiming|Dai, Xianhua|Xiang, Qian|Feng, Jihua|Deng, Yangyang|Wang, Jiang|He, Caisheng', 'Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com|Electronic Department, Sun Yat-Sen University, Guangzhou, 510275, PR China. zhimdai@gmail.com', 'China|China|China|China|China|China|China', 0, 'Base Sequence|Computational Biology|DNA|Genomics|Nucleosomes|Reproducibility of Results|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-S1-S31', '19208132', 'Nucleosomes regulate DNA accessibility and therefore play a central role in transcription control. Computational methods have been developed to predict static nucleosome positions from DNA sequences, but nucleosomes are dynamic in vivo.'),
(92668, 2009, 'bmc', 'Predicting microRNA targets in time-series microarray experiments via functional data analysis', 'Parker, Brian J|Wen, Jiayu', 'The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark. bparker@binf.ku.dk|The Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark. bparker@binf.ku.dk', 'Denmark|Denmark', 0, 'Computational Biology|Gene Expression Profiling|MicroRNAs|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Sequence Analysis, RNA|Statistics as Topic|Transfection', 'Journal Article', '10.1186/1471-2105-10-S1-S32', '19208133', 'MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional.'),
(92669, 2009, 'bmc', 'A structural interpretation of the effect of GC-content on efficiency of RNA interference', 'Chan, Chi Yu|Carmack, C Steven|Long, Dang D|Maliyekkel, Anil|Shao, Yu|Roninson, Igor B|Ding, Ye', 'Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com|Wadsworth Center, New York State Department of Health, 150 New Scotland Avenue, Albany, NY 12208, USA. c.clarence@yahoo.com', 'United States|United States|United States|United States|United States|United States|United States', 46, 'Base Composition|Cytosine|Guanine|Nucleic Acid Conformation|RNA Interference|RNA, Messenger|RNA, Small Interfering|RNA, Untranslated', 'Journal Article', '10.1186/1471-2105-10-S1-S33', '19208134', 'RNA interference (RNAi) mediated by small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs) has become a powerful technique for eukaryotic gene knockdown. siRNA GC-content negatively correlates with RNAi efficiency, and it is of interest to have a convincing mechanistic interpretation of this observation. We here examine this issue by considering the secondary structures for both the target messenger RNA (mRNA) and the siRNA guide strand.'),
(92670, 2009, 'bmc', 'Computational identification of condition-specific miRNA targets based on gene expression profiles and sequence information', 'Joung, Je-Gun|Fei, Zhangjun', 'Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853, USA. jj294@cornell.edu|Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY 14853, USA. jj294@cornell.edu', 'United States|United States', 0, 'Algorithms|Arabidopsis|Computational Biology|Gene Expression Profiling|MicroRNAs|RNA, Messenger|Sequence Analysis, RNA|Stress, Physiological', 'Journal Article', '10.1186/1471-2105-10-S1-S34', '19208135', 'MicroRNAs (miRNAs) are small and noncoding RNAs that play important roles in various biological processes. They regulate target mRNAs post-transcriptionally through complementary base pairing. Since the changes of miRNAs affect the expression of target genes, the expression levels of target genes in specific biological processes could be different from those of non-target genes. Here we demonstrate that gene expression profiles contain useful information in separating miRNA targets from non-targets.'),
(92671, 2009, 'bmc', 'HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models', 'Kadri, Sabah|Hinman, Veronica|Benos, Panayiotis V', 'Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sskadri@andrew.cmu.edu|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sskadri@andrew.cmu.edu|Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. sskadri@andrew.cmu.edu', 'United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Databases, Genetic|Humans|Markov Chains|MicroRNAs|Models, Biological|Molecular Sequence Data|Nucleic Acid Conformation|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-10-S1-S35', '19208136', 'MicroRNAs (miRNAs) are small non-coding single-stranded RNAs (20-23 nts) that are known to act as post-transcriptional and translational regulators of gene expression. Although, they were initially overlooked, their role in many important biological processes, such as development, cell differentiation, and cancer has been established in recent times. In spite of their biological significance, the identification of miRNA genes in newly sequenced organisms is still based, to a large degree, on extensive use of evolutionary conservation, which is not always available.'),
(92672, 2009, 'bmc', 'Computational prediction of novel non-coding RNAs in Arabidopsis thaliana', 'Song, Dandan|Yang, Yang|Yu, Bin|Zheng, Binglian|Deng, Zhidong|Lu, Bao-Liang|Chen, Xuemei|Jiang, Tao', 'Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn|Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. sdd00@mails.tsinghua.edu.cn', 'China|China|China|China|China|China|China|China', 0, 'Arabidopsis|Arabidopsis Proteins|Computational Biology|Conserved Sequence|Databases, Genetic|Genome, Plant|RNA, Untranslated|Reproducibility of Results|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-10-S1-S36', '19208137', 'Non-coding RNA (ncRNA) genes do not encode proteins but produce functional RNA molecules that play crucial roles in many key biological processes. Recent genome-wide transcriptional profiling studies using tiling arrays in organisms such as human and Arabidopsis have revealed a great number of transcripts, a large portion of which have little or no capability to encode proteins. This unexpected finding suggests that the currently known repertoire of ncRNAs may only represent a small fraction of ncRNAs of the organisms. Thus, efficient and effective prediction of ncRNAs has become an important task in bioinformatics in recent years. Among the available computational methods, the comparative genomic approach seems to be the most powerful to detect ncRNAs. The recent completion of the sequencing of several major plant genomes has made the approach possible for plants.'),
(92673, 2009, 'bmc', 'Fine-grained parallel RNAalifold algorithm for RNA secondary structure prediction on FPGA', 'Xia, Fei|Dou, Yong|Zhou, Xingming|Yang, Xuejun|Xu, Jiaqing|Zhang, Yang', 'National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, PR China. xcyphoenix@nudt.edu.cn', 'China|China|China|China|China|China', 0, 'Algorithms|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-10-S1-S37', '19208138', 'In the field of RNA secondary structure prediction, the RNAalifold algorithm is one of the most popular methods using free energy minimization. However, general-purpose computers including parallel computers or multi-core computers exhibit parallel efficiency of no more than 50%. Field Programmable Gate-Array (FPGA) chips provide a new approach to accelerate RNAalifold by exploiting fine-grained custom design.'),
(92674, 2009, 'bmc', 'Prediction of RNA secondary structure with pseudoknots using integer programming', 'Poolsap, Unyanee|Kato, Yuki|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. unyanee@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. unyanee@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. unyanee@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan', 21, 'Base Sequence|Computational Biology|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-10-S1-S38', '19208139', 'RNA secondary structure prediction is one major task in bioinformatics, and various computational methods have been proposed so far. Pseudoknot is one of the typical substructures appearing in several RNAs, and plays an important role in some biological processes. Prediction of RNA secondary structure with pseudoknots is still challenging since the problem is NP-hard when arbitrary pseudoknots are taken into consideration.'),
(92675, 2009, 'bmc', 'Sequence-structure relations of pseudoknot RNA', 'Huang, Fenix W D|Li, Linda Y M|Reidys, Christian M', 'Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China. fenixprotoss@163.com|Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China. fenixprotoss@163.com|Center for Combinatorics, LPMC-TJKLC, Nankai University, Tianjin 300071, PR China. fenixprotoss@163.com', 'China|China|China', 4, 'Algorithms|Nucleic Acid Conformation|Phenotype|RNA|Sequence Analysis, RNA|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-S1-S39', '19208140', 'The analysis of sequence-structure relations of RNA is based on a specific notion and folding of RNA structure. The notion of coarse grained structure employed here is that of canonical RNA pseudoknot contact-structures with at most two mutually crossing bonds (3-noncrossing). These structures are folded by a novel, ab initio prediction algorithm, cross, capable of searching all 3-noncrossing RNA structures. The algorithm outputs the minimum free energy structure.'),
(92676, 2009, 'bmc', 'SE: an algorithm for deriving sequence alignment from a pair of superimposed structures', 'Tai, Chin-Hsien|Vincent, James J|Kim, Changhoon|Lee, Byungkook', 'Molecular Modeling and Bioinformatics Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. taic@mail.nih.gov|Molecular Modeling and Bioinformatics Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. taic@mail.nih.gov|Molecular Modeling and Bioinformatics Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. taic@mail.nih.gov|Molecular Modeling and Bioinformatics Section, Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. taic@mail.nih.gov', 'United States|United States|United States|United States', 8, 'Algorithms|Amino Acid Sequence|Databases, Protein|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S1-S4', '19208141', 'Generating sequence alignments from superimposed structures is an important part of many structure comparison programs. The accuracy of the alignment affects structure recognition, classification and possibly function prediction. Many programs use a dynamic programming algorithm to generate the sequence alignment from superimposed structures. However, this procedure requires using a gap penalty and, depending on the value of the penalty used, can introduce spurious gaps and misalignments. Here we present a new algorithm, Seed Extension (SE), for generating the sequence alignment from a pair of superimposed structures. The SE algorithm first finds seeds, which are the pairs of residues, one from each structure, that meet certain stringent criteria for being structurally equivalent. Three consecutive seeds form a seed segment, which is extended along the diagonal of the alignment matrix in both directions. Distance and the amino acid type similarity between the residues are used to resolve conflicts that arise during extension of more than one diagonal. The manually curated alignments in the Conserved Domain Database were used as the standard to assess the quality of the sequence alignments.'),
(92677, 2009, 'bmc', 'Reduction of the secondary structure topological space through direct estimation of the contact energy formed by the secondary structures', 'Sun, Weitao|He, Jing', 'Department of Computer Science, New Mexico State University, Las Cruces, 88003, USA. sunwt@tsinghua.edu.cn|Department of Computer Science, New Mexico State University, Las Cruces, 88003, USA. sunwt@tsinghua.edu.cn', 'Mexico|Mexico', 0, 'Binding Sites|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Protein Structure, Secondary|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-S1-S40', '19208142', 'Electron cryomicroscopy is a fast developing technique aiming at the determination of the 3-dimensional structures of large protein complexes. Using this technique, protein density maps can be generated with 6 to 10 A resolution. At such resolutions, the secondary structure elements such as helices and beta-strands appear to be skeletons and can be computationally detected. However, it is not known which segment of the protein sequence corresponds to which of the skeletons. The topology in this paper refers to the linear order and the directionality of the secondary structures. For a protein with N helices and M strands, there are (N!2N)(M!2M) different topologies, each of which maps N helix segments and M strand segments on the protein sequence to N helix and M strand skeletons. Since the backbone position is not available in the skeleton, each topology of the skeletons corresponds to additional freedom to position the atoms in the skeletons.'),
(92678, 2009, 'bmc', 'Co-evolution positions and rules for antigenic variants of human influenza A/H3N2 viruses', 'Huang, Jhang-Wei|King, Chwan-Chuen|Yang, Jinn-Moon', 'Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan. popblue.bi93g@nctu.edu.tw|Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan. popblue.bi93g@nctu.edu.tw|Institute of Bioinformatics, National Chiao Tung University, Hsinchu, 30050, Taiwan. popblue.bi93g@nctu.edu.tw', '-|-|-', 32, 'Antigenic Variation|Biological Evolution|Hemagglutinins, Viral|Humans|Influenza A Virus, H3N2 Subtype|Influenza A virus|Influenza, Human|Models, Molecular', 'Journal Article', '10.1186/1471-2105-10-S1-S41', '19208143', 'In pandemic and epidemic forms, avian and human influenza viruses often cause significant damage to human society and economics. Gradually accumulated mutations on hemagglutinin (HA) cause immunologically distinct circulating strains, which lead to the antigenic drift (named as antigenic variants). The antigenic variants often requires a new vaccine to be formulated before each annual epidemic. Mapping the genetic evolution to the antigenic drift of influenza viruses is an emergent issue to public health and vaccine development'),
(92679, 2009, 'bmc', 'Predicting disordered regions in proteins using the profiles of amino acid indices', 'Han, Pengfei|Zhang, Xiuzhen|Feng, Zhi-Ping', 'School of Computer Science and IT, RMIT University, Melbourne, VIC 3001, Australia. phan@cs.rmit.edu.au|School of Computer Science and IT, RMIT University, Melbourne, VIC 3001, Australia. phan@cs.rmit.edu.au|School of Computer Science and IT, RMIT University, Melbourne, VIC 3001, Australia. phan@cs.rmit.edu.au', 'Australia|Australia|Australia', 17, 'Algorithms|Amino Acids|Artificial Intelligence|Databases, Protein|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S1-S42', '19208144', 'Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures.'),
(92680, 2009, 'bmc', 'A method to improve protein subcellular localization prediction by integrating various biological data sources', 'Tung, Thai Quang|Lee, Doheon', 'Department of Bio & Brain Engineering, KAIST, Daejeon City, Republic of Korea. tqtung@kaist.ac.kr|Department of Bio & Brain Engineering, KAIST, Daejeon City, Republic of Korea. tqtung@kaist.ac.kr', '-|-', 16, 'Algorithms|Computational Biology|Databases, Protein|Fuzzy Logic|Gene Regulatory Networks|Proteins|Saccharomycetales', 'Journal Article', '10.1186/1471-2105-10-S1-S43', '19208145', 'Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large, distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different, and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.'),
(92681, 2009, 'bmc', 'Studying the unfolding process of protein G and protein L under physical property space', 'Zhao, Liling|Wang, Jihua|Dou, Xianghua|Cao, Zanxia', 'Key Lab of Biophysics in universities of Shandong (Dezhou University), Dezhou 253023, PR China. zhaoll@sina.com|Key Lab of Biophysics in universities of Shandong (Dezhou University), Dezhou 253023, PR China. zhaoll@sina.com|Key Lab of Biophysics in universities of Shandong (Dezhou University), Dezhou 253023, PR China. zhaoll@sina.com|Key Lab of Biophysics in universities of Shandong (Dezhou University), Dezhou 253023, PR China. zhaoll@sina.com', 'China|China|China|China', 6, 'Bacterial Proteins|Binding Sites|Computer Simulation|Kinetics|Models, Molecular|Protein Conformation|Protein Folding', 'Journal Article', '10.1186/1471-2105-10-S1-S44', '19208146', 'The studies on protein folding/unfolding indicate that the native state topology is an important determinant of protein folding mechanism. The folding/unfolding behaviors of proteins which have similar topologies have been studied under Cartesian space and the results indicate that some proteins share the similar folding/unfolding characters.'),
(92682, 2009, 'bmc', 'Prediction of amyloid fibril-forming segments based on a support vector machine', 'Tian, Jian|Wu, Ningfeng|Guo, Jun|Fan, Yunliu', 'Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. tianjian3721@163.com|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. tianjian3721@163.com|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. tianjian3721@163.com|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China. tianjian3721@163.com', 'China|China|China|China', 65, 'Algorithms|Amyloid|Artificial Intelligence|Computational Biology|Databases, Protein|Humans|Peptides', 'Journal Article', '10.1186/1471-2105-10-S1-S45', '19208147', 'Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.'),
(92683, 2009, 'bmc', 'Towards comprehensive structural motif mining for better fold annotation in the twilight zone of sequence dissimilarity', 'Jia, Yi|Huan, Jun|Buhr, Vincent|Zhang, Jintao|Carayannopoulos, Leonidas N', 'Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA. jiayi@ittc.ku.edu|Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA. jiayi@ittc.ku.edu|Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA. jiayi@ittc.ku.edu|Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA. jiayi@ittc.ku.edu|Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS, 66045, USA. jiayi@ittc.ku.edu', 'United States|United States|United States|United States|United States', 4, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Computational Biology|Databases, Protein|Models, Molecular|Molecular Sequence Data|Protein Folding|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-S1-S46', '19208148', 'Automatic identification of structure fingerprints from a group of diverse protein structures is challenging, especially for proteins whose divergent amino acid sequences may fall into the twilight- or midnight- zones where pair-wise sequence identities to known sequences fall below 25% and sequence-based functional annotations often fail.'),
(92684, 2009, 'bmc', 'Semi-supervised protein subcellular localization', 'Xu, Qian|Hu, Derek Hao|Xue, Hong|Yu, Weichuan|Yang, Qiang', 'Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk|Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk|Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk|Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk|Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 27, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Models, Biological|Protein Transport|Proteins', 'Journal Article', '10.1186/1471-2105-10-S1-S47', '19208149', 'Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.'),
(92685, 2009, 'bmc', 'A computational analysis of SARS cysteine proteinase-octapeptide substrate interaction: implication for structure and active site binding mechanism', 'Phakthanakanok, Krongsakda|Ratanakhanokchai, Khanok|Kyu, Khin Lay|Sompornpisut, Pornthep|Watts, Aaron|Pinitglang, Surapong', 'Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th|Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th|Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th|Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th|Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th|Division of Biochemical Technology, School of Bioresources and Technology, King Mongkuts University of Technology Thonburi, Bangkok, Thailand. s7500802@st.kmutt.ac.th', 'Thailand|Thailand|Thailand|Thailand|Thailand|Thailand', 4, 'Binding Sites|Catalytic Domain|Computational Biology|Cysteine Endopeptidases|Cysteine Proteinase Inhibitors|Hydrogen Bonding|Models, Molecular|Oligopeptides|Protein Conformation|SARS Virus|Structure-Activity Relationship|Substrate Specificity|Viral Proteins', 'Journal Article', '10.1186/1471-2105-10-S1-S48', '19208150', 'SARS coronavirus main proteinase (SARS CoVMpro) is an important enzyme for the replication of Severe Acute Respiratory Syndrome virus. The active site region of SARS CoVMpro is divided into 8 subsites. Understanding the binding mode of SARS CoVMpro with a specific substrate is useful and contributes to structural-based drug design. The purpose of this research is to investigate the binding mode between the SARS CoVMpro and two octapeptides, especially in the region of the S3 subsite, through a molecular docking and molecular dynamics (MD) simulation approach.'),
(92686, 2009, 'bmc', 'Quality assessment of tandem mass spectra using support vector machine (SVM)', 'Zou, An-Min|Wu, Fang-Xiang|Ding, Jia-Rui|Poirier, Guy G', 'Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 59A, Canada. anmin.zou@ia.ac.cn|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 59A, Canada. anmin.zou@ia.ac.cn|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 59A, Canada. anmin.zou@ia.ac.cn|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK, S7N 59A, Canada. anmin.zou@ia.ac.cn', 'Canada|Canada|Canada|Canada', 7, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Proteins|Proteomics|Tandem Mass Spectrometry', 'Comparative Study', '10.1186/1471-2105-10-S1-S49', '19208151', 'Tandem mass spectrometry has become particularly useful for the rapid identification and characterization of protein components of complex biological mixtures. Powerful database search methods have been developed for the peptide identification, such as SEQUEST and MASCOT, which are implemented by comparing the mass spectra obtained from unknown proteins or peptides with theoretically predicted spectra derived from protein databases. However, the majority of spectra generated from a mass spectrometry experiment are of too poor quality to be interpreted while some of spectra with high quality cannot be interpreted by one method but perhaps by others. Hence a filtering algorithm that removes those spectra with poor quality prior to the database search is appealing.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92687, 2009, 'bmc', 'ComPhy: prokaryotic composite distance phylogenies inferred from whole-genome gene sets', 'Lin, Guan Ning|Cai, Zhipeng|Lin, Guohui|Chakraborty, Sounak|Xu, Dong', 'Digital Biology Laboratory, Informatics Institute, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. gnln66@mizzou.edu|Digital Biology Laboratory, Informatics Institute, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. gnln66@mizzou.edu|Digital Biology Laboratory, Informatics Institute, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. gnln66@mizzou.edu|Digital Biology Laboratory, Informatics Institute, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. gnln66@mizzou.edu|Digital Biology Laboratory, Informatics Institute, Computer Science Department and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. gnln66@mizzou.edu', 'United States|United States|United States|United States|United States', 11, 'Computational Biology|Databases, Genetic|Evolution, Molecular|Genome, Archaeal|Genome, Bacterial|Internet|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-10-S1-S5', '19208152', 'With the increasing availability of whole genome sequences, it is becoming more and more important to use complete genome sequences for inferring species phylogenies. We developed a new tool ComPhy, Composite Distance Phylogeny, based on a composite distance matrix calculated from the comparison of complete gene sets between genome pairs to produce a prokaryotic phylogeny.'),
(92688, 2009, 'bmc', 'Efficient discovery of abundant post-translational modifications and spectral pairs using peptide mass and retention time differences', 'Fu, Yan|Jia, Wei|Lu, Zhuang|Wang, Haipeng|Yuan, Zuofei|Chi, Hao|Li, You|Xiu, Liyun|Wang, Wenping|Liu, Chao|Wang, Leheng|Sun, Ruixiang|Gao, Wen|Qian, Xiaohong|He, Si-Min', 'Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, PR China. yfu@ict.ac.cn', 'China|China|China|China|China|China|China|China|China|China|China|China|China|China|China', 9, 'Algorithms|Computational Biology|Databases, Protein|Mass Spectrometry|Molecular Weight|Peptides|Protein Processing, Post-Translational|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S1-S50', '19208153', 'Peptide identification via tandem mass spectrometry is the basic task of current proteomics research. Due to the complexity of mass spectra, the majority of mass spectra cannot be interpreted at present. The existence of unexpected or unknown protein post-translational modifications is a major reason.'),
(92689, 2009, 'bmc', 'Analysis of mass spectrometry data using sub-spectra', 'Meuleman, Wouter|Engwegen, Judith Y M N|Gast, Marie-Christine W|Wessels, Lodewyk F A|Reinders, Marcel J T', 'Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl|Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl|Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl|Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl|Bioinformatics and Statistics, Department of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. w.meuleman@tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 7, 'Algorithms|Pattern Recognition, Automated|Peptides|Signal Processing, Computer-Assisted|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-10-S1-S51', '19208154', 'Spectra resulting from Surface-Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry measurements are constructed by combining sub-spectra, each of which are the result of a single firing of the laser responsible for the process of desorption/ionisation. These firings are performed at different locations of the spot on which the sample is analysed. The final spectrum is then constructed by summing over all these sub-spectra. This process is sub-optimal in that it can average out peaks from peptides that are present in low abundance or are unevenly distributed across the spot, particularly because the amount of noise varies considerably between sub-spectra. This argues for analysing sub-spectra separately and combining results afterwards.'),
(92690, 2009, 'bmc', 'Mutual information estimation reveals global associations between stimuli and biological processes', 'Suzuki, Taiji|Sugiyama, Masashi|Kanamori, Takafumi|Sese, Jun', 'Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. s-taiji@stat.t.u-tokyo.ac.jp|Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. s-taiji@stat.t.u-tokyo.ac.jp|Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. s-taiji@stat.t.u-tokyo.ac.jp|Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan. s-taiji@stat.t.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan', 73, 'Algorithms|Biological Phenomena|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S1-S52', '19208155', 'Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions.'),
(92691, 2009, 'bmc', 'Comparative analysis of the JAK/STAT signaling through erythropoietin receptor and thrombopoietin receptor using a systems approach', 'Won, Hong-Hee|Park, Inho|Lee, Eunjung|Kim, Jong-Won|Lee, Doheon', 'Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea. honghee.won@gmail.com|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea. honghee.won@gmail.com|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea. honghee.won@gmail.com|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea. honghee.won@gmail.com|Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-710, South Korea. honghee.won@gmail.com', '-|-|-|-|-', 0, 'Computer Simulation|Janus Kinases|Kinetics|Mutation|Myeloproliferative Disorders|Receptors, Erythropoietin|Receptors, Thrombopoietin|STAT Transcription Factors|Signal Transduction|Systems Biology', 'Comparative Study', '10.1186/1471-2105-10-S1-S53', '19208156', 'The Janus kinase-signal transducer and activator of transcription (JAK/STAT) pathway is one of the most important targets for myeloproliferative disorder (MPD). Although several efforts toward modeling the pathway using systems biology have been successful, the pathway was not fully investigated in regard to understanding pathological context and to model receptor kinetics and mutation effects.'),
(92692, 2009, 'bmc', 'Semi-supervised gene shaving method for predicting low variation biological pathways from genome-wide data', 'Zhu, Dongxiao', 'Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA. dzhu@cs.uno.edu', 'United States', 3, 'Algorithms|Gene Expression Profiling|Genome|Multigene Family|Oligonucleotide Array Sequence Analysis|Receptors, Notch|Wnt Proteins', 'Journal Article', '10.1186/1471-2105-10-S1-S54', '19208157', 'The gene shaving algorithm and many other clustering algorithms identify gene clusters showing high variation across samples. However, gene expression in many signaling pathways show only modest and concordant changes that fail to be identified by these methods. The increasingly available signaling pathway prior knowledge provide new opportunity to solve this problem.'),
(92693, 2009, 'bmc', 'Extract interaction detection methods from the biological literature', 'Wang, Hongning|Huang, Minlie|Zhu, Xiaoyan', 'State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. whn03@mails.tsinghua.edu.cn|State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. whn03@mails.tsinghua.edu.cn|State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China. whn03@mails.tsinghua.edu.cn', 'China|China|China', 0, 'Computational Biology|Information Storage and Retrieval|Pattern Recognition, Automated|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-10-S1-S55', '19208158', 'Considerable efforts have been made to extract protein-protein interactions from the biological literature, but little work has been done on the extraction of interaction detection methods. It is crucial to annotate the detection methods in the literature, since different detection methods shed different degrees of reliability on the reported interactions. However, the diversity of method mentions in the literature makes the automatic extraction quite challenging.'),
(92694, 2009, 'bmc', 'Human gene expression sensitivity according to large scale meta-analysis', 'Hao, Pei|Zheng, Siyuan|Ping, Jie|Tu, Kang|Gieger, Christian|Wang-Sattler, Rui|Zhong, Yang|Li, Yixue', 'Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn|Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. phao@sibs.ac.cn', 'China|China|China|China|China|China|China|China', 0, 'Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genome, Fungal|Humans|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-10-S1-S56', '19208159', 'Genes show different sensitivities in expression corresponding to various biological conditions. Systematical study of this concept is required because of its important implications in microarray analysis etc. J.H. Ohn et al. first studied this gene property with yeast transcriptional profiling data.'),
(92695, 2009, 'bmc', 'Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting', 'Kim, Jisu|Huang, De-Shuang|Han, Kyungsook', 'School of Computer Science and Engineering, Inha University, Incheon, South Korea. sujiper@inhainan.net|School of Computer Science and Engineering, Inha University, Incheon, South Korea. sujiper@inhainan.net|School of Computer Science and Engineering, Inha University, Incheon, South Korea. sujiper@inhainan.net', '-|-|-', 0, 'Algorithms|Amino Acid Motifs|Computational Biology|Databases, Protein|Humans|Models, Molecular|Proteins', 'Journal Article', '10.1186/1471-2105-10-S1-S57', '19208160', 'Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.'),
(92696, 2009, 'bmc', 'Analysis on relationship between extreme pathways and correlated reaction sets', 'Xi, Yanping|Chen, Yi-Ping Phoebe|Cao, Ming|Wang, Weirong|Wang, Fei', 'School of computer science and technology, Fudan University, Shanghai, PR China. 071021055@fudan.edu.cn|School of computer science and technology, Fudan University, Shanghai, PR China. 071021055@fudan.edu.cn|School of computer science and technology, Fudan University, Shanghai, PR China. 071021055@fudan.edu.cn|School of computer science and technology, Fudan University, Shanghai, PR China. 071021055@fudan.edu.cn|School of computer science and technology, Fudan University, Shanghai, PR China. 071021055@fudan.edu.cn', 'China|China|China|China|China', 4, 'Animals|Computational Biology|Energy Metabolism|Erythrocytes|Escherichia coli|Genome|Humans|Metabolic Networks and Pathways|Phenotype|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-S1-S58', '19208161', 'Constraint-based modeling of reconstructed genome-scale metabolic networks has been successfully applied on several microorganisms. In constraint-based modeling, in order to characterize all allowable phenotypes, network-based pathways, such as extreme pathways and elementary flux modes, are defined. However, as the scale of metabolic network rises, the number of extreme pathways and elementary flux modes increases exponentially. Uniform random sampling solves this problem to some extent to study the contents of the available phenotypes. After uniform random sampling, correlated reaction sets can be identified by the dependencies between reactions derived from sample phenotypes. In this paper, we study the relationship between extreme pathways and correlated reaction sets.'),
(92697, 2009, 'bmc', 'A new graph-based method for pairwise global network alignment', 'Klau, Gunnar W', 'CWI, P,O, Box 94079, 1090 GB Amsterdam, The Netherlands. gunnar.klau@cwi.nl', 'Netherlands', 136, 'Computational Biology|Computer Graphics|Databases, Protein|Metabolic Networks and Pathways|Protein Interaction Mapping|Proteins|Sequence Alignment|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S1-S59', '19208162', 'In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature.'),
(92698, 2009, 'bmc', 'Maximum independent sets of commuting and noninterfering inversions', 'Swenson, Krister M|To, Yokuki|Tang, Jijun|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland. krister.swenson@epfl.ch|Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland. krister.swenson@epfl.ch|Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland. krister.swenson@epfl.ch|Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland. krister.swenson@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 7, 'Algorithms|Chromosome Inversion|Computational Biology|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-S1-S6', '19208163', 'Given three signed permutations, an inversion median is a fourth permutation that minimizes the sum of the pairwise inversion distances between it and the three others. This problem is NP-hard as well as hard to approximate. Yet median-based approaches to phylogenetic reconstruction have been shown to be among the most accurate, especially in the presence of long branches. Most existing approaches have used heuristics that attempt to find a longest sequence of inversions from one of the three permutations that, at each step in the sequence, moves closer to the other two permutations, yet very little is known about the quality of solutions returned by such approaches.'),
(92699, 2009, 'bmc', 'GAIA: a gram-based interaction analysis tool--an approach for identifying interacting domains in yeast', 'Zhang, Kelvin X|Ouellette, B F Francis', 'Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada. kelvin.zhang@oicr.on.ca|Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada. kelvin.zhang@oicr.on.ca', 'Canada|Canada', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computational Biology|Models, Molecular|Molecular Sequence Data|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Protein Structure, Tertiary|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-S1-S60', '19208164', 'Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.'),
(92700, 2009, 'bmc', 'On optimal comparability editing with applications to molecular diagnostics', 'Böcker, Sebastian|Briesemeister, Sebastian|Klau, Gunnar W', 'Institut für Informatik, Friedrich-Schiller-Universität, Jena, Germany. boecker@minet.uni-jena.de|Institut für Informatik, Friedrich-Schiller-Universität, Jena, Germany. boecker@minet.uni-jena.de|Institut für Informatik, Friedrich-Schiller-Universität, Jena, Germany. boecker@minet.uni-jena.de', 'Germany|Germany|Germany', 4, 'Algorithms|Humans|Molecular Diagnostic Techniques|Programming, Linear|Software', 'Comparative Study', '10.1186/1471-2105-10-S1-S61', '19208165', 'The COMPARABILITY EDITING problem appears in the context of hierarchical disease classification based on noisy data. We are given a directed graph G representing hierarchical relationships between patient subgroups. The task is to identify the minimum number of edge insertions or deletions to transform G into a transitive graph, that is, if edges (u, v) and (v, w) are present then edge (u, w) must be present, too.'),
(92701, 2009, 'bmc', 'Genome-scale analysis to the impact of gene deletion on the metabolism of E coli: constraint-based simulation approach', 'Xu, Zixiang|Sun, Xiao|Yu, Shihai', 'State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, PR China. xzx21c@163.com|State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, PR China. xzx21c@163.com|State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, PR China. xzx21c@163.com', 'China|China|China', 0, 'Bacterial Proteins|Computer Simulation|Escherichia coli|Gene Deletion|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genes, Essential|Genome, Bacterial', 'Journal Article', '10.1186/1471-2105-10-S1-S62', '19208166', 'Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy. Some gene deletion studies on in silico organism models at genome-scale have been made, but most of them were from the aspects of distinguishing lethal and non-lethal genes or growth rate. The impact of gene deletion on flux redistribution, the functions and characters of key genes, and the performance of different reactions in entire gene deletion still lack research.'),
(92702, 2009, 'bmc', 'An unsupervised partition method based on association delineated revised mutual information', 'Chen, Jing|Xi, Guangcheng', 'Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 086-100190, PR China. jing.chen@ia.ac.cn|Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 086-100190, PR China. jing.chen@ia.ac.cn', 'China|China', 0, 'Algorithms|Animals|Cluster Analysis|Data Interpretation, Statistical|Diagnosis, Differential|Humans|Medicine, Chinese Traditional|Rats|Research Design|Syndrome', 'Journal Article', '10.1186/1471-2105-10-S1-S63', '19208167', 'The syndrome is the basic pathological unit and the key concept in traditional Chinese medicine (TCM) and the herbal remedy is prescribed according to the syndrome a patient catches. Nevertheless, few studies are dedicated to investigate the number of syndromes and what these syndromes are. Correlative measure based on mutual information can measure arbitrary statistical dependences between discrete and continuous variables.'),
(92703, 2009, 'bmc', 'A new classification method using array Comparative Genome Hybridization data, based on the concept of Limited Jumping Emerging Patterns', 'Gambin, Tomasz|Walczak, Krzysztof', 'Faculty of Electronics and Information Technology of Warsaw University of Technology, Institute of Computer Science, Nowowiejska 15/19, Warsaw, 00-665, Poland. T.Gambin@ii.pw.edu.pl|Faculty of Electronics and Information Technology of Warsaw University of Technology, Institute of Computer Science, Nowowiejska 15/19, Warsaw, 00-665, Poland. T.Gambin@ii.pw.edu.pl', 'Poland|Poland', 7, 'Comparative Genomic Hybridization|Computational Biology|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-10-S1-S64', '19208168', 'Classification using aCGH data is an important and insufficiently investigated problem in bioinformatics. In this paper we propose a new classification method of DNA copy number data based on the concept of limited Jumping Emerging Patterns. We present the comparison of our limJEPClassifier to SVM which is considered the most successful classifier in the case of high-throughput data.'),
(92704, 2009, 'bmc', 'A random forest approach to the detection of epistatic interactions in case-control studies', 'Jiang, Rui|Tang, Wanwan|Wu, Xuebing|Fu, Wenhui', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. ruijiang@tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. ruijiang@tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. ruijiang@tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. ruijiang@tsinghua.edu.cn', 'China|China|China|China', 0, 'Artificial Intelligence|Case-Control Studies|Epistasis, Genetic|Genetic Predisposition to Disease|Genome, Human|Genomics|Humans|Polymorphism, Single Nucleotide|Selection, Genetic', 'Journal Article', '10.1186/1471-2105-10-S1-S65', '19208169', 'The key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the combination explosion course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates.'),
(92705, 2009, 'bmc', 'Partial correlation analysis indicates causal relationships between GC-content, exon density and recombination rate in the human genome', 'Freudenberg, Jan|Wang, Mingyi|Yang, Yaning|Li, Wentian', 'The Robert S Boas Center for Genomics and Human GeneticsFeinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. jan.freudenberg@gmail.com|The Robert S Boas Center for Genomics and Human GeneticsFeinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. jan.freudenberg@gmail.com|The Robert S Boas Center for Genomics and Human GeneticsFeinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. jan.freudenberg@gmail.com|The Robert S Boas Center for Genomics and Human GeneticsFeinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. jan.freudenberg@gmail.com', 'United States|United States|United States|United States', 23, 'Base Composition|Computer Simulation|Evolution, Molecular|Exons|Genome, Human|Genomics|Humans|Recombination, Genetic|Telomere', 'Journal Article', '10.1186/1471-2105-10-S1-S66', '19208170', 'Several features are known to correlate with the GC-content in the human genome, including recombination rate, gene density and distance to telomere. However, by testing for pairwise correlation only, it is impossible to distinguish direct associations from indirect ones and to distinguish between causes and effects.'),
(92706, 2009, 'bmc', 'Copy-number-variation and copy-number-alteration region detection by cumulative plots', 'Li, Wentian|Lee, Annette|Gregersen, Peter K', 'The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. wli@nslij-genetics.org|The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. wli@nslij-genetics.org|The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY 11030, USA. wli@nslij-genetics.org', 'United States|United States|United States', 13, 'Algorithms|Gene Dosage|Genetic Variation|Genome, Human|Genotype|Homozygote|Humans|Mutation', 'Journal Article', '10.1186/1471-2105-10-S1-S67', '19208171', 'Regions with copy number variations (in germline cells) or copy number alteration (in somatic cells) are of great interest for human disease gene mapping and cancer studies. They represent a new type of mutation and are larger-scaled than the single nucleotide polymorphisms. Using genotyping microarray for copy number variation detection has become standard, and there is a need for improving analysis methods.'),
(92707, 2009, 'bmc', 'Identifying disease associations via genome-wide association studies', 'Huang, Wenhui|Wang, Pengyuan|Liu, Zhen|Zhang, Liqing', 'Department of Computer Science, Virginia Tech, 2050 Torgerson Hall, Blacksburg, VA 24061-0106, USA. wenhui@cs.vt.edu|Department of Computer Science, Virginia Tech, 2050 Torgerson Hall, Blacksburg, VA 24061-0106, USA. wenhui@cs.vt.edu|Department of Computer Science, Virginia Tech, 2050 Torgerson Hall, Blacksburg, VA 24061-0106, USA. wenhui@cs.vt.edu|Department of Computer Science, Virginia Tech, 2050 Torgerson Hall, Blacksburg, VA 24061-0106, USA. wenhui@cs.vt.edu', 'United States|United States|United States|United States', 0, 'Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-S1-S68', '19208172', 'Genome-wide association studies prove to be a powerful approach to identify the genetic basis of different human diseases. We studied the relationship between seven diseases characterized in a previous genome-wide association study by the Wellcome Trust Case Control Consortium. Instead of doing a horizontal association of SNPs to diseases, we did a vertical analysis of disease associations by comparing the genetic similarities of diseases. Our analysis was carried out at four levels - the nucleotide level (SNPs), the gene level, the protein level (through protein-protein interaction network), and the phenotype level.'),
(92708, 2009, 'bmc', 'Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies', 'Teber, Erdahl T|Liu, Jason Y|Ballouz, Sara|Fatkin, Diane|Wouters, Merridee A', 'Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au|Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au|Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au|Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au|Victor Chang Cardiac Research Institute, 384 Victoria St, Darlinghurst, 2010, NSW, Australia. e.teber@victorchang.edu.au', 'Australia|Australia|Australia|Australia|Australia', 16, 'Databases, Genetic|Diabetes Mellitus, Type 2|Gene Regulatory Networks|Genetic Predisposition to Disease|Genome, Human|Genome-Wide Association Study|Humans|Models, Genetic|Polymorphism, Single Nucleotide', 'Comparative Study', '10.1186/1471-2105-10-S1-S69', '19208173', 'Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.'),
(92709, 2009, 'bmc', 'Inversion-based genomic signatures', 'Swenson, Krister M|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, EPFL (Swiss Federal Institute of Technology), EPFL-IC-LCBB, INJ 230, Station 14, CH-1014 Lausanne, Switzerland. krister.swenson@epfl.ch|Laboratory for Computational Biology and Bioinformatics, EPFL (Swiss Federal Institute of Technology), EPFL-IC-LCBB, INJ 230, Station 14, CH-1014 Lausanne, Switzerland. krister.swenson@epfl.ch', 'Switzerland|Switzerland', 15, 'Animals|Chromosome Inversion|Evolution, Molecular|Gene Rearrangement|Genome|Humans', 'Journal Article', '10.1186/1471-2105-10-S1-S7', '19208174', 'Reconstructing complete ancestral genomes (at least in terms of their gene inventory and arrangement) is attracting much interest due to the rapidly increasing availability of whole genome sequences. While modest successes have been reported for mammalian and even vertebrate genomes, more divergent groups continue to pose a stiff challenge, mostly because current models of genomic evolution support too many choices.'),
(92710, 2009, 'bmc', 'Advanced analysis and visualization of gene copy number and expression data', 'Autio, Reija|Saarela, Matti|Järvinen, Anna-Kaarina|Hautaniemi, Sampsa|Astola, Jaakko', 'Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland. reija.autio@tut.fi|Department of Signal Processing, Tampere University of Technology, 33101, Tampere, Finland. reija.autio@tut.fi', 'Finland|Finland|Finland|Finland|Finland', 0, 'Comparative Genomic Hybridization|Gene Dosage|Gene Expression|Gene Expression Profiling|Genome, Human|Head and Neck Neoplasms|Humans|Software|Tongue Neoplasms', 'Journal Article', '10.1186/1471-2105-10-S1-S70', '19208175', 'Gene copy number and gene expression values play important roles in cancer initiation and progression. Both can be measured with high-throughput microarrays and some methodologies to integrate and analyze these data exist. However, varying gene sets within different gene expression and copy number microarrays present significant challenges.'),
(92711, 2009, 'bmc', 'CGTS: a site-clustering graph based tagSNP selection algorithm in genotype data', 'Wang, Jun|Guo, Mao-zu|Wang, Chun-yu', 'Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, PR China. wjking@hit.edu.cn|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, PR China. wjking@hit.edu.cn|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, PR China. wjking@hit.edu.cn', 'China|China|China', 6, 'Algorithms|Cluster Analysis|Computational Biology|Genotype|Pattern Recognition, Automated|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-S1-S71', '19208176', 'Recent studies have shown genetic variation is the basis of the genome-wide disease association research. However, due to the high cost on genotyping large number of single nucleotide polymorphisms (SNPs), it is essential to choose a small subset of informative SNPs (tagSNPs), which are able to capture most variation in a population, to represent the rest SNPs. Several methods have been proposed to find the minimum set of tagSNPs, but most of them still have some disadvantages such as information loss and block-partition limit.'),
(92712, 2009, 'bmc', 'Minimizing recombinations in consensus networks for phylogeographic studies', 'Parida, Laxmi|Javed, Asif|Melé, Marta|Calafell, Francesc|Bertranpetit, Jaume|Genographic Consortium', 'Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com|Computational Biology Center, IBM T J Watson Research, Yorktown, USA. parida@us.ibm.com', 'United States|United States|United States|United States|United States|United States', 0, 'Africa|Algorithms|Chromosome Mapping|Chromosomes, Human, X|Gene Regulatory Networks|Genetics, Population|Humans|Models, Genetic|Phylogeny|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-10-S1-S72', '19208177', 'We address the problem of studying recombinational variations in (human) populations. In this paper, our focus is on one computational aspect of the general task: Given two networks G1 and G2, with both mutation and recombination events, defined on overlapping sets of extant units the objective is to compute a consensus network G3 with minimum number of additional recombinations. We describe a polynomial time algorithm with a guarantee that the number of computed new recombination events is within = sz(G1, G2) (function sz is a well-behaved function of the sizes and topologies of G1 and G2) of the optimal number of recombinations. To date, this is the best known result for a network consensus problem.'),
(92713, 2009, 'bmc', 'PCA-based population structure inference with generic clustering algorithms', 'Lee, Chih|Abdool, Ali|Huang, Chun-Hsi', 'Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA. chih.lee@uconn.edu|Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA. chih.lee@uconn.edu|Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA. chih.lee@uconn.edu', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Genetics, Population|Genome, Human|Genotype|Humans|Population Groups', 'Journal Article', '10.1186/1471-2105-10-S1-S73', '19208178', 'Handling genotype data typed at hundreds of thousands of loci is very time-consuming and it is no exception for population structure inference. Therefore, we propose to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms.'),
(92714, 2009, 'bmc', 'A fast algorithm for genome-wide haplotype pattern mining', 'Besenbacher, Søren|Pedersen, Christian N S|Mailund, Thomas', 'Bioinformatics Research Center, University of Aarhus, Denmark. besen@birc.au.dk|Bioinformatics Research Center, University of Aarhus, Denmark. besen@birc.au.dk|Bioinformatics Research Center, University of Aarhus, Denmark. besen@birc.au.dk', 'Denmark|Denmark|Denmark', 2, 'Algorithms|Computational Biology|Databases, Genetic|Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Genome, Human|Haplotypes|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-S1-S74', '19208179', 'Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The Haplotype Pattern Mining (HPM) method is a machine learning approach to do exactly this.'),
(92715, 2009, 'bmc', 'Detecting disease-associated genotype patterns', 'Long, Quan|Zhang, Qingrun|Ott, Jurg', 'Beijing Institute of Genomics, Chinese Academy of Sciences, No, 7 Bei Tu Cheng West Road, Beijing 100029, PR China. ql2@sanger.ac.uk|Beijing Institute of Genomics, Chinese Academy of Sciences, No, 7 Bei Tu Cheng West Road, Beijing 100029, PR China. ql2@sanger.ac.uk|Beijing Institute of Genomics, Chinese Academy of Sciences, No, 7 Bei Tu Cheng West Road, Beijing 100029, PR China. ql2@sanger.ac.uk', 'China|China|China', 0, 'Computer Simulation|Genetic Markers|Genetic Predisposition to Disease|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-S1-S75', '19208180', 'In addition to single-locus (main) effects of disease variants, there is a growing consensus that gene-gene and gene-environment interactions may play important roles in disease etiology. However, for the very large numbers of genetic markers currently in use, it has proven difficult to develop suitable and efficient approaches for detecting effects other than main effects due to single variants.'),
(92716, 2009, 'bmc', 'Triplet supertree heuristics for the tree of life', 'Lin, Harris T|Burleigh, J Gordon|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA, USA. htlin@cs.iastate.edu|Department of Computer Science, Iowa State University, Ames, IA, USA. htlin@cs.iastate.edu|Department of Computer Science, Iowa State University, Ames, IA, USA. htlin@cs.iastate.edu', 'United States|United States|United States', 30, 'Algorithms|Computational Biology|Evolution, Molecular|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-S1-S8', '19208181', 'There is much interest in developing fast and accurate supertree methods to infer the tree of life. Supertree methods combine smaller input trees with overlapping sets of taxa to make a comprehensive phylogenetic tree that contains all of the taxa in the input trees. The intrinsically hard triplet supertree problem takes a collection of input species trees and seeks a species tree (supertree) that maximizes the number of triplet subtrees that it shares with the input trees. However, the utility of this supertree problem has been limited by a lack of efficient and effective heuristics.'),
(92717, 2009, 'bmc', 'Sorting by reversals, block interchanges, tandem duplications, and deletions', 'Bader, Martin', 'Institute of Theoretical Computer Science, University of Ulm, 89069 Ulm, Germany. martin.bader@uni-ulm.de', 'Germany', 46, 'Algorithms|Evolution, Molecular|Gene Deletion|Gene Duplication|Genome|Genomics', 'Journal Article', '10.1186/1471-2105-10-S1-S9', '19208182', 'Finding sequences of evolutionary operations that transform one genome into another is a classic problem in comparative genomics. While most of the genome rearrangement algorithms assume that there is exactly one copy of each gene in both genomes, this does not reflect the biological reality very well--most of the studied genomes contain duplicated gene content, which has to be removed before applying those algorithms. However, dealing with unequal gene content is a very challenging task, and only few algorithms allow operations like duplications and deletions. Almost all of these algorithms restrict these operations to have a fixed size.'),
(92718, 2009, 'bmc', 'Selected proceedings of the First Summit on Translational Bioinformatics 2008', 'Butte, Atul J|Sarkar, Indra Neil|Ramoni, Marco|Lussier, Yves|Troyanskaya, Olga', 'Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 USA. abutte@stanford.edu|Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 USA. abutte@stanford.edu|Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 USA. abutte@stanford.edu|Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 USA. abutte@stanford.edu|Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 USA. abutte@stanford.edu', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Congresses as Topic|Genome|Pharmacogenetics', 'Introductory Journal Article', '10.1186/1471-2105-10-S2-I1', '19208183', '-'),
(92719, 2009, 'bmc', 'Ontology-driven indexing of public datasets for translational bioinformatics', 'Shah, Nigam H|Jonquet, Clement|Chiang, Annie P|Butte, Atul J|Chen, Rong|Musen, Mark A', 'Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu', 'United States|United States|United States|United States|United States|United States', 69, 'Computational Biology|Databases, Factual|Gene Expression Profiling|Genomics|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S2-S1', '19208184', 'The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields describing the pathological state of the studied sample. These annotations are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT. In this work we generalize our methods to map text annotations of gene expression datasets to concepts in the UMLS. We demonstrate the utility of our methods by processing annotations of datasets in the Gene Expression Omnibus. We demonstrate that we enable ontology-based querying and integration of tissue and gene expression microarray data. We enable identification of datasets on specific diseases across both repositories. Our approach provides the basis for ontology-driven data integration for translational research on gene and protein expression data. Based on this work we have built a prototype system for ontology based annotation and indexing of biomedical data. The system processes the text metadata of diverse resource elements such as gene expression data sets, descriptions of radiology images, clinical-trial reports, and PubMed article abstracts to annotate and index them with concepts from appropriate ontologies. The key functionality of this system is to enable users to locate biomedical data resources related to particular ontology concepts.');
