INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92007, 2008, 'bmc', 'Merging microarray data from separate breast cancer studies provides a robust prognostic test', 'Xu, Lei|Tan, Aik Choon|Winslow, Raimond L|Geman, Donald', 'The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu', 'United States|United States|United States|United States', 0, 'Biomarkers, Tumor|Breast Neoplasms|Database Management Systems|Databases, Factual|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Prognosis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-9-125', '18304324', 'There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of developing distant metastases. However, due to the small sample sizes of individual studies, the overlap among signatures is almost zero and their predictive power is often limited. Integrating microarray data from multiple studies in order to increase sample size is therefore a promising approach to the development of more robust prognostic tests.'),
(92008, 2008, 'bmc', 'DOVIS: an implementation for high-throughput virtual screening using AutoDock', 'Zhang, Shuxing|Kumar, Kamal|Jiang, Xiaohui|Wallqvist, Anders|Reifman, Jaques', 'Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. shuzhang@mdanderson.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. shuzhang@mdanderson.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. shuzhang@mdanderson.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. shuzhang@mdanderson.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. shuzhang@mdanderson.org', 'United States|United States|United States|United States|United States', 74, 'Binding Sites|Computer Simulation|Drug Design|Models, Chemical|Pharmaceutical Preparations|Protein Binding|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-126', '18304355', 'Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to in silico screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. However, the development of an integrated HPC system that makes efficient use of its elements is not trivial.'),
(92009, 2008, 'bmc', 'DBMLoc: a Database of proteins with multiple subcellular localizations', 'Zhang, Song|Xia, Xuefeng|Shen, Jincheng|Zhou, Yun|Sun, Zhirong', 'MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China. zhangsong04@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China. zhangsong04@mails.tsinghua.edu.cn', 'China|China|China|China|China', 0, 'Database Management Systems|Databases, Protein|Information Storage and Retrieval|Proteins|Subcellular Fractions|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-127', '18304364', 'Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.'),
(92010, 2008, 'bmc', 'Using quality scores and longer reads improves accuracy of Solexa read mapping', 'Smith, Andrew D|Xuan, Zhenyu|Zhang, Michael Q', 'Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11274, USA. asmith@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11274, USA. asmith@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11274, USA. asmith@cshl.edu', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1186/1471-2105-9-128', '18307793', 'Second-generation sequencing has the potential to revolutionize genomics and impact all areas of biomedical science. New technologies will make re-sequencing widely available for such applications as identifying genome variations or interrogating the oligonucleotide content of a large sample (e.g. ChIP-sequencing). The increase in speed, sensitivity and availability of sequencing technology brings demand for advances in computational technology to perform associated analysis tasks. The Solexa/Illumina 1G sequencer can produce tens of millions of reads, ranging in length from approximately 25-50 nt, in a single experiment. Accurately mapping the reads back to a reference genome is a critical task in almost all applications. Two sources of information that are often ignored when mapping reads from the Solexa technology are the 3 ends of longer reads, which contain a much higher frequency of sequencing errors, and the base-call quality scores.'),
(92011, 2008, 'bmc', 'Term-tissue specific models for prediction of gene ontology biological processes using transcriptional profiles of aging in drosophila melanogaster', 'Zhang, Wensheng|Zou, Sige|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', 6, 'Aging|Animals|Computer Simulation|Drosophila Proteins|Drosophila melanogaster|Gene Expression Profiling|Models, Biological|Organ Specificity|Signal Transduction|Tissue Distribution|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-129', '18307794', 'Predictive classification on the base of gene expression profiles appeared recently as an attractive strategy for identifying the biological functions of genes. Gene Ontology (GO) provides a valuable source of knowledge for model training and validation. The increasing collection of microarray data represents a valuable source for generating functional hypotheses of uncharacterized genes.'),
(92012, 2008, 'bmc', 'Catmap: case-control and TDT meta-analysis package', 'Nicodemus, Kristin K', 'Genes, Cognition and Psychosis Program, Clinical Brain Disorders Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA. kristin.nicodemus@well.ox.ac.uk', 'United States', 48, 'Algorithms|Biometry|Case-Control Studies|Data Interpretation, Statistical|Epidemiologic Methods|Genetic Predisposition to Disease|Humans|Meta-Analysis as Topic|Software', 'Journal Article', '10.1186/1471-2105-9-130', '18307795', 'Risk for complex disease is thought to be controlled by multiple genetic risk factors, each with small individual effects. Meta-analyses of several independent studies may be helpful to increase the ability to detect association when effect sizes are modest. Although many software options are available for meta-analysis of genetic case-control data, no currently available software implements the method described by Kazeem and Farrall (2005), which combines data from independent family-based and case-control studies.'),
(92013, 2008, 'bmc', 'Shape based indexing for faster search of RNA family databases', 'Janssen, Stefan|Reeder, Jens|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany. stefan.janssen@uni-bielefeld.de|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany. stefan.janssen@uni-bielefeld.de|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany. stefan.janssen@uni-bielefeld.de', 'Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-9-131', '18312625', 'Most non-coding RNA families exert their function by means of a conserved, common secondary structure. The Rfam data base contains more than five hundred structurally annotated RNA families. Unfortunately, searching for new family members using covariance models (CMs) is very time consuming. Filtering approaches that use the sequence conservation to reduce the number of CM searches, are fast, but it is unknown to which sacrifice.'),
(92014, 2008, 'bmc', 'Objective and automated protocols for the evaluation of biomedical search engines using No Title Evaluation protocols', 'Campagne, Fabien', 'HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY 10021, USA. fac2003@med.cornell.edu', 'United States', 3, 'Algorithms|Artificial Intelligence|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Software Validation|Vocabulary, Controlled', 'Evaluation Studies', '10.1186/1471-2105-9-132', '18312673', 'The evaluation of information retrieval techniques has traditionally relied on human judges to determine which documents are relevant to a query and which are not. This protocol is used in the Text Retrieval Evaluation Conference (TREC), organized annually for the past 15 years, to support the unbiased evaluation of novel information retrieval approaches. The TREC Genomics Track has recently been introduced to measure the performance of information retrieval for biomedical applications.'),
(92015, 2008, 'bmc', 'A software application for comparing large numbers of high resolution MALDI-FTICR MS spectra demonstrated by searching candidate biomarkers for glioma blood vessel formation', 'Titulaer, Mark K|Mustafa, Dana A N|Siccama, Ivar|Konijnenburg, Marco|Burgers, Peter C|Andeweg, Arno C|Smitt, Peter A E Sillevis|Kros, Johan M|Luider, Theo M', 'Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl|Department of Neurology, Laboratory of Neuro-Oncology, Clinical and Cancer Proteomics, Erasmus Medical Center, Dr, Molewaterplein 50, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.titulaer@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 14, 'Algorithms|Angiogenic Proteins|Biomarkers, Tumor|Cyclotrons|Glioma|Humans|Neoplasm Proteins|Neovascularization, Pathologic|Peptide Mapping|Sensitivity and Specificity|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Spectroscopy, Fourier Transform Infrared', 'Evaluation Studies', '10.1186/1471-2105-9-133', '18312684', 'A Java application is presented, which compares large numbers (n > 100) of raw FTICR mass spectra from patients and controls. Two peptide profile matrices can be produced simultaneously, one with occurrences of peptide masses in samples and another with the intensity of common peak masses in all the measured samples, using the peak- and background intensities of the raw data. In latter way, more significantly differentially expressed peptides are found between groups than just using the presence or absence in samples of common peak masses. The software application is tested by searching angiogenesis related proteins in glioma by comparing laser capture micro dissected- and enzymatic by trypsin digested tissue sections.'),
(92016, 2008, 'bmc', 'Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling', 'Shieh, Grace S|Chen, Chung-Ming|Yu, Ching-Yun|Huang, Juiling|Wang, Woei-Fuh|Lo, Yi-Chen', 'Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan. gshieh@stat.sinica.edu.tw', '-|-|-|-|-|-', 0, 'Computer Simulation|Gene Expression Regulation|Models, Biological|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-9-134', '18312694', 'With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.'),
(92017, 2008, 'bmc', 'Functional discrimination of membrane proteins using machine learning techniques', 'Gromiha, M Michael|Yabuki, Yukimitsu', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp', 'Japan|Japan', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Discriminant Analysis|Membrane Proteins|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-135', '18312695', 'Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters.'),
(92018, 2008, 'bmc', 'Bioinformatics research in the Asia Pacific: a 2007 update', 'Ranganathan, Shoba|Gribskov, Michael|Tan, Tin Wee', 'Department of Chemistry and Biomolecular Sciences and Biotechnology Research Institute, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au|Department of Chemistry and Biomolecular Sciences and Biotechnology Research Institute, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au|Department of Chemistry and Biomolecular Sciences and Biotechnology Research Institute, Macquarie University, Sydney NSW 2109, Australia. shoba.ranganathan@mq.edu.au', 'Australia|Australia|Australia', 0, 'Asia|Biomedical Research|Computational Biology|Genomics|Pacific Islands', 'Introductory Journal Article', '10.1186/1471-2105-9-S1-S1', '18315840', 'We provide a 2007 update on the bioinformatics research in the Asia-Pacific from the Asia Pacific Bioinformatics Network (APBioNet), Asias oldest bioinformatics organisation set up in 1998. From 2002, APBioNet has organized the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2007 Conference was organized as the 6th annual conference of the Asia-Pacific Bioinformatics Network, on Aug. 27-30, 2007 at Hong Kong, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea) and New Delhi (India). Besides a scientific meeting at Hong Kong, satellite events organized are a pre-conference training workshop at Hanoi, Vietnam and a post-conference workshop at Nansha, China. This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. We have organized the papers into thematic areas, highlighting the growing contribution of research excellence from this region, to global bioinformatics endeavours.'),
(92019, 2008, 'bmc', 'In silico analysis of expressed sequence tags from Trichostrongylus vitrinus (Nematoda): comparison of the automated ESTExplorer workflow platform with conventional database searches', 'Nagaraj, Shivashankar H|Gasser, Robin B|Nisbet, Alasdair J|Ranganathan, Shoba', 'Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. snagaraj@els.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. snagaraj@els.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. snagaraj@els.mq.edu.au|Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. snagaraj@els.mq.edu.au', 'Australia|Australia|Australia|Australia', 17, 'Animals|Base Sequence|Databases, Genetic|Expressed Sequence Tags|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Species Specificity|Transcription Factors|Trichostrongylus', 'Comparative Study', '10.1186/1471-2105-9-S1-S10', '18315841', 'The analysis of expressed sequence tags (EST) offers a rapid and cost effective approach to elucidate the transcriptome of an organism, but requires several computational methods for assembly and annotation. Researchers frequently analyse each step manually, which is laborious and time consuming. We have recently developed ESTExplorer, a semi-automated computational workflow system, in order to achieve the rapid analysis of EST datasets. In this study, we evaluated EST data analysis for the parasitic nematode Trichostrongylus vitrinus (order Strongylida) using ESTExplorer, compared with database matching alone.'),
(92020, 2008, 'bmc', 'Analyzing the simplicial decomposition of spatial protein structures', 'Ordög, Rafael|Szabadka, Zoltán|Grolmusz, Vince', 'Protein Information Technology Group, Department of Computer Science, Eötvös University, Pázmány P, stny., 1/C, H-1117 Budapest, Hungary. devill@cs.elte.hu|Protein Information Technology Group, Department of Computer Science, Eötvös University, Pázmány P, stny., 1/C, H-1117 Budapest, Hungary. devill@cs.elte.hu|Protein Information Technology Group, Department of Computer Science, Eötvös University, Pázmány P, stny., 1/C, H-1117 Budapest, Hungary. devill@cs.elte.hu', 'Hungary|Hungary|Hungary', 7, 'Algorithms|Amino Acid Sequence|Computer Simulation|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S1-S11', '18315842', 'The fast growing Protein Data Bank contains the three-dimensional description of more than 45000 protein- and nucleic-acid structures today. The large majority of the data in the PDB are measured by X-ray crystallography by thousands of researchers in millions of work-hours. Unfortunately, lots of structural errors, bad labels, missing atoms, falsely identified chains and groups make dificult the automated processing of this treasury of structural biological data.'),
(92021, 2008, 'bmc', 'Improved general regression network for protein domain boundary prediction', 'Yoo, Paul D|Sikder, Abdur R|Zhou, Bing Bing|Zomaya, Albert Y', 'Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au', 'Australia|Australia|Australia|Australia', 18, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Structure, Tertiary|Proteins|Regression Analysis|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S1-S12', '18315843', 'Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence.'),
(92022, 2008, 'bmc', 'Functional site prediction selects correct protein models', 'Chelliah, Vijayalakshmi|Taylor, William R', 'Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, Mill Hill, London NW7 1AA, UK. vchelli@nimr.mrc.ac.uk|Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, Mill Hill, London NW7 1AA, UK. vchelli@nimr.mrc.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-S1-S13', '18315844', 'The prediction of protein structure can be facilitated by the use of constraints based on a knowledge of functional sites. Without this information it is still possible to predict which residues are likely to be part of a functional site and this information can be used to select model structures from a variety of alternatives that would correspond to a functional protein.'),
(92023, 2008, 'bmc', 'Modelling the structures of G protein-coupled receptors aided by three-dimensional validation', 'Dastmalchi, Siavoush|Church, W Bret|Morris, Michael B', 'School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran. dastmalchi.s@tbzmed.ac.ir|School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran. dastmalchi.s@tbzmed.ac.ir|School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 51664, Iran. dastmalchi.s@tbzmed.ac.ir', '-|-|-', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Receptors, G-Protein-Coupled|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-9-S1-S14', '18315845', 'G protein-coupled receptors (GPCRs) are abundant, activate complex signalling and represent the targets for up to approximately 60% of pharmaceuticals but there is a paucity of structural data. Bovine rhodopsin is the first GPCR for which high-resolution structures have been completed but significant variations in structure are likely to exist among the GPCRs. Because of this, considerable effort has been expended on developing in silico tools for refining structures of individual GPCRs. We have developed REPIMPS, a modification of the inverse-folding software Profiles-3D, to assess and predict the rotational orientation and vertical position of helices within the helix bundle of individual GPCRs. We highlight the value of the method by applying it to the Baldwin GPCR template but the method can, in principle, be applied to any low- or high-resolution membrane protein template or structure.'),
(92024, 2008, 'bmc', 'Modeling Escherichia coli signal peptidase complex with bound substrate: determinants in the mature peptide influencing signal peptide cleavage', 'Choo, Khar Heng|Tong, Joo Chuan|Ranganathan, Shoba', 'Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. khchoo@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. khchoo@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. khchoo@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Enzyme Activation|Escherichia coli Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Disulfide-Isomerases|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-S1-S15', '18315846', 'Type I signal peptidases (SPases) are essential membrane-bound serine proteases responsible for the cleavage of signal peptides from proteins that are translocated across biological membranes. The crystal structure of SPase in complex with signal peptide has not been solved and their substrate-binding site and binding specificities remain poorly understood. We report here a structure-based model for Escherichia coli DsbA 13-25 in complex with its endogenous type I SPase.'),
(92025, 2008, 'bmc', 'Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors', 'Yanamala, Naveena|Tirupula, Kalyan C|Klein-Seetharaman, Judith', 'Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA. naveena@cs.cmu.edu|Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA. naveena@cs.cmu.edu|Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA. naveena@cs.cmu.edu', 'United States|United States|United States', 27, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Isomerism|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Receptors, Metabotropic Glutamate|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-S1-S16', '18315847', 'Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects--enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand--despite the extensive differences in sequences between mGluRs and rhodopsin.'),
(92026, 2008, 'bmc', 'Stability of the core domain of p53: insights from computer simulations', 'Madhumalar, Arumugam|Smith, Derek John|Verma, Chandra', 'Bioinformatics Institute, A-STAR (Agency for Science, Technology & Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671. madhumalar@bii.a-star.edu.sg|Bioinformatics Institute, A-STAR (Agency for Science, Technology & Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671. madhumalar@bii.a-star.edu.sg|Bioinformatics Institute, A-STAR (Agency for Science, Technology & Research), 30 Biopolis Street, #07-01 Matrix, Singapore 138671. madhumalar@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, Protein|Structure-Activity Relationship|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-9-S1-S17', '18315848', 'The tumour suppressor protein p53 protein has a core domain that binds DNA and is the site for most oncogenic mutations. This domain is quite unstable compared to its homologs p63 and p73. Two key residues in the core domain of p53 (Tyr236, Thr253), have been mutated in-silico, to their equivalent residues in p63 (Phe238 and Ile255) and p73 (Phe238 and Ile255), with subsequent increase in stability of p53. Computational studies have been performed to examine the basis of instability in p53.'),
(92027, 2008, 'bmc', 'Identification of human-to-human transmissibility factors in PB2 proteins of influenza A by large-scale mutual information analysis', 'Miotto, Olivo|Heiny, At|Tan, Tin Wee|August, J Thomas|Brusic, Vladimir', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. olivo@nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. olivo@nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. olivo@nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. olivo@nus.edu.sg|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore. olivo@nus.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 66, 'Amino Acid Sequence|Disease Transmission, Infectious|Humans|Influenza, Human|Molecular Sequence Data|RNA Replicase|Sequence Analysis, Protein|Structure-Activity Relationship|Viral Proteins', 'Journal Article', '10.1186/1471-2105-9-S1-S18', '18315849', 'The identification of mutations that confer unique properties to a pathogen, such as host range, is of fundamental importance in the fight against disease. This paper describes a novel method for identifying amino acid sites that distinguish specific sets of protein sequences, by comparative analysis of matched alignments. The use of mutual information to identify distinctive residues responsible for functional variants makes this approach highly suitable for analyzing large sets of sequences. To support mutual information analysis, we developed the AVANA software, which utilizes sequence annotations to select sets for comparison, according to user-specified criteria. The method presented was applied to an analysis of influenza A PB2 protein sequences, with the objective of identifying the components of adaptation to human-to-human transmission, and reconstructing the mutation history of these components.'),
(92028, 2008, 'bmc', 'Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes', 'Zhang, Guang Lan|Khan, Asif M|Srinivasan, Kellathur N|Heiny, At|Lee, Kx|Kwoh, Chee Keong|August, J Thomas|Brusic, Vladimir', 'Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. guanglanzhang@gmail.com', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Amino Acid Sequence|Binding Sites|Epitope Mapping|Epitopes, T-Lymphocyte|Immunodominant Epitopes|Molecular Sequence Data|Protein Binding|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S1-S19', '18315850', 'T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.'),
(92029, 2008, 'bmc', 'SNP@Promoter: a database of human SNPs (single nucleotide polymorphisms) within the putative promoter regions', 'Kim, Byoung-Chul|Kim, Woo-Yeon|Park, Daeui|Chung, Won-Hyong|Shin, Kwang-Sik|Bhak, Jong', 'Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr|Korean BioInformation Center (KOBIC), KRIBB, Daejeon 305-806, Korea. bckim@kribb.re.kr', '-|-|-|-|-|-', 0, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Genome, Human|Humans|Information Storage and Retrieval|Molecular Sequence Data|Polymorphism, Single Nucleotide|Promoter Regions, Genetic|RNA, Untranslated|Regulatory Elements, Transcriptional|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S1-S2', '18315851', 'Analysis of single nucleotide polymorphism (SNP) is becoming a key research in genomics fields. Many functional analyses of SNPs have been carried out for coding regions and splicing sites that can alter proteins and mRNA splicing. However, SNPs in non-coding regulatory regions can also influence important biological regulation. Presently, there are few databases for SNPs in non-coding regulatory regions.'),
(92030, 2008, 'bmc', 'SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp PCC 6803', 'Kim, Woo-Yeon|Kang, Sungsoo|Kim, Byoung-Chul|Oh, Jeehyun|Cho, Seongwoong|Bhak, Jong|Choi, Jong-Soon', 'Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr|Korean BioInformation Center, KRIBB, Daejeon 305-806, Korea. kimplove@kribb.re.kr', '-|-|-|-|-|-|-', 11, 'Bacterial Proteins|Database Management Systems|Databases, Protein|Protein Interaction Mapping|Synechocystis|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S1-S20', '18315852', 'Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date.'),
(92031, 2008, 'bmc', 'Motif-directed network component analysis for regulatory network inference', 'Wang, Chen|Xuan, Jianhua|Chen, Li|Zhao, Po|Wang, Yue|Clarke, Robert|Hoffman, Eric', 'Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. topsoil@vt.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Base Sequence|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-S1-S21', '18315853', 'Network Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies due to limited topology information available, such as lack of ChIP-on-chip data. We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks.'),
(92032, 2008, 'bmc', 'Tandem duplication, circular permutation, molecular adaptation: how Solanaceae resist pests via inhibitors', 'Kong, Lesheng|Ranganathan, Shoba', 'Computational Biology Group, Temasek Life Sciences Laboratory, 1 Reseach Link National University of Singapore, Singapore 117604. lesheng@tll.org.sg|Computational Biology Group, Temasek Life Sciences Laboratory, 1 Reseach Link National University of Singapore, Singapore 117604. lesheng@tll.org.sg', 'Singapore|Singapore', 0, 'Adaptation, Physiological|Arabidopsis Proteins|DNA Mutational Analysis|Evolution, Molecular|Mutation|Solanaceae|Structure-Activity Relationship|Tandem Repeat Sequences|Telomere-Binding Proteins', 'Journal Article', '10.1186/1471-2105-9-S1-S22', '18315854', 'The Potato type II (Pot II) family of proteinase inhibitors plays critical roles in the defense system of plants from Solanaceae family against pests. To better understand the evolution of this family, we investigated the correlation between sequence and structural repeats within this family and the evolution and molecular adaptation of Pot II genes through computational analysis, using the putative ancestral domain sequence as the basic repeat unit.'),
(92033, 2008, 'bmc', 'Comparative phyloinformatics of virus genes at micro and macro levels in a distributed computing environment', 'Singh, Dadabhai T|Trehan, Rahul|Schmidt, Bertil|Bretschneider, Timo', 'Genvea Biosciences, 53 Craig Road, #04-01, Singapore 089691. dtsingh@genvea.com|Genvea Biosciences, 53 Craig Road, #04-01, Singapore 089691. dtsingh@genvea.com|Genvea Biosciences, 53 Craig Road, #04-01, Singapore 089691. dtsingh@genvea.com|Genvea Biosciences, 53 Craig Road, #04-01, Singapore 089691. dtsingh@genvea.com', 'Singapore|Singapore|Singapore|Singapore', 0, 'Computational Biology|Computer Communication Networks|Computing Methodologies|DNA Mutational Analysis|Evolution, Molecular|Influenza A Virus, H5N1 Subtype|Neuraminidase|Phylogeny|Viral Proteins', 'Journal Article', '10.1186/1471-2105-9-S1-S23', '18315855', 'Preparedness for a possible global pandemic caused by viruses such as the highly pathogenic influenza A subtype H5N1 has become a global priority. In particular, it is critical to monitor the appearance of any new emerging subtypes. Comparative phyloinformatics can be used to monitor, analyze, and possibly predict the evolution of viruses. However, in order to utilize the full functionality of available analysis packages for large-scale phyloinformatics studies, a team of computer scientists, biostatisticians and virologists is needed--a requirement which cannot be fulfilled in many cases. Furthermore, the time complexities of many algorithms involved leads to prohibitive runtimes on sequential computer platforms. This has so far hindered the use of comparative phyloinformatics as a commonly applied tool in this area.'),
(92034, 2008, 'bmc', 'Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles', 'Tsai, Richard Tzong-Han|Hung, Hsi-Chuan|Dai, Hong-Jie|Lin, Yi-Wen|Hsu, Wen-Lian', 'Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taoyuan 32003, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw', '-|-|-|-|-', 0, 'Abstracting and Indexing as Topic|Database Management Systems|Databases, Factual|Documentation|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S1-S3', '18315856', 'Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92035, 2008, 'bmc', 'Transmembrane helix prediction using amino acid property features and latent semantic analysis', 'Ganapathiraju, Madhavi|Balakrishnan, N|Reddy, Raj|Klein-Seetharaman, Judith', 'Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA. madhavi+@cs.cmu.edu|Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA. madhavi+@cs.cmu.edu|Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA. madhavi+@cs.cmu.edu|Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA. madhavi+@cs.cmu.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Membrane Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Semantics|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S1-S4', '18315857', 'Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology cytoplasmic-transmembrane-extracellular and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families.'),
(92036, 2008, 'bmc', 'Towards ontology-driven navigation of the lipid bibliosphere', 'Baker, Christopher Jo|Kanagasabai, Rajaraman|Ang, Wee Tiong|Veeramani, Anitha|Low, Hong-Sang|Wenk, Markus R', 'Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg|Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg|Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg|Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg|Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg|Department of Data Mining, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613. cbaker@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Bibliometrics|Database Management Systems|Databases, Factual|Humans|Information Storage and Retrieval|Lipid Metabolism|Lipids|Metabolic Diseases|Natural Language Processing|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-9-S1-S5', '18315858', 'The indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. The emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimers syndrome, Mycobacterium infections and cancer.'),
(92037, 2008, 'bmc', 'GORouter: an RDF model for providing semantic query and inference services for Gene Ontology and its associations', 'Xu, Qingwei|Shi, Yixiang|Lu, Qiang|Zhang, Guoqing|Luo, Qingming|Li, Yixue', 'The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn|The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn|The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn|The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn|The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn|The Key Laboratory of Biomedical Photonics of the Ministry of Education, HUST, Wuhan 430074, China. xuqingwei@sina.com.cn', 'China|China|China|China|China|China', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Genes|Information Storage and Retrieval|Models, Genetic|Natural Language Processing|Software', 'Journal Article', '10.1186/1471-2105-9-S1-S6', '18315859', 'The most renowned biological ontology, Gene Ontology (GO) is widely used for annotations of genes and gene products of different organisms. However, there are shortcomings in the Resource Description Framework (RDF) data file provided by the GO consortium: 1) Lack of sufficient semantic relationships between pairs of terms coming from the three independent GO sub-ontologies, that limit the power to provide complex semantic queries and inference services based on it. 2) The term-centric view of GO annotation data and the fact that all information is stored in a single file. This makes attempts to retrieve GO annotations based on big volume datasets unmanageable. 3) No support of GOSlim.'),
(92038, 2008, 'bmc', 'Rule-based knowledge aggregation for large-scale protein sequence analysis of influenza A viruses', 'Miotto, Olivo|Tan, Tin Wee|Brusic, Vladimir', 'Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore. olivo@nus.edu.sg|Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore. olivo@nus.edu.sg|Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore. olivo@nus.edu.sg', 'Singapore|Singapore|Singapore', 9, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Database Management Systems|Databases, Protein|Influenza A virus|Information Storage and Retrieval|Meta-Analysis as Topic|Molecular Sequence Data|Sequence Analysis, Protein|Viral Proteins', 'Journal Article', '10.1186/1471-2105-9-S1-S7', '18315860', 'The explosive growth of biological data provides opportunities for new statistical and comparative analyses of large information sets, such as alignments comprising tens of thousands of sequences. In such studies, sequence annotations frequently play an essential role, and reliable results depend on metadata quality. However, the semantic heterogeneity and annotation inconsistencies in biological databases greatly increase the complexity of aggregating and cleaning metadata. Manual curation of datasets, traditionally favoured by life scientists, is impractical for studies involving thousands of records. In this study, we investigate quality issues that affect major public databases, and quantify the effectiveness of an automated metadata extraction approach that combines structural and semantic rules. We applied this approach to more than 90,000 influenza A records, to annotate sequences with protein name, virus subtype, isolate, host, geographic origin, and year of isolation.'),
(92039, 2008, 'bmc', 'The unique probe selector: a comprehensive web service for probe design and oligonucleotide arrays', 'Chen, Shu-Hwa|Lo, Chen-Zen|Tsai, Ming-Chi|Hsiung, Chao A|Lin, Chung-Yen', 'Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica, No, 128 Academia Rd, Sec. 2, Taipei 115, Taiwan. seven@gate.sinica.edu.tw|Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica, No, 128 Academia Rd, Sec. 2, Taipei 115, Taiwan. seven@gate.sinica.edu.tw|Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica, No, 128 Academia Rd, Sec. 2, Taipei 115, Taiwan. seven@gate.sinica.edu.tw|Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica, No, 128 Academia Rd, Sec. 2, Taipei 115, Taiwan. seven@gate.sinica.edu.tw|Stem Cell/Regenerative Medicine Program, Genomics Research Center, Academia Sinica, No, 128 Academia Rd, Sec. 2, Taipei 115, Taiwan. seven@gate.sinica.edu.tw', '-|-|-|-|-', 0, 'Base Sequence|DNA Probes|Equipment Design|Equipment Failure Analysis|Gene Targeting|In Situ Hybridization|Internet|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S1-S8', '18315861', 'Nucleic acid hybridization, a fundamental technique in molecular biology, can be modified into very effective and sensitive methods for detecting particular targets mixed with millions of non-target sequences. Therefore, avoiding cross-hybridization is the most crucial issue for developing diagnostic methods based on hybridization.'),
(92040, 2008, 'bmc', 'Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments', 'Zhao, Hongya|Chan, Kwok-Leung|Cheng, Lee-Ming|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 6, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-9-S1-S9', '18315862', 'Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is.'),
(92041, 2008, 'bmc', 'Mining phenotypes for gene function prediction', 'Groth, Philip|Weiss, Bertram|Pohlenz, Hans-Dieter|Leser, Ulf', 'Research Laboratories of Bayer Schering Pharma AG, Berlin, Germany. groth@informatik.hu-berlin.de|Research Laboratories of Bayer Schering Pharma AG, Berlin, Germany. groth@informatik.hu-berlin.de|Research Laboratories of Bayer Schering Pharma AG, Berlin, Germany. groth@informatik.hu-berlin.de|Research Laboratories of Bayer Schering Pharma AG, Berlin, Germany. groth@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany', 43, 'Algorithms|Databases, Protein|Information Storage and Retrieval|Multigene Family|Natural Language Processing|Phenotype|Protein Interaction Mapping|Proteome', 'Journal Article', '10.1186/1471-2105-9-136', '18315868', 'Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships.'),
(92042, 2008, 'bmc', 'How reliably can we predict the reliability of protein structure predictions?', 'Miklós, István|Novák, Adám|Dombai, Balázs|Hein, Jotun', 'Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK. miklos@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK. miklos@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK. miklos@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG Oxford, UK. miklos@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Algorithms|Amino Acid Sequence|Computer Simulation|Data Interpretation, Statistical|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-137', '18315874', 'Comparative methods have been the standard techniques for in silico protein structure prediction. The prediction is based on a multiple alignment that contains both reference sequences with known structures and the sequence whose unknown structure is predicted. Intensive research has been made to improve the quality of multiple alignments, since misaligned parts of the multiple alignment yield misleading predictions. However, sometimes all methods fail to predict the correct alignment, because the evolutionary signal is too weak to find the homologous parts due to the large number of mutations that separate the sequences.'),
(92043, 2008, 'bmc', 'Goldsurfer2 (Gs2): a comprehensive tool for the analysis and visualization of genome wide association studies', 'Pettersson, Fredrik|Morris, Andrew P|Barnes, Michael R|Cardon, Lon R', 'Dept Bioinformatics, Wellcome Trust Centre, Oxford, UK. fredrikp@well.ox.ac.uk|Dept Bioinformatics, Wellcome Trust Centre, Oxford, UK. fredrikp@well.ox.ac.uk|Dept Bioinformatics, Wellcome Trust Centre, Oxford, UK. fredrikp@well.ox.ac.uk|Dept Bioinformatics, Wellcome Trust Centre, Oxford, UK. fredrikp@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Linkage Disequilibrium|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-138', '18318908', 'Genome wide association (GWA) studies are now being widely undertaken aiming to find the link between genetic variations and common diseases. Ideally, a well-powered GWA study will involve the measurement of hundreds of thousands of single nucleotide polymorphisms (SNPs) in thousands of individuals. The sheer volume of data generated by these experiments creates very high analytical demands. There are a number of important steps during the analysis of such data, many of which may present severe bottlenecks. The data need to be imported and reviewed to perform initial quality control (QC) before proceeding to association testing. Evaluation of results may involve further statistical analysis, such as permutation testing, or further QC of associated markers, for example, reviewing raw genotyping intensities. Finally significant associations need to be prioritised using functional and biological interpretation methods, browsing available biological annotation, pathway information and patterns of linkage disequilibrium (LD).'),
(92044, 2008, 'bmc', 'Information transfer in signaling pathways: a study using coupled simulated and experimental data', 'Pahle, Jürgen|Green, Anne K|Dixon, C Jane|Kummer, Ursula', 'Bioinformatics and Computational Biochemistry, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany. juergen@pahle.de|Bioinformatics and Computational Biochemistry, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany. juergen@pahle.de|Bioinformatics and Computational Biochemistry, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany. juergen@pahle.de|Bioinformatics and Computational Biochemistry, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany. juergen@pahle.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Information Storage and Retrieval|Models, Biological|Models, Statistical|Proteins|Signal Transduction|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-139', '18318909', 'The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the amplitude, frequency and waveform of the oscillatory Ca(2+)-signal.'),
(92045, 2008, 'bmc', 'Normalization of oligonucleotide arrays based on the least-variant set of genes', 'Calza, Stefano|Valentini, Davide|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. calza@med.unibs.it|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. calza@med.unibs.it|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. calza@med.unibs.it', 'Sweden|Sweden|Sweden', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Genes|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-140', '18318917', 'It is well known that the normalization step of microarray data makes a difference in the downstream analysis. All normalization methods rely on certain assumptions, so differences in results can be traced to different sensitivities to violation of the assumptions. Illustrating the lack of robustness, in a striking spike-in experiment all existing normalization methods fail because of an imbalance between up- and down-regulated genes. This means it is still important to develop a normalization method that is robust against violation of the standard assumptions'),
(92046, 2008, 'bmc', 'Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence', 'Barbosa-Silva, Adriano|Satagopam, Venkata P|Schneider, Reinhard|Ortega, J Miguel', 'Laboratório de Biodados, Dep, Bioquímica e Imunologia, Instituto de Ciências Biológicas, UFMG, Av, Antônio Carlos 6627, Belo Horizonte, MG, Brasil. barbosa@embl.de|Laboratório de Biodados, Dep, Bioquímica e Imunologia, Instituto de Ciências Biológicas, UFMG, Av, Antônio Carlos 6627, Belo Horizonte, MG, Brasil. barbosa@embl.de|Laboratório de Biodados, Dep, Bioquímica e Imunologia, Instituto de Ciências Biológicas, UFMG, Av, Antônio Carlos 6627, Belo Horizonte, MG, Brasil. barbosa@embl.de|Laboratório de Biodados, Dep, Bioquímica e Imunologia, Instituto de Ciências Biológicas, UFMG, Av, Antônio Carlos 6627, Belo Horizonte, MG, Brasil. barbosa@embl.de', '-|-|-|-', 4, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Molecular Sequence Data|Multigene Family|Pattern Recognition, Automated|Proteome|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-9-141', '18321373', 'Modern proteomes evolved by modification of pre-existing ones. It is extremely important to comparative biology that related proteins be identified as members of the same cognate group, since a characterized putative homolog could be used to find clues about the function of uncharacterized proteins from the same group. Typically, databases of related proteins focus on those from completely-sequenced genomes. Unfortunately, relatively few organisms have had their genomes fully sequenced, accordingly, many proteins are ignored by the currently available databases of cognate proteins, despite the high amount of important genes that are functionally described only for these incomplete proteomes.'),
(92047, 2008, 'bmc', 'Ranking analysis of F-statistics for microarray data', 'Tan, Yuan-De|Fornage, Myriam|Xu, Hongyan', 'College of Life Sciences, Hunan Normal University, Changsha, 410081, China. tanyuande@hotmail.com|College of Life Sciences, Hunan Normal University, Changsha, 410081, China. tanyuande@hotmail.com|College of Life Sciences, Hunan Normal University, Changsha, 410081, China. tanyuande@hotmail.com', 'China|China|China', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Proteome', 'Journal Article', '10.1186/1471-2105-9-142', '18325100', 'Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data.'),
(92048, 2008, 'bmc', 'Discovering gene annotations in biomedical text databases', 'Cakmak, Ali|Ozsoyoglu, Gultekin', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA. ali.cakmak@case.edu|Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA. ali.cakmak@case.edu', 'United States|United States', 5, 'Artificial Intelligence|Database Management Systems|Genes|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Proteins|PubMed|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-143', '18325104', 'Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.'),
(92049, 2008, 'bmc', 'Empirical Bayes analysis of single nucleotide polymorphisms', 'Schwender, Holger|Ickstadt, Katja', 'Collaborative Research Center 475, Faculty of Statistics, Dortmund University of Technology, 44221 Dortmund, Germany. holger.schw@gmx.de|Collaborative Research Center 475, Faculty of Statistics, Dortmund University of Technology, 44221 Dortmund, Germany. holger.schw@gmx.de', 'Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|DNA|DNA Mutational Analysis|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/1471-2105-9-144', '18325106', 'An important goal of whole-genome studies concerned with single nucleotide polymorphisms (SNPs) is the identification of SNPs associated with a covariate of interest such as the case-control status or the type of cancer. Since these studies often comprise the genotypes of hundreds of thousands of SNPs, methods are required that can cope with the corresponding multiple testing problem. For the analysis of gene expression data, approaches such as the empirical Bayes analysis of microarrays have been developed particularly for the detection of genes associated with the response. However, the empirical Bayes analysis of microarrays has only been suggested for binary responses when considering expression values, i.e. continuous predictors.'),
(92050, 2008, 'bmc', 'ComPath: comparative enzyme analysis and annotation in pathway/subsystem contexts', 'Choi, Kwangmin|Kim, Sun', 'School of Informatics, Indiana University, Bloomington, IN 47408, USA. kwchoi@indiana.edu|School of Informatics, Indiana University, Bloomington, IN 47408, USA. kwchoi@indiana.edu', 'India|India', 5, 'Algorithms|Amino Acid Sequence|Enzymes|Molecular Sequence Data|Multienzyme Complexes|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-145', '18325116', 'Once a new genome is sequenced, one of the important questions is to determine the presence and absence of biological pathways. Analysis of biological pathways in a genome is a complicated task since a number of biological entities are involved in pathways and biological pathways in different organisms are not identical. Computational pathway identification and analysis thus involves a number of computational tools and databases and typically done in comparison with pathways in other organisms. This computational requirement is much beyond the capability of biologists, so information systems for reconstructing, annotating, and analyzing biological pathways are much needed. We introduce a new comparative pathway analysis workbench, ComPath, which integrates various resources and computational tools using an interactive spreadsheet-style web interface for reliable pathway analyses.'),
(92051, 2008, 'bmc', 'Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions', 'Mechanic, Leah E|Luke, Brian T|Goodman, Julie E|Chanock, Stephen J|Harris, Curtis C', 'Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov|Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov|Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov|Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov|Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov', 'United States|United States|United States|United States|United States', 50, 'Algorithms|Base Sequence|DNA Mutational Analysis|Molecular Sequence Data|Multigene Family|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Protein Interaction Mapping|Proteome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-146', '18325117', 'The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.'),
(92052, 2008, 'bmc', 'Conditional clustering of temporal expression profiles', 'Wang, Ling|Montano, Monty|Rarick, Matt|Sebastiani, Paola', 'Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA. ling-1.wang@novartis.com|Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA. ling-1.wang@novartis.com|Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA. ling-1.wang@novartis.com|Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA. ling-1.wang@novartis.com', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-147', '18334028', 'Many microarray experiments produce temporal profiles in different biological conditions but common cluster techniques are not able to analyze the data conditional on the biological conditions.'),
(92053, 2008, 'bmc', 'Testing the Coulomb/Accessible Surface Area solvent model for protein stability, ligand binding, and protein design', 'am Busch, Marcel Schmidt|Lopes, Anne|Amara, Najette|Bathelt, Christine|Simonson, Thomas', 'Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. marcel.schmidt-am-busch@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. marcel.schmidt-am-busch@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. marcel.schmidt-am-busch@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. marcel.schmidt-am-busch@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. marcel.schmidt-am-busch@polytechnique.edu', 'France|France|France|France|France', 25, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Drug Stability|Ligands|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Solvents', 'Journal Article', '10.1186/1471-2105-9-148', '18366628', 'Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters.'),
(92054, 2008, 'bmc', 'Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses', 'Natowicz, René|Incitti, Roberto|Horta, Euler Guimarães|Charles, Benoît|Guinot, Philippe|Yan, Kai|Coutant, Charles|Andre, Fabrice|Pusztai, Lajos|Rouzier, Roman', 'AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr|AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France. natowicr@esiee.fr', 'France|France|France|France|France|France|France|France|France|France', 0, 'Antineoplastic Agents|Biomarkers, Tumor|Breast Neoplasms|DNA Probes|Female|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Outcome Assessment (Health Care)|Preoperative Care|Prognosis|Reproducibility of Results|Sensitivity and Specificity|Treatment Outcome', 'Evaluation Studies', '10.1186/1471-2105-9-149', '18366635', 'DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patients response to chemotherapy is one such issue, successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.'),
(92055, 2008, 'bmc', 'Optimizations for the EcoPod field identification tool', 'Manoharan, Aswath|Stamberger, Jeannie|Yu, YuanYuan|Paepcke, Andreas', 'Department of Computer Science, Stanford University, Stanford, CA 95305, USA. aswath.manohar@gmail.com|Department of Computer Science, Stanford University, Stanford, CA 95305, USA. aswath.manohar@gmail.com|Department of Computer Science, Stanford University, Stanford, CA 95305, USA. aswath.manohar@gmail.com|Department of Computer Science, Stanford University, Stanford, CA 95305, USA. aswath.manohar@gmail.com', 'United States|United States|United States|United States', 1, 'Animals|Artificial Intelligence|Birds|Computers, Handheld|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-150', '18366649', 'We sketch our species identification tool for palm sized computers that helps knowledgeable observers with census activities. An algorithm turns an identification matrix into a minimal length series of questions that guide the operator towards identification. Historic observation data from the census geographic area helps minimize question volume. We explore how much historic data is required to boost performance, and whether the use of history negatively impacts identification of rare species. We also explore how characteristics of the matrix interact with the algorithm, and how best to predict the probability of observing a previously unseen species.'),
(92056, 2008, 'bmc', 'H2r: identification of evolutionary important residues by means of an entropy based analysis of multiple sequence alignments', 'Merkl, Rainer|Zwick, Matthias', 'Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, D-93040 Regensburg, Germany. Rainer.Merkl@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, D-93040 Regensburg, Germany. Rainer.Merkl@biologie.uni-regensburg.de', 'Germany|Germany', 15, 'Algorithms|Amino Acid Sequence|Base Sequence|Entropy|Evolution, Molecular|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-151', '18366663', 'A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues.'),
(92057, 2008, 'bmc', '13C labeling experiments at metabolic nonstationary conditions: an exploratory study', 'Wahl, Sebastian Aljoscha|Nöh, Katharina|Wiechert, Wolfgang', 'Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. awahl@mpi-magdeburg.mpg.de|Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. awahl@mpi-magdeburg.mpg.de|Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. awahl@mpi-magdeburg.mpg.de', 'Germany|Germany|Germany', 48, 'Carbon Isotopes|Computer Simulation|Data Interpretation, Statistical|Enzymes|Isotope Labeling|Kinetics|Metabolic Networks and Pathways|Models, Biological|Reproducibility of Results|Research Design|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-152', '18366666', 'Stimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of 13C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.'),
(92058, 2008, 'bmc', 'Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain', 'Lau, Christopher|Ng, Lydia|Thompson, Carol|Pathak, Sayan|Kuan, Leonard|Jones, Allan|Hawrylycz, Mike', 'Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org|Allen Institute for Brain Science, Seattle, WA, USA. chrisl@alleninstitute.org', 'United States|United States|United States|United States|United States|United States|United States', 65, 'Animals|Brain|Computer Graphics|Computer Simulation|Gene Expression|Gene Expression Profiling|Mice|Mice, Inbred C57BL|Models, Anatomic|Models, Biological|Nerve Tissue Proteins|Oligonucleotide Array Sequence Analysis|Software|Tissue Distribution|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-153', '18366675', 'Spatially mapped large scale gene expression databases enable quantitative comparison of data measurements across genes, anatomy, and phenotype. In most ongoing efforts to study gene expression in the mammalian brain, significant resources are applied to the mapping and visualization of data. This paper describes the implementation and utility of Brain Explorer, a 3D visualization tool for studying in situ hybridization-based (ISH) expression patterns in the Allen Brain Atlas, a genome-wide survey of 21,000 expression patterns in the C57BL6J adult mouse brain.'),
(92059, 2008, 'bmc', 'DNAAlignEditor: DNA alignment editor tool', 'Sanchez-Villeda, Hector|Schroeder, Steven|Flint-Garcia, Sherry|Guill, Katherine E|Yamasaki, Masanori|McMullen, Michael D', 'Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA. sanchezvilledah@missouri.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface|Word Processing', 'Journal Article', '10.1186/1471-2105-9-154', '18366684', 'With advances in DNA re-sequencing methods and Next-Generation parallel sequencing approaches, there has been a large increase in genomic efforts to define and analyze the sequence variability present among individuals within a species. For very polymorphic species such as maize, this has lead to a need for intuitive, user-friendly software that aids the biologist, often with naïve programming capability, in tracking, editing, displaying, and exporting multiple individual sequence alignments. To fill this need we have developed a novel DNA alignment editor.'),
(92060, 2008, 'bmc', 'Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles', 'Tien, Yin-Jing|Lee, Yun-Shien|Wu, Han-Ming|Chen, Chun-Houh', 'Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan. gary@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan. gary@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan. gary@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan. gary@stat.sinica.edu.tw', '-|-|-|-', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-9-155', '18366693', 'The hierarchical clustering tree (HCT) with a dendrogram 1 and the singular value decomposition (SVD) with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.'),
(92061, 2008, 'bmc', 'Empirical Bayes models for multiple probe type microarrays at the probe level', 'Astrand, Magnus|Mostad, Petter|Rudemo, Mats', 'Mathematical Sciences, Chalmers University of Technology, and Mathematical Sciences, Göteborg University, S-41296, Göteborg, Sweden. magnus.astrand@astrazeneca.com|Mathematical Sciences, Chalmers University of Technology, and Mathematical Sciences, Göteborg University, S-41296, Göteborg, Sweden. magnus.astrand@astrazeneca.com|Mathematical Sciences, Chalmers University of Technology, and Mathematical Sciences, Göteborg University, S-41296, Göteborg, Sweden. magnus.astrand@astrazeneca.com', 'Sweden|Sweden|Sweden', 7, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computer Simulation|DNA Probes|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-9-156', '18366694', 'When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.'),
(92062, 2008, 'bmc', 'BioSAVE: display of scored annotation within a sequence context', 'Pollock, Richard F|Adryan, Boris', 'MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK. rfp26@mrc-lmb.cam.ac.uk|MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK. rfp26@mrc-lmb.cam.ac.uk', 'United Kingdom|United Kingdom', 2, 'Algorithms|Computer Graphics|Sequence Alignment|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-157', '18366701', 'Visualization of sequence annotation is a common feature in many bioinformatics tools. For many applications it is desirable to restrict the display of such annotation according to a score cutoff, as biological interpretation can be difficult in the presence of the entire data. Unfortunately, many visualisation solutions are somewhat static in the way they handle such score cutoffs.'),
(92063, 2008, 'bmc', 'Improving the prediction of mRNA extremities in the parasitic protozoan Leishmania', 'Smith, Martin|Blanchette, Mathieu|Papadopoulou, Barbara', 'Research Centre in Infectious Diseases, CHUL Research Centre, 2705 Laurier Blvd,, Quebec, QC G1V 4G2, Canada. martin.smith@umontreal.ca|Research Centre in Infectious Diseases, CHUL Research Centre, 2705 Laurier Blvd,, Quebec, QC G1V 4G2, Canada. martin.smith@umontreal.ca|Research Centre in Infectious Diseases, CHUL Research Centre, 2705 Laurier Blvd,, Quebec, QC G1V 4G2, Canada. martin.smith@umontreal.ca', 'Canada|Canada|Canada', 0, 'Animals|Base Sequence|Computer Simulation|Expressed Sequence Tags|Leishmania|Models, Genetic|Molecular Sequence Data|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-9-158', '18366710', 'Leishmania and other members of the Trypanosomatidae family diverged early on in eukaryotic evolution and consequently display unique cellular properties. Their apparent lack of transcriptional regulation is compensated by complex post-transcriptional control mechanisms, including the processing of polycistronic transcripts by means of coupled trans-splicing and polyadenylation. Trans-splicing signals are often U-rich polypyrimidine (poly(Y)) tracts, which precede AG splice acceptor sites. However, as opposed to higher eukaryotes there is no consensus polyadenylation signal in trypanosomatid mRNAs.'),
(92064, 2008, 'bmc', 'Synonym set extraction from the biomedical literature by lexical pattern discovery', 'McCrae, John|Collier, Nigel', 'National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, 101-8430, Japan. jmccrae@nii.ac.jp|National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, 101-8430, Japan. jmccrae@nii.ac.jp', 'Japan|Japan', 23, 'Artificial Intelligence|Computational Biology|Database Management Systems|Dictionaries as Topic|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-159', '18366721', 'Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst 1, but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92065, 2008, 'bmc', 'Computational evaluation of TIS annotation for prokaryotic genomes', 'Hu, Gang-Qing|Zheng, Xiaobin|Ju, Li-Ning|Zhu, Huaiqiu|She, Zhen-Su', 'State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China. hugangqing@ctb.pku.edu.cn|State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China. hugangqing@ctb.pku.edu.cn|State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China. hugangqing@ctb.pku.edu.cn|State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China. hugangqing@ctb.pku.edu.cn|State Key Lab for Turbulence and Complex System and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China. hugangqing@ctb.pku.edu.cn', 'China|China|China|China|China', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Codon, Initiator|Databases, Genetic|Molecular Sequence Data|Prokaryotic Cells|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-160', '18366730', 'Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.'),
(92066, 2008, 'bmc', 'A simple and fast heuristic for protein structure comparison', 'Pelta, David A|González, Juan R|Moreno Vega, Marcos', 'Models of Decision and Optimization Research Group, Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain. dpelta@decsai.ugr.es|Models of Decision and Optimization Research Group, Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain. dpelta@decsai.ugr.es|Models of Decision and Optimization Research Group, Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain. dpelta@decsai.ugr.es', 'Spain|Spain|Spain', 41, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-161', '18366735', 'Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers.'),
(92067, 2008, 'bmc', 'Adaptive diffusion kernel learning from biological networks for protein function prediction', 'Sun, Liang|Ji, Shuiwang|Ye, Jieping', 'Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA. sun.liang@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA. sun.liang@asu.edu|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA. sun.liang@asu.edu', 'United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression Profiling|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-162', '18366736', 'Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods are suitable for learning from graph-based data such as biological networks, as they only require the abstraction of the similarities between objects into the kernel matrix. One key issue in kernel methods is the selection of a good kernel function. Diffusion kernels, the discretization of the familiar Gaussian kernel of Euclidean space, are commonly used for graph-based data.'),
(92068, 2008, 'bmc', 'OpenMS - an open-source software framework for mass spectrometry', 'Sturm, Marc|Bertsch, Andreas|Gröpl, Clemens|Hildebrandt, Andreas|Hussong, Rene|Lange, Eva|Pfeifer, Nico|Schulz-Trieglaff, Ole|Zerck, Alexandra|Reinert, Knut|Kohlbacher, Oliver', 'Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de|Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany. sturm@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Mass Spectrometry|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-9-163', '18366760', 'Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.'),
(92069, 2008, 'bmc', 'A comprehensive re-analysis of the Golden Spike data: towards a benchmark for differential expression methods', 'Pearson, Richard D', 'School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@postgrad.manchester.ac.uk', 'United Kingdom', 27, 'Algorithms|Artifacts|Benchmarking|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-164', '18366762', 'The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws.'),
(92070, 2008, 'bmc', 'Stability analysis of mixtures of mutagenetic trees', 'Bogojeska, Jasmina|Lengauer, Thomas|Rahnenführer, Jörg', 'Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. jasmina@mpi-inf.mpg.de|Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. jasmina@mpi-inf.mpg.de', 'Germany|Germany|Germany', 6, 'Base Sequence|DNA|DNA Mutational Analysis|Data Interpretation, Statistical|Disease Progression|Genetic Predisposition to Disease|Genomic Instability|Humans|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-165', '18366778', 'Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.'),
(92071, 2008, 'bmc', 'Fast NJ-like algorithms to deal with incomplete distance matrices', 'Criscuolo, Alexis|Gascuel, Olivier', 'Equipe Méthodes et Algorithmes pour Bioinformatique, LIRMM, CNRS - Université Montpellier 2, 161 rue Ada, 34392 Montpellier Cedex 05, France. criscuol@lirmm.fr|Equipe Méthodes et Algorithmes pour Bioinformatique, LIRMM, CNRS - Université Montpellier 2, 161 rue Ada, 34392 Montpellier Cedex 05, France. criscuol@lirmm.fr', 'France|France', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Linkage Disequilibrium|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-166', '18366787', 'Distance-based phylogeny inference methods first estimate evolutionary distances between every pair of taxa, then build a tree from the so-obtained distance matrix. These methods are fast and fairly accurate. However, they hardly deal with incomplete distance matrices. Such matrices are frequent with recent multi-gene studies, when two species do not share any gene in analyzed data. The few existing algorithms to infer trees with satisfying accuracy from incomplete distance matrices have time complexity in O(n4) or more, where n is the number of taxa, which precludes large scale studies. Agglomerative distance algorithms (e.g. NJ 12) are much faster, with time complexity in O(n3) which allows huge datasets and heavy bootstrap analyses to be dealt with. These algorithms proceed in three steps: (a) search for the taxon pair to be agglomerated, (b) estimate the lengths of the two so-created branches, (c) reduce the distance matrix and return to (a) until the tree is fully resolved. But available agglomerative algorithms cannot deal with incomplete matrices.'),
(92072, 2008, 'bmc', 'Efficient computation of absent words in genomic sequences', 'Herold, Julia|Kurtz, Stefan|Giegerich, Robert', 'Center of Biotechnology, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. jherold@cebitec.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. jherold@cebitec.uni-bielefeld.de|Center of Biotechnology, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. jherold@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|GC Rich Sequence|Molecular Sequence Data|Semantics|Sequence Alignment|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1186/1471-2105-9-167', '18366790', 'Analysis of sequence composition is a routine task in genome research. Organisms are characterized by their base composition, dinucleotide relative abundance, codon usage, and so on. Unique subsequences are markers of special interest in genome comparison, expression profiling, and genetic engineering. Relative to a random sequence of the same length, unique subsequences are overrepresented in real genomes. Shortest words absent from a genome have been addressed in two recent studies.'),
(92073, 2008, 'bmc', 'Chromhome: a rich internet application for accessing comparative chromosome homology maps', 'Nagarajan, Sridevi|Rens, Willem|Stalker, James|Cox, Tony|Ferguson-Smith, Malcolm A', 'Cambridge Resource Centre for Comparative Genomics, Department of Veterinary Medicine, University of Cambridge, Madingley Road Cambridge CB3 0ES, UK. sn324@cam.ac.uk|Cambridge Resource Centre for Comparative Genomics, Department of Veterinary Medicine, University of Cambridge, Madingley Road Cambridge CB3 0ES, UK. sn324@cam.ac.uk|Cambridge Resource Centre for Comparative Genomics, Department of Veterinary Medicine, University of Cambridge, Madingley Road Cambridge CB3 0ES, UK. sn324@cam.ac.uk|Cambridge Resource Centre for Comparative Genomics, Department of Veterinary Medicine, University of Cambridge, Madingley Road Cambridge CB3 0ES, UK. sn324@cam.ac.uk|Cambridge Resource Centre for Comparative Genomics, Department of Veterinary Medicine, University of Cambridge, Madingley Road Cambridge CB3 0ES, UK. sn324@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Base Sequence|Chromosome Mapping|Chromosome Painting|Internet|Molecular Sequence Data|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-168', '18366796', 'Comparative genomics has become a significant research area in recent years, following the availability of a number of sequenced genomes. The comparison of genomes is of great importance in the analysis of functionally important genome regions. It can also be used to understand the phylogenetic relationships of species and the mechanisms leading to rearrangement of karyotypes during evolution. Many species have been studied at the cytogenetic level by cross species chromosome painting. With the large amount of such information, it has become vital to computerize the data and make them accessible worldwide. Chromhome http://www.chromhome.org is a comprehensive web application that is designed to provide cytogenetic comparisons among species and to fulfil this need.'),
(92074, 2008, 'bmc', 'MausDB: an open source application for phenotype data and mouse colony management in large-scale mouse phenotyping projects', 'Maier, Holger|Lengger, Christoph|Simic, Bruno|Fuchs, Helmut|Gailus-Durner, Valérie|Hrabé de Angelis, Martin', 'Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de|Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Experimental Genetics (IEG), Ingolstädter Landstr, 1, D-85764 Neuherberg, Germany. holger.maier@helmholtz-muenchen.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Mice|Phenotype|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-169', '18366799', 'Large-scale, comprehensive and standardized high-throughput mouse phenotyping has been established as a tool of functional genome research by the German Mouse Clinic and others. In all these projects, vast amounts of data are continuously generated and need to be stored, prepared for data-mining procedures and eventually be made publicly available. Thus, central storage and integrated management of mouse phenotype data, genotype data, metadata and linked external data are highly important. Requirements most probably depend on the individual mouse housing unit or project and the demand for either very specific individual database solutions or very flexible solutions that can be easily adapted to local demands. Not every group has the resources and/or the know-how to develop software for this purpose. A database application has been developed for the German Mouse Clinic in order to meet all requirements mentioned above.'),
(92075, 2008, 'bmc', 'PSAT: a web tool to compare genomic neighborhoods of multiple prokaryotic genomes', 'Fong, Christine|Rohmer, Laurence|Radey, Matthew|Wasnick, Michael|Brittnacher, Mitchell J', 'Department of Genome Sciences, University of Washington, Box 357710, Seattle, Washington 98195, USA. cfong@u.washington.edu|Department of Genome Sciences, University of Washington, Box 357710, Seattle, Washington 98195, USA. cfong@u.washington.edu|Department of Genome Sciences, University of Washington, Box 357710, Seattle, Washington 98195, USA. cfong@u.washington.edu|Department of Genome Sciences, University of Washington, Box 357710, Seattle, Washington 98195, USA. cfong@u.washington.edu|Department of Genome Sciences, University of Washington, Box 357710, Seattle, Washington 98195, USA. cfong@u.washington.edu', 'United States|United States|United States|United States|United States', 25, 'Algorithms|Base Sequence|Chromosome Mapping|Genome, Archaeal|Genome, Bacterial|Internet|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-170', '18366802', 'The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved. Although some tools are available for visualizing and comparing the order of genes between genomes of study, few support an efficient and organized analysis between large numbers of genomes. The Prokaryotic Sequence homology Analysis Tool (PSAT) is a web tool for comparing gene neighborhoods among multiple prokaryotic genomes.'),
(92076, 2008, 'bmc', 'Interrogating domain-domain interactions with parsimony based approaches', 'Guimarães, Katia S|Przytycka, Teresa M', 'National Center of Biotechnology, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. katiaguim@gmail.com|National Center of Biotechnology, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. katiaguim@gmail.com', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-171', '18366803', 'The identification and characterization of interacting domain pairs is an important step towards understanding protein interactions. In the last few years, several methods to predict domain interactions have been proposed. Understanding the power and the limitations of these methods is key to the development of improved approaches and better understanding of the nature of these interactions.'),
(92077, 2008, 'bmc', 'Predicting cancer involvement of genes from heterogeneous data', 'Aragues, Ramon|Sander, Chris|Oliva, Baldo', 'Structural Bioinformatics Lab, (GRIB), Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine (PRBB), 08003-Barcelona, Catalonia, Spain. ramon.aragues@upf.edu|Structural Bioinformatics Lab, (GRIB), Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine (PRBB), 08003-Barcelona, Catalonia, Spain. ramon.aragues@upf.edu|Structural Bioinformatics Lab, (GRIB), Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine (PRBB), 08003-Barcelona, Catalonia, Spain. ramon.aragues@upf.edu', 'Spain|Spain|Spain', 60, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Gene Expression Profiling|Humans|Models, Biological|Neoplasm Proteins|Neoplasms|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-172', '18371197', 'Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.'),
(92078, 2008, 'bmc', 'LocateP: genome-scale subcellular-location predictor for bacterial proteins', 'Zhou, Miaomiao|Boekhorst, Jos|Francke, Christof|Siezen, Roland J', 'Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. m.zhou@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. m.zhou@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. m.zhou@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands. m.zhou@cmbi.ru.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Amino Acid Sequence|Bacterial Proteins|Chromosome Mapping|Molecular Sequence Data|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-9-173', '18371216', 'In the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase) cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP.'),
(92079, 2008, 'bmc', 'Snagger: a user-friendly program for incorporating additional information for tagSNP selection', 'Edlund, Christopher K|Lee, Won H|Li, Dalin|Van Den Berg, David J|Conti, David V', 'Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. cedlund@usc.edu|Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. cedlund@usc.edu|Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. cedlund@usc.edu|Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. cedlund@usc.edu|Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA. cedlund@usc.edu', 'United States|United States|United States|United States|United States', 0, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Expressed Sequence Tags|Genetic Variation|Linkage Disequilibrium|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-174', '18371222', 'There has been considerable effort focused on developing efficient programs for tagging single-nucleotide polymorphisms (SNPs). Many of these programs do not account for potential reduced genomic coverage resulting from genotyping failures nor do they preferentially select SNPs based on functionality, which may be more likely to be biologically important.'),
(92080, 2008, 'bmc', 'A perl package and an alignment tool for phylogenetic networks', 'Cardona, Gabriel|Rosselló, Francesc|Valiente, Gabriel', 'Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es', 'Spain|Spain|Spain', 11, 'Algorithms|Base Sequence|Evolution, Molecular|Gene Expression Profiling|Molecular Sequence Data|Phylogeny|Programming Languages|Proteome|Sequence Alignment|Sequence Analysis, DNA|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-175', '18371228', 'Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of evolutionary events acting at the population level, like recombination between genes, hybridization between lineages, and lateral gene transfer. While most phylogenetics tools implement a wide range of algorithms on phylogenetic trees, there exist only a few applications to work with phylogenetic networks, none of which are open-source libraries, and they do not allow for the comparative analysis of phylogenetic networks by computing distances between them or aligning them.'),
(92081, 2008, 'bmc', 'RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure', 'Liu, Qi|Yang, Yu|Chen, Chun|Bu, Jiajun|Zhang, Yin|Ye, Xiuzi', 'Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn|Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn|Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn|Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn|Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn|Zhejiang California International Nanosystems Institute, Zhejiang University, Hangzhou, 310029, China. bio_qiliu@yahoo.com.cn', 'China|China|China|China|China|China', 13, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Data Compression|Databases, Genetic|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Semantics|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-9-176', '18373878', 'With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression.'),
(92082, 2008, 'bmc', 'JCoast - a biologist-centric software tool for data mining and comparison of prokaryotic (meta)genomes', 'Richter, Michael|Lombardot, Thierry|Kostadinov, Ivaylo|Kottmann, Renzo|Duhaime, Melissa Beth|Peplies, Jörg|Glöckner, Frank Oliver', 'Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de|Microbial Genomics Group, Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, D-28359 Bremen, Germany. mrichter@mpi-bremen.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Chromosome Mapping|Database Management Systems|Databases, Genetic|Programming Languages|Prokaryotic Cells|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-177', '18380896', 'Current sequencing technologies give access to sequence information for genomes and metagenomes at a tremendous speed. Subsequent data processing is mainly performed by automatic pipelines provided by the sequencing centers. Although, standardised workflows are desirable and useful in many respects, rational data mining, comparative genomics, and especially the interpretation of the sequence information in the biological context, demands for intuitive, flexible, and extendable solutions.'),
(92083, 2008, 'bmc', 'Ornithine decarboxylase antizyme finder (OAF): fast and reliable detection of antizymes with frameshifts in mRNAs', 'Bekaert, Michaël|Ivanov, Ivaylo P|Atkins, John F|Baranov, Pavel V', 'School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland. mbekaert@gmail.com|School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland. mbekaert@gmail.com|School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland. mbekaert@gmail.com|School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland. mbekaert@gmail.com', 'Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Base Sequence|Enzyme Inhibitors|Frameshifting, Ribosomal|Molecular Sequence Data|Ornithine Decarboxylase|Ornithine Decarboxylase Inhibitors|RNA, Messenger|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-9-178', '18384676', 'Ornithine decarboxylase antizymes are proteins which negatively regulate cellular polyamine levels via their affects on polyamine synthesis and cellular uptake. In virtually all organisms from yeast to mammals, antizymes are encoded by two partially overlapping open reading frames (ORFs). A +1 frameshift between frames is required for the synthesis of antizyme. Ribosomes change translation phase at the end of the first ORF in response to stimulatory signals embedded in mRNA. Since standard sequence analysis pipelines are currently unable to recognise sites of programmed ribosomal frameshifting, proper detection of full length antizyme coding sequences (CDS) requires conscientious manual evaluation by a human expert. The rapid growth of sequence information demands less laborious and more cost efficient solutions for this problem. This manuscript describes a rapid and accurate computer tool for antizyme CDS detection that requires minimal human involvement.'),
(92084, 2008, 'bmc', 'CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment', 'Manavski, Svetlin A|Valle, Giorgio', 'CRIBI, University of Padova, Padova, Italy. svetlin.manavski@cribi.unipd.it|CRIBI, University of Padova, Padova, Italy. svetlin.manavski@cribi.unipd.it', 'Italy|Italy', 0, 'Computer Graphics|Equipment Design|Equipment Failure Analysis|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis|Signal Processing, Computer-Assisted', 'Journal Article', '10.1186/1471-2105-9-S2-S10', '18387198', 'Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences, as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment.'),
(92085, 2008, 'bmc', 'The MoVIN server for the analysis of protein interaction networks', 'Marcatili, Paolo|Bussotti, Giovanni|Tramontano, Anna', 'Department of Biochemical Sciences, Sapienza University, Rome, Italy. paolo.marcatili@uniroma1.it|Department of Biochemical Sciences, Sapienza University, Rome, Italy. paolo.marcatili@uniroma1.it|Department of Biochemical Sciences, Sapienza University, Rome, Italy. paolo.marcatili@uniroma1.it', 'Italy|Italy|Italy', 7, 'Binding Sites|Internet|Protein Binding|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-S2-S11', '18387199', 'Protein-protein interactions are at the basis of most cellular processes and crucial for many bio-technological applications. During the last few years the development of high-throughput technologies has produced several large-scale protein-protein interaction data sets for various organisms. It is important to develop tools for dissecting their content and analyse the information they embed by data-integration and computational methods.'),
(92086, 2008, 'bmc', 'Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells', 'Mutarelli, Margherita|Cicatiello, Luigi|Ferraro, Lorenzo|Grober, Olì Mv|Ravo, Maria|Facchiano, Angelo M|Angelini, Claudia|Weisz, Alessandro', 'Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it|Department of General Pathology - Second University of Napoli, Napoli, Italy. margherita.mutarelli@isa.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Biomarkers, Tumor|Cell Line, Tumor|Estrogens|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Proteome|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-S2-S12', '18387200', 'Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a molecular picture of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.'),
(92087, 2008, 'bmc', 'SNPLims: a data management system for genome wide association studies', 'Orro, Alessandro|Guffanti, Guia|Salvi, Erika|Macciardi, Fabio|Milanesi, Luciano', 'Consorzio Interuniversitario Lombardo per lElaborazione Automatica, Via Sanzio Raffaello 4, 20090 Segrate (MI), Italy. alessandro.orro@itb.cnr.it|Consorzio Interuniversitario Lombardo per lElaborazione Automatica, Via Sanzio Raffaello 4, 20090 Segrate (MI), Italy. alessandro.orro@itb.cnr.it|Consorzio Interuniversitario Lombardo per lElaborazione Automatica, Via Sanzio Raffaello 4, 20090 Segrate (MI), Italy. alessandro.orro@itb.cnr.it|Consorzio Interuniversitario Lombardo per lElaborazione Automatica, Via Sanzio Raffaello 4, 20090 Segrate (MI), Italy. alessandro.orro@itb.cnr.it|Consorzio Interuniversitario Lombardo per lElaborazione Automatica, Via Sanzio Raffaello 4, 20090 Segrate (MI), Italy. alessandro.orro@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Chromosome Mapping|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Linkage Disequilibrium|Polymorphism, Single Nucleotide|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S2-S13', '18387201', 'Recent progresses in genotyping technologies allow the generation high-density genetic maps using hundreds of thousands of genetic markers for each DNA sample. The availability of this large amount of genotypic data facilitates the whole genome search for genetic basis of diseases. We need a suitable information management system to efficiently manage the data flow produced by whole genome genotyping and to make it available for further analyses.'),
(92088, 2008, 'bmc', 'Is plant mitochondrial RNA editing a source of phylogenetic incongruence? An answer from in silico and in vivo data sets', 'Picardi, Ernesto|Quagliariello, Carla', 'Dipartimento di Biologia Cellulare, Università della Calabria, Arcavacata di Rende, (87036), Italy. e.picardi@unical.it|Dipartimento di Biologia Cellulare, Università della Calabria, Arcavacata di Rende, (87036), Italy. e.picardi@unical.it', 'Italy|Italy', 4, 'Base Sequence|Computer Simulation|Databases, Genetic|Models, Genetic|Molecular Sequence Data|Phylogeny|Polymorphism, Single Nucleotide|RNA|RNA, Mitochondrial|RNA, Plant|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-9-S2-S14', '18387202', 'In plant mitochondria, the post-transcriptional RNA editing process converts C to U at a number of specific sites of the mRNA sequence and usually restores phylogenetically conserved codons and the encoded amino acid residues. Sites undergoing RNA editing evolve at a higher rate than sites not modified by the process. As a result, editing sites strongly affect the evolution of plant mitochondrial genomes, representing an important source of sequence variability and potentially informative characters. To date no clear and convincing evidence has established whether or not editing sites really affect the topology of reconstructed phylogenetic trees. For this reason, we investigated here the effect of RNA editing on the tree building process of twenty different plant mitochondrial gene sequences and by means of computer simulations.'),
(92089, 2008, 'bmc', 'SWS: accessing SRS sites contents through Web Services', 'Romano, Paolo|Marra, Domenico', 'Bioinformatics, National Cancer Research Institute (IST), Genova, I-16132, Italy. paolo.romano@istge.it|Bioinformatics, National Cancer Research Institute (IST), Genova, I-16132, Italy. paolo.romano@istge.it', 'Italy|Italy', 1, 'Algorithms|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S2-S15', '18387203', 'Web Services and Workflow Management Systems can support creation and deployment of network systems, able to automate data analysis and retrieval processes in biomedical research. Web Services have been implemented at bioinformatics centres and workflow systems have been proposed for biological data analysis. New databanks are often developed by taking into account these technologies, but many existing databases do not allow a programmatic access. Only a fraction of available databanks can thus be queried through programmatic interfaces. SRS is a well know indexing and search engine for biomedical databanks offering public access to many databanks and analysis tools. Unfortunately, these data are not easily and efficiently accessible through Web Services.'),
(92090, 2008, 'bmc', 'FunClust: a web server for the identification of structural motifs in a set of non-homologous protein structures', 'Ausiello, Gabriele|Gherardini, Pier Federico|Marcatili, Paolo|Tramontano, Anna|Via, Allegra|Helmer-Citterich, Manuela', 'Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Rome, Italy. gabriele.ausiello@uniroma2.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Amino Acid Motifs|Amino Acid Sequence|Cluster Analysis|Internet|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1186/1471-2105-9-S2-S2', '18387204', 'The occurrence of very similar structural motifs brought about by different parts of non homologous proteins is often indicative of a common function. Indeed, relatively small local structures can mediate binding to a common partner, be it a protein, a nucleic acid, a cofactor or a substrate. While it is relatively easy to identify short amino acid or nucleotide sequence motifs in a given set of proteins or genes, and many methods do exist for this purpose, much more challenging is the identification of common local substructures, especially if they are formed by non consecutive residues in the sequence.'),
(92091, 2008, 'bmc', 'CellExcite: an efficient simulation environment for excitable cells', 'Bartocci, Ezio|Corradini, Flavio|Entcheva, Emilia|Grosu, Radu|Smolka, Scott A', 'Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n,9, Camerino, Italy. ezio.bartocci@unicam.it|Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n,9, Camerino, Italy. ezio.bartocci@unicam.it|Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n,9, Camerino, Italy. ezio.bartocci@unicam.it|Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n,9, Camerino, Italy. ezio.bartocci@unicam.it|Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri n,9, Camerino, Italy. ezio.bartocci@unicam.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Action Potentials|Algorithms|Animals|Computer Simulation|Heart Conduction System|Humans|Models, Biological|Myocytes, Cardiac|Nerve Net|Neurons|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S2-S3', '18387205', 'Brain, heart and skeletal muscle share similar properties of excitable tissue, featuring both discrete behavior (all-or-nothing response to electrical activation) and continuous behavior (recovery to rest follows a temporal path, determined by multiple competing ion flows). Classical mathematical models of excitable cells involve complex systems of nonlinear differential equations. Such models not only impair formal analysis but also impose high computational demands on simulations, especially in large-scale 2-D and 3-D cell networks. In this paper, we show that by choosing Hybrid Automata as the modeling formalism, it is possible to construct a more abstract model of excitable cells that preserves the properties of interest while reducing the computational effort, thereby admitting the possibility of formal analysis and efficient simulation.'),
(92092, 2008, 'bmc', 'Discovering multi-level structures in bio-molecular data through the Bernstein inequality', 'Bertoni, Alberto|Valentini, Giorgio', 'DSI, Dipartimento di Scienze dell Informazione, Universitá degli Studi di Milano, Via Comelico 39, Milano, Italy. bertoni@dsi.unimi.it|DSI, Dipartimento di Scienze dell Informazione, Universitá degli Studi di Milano, Via Comelico 39, Milano, Italy. bertoni@dsi.unimi.it', 'Italy|Italy', 16, 'Algorithms|Computer Simulation|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S2-S4', '18387206', 'The unsupervised discovery of structures (i.e. clusterings) underlying data is a central issue in several branches of bioinformatics. Methods based on the concept of stability have been recently proposed to assess the reliability of a clustering procedure and to estimate the optimal number of clusters in bio-molecular data. A major problem with stability-based methods is the detection of multi-level structures (e.g. hierarchical functional classes of genes), and the assessment of their statistical significance. In this context, a chi-square based statistical test of hypothesis has been proposed, however, to assure the correctness of this technique some assumptions about the distribution of the data are needed.'),
(92093, 2008, 'bmc', 'Gene expression profile of peripheral blood mononuclear cells in response to HIV-VLPs stimulation', 'Buonaguro, Luigi|Monaco, Alessandro|Aricò, Eleonora|Wang, Ena|Tornesello, Maria Lina|Lewis, George K|Marincola, Franco M|Buonaguro, Franco M', 'Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it|Lab, Viral Oncogenesis and Immunotherapies & AIDS Reference Center, Department of Experimental Oncology, Istituto Nazionale Tumori Fond, G, Pascale, 80131 Napoli, Italy. irccsvir@unina.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Blood Proteins|Cells, Cultured|Gene Expression Profiling|Gene Expression Regulation|HIV|Human Immunodeficiency Virus Proteins|Humans|Leukocytes, Mononuclear|Virion', 'Journal Article', '10.1186/1471-2105-9-S2-S5', '18387207', 'Baculovirus-expressed HIV-1 Pr55gag Virus-Like Particles (HIV-VLPs) induce maturation and activation of monocyte-derived dendritic cells (MDDCs) with a production of Th1- and Th2-specific cytokines.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92094, 2008, 'bmc', 'A three-state prediction of single point mutations on protein stability changes', 'Capriotti, Emidio|Fariselli, Piero|Rossi, Ivan|Casadio, Rita', 'Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain. ecapriotti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain. ecapriotti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain. ecapriotti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain. ecapriotti@cipf.es', 'Spain|Spain|Spain|Spain', 199, 'Amino Acid Sequence|Amino Acid Substitution|Computer Simulation|Drug Stability|Models, Chemical|Models, Molecular|Molecular Sequence Data|Mutagenesis, Site-Directed|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-S2-S6', '18387208', 'A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DeltaDeltaG) upon single point mutation may also help the annotation process. The experimental DeltaDeltaG values are affected by uncertainty as measured by standard deviations. Most of the DeltaDeltaG values are nearly zero (about 32% of the DeltaDeltaG data set ranges from -0.5 to 0.5 kcal/mole) and both the value and sign of DeltaDeltaG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DeltaDeltaG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DeltaDeltaG<-1.0 kcal/mol), stabilizing mutations (DeltaDeltaG>1.0 kcal/mole) and neutral mutations (-1.0</=DeltaDeltaG</=1.0 kcal/mole).'),
(92095, 2008, 'bmc', 'ISOL@: an Italian SOLAnaceae genomics resource', 'Chiusano, Maria Luisa|DAgostino, Nunzio|Traini, Alessandra|Licciardello, Concetta|Raimondo, Enrico|Aversano, Mario|Frusciante, Luigi|Monti, Luigi', 'Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it|Department of Soil, Plant, Environmental and Animal Production Sciences, University Federico II of Naples, Portici (NA), Italy. chiusano@unina.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 8, 'Database Management Systems|Databases, Genetic|Genome, Plant|Genomics|Information Storage and Retrieval|Internet|Italy|Lycopersicon esculentum|Plant Proteins|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S2-S7', '18387209', 'Present-day -omics technologies produce overwhelming amounts of data which include genome sequences, information on gene expression (transcripts and proteins) and on cell metabolic status. These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality. The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations. In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource.'),
(92096, 2008, 'bmc', 'In silico docking of urokinase plasminogen activator and integrins', 'Degryse, Bernard|Fernandez-Recio, Juan|Citro, Valentina|Blasi, Francescol|Cubellis, Maria Vittoria', 'San Raffaele Scientific Institute, Milan, Italy. bernard_degryse@yahoo.fr|San Raffaele Scientific Institute, Milan, Italy. bernard_degryse@yahoo.fr|San Raffaele Scientific Institute, Milan, Italy. bernard_degryse@yahoo.fr|San Raffaele Scientific Institute, Milan, Italy. bernard_degryse@yahoo.fr|San Raffaele Scientific Institute, Milan, Italy. bernard_degryse@yahoo.fr', 'Italy|Italy|Italy|Italy|Italy', 9, 'Binding Sites|Computer Simulation|Integrins|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Mapping|Urokinase-Type Plasminogen Activator', 'Journal Article', '10.1186/1471-2105-9-S2-S8', '18387210', 'Urokinase, its receptor and the integrins are functionally associated and involved in regulation of cell signaling, migration, adhesion and proliferation. No structural information is available on this potential multimolecular complex. However, the tri-dimensional structure of urokinase, urokinase receptor and integrins is known.'),
(92097, 2008, 'bmc', 'Version VI of the ESTree db: an improved tool for peach transcriptome analysis', 'Lazzari, Barbara|Caprera, Andrea|Vecchietti, Alberto|Merelli, Ivan|Barale, Francesca|Milanesi, Luciano|Stella, Alessandra|Pozzi, Carlo', 'Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org|Parco Tecnologico Padano, Via Einstein - Località Cascina Codazza, Lodi, 26900, Italy. barbara.lazzari@tecnoparco.org', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 23, 'Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Expressed Sequence Tags|Information Storage and Retrieval|Molecular Sequence Data|Plant Proteins|Proteome|Prunus|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S2-S9', '18387211', 'The ESTree database (db) is a collection of Prunus persica and Prunus dulcis EST sequences that in its current version encompasses 75,404 sequences from 3 almond and 19 peach libraries. Nine peach genotypes and four peach tissues are represented, from four fruit developmental stages. The aim of this work was to implement the already existing ESTree db by adding new sequences and analysis programs. Particular care was given to the implementation of the web interface, that allows querying each of the database features.'),
(92098, 2008, 'bmc', 'Multidimensional scaling for large genomic data sets', 'Tzeng, Jengnan|Lu, Henry Horng-Shing|Li, Wen-Hsiung', 'Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan. jengnan@gate.sinica.edu.tw|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan. jengnan@gate.sinica.edu.tw|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan. jengnan@gate.sinica.edu.tw', '-|-|-', 52, 'Algorithms|Chromosome Mapping|DNA|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-179', '18394154', 'Multi-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.'),
(92099, 2008, 'bmc', 'annot8r: GO, EC and KEGG annotation of EST datasets', 'Schmid, Ralf|Blaxter, Mark L', 'Department of Biochemistry, University of Leicester, Lancaster Road, Leicester LE1 9HN, UK. R.Schmid@le.ac.uk|Department of Biochemistry, University of Leicester, Lancaster Road, Leicester LE1 9HN, UK. R.Schmid@le.ac.uk', 'United Kingdom|United Kingdom', 77, 'Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Expressed Sequence Tags|Information Storage and Retrieval|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-180', '18400082', 'The expressed sequence tag (EST) methodology is an attractive option for the generation of sequence data for species for which no completely sequenced genome is available. The annotation and comparative analysis of such datasets poses a formidable challenge for research groups that do not have the bioinformatics infrastructure of major genome sequencing centres. Therefore, there is a need for user-friendly tools to facilitate the annotation of non-model species EST datasets with well-defined ontologies that enable meaningful cross-species comparisons. To address this, we have developed annot8r, a platform for the rapid annotation of EST datasets with GO-terms, EC-numbers and KEGG-pathways.'),
(92100, 2008, 'bmc', 'Proteochemometric modeling of HIV protease susceptibility', 'Lapins, Maris|Eklund, Martin|Spjuth, Ola|Prusis, Peteris|Wikberg, Jarl E S', 'Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden. maris.lapins@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden. maris.lapins@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden. maris.lapins@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden. maris.lapins@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, SE-751 24, Sweden. maris.lapins@farmbio.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 65, 'Binding Sites|Combinatorial Chemistry Techniques|Computer Simulation|Drug Delivery Systems|Drug Resistance, Viral|HIV Protease|HIV Protease Inhibitors|Models, Chemical|Protein Binding|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-9-181', '18402661', 'A major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use, the data set comprised 4792 protease-inhibitor combinations.'),
(92101, 2008, 'bmc', 'Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering', 'Yooseph, Shibu|Li, Weizhong|Sutton, Granger', 'J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. syooseph@venterinstitute.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. syooseph@venterinstitute.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. syooseph@venterinstitute.org', 'United States|United States|United States', 39, 'Algorithms|Bacterial Proteins|Base Sequence|Chromosome Mapping|Cluster Analysis|Molecular Sequence Data|Multigene Family|Open Reading Frames|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-182', '18402669', 'The identification and study of proteins from metagenomic datasets can shed light on the roles and interactions of the source organisms in their communities. However, metagenomic datasets are characterized by the presence of organisms with varying GC composition, codon usage biases etc., and consequently gene identification is challenging. The vast amount of sequence data also requires faster protein family classification tools.'),
(92102, 2008, 'bmc', 'Data capture in bioinformatics: requirements and experiences with Pedro', 'Jameson, Daniel|Garwood, Kevin|Garwood, Chris|Booth, Tim|Alper, Pinar|Oliver, Stephen G|Paton, Norman W', 'School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Software', 'Journal Article', '10.1186/1471-2105-9-183', '18402673', 'The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing - both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort.'),
(92103, 2008, 'bmc', 'MS-DOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening', 'Sauton, Nicolas|Lagorce, David|Villoutreix, Bruno O|Miteva, Maria A', 'INSERM, U648, 45 rue des Sts Peres, University Paris Descartes, 75006 Paris, France. nicolas.sauton@laposte.net|INSERM, U648, 45 rue des Sts Peres, University Paris Descartes, 75006 Paris, France. nicolas.sauton@laposte.net|INSERM, U648, 45 rue des Sts Peres, University Paris Descartes, 75006 Paris, France. nicolas.sauton@laposte.net|INSERM, U648, 45 rue des Sts Peres, University Paris Descartes, 75006 Paris, France. nicolas.sauton@laposte.net', 'France|France|France|France', 82, 'Algorithms|Binding Sites|Computational Biology|Computer Simulation|Databases, Factual|Isomerism|Ligands|Models, Molecular|Molecular Conformation|Particle Size|Protein Binding|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-184', '18402678', 'The number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most protein-ligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites.'),
(92104, 2008, 'bmc', 'A high-throughput pipeline for designing microarray-based pathogen diagnostic assays', 'Vijaya Satya, Ravi|Zavaljevski, Nela|Kumar, Kamal|Reifman, Jaques', 'Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. rvijaya@bioanalysis.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. rvijaya@bioanalysis.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. rvijaya@bioanalysis.org|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA. rvijaya@bioanalysis.org', 'United States|United States|United States|United States', 22, 'Algorithms|Bacteria|Chromosome Mapping|DNA Fingerprinting|DNA Probes|DNA, Bacterial|Genomic Islands|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-185', '18402679', 'We present a methodology for high-throughput design of oligonucleotide fingerprints for microarray-based pathogen diagnostic assays. The oligonucleotide fingerprints, or DNA microarray probes, are designed for identifying target organisms in environmental or clinical samples. The design process is implemented in a high-performance computing software pipeline that incorporates major algorithmic improvements over a previous version to both reduce computation time and improve specificity assessment.'),
(92105, 2008, 'bmc', 'EST Express: PHP/MySQL based automated annotation of ESTs from expression libraries', 'Smith, Robin P|Buchser, William J|Lemmon, Marcus B|Pardinas, Jose R|Bixby, John L|Lemmon, Vance P', 'The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu|The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu|The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu|The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu|The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu|The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, USA. rsmith@med.miami.edu', 'United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Databases, Genetic|Expressed Sequence Tags|Gene Expression Profiling|Gene Library|Information Storage and Retrieval|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-9-186', '18402700', 'Several biological techniques result in the acquisition of functional sets of cDNAs that must be sequenced and analyzed. The emergence of redundant databases such as UniGene and centralized annotation engines such as Entrez Gene has allowed the development of software that can analyze a great number of sequences in a matter of seconds.'),
(92106, 2008, 'bmc', 'Significance analysis of microarray for relative quantitation of LC/MS data in proteomics', 'Roxas, Bryan A P|Li, Qingbo', 'Center for Pharmaceutical Biotechnology, University of Illinois at Chicago, Chicago, IL 60607, USA. baproxas@uic.edu|Center for Pharmaceutical Biotechnology, University of Illinois at Chicago, Chicago, IL 60607, USA. baproxas@uic.edu', 'United States|United States', 45, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Peptide Mapping|Proteome|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Spectrometry, Mass, Electrospray Ionization', 'Journal Article', '10.1186/1471-2105-9-187', '18402702', 'Although fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test.'),
(92107, 2008, 'bmc', 'A novel representation of RNA secondary structure based on element-contact graphs', 'Shu, Wenjie|Bo, Xiaochen|Zheng, Zhiqiang|Wang, Shengqi', 'Beijing Institute of Radiation Medicine, Beijing 100850, China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing 100850, China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing 100850, China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing 100850, China. shuwj@bmi.ac.cn', 'China|China|China|China', 14, 'Cluster Analysis|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Decision Trees|Humans|Models, Molecular|Neural Networks (Computer)|Nucleic Acid Conformation|Quantitative Structure-Activity Relationship|RNA, Untranslated', 'Journal Article', '10.1186/1471-2105-9-188', '18402706', 'Depending on their specific structures, noncoding RNAs (ncRNAs) play important roles in many biological processes. Interest in developing new topological indices based on RNA graphs has been revived in recent years, as such indices can be used to compare, identify and classify RNAs. Although the topological indices presented before characterize the main topological features of RNA secondary structures, information on RNA structural details is ignored to some degree. Therefore, it is necessity to identify topological features with low degeneracy based on complete and fine-grained RNA graphical representations.'),
(92108, 2008, 'bmc', 'Proceedings of the Asia Pacific Bioinformatics Network (APBioNet) 6th International Conference on Bioinformatics (InCoB207), Hong Kong, China, 27-30 August 2007', '-', '-', '-', 14, 'Computational Biology', 'Congress', '10.1186/1471-2105-9-188', '18404817', '-'),
(92109, 2008, 'bmc', 'Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci', 'Lee, Sang Hong|Van der Werf, Julius H J|Kinghorn, Brian P', 'The Institute for Genetics and Bioinformatics, School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia. slee38@une.edu.au|The Institute for Genetics and Bioinformatics, School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia. slee38@une.edu.au|The Institute for Genetics and Bioinformatics, School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia. slee38@une.edu.au', 'Australia|Australia|Australia', 3, 'Algorithms|Base Sequence|Chromosome Mapping|Evolution, Molecular|Genetic Markers|Haplotypes|Molecular Sequence Data|Pedigree|Quantitative Trait Loci|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-189', '18405361', 'Haplotype reconstruction is important in linkage mapping and association mapping of quantitative trait loci (QTL). One widely used statistical approach for haplotype reconstruction is simulated annealing (SA), implemented in SimWalk2. However, the algorithm needs a very large number of sequential iterations, and it does not clearly show if convergence of the likelihood is obtained.'),
(92110, 2008, 'bmc', 'Local conservation scores without a priori assumptions on neutral substitution rates', 'Dingel, Janis|Hanus, Pavol|Leonardi, Niccolò|Hagenauer, Joachim|Zech, Jürgen|Mueller, Jakob C', 'Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de|Institute for Communications Engineering, Technische Universität München, Munich, Germany. janis.dingel@tum.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Algorithms|Computer Simulation|Conserved Sequence|DNA|Evolution, Molecular|Genetic Variation|Likelihood Functions|Models, Genetic|Models, Statistical|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-9-190', '18405366', 'Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics. All methods we are aware of rely on a comparison with the neutral rate and conservation is estimated by measuring the deviation of a particular genomic region from this rate. Consequently, it is a reasonable assumption that inaccurate neutral rate estimates may lead to biased conservation and constraint estimates.'),
(92111, 2008, 'bmc', 'Novel computational methods for increasing PCR primer design effectiveness in directed sequencing', 'Li, Kelvin|Brownley, Anushka|Stockwell, Timothy B|Beeson, Karen|McIntosh, Tina C|Busam, Dana|Ferriera, Steve|Murphy, Sean|Levy, Samuel', 'The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org|The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@venterinstitute.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 25, 'Algorithms|Base Sequence|DNA Primers|Molecular Sequence Data|Polymerase Chain Reaction|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-191', '18405373', 'Polymerase chain reaction (PCR) is used in directed sequencing for the discovery of novel polymorphisms. As the first step in PCR directed sequencing, effective PCR primer design is crucial for obtaining high-quality sequence data for target regions. Since current computational primer design tools are not fully tuned with stable underlying laboratory protocols, researchers may still be forced to iteratively optimize protocols for failed amplifications after the primers have been ordered. Furthermore, potentially identifiable factors which contribute to PCR failures have yet to be elucidated. This inefficient approach to primer design is further intensified in a high-throughput laboratory, where hundreds of genes may be targeted in one experiment.'),
(92112, 2008, 'bmc', 'uShuffle: a useful tool for shuffling biological sequences while preserving the k-let counts', 'Jiang, Minghui|Anderson, James|Gillespie, Joel|Mayne, Martin', 'Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. mjiang@cc.usu.edu|Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. mjiang@cc.usu.edu|Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. mjiang@cc.usu.edu|Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA. mjiang@cc.usu.edu', 'United States|United States|United States|United States', 75, 'Algorithms|Computer Simulation|Models, Genetic|Models, Statistical|Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-9-192', '18405375', 'Randomly shuffled sequences are routinely used in sequence analysis to evaluate the statistical significance of a biological sequence. In many cases, biologists need sophisticated shuffling tools that preserve not only the counts of distinct letters but also higher-order statistics such as doublet counts, triplet counts, and, in general, k-let counts.'),
(92113, 2008, 'bmc', 'Natural language processing in aid of FlyBase curators', 'Karamanis, Nikiforos|Seal, Ruth|Lewin, Ian|McQuilton, Peter|Vlachos, Andreas|Gasperin, Caroline|Drysdale, Rachel|Briscoe, Ted', 'Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, William Gates Building, Cambridge, CB3 0FD, UK. nikiforos.karamanis@cl.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Artificial Intelligence|Database Management Systems|Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-193', '18410678', 'Despite increasing interest in applying Natural Language Processing (NLP) to biomedical text, whether this technology can facilitate tasks such as database curation remains unclear.'),
(92114, 2008, 'bmc', 'A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays', 'Ge, Huanying|Cheng, Chao|Li, Lei M', 'Molecular and Computational Biology, Department of Biology Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. hge@usc.edu|Molecular and Computational Biology, Department of Biology Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. hge@usc.edu|Molecular and Computational Biology, Department of Biology Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. hge@usc.edu', 'United States|United States|United States', 5, 'Algorithms|Base Sequence|Computer Simulation|DNA Probes|Gene Expression Profiling|Models, Genetic|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Reference Values|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-194', '18410691', 'Microarray pre-processing usually consists of normalization and summarization. Normalization aims to remove non-biological variations across different arrays. The normalization algorithms generally require the specification of reference and target arrays. The issue of reference selection has not been fully addressed. Summarization aims to estimate the transcript abundance from normalized intensities. In this paper, we consider normalization and summarization jointly by a new strategy of reference selection.'),
(92115, 2008, 'bmc', 'A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations', 'Kiiveri, Harri T', 'CSIRO Mathematical and Information Sciences, The Leeuwin Center, 65 Brockway Road, Floreat, 6014, Western Australia. harri.kiiveri@csiro.au', 'Australia', 21, 'Female|Gene Expression Profiling|Humans|Information Storage and Retrieval|Leukemia|Lymphoma|Male|Models, Biological|Models, Statistical|Neoplasm Staging|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Prostatic Neoplasms|Reference Values|Research Design|Smoking|Software', 'Journal Article', '10.1186/1471-2105-9-195', '18410693', 'With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables than observations are now routinely being collected. Finding relationships between response variables of interest and variables in such data sets is an important problem akin to finding needles in a haystack. Whilst methods for a number of response types have been developed a general approach has been lacking.'),
(92116, 2008, 'bmc', 'MPDA: microarray pooled DNA analyzer', 'Yang, Hsin-Chou|Huang, Mei-Chu|Li, Ling-Hui|Lin, Chien-Hsing|Yu, Alice L T|Diccianni, Mitchell B|Wu, Jer-Yuarn|Chen, Yuan-Tsong|Fann, Cathy S J', 'Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw|Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. hsinchou@stat.sinica.edu.tw', '-|-|-|-|-|-|-|-|-', 11, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-196', '18412951', 'Microarray-based pooled DNA experiments that combine the merits of DNA pooling and gene chip technology constitute a pivotal advance in biotechnology. This new technique uses pooled DNA, thereby reducing costs associated with the typing of DNA from numerous individuals. Moreover, use of an oligonucleotide gene chip reduces costs related to processing various DNA segments (e.g., primers, reagents). Thus, the technique provides an overall cost-effective solution for large-scale genomic/genetic research. However, few publicly shared tools are available to systematically analyze the rapidly accumulating volume of whole-genome pooled DNA data.'),
(92117, 2008, 'bmc', 'Accelerating string set matching in FPGA hardware for bioinformatics research', 'Dandass, Yoginder S|Burgess, Shane C|Lawrence, Mark|Bridges, Susan M', 'Institute of Digital Biology, Mississippi State University, Mississippi 39762, USA. yogi@cse.msstate.edu|Institute of Digital Biology, Mississippi State University, Mississippi 39762, USA. yogi@cse.msstate.edu|Institute of Digital Biology, Mississippi State University, Mississippi 39762, USA. yogi@cse.msstate.edu|Institute of Digital Biology, Mississippi State University, Mississippi 39762, USA. yogi@cse.msstate.edu', 'United States|United States|United States|United States', 30, 'Algorithms|Computers|Equipment Design|Gene Expression Profiling|Information Storage and Retrieval|Logic|Models, Theoretical|Neural Networks (Computer)|Open Reading Frames|Proteome|Proteomics|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-197', '18412963', 'This paper describes techniques for accelerating the performance of the string set matching problem with particular emphasis on applications in computational proteomics. The process of matching peptide sequences against a genome translated in six reading frames is part of a proteogenomic mapping pipeline that is used as a case-study. The Aho-Corasick algorithm is adapted for execution in field programmable gate array (FPGA) devices in a manner that optimizes space and performance. In this approach, the traditional Aho-Corasick finite state machine (FSM) is split into smaller FSMs, operating in parallel, each of which matches up to 20 peptides in the input translated genome. Each of the smaller FSMs is further divided into five simpler FSMs such that each simple FSM operates on a single bit position in the input (five bits are sufficient for representing all amino acids and special symbols in protein sequences).'),
(92118, 2008, 'bmc', 'Improving protein function prediction methods with integrated literature data', 'Gabow, Aaron P|Leach, Sonia M|Baumgartner, William A|Hunter, Lawrence E|Goldberg, Debra S', 'Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org|Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org|Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org|Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org|Department of Pharmacology, University of Colorado at Denver and Health Sciences Center, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6101, PO Box 6511, Aurora, CO 80045, USA. gabow@cbio.mskcc.org', 'United States|United States|United States|United States|United States', 23, 'Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteins|Sensitivity and Specificity|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-198', '18412966', 'Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problems complexity and scale. Identifying a proteins function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.'),
(92119, 2008, 'bmc', 'anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data', 'Zamboni, Nicola|Kümmel, Anne|Heinemann, Matthias', 'ETH Zurich, Institute of Molecular Systems Biology, Wolfgang-Pauli Strasse 16, 8093 Zurich, Switzerland. zamboni@imsb.biol.ethz.ch|ETH Zurich, Institute of Molecular Systems Biology, Wolfgang-Pauli Strasse 16, 8093 Zurich, Switzerland. zamboni@imsb.biol.ethz.ch|ETH Zurich, Institute of Molecular Systems Biology, Wolfgang-Pauli Strasse 16, 8093 Zurich, Switzerland. zamboni@imsb.biol.ethz.ch', 'Switzerland|Switzerland|Switzerland', 60, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Chemical|Protein Interaction Mapping|Proteome|Signal Transduction|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-199', '18416814', 'Compared to other omics techniques, quantitative metabolomics is still at its infancy. Complex sample preparation and analytical procedures render exact quantification extremely difficult. Furthermore, not only the actual measurement but also the subsequent interpretation of quantitative metabolome data to obtain mechanistic insights is still lacking behind the current expectations. Recently, the method of network-embedded thermodynamic (NET) analysis was introduced to address some of these open issues. Building upon principles of thermodynamics, this method allows for a quality check of measured metabolite concentrations and enables to spot metabolic reactions where active regulation potentially controls metabolic flux. So far, however, widespread application of NET analysis in metabolomics labs was hindered by the absence of suitable software.'),
(92120, 2008, 'bmc', 'ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use', 'Kraj, Piotr|Sharma, Ashok|Garge, Nikhil|Podolsky, Robert|McIndoe, Richard A', 'Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA USA. pkraj@mail.mcg.edu|Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA USA. pkraj@mail.mcg.edu|Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA USA. pkraj@mail.mcg.edu|Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA USA. pkraj@mail.mcg.edu|Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA USA. pkraj@mail.mcg.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia', 21, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-9-200', '18416829', 'During the last decade, the use of microarrays to assess the transcriptome of many biological systems has generated an enormous amount of data. A common technique used to organize and analyze microarray data is to perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant decrease in computational performance as the size of the dataset being analyzed becomes very large. For example, clustering 10000 genes from an experiment containing 200 microarrays can be quite time consuming and challenging on a desktop PC. One solution to the scalability problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers.'),
(92121, 2008, 'bmc', 'Identification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles', 'Verma, Ruchi|Tiwari, Ajit|Kaur, Sukhwinder|Varshney, Grish C|Raghava, Gajendra Ps', 'Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector 39-A, Chandigarh, India. ruchi@imtech.res.in', 'India|India|India|India|India', 24, 'Amino Acid Sequence|Animals|Artificial Intelligence|Blood Proteins|Erythrocytes|Gene Expression Profiling|Humans|Malaria, Falciparum|Molecular Sequence Data|Plasmodium falciparum|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-201', '18416838', 'Malaria parasite secretes various proteins in infected RBC for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine/drug against malaria. The existing motif-based methods have got limited success due to lack of universal motif in all secretory proteins of malaria parasite.'),
(92122, 2008, 'bmc', 'Missing value imputation improves clustering and interpretation of gene expression microarray data', 'Tuikkala, Johannes|Elo, Laura L|Nevalainen, Olli S|Aittokallio, Tero', 'Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland. jotatu@utu.fi|Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland. jotatu@utu.fi|Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland. jotatu@utu.fi|Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland. jotatu@utu.fi', 'Finland|Finland|Finland|Finland', 49, 'Algorithms|Artifacts|Artificial Intelligence|Cluster Analysis|Computer Simulation|Confidence Intervals|Fuzzy Logic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reference Values|Sample Size|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-9-202', '18423022', 'Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used.'),
(92123, 2008, 'bmc', 'The Second International Symposium on Languages in Biology and Medicine (LBM) 2007', 'Jian, Su|Baker, Christopher J O', 'Data Mining Department, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore. sujian@i2r.a-star.edu.sg|Data Mining Department, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore. sujian@i2r.a-star.edu.sg', 'Singapore|Singapore', 10, 'Biology|Language|Medicine|Natural Language Processing|Programming Languages|Terminology as Topic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S3-S1', '18426544', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92124, 2008, 'bmc', 'Automatic construction of rule-based ICD-9-CM coding systems', 'Farkas, Richárd|Szarvas, György', 'Research Group on Artificial Intelligence, Hungarian Academy of Sciences, Aradi Vértanúk tere 1, Szeged, Hungary. rfarkas@inf.u-szeged.hu|Research Group on Artificial Intelligence, Hungarian Academy of Sciences, Aradi Vértanúk tere 1, Szeged, Hungary. rfarkas@inf.u-szeged.hu', 'Hungary|Hungary', 68, 'Algorithms|Artificial Intelligence|Decision Support Systems, Clinical|International Classification of Diseases|Natural Language Processing|Pattern Recognition, Automated|Radiology|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S10', '18426545', 'In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health institutes and are assigned to clinical records manually following clinical treatment. Since this labeling task requires expert knowledge in the field of medicine, the process itself is costly and is prone to errors as human annotators have to consider thousands of possible codes when assigning the right ICD-9-CM labels to a document. In this study we use the datasets made available for training and testing automated ICD-9-CM coding systems by the organisers of an International Challenge on Classifying Clinical Free Text Using Natural Language Processing in spring 2007. The challenge itself was dominated by entirely or partly rule-based systems that solve the coding task using a set of hand crafted expert rules. Since the feasibility of the construction of such systems for thousands of ICD codes is indeed questionable, we decided to examine the problem of automatically constructing similar rule sets that turned out to achieve a remarkable accuracy in the shared task challenge.'),
(92125, 2008, 'bmc', 'Identification of transcription factor contexts in literature using machine learning approaches', 'Yang, Hui|Nenadic, Goran|Keane, John A', 'School of Computer Science, University of Manchester, Manchester, UK. Hui.Yang@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, UK. Hui.Yang@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, UK. Hui.Yang@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 29, 'Algorithms|Artificial Intelligence|Natural Language Processing|Pattern Recognition, Automated|Terminology as Topic|Transcription Factors|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S11', '18426546', 'Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While manual literature curation is expensive and labour intensive, the development of semi-automated text mining support is hindered by unavailability of training data. There have been no studies on how existing data sources (e.g. TF-related data from the MeSH thesaurus and GO ontology) or potentially noisy example data (e.g. protein-protein interaction, PPI) could be used to provide training data for identification of TF-contexts in literature.'),
(92126, 2008, 'bmc', 'Normalizing biomedical terms by minimizing ambiguity and variability', 'Tsuruoka, Yoshimasa|McNaught, John|Ananiadou, Sophia', 'School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester, M1 7DN, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester, M1 7DN, UK. yoshimasa.tsuruoka@manchester.ac.uk|School of Computer Science, The University of Manchester, MIB, 131 Princess Street, Manchester, M1 7DN, UK. yoshimasa.tsuruoka@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 33, 'Algorithms|Artifacts|Artificial Intelligence|Dictionaries as Topic|Natural Language Processing|Pattern Recognition, Automated|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S2', '18426547', 'One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.'),
(92127, 2008, 'bmc', 'Assessment of disease named entity recognition on a corpus of annotated sentences', 'Jimeno, Antonio|Jimenez-Ruiz, Ernesto|Lee, Vivian|Gaudan, Sylvain|Berlanga, Rafael|Rebholz-Schuhmann, Dietrich', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 78, 'Algorithms|Artificial Intelligence|Dictionaries as Topic|Disease|Natural Language Processing|Pattern Recognition, Automated|Semantics|Terminology as Topic|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S3', '18426548', 'In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions.'),
(92128, 2008, 'bmc', 'Exploiting and integrating rich features for biological literature classification', 'Wang, Hongning|Huang, Minlie|Ding, Shilin|Zhu, Xiaoyan', 'State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. whn03@mails.tsinghua.edu.cn|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. whn03@mails.tsinghua.edu.cn|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. whn03@mails.tsinghua.edu.cn|State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. whn03@mails.tsinghua.edu.cn', 'China|China|China|China', 25, 'Algorithms|Artificial Intelligence|Dictionaries as Topic|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S4', '18426549', 'Efficient features play an important role in automated text classification, which definitely facilitates the access of large-scale data. In the bioscience field, biological structures and terminologies are described by a large number of features, domain dependent features would significantly improve the classification performance. How to effectively select and integrate different types of features to improve the biological literature classification performance is the major issue studied in this paper.'),
(92129, 2008, 'bmc', 'New challenges for text mining: mapping between text and manually curated pathways', 'Oda, Kanae|Kim, Jin-Dong|Ohta, Tomoko|Okanohara, Daisuke|Matsuzaki, Takuya|Tateisi, Yuka|Tsujii, Junichi', 'Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp|Department of Computer Science, Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan. k.oda@is.s.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Artificial Intelligence|Dictionaries as Topic|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S5', '18426550', 'Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.'),
(92130, 2008, 'bmc', 'Comparative analysis of five protein-protein interaction corpora', 'Pyysalo, Sampo|Airola, Antti|Heimonen, Juho|Björne, Jari|Ginter, Filip|Salakoski, Tapio', 'Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi|Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi|Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi|Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi|Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi|Turku Centre for Computer Science (TUCS), Department of IT, University of Turku, Joukahaisenkatu 3-5, 20520 Turku, Finland. sampo.pyysalo@it.utu.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Artificial Intelligence|Dictionaries as Topic|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Protein Interaction Mapping|Terminology as Topic|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-9-S3-S6', '18426551', 'Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate.'),
(92131, 2008, 'bmc', 'Monitoring the evolutionary aspect of the Gene Ontology to enhance predictability and usability', 'Park, Jong C|Kim, Tak-eun|Park, Jinah', 'Computer Science Division, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, South Korea. park@nlp.kaist.ac.kr|Computer Science Division, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, South Korea. park@nlp.kaist.ac.kr|Computer Science Division, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, South Korea. park@nlp.kaist.ac.kr', '-|-|-', 14, 'Algorithms|Artificial Intelligence|Evolution, Molecular|Genes|Natural Language Processing|Pattern Recognition, Automated|Proteins|Sensitivity and Specificity|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S7', '18426552', 'Much effort is currently made to develop the Gene Ontology (GO). Due to the dynamic nature of information it addresses, GO undergoes constant updates whose results are released at regular intervals as separate versions. Although there are a large number of computational tools to aid the development of GO, they are operating on a particular version of GO, making it difficult for GO curators to anticipate the full impact of particular changes along the time axis on a larger scale. We present a method for tapping into such an evolutionary aspect of GO, by making it possible to keep track of important temporal changes to any of the terms and relations of GO and by consequently making it possible to recognize associated trends.'),
(92132, 2008, 'bmc', 'Structuring an event ontology for disease outbreak detection', 'Kawazoe, Ai|Chanlekha, Hutchatai|Shigematsu, Mika|Collier, Nigel', 'National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan. zoeai@nii.ac.jp|National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan. zoeai@nii.ac.jp|National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan. zoeai@nii.ac.jp|National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan. zoeai@nii.ac.jp', 'Japan|Japan|Japan|Japan', 13, 'Algorithms|Artificial Intelligence|Disease Outbreaks|Natural Language Processing|Pattern Recognition, Automated|Population Surveillance|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S8', '18426553', 'This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between laymans language used in disease outbreak reports and public health experts deep knowledge, and 2) making multi-lingual information available.'),
(92133, 2008, 'bmc', 'Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction', 'Gobeill, Julien|Tbahriti, Imad|Ehrler, Frédéric|Mottaz, Anaïs|Veuthey, Anne-Lise|Ruch, Patrick', 'University and Hospitals of Geneva, Geneva, Switzerland.|University and Hospitals of Geneva, Geneva, Switzerland.|University and Hospitals of Geneva, Geneva, Switzerland.|University and Hospitals of Geneva, Geneva, Switzerland.|University and Hospitals of Geneva, Geneva, Switzerland.|University and Hospitals of Geneva, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 10, 'Algorithms|Artificial Intelligence|Genes|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Proteins|Sensitivity and Specificity|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S3-S9', '18426554', 'This paper describes and evaluates a sentence selection engine that extracts a GeneRiF (Gene Reference into Functions) as defined in ENTREZ-Gene based on a MEDLINE record. Inputs for this task include both a gene and a pointer to a MEDLINE reference. In the suggested approach we merge two independent sentence extraction strategies. The first proposed strategy (LASt) uses argumentative features, inspired by discourse-analysis models. The second extraction scheme (GOEx) uses an automatic text categorizer to estimate the density of Gene Ontology categories in every sentence, thus providing a full ranking of all possible candidate GeneRiFs. A combination of the two approaches is proposed, which also aims at reducing the size of the selected segment by filtering out non-content bearing rhetorical phrases.'),
(92134, 2008, 'bmc', 'Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data', 'Zhang, Yuji|Xuan, Jianhua|de los Reyes, Benildo G|Clarke, Robert|Ressom, Habtom W', 'Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA. yjzhang@vt.edu|Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA. yjzhang@vt.edu|Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA. yjzhang@vt.edu|Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA. yjzhang@vt.edu|Lombardi Comprehensive Cancer Center, Georgetown University, 4000 Reservoir Rd, Washington, DC, USA. yjzhang@vt.edu', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Motifs|Computer Simulation|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Gene Targeting|Models, Biological|Signal Transduction|Systems Integration|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-203', '18426580', 'Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.'),
(92135, 2008, 'bmc', 'Major copy proportion analysis of tumor samples using SNP arrays', 'Li, Cheng|Beroukhim, Rameen|Weir, Barbara A|Winckler, Wendy|Garraway, Levi A|Sellers, William R|Meyerson, Matthew', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States', 33, 'Algorithms|Base Sequence|DNA, Neoplasm|Gene Dosage|Genetic Variation|Humans|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-204', '18426588', 'Single nucleotide polymorphisms (SNPs) are the most common genetic variations in the human genome and are useful as genomic markers. Oligonucleotide SNP microarrays have been developed for high-throughput genotyping of up to 900,000 human SNPs and have been used widely in linkage and cancer genomics studies. We have previously used Hidden Markov Models (HMM) to analyze SNP array data for inferring copy numbers and loss-of-heterozygosity (LOH) from paired normal and tumor samples and unpaired tumor samples.'),
(92136, 2008, 'bmc', 'GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique', 'Yu, Wei|Clyne, Melinda|Dolan, Siobhan M|Yesupriya, Ajay|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|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|United States|United States|United States', 35, 'Artificial Intelligence|Chromosome Mapping|Genetic Linkage|Genome, Human|Humans|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|PubMed|Software', 'Journal Article', '10.1186/1471-2105-9-205', '18430222', 'Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.'),
(92137, 2008, 'bmc', 'VariVis: a visualisation toolkit for variation databases', 'Smith, Timothy D|Cotton, Richard G H', 'Genomic Disorders Research Centre, Carlton South, VIC 3053, Australia. t.smith6@pgrad.unimelb.edu.au|Genomic Disorders Research Centre, Carlton South, VIC 3053, Australia. t.smith6@pgrad.unimelb.edu.au', 'Australia|Australia', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|DNA|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Models, Genetic|Molecular Sequence Data|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-9-206', '18430251', 'With the completion of the Human Genome Project and recent advancements in mutation detection technologies, the volume of data available on genetic variations has risen considerably. These data are stored in online variation databases and provide important clues to the cause of diseases and potential side effects or resistance to drugs. However, the data presentation techniques employed by most of these databases make them difficult to use and understand.'),
(92138, 2008, 'bmc', 'Extraction of semantic biomedical relations from text using conditional random fields', 'Bundschus, Markus|Dejori, Mathaeus|Stetter, Martin|Tresp, Volker|Kriegel, Hans-Peter', 'Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany. bundschu@dbs.ifi.lmu.de|Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany. bundschu@dbs.ifi.lmu.de|Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany. bundschu@dbs.ifi.lmu.de|Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany. bundschu@dbs.ifi.lmu.de|Siemens AG, Corporate Technology, Information and Communications, Otto-Hahn-Ring 6, 81739 Munich, Germany. bundschu@dbs.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany', 139, 'Biomedical Research|Database Management Systems|Databases, Genetic|Disease|Genes|Humans|MEDLINE|Models, Statistical|Natural Language Processing|Semantics|Sequence Analysis|Terminology as Topic|Therapeutics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-207', '18433469', 'The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.'),
(92139, 2008, 'bmc', 'Conserved co-expression for candidate disease gene prioritization', 'Oti, Martin|van Reeuwijk, Jeroen|Huynen, Martijn A|Brunner, Han G', 'Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands. m.oti@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands. m.oti@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands. m.oti@cmbi.ru.nl|Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Geert Grooteplein 26-28, 6525 GA, Nijmegen, The Netherlands. m.oti@cmbi.ru.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 28, 'Animals|Base Sequence|Caenorhabditis elegans|Conserved Sequence|Databases, Genetic|Disease|Drosophila melanogaster|Evolution, Molecular|Gene Dosage|Gene Expression|Gene Expression Profiling|Gene Frequency|Genetic Predisposition to Disease|Humans|Mice|Oligonucleotide Array Sequence Analysis|Penetrance|Predictive Value of Tests|Saccharomyces cerevisiae|Sample Size|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-208', '18433471', 'Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.'),
(92140, 2008, 'bmc', 'Discovering biclusters in gene expression data based on high-dimensional linear geometries', 'Gan, Xiangchao|Liew, Alan Wee-Chung|Yan, Hong', 'Department of Computer Science, Kings College London, UK. xiang.gan@kcl.ac.uk|Department of Computer Science, Kings College London, UK. xiang.gan@kcl.ac.uk|Department of Computer Science, Kings College London, UK. xiang.gan@kcl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 75, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Decision Theory|Gene Expression|Gene Expression Profiling|Humans|Information Storage and Retrieval|Linear Models|Lymphoma|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-9-209', '18433477', 'In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns.'),
(92141, 2008, 'bmc', 'Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization', 'Cheng, Kin-On|Law, Ngai-Fong|Siu, Wan-Chi|Liew, Alan Wee-Chung', 'School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk|School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk|School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk|School of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland, Australia. k.o.cheng@polyu.edu.hk', 'Australia|Australia|Australia|Australia', 62, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Graphics|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-210', '18433478', 'The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.'),
(92142, 2008, 'bmc', 'Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework', 'Katoh, Kazutaka|Toh, Hiroyuki', 'Digital Medicine Initiative, Kyushu University, Fukuoka, 812-8582, Japan. katoh@bioreg.kyushu-u.ac.jp|Digital Medicine Initiative, Kyushu University, Fukuoka, 812-8582, Japan. katoh@bioreg.kyushu-u.ac.jp', 'Japan|Japan', 431, 'Algorithms|Base Sequence|Molecular Sequence Data|RNA, Untranslated|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-212', '18439255', 'Structural alignment of RNAs is becoming important, since the discovery of functional non-coding RNAs (ncRNAs). Recent studies, mainly based on various approximations of the Sankoff algorithm, have resulted in considerable improvement in the accuracy of pairwise structural alignment. In contrast, for the cases with more than two sequences, the practical merit of structural alignment remains unclear as compared to traditional sequence-based methods, although the importance of multiple structural alignment is widely recognized.'),
(92143, 2008, 'bmc', 'A new protein linear motif benchmark for multiple sequence alignment software', 'Perrodou, Emmanuel|Chica, Claudia|Poch, Olivier|Gibson, Toby J|Thompson, Julie D', 'Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr|Institut de Génétique et de Biologie Moléculaire et Cellulaire, Department of Structural Biology and Genomics, F-67400 Illkirch, France. perrodou@igbmc.u-strasbg.fr', 'France|France|France|France|France', 25, 'Amino Acid Motifs|Artificial Intelligence|Pattern Recognition, Automated|Proteins|Proteomics|Quality Control|Reproducibility of Results|Sequence Alignment|Sequence Homology, Amino Acid|Software Validation|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-213', '18439277', 'Linear motifs (LMs) are abundant short regulatory sites used for modulating the functions of many eukaryotic proteins. They play important roles in post-translational modification, cell compartment targeting, docking sites for regulatory complex assembly and protein processing and cleavage. Methods for LM detection are now being developed that are strongly dependent on scores for motif conservation in homologous proteins. However, most LMs are found in natively disordered polypeptide segments that evolve rapidly, unhindered by structural constraints on the sequence. These regions of modular proteins are difficult to align using classical multiple sequence alignment programs that are specifically optimised to align the globular domains. As a consequence, poor motif alignment quality is hindering efforts to detect new LMs.'),
(92144, 2008, 'bmc', 'M-BISON: microarray-based integration of data sources using networks', 'Daigle, Bernie J|Altman, Russ B', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. bdaigle@stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. bdaigle@stanford.edu', 'United States|United States', 6, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Software|Systems Integration', 'Evaluation Studies', '10.1186/1471-2105-9-214', '18439292', 'The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes.'),
(92145, 2008, 'bmc', 'Binning sequences using very sparse labels within a metagenome', 'Chan, Chon-Kit Kenneth|Hsu, Arthur L|Halgamuge, Saman K|Tang, Sen-Lin', 'Research Centre for Biodiversity, Academia Sinica, Taipei, Taiwan. cckenneth@gmail.com|Research Centre for Biodiversity, Academia Sinica, Taipei, Taiwan. cckenneth@gmail.com|Research Centre for Biodiversity, Academia Sinica, Taipei, Taiwan. cckenneth@gmail.com|Research Centre for Biodiversity, Academia Sinica, Taipei, Taiwan. cckenneth@gmail.com', '-|-|-|-', 59, 'Algorithms|Artificial Intelligence|Base Sequence|Confidence Intervals|Database Management Systems|Databases, Genetic|Genes, rRNA|Genomics|Information Storage and Retrieval|Pattern Recognition, Automated|Phylogeny|RNA, Archaeal|RNA, Bacterial|Sample Size|Sequence Analysis, RNA|Species Specificity|Uncertainty', 'Comparative Study', '10.1186/1471-2105-9-215', '18442374', 'In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.'),
(92146, 2008, 'bmc', 'Normalization for triple-target microarray experiments', 'Martin-Magniette, Marie-Laure|Aubert, Julie|Bar-Hen, Avner|Elftieh, Samira|Magniette, Frederic|Renou, Jean-Pierre|Daudin, Jean-Jacques', 'UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr|UMR AgroParisTech-INRA MIA 518, 75231 Paris Cedex05, France. marie_laure.martin@agroparistech.fr', 'France|France|France|France|France|France|France', 4, 'Arabidopsis|Artifacts|Calibration|Carbocyanines|Fluorescent Dyes|Gene Expression Profiling|Genes, Plant|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Organic Chemicals|Reference Standards|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-216', '18442385', 'Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are Cy3 and Cy5. Recent improvements in the technology (dye-labelling, scanner and, image analysis) allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data.'),
(92147, 2008, 'bmc', 'Gene prediction in metagenomic fragments: a large scale machine learning approach', 'Hoff, Katharina J|Tech, Maike|Lingner, Thomas|Daniel, Rolf|Morgenstern, Burkhard|Meinicke, Peter', 'Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de|Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de|Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de|Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de|Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de|Abteilung Bioinformatik, Georg-August-Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany. katharina@gobics.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 60, 'Algorithms|Artificial Intelligence|Chromosome Mapping|DNA, Bacterial|Genome, Bacterial|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-217', '18442389', 'Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.'),
(92148, 2008, 'bmc', 'An efficient visualization tool for the analysis of protein mutation matrices', 'David, Maria Pamela C|Lapid, Carlo M|Daria, Vincent Ricardo M', 'Computational Science Research Center, University of the Philippines, Diliman 1101, Philippines. maria_pamela.david@up.edu.ph|Computational Science Research Center, University of the Philippines, Diliman 1101, Philippines. maria_pamela.david@up.edu.ph|Computational Science Research Center, University of the Philippines, Diliman 1101, Philippines. maria_pamela.david@up.edu.ph', 'Philippines|Philippines|Philippines', 1, 'Amino Acids|Amyloid|Animals|Antibodies|Audiovisual Aids|Computer Graphics|Databases, Protein|Electric Capacitance|Hemagglutinins|Humans|Hydrophobic and Hydrophilic Interactions|Iodide Peroxidase|Mutagenesis|Mutation|Protein Folding|Proteins|Receptors, Odorant|Research Design|Sequence Alignment|Sequence Analysis, Protein|Static Electricity|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-218', '18442400', 'It is useful to develop a tool that would effectively describe protein mutation matrices specifically geared towards the identification of mutations that produce either wanted or unwanted effects, such as an increase or decrease in affinity, or a predisposition towards misfolding. Here, we describe a tool where such mutations are efficiently identified, categorized and visualized. To categorize the mutations, amino acids in a mutation matrix are arranged according to one of three sets of physicochemical characteristics, namely hydrophilicity, size and polarizability, and charge and polarity. The magnitude and frequencies of mutations for an alignment are subsequently described using color information and scaling factors.'),
(92149, 2008, 'bmc', 'ConStruct: Improved construction of RNA consensus structures', 'Wilm, Andreas|Linnenbrink, Kornelia|Steger, Gerhard', 'Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Universitätsstr, 1, D-40225 Düsseldorf, Germany. wilm@biophys.uni-duesseldorf.de|Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Universitätsstr, 1, D-40225 Düsseldorf, Germany. wilm@biophys.uni-duesseldorf.de|Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie, Universitätsstr, 1, D-40225 Düsseldorf, Germany. wilm@biophys.uni-duesseldorf.de', 'Germany|Germany|Germany', 29, 'Algorithms|Base Sequence|Consensus Sequence|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-9-219', '18442401', 'Aligning homologous non-coding RNAs (ncRNAs) correctly in terms of sequence and structure is an unresolved problem, due to both mathematical complexity and imperfect scoring functions. High quality alignments, however, are a prerequisite for most consensus structure prediction approaches, homology searches, and tools for phylogeny inference. Automatically created ncRNA alignments often need manual corrections, yet this manual refinement is tedious and error-prone.'),
(92150, 2008, 'bmc', 'Studying the functional conservation of cis-regulatory modules and their transcriptional output', 'Bauer, Denis C|Bailey, Timothy L', 'Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld. 4072 Australia. d.bauer@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld. 4072 Australia. d.bauer@imb.uq.edu.au', 'Australia|Australia', 9, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|Molecular Sequence Data|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-220', '18442418', 'Cis-regulatory modules (CRMs) are distinct, genomic regions surrounding the target gene that can independently activate the promoter to drive transcription. The activation of a CRM is controlled by the binding of a certain combination of transcription factors (TFs). It would be of great benefit if the transcriptional output mediated by a specific CRM could be predicted. Of equal benefit would be identifying in silico a specific CRM as the driver of the expression in a specific tissue or situation. We extend a recently developed biochemical modeling approach to manage both prediction tasks. Given a set of TFs, their protein concentrations, and the positions and binding strengths of each of the TFs in a putative CRM, the model predicts the transcriptional output of the gene. Our approach predicts the location of the regulating CRM by using predicted TF binding sites in regions near the gene as input to the model and searching for the region that yields a predicted transcription rate most closely matching the known rate.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92151, 2008, 'bmc', 'Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry', 'Spiess, Andrej-Nikolai|Feig, Caroline|Ritz, Christian', 'Department of Andrology, University Hospital Hamburg-Eppendorf, Hamburg, Germany. a.spiess@uke.uni-hamburg.de|Department of Andrology, University Hospital Hamburg-Eppendorf, Hamburg, Germany. a.spiess@uke.uni-hamburg.de|Department of Andrology, University Hospital Hamburg-Eppendorf, Hamburg, Germany. a.spiess@uke.uni-hamburg.de', 'Germany|Germany|Germany', 80, 'Algorithms|Numerical Analysis, Computer-Assisted|Reproducibility of Results|Reverse Transcriptase Polymerase Chain Reaction|Sensitivity and Specificity|Spectrometry, Fluorescence', 'Evaluation Studies', '10.1186/1471-2105-9-221', '18445269', 'Fitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time PCR (qPCR) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal qPCR data.'),
(92152, 2008, 'bmc', 'An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions', 'Churkin, Alexander|Barash, Danny', 'Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel. churkin@cs.bgu.ac.il|Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel. churkin@cs.bgu.ac.il', 'Israel|Israel', 0, 'Algorithms|Animals|Biophysics|Computational Biology|Computer Simulation|Databases, Genetic|Hepacivirus|Humans|Molecular Sequence Data|Mutagenesis, Site-Directed|Nucleic Acid Conformation|Point Mutation|Predictive Value of Tests|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Deletion|Software Design|Tetrahymena thermophila|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-222', '18445289', 'RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Viennas RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.'),
(92153, 2008, 'bmc', 'GENOMEPOP: a program to simulate genomes in populations', 'Carvajal-Rodríguez, Antonio', 'Departamento de Bioquímica, Genética e Inmunología, Universidad de Vigo, 36310 Vigo, Spain. acraaj@uvigo.es.', 'Spain', 0, 'Algorithms|Base Sequence|Biological Evolution|Codon|Computer Simulation|Databases, Nucleic Acid|Emigration and Immigration|Game Theory|Gene Frequency|Genetics, Population|Genome, Human|Genotype|Humans|Markov Chains|Models, Genetic|Recombination, Genetic|Selection, Genetic|Software', 'Journal Article', '10.1186/1471-2105-9-223', '18447924', 'There are several situations in population biology research where simulating DNA sequences is useful. Simulation of biological populations under different evolutionary genetic models can be undertaken using backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally efficient. The reason is that they are based on the history of lineages with surviving offspring in the current population. On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. Hence, there is an increasing interest in forward population genetic simulation and efficient new tools have been developed recently. Software tools that allow efficient simulation of large DNA fragments under complex evolutionary models will be very helpful when trying to better understand the trace left on the DNA by the different interacting evolutionary forces. Here I will introduce GenomePop, a forward simulation program that fulfills the above requirements. The use of the program is demonstrated by studying the impact of intracodon recombination on global and site-specific dN/dS estimation.'),
(92154, 2008, 'bmc', 'Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory', 'Churbanov, Alexander|Winters-Hilt, Stephen', 'The Research Institute for Children, 200 Henry Clay Ave, New Orleans, LA 70118, USA. achurbanov@yahoo.com|The Research Institute for Children, 200 Henry Clay Ave, New Orleans, LA 70118, USA. achurbanov@yahoo.com', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computers|DNA|Data Interpretation, Statistical|Electric Impedance|Information Storage and Retrieval|Ion Channel Gating|Likelihood Functions|Linear Models|Markov Chains|Models, Molecular|Neural Networks (Computer)|Normal Distribution|Nucleic Acid Conformation|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Software Design|Weights and Measures', 'Journal Article', '10.1186/1471-2105-9-224', '18447951', 'The Baum-Welch learning procedure for Hidden Markov Models (HMMs) provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by Miklós, I. and Meyer, I.M. describes a memory sparse version of the Baum-Welch algorithm with modifications to the original probabilistic table topologies to make memory use independent of sequence length (and linearly dependent on state number). The original description of the technique has some errors that we amend. We then compare the corrected implementation on a variety of data sets with conventional and checkpointing implementations.'),
(92155, 2008, 'bmc', 'A copula method for modeling directional dependence of genes', 'Kim, Jong-Min|Jung, Yoon-Sung|Sungur, Engin A|Han, Kap-Hoon|Park, Changyi|Sohn, Insuk', 'Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu|Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu|Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu|Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu|Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu|Division of Science and Mathematics, University of Minnesota, Morris, MN, 56267, USA. jongmink@morris.umn.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Cell Cycle|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Gene Regulatory Networks|Genes, Fungal|Likelihood Functions|Models, Genetic|Models, Statistical|Multivariate Analysis|Pattern Recognition, Automated|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-225', '18447957', 'Genes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation.'),
(92156, 2008, 'bmc', 'SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences', 'Kurgan, Lukasz|Cios, Krzysztof|Chen, Ke', 'Department of Electrical and Computer Engineering, University of Alberta, ECEFR, 9701 116 Street, Edmonton, AB, T6G 2V4, Canada . lkurgan@ece.ualberta.ca|Department of Electrical and Computer Engineering, University of Alberta, ECEFR, 9701 116 Street, Edmonton, AB, T6G 2V4, Canada . lkurgan@ece.ualberta.ca|Department of Electrical and Computer Engineering, University of Alberta, ECEFR, 9701 116 Street, Edmonton, AB, T6G 2V4, Canada . lkurgan@ece.ualberta.ca', 'Canada|Canada|Canada', 129, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-226', '18452616', 'Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.'),
(92157, 2008, 'bmc', 'The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks', 'Potapov, Anatolij P|Goemann, Björn|Wingender, Edgar', 'Department of Bioinformatics, Medical School, Georg August University of Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany. anatolij.potapov@bioinf.med.uni-goettingen.de|Department of Bioinformatics, Medical School, Georg August University of Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany. anatolij.potapov@bioinf.med.uni-goettingen.de|Department of Bioinformatics, Medical School, Georg August University of Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany. anatolij.potapov@bioinf.med.uni-goettingen.de', 'Germany|Germany|Germany', 28, 'Algorithms|Computer Simulation|Gene Expression Regulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-227', '18454847', 'Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine general with particular properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter--the pairwise disconnectivity index of a networks element - that is capable of such bridging.'),
(92158, 2008, 'bmc', 'A Semantic Web for bioinformatics: goals, tools, systems, applications', 'Cannata, Nicola|Schröder, Michael|Marangoni, Roberto|Romano, Paolo', 'Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), I-62032, Italy. nicola.cannata@unicam.it|Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), I-62032, Italy. nicola.cannata@unicam.it|Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), I-62032, Italy. nicola.cannata@unicam.it|Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), I-62032, Italy. nicola.cannata@unicam.it', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Natural Language Processing|Semantics|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S4-S1', '18460170', '-'),
(92159, 2008, 'bmc', 'GraphFind: enhancing graph searching by low support data mining techniques', 'Ferro, Alfredo|Giugno, Rosalba|Mongiovì, Misael|Pulvirenti, Alfredo|Skripin, Dmitry|Shasha, Dennis', 'Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it|Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it|Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it|Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it|Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it|Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 19, 'Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S10', '18460171', 'Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.'),
(92160, 2008, 'bmc', 'PARPST: a PARallel algorithm to find peptide sequence tags', 'Brunetti, Sara|Lodi, Elena|Mori, Elisa|Stella, Maria', 'Dipartimento di Scienze Matematiche e Informatiche, Università degli studi di Siena, Siena I-53100, Italy. sara.brunetti@unisi.it|Dipartimento di Scienze Matematiche e Informatiche, Università degli studi di Siena, Siena I-53100, Italy. sara.brunetti@unisi.it|Dipartimento di Scienze Matematiche e Informatiche, Università degli studi di Siena, Siena I-53100, Italy. sara.brunetti@unisi.it|Dipartimento di Scienze Matematiche e Informatiche, Università degli studi di Siena, Siena I-53100, Italy. sara.brunetti@unisi.it', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S11', '18460172', 'Protein identification is one of the most challenging problems in proteomics. Tandem mass spectrometry provides an important tool to handle the protein identification problem.'),
(92161, 2008, 'bmc', 'Bio-jETI: a service integration, design, and provisioning platform for orchestrated bioinformatics processes', 'Margaria, Tiziana|Kubczak, Christian|Steffen, Bernhard', 'Universität Potsdam, D-14482 Potsdam, Germany. margaria@cs.uni-potsdam.de|Universität Potsdam, D-14482 Potsdam, Germany. margaria@cs.uni-potsdam.de|Universität Potsdam, D-14482 Potsdam, Germany. margaria@cs.uni-potsdam.de', 'Germany|Germany|Germany', 43, 'Computational Biology|Computer Graphics|Information Storage and Retrieval|Programming Languages|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S4-S12', '18460173', 'With Bio-jETI, we introduce a service platform for interdisciplinary work on biological application domains and illustrate its use in a concrete application concerning statistical data processing in R and xcms for an LC/MS analysis of FAAH gene knockout.'),
(92162, 2008, 'bmc', 'GeneFisher-P: variations of GeneFisher as processes in Bio-jETI', 'Lamprecht, Anna-Lena|Margaria, Tiziana|Steffen, Bernhard|Sczyrba, Alexander|Hartmeier, Sven|Giegerich, Robert', 'Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de|Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de|Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de|Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de|Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de|Dortmund University of Technology, Dortmund D-44227, Germany. anna-lena.lamprecht@cs.uni-dortmund.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 36, 'Algorithms|Base Sequence|DNA Primers|Gene Targeting|Molecular Sequence Data|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S13', '18460174', 'PCR primer design is an everyday, but not trivial task requiring state-of-the-art software. We describe the popular tool GeneFisher and explain its recent restructuring using workflow techniques. We apply a service-oriented approach to model and implement GeneFisher-P, a process-based version of the GeneFisher web application, as a part of the Bio-jETI platform for service modeling and execution. We show how to introduce a flexible process layer to meet the growing demand for improved user-friendliness and flexibility.'),
(92163, 2008, 'bmc', 'Terminologies for text-mining, an experiment in the lipoprotein metabolism domain', 'Alexopoulou, Dimitra|Wächter, Thomas|Pickersgill, Laura|Eyre, Cecilia|Schroeder, Michael', 'Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Dresden, D-01062, Germany. dimitra.alexopoulou@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany|Germany', 18, 'Algorithms|Database Management Systems|Databases, Factual|Lipoproteins|Natural Language Processing|Periodicals as Topic|Semantics|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S4-S2', '18460175', 'The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them.'),
(92164, 2008, 'bmc', 'Ontology-guided data preparation for discovering genotype-phenotype relationships', 'Coulet, Adrien|Smaïl-Tabbone, Malika|Benlian, Pascale|Napoli, Amedeo|Devignes, Marie-Dominique', 'KIKA Medical, Paris, F-75012, France. adrien.coulet@loria.fr|KIKA Medical, Paris, F-75012, France. adrien.coulet@loria.fr|KIKA Medical, Paris, F-75012, France. adrien.coulet@loria.fr|KIKA Medical, Paris, F-75012, France. adrien.coulet@loria.fr|KIKA Medical, Paris, F-75012, France. adrien.coulet@loria.fr', 'France|France|France|France|France', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Genes|Genotype|Natural Language Processing|Periodicals as Topic|Phenotype|Semantics|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S4-S3', '18460176', 'Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.'),
(92165, 2008, 'bmc', 'Ontology-based, Tissue MicroArray oriented, image centered tissue bank', 'Viti, Federica|Merelli, Ivan|Caprera, Andrea|Lazzari, Barbara|Stella, Alessandra|Milanesi, Luciano', 'Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it|Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it|Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it|Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it|Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it|Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy. federica.viti@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Database Management Systems|Databases, Factual|Italy|Medical Records Systems, Computerized|Natural Language Processing|Semantics|Software|Terminology as Topic|Tissue Array Analysis', 'Journal Article', '10.1186/1471-2105-9-S4-S4', '18460177', 'Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.'),
(92166, 2008, 'bmc', 'AMMO-Prot: amine system project 3D-model finder', 'Navas-Delgado, Ismael|Montañez, Raúl|Pino-Angeles, Almudena|Moya-García, Aurelio A|Urdiales, José Luis|Sánchez-Jiménez, Francisca|Aldana-Montes, José F', 'Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es|Computer Languages and Computing Science Department, University of Málaga, Málaga, 29071, Spain. ismael@lcc.uma.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Amines|Amino Acid Sequence|Computer Simulation|Database Management Systems|Databases, Protein|Imaging, Three-Dimensional|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S5', '18460178', 'Amines are biogenic amino acid derivatives, which play pleiotropic and very important yet complex roles in animal physiology. For many other relevant biomolecules, biochemical and molecular data are being accumulated, which need to be integrated in order to be effective in the advance of biological knowledge in the field. For this purpose, a multidisciplinary group has started an ontology-based system named the Amine System Project (ASP) for which amine-related information is the validation bench.'),
(92167, 2008, 'bmc', 'RDFScape: Semantic Web meets systems biology', 'Splendiani, Andrea', 'UPRES-EA 3888-Laboratoire dInformatique Médicale, Faculté de Médecine, Université Rennes 1, Rennes FR-35043, France. andrea.splendiani@univ-rennes1.fr', 'France', 31, 'Algorithms|Database Management Systems|Databases, Factual|Natural Language Processing|Periodicals as Topic|Semantics|Software|Systems Biology|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S4-S6', '18460179', 'The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.'),
(92168, 2008, 'bmc', 'Stochastic models for the in silico simulation of synaptic processes', 'Bracciali, Andrea|Brunelli, Marcello|Cataldo, Enrico|Degano, Pierpaolo', 'Dipartimento di Informatica, Università di Pisa, Pisa I-56127, Italy. braccia@di.unipi.it|Dipartimento di Informatica, Università di Pisa, Pisa I-56127, Italy. braccia@di.unipi.it|Dipartimento di Informatica, Università di Pisa, Pisa I-56127, Italy. braccia@di.unipi.it|Dipartimento di Informatica, Università di Pisa, Pisa I-56127, Italy. braccia@di.unipi.it', 'Italy|Italy|Italy|Italy', 9, 'Action Potentials|Animals|Computer Simulation|Humans|Models, Neurological|Nerve Net|Neurons|Neurotransmitter Agents|Synapses|Synaptic Transmission', 'Journal Article', '10.1186/1471-2105-9-S4-S7', '18460180', 'Research in life sciences is benefiting from a large availability of formal description techniques and analysis methodologies. These allow both the phenomena investigated to be precisely modeled and virtual experiments to be performed in silico. Such experiments may result in easier, faster, and satisfying approximations of their in vitro/vivo counterparts. A promising approach is represented by the study of biological phenomena as a collection of interactive entities through process calculi equipped with stochastic semantics. These exploit formal grounds developed in the theory of concurrency in computer science, account for the not continuous, nor discrete, nature of many phenomena, enjoy nice compositional properties and allow for simulations that have been demonstrated to be coherent with data in literature.'),
(92169, 2008, 'bmc', 'On deducing causality in metabolic networks', 'Bodei, Chiara|Bracciali, Andrea|Chiarugi, Davide', 'Dipartimento di Informatica, Università di Pisa, I-56127, Pisa, Italy. chiara@di.unipi.it|Dipartimento di Informatica, Università di Pisa, I-56127, Pisa, Italy. chiara@di.unipi.it|Dipartimento di Informatica, Università di Pisa, I-56127, Pisa, Italy. chiara@di.unipi.it', 'Italy|Italy|Italy', 8, 'Algorithms|Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S8', '18460181', 'Metabolic networks present a complex interconnected structure, whose understanding is in general a non-trivial task. Several formal approaches have been developed to support the investigation of such networks. One of the relevant problems in this context is the comprehension of causality dependencies amongst the molecules involved in the metabolic process.'),
(92170, 2008, 'bmc', 'Reconstructing networks of pathways via significance analysis of their intersections', 'Francesconi, Mirko|Remondini, Daniel|Neretti, Nicola|Sedivy, John M|Cooper, Leon N|Verondini, Ettore|Milanesi, Luciano|Castellani, Gastone', 'Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it|Centro Interdipartimentale L, Galvani, Università di Bologna, Bologna 40127, Italy. mirko.francesconi@studio.unibo.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Biological|Models, Statistical|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-S4-S9', '18460182', 'Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation.'),
(92171, 2008, 'bmc', 'Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology', 'Aranguren, Mikel Egaña|Antezana, Erick|Kuiper, Martin|Stevens, Robert', 'School of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK. mikel.eganaaranguren@cs.man.ac.uk|School of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK. mikel.eganaaranguren@cs.man.ac.uk|School of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK. mikel.eganaaranguren@cs.man.ac.uk|School of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK. mikel.eganaaranguren@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 54, 'Artificial Intelligence|Cell Cycle|Computational Biology|Database Management Systems|Databases, Factual|Models, Biological|Pattern Recognition, Automated|Semantics|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S5-S1', '18460183', 'Bio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training.'),
(92172, 2008, 'bmc', 'Gene Ontology annotations: what they mean and where they come from', 'Hill, David P|Smith, Barry|McAndrews-Hill, Monica S|Blake, Judith A', 'The Jackson Laboratory, Bar Harbor, ME, USA. dph@informatics.jax.org|The Jackson Laboratory, Bar Harbor, ME, USA. dph@informatics.jax.org|The Jackson Laboratory, Bar Harbor, ME, USA. dph@informatics.jax.org|The Jackson Laboratory, Bar Harbor, ME, USA. dph@informatics.jax.org', 'United States|United States|United States|United States', 0, 'Animals|Artificial Intelligence|Computational Biology|Database Management Systems|Databases, Genetic|Genomics|Humans|Models, Biological|Pattern Recognition, Automated|Semantics|Systems Integration|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S5-S2', '18460184', 'To address the challenges of information integration and retrieval, the computational genomics community increasingly has come to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). Here we address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations, in the hope that this will lead both to better representations of biological reality through annotation and ontology development and to more informed use of GO resources by experimental scientists.'),
(92173, 2008, 'bmc', 'Mapping proteins to disease terminologies: from UniProt to MeSH', 'Mottaz, Anaïs|Yip, Yum L|Ruch, Patrick|Veuthey, Anne-Lise', 'Swiss-Prot Group, Swiss Institute of Bioinformatics, 1211 Genève 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss-Prot Group, Swiss Institute of Bioinformatics, 1211 Genève 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss-Prot Group, Swiss Institute of Bioinformatics, 1211 Genève 4, Switzerland. anais.mottaz@isb-sib.ch|Swiss-Prot Group, Swiss Institute of Bioinformatics, 1211 Genève 4, Switzerland. anais.mottaz@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Biomedical Research|Databases, Protein|Disease|Humans|Knowledge Bases|Medical Subject Headings|Proteomics|Semantics|Systems Integration|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S5-S3', '18460185', 'Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology.'),
(92174, 2008, 'bmc', 'Metrics for GO based protein semantic similarity: a systematic evaluation', 'Pesquita, Catia|Faria, Daniel|Bastos, Hugo|Ferreira, António E N|Falcão, André O|Couto, Francisco M', 'XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt|XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt|XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt|XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt|XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt|XLDB, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande-Edifício C6, Lisboa, Portugal. cpesquita@xldb.di.fc.ul.pt', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 214, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Gene Expression Profiling|Normal Distribution|Pattern Recognition, Automated|Proteins|Reference Standards|Reference Values|Semantics|Sensitivity and Specificity|Sequence Homology|Structure-Activity Relationship|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S5-S4', '18460186', 'Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.'),
(92175, 2008, 'bmc', 'Facilitating the development of controlled vocabularies for metabolomics technologies with text mining', 'Spasixc4x87, Irena|Schober, Daniel|Sansone, Susanna-Assunta|Rebholz-Schuhmann, Dietrich|Kell, Douglas B|Paton, Norman W', 'Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. i.spasic@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Abstracting and Indexing as Topic|Chromatography, Gas|Information Storage and Retrieval|MEDLINE|Magnetic Resonance Spectroscopy|Metabolism|Natural Language Processing|Pattern Recognition, Automated|Systems Biology|Systems Integration|Technology|Terminology as Topic|United States|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-S5-S5', '18460187', 'Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually.'),
(92176, 2008, 'bmc', 'Inferring the role of transcription factors in regulatory networks', 'Veber, Philippe|Guziolowski, Carito|Le Borgne, Michel|Radulescu, Ovidiu|Siegel, Anne', 'Centre INRIA Rennes Bretagne Atlantique, IRISA, Rennes, France. philippe.veber@googlemail.com|Centre INRIA Rennes Bretagne Atlantique, IRISA, Rennes, France. philippe.veber@googlemail.com|Centre INRIA Rennes Bretagne Atlantique, IRISA, Rennes, France. philippe.veber@googlemail.com|Centre INRIA Rennes Bretagne Atlantique, IRISA, Rennes, France. philippe.veber@googlemail.com|Centre INRIA Rennes Bretagne Atlantique, IRISA, Rennes, France. philippe.veber@googlemail.com', 'France|France|France|France|France', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-9-228', '18460200', 'Expression profiles obtained from multiple perturbation experiments are increasingly used to reconstruct transcriptional regulatory networks, from well studied, simple organisms up to higher eukaryotes. Admittedly, a key ingredient in developing a reconstruction method is its ability to integrate heterogeneous sources of information, as well as to comply with practical observability issues: measurements can be scarce or noisy. In this work, we show how to combine a network of genetic regulations with a set of expression profiles, in order to infer the functional effect of the regulations, as inducer or repressor. Our approach is based on a consistency rule between a network and the signs of variation given by expression arrays.'),
(92177, 2008, 'bmc', 'A tree-based conservation scoring method for short linear motifs in multiple alignments of protein sequences', 'Chica, Claudia|Labarga, Alberto|Gould, Cathryn M|López, Rodrigo|Gibson, Toby J', 'EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. claudia.chica@embl.de|EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. claudia.chica@embl.de|EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. claudia.chica@embl.de|EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. claudia.chica@embl.de|EMBL Structural and Computational Biology Unit, Meyerhofstrasse 1, 69117 Heidelberg, Germany. claudia.chica@embl.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Conserved Sequence|Linear Models|Models, Chemical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-229', '18460207', 'The structure of many eukaryotic cell regulatory proteins is highly modular. They are assembled from globular domains, segments of natively disordered polypeptides and short linear motifs. The latter are involved in protein interactions and formation of regulatory complexes. The function of such proteins, which may be difficult to define, is the aggregate of the subfunctions of the modules. It is therefore desirable to efficiently predict linear motifs with some degree of accuracy, yet sequence database searches return results that are not significant.'),
(92178, 2008, 'bmc', 'CPSP-tools--exact and complete algorithms for high-throughput 3D lattice protein studies', 'Mann, Martin|Will, Sebastian|Backofen, Rolf', 'Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany. mmann@informatik.uni-freiburg.de|Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany. mmann@informatik.uni-freiburg.de|Bioinformatics Group, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany. mmann@informatik.uni-freiburg.de', 'Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-230', '18462492', 'The principles of protein folding and evolution pose problems of very high inherent complexity. Often these problems are tackled using simplified protein models, e.g. lattice proteins. The CPSP-tools package provides programs to solve exactly and completely the problems typical of studies using 3D lattice protein models. Among the tasks addressed are the prediction of (all) globally optimal and/or suboptimal structures as well as sequence design and neutral network exploration.'),
(92179, 2008, 'bmc', 'Automating dChip: toward reproducible sharing of microarray data analysis', 'Li, Cheng', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney St, Boston, MA 02115, USA. cli@hsph.harvard.edu', 'United States', 55, 'Database Management Systems|Databases, Genetic|Electronic Data Processing|Humans|Information Dissemination|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Work Simplification', 'Journal Article', '10.1186/1471-2105-9-231', '18466620', 'During the past decade, many software packages have been developed for analysis and visualization of various types of microarrays. We have developed and maintained the widely used dChip as a microarray analysis software package accessible to both biologist and data analysts. However, challenges arise when dChip users want to analyze large number of arrays automatically and share data analysis procedures and parameters. Improvement is also needed when the dChip user support team tries to identify the causes of reported analysis errors or bugs from users.'),
(92180, 2008, 'bmc', 'Bioinformatic analyses of mammalian 5-UTR sequence properties of mRNAs predicts alternative translation initiation sites', 'Wegrzyn, Jill L|Drudge, Thomas M|Valafar, Faramarz|Hook, Vivian', 'Computational Science Research Center, San Diego State University, San Diego, USA. jwegrzyn@ucsd.edu|Computational Science Research Center, San Diego State University, San Diego, USA. jwegrzyn@ucsd.edu|Computational Science Research Center, San Diego State University, San Diego, USA. jwegrzyn@ucsd.edu|Computational Science Research Center, San Diego State University, San Diego, USA. jwegrzyn@ucsd.edu', 'United States|United States|United States|United States', 0, '5 Untranslated Regions|Animals|Computational Biology|Computer Simulation|Humans|Models, Genetic|RNA Splice Sites|RNA, Messenger|Sequence Analysis, RNA|Transcription Initiation Site', 'Journal Article', '10.1186/1471-2105-9-232', '18466625', 'Utilization of alternative initiation sites for protein translation directed by non-AUG codons in mammalian mRNAs is observed with increasing frequency. Alternative initiation sites are utilized for the synthesis of important regulatory proteins that control distinct biological functions. It is, therefore, of high significance to define the parameters that allow accurate bioinformatic prediction of alternative translation initiation sites (aTIS). This study has investigated 5-UTR regions of mRNAs to define consensus sequence properties and structural features that allow identification of alternative initiation sites for protein translation.'),
(92181, 2008, 'bmc', 'Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes', 'Dekhtyar, Michael|Morin, Amelie|Sakanyan, Vehary', 'University of Tver, 33 Jelyabova, 170000 Tver, Russia. Michael.Dekhtyar@tversu.ru|University of Tver, 33 Jelyabova, 170000 Tver, Russia. Michael.Dekhtyar@tversu.ru|University of Tver, 33 Jelyabova, 170000 Tver, Russia. Michael.Dekhtyar@tversu.ru', '-|-|-', 14, 'AT Rich Sequence|Algorithms|Amino Acid Motifs|Cell-Free System|Computational Biology|DNA-Directed RNA Polymerases|Escherichia coli Proteins|Genome, Bacterial|Pattern Recognition, Automated|Promoter Regions, Genetic|Protein Subunits|Sequence Alignment|Sequence Analysis, DNA|Sigma Factor|Thermotoga maritima', 'Journal Article', '10.1186/1471-2105-9-233', '18471287', 'Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes.'),
(92182, 2008, 'bmc', 'Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces', 'Bordner, Andrew J|Gorin, Andrey A', 'Computational Science and Mathematics Division and BioEnergy Science Center, Oak Ridge National Laboratory, PO Box 2008, MS 6173, Oak Ridge, TN 37831, USA. bordner.andrew@mayo.edu|Computational Science and Mathematics Division and BioEnergy Science Center, Oak Ridge National Laboratory, PO Box 2008, MS 6173, Oak Ridge, TN 37831, USA. bordner.andrew@mayo.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Cluster Analysis|Consensus Sequence|Crystallography, X-Ray|Database Management Systems|Databases, Protein|Evolution, Molecular|Models, Molecular|Multiprotein Complexes|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteomics|Reproducibility of Results|Sequence Analysis, Protein|Structural Homology, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-234', '18474114', 'Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92183, 2008, 'bmc', 'Discovery and assembly of repeat family pseudomolecules from sparse genomic sequence data using the Assisted Automated Assembler of Repeat Families (AAARF) algorithm', 'DeBarry, Jeremy D|Liu, Renyi|Bennetzen, Jeffrey L', 'Department of Genetics, University of Georgia, Athens, GA 30602-7223, USA. jdebarry@uga.edu|Department of Genetics, University of Georgia, Athens, GA 30602-7223, USA. jdebarry@uga.edu|Department of Genetics, University of Georgia, Athens, GA 30602-7223, USA. jdebarry@uga.edu', 'Georgia|Georgia|Georgia', 0, 'Algorithms|DNA, Plant|Databases, Genetic|Gene Dosage|Genome|Multigene Family|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software|Zea mays', 'Journal Article', '10.1186/1471-2105-9-235', '18474116', 'Higher eukaryotic genomes are typically large, complex and filled with both genes and multiple classes of repetitive DNA. The repetitive DNAs, primarily transposable elements, are a rapidly evolving genome component that can provide the raw material for novel selected functions and also indicate the mechanisms and history of genome evolution in any ancestral lineage. Despite their abundance, universality and significance, studies of genomic repeat content have been largely limited to analyses of the repeats in fully sequenced genomes.'),
(92184, 2008, 'bmc', 'A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins', 'Brick, Kevin|Pizzi, Elisabetta', 'Dipartimento di Malattie Infettive, Parassitarie ed Immunomediate - Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Roma, Italy. kevbrick@gmail.com|Dipartimento di Malattie Infettive, Parassitarie ed Immunomediate - Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Roma, Italy. kevbrick@gmail.com', 'Italy|Italy', 11, 'Algorithms|Amino Acid Sequence|Animals|Molecular Sequence Data|Plasmodium|Protozoan Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-9-236', '18485187', 'The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent a fully accurate substitution model for proteins characterized by a biased amino acid composition. This problem has been addressed recently by adjusting existing matrices, however, to date, no empirical approach has been taken to build matrices which offer a substitution model for comparing proteins sharing an amino acid compositional bias. Here, we present a novel procedure to construct series of symmetrical substitution matrices to align proteins from similarly biased Plasmodium proteomes.'),
(92185, 2008, 'bmc', 'Visualization of large influenza virus sequence datasets using adaptively aggregated trees with sampling-based subscale representation', 'Zaslavsky, Leonid|Bao, Yiming|Tatusova, Tatiana A', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. zaslavsk@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. zaslavsk@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. zaslavsk@ncbi.nlm.nih.gov', 'United States|United States|United States', 21, 'Algorithms|Chromosome Mapping|Computer Graphics|Computer Simulation|Genome, Viral|Models, Genetic|Orthomyxoviridae|Sample Size|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-237', '18485197', 'With the amount of influenza genome sequence data growing rapidly, researchers need machine assistance in selecting datasets and exploring the data. Enhanced visualization tools are required to represent results of the exploratory analysis on the web in an easy-to-comprehend form and to facilitate convenient information retrieval.'),
(92186, 2008, 'bmc', 'Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction', 'Bush, William S|Edwards, Todd L|Dudek, Scott M|McKinney, Brett A|Ritchie, Marylyn D', 'Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. wbush@chgr.mc.vanderbilt.edu|Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. wbush@chgr.mc.vanderbilt.edu|Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. wbush@chgr.mc.vanderbilt.edu|Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. wbush@chgr.mc.vanderbilt.edu|Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. wbush@chgr.mc.vanderbilt.edu', 'United States|United States|United States|United States|United States', 0, 'Bias|Diagnostic Errors|Gene Frequency|Gene Regulatory Networks|Genetic Markers|Genotype|Information Storage and Retrieval|Models, Genetic|Odds Ratio|Risk Assessment|Sensitivity and Specificity|Statistics, Nonparametric|Weights and Measures', 'Journal Article', '10.1186/1471-2105-9-238', '18485205', 'Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification - the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.'),
(92187, 2008, 'bmc', 'Aspects of coverage in medical DNA sequencing', 'Wendl, Michael C|Wilson, Richard K', 'Genome Sequencing Center and Department of Genetics, Washington University, St Louis MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center and Department of Genetics, Washington University, St Louis MO 63108, USA. mwendl@wustl.edu', 'United States|United States', 0, 'Alleles|Animals|Caenorhabditis elegans|Calibration|Chromosome Mapping|Computational Biology|Gene Library|Genome, Helminth|Neoplasms|Ploidies|Predictive Value of Tests|Reference Standards|Reference Values|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-239', '18485222', 'DNA sequencing is now emerging as an important component in biomedical studies of diseases like cancer. Short-read, highly parallel sequencing instruments are expected to be used heavily for such projects, but many design specifications have yet to be conclusively established. Perhaps the most fundamental of these is the redundancy required to detect sequence variations, which bears directly upon genomic coverage and the consequent resolving power for discerning somatic mutations.'),
(92188, 2008, 'bmc', 'Estimating the size of the solution space of metabolic networks', 'Braunstein, Alfredo|Mulet, Roberto|Pagnani, Andrea', 'Politecnico di Torino, Corso Duca degli Abruzzi 34, I-10129, Torino, Italy. alfredo.braunstein@polito.it|Politecnico di Torino, Corso Duca degli Abruzzi 34, I-10129, Torino, Italy. alfredo.braunstein@polito.it|Politecnico di Torino, Corso Duca degli Abruzzi 34, I-10129, Torino, Italy. alfredo.braunstein@polito.it', 'Italy|Italy|Italy', 0, 'Adaptation, Physiological|Algorithms|Computational Biology|Erythrocytes|Escherichia coli|Gene Deletion|Information Theory|Metabolic Networks and Pathways|Models, Biological|Models, Statistical|Neural Networks (Computer)|Systems Biology|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-240', '18489757', 'Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.'),
(92189, 2008, 'bmc', 'A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model', 'Judson, Richard|Elloumi, Fathi|Setzer, R Woodrow|Li, Zhen|Shah, Imran', 'National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov|National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov|National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov|National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov|National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov', 'United States|United States|United States|United States|United States', 52, 'Algorithms|Artificial Intelligence|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Toxicity Tests', 'Comparative Study', '10.1186/1471-2105-9-241', '18489778', 'Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.'),
(92190, 2008, 'bmc', 'Compressing DNA sequence databases with coil', 'White, W Timothy J|Hendy, Michael D', 'Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. w.t.white@massey.ac.nz|Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Palmerston North, New Zealand. w.t.white@massey.ac.nz', 'New Zealand|New Zealand', 0, 'Animals|Data Compression|Database Management Systems|Databases, Nucleic Acid|Evolution, Molecular|Expressed Sequence Tags|Humans|Neural Networks (Computer)|Phylogeny|Point Mutation|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-242', '18489794', 'Publicly available DNA sequence databases such as GenBank are large, and are growing at an exponential rate. The sheer volume of data being dealt with presents serious storage and data communications problems. Currently, sequence data is usually kept in large flat files, which are then compressed using standard Lempel-Ziv (gzip) compression - an approach which rarely achieves good compression ratios. While much research has been done on compressing individual DNA sequences, surprisingly little has focused on the compression of entire databases of such sequences. In this study we introduce the sequence database compression software coil.'),
(92191, 2008, 'bmc', 'MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data', 'Chi, Bryan|deLeeuw, Ronald J|Coe, Bradley P|Ng, Raymond T|MacAulay, Calum|Lam, Wan L', 'Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca|Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca|Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca|Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca|Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca|Department of Cancer Genetics and Developmental Biology, British Columbia Cancer Research Centre, Vancouver, BC, Canada. bchi@bccrc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-243', '18492270', 'Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms.'),
(92192, 2008, 'bmc', 'Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis', 'Biswas, Shameek|Storey, John D|Akey, Joshua M', 'Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA. sbiswas@u.washington.edu|Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA. sbiswas@u.washington.edu|Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA. sbiswas@u.washington.edu', 'United States|United States|United States', 30, 'Chromosome Mapping|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Principal Component Analysis|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-9-244', '18492285', 'The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, most studies have searched for eQTL by analyzing gene expression traits one at a time. As thousands of expression traits are typically analyzed, this can reduce power because of the need to correct for the number of hypothesis tests performed. In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually.'),
(92193, 2008, 'bmc', 'Predikin and PredikinDB: a computational framework for the prediction of protein kinase peptide specificity and an associated database of phosphorylation sites', 'Saunders, Neil F W|Brinkworth, Ross I|Huber, Thomas|Kemp, Bruce E|Kobe, Bostjan', 'School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia. n.saunders@uq.edu.au|School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia. n.saunders@uq.edu.au|School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia. n.saunders@uq.edu.au|School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia. n.saunders@uq.edu.au|School of Molecular and Microbial Sciences, University of Queensland, Brisbane 4072, Australia. n.saunders@uq.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Amino Acid Sequence|Animals|Binding Sites|Catalytic Domain|Databases, Protein|Mice|Phosphorylation|Protein-Serine-Threonine Kinases|Sequence Analysis, Protein|Software|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-9-245', '18501020', 'We have previously described an approach to predicting the substrate specificity of serine-threonine protein kinases. The method, named Predikin, identifies key conserved substrate-determining residues in the kinase catalytic domain that contact the substrate in the region of the phosphorylation site and so determine the sequence surrounding the phosphorylation site. Predikin was implemented originally as a web application written in Javascript.'),
(92194, 2008, 'bmc', 'Using the longest significance run to estimate region-specific p-values in genetic association mapping studies', 'Lian, Ie-Bin|Lin, Yi-Hsien|Lin, Ying-Chao|Yang, Hsin-Chou|Chang, Chee-Jang|Fann, Cathy S J', '1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw|1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw|1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw|1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw|1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw|1Department of Mathematics, National Changhua University of Education, Changhua 500, Taiwan. maiblian@cc.ncue.edu.tw', '-|-|-|-|-|-', 3, 'Chi-Square Distribution|Confidence Intervals|DNA|Gene Frequency|Genetic Markers|Genetic Predisposition to Disease|Genetic Testing|Genome, Human|Haplotypes|Humans|Likelihood Functions|Linkage Disequilibrium|Logistic Models|Predictive Value of Tests|Psoriasis|Reference Values|Sequence Homology, Nucleic Acid|Uncertainty', 'Journal Article', '10.1186/1471-2105-9-246', '18503718', 'Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region.'),
(92195, 2008, 'bmc', 'A visual analytics approach for understanding biclustering results from microarray data', 'Santamaría, Rodrigo|Therón, Roberto|Quintales, Luis', 'Departamento de Informática y Automática, Universidad de Salamanca, Pz, de Los Caídos S/N, 37007 Salamanca, Spain. rodri@usal.es|Departamento de Informática y Automática, Universidad de Salamanca, Pz, de Los Caídos S/N, 37007 Salamanca, Spain. rodri@usal.es|Departamento de Informática y Automática, Universidad de Salamanca, Pz, de Los Caídos S/N, 37007 Salamanca, Spain. rodri@usal.es', 'Spain|Spain|Spain', 0, 'Algorithms|Cluster Analysis|Computer Graphics|Computer Simulation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-247', '18505552', 'Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results.'),
(92196, 2008, 'bmc', 'Dinucleotide controlled null models for comparative RNA gene prediction', 'Gesell, Tanja|Washietl, Stefan', 'Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, Dr. Bohr-Gasse 9, A-1030 Vienna, Austria. tanja.gesell@univie.ac.at|Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, Dr. Bohr-Gasse 9, A-1030 Vienna, Austria. tanja.gesell@univie.ac.at', 'Austria|Austria', 0, 'Algorithms|Animals|Base Composition|Computational Biology|Humans|Markov Chains|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-9-248', '18505553', 'Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available.'),
(92197, 2008, 'bmc', 'Prediction of enzyme function by combining sequence similarity and protein interactions', 'Espadaler, Jordi|Eswar, Narayanan|Querol, Enrique|Avilés, Francesc X|Sali, Andrej|Marti-Renom, Marc A|Oliva, Baldomero', 'Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es|Laboratori de Bioinformàtica Estructural (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 19, 'Amino Acid Sequence|Databases, Protein|Enzymes|Fuzzy Logic|Pattern Recognition, Automated|Predictive Value of Tests|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Structure-Activity Relationship|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-9-249', '18505562', 'A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners.'),
(92198, 2008, 'bmc', 'TileQC: a system for tile-based quality control of Solexa data', 'Dolan, Peter C|Denver, Dee R', 'Department of Zoology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon 97331, USA. dolanp@science.oregonstate.edu|Department of Zoology and Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon 97331, USA. dolanp@science.oregonstate.edu', 'United States|United States', 0, 'Animals|Base Sequence|Caenorhabditis elegans|Quality Control|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-250', '18507856', 'Next-generation DNA sequencing technologies such as Illuminas Solexa platform and Roches 454 approach provide new avenues for investigating genome-scale questions. However, they also present novel analytical challenges that must be met for their effective application to biological questions.'),
(92199, 2008, 'bmc', 'Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases', 'Zhang, Min|Zhang, Dabao|Wells, Martin T', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. minzhang@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. minzhang@stat.purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. minzhang@stat.purdue.edu', 'United States|United States|United States', 0, 'Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Epistasis, Genetic|Models, Genetic|Models, Statistical|Quantitative Trait Loci|Regression Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-9-251', '18510743', 'Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaikes information criterion (AIC) and Bayesian information criterion (BIC).'),
(92200, 2008, 'bmc', 'Missing value imputation for microarray gene expression data using histone acetylation information', 'Xiang, Qian|Dai, Xianhua|Deng, Yangyang|He, Caisheng|Wang, Jiang|Feng, Jihua|Dai, Zhiming', 'Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn|Department of Electronics & Communications Engineering, School of Information Science and Technology, Sun Yat-Sen University, 135 West Xingang Road, Guangzhou, PR China. xiangq@mail.sysu.edu.cn', 'China|China|China|China|China|China|China', 23, 'Acetylation|Artifacts|Artificial Intelligence|Chromatin Assembly and Disassembly|Confidence Intervals|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genes, Fungal|Histones|Least-Squares Analysis|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Saccharomyces cerevisiae|Sample Size|Transcription Factors|Uncertainty', 'Journal Article', '10.1186/1471-2105-9-252', '18510747', 'It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.'),
(92201, 2008, 'bmc', 'BatchPrimer3: a high throughput web application for PCR and sequencing primer design', 'You, Frank M|Huo, Naxin|Gu, Yong Qiang|Luo, Ming-Cheng|Ma, Yaqin|Hane, Dave|Lazo, Gerard R|Dvorak, Jan|Anderson, Olin D', 'Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, CA 95616, USA. frank.you@ars.usda.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|DNA Primers|Genetic Markers|Internet|Microsatellite Repeats|Molecular Sequence Data|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-253', '18510760', 'Microsatellite (simple sequence repeat - SSR) and single nucleotide polymorphism (SNP) markers are two types of important genetic markers useful in genetic mapping and genotyping. Often, large-scale genomic research projects require high-throughput computer-assisted primer design. Numerous such web-based or standard-alone programs for PCR primer design are available but vary in quality and functionality. In particular, most programs lack batch primer design capability. Such a high-throughput software tool for designing SSR flanking primers and SNP genotyping primers is increasingly demanded.'),
(92202, 2008, 'bmc', 'New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background', 'Penco, Silvana|Buscema, Massimo|Patrosso, Maria Cristina|Marocchi, Alessandro|Grossi, Enzo', 'Medical Genetics, Clinical Chemistry and Clinical Pathology Laboratory, Niguarda Ca Granda Hospital P.za Ospedale Maggiore 3, 20100 Milan, Italy. silvana.penco@ospedaleniguarda.it|Medical Genetics, Clinical Chemistry and Clinical Pathology Laboratory, Niguarda Ca Granda Hospital P.za Ospedale Maggiore 3, 20100 Milan, Italy. silvana.penco@ospedaleniguarda.it|Medical Genetics, Clinical Chemistry and Clinical Pathology Laboratory, Niguarda Ca Granda Hospital P.za Ospedale Maggiore 3, 20100 Milan, Italy. silvana.penco@ospedaleniguarda.it|Medical Genetics, Clinical Chemistry and Clinical Pathology Laboratory, Niguarda Ca Granda Hospital P.za Ospedale Maggiore 3, 20100 Milan, Italy. silvana.penco@ospedaleniguarda.it|Medical Genetics, Clinical Chemistry and Clinical Pathology Laboratory, Niguarda Ca Granda Hospital P.za Ospedale Maggiore 3, 20100 Milan, Italy. silvana.penco@ospedaleniguarda.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Adult|Aged|Amyotrophic Lateral Sclerosis|Case-Control Studies|Female|Genetic Predisposition to Disease|Genome, Human|Humans|Italy|Male|Middle Aged|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Polymorphism, Genetic', 'Journal Article', '10.1186/1471-2105-9-254', '18513389', 'Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis'),
(92203, 2008, 'bmc', 'Protein contact order prediction from primary sequences', 'Shi, Yi|Zhou, Jianjun|Arndt, David|Wishart, David S|Lin, Guohui', 'Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada. ys3@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada. ys3@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada. ys3@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada. ys3@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada. ys3@cs.ualberta.ca', 'Canada|Canada|Canada|Canada|Canada', 12, 'Algorithms|Amino Acid Sequence|Binding Sites|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-255', '18513429', 'Contact order is a topological descriptor that has been shown to be correlated with several interesting protein properties such as protein folding rates and protein transition state placements. Contact order has also been used to select for viable protein folds from ab initio protein structure prediction programs. For proteins of known three-dimensional structure, their contact order can be calculated directly. However, for proteins with unknown three-dimensional structure, there is no effective prediction method currently available.'),
(92204, 2008, 'bmc', 'poolHiTS: a shifted transversal design based pooling strategy for high-throughput drug screening', 'Kainkaryam, Raghunandan M|Woolf, Peter J', 'Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA. raghu@umich.edu|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA. raghu@umich.edu', 'United States|United States', 0, 'Algorithms|Biological Assay|Cell Physiological Phenomena|Drug Evaluation, Preclinical|Pharmaceutical Preparations|Software', 'Journal Article', '10.1186/1471-2105-9-256', '18513431', 'A key goal of drug discovery is to increase the throughput of small molecule screens without sacrificing screening accuracy. High-throughput screening (HTS) in drug discovery involves testing a large number of compounds in a biological assay to identify active compounds. Normally, molecules from a large compound library are tested individually to identify the activity of each molecule. Usually a small number of compounds are found to be active, however the presence of false positive and negative testing errors suggests that this one-drug one-assay screening strategy can be significantly improved. Pooling designs are testing schemes that test mixtures of compounds in each assay, thereby generating a screen of the whole compound library in fewer tests. By repeatedly testing compounds in different combinations, pooling designs also allow for error-correction. These pooled designs, for specific experiment parameters, can be simply and efficiently created using the Shifted Transversal Design (STD) pooling algorithm. However, drug screening contains a number of key constraints that require specific modifications if this pooling approach is to be useful for practical screen designs.'),
(92205, 2008, 'bmc', 'SODa: an Mn/Fe superoxide dismutase prediction and design server', 'Kwasigroch, Jean Marc|Wintjens, René|Gilis, Dimitri|Rooman, Marianne', 'Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, avenue Roosevelt 50, B-1050 Brussels, Belgium. jkwasigr@ulb.ac.be|Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, avenue Roosevelt 50, B-1050 Brussels, Belgium. jkwasigr@ulb.ac.be|Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, avenue Roosevelt 50, B-1050 Brussels, Belgium. jkwasigr@ulb.ac.be|Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, avenue Roosevelt 50, B-1050 Brussels, Belgium. jkwasigr@ulb.ac.be', 'Belgium|Belgium|Belgium|Belgium', 12, 'Algorithms|Amino Acid Sequence|Binding Sites|Drug Design|Enzyme Activation|Iron|Manganese|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein|Software|Superoxide Dismutase', 'Journal Article', '10.1186/1471-2105-9-257', '18518943', 'Superoxide dismutases (SODs) are ubiquitous metalloenzymes that play an important role in the defense of aerobic organisms against oxidative stress, by converting reactive oxygen species into nontoxic molecules. We focus here on the SOD family that uses Fe or Mn as cofactor.'),
(92206, 2008, 'bmc', 'A simple and robust method for connecting small-molecule drugs using gene-expression signatures', 'Zhang, Shu-Dong|Gant, Timothy W', 'MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK. sdz1@le.ac.uk|MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK. sdz1@le.ac.uk', 'United Kingdom|United Kingdom', 63, 'Algorithms|Cell Line|Databases, Genetic|Estrogens|Gene Expression Profiling|Genomics|Histone Deacetylase Inhibitors|Humans|Immunosuppressive Agents|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Pharmaceutical Preparations|Pharmacology|Reference Standards|Research Design|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-258', '18518950', 'Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Using a suitably robust algorithm these signatures can potentially be used to connect molecules with similar pharmacological or toxicological properties by gene expression profile. Lamb et al first proposed the Connectivity Map [Lamb et al (2006), Science 313, 1929-1935] to make successful connections among small molecules, genes, and diseases using genomic signatures.'),
(92207, 2008, 'bmc', 'Word correlation matrices for protein sequence analysis and remote homology detection', 'Lingner, Thomas|Meinicke, Peter', 'Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Göttingen, Germany. thomas@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Göttingen, Germany. thomas@gobics.de', 'Germany|Germany', 21, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Semantics|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-9-259', '18522726', 'Classification of protein sequences is a central problem in computational biology. Currently, among computational methods discriminative kernel-based approaches provide the most accurate results. However, kernel-based methods often lack an interpretable model for analysis of discriminative sequence features, and predictions on new sequences usually are computationally expensive.'),
(92208, 2008, 'bmc', 'GMDD: a database of GMO detection methods', 'Dong, Wei|Yang, Litao|Shen, Kailin|Kim, Banghyun|Kleter, Gijs A|Marvin, Hans J P|Guo, Rong|Liang, Wanqi|Zhang, Dabing', 'GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn|GMO Detection Laboratory, SJTU-Bor Luh Food Safety Center, Key Laboratory of Microbial Metabolism, Ministry of Education, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China. dong_wei@sjtu.edu.cn', 'China|China|China|China|China|China|China|China|China', 94, 'Animals|Database Management Systems|Databases, Factual|Genetic Engineering|Organisms, Genetically Modified|Reference Standards|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-260', '18522755', 'Since more than one hundred events of genetically modified organisms (GMOs) have been developed and approved for commercialization in global area, the GMO analysis methods are essential for the enforcement of GMO labelling regulations. Protein and nucleic acid-based detection techniques have been developed and utilized for GMOs identification and quantification. However, the information for harmonization and standardization of GMO analysis methods at global level is needed.'),
(92209, 2008, 'bmc', 'Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)', 'González, Juan R|Carrasco, Josep L|Armengol, Lluís|Villatoro, Sergi|Jover, Lluís|Yasui, Yutaka|Estivill, Xavier', 'Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@imim.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Autistic Disorder|Breast Neoplasms|Confidence Intervals|DNA Probes|DiGeorge Syndrome|Gene Dosage|Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Humans|Linear Models|Molecular Probe Techniques|Polymerase Chain Reaction|Prader-Willi Syndrome|Predictive Value of Tests|Reference Standards|Reference Values|Single-Blind Method|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-261', '18522760', 'MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample.'),
(92210, 2008, 'bmc', 'Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor binding sites', 'Kim, Nak-Kyeong|Tharakaraman, Kannan|Mariño-Ramírez, Leonardo|Spouge, John L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA. kimnak@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA. kimnak@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA. kimnak@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA. kimnak@ncbi.nlm.nih.gov', 'United States|United States|United States|United States', 26, 'Base Sequence|Bayes Theorem|Binding Sites|Computational Biology|Consensus Sequence|DNA|Databases, Genetic|Humans|Models, Statistical|Nucleic Acid Conformation|Predictive Value of Tests|Protein Binding|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors|Weights and Measures', 'Journal Article', '10.1186/1471-2105-9-262', '18533028', 'Biologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream of a transcription start site (TSS). Although some programs for identifying sequence motifs exploit positional information, most of them model it only implicitly and with ad hoc methods, making them unsuitable for general motif searches.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92211, 2008, 'bmc', 'GPR17: molecular modeling and dynamics studies of the 3-D structure and purinergic ligand binding features in comparison with P2Y receptors', 'Parravicini, Chiara|Ranghino, Graziella|Abbracchio, Maria P|Fantucci, Piercarlo', 'Laboratory of Cellular and Molecular Pharmacology of Purinergic Transmission, Department of Pharmacological Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy. mariapia.abbracchio@unimi.it|Laboratory of Cellular and Molecular Pharmacology of Purinergic Transmission, Department of Pharmacological Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy. mariapia.abbracchio@unimi.it|Laboratory of Cellular and Molecular Pharmacology of Purinergic Transmission, Department of Pharmacological Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy. mariapia.abbracchio@unimi.it|Laboratory of Cellular and Molecular Pharmacology of Purinergic Transmission, Department of Pharmacological Sciences, University of Milan, Via Balzaretti 9, 20133 Milan, Italy. mariapia.abbracchio@unimi.it', 'Italy|Italy|Italy|Italy', 36, 'Adenosine Diphosphate|Adenosine Monophosphate|Animals|Binding Sites|Computer Simulation|Crystallography, X-Ray|Humans|Models, Molecular|Purinergic P2 Receptor Antagonists|Rats|Receptors, G-Protein-Coupled|Receptors, Purinergic P2|Structural Homology, Protein|Uridine Diphosphate', 'Journal Article', '10.1186/1471-2105-9-263', '18533035', 'GPR17 is a G-protein-coupled receptor located at intermediate phylogenetic position between two distinct receptor families: the P2Y and CysLT receptors for extracellular nucleotides and cysteinyl-LTs, respectively. We previously showed that GPR17 can indeed respond to both classes of endogenous ligands and to synthetic compounds active at the above receptor families, thus representing the first fully characterized non-peptide hybrid GPCR. In a rat brain focal ischemia model, the selective in vivo knock down of GPR17 by anti-sense technology or P2Y/CysLT antagonists reduced progression of ischemic damage, thus highlighting GPR17 as a novel therapeutic target for stroke. Elucidation of the structure of GPR17 and of ligand binding mechanisms are the necessary steps to obtain selective and potent drugs for this new potential target. On this basis, a 3-D molecular model of GPR17 embedded in a solvated phospholipid bilayer and refined by molecular dynamics simulations has been the first aim of this study. To explore the binding mode of the purinergic component of the receptor, the endogenous agonist UDP and two P2Y receptor antagonists demonstrated to be active on GPR17 (MRS2179 and cangrelor) were then modeled on the receptor.'),
(92212, 2008, 'bmc', 'Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens', 'Yin, Zheng|Zhou, Xiaobo|Bakal, Chris|Li, Fuhai|Sun, Youxian|Perrimon, Norbert|Wong, Stephen T C', 'Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org|Center for Bioinformatics, The Methodist Hospital Research Institute and Weill Cornell College of Medicine, 6565 Fannin Street, Houston, TX 77030, USA. ZYin@tmhs.org', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Cluster Analysis|Computer Simulation|Cytological Techniques|Drosophila|HeLa Cells|Humans|Internet|Pattern Recognition, Automated|Phenotype|RNA Interference|RNA, Small Interfering', 'Journal Article', '10.1186/1471-2105-9-264', '18534020', 'The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.'),
(92213, 2008, 'bmc', 'StAR: a simple tool for the statistical comparison of ROC curves', 'Vergara, Ismael A|Norambuena, Tomás|Ferrada, Evandro|Slater, Alex W|Melo, Francisco', 'Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile. ivergara@gmail.com', 'Chile|Chile|Chile|Chile|Chile', 136, 'Algorithms|Data Interpretation, Statistical|Diagnosis, Computer-Assisted|ROC Curve|Software', 'Journal Article', '10.1186/1471-2105-9-265', '18534022', 'As in many different areas of science and technology, most important problems in bioinformatics rely on the proper development and assessment of binary classifiers. A generalized assessment of the performance of binary classifiers is typically carried out through the analysis of their receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) constitutes a popular indicator of the performance of a binary classifier. However, the assessment of the statistical significance of the difference between any two classifiers based on this measure is not a straightforward task, since not many freely available tools exist. Most existing software is either not free, difficult to use or not easy to automate when a comparative assessment of the performance of many binary classifiers is intended. This constitutes the typical scenario for the optimization of parameters when developing new classifiers and also for their performance validation through the comparison to previous art.'),
(92214, 2008, 'bmc', 'An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments', 'Rantanen, Ari|Rousu, Juho|Jouhten, Paula|Zamboni, Nicola|Maaheimo, Hannu|Ukkonen, Esko', 'Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi|Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi|Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi|Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi|Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi|Department of Computer Science, University of Helsinki, Finland. ari.rantanen@iki.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 36, 'Artificial Intelligence|Bacillus subtilis|Bacterial Proteins|Carbon Isotopes|Citric Acid Cycle|Computer Simulation|Databases, Factual|Fungal Proteins|Glucose|Glycolysis|Isomerism|Isotope Labeling|Magnetic Resonance Spectroscopy|Mass Spectrometry|Neural Networks (Computer)|Pentose Phosphate Pathway|Research Design|Saccharomyces cerevisiae|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-9-266', '18534038', 'Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from 13C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the 13C isotopomer data are typically needed.'),
(92215, 2008, 'bmc', 'Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data', 'Jonnalagadda, Sudhakar|Srinivasan, Rajagopalan', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore. sudhakar@nus.edu.sg|Department of Chemical and Biomolecular Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore. sudhakar@nus.edu.sg', 'Singapore|Singapore', 0, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-9-267', '18534040', 'Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.'),
(92216, 2008, 'bmc', 'Proceedings of the Second International Symposium on Languages in Biology and Medicine December 6-7, 2007 Singapore', '-', '-', '-', 23, 'Biology|Language|Medicine|Natural Language Processing|Programming Languages|Terminology as Topic|User-Computer Interface', 'Congress', '10.1186/1471-2105-9-267', '18536084', '-'),
(92217, 2008, 'bmc', 'A Semantic Web for bioinformatics: goals, tools, systems, applications Proceedings of the Seventh NETTAB (Network Tools and Applications in Biology) Workshop June 12-15, 2007 Pisa, Italy', '-', '-', '-', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Natural Language Processing|Semantics|Software|Terminology as Topic', 'Congress', '10.1186/1471-2105-9-267', '18536085', '-'),
(92218, 2008, 'bmc', 'Microarray data mining: a novel optimization-based approach to uncover biologically coherent structures', 'Tan, Meng P|Smith, Erin N|Broach, James R|Floudas, Christodoulos A', 'Department of Chemical Engineering, Princeton University, NJ, USA. mtan@princeton.edu|Department of Chemical Engineering, Princeton University, NJ, USA. mtan@princeton.edu|Department of Chemical Engineering, Princeton University, NJ, USA. mtan@princeton.edu|Department of Chemical Engineering, Princeton University, NJ, USA. mtan@princeton.edu', 'United States|United States|United States|United States', 26, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Multigene Family|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-9-268', '18538024', 'DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust.'),
(92219, 2008, 'bmc', 'Flexible boosting of accelerated failure time models', 'Schmid, Matthias|Hothorn, Torsten', '1Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstrasse 6, D-91054 Erlangen, Germany. matthias.schmid@imbe.imed.uni-erlangen.de|1Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstrasse 6, D-91054 Erlangen, Germany. matthias.schmid@imbe.imed.uni-erlangen.de', 'Germany|Germany', 30, 'Algorithms|Colonic Neoplasms|Computer Simulation|Humans|Likelihood Functions|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-269', '18538026', 'When boosting algorithms are used for building survival models from high-dimensional data, it is common to fit a Cox proportional hazards model or to use least squares techniques for fitting semiparametric accelerated failure time models. There are cases, however, where fitting a fully parametric accelerated failure time model is a good alternative to these methods, especially when the proportional hazards assumption is not justified. Boosting algorithms for the estimation of parametric accelerated failure time models have not been developed so far, since these models require the estimation of a model-specific scale parameter which traditional boosting algorithms are not able to deal with.'),
(92220, 2008, 'bmc', 'PageRank without hyperlinks: reranking with PubMed related article networks for biomedical text retrieval', 'Lin, Jimmy', 'National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, USA. jimmylin@umd.edu', 'United States', 0, 'Artificial Intelligence|Database Management Systems|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|PubMed|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-270', '18538027', 'Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed(R) search interface, a MEDLINE(R) citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web.'),
(92221, 2008, 'bmc', 'TiGER: a database for tissue-specific gene expression and regulation', 'Liu, Xiong|Yu, Xueping|Zack, Donald J|Zhu, Heng|Qian, Jiang', 'Wilmer Institute, Johns Hopkins University School of Medicine, Maumenee Building 844, 600 N, Wolfe Street, Baltimore, MD 21287, USA. xliu09@gmail.com|Wilmer Institute, Johns Hopkins University School of Medicine, Maumenee Building 844, 600 N, Wolfe Street, Baltimore, MD 21287, USA. xliu09@gmail.com|Wilmer Institute, Johns Hopkins University School of Medicine, Maumenee Building 844, 600 N, Wolfe Street, Baltimore, MD 21287, USA. xliu09@gmail.com|Wilmer Institute, Johns Hopkins University School of Medicine, Maumenee Building 844, 600 N, Wolfe Street, Baltimore, MD 21287, USA. xliu09@gmail.com|Wilmer Institute, Johns Hopkins University School of Medicine, Maumenee Building 844, 600 N, Wolfe Street, Baltimore, MD 21287, USA. xliu09@gmail.com', 'United States|United States|United States|United States|United States', 0, 'Database Management Systems|Databases, Genetic|Gene Expression Profiling|Organ Specificity|Proteome|Signal Transduction|Tissue Array Analysis', 'Journal Article', '10.1186/1471-2105-9-271', '18541026', 'Understanding how genes are expressed and regulated in different tissues is a fundamental and challenging question. However, most of currently available biological databases do not focus on tissue-specific gene regulation.'),
(92222, 2008, 'bmc', 'SiteSeek: post-translational modification analysis using adaptive locality-effective kernel methods and new profiles', 'Yoo, Paul D|Ho, Yung Shwen|Zhou, Bing Bing|Zomaya, Albert Y', 'Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au|Advanced Networks Research Group, School of Information Technologies (J12), The University of Sydney, Sydney, NSW 2006, Australia. dyoo4334@it.usyd.edu.au', 'Australia|Australia|Australia|Australia', 26, 'Algorithms|Amino Acid Sequence|Binding Sites|Molecular Sequence Data|Phosphorylation|Protein Binding|Proteins|RNA Processing, Post-Transcriptional|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-272', '18541042', 'Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. Accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. In this study, we propose a new machine learning based protein phosphorylation site predictor, SiteSeek. SiteSeek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. The newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.'),
(92223, 2008, 'bmc', 'Highlighting computations in bioscience and bioinformatics: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB07)', 'Lu, Guoqing|Ni, Jun', 'Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu|Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu', 'United States|United States', 0, 'Biological Science Disciplines|Computational Biology|Genomics', 'Congress', '10.1186/1471-2105-9-S6-S1', '18541044', 'The Second Symposium on Computations in Bioinformatics and Bioscience (SCBB07) was held in Iowa City, Iowa, USA, on August 13-15, 2007. This annual event attracted dozens of bioinformatics professionals and students, who are interested in solving emerging computational problems in bioscience, from China, Japan, Taiwan and the United States. The Scientific Committee of the symposium selected 18 peer-reviewed papers for publication in this supplemental issue of BMC Bioinformatics. These papers cover a broad spectrum of topics in computational biology and bioinformatics, including DNA, protein and genome sequence analysis, gene expression and microarray analysis, computational proteomics and protein structure classification, systems biology and machine learning.'),
(92224, 2008, 'bmc', 'Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs', 'Mishima, Hiroyuki|Lidral, Andrew C|Ni, Jun', 'Dows Institute for Dental Research, College of Dentistry, University of Iowa, Iowa City, IA 52242, USA. hmishima@nagasaki-u.ac.jp|Dows Institute for Dental Research, College of Dentistry, University of Iowa, Iowa City, IA 52242, USA. hmishima@nagasaki-u.ac.jp|Dows Institute for Dental Research, College of Dentistry, University of Iowa, Iowa City, IA 52242, USA. hmishima@nagasaki-u.ac.jp', 'United States|United States|United States', 0, 'Base Sequence|Chromosome Mapping|Computer Communication Networks|DNA Mutational Analysis|Haplotypes|Molecular Sequence Data|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S6-S10', '18541045', 'Genetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies.'),
(92225, 2008, 'bmc', 'Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation', 'Arikuma, Takeshi|Yoshikawa, Sumi|Azuma, Ryuzo|Watanabe, Kentaro|Matsumura, Kazumi|Konagaya, Akihiko', 'Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp|Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp|Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp|Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp|Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp|Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro, Tokyo, Japan. atake@bio.cs.titech.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Binding Sites|Computer Simulation|Models, Biological|Pharmaceutical Preparations|Protein Binding|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S6-S11', '18541046', 'In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL).'),
(92226, 2008, 'bmc', 'Genetic weighted k-means algorithm for clustering large-scale gene expression data', 'Wu, Fang-Xiang', 'Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada. faw341@mail.usask.ca', 'Canada', 35, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Proteome', 'Journal Article', '10.1186/1471-2105-9-S6-S12', '18541047', 'The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It is sensitive to initial partitions, its result is prone to the local minima, and it is only applicable to data with spherical-shape clusters. The last shortcoming means that we must assume that gene expression data at the different conditions follow the independent distribution with the same variances. Nevertheless, this assumption is not true in practice.'),
(92227, 2008, 'bmc', 'Quality assessment of peptide tandem mass spectra', 'Wu, Fang-Xiang|Gagné, Pierre|Droit, Arnaud|Poirier, Guy G', 'Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK Canada, S7N 5A9, Canada. faw341@mail.usak.ca|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK Canada, S7N 5A9, Canada. faw341@mail.usak.ca|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK Canada, S7N 5A9, Canada. faw341@mail.usak.ca|Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr, Saskatoon, SK Canada, S7N 5A9, Canada. faw341@mail.usak.ca', 'Canada|Canada|Canada|Canada', 22, 'Algorithms|Amino Acid Sequence|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Quality Control|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S6-S13', '18541048', 'Tandem mass spectrometry has emerged as a cornerstone of high throughput proteomic studies owing in part to various high throughput search engines which are used to interpret these tandem mass spectra. However, majority of experimental tandem mass spectra cannot be interpreted by any existing methods. There are many reasons why this happens. However, one of the most important reasons is that majority of experimental spectra are of too poor quality to be interpretable. It wastes time to interpret these uninterpretable spectra by any methods. On the other hand, some spectra of high quality are not able to get a score high enough to be interpreted by existing search engines because there are many similar peptides in the searched database. However, such spectra may be good enough to be interpreted by de novo methods or manually verifying methods. Therefore, it is worth in developing a method for assessing spectral quality, which can used for filtering the spectra of poor quality before any interpretation attempts or for finding the most potential candidates for de novo methods or manually verifying methods.'),
(92228, 2008, 'bmc', 'Improving the power for detecting overlapping genes from multiple DNA microarray-derived gene lists', 'Deng, Xutao|Xu, Jun|Wang, Charles', 'Transcriptional Genomics Core, Burns Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. dengx@ucla.edu|Transcriptional Genomics Core, Burns Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. dengx@ucla.edu|Transcriptional Genomics Core, Burns Allen Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. dengx@ucla.edu', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S6-S14', '18541049', 'In DNA microarray gene expression profiling studies, a fundamental task is to extract statistically significant genes that meet certain research hypothesis. Currently, Venn diagram is a frequently used method for identifying overlapping genes that meet the investigators research hypotheses. However this simple operation of intersecting multiple gene lists, known as the Intersection-Union Tests (IUTs), is performed without knowing the incurred changes in Type 1 error rate and can lead to loss of discovery power.'),
(92229, 2008, 'bmc', 'An improved string composition method for sequence comparison', 'Lu, Guoqing|Zhang, Shunpu|Fang, Xiang', 'Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu|Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu|Department of Biology, University of Nebraska, Omaha, NE 68182, USA. glu3@mail.unomaha.edu', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|DNA|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S6-S15', '18541050', 'Historically, two categories of computational algorithms (alignment-based and alignment-free) have been applied to sequence comparison-one of the most fundamental issues in bioinformatics. Multiple sequence alignment, although dominantly used by biologists, possesses both fundamental as well as computational limitations. Consequently, alignment-free methods have been explored as important alternatives in estimating sequence similarity. Of the alignment-free methods, the string composition vector (CV) methods, which use the frequencies of nucleotide or amino acid strings to represent sequence information, show promising results in genome sequence comparison of prokaryotes. The existing CV-based methods, however, suffer certain statistical problems, thereby underestimating the amount of evolutionary information in genetic sequences.'),
(92230, 2008, 'bmc', 'MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways', 'Liang, Lily R|Mandal, Vinay|Lu, Yi|Kumar, Deepak', 'Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA. lliang@udc.edu|Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA. lliang@udc.edu|Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA. lliang@udc.edu|Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA. lliang@udc.edu', 'United States|United States|United States|United States', 8, 'Algorithms|Computer Simulation|Fuzzy Logic|Gene Expression Profiling|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-S6-S16', '18541051', 'Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases.'),
(92231, 2008, 'bmc', 'Quantitative analysis of numerical solvers for oscillatory biomolecular system models', 'Quo, Chang F|Wang, May D', 'Wallace H, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA. gtg801f@mail.gatech.edu|Wallace H, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA. gtg801f@mail.gatech.edu', 'Georgia|Georgia', 0, 'Algorithms|Biological Clocks|Computer Simulation|Models, Biological|Numerical Analysis, Computer-Assisted|Oscillometry|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S6-S17', '18541052', 'This article provides guidelines for selecting optimal numerical solvers for biomolecular system models. Because various parameters of the same system could have drastically different ranges from 10(-15) to 10(10), the ODEs can be stiff and ill-conditioned, resulting in non-unique, non-existing, or non-reproducible modeling solutions. Previous studies have not examined in depth how to best select numerical solvers for biomolecular system models, which makes it difficult to experimentally validate the modeling results. To address this problem, we have chosen one of the well-known stiff initial value problems with limit cycle behavior as a test-bed system model. Solving this model, we have illustrated that different answers may result from different numerical solvers. We use MATLAB numerical solvers because they are optimized and widely used by the modeling community. We have also conducted a systematic study of numerical solver performances by using qualitative and quantitative measures such as convergence, accuracy, and computational cost (i.e. in terms of function evaluation, partial derivative, LU decomposition, and take-off points). The results show that the modeling solutions can be drastically different using different numerical solvers. Thus, it is important to intelligently select numerical solvers when solving biomolecular system models.'),
(92232, 2008, 'bmc', 'ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses', 'Stokes, Todd H|Torrance, J T|Li, Henry|Wang, May D', 'Department of Electrical and Computer Engineering, Georgia Institute of Technology, Van Leer Building, 777 Atlantic Drive NW, Atlanta, GA 30332, USA. todd.stokes@bme.gatech.edu|Department of Electrical and Computer Engineering, Georgia Institute of Technology, Van Leer Building, 777 Atlantic Drive NW, Atlanta, GA 30332, USA. todd.stokes@bme.gatech.edu|Department of Electrical and Computer Engineering, Georgia Institute of Technology, Van Leer Building, 777 Atlantic Drive NW, Atlanta, GA 30332, USA. todd.stokes@bme.gatech.edu|Department of Electrical and Computer Engineering, Georgia Institute of Technology, Van Leer Building, 777 Atlantic Drive NW, Atlanta, GA 30332, USA. todd.stokes@bme.gatech.edu', 'Georgia|Georgia|Georgia|Georgia', 30, 'Artificial Intelligence|Biotechnology|Database Management Systems|Databases, Genetic|Information Dissemination|Internet|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-S6-S18', '18541053', 'A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the curse-of-dimension problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create intelligent microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources.'),
(92233, 2008, 'bmc', 'A practical comparison of two K-Means clustering algorithms', 'Wilkin, Gregory A|Huang, Xiuzhen', 'Department of Computer Science, Arkansas State University, Arkansas 72467, USA. awilkin@csm.astate.edu|Department of Computer Science, Arkansas State University, Arkansas 72467, USA. awilkin@csm.astate.edu', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Comparative Study', '10.1186/1471-2105-9-S6-S19', '18541054', 'Data clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementations of clustering algorithms yield the same performance or the same clusters.'),
(92234, 2008, 'bmc', 'MLIP: using multiple processors to compute the posterior probability of linkage', 'Govil, Manika|Segre, Alberto M|Vieland, Veronica J', 'Department of Oral Biology and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. govil@pitt.edu|Department of Oral Biology and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. govil@pitt.edu|Department of Oral Biology and Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. govil@pitt.edu', 'United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Genetic Linkage|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Software', 'Journal Article', '10.1186/1471-2105-9-S6-S2', '18541055', 'Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PPL), a class of statistics for complex trait genetic mapping in humans, is designed to model the trait model complexity represented by the multidimensional parameter space in a mathematically rigorous fashion. However, the method requires the evaluation of integrals with no functional form, making it difficult to compute, and thus further test, develop and apply. This paper describes MLIP, a multiprocessor two-point genetic linkage analysis system that supports statistical calculations, such as the PPL, based on the full parameter space implicit in the linkage likelihood.'),
(92235, 2008, 'bmc', 'Evolutionary conservation of DNA-contact residues in DNA-binding domains', 'Chang, Yao-Lin|Tsai, Huai-Kuang|Kao, Cheng-Yan|Chen, Yung-Chian|Hu, Yuh-Jyh|Yang, Jinn-Moon', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. b4506046@csie.ntu.edu.tw', '-|-|-|-|-|-', 3, 'Amino Acid Sequence|Base Sequence|Binding Sites|Conserved Sequence|DNA|DNA-Binding Proteins|Evolution, Molecular|Molecular Sequence Data|Protein Binding|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S6-S3', '18541056', 'DNA-binding proteins are of utmost importance to gene regulation. The identification of DNA-binding domains is useful for understanding the regulation mechanisms of DNA-binding proteins. In this study, we proposed a method to determine whether a domain or a protein can has DNA binding capability by considering evolutionary conservation of DNA-binding residues.'),
(92236, 2008, 'bmc', 'A topological transformation in evolutionary tree search methods based on maximum likelihood combining p-ECR and neighbor joining', 'Guo, Mao-Zu|Li, Jian-Fu|Liu, Yang', 'Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, PR China. maozuguo@hit.edu.cn|Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, PR China. maozuguo@hit.edu.cn|Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, PR China. maozuguo@hit.edu.cn', 'China|China|China', 0, 'Algorithms|Base Sequence|Biological Evolution|Computer Simulation|Evolution, Molecular|Likelihood Functions|Models, Molecular|Molecular Sequence Data|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S6-S4', '18541057', 'Inference of evolutionary trees using the maximum likelihood principle is NP-hard. Therefore, all practical methods rely on heuristics. The topological transformations often used in heuristics are Nearest Neighbor Interchange (NNI), Subtree Prune and Regraft (SPR) and Tree Bisection and Reconnection (TBR). However, these topological transformations often fall easily into local optima, since there are not many trees accessible in one step from any given tree. Another more exhaustive topological transformation is p-Edge Contraction and Refinement (p-ECR). However, due to its high computation complexity, p-ECR has rarely been used in practice.'),
(92237, 2008, 'bmc', 'Protein structural class prediction based on an improved statistical strategy', 'Gu, Fei|Chen, Hang|Ni, Jun', 'Department of Biotechnology, College of Life Sciences, Zhejiang University, Hangzhou, 310027, China. alickgf@hotmail.com|Department of Biotechnology, College of Life Sciences, Zhejiang University, Hangzhou, 310027, China. alickgf@hotmail.com|Department of Biotechnology, College of Life Sciences, Zhejiang University, Hangzhou, 310027, China. alickgf@hotmail.com', 'China|China|China', 11, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Chemical|Models, Molecular|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-S6-S5', '18541058', 'A protein structural class (PSC) belongs to the most basic but important classification in protein structures. The prediction technique of protein structural class has been developing for decades. Two popular indices are the amino-acid-frequency (AAF) based, and amino-acid-arrangement (AAA) with long-term correlation (LTC) - based indices. They were proposed in many works. Both indices have its pros and cons. For example, the AAF index focuses on a statistical analysis, while the AAA-LTC emphasizes the long-term, biological significance. Unfortunately, the datasets used in previous work were not very reliable for a small number of sequences with a high-sequence similarity.'),
(92238, 2008, 'bmc', 'BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment', 'Chin, George|Chavarria, Daniel G|Nakamura, Grant C|Sofia, Heidi J', 'High Performance Computing Group, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, USA. george.chin@pnl.gov|High Performance Computing Group, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, USA. george.chin@pnl.gov|High Performance Computing Group, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, USA. george.chin@pnl.gov|High Performance Computing Group, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, Washington, USA. george.chin@pnl.gov', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-S6-S6', '18541059', 'Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data.'),
(92239, 2008, 'bmc', 'Asymmetric bagging and feature selection for activities prediction of drug molecules', 'Li, Guo-Zheng|Meng, Hao-Hua|Lu, Wen-Cong|Yang, Jack Y|Yang, Mary Qu', 'Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn|Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn|Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn|Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn|Institute of Systems Biology, Shanghai University, Shanghai 200444, China. gzli@shu.edu.cn', 'China|China|China|China|China', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Models, Chemical|Pattern Recognition, Automated|Pharmaceutical Preparations|Quantitative Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-S6-S7', '18541060', 'Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.'),
(92240, 2008, 'bmc', 'Dimension reduction with redundant gene elimination for tumor classification', 'Zeng, Xue-Qiang|Li, Guo-Zheng|Yang, Jack Y|Yang, Mary Qu|Wu, Geng-Feng', 'School of Computer Engineering & Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn|School of Computer Engineering & Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn|School of Computer Engineering & Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn|School of Computer Engineering & Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn|School of Computer Engineering & Science, Shanghai University, Shanghai 200072, China. stamina_zeng@shu.edu.cn', 'China|China|China|China|China', 18, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-S6-S8', '18541061', 'Analysis of gene expression data for tumor classification is an important application of bioinformatics methods. But it is hard to analyse gene expression data from DNA microarray experiments by commonly used classifiers, because there are only a few observations but with thousands of measured genes in the data set. Dimension reduction is often used to handle such a high dimensional problem, but it is obscured by the existence of amounts of redundant features in the microarray data set.'),
(92241, 2008, 'bmc', 'Comparative analyses of bidirectional promoters in vertebrates', 'Yang, Mary Qu|Taylor, James|Elnitski, Laura', 'Genome Technology Branch, NHGRI, NIH, MD, USA. yangma@mail.nih.gov|Genome Technology Branch, NHGRI, NIH, MD, USA. yangma@mail.nih.gov|Genome Technology Branch, NHGRI, NIH, MD, USA. yangma@mail.nih.gov', 'United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Biological Evolution|Chromosome Mapping|Evolution, Molecular|Molecular Sequence Data|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Vertebrates', 'Journal Article', '10.1186/1471-2105-9-S6-S9', '18541062', 'Orthologous genes with deep phylogenetic histories are likely to retain similar regulatory features. In this report we utilize orthology assignments for pairs of genes co-regulated by bidirectional promoters to map the ancestral history of the promoter regions.');
