INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93483, 2010, 'bmc', 'Effects of scanning sensitivity and multiple scan algorithms on microarray data quality', 'Williams, Andrew|Thomson, Errol M', 'Population Health Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, K1A 0K9, Canada. Andrew_Williams@hc-sc.gc.ca|Population Health Studies Division, Environmental Health Science and Research Bureau, Health Canada, Ottawa, K1A 0K9, Canada. Andrew_Williams@hc-sc.gc.ca', 'Canada|Canada', 4, 'Algorithms|Gene Expression Profiling|Image Interpretation, Computer-Assisted|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-127', '20226031', 'Maximizing the utility of DNA microarray data requires optimization of data acquisition through selection of an appropriate scanner setting. To increase the amount of useable data, several approaches have been proposed that incorporate multiple scans at different sensitivities to reduce the quantification error and to minimize effects of saturation. However, no direct comparison of their efficacy has been made. In the present study we compared individual scans at low, medium and high sensitivity with three methods for combining data from multiple scans (either 2-scan or 3-scan cases) using an actual dataset comprising 40 technical replicates of a reference RNA standard.'),
(93484, 2010, 'bmc', 'New statistical potential for quality assessment of protein models and a survey of energy functions', 'Rykunov, Dmitry|Fiser, Andras', 'Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Ave,, Bronx, NY 10461, USA.|Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Ave,, Bronx, NY 10461, USA.', 'United States|United States', 54, 'Databases, Protein|Models, Statistical|Protein Conformation|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-128', '20226048', 'Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment.'),
(93485, 2010, 'bmc', 'RNAstructure: software for RNA secondary structure prediction and analysis', 'Reuter, Jessica S|Mathews, David H', 'Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, NY 14642, USA.|Department of Biochemistry & Biophysics and Center for RNA Biology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, NY 14642, USA.', 'United States|United States', 808, 'Algorithms|Base Sequence|Binding Sites|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-129', '20230624', 'To understand an RNA sequences mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence.'),
(93486, 2010, 'bmc', 'Calling SNPs without a reference sequence', 'Ratan, Aakrosh|Zhang, Yu|Hayes, Vanessa M|Schuster, Stephan C|Miller, Webb', 'Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, USA. ratan@bx.psu.edu.|Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, USA. ratan@bx.psu.edu.|Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, USA. ratan@bx.psu.edu.|Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, USA. ratan@bx.psu.edu.|Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, USA. ratan@bx.psu.edu.', 'United States|United States|United States|United States|United States', 30, 'Algorithms|Alleles|Base Sequence|Computational Biology|Databases, Genetic|Genome|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-130', '20230626', 'The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies.'),
(93487, 2010, 'bmc', 'Missing genes in the annotation of prokaryotic genomes', 'Warren, Andrew S|Archuleta, Jeremy|Feng, Wu-Chun|Setubal, João Carlos', 'Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu|Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA. anwarren@vt.edu', 'United States|United States|United States|United States', 50, 'Databases, Genetic|Genes, Bacterial|Genome, Bacterial|Genomics|Open Reading Frames|Prokaryotic Cells', 'Journal Article', '10.1186/1471-2105-11-131', '20230630', 'Protein-coding gene detection in prokaryotic genomes is considered a much simpler problem than in intron-containing eukaryotic genomes. However there have been reports that prokaryotic gene finder programs have problems with small genes (either over-predicting or under-predicting). Therefore the question arises as to whether current genome annotations have systematically missing, small genes.'),
(93488, 2010, 'bmc', 'A parallel and incremental algorithm for efficient unique signature discovery on DNA databases', 'Lee, Hsiao Ping|Sheu, Tzu-Fang|Tang, Chuan Yi', 'Department of Computer Science and Communication Engineering, Providence University, Taichung, 43301 Taiwan, ROC.|Department of Computer Science and Communication Engineering, Providence University, Taichung, 43301 Taiwan, ROC.|Department of Computer Science and Communication Engineering, Providence University, Taichung, 43301 Taiwan, ROC.', '-|-|-', 0, 'Algorithms|Computational Biology|DNA|Databases, Nucleic Acid|Gene Expression Profiling|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-132', '20230647', 'DNA signatures are distinct short nucleotide sequences that provide valuable information that is used for various purposes, such as the design of Polymerase Chain Reaction primers and microarray experiments. Biologists usually use a discovery algorithm to find unique signatures from DNA databases, and then apply the signatures to microarray experiments. Such discovery algorithms require to set some input factors, such as signature length l and mismatch tolerance d, which affect the discovery results. However, suggestions about how to select proper factor values are rare, especially when an unfamiliar DNA database is used. In most cases, biologists typically select factor values based on experience, or even by guessing. If the discovered result is unsatisfactory, biologists change the input factors of the algorithm to obtain a new result. This process is repeated until a proper result is obtained. Implicit signatures under the discovery condition (l, d) are defined as the signatures of length < or = l with mismatch tolerance > or = d. A discovery algorithm that could discover all implicit signatures, such that those that meet the requirements concerning the results, would be more helpful than one that depends on trial and error. However, existing discovery algorithms do not address the need to discover all implicit signatures.'),
(93489, 2010, 'bmc', 'MapMi: automated mapping of microRNA loci', 'Guerra-Assunção, José Afonso|Enright, Anton J', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom', 42, 'Databases, Nucleic Acid|Genomics|Internet|MicroRNAs|Phylogeny|RNA, Messenger|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-11-133', '20233390', 'A large effort to discover microRNAs (miRNAs) has been under way. Currently miRBase is their primary repository, providing annotations of primary sequences, precursors and probable genomic loci. In many cases miRNAs are identical or very similar between related (or in some cases more distant) species. However, miRBase focuses on those species for which miRNAs have been directly confirmed. Secondly, specific miRNAs or their loci are sometimes not annotated even in well-covered species. We sought to address this problem by developing a computational system for automated mapping of miRNAs within and across species. Given the sequence of a known miRNA in one species it is relatively straightforward to determine likely loci of that miRNA in other species. Our primary goal is not the discovery of novel miRNAs but the mapping of validated miRNAs in one species to their most likely orthologues in other species.'),
(93490, 2010, 'bmc', 'ProbABEL package for genome-wide association analysis of imputed data', 'Aulchenko, Yurii S|Struchalin, Maksim V|van Duijn, Cornelia M', 'Department of Epidemiology, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands. i.aoultchenko@erasmusmc.nl|Department of Epidemiology, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands. i.aoultchenko@erasmusmc.nl|Department of Epidemiology, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands. i.aoultchenko@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands', 292, 'Genome-Wide Association Study|Genotype|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-11-134', '20233392', 'Over the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account.'),
(93491, 2010, 'bmc', 'miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature', 'Naeem, Haroon|Küffner, Robert|Csaba, Gergely|Zimmer, Ralf', 'Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr, 17 80333 München, Germany.|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr, 17 80333 München, Germany.|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr, 17 80333 München, Germany.|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr, 17 80333 München, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Data Mining|Databases, Genetic|Internet|MicroRNAs|PubMed|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-135', '20233441', 'MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories.'),
(93492, 2010, 'bmc', 'Analysis of DNA strand-specific differential expression with high density tiling microarrays', 'Quintales, Luis|Sánchez, Mar|Antequera, Francisco', 'Instituto de Microbiología Bioquímica, Consejo Superior de Investigaciones Científicas (CSIC)/Universidad de Salamanca, Edificio Departamental, Campus Miguel de Unamuno, 37007-Salamanca, Spain.|Instituto de Microbiología Bioquímica, Consejo Superior de Investigaciones Científicas (CSIC)/Universidad de Salamanca, Edificio Departamental, Campus Miguel de Unamuno, 37007-Salamanca, Spain.|Instituto de Microbiología Bioquímica, Consejo Superior de Investigaciones Científicas (CSIC)/Universidad de Salamanca, Edificio Departamental, Campus Miguel de Unamuno, 37007-Salamanca, Spain.', 'Spain|Spain|Spain', 0, 'DNA, Fungal|Databases, Genetic|Gene Expression Profiling|Genome, Fungal|Genomics|Oligonucleotide Array Sequence Analysis|Schizosaccharomyces', 'Journal Article', '10.1186/1471-2105-11-136', '20233458', 'DNA microarray technology allows the analysis of genome structure and dynamics at genome-wide scale. Expression microarrays (EMA) contain probes for annotated open reading frames (ORF) and are widely used for the analysis of differential gene expression. By contrast, tiling microarrays (TMA) have a much higher probe density and provide unbiased genome-wide coverage. The purpose of this study was to develop a protocol to exploit the high resolution of TMAs for quantitative measurement of DNA strand-specific differential expression of annotated and non-annotated transcripts.'),
(93493, 2010, 'bmc', 'Indirect two-sided relative ranking: a robust similarity measure for gene expression data', 'Licamele, Louis|Getoor, Lise', 'Computer Science Department, University of Maryland, College Park, USA. licamele@cs.umd.edu|Computer Science Department, University of Maryland, College Park, USA. licamele@cs.umd.edu', 'United States|United States', 3, 'Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-137', '20236517', 'There is a large amount of gene expression data that exists in the public domain. This data has been generated under a variety of experimental conditions. Unfortunately, these experimental variations have generally prevented researchers from accurately comparing and combining this wealth of data, which still hides many novel insights.'),
(93494, 2010, 'bmc', 'An optimized TOPS+ comparison method for enhanced TOPS models', 'Veeramalai, Mallika|Gilbert, David|Valiente, Gabriel', 'Joint Center for Molecular Modeling, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. mallikav@burnham.org|Joint Center for Molecular Modeling, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. mallikav@burnham.org|Joint Center for Molecular Modeling, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA. mallikav@burnham.org', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Ligands|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Comparative Study', '10.1186/1471-2105-11-138', '20236520', 'Although methods based on highly abstract descriptions of protein structures, such as VAST and TOPS, can perform very fast protein structure comparison, the results can lack a high degree of biological significance. Previously we have discussed the basic mechanisms of our novel method for structure comparison based on our TOPS+ model (Topological descriptions of Protein Structures Enhanced with Ligand Information). In this paper we show how these results can be significantly improved using parameter optimization, and we call the resulting optimised TOPS+ method as advanced TOPS+ comparison method i.e. advTOPS+.'),
(93495, 2010, 'bmc', 'Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry', 'Moseley, Hunter Nb', 'Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky, USA. hunter.moseley@louisville.edu', 'United States', 61, 'Carbon Isotopes|Fourier Analysis|Isotope Labeling|Mass Spectrometry|Metabolomics|Nitrogen Isotopes', 'Journal Article', '10.1186/1471-2105-11-139', '20236542', 'Stable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism. However, contributions to the observed intensities arising from the stable isotopes natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks. This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment.'),
(93496, 2010, 'bmc', 'Gene regulatory networks modelling using a dynamic evolutionary hybrid', 'Maraziotis, Ioannis A|Dragomir, Andrei|Thanos, Dimitris', 'Institute of Molecular Biology, Genetics and Biotechnology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece.|Institute of Molecular Biology, Genetics and Biotechnology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece.|Institute of Molecular Biology, Genetics and Biotechnology, Biomedical Research Foundation, Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece.', 'Greece|Greece|Greece', 20, 'Algorithms|Biological Evolution|Computational Biology|Databases, Genetic|Fuzzy Logic|Gene Regulatory Networks|Models, Genetic', 'Journal Article', '10.1186/1471-2105-11-140', '20298548', 'Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the datas high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.'),
(93497, 2010, 'bmc', 'YODA: software to facilitate high-throughput analysis of chronological life span, growth rate, and survival in budding yeast', 'Olsen, Brady|Murakami, Christopher J|Kaeberlein, Matt', 'Department of Pathology, University of Washington, Seattle, WA 98195, USA.|Department of Pathology, University of Washington, Seattle, WA 98195, USA.|Department of Pathology, University of Washington, Seattle, WA 98195, USA.', 'United States|United States|United States', 0, 'Cell Survival|Cellular Senescence|Gene Expression Regulation, Fungal|Genomics|Saccharomycetales|Software', 'Journal Article', '10.1186/1471-2105-11-141', '20298554', 'The budding yeast Saccharomyces cerevisiae is one of the most widely studied model organisms in aging-related science. Although several genetic modifiers of yeast longevity have been identified, the utility of this system for longevity studies has been limited by a lack of high-throughput assays for quantitatively measuring survival of individual yeast cells during aging.'),
(93498, 2010, 'bmc', 'MISHIMA--a new method for high speed multiple alignment of nucleotide sequences of bacterial genome scale data', 'Kryukov, Kirill|Saitou, Naruya', 'Division of Population Genetics, National Institute of Genetics 1111 Yata, Mishima, 411-8540, Japan.|Division of Population Genetics, National Institute of Genetics 1111 Yata, Mishima, 411-8540, Japan.', 'Japan|Japan', 11, 'Algorithms|Base Sequence|Genome, Bacterial|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-142', '20298584', 'Large nucleotide sequence datasets are becoming increasingly common objects of comparison. Complete bacterial genomes are reported almost everyday. This creates challenges for developing new multiple sequence alignment methods. Conventional multiple alignment methods are based on pairwise alignment and/or progressive alignment techniques. These approaches have performance problems when the number of sequences is large and when dealing with genome scale sequences.'),
(93499, 2010, 'bmc', 'MPprimer: a program for reliable multiplex PCR primer design', 'Shen, Zhiyong|Qu, Wubin|Wang, Wen|Lu, Yiming|Wu, Yonghong|Li, Zhifeng|Hang, Xingyi|Wang, Xiaolei|Zhao, Dongsheng|Zhang, Chenggang', 'Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.|Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.', 'China|China|China|China|China|China|China|China|China|China', 86, 'Algorithms|DNA Primers|Databases, Nucleic Acid|Dystrophin|Genomics|Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-11-143', '20298595', 'Multiplex PCR, defined as the simultaneous amplification of multiple regions of a DNA template or multiple DNA templates using more than one primer set (comprising a forward primer and a reverse primer) in one tube, has been widely used in diagnostic applications of clinical and environmental microbiology studies. However, primer design for multiplex PCR is still a challenging problem and several factors need to be considered. These problems include mis-priming due to nonspecific binding to non-target DNA templates, primer dimerization, and the inability to separate and purify DNA amplicons with similar electrophoretic mobility.'),
(93500, 2010, 'bmc', 'Large-scale prediction of protein-protein interactions from structures', 'Hue, Martial|Riffle, Michael|Vert, Jean-Philippe|Noble, William S', 'Department of Genome Sciences University of Washington, Seattle, WA, USA.|Department of Genome Sciences University of Washington, Seattle, WA, USA.|Department of Genome Sciences University of Washington, Seattle, WA, USA.|Department of Genome Sciences University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States', 68, 'Binding Sites|Databases, Protein|Protein Conformation|Protein Interaction Mapping|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-144', '20298601', 'The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins.'),
(93501, 2010, 'bmc', 'Physicochemical property distributions for accurate and rapid pairwise protein homology detection', 'Webb-Robertson, Bobbie-Jo M|Ratuiste, Kyle G|Oehmen, Christopher S', 'Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA. bj@pnl.gov', 'United States|United States|United States', 0, 'Computational Biology|Databases, Protein|Pattern Recognition, Automated|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-145', '20302613', 'The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.'),
(93502, 2010, 'bmc', 'Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments', 'Sierk, Michael L|Smoot, Michael E|Bass, Ellen J|Pearson, William R', 'Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.', 'United States|United States|United States|United States', 9, 'Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-146', '20307279', 'While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure, pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.'),
(93503, 2010, 'bmc', 'Identification of recurrent regions of Copy-Number Variants across multiple individuals', 'Mei, Teo Shu|Salim, Agus|Calza, Stefano|Seng, Ku Chee|Seng, Chia Kee|Pawitan, Yudi', 'Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.|Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.|Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.|Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.|Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.|Department of Epidemiology and Public Health, National University of Singapore, 16 Medical Drive, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Databases, Genetic|Gene Dosage|Genetic Variation|Genome, Human|Genomics|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-147', '20307285', 'Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed.'),
(93504, 2010, 'bmc', 'In silico fragmentation for computer assisted identification of metabolite mass spectra', 'Wolf, Sebastian|Schmidt, Stephan|Müller-Hannemann, Matthias|Neumann, Steffen', 'Leibniz Institute of Plant Biochemistry- Department of Stress- and Developmental Biology, Weinberg 3, 06120 Halle(Saale), Germany. swolf@ipb-halle.de|Leibniz Institute of Plant Biochemistry- Department of Stress- and Developmental Biology, Weinberg 3, 06120 Halle(Saale), Germany. swolf@ipb-halle.de|Leibniz Institute of Plant Biochemistry- Department of Stress- and Developmental Biology, Weinberg 3, 06120 Halle(Saale), Germany. swolf@ipb-halle.de|Leibniz Institute of Plant Biochemistry- Department of Stress- and Developmental Biology, Weinberg 3, 06120 Halle(Saale), Germany. swolf@ipb-halle.de', 'Germany|Germany|Germany|Germany', 366, 'Databases, Factual|Mass Spectrometry|Metabolomics|Proteomics', 'Journal Article', '10.1186/1471-2105-11-148', '20307295', 'Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.'),
(93505, 2010, 'bmc', 'Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis', 'Keilwagen, Jens|Grau, Jan|Posch, Stefan|Grosse, Ivo', 'Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de|Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany', 0, 'Bayes Theorem|Binding Sites|DNA|Markov Chains|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-149', '20307305', 'One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions.'),
(93506, 2010, 'bmc', 'Identifying common prognostic factors in genomic cancer studies: a novel index for censored outcomes', 'Rouam, Sigrid|Moreau, Thierry|Broët, Philippe', 'Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore. sigrid.rouam@inserm.fr|Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore. sigrid.rouam@inserm.fr|Computational and Mathematical Biology, Genome Institute of Singapore, Singapore, Singapore. sigrid.rouam@inserm.fr', 'Singapore|Singapore|Singapore', 28, 'Computer Simulation|Databases, Genetic|Gene Expression Profiling|Genome, Human|Humans|Likelihood Functions|Neoplasms|Oligonucleotide Array Sequence Analysis|Prognosis|Sample Size', 'Journal Article', '10.1186/1471-2105-11-150', '20334636', 'With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements.'),
(93507, 2010, 'bmc', 'Evolutionary rates at codon sites may be used to align sequences and infer protein domain function', 'Durand, Pierre M|Hazelhurst, Scott|Coetzer, Theresa L', 'Evolutionary Medicine Unit, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa. pierre.durand@wits.ac.za|Evolutionary Medicine Unit, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa. pierre.durand@wits.ac.za|Evolutionary Medicine Unit, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa. pierre.durand@wits.ac.za', 'South Africa|South Africa|South Africa', 4, 'Base Sequence|Codon|Databases, Protein|Evolution, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-151', '20334658', 'Sequence alignments form part of many investigations in molecular biology, including the determination of phylogenetic relationships, the prediction of protein structure and function, and the measurement of evolutionary rates. However, to obtain meaningful results, a significant degree of sequence similarity is required to ensure that the alignments are accurate and the inferences correct. Limitations arise when sequence similarity is low, which is particularly problematic when working with fast-evolving genes, evolutionary distant taxa, genomes with nucleotide biases, and cases of convergent evolution.'),
(93508, 2010, 'bmc', 'Alignment and clustering of phylogenetic markers--implications for microbial diversity studies', 'White, James R|Navlakha, Saket|Nagarajan, Niranjan|Ghodsi, Mohammad-Reza|Kingsford, Carl|Pop, Mihai', 'Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.|Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.|Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.|Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.|Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.|Department of Computer Science, University of Maryland-College Park, College Park, MD 20742, USA.', 'United States|United States|United States|United States|United States|United States', 47, 'Cluster Analysis|DNA, Bacterial|Genetic Markers|Genetic Variation|Phylogeny|RNA, Bacterial|RNA, Ribosomal, 16S|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-152', '20334679', 'Molecular studies of microbial diversity have provided many insights into the bacterial communities inhabiting the human body and the environment. A common first step in such studies is a survey of conserved marker genes (primarily 16S rRNA) to characterize the taxonomic composition and diversity of these communities. To date, however, there exists significant variability in analysis methods employed in these studies.'),
(93509, 2010, 'bmc', 'The ancient history of the structure of ribonuclease P and the early origins of Archaea', 'Sun, Feng-Jie|Caetano-Anollés, Gustavo', 'Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.|Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.', 'United States|United States', 0, 'Archaea|Evolution, Molecular|Genetic Variation|Genomics|Phylogeny|Protein Conformation|Protein Structure, Tertiary|RNA, Archaeal|RNA, Transfer|Ribonuclease P', 'Journal Article', '10.1186/1471-2105-11-153', '20334683', 'Ribonuclease P is an ancient endonuclease that cleaves precursor tRNA and generally consists of a catalytic RNA subunit (RPR) and one or more proteins (RPPs). It represents an important macromolecular complex and model system that is universally distributed in life. Its putative origins have inspired fundamental hypotheses, including the proposal of an ancient RNA world.'),
(93510, 2010, 'bmc', 'TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach', 'Zoppoli, Pietro|Morganella, Sandro|Ceccarelli, Michele', 'Department of Biological and Environmental Studies, University of Sannio, Benevento, I-82100, Italy.|Department of Biological and Environmental Studies, University of Sannio, Benevento, I-82100, Italy.|Department of Biological and Environmental Studies, University of Sannio, Benevento, I-82100, Italy.', 'Italy|Italy|Italy', 171, 'Algorithms|Computational Biology|Escherichia coli|Gene Expression Profiling|Gene Regulatory Networks|Genome, Bacterial|Genome, Fungal|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-11-154', '20338053', 'One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory.'),
(93511, 2010, 'bmc', 'Reduced representation of protein structure: implications on efficiency and scope of detection of structural similarity', 'Zhang, Zong Hong|Lee, Hwee Kuan|Mihalek, Ivana', 'Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671.|Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671.|Bioinformatics Institute, A*STAR, 30 Biopolis Street, #07-01 Matrix, Singapore 138671.', 'Singapore|Singapore|Singapore', 0, 'Binding Sites|Databases, Protein|Models, Molecular|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-11-155', '20338066', 'Computational comparison of two protein structures is the starting point of many methods that build on existing knowledge, such as structure modeling (including modeling of protein complexes and conformational changes), molecular replacement, or annotation by structural similarity. In a commonly used strategy, significant effort is invested in matching two sets of atoms. In a complementary approach, a global descriptor is assigned to the overall structure, thus losing track of the substructures within.'),
(93512, 2006, 'bmc', 'Proceedings of the Third Annual Conference of the MidSouth Computational Biology and Bioinformatics Society March 2-4, 2006 Baton Rouge, Louisiana, USA', '-', '-', '-', 8, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-11-155', '20344819', '-'),
(93513, 2010, 'bmc', 'Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization', 'Hulsman, Marc|Mentink, Anouk|van Someren, Eugene P|Dechering, Koen J|de Boer, Jan|Reinders, Marcel Jt', 'Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl|Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands. m.hulsman@tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genomics|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-156', '20346103', 'Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequently, we encounter datasets with significant technical effects that currently available methods are not able to correct.'),
(93514, 2010, 'bmc', 'Moara: a Java library for extracting and normalizing gene and protein mentions', 'Neves, Mariana L|Carazo, José-María|Pascual-Montano, Alberto', 'BioComputing Unit, National Center of Biotechnology (CNB-CSIC), Madrid, Spain.|BioComputing Unit, National Center of Biotechnology (CNB-CSIC), Madrid, Spain.|BioComputing Unit, National Center of Biotechnology (CNB-CSIC), Madrid, Spain.', 'Spain|Spain|Spain', 19, 'Data Mining|Databases, Genetic|Databases, Protein|Genes|Information Storage and Retrieval|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-157', '20346105', 'Gene/protein recognition and normalization are important preliminary steps for many biological text mining tasks, such as information retrieval, protein-protein interactions, and extraction of semantic information, among others. Despite dedication to these problems and effective solutions being reported, easily integrated tools to perform these tasks are not readily available.'),
(93515, 2010, 'bmc', 'Integrating gene expression and GO classification for PCA by preclustering', 'De Haan, Jorn R|Piek, Ester|van Schaik, Rene C|de Vlieg, Jacob|Bauerschmidt, Susanne|Buydens, Lutgarde M C|Wehrens, Ron', 'Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.|Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 12, 'Cell Cycle|Cell Differentiation|Cluster Analysis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Mesenchymal Stem Cells|Principal Component Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-11-158', '20346140', 'Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering.'),
(93516, 2010, 'bmc', 'CDK-Taverna: an open workflow environment for cheminformatics', 'Kuhn, Thomas|Willighagen, Egon L|Zielesny, Achim|Steinbeck, Christoph', 'Chemoinformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK.|Chemoinformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK.|Chemoinformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK.|Chemoinformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 48, 'Computational Biology|Databases, Factual|Drug Discovery|Ligands|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-159', '20346188', 'Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new knowledge. To freely support open science based on these data resources, it is desirable for the processing tools to be open source and available for everyone.'),
(93517, 2010, 'bmc', 'Identification of NAD interacting residues in proteins', 'Ansari, Hifzur R|Raghava, Gajendra P S', 'Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India.|Institute of Microbial Technology, Sector 39A, Chandigarh, 160036, India.', 'India|India', 0, 'Amino Acid Sequence|Binding Sites|Databases, Protein|NAD|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-160', '20353553', 'Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD+ or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD+ to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93518, 2010, 'bmc', 'ProbFAST: Probabilistic functional analysis system tool', 'Silva, Israel T|Vêncio, Ricardo Z N|Oliveira, Thiago Y K|Molfetta, Greice A|Silva, Wilson A', 'Department of Genetics, Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. itojal@usp.br|Department of Genetics, Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. itojal@usp.br|Department of Genetics, Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. itojal@usp.br|Department of Genetics, Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. itojal@usp.br|Department of Genetics, Faculty of Medicine, University of São Paulo, Ribeirão Preto, Brazil. itojal@usp.br', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Gene Expression|Gene Expression Profiling|Genome, Human|Genomics|Humans|Internet|Probability|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-161', '20353576', 'The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis.'),
(93519, 2010, 'bmc', 'Knowledge-guided gene ranking by coordinative component analysis', 'Wang, Chen|Xuan, Jianhua|Li, Huai|Wang, Yue|Zhan, Ming|Hoffman, Eric P|Clarke, Robert', 'Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Gene Expression Profiling|Knowledge Bases|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-11-162', '20353603', 'In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data.'),
(93520, 2010, 'bmc', 'New components of the Dictyostelium PKA pathway revealed by Bayesian analysis of expression data', 'Parikh, Anup|Huang, Eryong|Dinh, Christopher|Zupan, Blaz|Kuspa, Adam|Subramanian, Devika|Shaulsky, Gad', 'Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.|Graduate Program in Structural Computational Biology and Molecular Biophysics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Cyclic AMP-Dependent Protein Kinases|Dictyostelium|Gene Expression|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-163', '20356373', 'Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.'),
(93521, 2010, 'bmc', 'Multiconstrained gene clustering based on generalized projections', 'Zeng, Jia|Zhu, Shanfeng|Liew, Alan Wee-Chung|Yan, Hong', 'School of Computer Science and Technology, Soochow University, Suzhou 215006, China. j.zeng@ieee.org|School of Computer Science and Technology, Soochow University, Suzhou 215006, China. j.zeng@ieee.org|School of Computer Science and Technology, Soochow University, Suzhou 215006, China. j.zeng@ieee.org|School of Computer Science and Technology, Soochow University, Suzhou 215006, China. j.zeng@ieee.org', 'China|China|China|China', 2, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Likelihood Functions|Multigene Family|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-164', '20356386', 'Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem.'),
(93522, 2010, 'bmc', 'Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data', 'McLeay, Robert C|Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.', 'Australia|Australia', 0, 'Algorithms|Chromatin Immunoprecipitation|Computational Biology|DNA-Binding Proteins|Linear Models|Oligonucleotide Array Sequence Analysis|Regulatory Elements, Transcriptional|Sequence Alignment|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-11-165', '20356413', 'A major goal of molecular biology is determining the mechanisms that control the transcription of genes. Motif Enrichment Analysis (MEA) seeks to determine which DNA-binding transcription factors control the transcription of a set of genes by detecting enrichment of known binding motifs in the genes regulatory regions. Typically, the biologist specifies a set of genes believed to be co-regulated and a library of known DNA-binding models for transcription factors, and MEA determines which (if any) of the factors may be direct regulators of the genes. Since the number of factors with known DNA-binding models is rapidly increasing as a result of high-throughput technologies, MEA is becoming increasingly useful. In this paper, we explore ways to make MEA applicable in more settings, and evaluate the efficacy of a number of MEA approaches.'),
(93523, 2010, 'bmc', 'GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms', 'Jani, Saurin D|Argraves, Gary L|Barth, Jeremy L|Argraves, W Scott', 'Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC 29425, USA.|Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC 29425, USA.', 'United States|United States|United States|United States', 9, 'Databases, Genetic|Gene Expression Profiling|Internet|Medical Subject Headings|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-166', '20359363', 'An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases.'),
(93524, 2010, 'bmc', 'Predicting protein-protein interactions in unbalanced data using the primary structure of proteins', 'Yu, Chi-Yuan|Chou, Lih-Ching|Chang, Darby Tien-Hao', 'Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan|Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan', '-|-|-', 58, 'Amino Acid Sequence|Binding Sites|Databases, Protein|Protein Interaction Mapping|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-167', '20361868', 'Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks.'),
(93525, 2010, 'bmc', 'Using jackknife to assess the quality of gene order phylogenies', 'Shi, Jian|Zhang, Yiwei|Luo, Haiwei|Tang, Jijun', 'Department of Computer Science and Engineering, University of South Carolina Columbia, SC 29028, USA.|Department of Computer Science and Engineering, University of South Carolina Columbia, SC 29028, USA.|Department of Computer Science and Engineering, University of South Carolina Columbia, SC 29028, USA.|Department of Computer Science and Engineering, University of South Carolina Columbia, SC 29028, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Evolution, Molecular|Gene Expression Profiling|Gene Order|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-168', '20370914', 'In recent years, gene order data has attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis. If gene orders are viewed as one character with a large number of states, traditional bootstrap procedures cannot be applied. Researchers began to use a jackknife resampling method to assess the quality of gene order phylogenies.'),
(93526, 2010, 'bmc', 'A highly efficient multi-core algorithm for clustering extremely large datasets', 'Kraus, Johann M|Kestler, Hans A', 'Institute of Neural Information Processing, University of Ulm, 89069 Ulm, Germany.|Institute of Neural Information Processing, University of Ulm, 89069 Ulm, Germany.', 'Germany|Germany', 0, 'Algorithms|Cluster Analysis|Databases, Factual|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-169', '20370922', 'In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.'),
(93527, 2010, 'bmc', 'Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections', 'Tatebe, Ken|Zeytun, Ahmet|Ribeiro, Ruy M|Hoffmann, Robert|Harrod, Kevin S|Forst, Christian V', 'Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.|Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Animals|Epithelial Cells|Gene Expression Profiling|Gene Regulatory Networks|Humans|Influenza A Virus, H5N1 Subtype|Lung|Respiratory Syncytial Viruses', 'Journal Article', '10.1186/1471-2105-11-170', '20370926', 'The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily infect humans. We analyzed differential gene expression of lung epithelial cells to compare the response to H5N1 infection with a more benign infection with Respiratory Syncytial Virus (RSV). These gene expression data are then used as seeds to find important nodes by using a novel combination of the Gene Ontology database and the Human Network of gene interactions. Additional analysis of the data is conducted by training support vector machines (SVM) with the data and examining the orientations of the optimal hyperplanes generated.'),
(93528, 2010, 'bmc', 'Assignment of chromosomal locations for unassigned SNPs/scaffolds based on pair-wise linkage disequilibrium estimates', 'Khatkar, Mehar S|Hobbs, Matthew|Neuditschko, Markus|Sölkner, Johann|Nicholas, Frank W|Raadsma, Herman W', 'Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au|Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au|Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au|Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au|Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au|Reprogen-Animal Bioscience, Faculty of Veterinary Science, University of Sydney, Camden NSW 2570, Australia. mehar.khatkar@sydney.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Animals|Cattle|Chromosome Mapping|Chromosomes, Mammalian|Gene Frequency|Genome|Genomics|Genotype|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-171', '20370931', 'Recent developments of high-density SNP chips across a number of species require accurate genetic maps. Despite rapid advances in genome sequence assembly and availability of a number of tools for creating genetic maps, the exact genome location for a number of SNPs from these SNP chips still remains unknown. We have developed a locus ordering procedure based on linkage disequilibrium (LODE) which provides estimation of the chromosomal positions of unaligned SNPs and scaffolds. It also provides an alternative means for verification of genetic maps. We exemplified LODE in cattle.'),
(93529, 2010, 'bmc', 'Directionality in protein fold prediction', 'Ellis, Jonathan J|Huard, Fabien P E|Deane, Charlotte M|Srivastava, Sheenal|Wood, Graham R', 'Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.|Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.|Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.|Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.|Department of Statistics, Macquarie University, Sydney, NSW 2109, Australia.', 'Australia|Australia|Australia|Australia|Australia', 15, 'Algorithms|Databases, Protein|Models, Molecular|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-172', '20374616', 'Ever since the ground-breaking work of Anfinsen et al. in which a denatured protein was found to refold to its native state, it has been frequently stated by the protein fold prediction community that all the information required for protein folding lies in the amino acid sequence. Recent in vitro experiments and in silico computational studies, however, have shown that cotranslation may affect the folding pathway of some proteins, especially those of ancient folds. In this paper aspects of cotranslational folding have been incorporated into a protein structure prediction algorithm by adapting the Rosetta program to fold proteins as the nascent chain elongates. This makes it possible to conduct a pairwise comparison of folding accuracy, by comparing folds created sequentially from each end of the protein.'),
(93530, 2010, 'bmc', 'SNP-RFLPing 2: an updated and integrated PCR-RFLP tool for SNP genotyping', 'Chang, Hsueh-Wei|Cheng, Yu-Huei|Chuang, Li-Yeh|Yang, Cheng-Hong', 'Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.|Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.|Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.|Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.', '-|-|-|-', 37, 'DNA Primers|Genomics|Genotype|Polymerase Chain Reaction|Polymorphism, Restriction Fragment Length|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-173', '20377871', 'PCR-restriction fragment length polymorphism (RFLP) assay is a cost-effective method for SNP genotyping and mutation detection, but the manual mining for restriction enzyme sites is challenging and cumbersome. Three years after we constructed SNP-RFLPing, a freely accessible database and analysis tool for restriction enzyme mining of SNPs, significant improvements over the 2006 version have been made and incorporated into the latest version, SNP-RFLPing 2.'),
(93531, 2010, 'bmc', 'APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility', 'Xia, Jun-Feng|Zhao, Xing-Ming|Song, Jiangning|Huang, De-Shuang', 'Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.|Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.|Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.|Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.', 'China|China|China|China', 0, 'Binding Sites|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Protein Interaction Mapping|Proteins|Software|Solvents|Surface Properties', 'Journal Article', '10.1186/1471-2105-11-174', '20377884', 'It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.'),
(93532, 2010, 'bmc', 'Protein sequences classification by means of feature extraction with substitution matrices', 'Saidi, Rabie|Maddouri, Mondher|Mephu Nguifo, Engelbert', 'LIMOS - Blaise Pascal University - Clermont University, BP 10448, Clermont-Ferrand 63000, France.|LIMOS - Blaise Pascal University - Clermont University, BP 10448, Clermont-Ferrand 63000, France.|LIMOS - Blaise Pascal University - Clermont University, BP 10448, Clermont-Ferrand 63000, France.', 'France|France|France', 0, 'Amino Acid Sequence|Amino Acid Substitution|Artificial Intelligence|Databases, Protein|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-175', '20377887', 'This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.'),
(93533, 2010, 'bmc', 'Gene set enrichment meta-learning analysis: next- generation sequencing versus microarrays', 'Stiglic, Gregor|Bajgot, Mateja|Kokol, Peter', 'Faculty of Health Sciences, University of Maribor, Zitna ulica 15, 2000 Maribor, Slovenia. gregor.stiglic@uni-mb.si|Faculty of Health Sciences, University of Maribor, Zitna ulica 15, 2000 Maribor, Slovenia. gregor.stiglic@uni-mb.si|Faculty of Health Sciences, University of Maribor, Zitna ulica 15, 2000 Maribor, Slovenia. gregor.stiglic@uni-mb.si', 'Slovenia|Slovenia|Slovenia', 0, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-11-176', '20377890', 'Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.'),
(93534, 2010, 'bmc', 'A Bayesian network approach to feature selection in mass spectrometry data', 'Kuschner, Karl W|Malyarenko, Dariya I|Cooke, William E|Cazares, Lisa H|Semmes, O J|Tracy, Eugene R', 'Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu|Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu|Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu|Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu|Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu|Department of Physics, The College of William and Mary, Williamsburg, VA, USA. kwkusc@wm.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Biomarkers|Mass Spectrometry|Pattern Recognition, Automated|Proteomics', 'Journal Article', '10.1186/1471-2105-11-177', '20377906', 'Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates.'),
(93535, 2010, 'bmc', 'An overview of the CellML API and its implementation', 'Miller, Andrew K|Marsh, Justin|Reeve, Adam|Garny, Alan|Britten, Randall|Halstead, Matt|Cooper, Jonathan|Nickerson, David P|Nielsen, Poul F', 'Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Auckland, NZ. ak.miller@auckland.ac.nz', '-|-|-|-|-|-|-|-|-', 55, 'Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Models, Theoretical|Software', 'Journal Article', '10.1186/1471-2105-11-178', '20377909', 'CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models.However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML.'),
(93536, 2010, 'bmc', 'The value of position-specific priors in motif discovery using MEME', 'Bailey, Timothy L|Bodén, Mikael|Whitington, Tom|Machanick, Philip', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia. t.bailey@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia. t.bailey@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia. t.bailey@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia. t.bailey@imb.uq.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Motifs|Animals|Base Sequence|Computational Biology|Databases, Genetic|Databases, Protein|Mice|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-179', '20380693', 'Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).'),
(93537, 2010, 'bmc', 'Optimization algorithms for functional deimmunization of therapeutic proteins', 'Parker, Andrew S|Zheng, Wei|Griswold, Karl E|Bailey-Kellogg, Chris', 'Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Epitopes, T-Lymphocyte|Molecular Sequence Data|Protein Engineering|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-180', '20380721', 'To develop protein therapeutics from exogenous sources, it is necessary to mitigate the risks of eliciting an anti-biotherapeutic immune response. A key aspect of the response is the recognition and surface display by antigen-presenting cells of epitopes, short peptide fragments derived from the foreign protein. Thus, developing minimal-epitope variants represents a powerful approach to deimmunizing protein therapeutics. Critically, mutations selected to reduce immunogenicity must not interfere with the proteins therapeutic activity.'),
(93538, 2010, 'bmc', 'Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study', 'Liu, Qi|Xu, Qian|Zheng, Vincent W|Xue, Hong|Cao, Zhiwei|Yang, Qiang', 'Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.|Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.|Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.|Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.|Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.|Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 30, 'Computational Biology|Gene Silencing|RNA Interference|RNA, Messenger|RNA, Small Interfering|RNA-Induced Silencing Complex', 'Journal Article', '10.1186/1471-2105-11-181', '20380733', 'Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs.'),
(93539, 2010, 'bmc', 'Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry', 'Wang, Bing|Valentine, Steve|Plasencia, Manolo|Raghuraman, Sriram|Zhang, Xiang', 'Department of Electronics and Information Engineering, Anhui University of Technology, Maanshan, 243002, China. wangbing@ustc.edu|Department of Electronics and Information Engineering, Anhui University of Technology, Maanshan, 243002, China. wangbing@ustc.edu|Department of Electronics and Information Engineering, Anhui University of Technology, Maanshan, 243002, China. wangbing@ustc.edu|Department of Electronics and Information Engineering, Anhui University of Technology, Maanshan, 243002, China. wangbing@ustc.edu|Department of Electronics and Information Engineering, Anhui University of Technology, Maanshan, 243002, China. wangbing@ustc.edu', 'China|China|China|China|China', 19, 'Ions|Mass Spectrometry|Neural Networks (Computer)|Peptides|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-11-182', '20380738', 'There is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS). It separates and detects peptide ions on a millisecond time-scale. IMS separates peptide ions based on drift time that is determined by the collision cross-section of each peptide ion in a given experiment condition. A peptide ions collision cross-section is related to the ion size and shape resulted from the peptide amino acid sequence and their modifications. This inherent relation between the drift time of peptide ion and peptide sequence indicates that the drift time of peptide ions can be used to infer peptide sequence and therefore, for peptide identification.'),
(93540, 2010, 'bmc', 'A robust method for estimating gene expression states using Affymetrix microarray probe level data', 'Ohtaki, Megu|Otani, Keiko|Hiyama, Keiko|Kamei, Naomi|Satoh, Kenichi|Hiyama, Eiso', 'Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp|Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp|Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp|Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp|Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp|Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Genome, Human|Humans|Neuroblastoma|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1186/1471-2105-11-183', '20380745', 'Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-specific binding, which is a major disadvantage with microarray data. The Affymetrix GeneChip assigned a mismatch (MM) probe with the intention of measuring non-specific binding, but various opinions exist regarding usefulness of MM measures. It should be noted that not all observed intensities are associated with expressed genes and many of those are associated with unexpressed genes, of which measured values express mere noise due to non-specific binding, cross-hybridization, or stray signals. The implicit assumption that all genes are expressed leads to poor performance of microarray data analyses. We assume two functional states of a gene - expressed or unexpressed - and propose a robust method to estimate gene expression states using an order relationship between PM and MM measures.'),
(93541, 2010, 'bmc', 'FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods', 'Zierke, Stephanie|Bakos, Jason D', 'Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.', 'United States|United States', 0, 'Bayes Theorem|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-184', '20385005', 'Likelihood (ML)-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data. This method is used in applications such as RAxML, GARLI, MrBayes, PAML, and PAUP. The Phylogenetic Likelihood Function (PLF) is an important kernel computation for this method. The PLF consists of a loop with no conditional behavior or dependencies between iterations. As such it contains a high potential for exploiting parallelism using micro-architectural techniques. In this paper, we describe a technique for mapping the PLF and supporting logic onto a Field Programmable Gate Array (FPGA)-based co-processor. By leveraging the FPGAs on-chip DSP modules and the high-bandwidth local memory attached to the FPGA, the resultant co-processor can accelerate ML-based methods and outperform state-of-the-art multi-core processors.'),
(93542, 2010, 'bmc', 'web cellHTS2: a web-application for the analysis of high-throughput screening data', 'Pelz, Oliver|Gilsdorf, Moritz|Boutros, Michael', 'German Cancer Research Center (DKFZ), Div Signaling and Functional Genomics and University of Heidelberg, Faculty of Medicine Mannheim, Dept Cell and Molecular Biology, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.|German Cancer Research Center (DKFZ), Div Signaling and Functional Genomics and University of Heidelberg, Faculty of Medicine Mannheim, Dept Cell and Molecular Biology, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.|German Cancer Research Center (DKFZ), Div Signaling and Functional Genomics and University of Heidelberg, Faculty of Medicine Mannheim, Dept Cell and Molecular Biology, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany.', 'Germany|Germany|Germany', 43, 'Databases, Genetic|Gene Expression Profiling|Genomics|High-Throughput Screening Assays|Internet|RNA Interference|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-185', '20385013', 'The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.'),
(93543, 2010, 'bmc', 'Shape based kinetic outlier detection in real-time PCR', 'Sisti, Davide|Guescini, Michele|Rocchi, Marco B L|Tibollo, Pasquale|DAtri, Mario|Stocchi, Vilberto', 'Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it|Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it|Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it|Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it|Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it|Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino Carlo Bo, Campus Scientifico Sogesta, Località Crocicchia - 61029 Urbino, Italy. davide.sisti@uniurb.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 20, 'Computational Biology|Kinetics|Pattern Recognition, Automated|Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-186', '20385019', 'Real-time PCR has recently become the technique of choice for absolute and relative nucleic acid quantification. The gold standard quantification method in real-time PCR assumes that the compared samples have similar PCR efficiency. However, many factors present in biological samples affect PCR kinetic, confounding quantification analysis. In this work we propose a new strategy to detect outlier samples, called SOD.'),
(93544, 2010, 'bmc', 'Artificial and natural duplicates in pyrosequencing reads of metagenomic data', 'Niu, Beifang|Fu, Limin|Sun, Shulei|Li, Weizhong', 'California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, California 92093, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, California 92093, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, California 92093, USA.|California Institute for Telecommunications and Information Technology, University of California San Diego, La Jolla, California 92093, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Genome|Internet|Metagenomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-187', '20388221', 'Artificial duplicates from pyrosequencing reads may lead to incorrect interpretation of the abundance of species and genes in metagenomic studies. Duplicated reads were filtered out in many metagenomic projects. However, since the duplicated reads observed in a pyrosequencing run also include natural (non-artificial) duplicates, simply removing all duplicates may also cause underestimation of abundance associated with natural duplicates.'),
(93545, 2010, 'bmc', 'Dynamics based alignment of proteins: an alternative approach to quantify dynamic similarity', 'Münz, Márton|Lyngsø, Rune|Hein, Jotun|Biggin, Philip C', 'Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.|Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.|Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.|Structural Bioinformatics and Computational Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Databases, Protein|Models, Molecular|Molecular Dynamics Simulation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-188', '20398246', 'The dynamic motions of many proteins are central to their function. It therefore follows that the dynamic requirements of a protein are evolutionary constrained. In order to assess and quantify this, one needs to compare the dynamic motions of different proteins. Comparing the dynamics of distinct proteins may also provide insight into how protein motions are modified by variations in sequence and, consequently, by structure. The optimal way of comparing complex molecular motions is, however, far from trivial. The majority of comparative molecular dynamics studies performed to date relied upon prior sequence or structural alignment to define which residues were equivalent in 3-dimensional space.'),
(93546, 2010, 'bmc', 'JISTIC: identification of significant targets in cancer', 'Sanchez-Garcia, Felix|Akavia, Uri David|Mozes, Eyal|Peer, Dana', 'Department of Computer Science, Columbia University, New York, NY, USA. fs2282@columbia.edu|Department of Computer Science, Columbia University, New York, NY, USA. fs2282@columbia.edu|Department of Computer Science, Columbia University, New York, NY, USA. fs2282@columbia.edu|Department of Computer Science, Columbia University, New York, NY, USA. fs2282@columbia.edu', 'United States|United States|United States|United States', 38, 'Chromosome Aberrations|Databases, Genetic|Genetic Variation|Genome, Human|Genomics|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-189', '20398270', 'Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect.'),
(93547, 2010, 'bmc', 'PuTmiR: a database for extracting neighboring transcription factors of human microRNAs', 'Bandyopadhyay, Sanghamitra|Bhattacharyya, Malay', 'Machine Intelligence Unit, Indian Statistical Institute, 203 B, T Road, Kolkata-700108, India.|Machine Intelligence Unit, Indian Statistical Institute, 203 B, T Road, Kolkata-700108, India.', 'India|India', 0, 'Binding Sites|Databases, Genetic|Gene Regulatory Networks|Humans|MicroRNAs|Sequence Analysis, RNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-190', '20398296', 'Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome. Recent studies suggest that these putative TFs are possible regulators of those miRNAs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93548, 2010, 'bmc', 'Integrative analysis of gene expression and copy number alterations using canonical correlation analysis', 'Soneson, Charlotte|Lilljebjörn, Henrik|Fioretos, Thoas|Fontes, Magnus', 'Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. lottas@maths.lth.se|Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. lottas@maths.lth.se|Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. lottas@maths.lth.se|Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. lottas@maths.lth.se', 'Sweden|Sweden|Sweden|Sweden', 45, 'Algorithms|Databases, Genetic|Gene Dosage|Gene Expression|Gene Expression Profiling|Genomics|Humans|Leukemia|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-11-191', '20398334', 'With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia.'),
(93549, 2010, 'bmc', 'Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design', 'Bordner, Andrew J', 'Mayo Clinic, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu', 'United States', 5, 'Computational Biology|Databases, Protein|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-192', '20398384', 'Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.'),
(93550, 2010, 'bmc', 'A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction', 'Cheng, Tiejun|Liu, Zhihai|Wang, Renxiao', 'State Key Laboratory of Bioorganic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, PR China.|State Key Laboratory of Bioorganic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, PR China.|State Key Laboratory of Bioorganic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032, PR China.', 'China|China|China', 0, 'Algorithms|Binding Sites|Databases, Protein|Drug Design|Ligands|Models, Molecular|Protein Conformation|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-193', '20398404', 'Current scoring functions are not very successful in protein-ligand binding affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a general knowledge-guided scoring (KGS) strategy to tackle this problem. Our KGS strategy computes the binding constant of a given protein-ligand complex based on the known binding constant of an appropriate reference complex. A good training set that includes a sufficient number of protein-ligand complexes with known binding data needs to be supplied for finding the reference complex. The reference complex is required to share a similar pattern of key protein-ligand interactions to that of the complex of interest. Thus, some uncertain factors in protein-ligand binding may cancel out, resulting in a more accurate prediction of absolute binding constants.'),
(93551, 2010, 'bmc', 'Starr: Simple Tiling ARRay analysis of Affymetrix ChIP-chip data', 'Zacher, Benedikt|Kuan, Pei Fen|Tresch, Achim', 'Gene Center, Department of Chemistry and Biochemistry, Ludwig-Maximilians-University of Munich, Feodor-Lynen-Str, 25, D-81377 Munich, Germany.|Gene Center, Department of Chemistry and Biochemistry, Ludwig-Maximilians-University of Munich, Feodor-Lynen-Str, 25, D-81377 Munich, Germany.|Gene Center, Department of Chemistry and Biochemistry, Ludwig-Maximilians-University of Munich, Feodor-Lynen-Str, 25, D-81377 Munich, Germany.', 'Germany|Germany|Germany', 24, 'Chromatin Immunoprecipitation|DNA|DNA-Binding Proteins|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-194', '20398407', 'Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression.'),
(93552, 2010, 'bmc', 'Properties and identification of antibiotic drug targets', 'Bakheet, Tala M|Doig, Andrew J', 'Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.|Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.', 'United Kingdom|United Kingdom', 0, 'Anti-Bacterial Agents|Bacterial Proteins|Computational Biology|Databases, Protein|Drug Discovery|Escherichia coli|Escherichia coli Proteins', 'Journal Article', '10.1186/1471-2105-11-195', '20406434', 'We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from E. coli only and 4243 non-drug targets from E. coli to identify differences in their properties and to predict new potential drug targets.'),
(93553, 2010, 'bmc', 'Shared probe design and existing microarray reanalysis using PICKY', 'Chou, Hui-Hsien', 'Department of Genetics, Development and Cell Biology, and Department of Computer Science, Iowa State University, Ames, IA, 50011-3223, USA. hhchou@iastate.edu', 'United States', 11, 'Databases, Genetic|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Software', 'Journal Article', '10.1186/1471-2105-11-196', '20406469', 'Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations.'),
(93554, 2010, 'bmc', 'Missing value imputation for epistatic MAPs', 'Ryan, Colm|Greene, Derek|Cagney, Gerard|Cunningham, Pádraig', 'School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. colm.ryan@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. colm.ryan@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. colm.ryan@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. colm.ryan@ucd.ie', 'Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Databases, Genetic|Epistasis, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-197', '20406472', 'Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.'),
(93555, 2010, 'bmc', 'Resolving anaphoras for the extraction of drug-drug interactions in pharmacological documents', 'Segura-Bedmar, Isabel|Crespo, Mario|de Pablo-Sánchez, César|Martínez, Paloma', 'Computer Science Department, University Carlos III of Madrid, Leganés, 28921, Spain. isegura@inf.uc3m.es|Computer Science Department, University Carlos III of Madrid, Leganés, 28921, Spain. isegura@inf.uc3m.es|Computer Science Department, University Carlos III of Madrid, Leganés, 28921, Spain. isegura@inf.uc3m.es|Computer Science Department, University Carlos III of Madrid, Leganés, 28921, Spain. isegura@inf.uc3m.es', 'Spain|Spain|Spain|Spain', 18, 'Computational Biology|Data Mining|Databases, Factual|Drug Interactions|Humans|Pharmacology|Semantics|Software|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-11-S2-S1', '20406499', 'Drug-drug interactions are frequently reported in the increasing amount of biomedical literature. Information Extraction (IE) techniques have been devised as a useful instrument to manage this knowledge. Nevertheless, IE at the sentence level has a limited effect because of the frequent references to previous entities in the discourse, a phenomenon known as anaphora. DrugNerAR, a drug anaphora resolution system is presented to address the problem of co-referring expressions in pharmacological literature. This development is part of a larger and innovative study about automatic drug-drug interaction extraction.'),
(93556, 2010, 'bmc', 'Linear predictive coding representation of correlated mutation for protein sequence alignment', 'Jeong, Chan-seok|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea.', '-|-', 2, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Linear Models|Models, Molecular|Mutation|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-S2-S2', '20406500', 'Although both conservation and correlated mutation (CM) are important information reflecting the different sorts of context in multiple sequence alignment, most of alignment methods use sequence profiles that only represent conservation. There is no general way to represent correlated mutation and incorporate it with sequence alignment yet.'),
(93557, 2010, 'bmc', 'MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge', 'Ijaz, Ali Z|Song, Min|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, South Korea.|Department of Bio and Brain Engineering, KAIST, South Korea.|Department of Bio and Brain Engineering, KAIST, South Korea.', '-|-|-', 9, 'Computational Biology|Data Mining|Databases, Factual|Humans|Information Services|MEDLINE|Models, Theoretical|Natural Language Processing|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-11-S2-S3', '20406501', 'Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.'),
(93558, 2010, 'bmc', 'Multivariate classification of urine metabolome profiles for breast cancer diagnosis', 'Kim, Younghoon|Koo, Imhoi|Jung, Byung Hwa|Chung, Bong Chul|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.|Department of Bio and Brain Engineering, KAIST, Daejeon, South Korea.', '-|-|-|-|-', 43, 'Adult|Artificial Intelligence|Biomarkers, Tumor|Breast Neoplasms|Case-Control Studies|Decision Trees|Female|Humans|Least-Squares Analysis|Metabolome|Metabolomics|Middle Aged|Multivariate Analysis', 'Journal Article', '10.1186/1471-2105-11-S2-S4', '20406502', 'Diagnosis techniques using urine are non-invasive, inexpensive, and easy to perform in clinical settings. The metabolites in urine, as the end products of cellular processes, are closely linked to phenotypes. Therefore, urine metabolome is very useful in marker discoveries and clinical applications. However, only univariate methods have been used in classification studies using urine metabolome. Since multiple genes or proteins would be involved in developments of complex diseases such as breast cancer, multiple compounds including metabolites would be related with the complex diseases, and multivariate methods would be needed to identify those multiple metabolite markers. Moreover, because combinatorial effects among the markers can seriously affect disease developments and there also exist individual differences in genetic makeup or heterogeneity in cancer progressions, single marker is not enough to identify cancers.'),
(93559, 2010, 'bmc', 'Unsupervised binning of environmental genomic fragments based on an error robust selection of l-mers', 'Yang, Bin|Peng, Yu|Leung, Henry Chi-Ming|Yiu, Siu-Ming|Chen, Jing-Chi|Chin, Francis Yuk-Lun', 'State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk|State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk|State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk|State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk|State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk|State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China. byang@cs.hku.hk', 'China|China|China|China|China|China', 0, 'Algorithms|Cluster Analysis|DNA|Data Mining|Databases, Genetic|Environmental Microbiology|Escherichia coli|Genome, Bacterial|Lactobacillus|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S2-S5', '20406503', 'With the rapid development of genome sequencing techniques, traditional research methods based on the isolation and cultivation of microorganisms are being gradually replaced by metagenomics, which is also known as environmental genomics. The first step, which is still a major bottleneck, of metagenomics is the taxonomic characterization of DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as binning. Existing binning methods are based on supervised or semi-supervised approaches which rely heavily on reference genomes of known microorganisms and phylogenetic marker genes. Due to the limited availability of reference genomes and the bias and instability of marker genes, existing binning methods may not be applicable in many cases.'),
(93560, 2010, 'bmc', 'Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS', 'Yu, Hwanjo|Kim, Taehoon|Oh, Jinoh|Ko, Ilhwan|Kim, Sungchul|Han, Wook-Shin', 'CSE Department, POSTECH, Pohang, South Korea.|CSE Department, POSTECH, Pohang, South Korea.|CSE Department, POSTECH, Pohang, South Korea.|CSE Department, POSTECH, Pohang, South Korea.|CSE Department, POSTECH, Pohang, South Korea.|CSE Department, POSTECH, Pohang, South Korea.', '-|-|-|-|-|-', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Feedback|PubMed|Reproducibility of Results|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S2-S6', '20406504', 'Finding relevant articles from PubMed is challenging because it is hard to express the users specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed.'),
(93561, 2010, 'bmc', 'Learning an enriched representation from unlabeled data for protein-protein interaction extraction', 'Li, Yanpeng|Hu, Xiaohua|Lin, Hongfei|Yang, Zhihao', 'Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China. liyanpeng.lyp@gmail.com|Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China. liyanpeng.lyp@gmail.com|Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China. liyanpeng.lyp@gmail.com|Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China. liyanpeng.lyp@gmail.com', 'China|China|China|China', 19, 'Algorithms|Artificial Intelligence|Chi-Square Distribution|Computational Biology|Data Mining|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-11-S2-S7', '20406505', 'Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task. We use feature coupling generalization (FCG) - a recently proposed semi-supervised learning strategy - to learn an enriched representation of local contexts in sentences from 47 million unlabeled examples and investigate the performance of the new features on AIMED corpus.'),
(93562, 2010, 'bmc', 'Predictors of natively unfolded proteins: unanimous consensus score to detect a twilight zone between order and disorder in generic datasets', 'Deiana, Antonio|Giansanti, Andrea', 'Department of Physics, La Sapienza University of Rome, Ple A, Moro 5, 00185 Rome, Italy.|Department of Physics, La Sapienza University of Rome, Ple A, Moro 5, 00185 Rome, Italy.', 'Italy|Italy', 0, 'Amino Acid Sequence|Binding Sites|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-198', '20409339', 'Natively unfolded proteins lack a well defined three dimensional structure but have important biological functions, suggesting a re-assignment of the structure-function paradigm. To assess that a given protein is natively unfolded requires laborious experimental investigations, then reliable sequence-only methods for predicting whether a sequence corresponds to a folded or to an unfolded protein are of interest in fundamental and applicative studies. Many proteins have amino acidic compositions compatible both with the folded and unfolded status, and belong to a twilight zone between order and disorder. This makes difficult a dichotomic classification of protein sequences into folded and natively unfolded ones. In this work we propose an operational method to identify proteins belonging to the twilight zone by combining into a consensus score good performing single predictors of folding.'),
(93563, 2010, 'bmc', 'BioPhysConnectoR: Connecting sequence information and biophysical models', 'Hoffgaard, Franziska|Weil, Philipp|Hamacher, Kay', 'Theoretical Biology and Bioinformatics, Institute of Microbiology and Genetics, Department of Biology, TU Darmstadt, Schnittspahnstrasse 10, 64289 Darmstadt, Germany. hoffgaard@bio.tu-darmstadt.de|Theoretical Biology and Bioinformatics, Institute of Microbiology and Genetics, Department of Biology, TU Darmstadt, Schnittspahnstrasse 10, 64289 Darmstadt, Germany. hoffgaard@bio.tu-darmstadt.de|Theoretical Biology and Bioinformatics, Institute of Microbiology and Genetics, Department of Biology, TU Darmstadt, Schnittspahnstrasse 10, 64289 Darmstadt, Germany. hoffgaard@bio.tu-darmstadt.de', 'Germany|Germany|Germany', 16, 'Binding Sites|Biophysics|Databases, Genetic|Models, Molecular|Protein Conformation|Proteins|Sequence Alignment|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-199', '20412558', 'One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s).A complete, theoretical picture of the selective advantage, and thus a functional annotation, of (co-)mutations is still lacking. Using sequence-based and information theoretical inspired methods we can identify coevolving residues in proteins without understanding the underlying biophysical properties giving rise to such coevolutionary dynamics. Detailed (atomistic) simulations are prohibitively expensive. At the same time reduced molecular models are an efficient way to determine the reduced dynamics around the native state. The combination of sequence based approaches with such reduced models is therefore a promising approach to annotate evolutionary sequence changes.'),
(93564, 2010, 'bmc', 'Conditional random pattern model for copy number aberration detection', 'Li, Fuhai|Zhou, Xiaobo|Huang, Wanting|Chang, Chung-Che|Wong, Stephen T C', 'Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Bioengineering and Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Comparative Genomic Hybridization|DNA Copy Number Variations|Gene Dosage|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-200', '20412592', 'DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.'),
(93565, 2010, 'bmc', 'A model-independent approach to infer hierarchical codon substitution dynamics', 'Görnerup, Olof|Jacobi, Martin Nilsson', 'Complex Systems Group, Department of Energy and Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden. olofgo@chalmers.se|Complex Systems Group, Department of Energy and Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden. olofgo@chalmers.se', 'Sweden|Sweden', 2, 'Amino Acid Substitution|Codon|Evolution, Molecular|Gene Regulatory Networks|Genetic Code|Genomics|Models, Genetic|Phylogeny|Proteins', 'Journal Article', '10.1186/1471-2105-11-201', '20412602', 'Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems.'),
(93566, 2010, 'bmc', 'Systematic calibration of a cell signaling network model', 'Kim, Kyoung Ae|Spencer, Sabrina L|Albeck, John G|Burke, John M|Sorger, Peter K|Gaudet, Suzanne|Kim, Do Hyun', 'Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.|Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea.', '-|-|-|-|-|-|-', 27, 'Cell Death|Computational Biology|Models, Biological|Signal Transduction|TNF-Related Apoptosis-Inducing Ligand', 'Journal Article', '10.1186/1471-2105-11-202', '20416044', 'Mathematical modeling is being applied to increasingly complex biological systems and datasets, however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death.'),
(93567, 2010, 'bmc', 'BS Seeker: precise mapping for bisulfite sequencing', 'Chen, Pao-Yang|Cokus, Shawn J|Pellegrini, Matteo', 'Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA.|Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA.|Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA.', 'United States|United States|United States', 159, 'Base Sequence|Genome|Genome, Human|Genomics|Humans|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/1471-2105-11-203', '20416082', 'Bisulfite sequencing using next generation sequencers yields genome-wide measurements of DNA methylation at single nucleotide resolution. Traditional aligners are not designed for mapping bisulfite-treated reads, where the unmethylated Cs are converted to Ts. We have developed BS Seeker, an approach that converts the genome to a three-letter alphabet and uses Bowtie to align bisulfite-treated reads to a reference genome. It uses sequence tags to reduce mapping ambiguity. Post-processing of the alignments removes non-unique and low-quality mappings.'),
(93568, 2010, 'bmc', 'Structural fragment clustering reveals novel structural and functional motifs in alpha-helical transmembrane proteins', 'Marsico, Annalisa|Henschel, Andreas|Winter, Christof|Tuukkanen, Anne|Vassilev, Boris|Scheubert, Kerstin|Schroeder, Michael', 'Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.|Bioinformatics department, Biotechnology Center TU Dresden, Dresden, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 11, 'Amino Acid Motifs|Binding Sites|Cluster Analysis|Computational Biology|Databases, Protein|Membrane Proteins|Models, Molecular|Protein Folding|Protein Interaction Mapping|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-11-204', '20420672', 'A large proportion of an organisms genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology.'),
(93569, 2010, 'bmc', 'Predicting conserved protein motifs with Sub-HMMs', 'Horan, Kevin|Shelton, Christian R|Girke, Thomas', 'Department of Computer Science and Engineering, University of California Riverside, Riverside, California, USA.|Department of Computer Science and Engineering, University of California Riverside, Riverside, California, USA.|Department of Computer Science and Engineering, University of California Riverside, Riverside, California, USA.', 'United States|United States|United States', 7, 'Amino Acid Motifs|Amino Acid Sequence|Conserved Sequence|Databases, Protein|Markov Chains|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-205', '20420695', 'Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins.'),
(93570, 2010, 'bmc', 'Comparative performances of DNA barcoding across insect orders', 'Virgilio, Massimiliano|Backeljau, Thierry|Nevado, Bruno|De Meyer, Marc', 'Royal Museum for Central Africa, Tervuren, Belgium. massimilano.virgilio@africamuseum.be|Royal Museum for Central Africa, Tervuren, Belgium. massimilano.virgilio@africamuseum.be|Royal Museum for Central Africa, Tervuren, Belgium. massimilano.virgilio@africamuseum.be|Royal Museum for Central Africa, Tervuren, Belgium. massimilano.virgilio@africamuseum.be', 'Belgium|Belgium|Belgium|Belgium', 134, 'Animals|DNA|Evolution, Molecular|Genetic Variation|Insecta|Species Specificity', 'Comparative Study', '10.1186/1471-2105-11-206', '20420717', 'Previous studies on insect DNA barcoding provide contradictory results and suggest not consistent performances across orders. This work aims at providing a general evaluation of insect DNA barcoding and mini-barcoding by performing simulations on a large database of 15,948 DNA barcodes. We compared the proportions of correctly identified queries across a) six insect orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera and Orthoptera), b) four identification criteria (Best Match: BM, Best Close Match: BCM, All Species Barcodes: ASB, tree-based identification: NJT), and c) reference databases with different taxon coverage (100, 500, 1,000, 1,500 and 1,995 insect species).'),
(93571, 2010, 'bmc', 'G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration', 'Fasold, Mario|Stadler, Peter F|Binder, Hans', 'Interdisciplinary Centre for Bioinformatics, University Leipzig, Germany.|Interdisciplinary Centre for Bioinformatics, University Leipzig, Germany.|Interdisciplinary Centre for Bioinformatics, University Leipzig, Germany.', 'Germany|Germany|Germany', 16, 'Calibration|DNA Probes|Gene Expression Profiling|Guanine|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-207', '20423484', 'The brightness of the probe spots on expression microarrays intends to measure the abundance of specific mRNA targets. Probes with runs of at least three guanines (G) in their sequence show abnormal high intensities which reflect rather probe effects than target concentrations. This G-bias requires correction prior to downstream expression analysis.'),
(93572, 2010, 'bmc', 'Identification and correction of previously unreported spatial phenomena using raw Illumina BeadArray data', 'Smith, Mike L|Dunning, Mark J|Tavaré, Simon|Lynch, Andy G', 'Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. mike.l.smith@cancer.org.uk|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. mike.l.smith@cancer.org.uk|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. mike.l.smith@cancer.org.uk|Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. mike.l.smith@cancer.org.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-208', '20423502', 'A key stage for all microarray analyses is the extraction of feature-intensities from an image. If this step goes wrong, then subsequent preprocessing and processing stages will stand little chance of rectifying the matter. Illumina employ random construction of their BeadArrays, making feature-intensity extraction even more important for the Illumina platform than for other technologies. In this paper we show that using raw Illumina data it is possible to identify, control, and perhaps correct for a range of spatial-related phenomena that affect feature-intensity extraction.'),
(93573, 2010, 'bmc', 'Phylogenetic representativeness: a new method for evaluating taxon sampling in evolutionary studies', 'Plazzi, Federico|Ferrucci, Ronald R|Passamonti, Marco', 'Department of Biologia Evoluzionistica Sperimentale, University of Bologna, Via Selmi 3, 40126 Bologna, Italy. federico.plazzi@unibo.it|Department of Biologia Evoluzionistica Sperimentale, University of Bologna, Via Selmi 3, 40126 Bologna, Italy. federico.plazzi@unibo.it|Department of Biologia Evoluzionistica Sperimentale, University of Bologna, Via Selmi 3, 40126 Bologna, Italy. federico.plazzi@unibo.it', 'Italy|Italy|Italy', 21, 'Algorithms|Evolution, Molecular|Models, Genetic|Phylogeny|Sampling Studies', 'Evaluation Studies', '10.1186/1471-2105-11-209', '20423512', 'Taxon sampling is a major concern in phylogenetic studies. Incomplete, biased, or improper taxon sampling can lead to misleading results in reconstructing evolutionary relationships. Several theoretical methods are available to optimize taxon choice in phylogenetic analyses. However, most involve some knowledge about the genetic relationships of the group of interest (i.e., the ingroup), or even a well-established phylogeny itself, these data are not always available in general phylogenetic applications.'),
(93574, 2010, 'bmc', 'AMS 30: prediction of post-translational modifications', 'Basu, Subhadip|Plewczynski, Dariusz', 'Department of Computer Science and Engineering, Jadavpur University, Kolkata - 700032, India.|Department of Computer Science and Engineering, Jadavpur University, Kolkata - 700032, India.', 'India|India', 39, 'Neural Networks (Computer)|Protein Processing, Post-Translational|Proteins|Proteome|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-210', '20423529', 'We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid, therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs.'),
(93575, 2010, 'bmc', 'Different effects of the probe summarization algorithms PLIER and RMA on high-level analysis of Affymetrix exon arrays', 'Qu, Yi|He, Fei|Chen, Yuchen', 'National Engineering Center for Biochip at Shanghai, Libing Rd, 151, Shanghai, 201203, China.|National Engineering Center for Biochip at Shanghai, Libing Rd, 151, Shanghai, 201203, China.|National Engineering Center for Biochip at Shanghai, Libing Rd, 151, Shanghai, 201203, China.', 'China|China|China', 17, 'Algorithms|Alternative Splicing|Databases, Genetic|Exons|Genome|Genomics|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-211', '20426803', 'Alternative splicing is an important mechanism that increases protein diversity and functionality in higher eukaryotes. Affymetrix exon arrays are a commercialized platform used to detect alternative splicing on a genome-wide scale. Two probe summarization algorithms, PLIER (Probe Logarithmic Intensity Error) and RMA (Robust Multichip Average), are commonly used to compute gene-level and exon-level expression values. However, a systematic comparison of these two algorithms on their effects on high-level analysis of the arrays has not yet been reported.'),
(93576, 2010, 'bmc', 'Concept-based query expansion for retrieving gene related publications from MEDLINE', 'Matos, Sérgio|Arrais, Joel P|Maia-Rodrigues, João|Oliveira, José Luis', 'Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 36, 'Abstracting and Indexing as Topic|Data Mining|Databases, Factual|Genes|Genomics|Information Storage and Retrieval|MEDLINE|PubMed|Publications|United States', 'Journal Article', '10.1186/1471-2105-11-212', '20426836', 'Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests.'),
(93577, 2010, 'bmc', 'BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions', 'Schellenberger, Jan|Park, Junyoung O|Conrad, Tom M|Palsson, Bernhard Ø', 'Bioinformatics Program, University of California San Diego, La Jolla, California, 92093-0419, USA.|Bioinformatics Program, University of California San Diego, La Jolla, California, 92093-0419, USA.|Bioinformatics Program, University of California San Diego, La Jolla, California, 92093-0419, USA.|Bioinformatics Program, University of California San Diego, La Jolla, California, 92093-0419, USA.', 'United States|United States|United States|United States', 349, 'Genome|Genomics|Knowledge Bases|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-11-213', '20426874', 'Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.'),
(93578, 2010, 'bmc', 'Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis', 'Redestig, Henning|Kusano, Miyako|Fukushima, Atsushi|Matsuda, Fumio|Saito, Kazuki|Arita, Masanori', 'Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp|Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp|Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp|Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp|Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp|Metabolomics Research Group, RIKEN Plant Science Center, 1-7-22 Tsurumi-ku, Suehiro-cho, Yokohama, Kanagawa, 230-0045, Japan. henning@psc.riken.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 31, 'Databases, Factual|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-11-214', '20426876', 'Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious.'),
(93579, 2010, 'bmc', 'PoGO: Prediction of Gene Ontology terms for fungal proteins', 'Jung, Jaehee|Yi, Gangman|Sukno, Serenella A|Thon, Michael R', 'Centro Hispano-Luso de Investigaciones Agrarias (CIALE), Department of Microbiology and Genetics, University of Salamanca, Villamayor 37185, Spain.|Centro Hispano-Luso de Investigaciones Agrarias (CIALE), Department of Microbiology and Genetics, University of Salamanca, Villamayor 37185, Spain.|Centro Hispano-Luso de Investigaciones Agrarias (CIALE), Department of Microbiology and Genetics, University of Salamanca, Villamayor 37185, Spain.|Centro Hispano-Luso de Investigaciones Agrarias (CIALE), Department of Microbiology and Genetics, University of Salamanca, Villamayor 37185, Spain.', 'Spain|Spain|Spain|Spain', 8, 'Databases, Protein|Fungal Proteins|Genome, Fungal|Genomics|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-215', '20429880', 'Automated protein function prediction methods are the only practical approach for assigning functions to genes obtained from model organisms. Many of the previously reported function annotation methods are of limited utility for fungal protein annotation. They are often trained only to one species, are not available for high-volume data processing, or require the use of data derived by experiments such as microarray analysis. To meet the increasing need for high throughput, automated annotation of fungal genomes, we have developed a tool for annotating fungal protein sequences with terms from the Gene Ontology.'),
(93580, 2010, 'bmc', 'TassDB2 - A comprehensive database of subtle alternative splicing events', 'Sinha, Rileen|Lenser, Thorsten|Jahn, Niels|Gausmann, Ulrike|Friedel, Swetlana|Szafranski, Karol|Huse, Klaus|Rosenstiel, Philip|Hampe, Jochen|Schuster, Stefan|Hiller, Michael|Backofen, Rolf|Platzer, Matthias', 'Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germ', 0, 'Alternative Splicing|Animals|Databases, Factual|Genome|Genomics|Humans|Internet|RNA Splice Sites|RNA Splicing|Software', 'Journal Article', '10.1186/1471-2105-11-216', '20429909', 'Subtle alternative splicing events involving tandem splice sites separated by a short (2-12 nucleotides) distance are frequent and evolutionarily widespread in eukaryotes, and a major contributor to the complexity of transcriptomes and proteomes. However, these events have been either omitted altogether in databases on alternative splicing, or only the cases of experimentally confirmed alternative splicing have been reported. Thus, a database which covers all confirmed cases of subtle alternative splicing as well as the numerous putative tandem splice sites (which might be confirmed once more transcript data becomes available), and allows to search for tandem splice sites with specific features and download the results, is a valuable resource for targeted experimental studies and large-scale bioinformatics analyses of tandem splice sites. Towards this goal we recently set up TassDB (Tandem Splice Site DataBase, version 1), which stores data about alternative splicing events at tandem splice sites separated by 3 nt in eight species.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93581, 2010, 'bmc', 'ParallABEL: an R library for generalized parallelization of genome-wide association studies', 'Sangket, Unitsa|Mahasirimongkol, Surakameth|Chantratita, Wasun|Tandayya, Pichaya|Aulchenko, Yurii S', 'Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com|Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com|Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com|Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com|Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com', 'Thailand|Thailand|Thailand|Thailand|Thailand', 0, 'Gene Library|Genome|Genome-Wide Association Study|Genomics|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-11-217', '20429914', 'Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files.'),
(93582, 2010, 'bmc', 'Adaptive coarse-grained Monte Carlo simulation of reaction and diffusion dynamics in heterogeneous plasma membranes', 'Collins, Stuart|Stamatakis, Michail|Vlachos, Dionisios G', 'Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA.|Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA.|Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA.', 'United States|United States|United States', 0, 'Cell Membrane|ErbB Receptors|Lipid Bilayers|Monte Carlo Method', 'Journal Article', '10.1186/1471-2105-11-218', '20429923', 'An adaptive coarse-grained (kinetic) Monte Carlo (ACGMC) simulation framework is applied to reaction and diffusion dynamics in inhomogeneous domains. The presented model is relevant to the diffusion and dimerization dynamics of epidermal growth factor receptor (EGFR) in the presence of plasma membrane heterogeneity and specifically receptor clustering. We perform simulations representing EGFR cluster dissipation in heterogeneous plasma membranes consisting of higher density clusters of receptors surrounded by low population areas using the ACGMC method. We further investigate the effect of key parameters on the cluster lifetime.'),
(93583, 2010, 'bmc', 'WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis', 'Mo, Fan|Mo, Qun|Chen, Yuanyuan|Goodlett, David R|Hood, Leroy|Omenn, Gilbert S|Li, Song|Lin, Biaoyang', 'Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.|Zhejiang-California Nanosystems Institute, Zhejiang University, Hangzhou, China.', 'China|China|China|China|China|China|China|China', 0, 'Algorithms|Isotope Labeling|Mass Spectrometry|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-219', '20429928', 'Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.'),
(93584, 2010, 'bmc', 'Integration of open access literature into the RCSB Protein Data Bank using BioLit', 'Prlixc4x87, Andreas|Martinez, Marco A|Dimitropoulos, Dimitris|Beran, Bojan|Yukich, Benjamin T|Rose, Peter W|Bourne, Philip E|Fink, J Lynn', 'San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com|San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, Mailcode 0505 La Jolla, CA 92093-0505, USA. andreas.prlic@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Databases, Protein|Proteins|PubMed|Publications|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-220', '20429930', 'Biological data have traditionally been stored and made publicly available through a variety of on-line databases, whereas biological knowledge has traditionally been found in the printed literature. With journals now on-line and providing an increasing amount of open access content, often free of copyright restriction, this distinction between database and literature is blurring. To exploit this opportunity we present the integration of open access literature with the RCSB Protein Data Bank (PDB).'),
(93585, 2010, 'bmc', 'GATExplorer: genomic and transcriptomic explorer, mapping expression probes to gene loci, transcripts, exons and ncRNAs', 'Risueño, Alberto|Fontanillo, Celia|Dinger, Marcel E|De Las Rivas, Javier', 'Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CiC-IBMCC, CSIC/USAL), Salamanca, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Animals|Chromosome Mapping|Databases, Genetic|Exons|Gene Expression Profiling|Genetic Loci|Genome|Genomics|Humans|Mice|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|RNA, Untranslated|Rats|Software', 'Journal Article', '10.1186/1471-2105-11-221', '20429936', 'Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of probesets to genes, without addressing the problem of gene definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.'),
(93586, 2010, 'bmc', 'Fine-tuning structural RNA alignments in the twilight zone', 'Bremges, Andreas|Schirmer, Stefanie|Giegerich, Robert', 'Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.|Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany.', 'Germany|Germany|Germany', 10, 'Algorithms|Base Sequence|Databases, Factual|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-222', '20433706', 'A widely used method to find conserved secondary structure in RNA is to first construct a multiple sequence alignment, and then fold the alignment, optimizing a score based on thermodynamics and covariance. This method works best around 75% sequence similarity. However, in a twilight zone below 55% similarity, the sequence alignment tends to obscure the covariance signal used in the second phase. Therefore, while the overall shape of the consensus structure may still be found, the degree of conservation cannot be estimated reliably.'),
(93587, 2010, 'bmc', 'From IMGT-ONTOLOGY to IMGT/LIGMotif: the IMGT standardized approach for immunoglobulin and T cell receptor gene identification and description in large genomic sequences', 'Lane, Jérôme|Duroux, Patrice|Lefranc, Marie-Paule', 'Université Montpellier 2, Laboratoire dImmunoGénétique Moléculaire LIGM, UPR CNRS 1142, Institut de Génétique Humaine IGH, 141 rue de la Cardonille, 34396 Montpellier cedex 5, France.|Université Montpellier 2, Laboratoire dImmunoGénétique Moléculaire LIGM, UPR CNRS 1142, Institut de Génétique Humaine IGH, 141 rue de la Cardonille, 34396 Montpellier cedex 5, France.|Université Montpellier 2, Laboratoire dImmunoGénétique Moléculaire LIGM, UPR CNRS 1142, Institut de Génétique Humaine IGH, 141 rue de la Cardonille, 34396 Montpellier cedex 5, France.', 'France|France|France', 18, 'Animals|Databases, Genetic|Genes, Immunoglobulin|Genes, T-Cell Receptor|Genome|Genomics|Humans|Immunoglobulins|Mice|Rats|Software', 'Journal Article', '10.1186/1471-2105-11-223', '20433708', 'The antigen receptors, immunoglobulins (IG) and T cell receptors (TR), are specific molecular components of the adaptive immune response of vertebrates. Their genes are organized in the genome in several loci (7 in humans) that comprise different gene types: variable (V), diversity (D), joining (J) and constant (C) genes. Synthesis of the IG and TR proteins requires rearrangements of V and J, or V, D and J genes at the DNA level, followed by the splicing at the RNA level of the rearranged V-J and V-D-J genes to C genes. Owing to the particularities of IG and TR gene structures related to these molecular mechanisms, conventional bioinformatic software and tools are not adapted to the identification and description of IG and TR genes in large genomic sequences. In order to answer that need, IMGT, the international ImMunoGeneTics information system, has developed IMGT/LIGMotif, a tool for IG and TR gene annotation. This tool is based on standardized rules defined in IMGT-ONTOLOGY, the first ontology in immunogenetics and immunoinformatics.'),
(93588, 2010, 'bmc', 'Cgaln: fast and space-efficient whole-genome alignment', 'Nakato, Ryuichiro|Gotoh, Osamu', 'Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto-shi, Kyoto 606-8501, Japan.|Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto-shi, Kyoto 606-8501, Japan.', 'Japan|Japan', 0, 'Animals|Base Sequence|Chromosome Mapping|Genome|Genomics|Humans|Mice|Sequence Alignment|Software', 'Comparative Study', '10.1186/1471-2105-11-224', '20433723', 'Whole-genome sequence alignment is an essential process for extracting valuable information about the functions, evolution, and peculiarities of genomes under investigation. As available genomic sequence data accumulate rapidly, there is great demand for tools that can compare whole-genome sequences within practical amounts of time and space. However, most existing genomic alignment tools can treat sequences that are only a few Mb long at once, and no state-of-the-art alignment program can align large sequences such as mammalian genomes directly on a conventional standalone computer.'),
(93589, 2010, 'bmc', 'Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies', 'Alamanova, Denitsa|Stegmaier, Philip|Kel, Alexander', 'BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbuettel, Germany. denitsa.alamanova@biobase-international.com|BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbuettel, Germany. denitsa.alamanova@biobase-international.com|BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbuettel, Germany. denitsa.alamanova@biobase-international.com', 'Germany|Germany|Germany', 0, 'Algorithms|Binding Sites|Computational Biology|DNA-Binding Proteins|Thermodynamics|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-225', '20438625', 'Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.'),
(93590, 2010, 'bmc', 'Partitioning of copy-number genotypes in pedigrees', 'Perreault, Louis-Philippe Lemieux|Andelfinger, Gregor U|Asselin, Géraldine|Dubé, Marie-Pierre', 'Montreal Heart Institute Research Center, Montréal, Canada. louis-philippe.lemieux.perreault@umontreal.ca|Montreal Heart Institute Research Center, Montréal, Canada. louis-philippe.lemieux.perreault@umontreal.ca|Montreal Heart Institute Research Center, Montréal, Canada. louis-philippe.lemieux.perreault@umontreal.ca|Montreal Heart Institute Research Center, Montréal, Canada. louis-philippe.lemieux.perreault@umontreal.ca', 'Canada|Canada|Canada|Canada', 0, 'DNA Copy Number Variations|Gene Dosage|Genetic Variation|Genotype|Humans|Pedigree|Phenotype', 'Journal Article', '10.1186/1471-2105-11-226', '20438641', 'Copy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic communitys attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in Birdsuite and PLINK for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrixs Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.'),
(93591, 2010, 'bmc', 'SeqRate: sequence-based protein folding type classification and rates prediction', 'Lin, Guan Ning|Wang, Zheng|Xu, Dong|Cheng, Jianlin', 'Informatics Institute, University of Missouri, Columbia, 65211, USA. guanlin@mail.missouri.edu|Informatics Institute, University of Missouri, Columbia, 65211, USA. guanlin@mail.missouri.edu|Informatics Institute, University of Missouri, Columbia, 65211, USA. guanlin@mail.missouri.edu|Informatics Institute, University of Missouri, Columbia, 65211, USA. guanlin@mail.missouri.edu', 'United States|United States|United States|United States', 22, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Computational Biology|Internet|Kinetics|Linear Models|Protein Conformation|Protein Folding|Proteins|Reproducibility of Results|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-S3-S1', '20438647', 'Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines.'),
(93592, 2010, 'bmc', 'Bacillus anthracis genome organization in light of whole transcriptome sequencing', 'Martin, Jeffrey|Zhu, Wenhan|Passalacqua, Karla D|Bergman, Nicholas|Borodovsky, Mark', 'DOE Joint Genome Institute, Walnut Creek, CA 94598, USA.|DOE Joint Genome Institute, Walnut Creek, CA 94598, USA.|DOE Joint Genome Institute, Walnut Creek, CA 94598, USA.|DOE Joint Genome Institute, Walnut Creek, CA 94598, USA.|DOE Joint Genome Institute, Walnut Creek, CA 94598, USA.', 'United States|United States|United States|United States|United States', 35, 'Algorithms|Bacillus anthracis|Chromosome Mapping|Codon|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genome, Bacterial|Markov Chains|Promoter Regions, Genetic|RNA, Bacterial|Ribosomes|Terminator Regions, Genetic', 'Journal Article', '10.1186/1471-2105-11-S3-S10', '20438648', 'Emerging knowledge of whole prokaryotic transcriptomes could validate a number of theoretical concepts introduced in the early days of genomics. What are the rules connecting gene expression levels with sequence determinants such as quantitative scores of promoters and terminators? Are translation efficiency measures, e.g. codon adaptation index and RBS score related to gene expression? We used the whole transcriptome shotgun sequencing of a bacterial pathogen Bacillus anthracis to assess correlation of gene expression level with promoter, terminator and RBS scores, codon adaptation index, as well as with a new measure of gene translational efficiency, average translation speed. We compared computational predictions of operon topologies with the transcript borders inferred from RNA-Seq reads. Transcriptome mapping may also improve existing gene annotation. Upon assessment of accuracy of current annotation of protein-coding genes in the B. anthracis genome we have shown that the transcriptome data indicate existence of more than a hundred genes missing in the annotation though predicted by an ab initio gene finder. Interestingly, we observed that many pseudogenes possess not only a sequence with detectable coding potential but also promoters that maintain transcriptional activity.'),
(93593, 2010, 'bmc', 'Exploratory analysis of protein translation regulatory networks using hierarchical random graphs', 'Wu, Daniel D|Hu, Xiaohua|Park, E K|Wang, Xiaofeng|Feng, Jiali|Wu, Xindong', 'Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu|Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu|Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu|Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu|Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu|Drexel University, Philadelphia, Pennsylvania, USA. daniel.wu@drexel.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Fungal Proteins|Markov Chains|Monte Carlo Method|Protein Biosynthesis|Protein Interaction Domains and Motifs|Protein Interaction Mapping|RNA, Messenger|Reproducibility of Results|Yeasts', 'Journal Article', '10.1186/1471-2105-11-S3-S2', '20438649', 'Protein translation is a vital cellular process for any living organism. The availability of interaction databases provides an opportunity for researchers to exploit the immense amount of data in silico such as studying biological networks. There has been an extensive effort using computational methods in deciphering the transcriptional regulatory networks. However, research on translation regulatory networks has caught little attention in the bioinformatics and computational biology community.'),
(93594, 2010, 'bmc', 'Identification of functional hubs and modules by converting interactome networks into hierarchical ordering of proteins', 'Cho, Young-Rae|Zhang, Aidong', 'Department of Computer Science Baylor University, Waco, TX 76798, USA. young-rae_cho@baylor.edu|Department of Computer Science Baylor University, Waco, TX 76798, USA. young-rae_cho@baylor.edu', 'United States|United States', 14, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Fungal Proteins|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Yeasts', 'Journal Article', '10.1186/1471-2105-11-S3-S3', '20438650', 'Protein-protein interactions play a key role in biological processes of proteins within a cell. Recent high-throughput techniques have generated protein-protein interaction data in a genome-scale. A wide range of computational approaches have been applied to interactome network analysis for uncovering functional organizations and pathways. However, they have been challenged because of complex connectivity. It has been investigated that protein interaction networks are typically characterized by intrinsic topological features: high modularity and hub-oriented structure. Elucidating the structural roles of modules and hubs is a critical step in complex interactome network analysis.'),
(93595, 2010, 'bmc', 'A conformal Bayesian network for classification of Mycobacterium tuberculosis complex lineages', 'Aminian, Minoo|Shabbeer, Amina|Bennett, Kristin P', 'Departments of Mathematical Science and Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA. aminim@cs.rpi.edu|Departments of Mathematical Science and Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA. aminim@cs.rpi.edu|Departments of Mathematical Science and Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA. aminim@cs.rpi.edu', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Biomarkers|Computational Biology|DNA Fingerprinting|DNA, Bacterial|DNA, Intergenic|Databases, Genetic|Humans|Internet|Interspersed Repetitive Sequences|Mycobacterium tuberculosis|Nucleic Acid Amplification Techniques|Software|Tuberculosis', 'Journal Article', '10.1186/1471-2105-11-S3-S4', '20438651', 'We present a novel conformal Bayesian network (CBN) to classify strains of Mycobacterium tuberculosis Complex (MTBC) into six major genetic lineages based on two high-throuput biomarkers: mycobacterial interspersed repetitive units (MIRU) and spacer oligonucleotide typing (spoligotyping). MTBC is the causative agent of tuberculosis (TB), which remains one of the leading causes of disease and morbidity world-wide. DNA fingerprinting methods such as MIRU and spoligotyping are key components in the control and tracking of modern TB.'),
(93596, 2010, 'bmc', 'A Markov blanket-based method for detecting causal SNPs in GWAS', 'Han, Bing|Park, Meeyoung|Chen, Xue-wen', 'Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, 66045, USA. hanbing@ittc.ku.edu|Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, 66045, USA. hanbing@ittc.ku.edu|Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, 66045, USA. hanbing@ittc.ku.edu', 'United States|United States|United States', 46, 'Algorithms|Artificial Intelligence|Case-Control Studies|Computer Simulation|Databases, Genetic|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Macular Degeneration|Markov Chains|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-S3-S5', '20438652', 'Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity.'),
(93597, 2010, 'bmc', 'Towards reliable isoform quantification using RNA-SEQ data', 'Howard, Brian E|Heber, Steffen', 'Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA. itsbehoward@hotmail.com|Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA. itsbehoward@hotmail.com', 'United States|United States', 0, 'Algorithms|Alternative Splicing|Arabidopsis|Base Sequence|Chi-Square Distribution|Computational Biology|Computer Simulation|Databases, Nucleic Acid|Gene Expression Regulation, Plant|Genes, Plant|Linear Models|Models, Genetic|Molecular Sequence Data|Protein Isoforms|Sequence Analysis, RNA|Stress, Physiological', 'Journal Article', '10.1186/1471-2105-11-S3-S6', '20438653', 'In eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here we explore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants.'),
(93598, 2010, 'bmc', 'Detection of gene orthology from gene co-expression and protein interaction networks', 'Towfic, Fadi|VanderPlas, Susan|Oliver, Casey A|Couture, Oliver|Tuggle, Christopher K|West Greenlee, M Heather|Honavar, Vasant', 'Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu|Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA . ftowfic@cs.iastate.edu', '-|-|-|-|-|-|-', 0, 'Algorithms|Animals|Artificial Intelligence|Bayes Theorem|Databases, Genetic|Decision Trees|Drosophila melanogaster|Gene Expression|Genomics|Humans|Mice|Protein Interaction Mapping|Proteins|ROC Curve|Saccharomyces cerevisiae|Sequence Alignment|Swine', 'Journal Article', '10.1186/1471-2105-11-S3-S7', '20438654', 'Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families.'),
(93599, 2010, 'bmc', 'Application of kernel functions for accurate similarity search in large chemical databases', 'Wang, Xiaohong|Huan, Jun|Smalter, Aaron|Lushington, Gerald H', 'School of Electrical Engineering and Computer Science University of Kansas, Lawrence, 66045, USA.|School of Electrical Engineering and Computer Science University of Kansas, Lawrence, 66045, USA.|School of Electrical Engineering and Computer Science University of Kansas, Lawrence, 66045, USA.|School of Electrical Engineering and Computer Science University of Kansas, Lawrence, 66045, USA.', 'United States|United States|United States|United States', 5, 'Algorithms|Computational Biology|Databases, Factual|Organic Chemicals|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-11-S3-S8', '20438655', 'Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases.'),
(93600, 2010, 'bmc', 'Learning biological network using mutual information and conditional independence', 'Kim, Dong-Chul|Wang, Xiaoyu|Yang, Chin-Rang|Gao, Jean', 'Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.|Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.|Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.|Department of Computer Science and Engineering, The University of Texas at Arlington, 76019, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Ataxia Telangiectasia Mutated Proteins|Bayes Theorem|Cell Cycle Proteins|Cell Line|DNA-Binding Proteins|Dose-Response Relationship, Radiation|Gene Regulatory Networks|Protein Array Analysis|Protein-Serine-Threonine Kinases|Proteomics|Quantum Dots|Reproducibility of Results|Signal Transduction|Tumor Suppressor Proteins', 'Journal Article', '10.1186/1471-2105-11-S3-S9', '20438656', 'Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses.'),
(93601, 2010, 'bmc', 'A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling', 'Meng, Hailong|Joyce, Andrew R|Adkins, Daniel E|Basu, Priyadarshi|Jia, Yankai|Li, Guoya|Sengupta, Tapas K|Zedler, Barbara K|Murrelle, E Lenn|van den Oord, Edwin J C G', 'Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com|Altria Client Services, Research Development & Engineering, Richmond, VA 23219, USA. hlmeng@yahoo.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 9, 'CpG Islands|DNA|DNA Methylation|Gene Expression Profiling|Humans|Models, Statistical', 'Journal Article', '10.1186/1471-2105-11-227', '20441598', 'High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called non-variable sites will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers.'),
(93602, 2010, 'bmc', 'Learning gene regulatory networks from only positive and unlabeled data', 'Cerulo, Luigi|Elkan, Charles|Ceccarelli, Michele', 'Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy. lcerulo@unisannio.it|Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy. lcerulo@unisannio.it|Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy. lcerulo@unisannio.it', 'Italy|Italy|Italy', 0, 'Artificial Intelligence|Data Mining|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks', 'Comparative Study', '10.1186/1471-2105-11-228', '20444264', 'Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.'),
(93603, 2010, 'bmc', 'GOAL: a software tool for assessing biological significance of genes groups', 'Tchagang, Alain B|Gawronski, Alexander|Bérubé, Hugo|Phan, Sieu|Famili, Fazel|Pan, Youlian', 'Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca|Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca|Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca|Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca|Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca|Knowledge Discovery Group, Institute for Information Technology, National Research Council, Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6 Canada. alain.tchagang@nrc-cnrc.gc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 20, 'Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genes|Genomics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-229', '20459620', 'Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then used to group together genes based on their similarity in expression profiles. Genes in each group are probably functionally related. The functional relevance among the genes in each group is usually characterized by utilizing available biological knowledge in public databases such as Gene Ontology (GO), KEGG pathways, association between a transcription factor (TF) and its target genes, and/or gene networks.'),
(93604, 2010, 'bmc', 'BISMA--fast and accurate bisulfite sequencing data analysis of individual clones from unique and repetitive sequences', 'Rohde, Christian|Zhang, Yingying|Reinhardt, Richard|Jeltsch, Albert', 'School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28725 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28725 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28725 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28725 Bremen, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Base Sequence|Clone Cells|DNA|DNA Methylation|Genomics|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-230', '20459626', 'Bisulfite sequencing is a popular method to analyze DNA methylation patterns at high resolution. A region of interest is targeted by PCR and about 20-50 subcloned DNA molecules are usually analyzed, to determine the methylation status at single CpG sites and molecule resolution.'),
(93605, 2010, 'bmc', 'RNA FRABASE 20: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures', 'Popenda, Mariusz|Szachniuk, Marta|Blazewicz, Marek|Wasik, Szymon|Burke, Edmund K|Blazewicz, Jacek|Adamiak, Ryszard W', 'Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.|Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Databases, Nucleic Acid|Genomics|Internet|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-231', '20459631', 'Recent discoveries concerning novel functions of RNA, such as RNA interference, have contributed towards the growing importance of the field. In this respect, a deeper knowledge of complex three-dimensional RNA structures is essential to understand their new biological functions. A number of bioinformatic tools have been proposed to explore two major structural databases (PDB, NDB) in order to analyze various aspects of RNA tertiary structures. One of these tools is RNA FRABASE 1.0, the first web-accessible database with an engine for automatic search of 3D fragments within PDB-derived RNA structures. This search is based upon the user-defined RNA secondary structure pattern. In this paper, we present and discuss RNA FRABASE 2.0. This second version of the system represents a major extension of this tool in terms of providing new data and a wide spectrum of novel functionalities. An intuitionally operated web server platform enables very fast user-tailored search of three-dimensional RNA fragments, their multi-parameter conformational analysis and visualization.'),
(93606, 2010, 'bmc', 'Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data', 'Imamura, Haruna|Yachie, Nozomu|Saito, Rintaro|Ishihama, Yasushi|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Epidermal Growth Factor|ErbB Receptors|Mass Spectrometry|Phosphorylation|Proteins|Proteomics|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-232', '20459641', 'Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.'),
(93607, 2010, 'bmc', 'Odefy--from discrete to continuous models', 'Krumsiek, Jan|Pölsterl, Sebastian|Wittmann, Dominik M|Theis, Fabian J', 'Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Munich-Neuherberg, Germany.|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Munich-Neuherberg, Germany.|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Munich-Neuherberg, Germany.|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764 Munich-Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Databases, Factual|Gene Regulatory Networks|Models, Statistical|Software', 'Journal Article', '10.1186/1471-2105-11-233', '20459647', 'Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable.'),
(93608, 2010, 'bmc', 'A semi-parametric Bayesian model for unsupervised differential co-expression analysis', 'Freudenberg, Johannes M|Sivaganesan, Siva|Wagner, Michael|Medvedovic, Mario', 'Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati OH 45267-0056, USA.|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati OH 45267-0056, USA.|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati OH 45267-0056, USA.|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati OH 45267-0056, USA.', 'United States|United States|United States|United States', 22, 'Bayes Theorem|Breast Neoplasms|Gene Expression Profiling|Gene Regulatory Networks', 'Journal Article', '10.1186/1471-2105-11-234', '20459663', 'Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.'),
(93609, 2010, 'bmc', 'MTRAP: pairwise sequence alignment algorithm by a new measure based on transition probability between two consecutive pairs of residues', 'Hara, Toshihide|Sato, Keiko|Ohya, Masanori', 'Department of Information Sciences, Tokyo University of Science, 2641 Yamazaki, Noda City, Chiba, Japan. hara@is.noda.tus.ac.jp|Department of Information Sciences, Tokyo University of Science, 2641 Yamazaki, Noda City, Chiba, Japan. hara@is.noda.tus.ac.jp|Department of Information Sciences, Tokyo University of Science, 2641 Yamazaki, Noda City, Chiba, Japan. hara@is.noda.tus.ac.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Base Sequence|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-235', '20459682', 'Sequence alignment is one of the most important techniques to analyze biological systems. It is also true that the alignment is not complete and we have to develop it to look for more accurate method. In particular, an alignment for homologous sequences with low sequence similarity is not in satisfactory level. Usual methods for aligning protein sequences in recent years use a measure empirically determined. As an example, a measure is usually defined by a combination of two quantities (1) and (2) below: (1) the sum of substitutions between two residue segments, (2) the sum of gap penalties in insertion/deletion region. Such a measure is determined on the assumption that there is no an intersite correlation on the sequences. In this paper, we improve the alignment by taking the correlation of consecutive residues.'),
(93610, 2010, 'bmc', 'Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes', 'Uchikoga, Nobuyuki|Hirokawa, Takatsugu', 'Japan Biological Informatics Consortium, 2-45 Aomi, Koto-ku, Tokyo, Japan. n.uchikoga@aist.go.jp|Japan Biological Informatics Consortium, 2-45 Aomi, Koto-ku, Tokyo, Japan. n.uchikoga@aist.go.jp', 'Japan|Japan', 6, 'Binding Sites|Calmodulin|Ligands|Magnetic Resonance Spectroscopy|Models, Molecular|Multiprotein Complexes|Protein Conformation|Protein Interaction Mapping|Proteomics', 'Journal Article', '10.1186/1471-2105-11-236', '20459766', 'Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles.'),
(93611, 2010, 'bmc', 'ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data', 'Zhu, Lihua J|Gazin, Claude|Lawson, Nathan D|Pagès, Hervé|Lin, Simon M|Lapointe, David S|Green, Michael R', 'Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu|Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. julie.zhu@umassmed.edu', 'United States|United States|United States|United States|United States|United States|United States', 377, 'Binding Sites|Chromatin Immunoprecipitation|Genome|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-237', '20459804', 'Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93612, 2010, 'bmc', 'A database and API for variation, dense genotyping and resequencing data', 'Rios, Daniel|McLaren, William M|Chen, Yuan|Birney, Ewan|Stabenau, Arne|Flicek, Paul|Cunningham, Fiona', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Genome|Genomics|Genotype|Phenotype|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-238', '20459810', 'Advances in sequencing and genotyping technologies are leading to the widespread availability of multi-species variation data, dense genotype data and large-scale resequencing projects. The 1000 Genomes Project and similar efforts in other species are challenging the methods previously used for storage and manipulation of such data necessitating the redesign of existing genome-wide bioinformatics resources.'),
(93613, 2010, 'bmc', 'Using caching and optimization techniques to improve performance of the Ensembl website', 'Parker, Anne|Bragin, Eugene|Brent, Simon|Pritchard, Bethan|Smith, James A|Trevanion, Stephen', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK. ap5@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Computational Biology|Databases, Factual|Genome|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-239', '20459812', 'The Ensembl web site has provided access to genomic information for almost 10 years. During this time the amount of data available through Ensembl has grown dramatically. At the same time, the World Wide Web itself has become a dramatically more important component of the scientific workflow and the way that scientists share and access data and scientific information. Since 2000, the Ensembl web interface has had three major updates and numerous smaller updates. These have largely been in response to expanding data types and valuable representations of existing data types. In 2007 it was realised that a radical new approach would be required in order to serve the projects future requirements, and development therefore focused on identifying suitable web technologies for implementation in the 2008 site redesign.'),
(93614, 2010, 'bmc', 'eHive: an artificial intelligence workflow system for genomic analysis', 'Severin, Jessica|Beal, Kathryn|Vilella, Albert J|Fitzgerald, Stephen|Schuster, Michael|Gordon, Leo|Ureta-Vidal, Abel|Flicek, Paul|Herrero, Javier', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Genetic|Genome|Genomics|Software', 'Journal Article', '10.1186/1471-2105-11-240', '20459813', 'The Ensembl project produces updates to its comparative genomics resources with each of its several releases per year. During each release cycle approximately two weeks are allocated to generate all the genomic alignments and the protein homology predictions. The number of calculations required for this task grows approximately quadratically with the number of species. We currently support 50 species in Ensembl and we expect the number to continue to grow in the future.'),
(93615, 2010, 'bmc', 'Error margin analysis for feature gene extraction', 'Chow, Chi Kin|Zhu, Hai Long|Lacy, Jessica|Kuo, Winston P', 'Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China.|Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China.|Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China.|Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China.', 'China|China|China|China', 0, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-241', '20459827', 'Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it.'),
(93616, 2010, 'bmc', 'Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction', 'Bryant, Drew H|Moll, Mark|Chen, Brian Y|Fofanov, Viacheslav Y|Kavraki, Lydia E', 'Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.|Department of Computer Science, Rice University, Houston, TX, USA.', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Motifs|Binding Sites|Databases, Protein|Enzymes|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-242', '20459833', 'Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels.'),
(93617, 2010, 'bmc', 'MOTIPS: automated motif analysis for predicting targets of modular protein domains', 'Lam, Hugo Y K|Kim, Philip M|Mok, Janine|Tonikian, Raffi|Sidhu, Sachdev S|Turk, Benjamin E|Snyder, Michael|Gerstein, Mark B', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 26, 'Algorithms|Amino Acid Motifs|Binding Sites|Models, Molecular|Protein Conformation|Protein Structure, Tertiary|Proteins|Proteome|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-243', '20459839', 'Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.'),
(93618, 2010, 'bmc', 'Protein binding hot spots and the residue-residue pairing preference: a water exclusion perspective', 'Liu, Qian|Li, Jinyan', 'Bioinformatics Research Center & School of Computer Engineering, Nanyang Technological University, Singapore 639798.|Bioinformatics Research Center & School of Computer Engineering, Nanyang Technological University, Singapore 639798.', 'Singapore|Singapore', 0, 'Binding Sites|Databases, Protein|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Water', 'Journal Article', '10.1186/1471-2105-11-244', '20462403', 'A protein binding hot spot is a small cluster of residues tightly packed at the center of the interface between two interacting proteins. Though a hot spot constitutes a small fraction of the interface, it is vital to the stability of protein complexes. Recently, there are a series of hypotheses proposed to characterize binding hot spots, including the pioneering O-ring theory, the insightful coupling and hot region principle, and our double water exclusion (DWE) hypothesis. As the perspective changes from the O-ring theory to the DWE hypothesis, we examine the physicochemical properties of the binding hot spots under the new hypothesis and compare with those under the O-ring theory.'),
(93619, 2010, 'bmc', 'TumorBoost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays', 'Bengtsson, Henrik|Neuvial, Pierre|Speed, Terence P', 'Department of Statistics, University of California, Berkeley, USA. hb@stat.berkeley.edu|Department of Statistics, University of California, Berkeley, USA. hb@stat.berkeley.edu|Department of Statistics, University of California, Berkeley, USA. hb@stat.berkeley.edu', 'United States|United States|United States', 38, 'Alleles|Gene Dosage|Gene Expression Profiling|Genomics|Genotype|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-245', '20462408', 'High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.'),
(93620, 2010, 'bmc', 'A comparison of approximation techniques for variance-based sensitivity analysis of biochemical reaction systems', 'Zhang, Hong-Xuan|Goutsias, John', 'Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA.|Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States', 9, 'Algorithms|Analysis of Variance|Biochemical Phenomena|Monte Carlo Method|Signal Transduction', 'Comparative Study', '10.1186/1471-2105-11-246', '20462443', 'Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of biochemical reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large biochemical reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by biochemical reaction systems that consist of a large number of reactions and molecular species.'),
(93621, 2010, 'bmc', 'Genome-wide estimation of firing efficiencies of origins of DNA replication from time-course copy number variation data', 'Luo, Huaien|Li, Juntao|Eshaghi, Majid|Liu, Jianhua|Karuturi, R Krishna Murthy', 'Computational and Systems Biology, Genome Institute of Singapore, Biopolis, Republic of Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Biopolis, Republic of Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Biopolis, Republic of Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Biopolis, Republic of Singapore.|Computational and Systems Biology, Genome Institute of Singapore, Biopolis, Republic of Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'DNA Copy Number Variations|DNA Replication|Genome|Genome, Fungal|Genome, Human|Genomics|Humans|Models, Statistical|S Phase|Saccharomyces cerevisiae|Schizosaccharomyces', 'Journal Article', '10.1186/1471-2105-11-247', '20462459', 'DNA replication is a fundamental biological process during S phase of cell division. It is initiated from several hundreds of origins along whole chromosome with different firing efficiencies (or frequency of usage). Direct measurement of origin firing efficiency by techniques such as DNA combing are time-consuming and lack the ability to measure all origins. Recent genome-wide study of DNA replication approximated origin firing efficiency by indirectly measuring other quantities related to replication. However, these approximation methods do not reflect properties of origin firing and may lead to inappropriate estimations.'),
(93622, 2010, 'bmc', 'Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images', 'Ruusuvuori, Pekka|Aijö, Tarmo|Chowdhury, Sharif|Garmendia-Torres, Cecilia|Selinummi, Jyrki|Birbaumer, Mirko|Dudley, Aimée M|Pelkmans, Lucas|Yli-Harja, Olli', 'Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, 33101, Finland. pekka.ruusuvuori@tut.fi', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland', 57, 'Algorithms|Cell Line, Tumor|Cells, Cultured|Humans|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-11-248', '20465797', 'Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.'),
(93623, 2010, 'bmc', 'Gene bi-targeting by viral and human miRNAs', 'Veksler-Lublinsky, Isana|Shemer-Avni, Yonat|Kedem, Klara|Ziv-Ukelson, Michal', 'Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel. zivukelson@gmail.com|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel. zivukelson@gmail.com|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel. zivukelson@gmail.com|Department of Computer Science, Ben-Gurion University, Beer-Sheva 84105, Israel. zivukelson@gmail.com', 'Israel|Israel|Israel|Israel', 0, 'Algorithms|Gene Regulatory Networks|Herpesvirus 4, Human|Humans|MicroRNAs|RNA Processing, Post-Transcriptional|RNA, Messenger|RNA, Viral', 'Journal Article', '10.1186/1471-2105-11-249', '20465802', 'MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression.'),
(93624, 2010, 'bmc', 'In-silico prediction of blood-secretory human proteins using a ranking algorithm', 'Liu, Qi|Cui, Juan|Yang, Qiang|Xu, Ying', 'Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia', 11, 'Algorithms|Blood Proteins|Data Mining|Databases, Protein|Humans|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-250', '20465853', 'Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum.'),
(93625, 2010, 'bmc', 'High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABER-TOOTH', 'Teichert, Florian|Minning, Jonas|Bastolla, Ugo|Porto, Markus', 'Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstr, Darmstadt, Germany.|Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstr, Darmstadt, Germany.|Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstr, Darmstadt, Germany.|Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstr, Darmstadt, Germany.', 'Germany|Germany|Germany|Germany', 16, 'Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-251', '20470364', 'Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called twilight zone where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins.'),
(93626, 2010, 'bmc', 'Antisense DNA parameters derived from next-nearest-neighbor analysis of experimental data', 'Gray, Donald M|Gray, Carla W|Yoo, Byong-Hoon|Lou, Tzu-Fang', 'Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W, Campbell Road, Richardson, Texas 75080, USA. dongray@utdallas.edu|Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W, Campbell Road, Richardson, Texas 75080, USA. dongray@utdallas.edu|Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W, Campbell Road, Richardson, Texas 75080, USA. dongray@utdallas.edu|Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W, Campbell Road, Richardson, Texas 75080, USA. dongray@utdallas.edu', 'United States|United States|United States|United States', 1, 'Base Sequence|Cells, Cultured|DNA, Antisense|Databases, Nucleic Acid|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-252', '20470414', 'The enumeration of tetrameric and other sequence motifs that are positively or negatively correlated with in vivo antisense DNA effects has been a useful addition to the arsenal of information needed to predict effective targets for antisense DNA control of gene expression. Such retrospective information derived from in vivo cellular experiments characterizes aspects of the sequence dependence of antisense inhibition that are not predicted by nearest-neighbor (NN) thermodynamic parameters derived from in vitro experiments. However, quantitation of the antisense contributions of motifs is problematic, since individual motifs are not isolated from the effects of neighboring nucleotides, and motifs may be overlapping. These problems are circumvented by a next-nearest-neighbor (NNN) analysis of antisense DNA effects in which the overlapping nature of nearest-neighbors is taken into account.'),
(93627, 2010, 'bmc', 'Selection of optimal reference genes for normalization in quantitative RT-PCR', 'Chervoneva, Inna|Li, Yanyan|Schulz, Stephanie|Croker, Sean|Wilson, Chantell|Waldman, Scott A|Hyslop, Terry', 'Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu|Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu', 'United States|United States|United States|United States|United States|United States|United States', 56, 'Gene Expression Profiling|Genes|Genomics|Guanylate Cyclase|RNA, Messenger|Receptors, Enterotoxin|Receptors, Guanylate Cyclase-Coupled|Receptors, Peptide|Reverse Transcriptase Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-11-253', '20470420', 'Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.'),
(93628, 2010, 'bmc', 'PhenoFam-gene set enrichment analysis through protein structural information', 'Paszkowski-Rogacz, Maciej|Slabicki, Mikolaj|Pisabarro, M Teresa|Buchholz, Frank', 'Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr, 108, 01307 Dresden, Germany. paszkows@mpi-cbg.de|Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr, 108, 01307 Dresden, Germany. paszkows@mpi-cbg.de|Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr, 108, 01307 Dresden, Germany. paszkows@mpi-cbg.de|Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr, 108, 01307 Dresden, Germany. paszkows@mpi-cbg.de', 'Germany|Germany|Germany|Germany', 4, 'Databases, Genetic|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-254', '20478033', 'With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.'),
(93629, 2010, 'bmc', 'Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data', 'Chen, Bin|Dong, Xiao|Jiao, Dazhi|Wang, Huijun|Zhu, Qian|Ding, Ying|Wild, David J', 'School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.|School of Informatics and Computing, Indiana University, Bloomington, IN, USA.', 'India|India|India|India|India|India|India', 148, 'Data Mining|Databases, Factual|Internet|Semantics|Software|Systems Biology|Systems Integration', 'Journal Article', '10.1186/1471-2105-11-255', '20478034', 'Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited'),
(93630, 2010, 'bmc', 'Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble', 'Sung, Edon|Kim, Sangsoo|Shin, Whanchul', 'Department of Chemistry, Seoul National University, Seoul 151-742, Korea.|Department of Chemistry, Seoul National University, Seoul 151-742, Korea.|Department of Chemistry, Seoul National University, Seoul 151-742, Korea.', '-|-|-', 1, 'Algorithms|Binding Sites|Crystallography, X-Ray|Databases, Protein|Drug Design|Ligands|Models, Molecular|Protein Conformation|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-256', '20478076', 'Modelling the ligand binding site of a protein is an important component of understanding protein-ligand interactions and is being actively studied. Even if the side chains are restricted to rotamers, a set of commonly-observed low-energy conformations, the exhaustive combinatorial search of ligand binding site conformers is known as NP-hard. Here we propose a new method, ROTAIMAGE, for modelling the plausible conformers for the ligand binding site given a fixed backbone structure.'),
(93631, 2010, 'bmc', 'Detecting microRNA activity from gene expression data', 'Madden, Stephen F|Carpenter, Susan B|Jeffery, Ian B|Björkbacka, Harry|Fitzgerald, Katherine A|ONeill, Luke A|Higgins, Desmond G', 'School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.|School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 0, 'Animals|Databases, Genetic|Gene Expression|Genomics|Humans|Mice|MicroRNAs|Oligonucleotide Array Sequence Analysis|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-11-257', '20482775', 'MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.'),
(93632, 2010, 'bmc', 'Cloud computing for comparative genomics', 'Wall, Dennis P|Kudtarkar, Parul|Fusaro, Vincent A|Pivovarov, Rimma|Patil, Prasad|Tonellato, Peter J', 'Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu|Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA. dpwall@hms.harvard.edu', 'United States|United States|United States|United States|United States|United States', 81, 'Algorithms|Computational Biology|Genome|Genomics', 'Comparative Study', '10.1186/1471-2105-11-259', '20482786', 'Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazons Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes.'),
(93633, 2010, 'bmc', 'Screensaver: an open source lab information management system (LIMS) for high throughput screening facilities', 'Tolopko, Andrew N|Sullivan, John P|Erickson, Sean D|Wrobel, David|Chiang, Su L|Rudnicki, Katrina|Rudnicki, Stewart|Nale, Jennifer|Selfors, Laura M|Greenhouse, Dara|Muhlich, Jeremy L|Shamu, Caroline E', 'ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu|ICCB-Longwood/NSRB Screening Facility, Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA. andrew_tolopko@hms.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 28, 'Database Management Systems|Drug Discovery|Genome|RNA Interference|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-260', '20482787', 'Shared-usage high throughput screening (HTS) facilities are becoming more common in academe as large-scale small molecule and genome-scale RNAi screening strategies are adopted for basic research purposes. These shared facilities require a unique informatics infrastructure that must not only provide access to and analysis of screening data, but must also manage the administrative and technical challenges associated with conducting numerous, interleaved screening efforts run by multiple independent research groups.'),
(93634, 2010, 'bmc', 'vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments', 'Ma, Jingming|Dykes, Carrie|Wu, Tao|Huang, Yangxin|Demeter, Lisa|Wu, Hulin', 'Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu|Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu|Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu|Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu|Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu|Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA. jma@bst.rochester.edu', 'United States|United States|United States|United States|United States|United States', 10, 'Computer Simulation|HIV-1|Internet|Linear Models|Software', 'Journal Article', '10.1186/1471-2105-11-261', '20482791', 'The replication rate (or fitness) between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV). HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness.'),
(93635, 2010, 'bmc', 'The Protein-DNA Interface database', 'Norambuena, Tomás|Melo, Francisco', 'Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.|Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile.', 'Chile|Chile', 45, 'Binding Sites|Crystallography, X-Ray|DNA|DNA-Binding Proteins|Databases, Nucleic Acid|Databases, Protein', 'Journal Article', '10.1186/1471-2105-11-262', '20482798', 'The Protein-DNA Interface database (PDIdb) is a repository containing relevant structural information of Protein-DNA complexes solved by X-ray crystallography and available at the Protein Data Bank. The database includes a simple functional classification of the protein-DNA complexes that consists of three hierarchical levels: Class, Type and Subtype. This classification has been defined and manually curated by humans based on the information gathered from several sources that include PDB, PubMed, CATH, SCOP and COPS. The current version of the database contains only structures with resolution of 2.5 A or higher, accounting for a total of 922 entries. The major aim of this database is to contribute to the understanding of the main rules that underlie the molecular recognition process between DNA and proteins. To this end, the database is focused on each specific atomic interface rather than on the separated binding partners. Therefore, each entry in this database consists of a single and independent protein-DNA interface.We hope that PDIdb will be useful to many researchers working in fields such as the prediction of transcription factor binding sites in DNA, the study of specificity determinants that mediate enzyme recognition events, engineering and design of new DNA binding proteins with distinct binding specificity and affinity, among others. Finally, due to its friendly and easy-to-use web interface, we hope that PDIdb will also serve educational and teaching purposes.'),
(93636, 2010, 'bmc', 'The Yeast Resource Center Public Image Repository: A large database of fluorescence microscopy images', 'Riffle, Michael|Davis, Trisha N', 'Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.|Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.', 'United States|United States', 28, 'Computational Biology|Databases, Factual|Microscopy, Fluorescence|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-263', '20482811', 'There is increasing interest in the development of computational methods to analyze fluorescent microscopy images and enable automated large-scale analysis of the subcellular localization of proteins. Determining the subcellular localization is an integral part of identifying a proteins function, and the application of bioinformatics to this problem provides a valuable tool for the annotation of proteomes. Training and validating algorithms used in image analysis research typically rely on large sets of image data, and would benefit from a large, well-annotated and highly-available database of images and associated metadata.'),
(93637, 2010, 'bmc', 'CNstream: a method for the identification and genotyping of copy number polymorphisms using Illumina microarrays', 'Alonso, Arnald|Julià, Antonio|Tortosa, Raül|Canaleta, Cristina|Cañete, Juan D|Ballina, Javier|Balsa, Alejandro|Tornero, Jesús|Marsal, Sara', 'Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.|Grup de Recerca de Reumatologia, Institut de Recerca de lHospital Universitari Vall dHebrón (UAB), Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 12, 'DNA Copy Number Variations|Databases, Genetic|Gene Dosage|Genome-Wide Association Study|Genotype|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-264', '20482829', 'Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.'),
(93638, 2010, 'bmc', 'Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP', 'Hawkins, Troy|Chitale, Meghana|Kihara, Daisuke', 'Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.|Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.', 'India|India|India', 15, 'Computational Biology|Databases, Genetic|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-265', '20482861', 'A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance.'),
(93639, 2010, 'bmc', 'FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data', 'Wong, Jason W H|Schwahn, Alexander B|Downard, Kevin M', 'Prince of Wales Clinical School & Lowy Cancer Research Centre, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia. jason.wong@unsw.edu.au|Prince of Wales Clinical School & Lowy Cancer Research Centre, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia. jason.wong@unsw.edu.au|Prince of Wales Clinical School & Lowy Cancer Research Centre, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia. jason.wong@unsw.edu.au', 'Australia|Australia|Australia', 16, 'Algorithms|Humans|Influenza A virus|Influenza B virus|Influenza, Human|Mass Spectrometry|Orthomyxoviridae|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Viral Matrix Proteins', 'Journal Article', '10.1186/1471-2105-11-266', '20482883', 'High resolution mass spectrometry has been employed to rapidly and accurately type and subtype influenza viruses. The detection of signature peptides with unique theoretical masses enables the unequivocal assignment of the type and subtype of a given strain. This analysis has, to date, required the manual inspection of mass spectra of whole virus and antigen digests.'),
(93640, 2010, 'bmc', 'Retraction: In silico screening of herbal and nanoparticle lead compounds for effectivity against H5N1, H1N1 neuraminidase and telomerase', '-', '-', '-', 0, '-', 'Retraction of Publication', '10.1186/1471-2105-11-258', '20482900', '-'),
(93641, 2010, 'bmc', 'A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes', 'Meng, Guofeng|Mosig, Axel|Vingron, Martin', 'CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, 200031, Shanghai, China. gfmeng@picb.ac.cn|CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, 200031, Shanghai, China. gfmeng@picb.ac.cn|CAS-MPG Partner Institute and Key Laboratory for Computational Biology, Shanghai Institutes for Biological Sciences, 320 Yue Yang Road, 200031, Shanghai, China. gfmeng@picb.ac.cn', 'China|China|China', 5, 'Animals|Computational Biology|Gene Expression Regulation|Humans|Mice|Promoter Regions, Genetic', 'Evaluation Studies', '10.1186/1471-2105-11-267', '20487530', 'Observed co-expression of a group of genes is frequently attributed to co-regulation by shared transcription factors. This assumption has led to the hypothesis that promoters of co-expressed genes should share common regulatory motifs, which forms the basis for numerous computational tools that search for these motifs. While frequently explored for yeast, the validity of the underlying hypothesis has not been assessed systematically in mammals. This demonstrates the need for a systematic and quantitative evaluation to what degree co-expressed genes share over-represented motifs for mammals.'),
(93642, 2010, 'bmc', 'Characterization of phylogenetic networks with NetTest', 'Arenas, Miguel|Patricio, Mateus|Posada, David|Valiente, Gabriel', 'Department of Biochemistry, Genetics and Immunology, University of Vigo, E-36310 Vigo, Spain. miguelab@uvigo.es|Department of Biochemistry, Genetics and Immunology, University of Vigo, E-36310 Vigo, Spain. miguelab@uvigo.es|Department of Biochemistry, Genetics and Immunology, University of Vigo, E-36310 Vigo, Spain. miguelab@uvigo.es|Department of Biochemistry, Genetics and Immunology, University of Vigo, E-36310 Vigo, Spain. miguelab@uvigo.es', 'Spain|Spain|Spain|Spain', 12, 'Databases, Genetic|Evolution, Molecular|Hybridization, Genetic|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-268', '20487540', 'Typical evolutionary events like recombination, hybridization or gene transfer make necessary the use of phylogenetic networks to properly depict the evolution of DNA and protein sequences. Although several theoretical classes have been proposed to characterize these networks, they make stringent assumptions that will likely not be met by the evolutionary process. We have recently shown that the complexity of simulated networks is a function of the population recombination rate, and that at moderate and large recombination rates the resulting networks cannot be categorized. However, we do not know whether these results extend to networks estimated from real data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93643, 2010, 'bmc', 'Analysing 454 amplicon resequencing experiments using the modular and database oriented Variant Identification Pipeline', 'De Schrijver, Joachim M|De Leeneer, Kim|Lefever, Steve|Sabbe, Nick|Pattyn, Filip|Van Nieuwerburgh, Filip|Coucke, Paul|Deforce, Dieter|Vandesompele, Jo|Bekaert, Sofie|Hellemans, Jan|Van Criekinge, Wim', 'Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be|Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Ghent University, 9000 Ghent, Belgium. Joachim.DeSchrijver@ugent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 14, 'Base Sequence|Databases, Genetic|Molecular Sequence Data|Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-269', '20487544', 'Next-generation amplicon sequencing enables high-throughput genetic diagnostics, sequencing multiple genes in several patients together in one sequencing run. Currently, no open-source out-of-the-box software solution exists that reliably reports detected genetic variations and that can be used to improve future sequencing effectiveness by analyzing the PCR reactions.'),
(93644, 2010, 'bmc', 'sdef: an R package to synthesize lists of significant features in related experiments', 'Blangiardo, Marta|Cassese, Alberto|Richardson, Sylvia', 'Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, St, Marys Campus, Norfolk Place London W2 1PG, UK. m.blangiardo@imperial.ac.uk|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, St, Marys Campus, Norfolk Place London W2 1PG, UK. m.blangiardo@imperial.ac.uk|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, St, Marys Campus, Norfolk Place London W2 1PG, UK. m.blangiardo@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 7, 'Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-270', '20487547', 'In microarray studies researchers are often interested in the comparison of relevant quantities between two or more similar experiments, involving different treatments, tissues, or species. Typically each experiment reports measures of significance (e.g. p-values) or other measures that rank its features (e.g genes). Our objective is to find a list of features that are significant in all experiments, to be further investigated. In this paper we present an R package called sdef, that allows the user to quantify the evidence of communality between the experiments using previously proposed statistical methods based on the ranked lists of p-values. sdef implements two approaches that address this objective: the first is a permutation test of the maximal ratio of observed to expected common features under the hypothesis of independence between the experiments. The second approach, set in a Bayesian framework, is more flexible as it takes into account the uncertainty on the number of genes differentially expressed in each experiment.'),
(93645, 2010, 'bmc', 'Incorporating gene co-expression network in identification of cancer prognosis markers', 'Ma, Shuangge|Shi, Mingyu|Li, Yang|Yi, Danhui|Shia, Ben-Chang', 'School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States|United States|United States|United States', 27, 'Biomarkers, Tumor|Breast Neoplasms|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genomics|Lymphoma|Neoplasms|Oligonucleotide Array Sequence Analysis|Prognosis', 'Journal Article', '10.1186/1471-2105-11-271', '20487548', 'Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.'),
(93646, 2010, 'bmc', 'Identifying overrepresented concepts in gene lists from literature: a statistical approach based on Poisson mixture model', 'He, Xin|Sarma, Moushumi Sen|Ling, Xu|Chee, Brant|Zhai, Chengxiang|Schatz, Bruce', 'Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.|Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.', 'United States|United States|United States|United States|United States|United States', 4, 'Computational Biology|Gene Expression Profiling|Genes|Models, Statistical', 'Journal Article', '10.1186/1471-2105-11-272', '20487560', 'Large-scale genomic studies often identify large gene lists, for example, the genes sharing the same expression patterns. The interpretation of these gene lists is generally achieved by extracting concepts overrepresented in the gene lists. This analysis often depends on manual annotation of genes based on controlled vocabularies, in particular, Gene Ontology (GO). However, the annotation of genes is a labor-intensive process, and the vocabularies are generally incomplete, leaving some important biological domains inadequately covered.'),
(93647, 2010, 'bmc', 'Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information', 'Biswas, Ashis Kumer|Noman, Nasimul|Sikder, Abdur Rahman', 'Department of Computer Science and Engineering, University of Dhaka, Dhaka - 1000, Bangladesh. ashis.csedu@gmail.com|Department of Computer Science and Engineering, University of Dhaka, Dhaka - 1000, Bangladesh. ashis.csedu@gmail.com|Department of Computer Science and Engineering, University of Dhaka, Dhaka - 1000, Bangladesh. ashis.csedu@gmail.com', 'Bangladesh|Bangladesh|Bangladesh', 61, 'Amino Acid Sequence|Artificial Intelligence|Binding Sites|Databases, Protein|Phosphorylation|Protein Kinases|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-273', '20492656', 'Most of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.'),
(93648, 2010, 'bmc', 'A high-level 3D visualization API for Java and ImageJ', 'Schmid, Benjamin|Schindelin, Johannes|Cardona, Albert|Longair, Mark|Heisenberg, Martin', 'Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany', 267, 'Computer Graphics|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Confocal|Pattern Recognition, Automated|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-11-274', '20492697', 'Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.'),
(93649, 2010, 'bmc', '\n', 'Schmid, Benjamin|Schindelin, Johannes|Cardona, Albert|Longair, Mark|Heisenberg, Martin', 'Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany', 267, 'Computer Graphics|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Confocal|Pattern Recognition, Automated|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-11-274', '20492697', 'Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.'),
(93650, 2010, 'HAT: hypergeometric analysis o', '\n', 'Schmid, Benjamin|Schindelin, Johannes|Cardona, Albert|Longair, Mark|Heisenberg, Martin', 'Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de|Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany. b.schmid@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany', 267, 'Computer Graphics|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Confocal|Pattern Recognition, Automated|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-11-274', '20492697', 'Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.'),
(93651, 2010, 'bmc', 'HAT: hypergeometric analysis of tiling-arrays with application to promoter-GeneChip data', 'Taskesen, Erdogan|Beekman, Renee|de Ridder, Jeroen|Wouters, Bas J|Peeters, Justine K|Touw, Ivo P|Reinders, Marcel J T|Delwel, Ruud', ' Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. | Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, the Netherlands. ', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Chromatin Immunoprecipitation|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|Software', 'Journal Article', '10.1186/1471-2105-11-275', '20492700', ' Tiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays. '),
(93652, 2010, 'bmc', 'Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes', 'Hellwig, Birte|Hengstler, Jan G|Schmidt, Marcus|Gehrmann, Mathias C|Schormann, Wiebke|Rahnenführer, Jörg', 'Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de|Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany. hellwig@statistik.tu-dortmund.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Biomarkers, Tumor|Breast Neoplasms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Genome|Humans|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-11-276', '20500820', 'A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.'),
(93653, 2010, 'bmc', 'Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context', 'Abraham, Gad|Kowalczyk, Adam|Loi, Sherene|Haviv, Izhak|Zobel, Justin', 'Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010, VIC, Australia.|Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010, VIC, Australia.|Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010, VIC, Australia.|Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010, VIC, Australia.|Department of Computer Science and Software Engineering, The University of Melbourne, Parkville 3010, VIC, Australia.', 'Australia|Australia|Australia|Australia|Australia', 61, 'Biomarkers, Tumor|Breast Neoplasms|Female|Gene Expression Profiling|Humans|Prognosis', 'Journal Article', '10.1186/1471-2105-11-277', '20500821', 'Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.'),
(93654, 2010, 'bmc', 'Semantic annotation of morphological descriptions: an overall strategy', 'Cui, Hong', 'School of Information Resources and Library Science, University of Arizona, 1515 E, First Street, Tucson, Arizona 85719, USA. hongcui@email.arizona.edu', 'United States', 14, 'Artificial Intelligence|Biodiversity|Database Management Systems|Natural Language Processing|Semantics', 'Journal Article', '10.1186/1471-2105-11-278', '20500882', 'Large volumes of morphological descriptions of whole organisms have been created as print or electronic text in a human-readable format. Converting the descriptions into computer- readable formats gives a new life to the valuable knowledge on biodiversity. Research in this area started 20 years ago, yet not sufficient progress has been made to produce an automated system that requires only minimal human intervention but works on descriptions of various plant and animal groups. This paper attempts to examine the hindering factors by identifying the mismatches between existing research and the characteristics of morphological descriptions.'),
(93655, 2010, 'bmc', 'Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver', 'Nguyen, Tung T|Almon, Richard R|DuBois, Debra C|Jusko, William J|Androulakis, Ioannis P', 'BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.|BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.|BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.|BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.|BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey, USA.', 'Jersey|Jersey|Jersey|Jersey|Jersey', 0, 'Adrenal Cortex Hormones|Animals|Computer Simulation|Gene Expression|Gene Expression Profiling|Liver|Period Circadian Proteins|Rats|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-279', '20500897', 'Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.'),
(93656, 2010, 'bmc', 'Data analysis issues for allele-specific expression using Illuminas GoldenGate assay', 'Ritchie, Matthew E|Forrest, Matthew S|Dimas, Antigone S|Daelemans, Caroline|Dermitzakis, Emmanouil T|Deloukas, Panagiotis|Tavaré, Simon', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia. mritchie@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Alleles|Databases, Genetic|Gene Expression|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-11-280', '20504309', 'High-throughput measurement of allele-specific expression (ASE) is a relatively new and exciting application area for array-based technologies. In this paper, we explore several data sets which make use of Illuminas GoldenGate BeadArray technology to measure ASE. This platform exploits coding SNPs to obtain relative expression measurements for alleles at approximately 1500 positions in the genome.'),
(93657, 2010, 'bmc', 'A comparison of probe-level and probeset models for small-sample gene expression data', 'Stevens, John R|Bell, Jason L|Aston, Kenneth I|White, Kenneth L', 'Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu|Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu|Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu|Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu', 'United States|United States|United States|United States', 0, 'Gene Expression|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size', 'Comparative Study', '10.1186/1471-2105-11-281', '20504334', 'Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.'),
(93658, 2010, 'bmc', 'An eScience-Bayes strategy for analyzing omics data', 'Eklund, Martin|Spjuth, Ola|Wikberg, Jarl E S', 'Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, SE 751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, SE 751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, SE 751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se', 'Sweden|Sweden|Sweden', 0, 'Algorithms|Bayes Theorem|Breast Neoplasms|Female|Gene Expression Profiling|Humans|Protein Interaction Mapping|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-282', '20504364', 'The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems.'),
(93659, 2010, 'bmc', 'Optimal contact definition for reconstruction of contact maps', 'Duarte, Jose M|Sathyapriya, Rajagopal|Stehr, Henning|Filippis, Ioannis|Lappe, Michael', 'Max Planck Institute for Molecular Genetics, Ihnestr, Berlin, Germany. duarte@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Ihnestr, Berlin, Germany. duarte@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Ihnestr, Berlin, Germany. duarte@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Ihnestr, Berlin, Germany. duarte@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Ihnestr, Berlin, Germany. duarte@molgen.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 36, 'Computational Biology|Models, Molecular|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-283', '20507547', 'Contact maps have been extensively used as a simplified representation of protein structures. They capture most important features of a proteins fold, being preferred by a number of researchers for the description and study of protein structures. Inspired by the models simplicity many groups have dedicated a considerable amount of effort towards contact prediction as a proxy for protein structure prediction. However a contact maps biological interest is subject to the availability of reliable methods for the 3-dimensional reconstruction of the structure.'),
(93660, 2010, 'bmc', 'JCoDA: a tool for detecting evolutionary selection', 'Steinway, Steven N|Dannenfelser, Ruth|Laucius, Christopher D|Hayes, James E|Nayak, Sudhir', 'Department of Biology, The College of New Jersey, 2000 Pennington Road, Ewing, NJ 08628, USA.|Department of Biology, The College of New Jersey, 2000 Pennington Road, Ewing, NJ 08628, USA.|Department of Biology, The College of New Jersey, 2000 Pennington Road, Ewing, NJ 08628, USA.|Department of Biology, The College of New Jersey, 2000 Pennington Road, Ewing, NJ 08628, USA.|Department of Biology, The College of New Jersey, 2000 Pennington Road, Ewing, NJ 08628, USA.', 'Jersey|Jersey|Jersey|Jersey|Jersey', 34, 'Codon|Databases, Genetic|Databases, Protein|Evolution, Molecular|Genomics|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-284', '20507581', 'The incorporation of annotated sequence information from multiple related species in commonly used databases (Ensembl, Flybase, Saccharomyces Genome Database, Wormbase, etc.) has increased dramatically over the last few years. This influx of information has provided a considerable amount of raw material for evaluation of evolutionary relationships. To aid in the process, we have developed JCoDA (Java Codon Delimited Alignment) as a simple-to-use visualization tool for the detection of site specific and regional positive/negative evolutionary selection amongst homologous coding sequences.'),
(93661, 2010, 'bmc', 'Preferred analysis methods for Affymetrix GeneChips II An expanded, balanced, wholly-defined spike-in dataset', 'Zhu, Qianqian|Miecznikowski, Jeffrey C|Halfon, Marc S', 'Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY 14214, USA.|Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY 14214, USA.|Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY 14214, USA.', 'United States|United States|United States', 0, 'Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|RNA|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-11-285', '20507584', 'Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled spike-in datasets are an invaluable but rare tool for assessing the performance of various methods.'),
(93662, 2010, 'bmc', 'Conserved residue clusters at protein-protein interfaces and their use in binding site identification', 'Guharoy, Mainak|Chakrabarti, Pinak', 'Bioinformatics Centre, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India.|Bioinformatics Centre, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India.', 'India|India', 63, 'Binding Sites|Models, Molecular|Protein Conformation|Protein Folding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-286', '20507585', 'Biological evolution conserves protein residues that are important for structure and function. Both protein stability and function often require a certain degree of structural co-operativity between spatially neighboring residues and it has previously been shown that conserved residues occur clustered together in protein tertiary structures, enzyme active sites and protein-DNA interfaces. Residues comprising protein interfaces are often more conserved compared to those occurring elsewhere on the protein surface. We investigate the extent to which conserved residues within protein-protein interfaces are clustered together in three-dimensions.'),
(93663, 2010, 'bmc', 'Colonyzer: automated quantification of micro-organism growth characteristics on solid agar', 'Lawless, Conor|Wilkinson, Darren J|Young, Alexander|Addinall, Stephen G|Lydall, David A', 'Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-Upon-Tyne NE45PL, UK. conor.lawless@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-Upon-Tyne NE45PL, UK. conor.lawless@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-Upon-Tyne NE45PL, UK. conor.lawless@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-Upon-Tyne NE45PL, UK. conor.lawless@ncl.ac.uk|Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle-Upon-Tyne NE45PL, UK. conor.lawless@ncl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 40, 'Agar|Algorithms|Cell Count|Culture Media|Image Processing, Computer-Assisted|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-11-287', '20509870', 'High-throughput screens comparing growth rates of arrays of distinct micro-organism cultures on solid agar are useful, rapid methods of quantifying genetic interactions. Growth rate is an informative phenotype which can be estimated by measuring cell densities at one or more times after inoculation. Precise estimates can be made by inoculating cultures onto agar and capturing cell density frequently by plate-scanning or photography, especially throughout the exponential growth phase, and summarising growth with a simple dynamic model (e.g. the logistic growth model). In order to parametrize such a model, a robust image analysis tool capable of capturing a wide range of cell densities from plate photographs is required.'),
(93664, 2010, 'bmc', 'GWAMA: software for genome-wide association meta-analysis', 'Mägi, Reedik|Morris, Andrew P', 'Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. reedik@well.ox.ac.uk|Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. reedik@well.ox.ac.uk', 'United Kingdom|United Kingdom', 0, 'Databases, Genetic|Genome|Genome-Wide Association Study|Genotype|Phenotype|Software', 'Journal Article', '10.1186/1471-2105-11-288', '20509871', 'Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies.'),
(93665, 2010, 'bmc', 'Spatio-structural granularity of biological material entities', 'Vogt, Lars', 'Institut für Evolutionsbiologie und Okologie, Universität Bonn, An der Immenburg 1, Bonn, Germany. lars.m.vogt@gmail.com', 'Germany', 11, 'Databases, Factual|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-11-289', '20509878', 'With the continuously increasing demands on knowledge- and data-management that databases have to meet, ontologies and the theories of granularity they use become more and more important. Unfortunately, currently used theories and schemes of granularity unnecessarily limit the performance of ontologies due to two shortcomings: (i) they do not allow the integration of multiple granularity perspectives into one granularity framework, (ii) they are not applicable to cumulative-constitutively organized material entities, which cover most of the biomedical material entities.'),
(93666, 2010, 'bmc', 'Revealing and avoiding bias in semantic similarity scores for protein pairs', 'Wang, Jing|Zhou, Xianxiao|Zhu, Jing|Zhou, Chenggui|Guo, Zheng', 'Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.|Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.', 'China|China|China|China|China', 37, 'Cluster Analysis|Computational Biology|Disease|Genome|Humans|Proteins|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/1471-2105-11-290', '20509916', 'Semantic similarity scores for protein pairs are widely applied in functional genomic researches for finding functional clusters of proteins, predicting protein functions and protein-protein interactions, and for identifying putative disease genes. However, because some proteins, such as those related to diseases, tend to be studied more intensively, annotations are likely to be biased, which may affect applications based on semantic similarity measures. Thus, it is necessary to evaluate the effects of the bias on semantic similarity scores between proteins and then find a method to avoid them.'),
(93667, 2010, 'bmc', 'Washing scaling of GeneChip microarray expression', 'Binder, Hans|Krohn, Knut|Burden, Conrad J', 'Interdisciplinary Centre for Bioinformatics, Universität Leipzig, D-4107 Leipzig, Haertelstr 16-18, Germany. binder@izbi.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, Universität Leipzig, D-4107 Leipzig, Haertelstr 16-18, Germany. binder@izbi.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, Universität Leipzig, D-4107 Leipzig, Haertelstr 16-18, Germany. binder@izbi.uni-leipzig.de', 'Germany|Germany|Germany', 12, 'Algorithms|Gene Expression Profiling|Kinetics|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-291', '20509934', 'Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.'),
(93668, 2010, 'bmc', 'TargetSpy: a supervised machine learning approach for microRNA target prediction', 'Sturm, Martin|Hackenberg, Michael|Langenberger, David|Frishman, Dmitrij', 'Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.|Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.', 'Germany|Germany|Germany|Germany', 104, 'Animals|Artificial Intelligence|Drosophila|MicroRNAs|Proteins|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-11-292', '20509939', 'Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5 end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.'),
(93669, 2010, 'bmc', 'Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition', 'Chen, Lei|Feng, Kai-Yan|Cai, Yu-Dong|Chou, Kuo-Chen|Li, Hai-Peng', 'College of Information Engineering, Shanghai Maritime University, Shanghai, PR China.|College of Information Engineering, Shanghai Maritime University, Shanghai, PR China.|College of Information Engineering, Shanghai Maritime University, Shanghai, PR China.|College of Information Engineering, Shanghai Maritime University, Shanghai, PR China.|College of Information Engineering, Shanghai Maritime University, Shanghai, PR China.', 'China|China|China|China|China', 83, 'Algorithms|Binding Sites|Enzymes|Kinetics|Metabolic Networks and Pathways|Structure-Activity Relationship|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-293', '20513238', 'Metabolic pathway is a highly regulated network consisting of many metabolic reactions involving substrates, enzymes, and products, where substrates can be transformed into products with particular catalytic enzymes. Since experimental determination of the network of substrate-enzyme-product triad (whether the substrate can be transformed into the product with a given enzyme) is both time-consuming and expensive, it would be very useful to develop a computational approach for predicting the network of substrate-enzyme-product triads.'),
(93670, 2010, 'bmc', 'EnvMine: a text-mining system for the automatic extraction of contextual information', 'Tamames, Javier|de Lorenzo, Victor', 'Centro Nacional de Biotecnología (CNB), CSIC, C/Darwin 3, 28049 Madrid, Spain. jtamames@cnb.csic.es|Centro Nacional de Biotecnología (CNB), CSIC, C/Darwin 3, 28049 Madrid, Spain. jtamames@cnb.csic.es', 'Spain|Spain', 11, 'Bayes Theorem|Data Mining|Databases, Factual|Information Storage and Retrieval|Pattern Recognition, Automated|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-11-294', '20515448', 'For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind.'),
(93671, 2010, 'bmc', 'Bayesian integrated modeling of expression data: a case study on RhoG', 'Gupta, Rashi|Greco, Dario|Auvinen, Petri|Arjas, Elja', 'Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. rashi1@live.com|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. rashi1@live.com|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. rashi1@live.com|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland. rashi1@live.com', 'Finland|Finland|Finland|Finland', 2, 'Bayes Theorem|Databases, Genetic|Gene Expression|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-11-295', '20515463', 'DNA microarrays provide an efficient method for measuring activity of genes in parallel and even covering all the known transcripts of an organism on a single array. This has to be balanced against that analyzing data emerging from microarrays involves several consecutive steps, and each of them is a potential source of errors. Errors tend to accumulate when moving from the lower level towards the higher level analyses because of the sequential nature. Eliminating such errors does not seem feasible without completely changing the technologies, but one should nevertheless try to meet the goal of being able to realistically assess degree of the uncertainties that are involved when drawing the final conclusions from such analyses.'),
(93672, 2010, 'bmc', 'Proceedings from the The Eighth Asia Pacific Bioinformatics Conference (APBC 2010) Bangalore, India 18-21 January 2010', '-', '-', '-', 2, 'Computational Biology', 'Congress', '10.1186/1471-2105-11-295', '20521369', '-'),
(93673, 2010, 'bmc', 'Super-sparse principal component analyses for high-throughput genomic data', 'Lee, Donghwan|Lee, Woojoo|Lee, Youngjo|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 31, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genome|Genomics|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-11-296', '20525176', 'Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings, for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients.'),
(93674, 2010, 'bmc', 'A classification model for distinguishing copy number variants from cancer-related alterations', 'Ostrovnaya, Irina|Nanjangud, Gouri|Olshen, Adam B', 'Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. ostrovni@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. ostrovni@mskcc.org|Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. ostrovni@mskcc.org', 'United States|United States|United States', 9, 'Comparative Genomic Hybridization|Databases, Genetic|Gene Dosage|Genome, Human|Humans|Models, Theoretical|Neoplasms|Nucleic Acid Hybridization', 'Journal Article', '10.1186/1471-2105-11-297', '20525196', 'Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.'),
(93675, 2010, 'bmc', 'An interaction-motif-based scoring function for protein-ligand docking', 'Xie, Zhong-Ru|Hwang, Ming-Jing', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.', '-|-', 26, 'Binding Sites|Computational Biology|Databases, Protein|Ligands|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-11-298', '20525216', 'A good scoring function is essential for molecular docking computations. In conventional scoring functions, energy terms modeling pairwise interactions are cumulatively summed, and the best docking solution is selected. Here, we propose to transform protein-ligand interactions into three-dimensional geometric networks, from which recurring network substructures, or network motifs, are selected and used to provide probability-ranked interaction templates with which to score docking solutions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93676, 2010, 'bmc', 'poolMC: smart pooling of mRNA samples in microarray experiments', 'Kainkaryam, Raghunandan M|Bruex, Angela|Gilbert, Anna C|Schiefelbein, John|Woolf, Peter J', 'Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States', 24, 'Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-11-299', '20525223', 'Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.'),
(93677, 2010, 'bmc', 'UFFizi: a generic platform for ranking informative features', 'Gottlieb, Assaf|Varshavsky, Roy|Linial, Michal|Horn, David', 'Tel Aviv University, Ramat Aviv, Israel. assafgot@tau.ac.il|Tel Aviv University, Ramat Aviv, Israel. assafgot@tau.ac.il|Tel Aviv University, Ramat Aviv, Israel. assafgot@tau.ac.il|Tel Aviv University, Ramat Aviv, Israel. assafgot@tau.ac.il', 'Israel|Israel|Israel|Israel', 3, 'Algorithms|Biomarkers, Tumor|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-11-300', '20525252', 'Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis. Numerous supervised methods have been suggested but only a few unsupervised ones exist. Unsupervised Feature Filtering (UFF) is such a method, based on an entropy measure of Singular Value Decomposition (SVD), ranking features and selecting a group of preferred ones.'),
(93678, 2010, 'bmc', 'Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information', 'Chauhan, Jagat S|Mishra, Nitish K|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh, India.|Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh, India.', 'India|India|India', 35, 'Binding Sites|Databases, Protein|Dipeptides|Evolution, Molecular|Guanosine Triphosphate|Oligopeptides|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-301', '20525281', 'Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc).'),
(93679, 2010, 'bmc', 'Saliva Ontology: an ontology-based framework for a Salivaomics Knowledge Base', 'Ai, Jiye|Smith, Barry|Wong, David T', 'University of California at Los Angeles School of Dentistry, University of California, Los Angeles, CA 90095-1668, USA.|University of California at Los Angeles School of Dentistry, University of California, Los Angeles, CA 90095-1668, USA.|University of California at Los Angeles School of Dentistry, University of California, Los Angeles, CA 90095-1668, USA.', 'United States|United States|United States', 35, 'Biomarkers|Data Mining|Knowledge Bases|Saliva|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-302', '20525291', 'The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO).'),
(93680, 2010, 'bmc', 'Detecting internally symmetric protein structures', 'Kim, Changhoon|Basner, Jodi|Lee, Byungkook', 'Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bldg 37, Room 5120, 37 Convent Dr MSC 4264, Bethesda, MD 20892-4264, USA.|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bldg 37, Room 5120, 37 Convent Dr MSC 4264, Bethesda, MD 20892-4264, USA.|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bldg 37, Room 5120, 37 Convent Dr MSC 4264, Bethesda, MD 20892-4264, USA.', 'United States|United States|United States', 28, 'Computational Biology|Databases, Protein|Evolution, Molecular|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-11-303', '20525292', 'Many functional proteins have a symmetric structure. Most of these are multimeric complexes, which are made of non-symmetric monomers arranged in a symmetric manner. However, there are also a large number of proteins that have a symmetric structure in the monomeric state. These internally symmetric proteins are interesting objects from the point of view of their folding, function, and evolution. Most algorithms that detect the internally symmetric proteins depend on finding repeating units of similar structure and do not use the symmetry information.'),
(93681, 2010, 'bmc', 'Scaffold filling, contig fusion and comparative gene order inference', 'Muñoz, Adriana|Zheng, Chunfang|Zhu, Qian|Albert, Victor A|Rounsley, Steve|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.|Department of Mathematics and Statistics, University of Ottawa, Ottawa, K1N 6N5, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 41, 'Algorithms|Gene Order|Genome, Plant|Genomics|Ricinus|Vitis', 'Comparative Study', '10.1186/1471-2105-11-304', '20525342', 'There has been a trend in increasing the phylogenetic scope of genome sequencing without finishing the sequence of the genome. Increasing numbers of genomes are being published in scaffold or contig form. Rearrangement algorithms, however, including gene order-based phylogenetic tools, require whole genome data on gene order or syntenic block order. How then can we use rearrangement algorithms to compare genomes available in scaffold form only? Can the comparative evidence predict the location of unsequenced genes?'),
(93682, 2010, 'bmc', 'Prediction of epigenetically regulated genes in breast cancer cell lines', 'Loss, Leandro A|Sadanandam, Anguraj|Durinck, Steffen|Nautiyal, Shivani|Flaucher, Diane|Carlton, Victoria E H|Moorhead, Martin|Lu, Yontao|Gray, Joe W|Faham, Malek|Spellman, Paul|Parvin, Bahram', 'Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. laloss@lbl.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 30, 'Antigens, Neoplasm|Breast Neoplasms|Cell Line, Tumor|Collagen Type I|CpG Islands|DNA Methylation|DNA Topoisomerases, Type II|DNA-Binding Proteins|Epigenesis, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Tumor Suppressor|Genome-Wide Association Study|Humans|Oligonucleotide Array Sequence Analysis|Poly-ADP-Ribose Binding Proteins|Promoter Regions, Genetic|Proto-Oncogene Proteins c-vav|Trefoil Factor-1|Tumor Suppressor Proteins', 'Journal Article', '10.1186/1471-2105-11-305', '20525369', 'Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.'),
(93683, 2010, 'bmc', 'Beyond rotamers: a generative, probabilistic model of side chains in proteins', 'Harder, Tim|Boomsma, Wouter|Paluszewski, Martin|Frellsen, Jes|Johansson, Kristoffer E|Hamelryck, Thomas', 'The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.|The Bioinformatics Section, Department of Biology, University of Copenhagen, Copenhagen, Denmark.', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 34, 'Models, Molecular|Models, Statistical|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-11-306', '20525384', 'Accurately covering the conformational space of amino acid side chains is essential for important applications such as protein design, docking and high resolution structure prediction. Today, the most common way to capture this conformational space is through rotamer libraries - discrete collections of side chain conformations derived from experimentally determined protein structures. The discretization can be exploited to efficiently search the conformational space. However, discretizing this naturally continuous space comes at the cost of losing detailed information that is crucial for certain applications. For example, rigorously combining rotamers with physical force fields is associated with numerous problems.'),
(93684, 2010, 'bmc', 'Rule-based spatial modeling with diffusing, geometrically constrained molecules', 'Gruenert, Gerd|Ibrahim, Bashar|Lenser, Thorsten|Lohel, Maiko|Hinze, Thomas|Dittrich, Peter', 'Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.|Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.|Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.|Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.|Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.|Friedrich Schiller University Jena, Bio Systems Analysis Group, 07743 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 74, 'Actin Cytoskeleton|Biological Transport, Active|Computer Simulation|DNA|Diffusion|Kinetics|Models, Biological|Models, Chemical|Virus Assembly', 'Journal Article', '10.1186/1471-2105-11-307', '20529264', 'We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided.'),
(93685, 2010, 'bmc', 'Computational modeling with forward and reverse engineering links signaling network and genomic regulatory responses: NF-kappaB signaling-induced gene expression responses in inflammation', 'Peng, Shih Chi|Wong, David Shan Hill|Tung, Kai Che|Chen, Yan Yu|Chao, Chun Cheih|Peng, Chien Hua|Chuang, Yung Jen|Tang, Chuan Yi', 'Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan, ROC.', '-|-|-|-|-|-|-|-', 19, 'Computer Simulation|Gene Expression|Genome|Genomics|Inflammation|NF-kappa B|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-308', '20529327', 'Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Diverse cellular stimuli create a wide variety of transcription factor activities through signal transduction pathways, resulting in different gene expression patterns. Understanding the relationship between external stimuli and the corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach is needed to integrate experimental data and theoretical hypotheses to identify the physiological consequences of environmental stimuli.'),
(93686, 2010, 'bmc', 'L2-norm multiple kernel learning and its application to biomedical data fusion', 'Yu, Shi|Falck, Tillmann|Daemen, Anneleen|Tranchevent, Leon-Charles|Suykens, Johan Ak|De Moor, Bart|Moreau, Yves', 'Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com|Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee B-3001, Belgium. shee.yu@gmail.com', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 86, 'Algorithms|Artificial Intelligence|Cluster Analysis|Disease|Genetic Predisposition to Disease|Humans|Male|Pattern Recognition, Automated|Prostatic Neoplasms|Protein Array Analysis|Proteomics|Rectal Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-11-309', '20529363', 'This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L(infinity), L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L(infinity) MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.'),
(93687, 2010, 'bmc', 'dConsensus: a tool for displaying domain assignments by multiple structure-based algorithms and for construction of a consensus assignment', 'Alden, Kieran|Veretnik, Stella|Bourne, Philip E', 'York Centre for Complex Systems Analysis (YCCSA), University of York, Heslington, York, YO10 5DD, UK.|York Centre for Complex Systems Analysis (YCCSA), University of York, Heslington, York, YO10 5DD, UK.|York Centre for Complex Systems Analysis (YCCSA), University of York, Heslington, York, YO10 5DD, UK.', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|Databases, Protein|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-310', '20529369', 'Partitioning of a protein into structural components, known as domains, is an important initial step in protein classification and for functional and evolutionary studies. While the systematic assignments of domains by human experts exist (CATH and SCOP), the introduction of high throughput technologies for structure determination threatens to overwhelm expert approaches. A variety of algorithmic methods have been developed to expedite this process, allowing almost instant structural decomposition into domains. The performance of algorithmic methods can approach 85% agreement on the number of domains with the consensus reached by experts. However, each algorithm takes a somewhat different conceptual approach, each with unique strengths and weaknesses. Currently there is no simple way to automatically compare assignments from different structure-based domain assignment methods, thereby providing a comprehensive understanding of possible structure partitioning as well as providing some insight into the tendencies of particular algorithms. Most importantly, a consensus assignment drawn from multiple assignment methods can provide a singular and presumably more accurate view.'),
(93688, 2010, 'bmc', 'FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases', 'Goodswen, Stephen J|Gondro, Cedric|Watson-Haigh, Nathan S|Kadarmideen, Haja N', 'CSIRO Livestock Industries, Davies Laboratory, University Drive, Townsville, QLD 4810, Australia.|CSIRO Livestock Industries, Davies Laboratory, University Drive, Townsville, QLD 4810, Australia.|CSIRO Livestock Industries, Davies Laboratory, University Drive, Townsville, QLD 4810, Australia.|CSIRO Livestock Industries, Davies Laboratory, University Drive, Townsville, QLD 4810, Australia.', 'Australia|Australia|Australia|Australia', 19, 'Databases, Genetic|Genetic Markers|Genomics|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-11-311', '20534127', 'Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.'),
(93689, 2010, 'bmc', 'GIGA: a simple, efficient algorithm for gene tree inference in the genomic age', 'Thomas, Paul D', 'Evolutionary Systems Biology Group, SRI International, Menlo Park, CA, USA. pdthomas@usc.edu', 'United States', 22, 'Algorithms|Animals|Base Sequence|Evolution, Molecular|Gene Duplication|Gene Transfer, Horizontal|Genome|Humans|Mice|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-312', '20534164', 'Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost.'),
(93690, 2010, 'bmc', 'Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates', 'Sobolev, Boris|Filimonov, Dmitry|Lagunin, Alexey|Zakharov, Alexey|Koborova, Olga|Kel, Alexander|Poroikov, Vladimir', 'Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru|Department of Bioinformatics, Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences, 119121, Pogodinskaya str, 10, Moscow, Russia. borissobolev-5@yandex.ru', '-|-|-|-|-|-|-', 15, 'Amino Acid Sequence|Databases, Protein|Models, Molecular|Protein Interaction Mapping|Protein Kinases|Proteins|Signal Transduction|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-313', '20537135', 'The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.'),
(93691, 2010, 'bmc', 'A boosting method for maximizing the partial area under the ROC curve', 'Komori, Osamu|Eguchi, Shinto', 'Prediction and Knowledge Discovery Research Center, The Institute of Statistical Mathematics, Midori-cho, Tachikawa, Tokyo 190-8562, Japan. komori@ism.ac.jp|Prediction and Knowledge Discovery Research Center, The Institute of Statistical Mathematics, Midori-cho, Tachikawa, Tokyo 190-8562, Japan. komori@ism.ac.jp', 'Japan|Japan', 0, 'Algorithms|Area Under Curve|Biomarkers|Pattern Recognition, Automated|ROC Curve', 'Journal Article', '10.1186/1471-2105-11-314', '20537139', 'The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function combining multiple markers. The area under the ROC curve (AUC) for a score function measures the intrinsic ability for the score function to discriminate between the controls and cases. Recently, the partial AUC (pAUC) has been paid more attention than the AUC, because a suitable range of the false positive rate can be focused according to various clinical situations. However, existing pAUC-based methods only handle a few markers and do not take nonlinear combination of markers into consideration.'),
(93692, 2010, 'bmc', 'mu-CS: an extension of the TM4 platform to manage Affymetrix binary data', 'Guzzi, Pietro H|Cannataro, Mario', 'Bioinformatics Laboratory, Department of Experimental Medicine and Clinic, Magna Graecia University, Catanzaro, Italy. hguzzi@unicz.it|Bioinformatics Laboratory, Department of Experimental Medicine and Clinic, Magna Graecia University, Catanzaro, Italy. hguzzi@unicz.it', 'Italy|Italy', 0, 'Computational Biology|Gene Expression Profiling|Gene Library|Humans|Internet|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-11-315', '20537149', 'A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.'),
(93693, 2010, 'bmc', 'A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization', 'van der Horst, Eelke|Peironcely, Julio E|Ijzerman, Adriaan P|Beukers, Margot W|Lane, Jonathan R|van Vlijmen, Herman W T|Emmerich, Michael T M|Okuno, Yasushi|Bender, Andreas', 'Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, 2333CC, The Netherlands. bendera@lacdr.leidenuniv.nl.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 41, 'Binding Sites|Drug Design|Genomics|Ligands|Models, Molecular|Phylogeny|Receptors, G-Protein-Coupled', 'Journal Article', '10.1186/1471-2105-11-316', '20537162', 'G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors.'),
(93694, 2010, 'bmc', 'Introducing WATERS: a workflow for the alignment, taxonomy, and ecology of ribosomal sequences', 'Hartman, Amber L|Riddle, Sean|McPhillips, Timothy|Ludäscher, Bertram|Eisen, Jonathan A', 'Department of Medical Microbiology and Immunology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Medical Microbiology and Immunology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Medical Microbiology and Immunology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Medical Microbiology and Immunology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.|Department of Medical Microbiology and Immunology, Genome Center, University of California Davis, One Shields Avenue, Davis, CA 95616, USA.', 'United States|United States|United States|United States|United States', 0, 'Base Sequence|Genes, rRNA|Genomics|Phylogeny|Ribosomes|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-317', '20540779', 'For more than two decades microbiologists have used a highly conserved microbial gene as a phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA sequence analysis has increased correspondingly.'),
(93695, 2010, 'bmc', 'CONAN: copy number variation analysis software for genome-wide association studies', 'Forer, Lukas|Schönherr, Sebastian|Weissensteiner, Hansi|Haider, Florian|Kluckner, Thomas|Gieger, Christian|Wichmann, Heinz-Erich|Specht, Günther|Kronenberg, Florian|Kloss-Brandstätter, Anita', 'Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, 6020 Innsbruck, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 14, 'DNA Copy Number Variations|Genome|Genome-Wide Association Study|Genomics|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-11-318', '20546565', 'Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs.'),
(93696, 2010, 'bmc', 'Accessing the SEED genome databases via Web services API: tools for programmers', 'Disz, Terry|Akhter, Sajia|Cuevas, Daniel|Olson, Robert|Overbeek, Ross|Vonstein, Veronika|Stevens, Rick|Edwards, Robert A', 'Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 96, 'Databases, Genetic|Genome|Metagenomics|Software', 'Journal Article', '10.1186/1471-2105-11-319', '20546611', 'The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups.'),
(93697, 2010, 'bmc', 'Pripper: prediction of caspase cleavage sites from whole proteomes', 'Piippo, Mirva|Lietzén, Niina|Nevalainen, Olli S|Salmi, Jussi|Nyman, Tuula A', 'Department of Information Technology, University of Turku, Turku, Finland. mirva.piippo@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. mirva.piippo@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. mirva.piippo@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. mirva.piippo@utu.fi|Department of Information Technology, University of Turku, Turku, Finland. mirva.piippo@utu.fi', 'Finland|Finland|Finland|Finland|Finland', 0, 'Caspases|Databases, Protein|Proteome|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-320', '20546630', 'Caspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.'),
(93698, 2010, 'bmc', 'An integrated Bayesian analysis of LOH and copy number data', 'Rancoita, Paola M V|Hutter, Marcus|Bertoni, Francesco|Kwee, Ivo', 'Istituto Dalle Molle di Studi sullIntelligenza Artificiale, Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale, Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale, Manno-Lugano, Switzerland. paola@idsia.ch|Istituto Dalle Molle di Studi sullIntelligenza Artificiale, Manno-Lugano, Switzerland. paola@idsia.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Alleles|Base Sequence|Bayes Theorem|Breast Neoplasms|DNA|Gene Dosage|Genome, Human|Humans|Leukemia, Lymphocytic, Chronic, B-Cell|Loss of Heterozygosity|Neoplasms|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Uniparental Disomy', 'Journal Article', '10.1186/1471-2105-11-321', '20550648', 'Cancer and other disorders are due to genomic lesions. SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphisms (SNPs) along the genome. CN is defined as the number of DNA copies, and the normal is two, since we have two copies of each chromosome. The genotype of a SNP is the status given by the nucleotides (alleles) which are present on the two copies of DNA. It is defined homozygous or heterozygous if the two alleles are the same or if they differ, respectively. Loss of heterozygosity (LOH) is the loss of the heterozygous status due to genomic events. Combining CN and LOH data, it is possible to better identify different types of genomic aberrations. For example, a long sequence of homozygous SNPs might be caused by either the physical loss of one copy or a uniparental disomy event (UPD), i.e. each SNP has two identical nucleotides both derived from only one parent. In this situation, the knowledge of the CN can help in distinguishing between these two events.'),
(93699, 2010, 'bmc', 'Quantifying the relationship between sequence and three-dimensional structure conservation in RNA', 'Capriotti, Emidio|Marti-Renom, Marc A', 'Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.|Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.', 'Spain|Spain', 0, 'Archaea|Bacteria|Base Sequence|Models, Molecular|Nucleic Acid Conformation|Proteins|RNA|RNA, Archaeal|RNA, Bacterial|RNA, Catalytic|RNA, Ribosomal|RNA, Transfer|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-322', '20550657', 'In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA.'),
(93700, 2010, 'bmc', 'A boundary delimitation algorithm to approximate cell soma volumes of bipolar cells from topographical data obtained by scanning probe microscopy', 'Happel, Patrick|Möller, Kerstin|Kunz, Ralf|Dietzel, Irmgard D', 'Ruhr University of Bochum, Germany. patrick.happel@rub.de|Ruhr University of Bochum, Germany. patrick.happel@rub.de|Ruhr University of Bochum, Germany. patrick.happel@rub.de|Ruhr University of Bochum, Germany. patrick.happel@rub.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Cell Movement|Cell Nucleus|Cell Size|Image Processing, Computer-Assisted|Microscopy, Scanning Probe|Oligodendroglia|Rats|Stem Cells', 'Journal Article', '10.1186/1471-2105-11-323', '20550692', 'Cell volume determination plays a pivotal role in the investigation of the biophysical mechanisms underlying various cellular processes. Whereas light microscopy in principle enables one to obtain three dimensional data, the reconstruction of cell volume from z-stacks is a time consuming procedure. Thus, three dimensional topographic representations of cells are easier to obtain by scanning probe microscopical measurements.'),
(93701, 2010, 'bmc', 'Detecting lateral gene transfers by statistical reconciliation of phylogenetic forests', 'Abby, Sophie S|Tannier, Eric|Gouy, Manolo|Daubin, Vincent', 'Université de Lyon, Université Lyon 1, CNRS, INRIA, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622 Villeurbanne, France.|Université de Lyon, Université Lyon 1, CNRS, INRIA, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622 Villeurbanne, France.|Université de Lyon, Université Lyon 1, CNRS, INRIA, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622 Villeurbanne, France.|Université de Lyon, Université Lyon 1, CNRS, INRIA, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622 Villeurbanne, France.', 'France|France|France|France', 0, 'Algorithms|Archaea|Bacteria|Gene Transfer, Horizontal|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-324', '20550700', 'To understand the evolutionary role of Lateral Gene Transfer (LGT), accurate methods are needed to identify transferred genes and infer their timing of acquisition. Phylogenetic methods are particularly promising for this purpose, but the reconciliation of a gene tree with a reference (species) tree is computationally hard. In addition, the application of these methods to real data raises the problem of sorting out real and artifactual phylogenetic conflict.'),
(93702, 2010, 'bmc', 'Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm', 'Li, Zhanchao|Zhou, Xuan|Dai, Zong|Zou, Xiaoyong', 'School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China.|School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China.|School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China.|School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, PR China.', 'China|China|China|China', 0, 'Algorithms|Receptors, G-Protein-Coupled|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-325', '20550715', 'Because a priori knowledge about function of G protein-coupled receptors (GPCRs) can provide useful information to pharmaceutical research, the determination of their function is a quite meaningful topic in protein science. However, with the rapid increase of GPCRs sequences entering into databanks, the gap between the number of known sequence and the number of known function is widening rapidly, and it is both time-consuming and expensive to determine their function based only on experimental techniques. Therefore, it is vitally significant to develop a computational method for quick and accurate classification of GPCRs.'),
(93703, 2010, 'bmc', 'PPLook: an automated data mining tool for protein-protein interaction', 'Zhang, Shao-Wu|Li, Yao-Jun|Xia, Li|Pan, Quan', 'Institute of Control and Information, School of Automation, Northwestern Polytechnical University, Xian, China. zhangsw@nwpu.edu.cn|Institute of Control and Information, School of Automation, Northwestern Polytechnical University, Xian, China. zhangsw@nwpu.edu.cn|Institute of Control and Information, School of Automation, Northwestern Polytechnical University, Xian, China. zhangsw@nwpu.edu.cn|Institute of Control and Information, School of Automation, Northwestern Polytechnical University, Xian, China. zhangsw@nwpu.edu.cn', 'China|China|China|China', 0, 'Algorithms|Data Mining|Internet|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-326', '20550717', 'Extracting and visualizing of protein-protein interaction (PPI) from text literatures are a meaningful topic in protein science. It assists the identification of interactions among proteins. There is a lack of tools to extract PPI, visualize and classify the results.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93704, 2010, 'bmc', 'FRASS: the web-server for RNA structural comparison', 'Kirillova, Svetlana|Tosatto, Silvio C E|Carugo, Oliviero', 'Department of Structural and Computational Biology, Max F Perutz Laboratories, Vienna University, Campus Vienna Biocenter 5, A-1030 Vienna, Austria. sviatlana.kirylava@univie.ac.at|Department of Structural and Computational Biology, Max F Perutz Laboratories, Vienna University, Campus Vienna Biocenter 5, A-1030 Vienna, Austria. sviatlana.kirylava@univie.ac.at|Department of Structural and Computational Biology, Max F Perutz Laboratories, Vienna University, Campus Vienna Biocenter 5, A-1030 Vienna, Austria. sviatlana.kirylava@univie.ac.at', 'Austria|Austria|Austria', 0, 'Databases, Protein|Genomics|Internet|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-327', '20553602', 'The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years, (ARTS, DIAL, SARA, SARSA, and LaJolla) are available for the structural comparison of large and intact RNA molecules.'),
(93705, 2010, 'bmc', 'permGPU: Using graphics processing units in RNA microarray association studies', 'Shterev, Ivo D|Jung, Sin-Ho|George, Stephen L|Owzar, Kouros', 'Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.|Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.', 'United States|United States|United States|United States', 17, 'Gene Expression Profiling|Genetic Association Studies|Humans|Microarray Analysis|Neoplasms|RNA|Software', 'Journal Article', '10.1186/1471-2105-11-329', '20553619', 'Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed.'),
(93706, 2010, 'bmc', 'MimoSA: a system for minimotif annotation', 'Vyas, Jay|Nowling, Ronald J|Meusburger, Thomas|Sargeant, David|Kadaveru, Krishna|Gryk, Michael R|Kundeti, Vamsi|Rajasekaran, Sanguthevar|Schiller, Martin R', 'Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.|Department of Molecular, Microbial, and Structural Biology, University of Connecticut Health Center, 263 Farmington Ave. Farmington, CT 06030-3305, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Animals|Artificial Intelligence|Data Mining|Databases, Protein|Humans|Protein Binding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-328', '20565705', 'Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature.'),
(93707, 2010, 'bmc', 'Computing and visually analyzing mutual information in molecular co-evolution', 'Bremm, Sebastian|Schreck, Tobias|Boba, Patrick|Held, Stephanie|Hamacher, Kay', 'Interactive Graphics Systems, Dept, of Computer Science, Technische Universität Darmstadt, Germany. sebastian.bremm@gris.tu-darmstadt.de|Interactive Graphics Systems, Dept, of Computer Science, Technische Universität Darmstadt, Germany. sebastian.bremm@gris.tu-darmstadt.de|Interactive Graphics Systems, Dept, of Computer Science, Technische Universität Darmstadt, Germany. sebastian.bremm@gris.tu-darmstadt.de|Interactive Graphics Systems, Dept, of Computer Science, Technische Universität Darmstadt, Germany. sebastian.bremm@gris.tu-darmstadt.de|Interactive Graphics Systems, Dept, of Computer Science, Technische Universität Darmstadt, Germany. sebastian.bremm@gris.tu-darmstadt.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Acetylcholinesterase|Animals|Base Sequence|Computational Biology|Evolution, Molecular|HIV Protease|HIV-1|Models, Molecular|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteins|Sequence Alignment|Software|Torpedo', 'Journal Article', '10.1186/1471-2105-11-330', '20565748', 'Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization--has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted.'),
(93708, 2010, 'bmc', 'CGHpower: exploring sample size calculations for chromosomal copy number experiments', 'Scheinin, Ilari|Ferreira, José A|Knuutila, Sakari|Meijer, Gerrit A|van de Wiel, Mark A|Ylstra, Bauke', 'Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.|Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 6, 'Animals|Chromosome Aberrations|Chromosomes, Human|Comparative Genomic Hybridization|Computer Simulation|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-11-331', '20565750', 'Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH).'),
(93709, 2010, 'bmc', 'VITCOMIC: visualization tool for taxonomic compositions of microbial communities based on 16S rRNA gene sequences', 'Mori, Hiroshi|Maruyama, Fumito|Kurokawa, Ken', 'Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama 226-8501, Japan.|Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama 226-8501, Japan.|Department of Biological Information, Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Yokohama 226-8501, Japan.', 'Japan|Japan|Japan', 22, 'Base Sequence|Biodiversity|DNA, Bacterial|Genome, Archaeal|Genome, Bacterial|Genomics|Phylogeny|RNA, Archaeal|RNA, Bacterial|RNA, Ribosomal, 16S|Software', 'Journal Article', '10.1186/1471-2105-11-332', '20565810', 'Understanding the community structure of microbes is typically accomplished by sequencing 16S ribosomal RNA (16S rRNA) genes. These community data can be represented by constructing a phylogenetic tree and comparing it with other samples using statistical methods. However, owing to high computational complexity, these methods are insufficient to effectively analyze the millions of sequences produced by new sequencing technologies such as pyrosequencing.'),
(93710, 2010, 'bmc', 'MPRAP: an accessibility predictor for a-helical transmembrane proteins that performs well inside and outside the membrane', 'Illergård, Kristoffer|Callegari, Simone|Elofsson, Arne', 'Center for Biomembrane Research, Stockholm Bioinformatics Center, Dept of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden.|Center for Biomembrane Research, Stockholm Bioinformatics Center, Dept of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden.|Center for Biomembrane Research, Stockholm Bioinformatics Center, Dept of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 0, 'Cell Membrane|Hydrophobic and Hydrophilic Interactions|Lipids|Membrane Proteins|Protein Conformation|Protein Folding|Protein Structure, Secondary|Software|Water', 'Journal Article', '10.1186/1471-2105-11-333', '20565847', 'In water-soluble proteins it is energetically favorable to bury hydrophobic residues and to expose polar and charged residues. In contrast to water soluble proteins, transmembrane proteins face three distinct environments, a hydrophobic lipid environment inside the membrane, a hydrophilic water environment outside the membrane and an interface region rich in phospholipid head-groups. Therefore, it is energetically favorable for transmembrane proteins to expose different types of residues in the different regions.'),
(93711, 2010, 'bmc', 'An effective approach for generating a three-Cys2His2 zinc-finger-DNA complex model by docking', 'Chou, Chun-Chi|Rajasekaran, M|Chen, Chinpan', 'Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.|Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.|Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan.', '-|-|-', 0, 'Crystallography, X-Ray|DNA|DNA-Binding Proteins|Humans|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Software|Sp1 Transcription Factor|Transcription Factors|Zinc Fingers', 'Journal Article', '10.1186/1471-2105-11-334', '20565873', 'Determination of protein-DNA complex structures with both NMR and X-ray crystallography remains challenging in many cases. High Ambiguity-Driven DOCKing (HADDOCK) is an information-driven docking program that has been used to successfully model many protein-DNA complexes. However, a protein-DNA complex model whereby the protein wraps around DNA has not been reported. Defining the ambiguous interaction restraints for the classical three-Cys2His2 zinc-finger proteins that wrap around DNA is critical because of the complicated binding geometry. In this study, we generated a Zif268-DNA complex model using three different sets of ambiguous interaction restraints (AIRs) to study the effect of the geometric distribution on the docking and used this approach to generate a newly reported Sp1-DNA complex model.'),
(93712, 2010, 'bmc', 'An efficient biological pathway layout algorithm combining grid-layout and spring embedder for complicated cellular location information', 'Kojima, Kaname|Nagasaki, Masao|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Japan.', 'Japan|Japan|Japan', 21, 'Algorithms|Animals|Apoptosis|Caenorhabditis elegans|Computational Biology|Endothelial Cells|Humans|Models, Biological', 'Journal Article', '10.1186/1471-2105-11-335', '20565884', 'Graph drawing is one of the important techniques for understanding biological regulations in a cell or among cells at the pathway level. Among many available layout algorithms, the spring embedder algorithm is widely used not only for pathway drawing but also for circuit placement and www visualization and so on because of the harmonized appearance of its results. For pathway drawing, location information is essential for its comprehension. However, complex shapes need to be taken into account when torus-shaped location information such as nuclear inner membrane, nuclear outer membrane, and plasma membrane is considered. Unfortunately, the spring embedder algorithm cannot easily handle such information. In addition, crossings between edges and nodes are usually not considered explicitly.'),
(93713, 2010, 'bmc', 'PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms', 'Bruckskotten, Marc|Looso, Mario|Cemixc4x89, Franz|Konzer, Anne|Hemberger, Jxc3xbcrgen|Krxc3xbcger, Marcus|Braun, Thomas', 'Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de|Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany. marc.bruckskotten@mpi-bn.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Databases, Genetic|Gene Expression Profiling|Multivariate Analysis|Principal Component Analysis|Proteins|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-336', '20565932', 'Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied.'),
(93714, 2010, 'bmc', 'Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach', 'Zou, Cunlu|Ladroue, Christophe|Guo, Shuixia|Feng, Jianfeng', 'Department of Computer Science, University of Warwick, Coventry, UK.|Department of Computer Science, University of Warwick, Coventry, UK.|Department of Computer Science, University of Warwick, Coventry, UK.|Department of Computer Science, University of Warwick, Coventry, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Factual|Gene Regulatory Networks|Proteins', 'Journal Article', '10.1186/1471-2105-11-337', '20565962', 'Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.'),
(93715, 2010, 'bmc', 'svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification', 'Zhang, Wensheng|Edwards, Andrea|Fan, Wei|Zhu, Dongxiao|Zhang, Kun', 'Department of Computer Science, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.|Department of Computer Science, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.|Department of Computer Science, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.|Department of Computer Science, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.|Department of Computer Science, Xavier University of Louisiana, 1 Drexel Drive, New Orleans, LA 70125, USA.', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Animals|Computational Biology|Drosophila melanogaster|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Organ Specificity', 'Journal Article', '10.1186/1471-2105-11-338', '20565989', 'Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes, however, practical difficulties often lie in the implementation of existing methods.'),
(93716, 2010, 'bmc', 'Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques', 'Lapins, Maris|Wikberg, Jarl Es', 'Department of Pharmaceutical Pharmacology, Uppsala University, Sweden.|Department of Pharmaceutical Pharmacology, Uppsala University, Sweden.', 'Sweden|Sweden', 45, 'Animals|Humans|Least-Squares Analysis|Models, Biological|Protein Kinase Inhibitors|Protein Kinases|ROC Curve|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-339', '20569422', 'Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.'),
(93717, 2010, 'bmc', 'A high-throughput pipeline for the design of real-time PCR signatures', 'Vijaya Satya, Ravi|Kumar, Kamal|Zavaljevski, Nela|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.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.', 'United States|United States|United States|United States', 0, 'Base Sequence|Burkholderia mallei|Burkholderia pseudomallei|Chromosome Mapping|Computational Biology|Genome, Bacterial|Polymerase Chain Reaction|Sensitivity and Specificity|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-11-340', '20573238', 'Pathogen diagnostic assays based on polymerase chain reaction (PCR) technology provide high sensitivity and specificity. However, the design of these diagnostic assays is computationally intensive, requiring high-throughput methods to identify unique PCR signatures in the presence of an ever increasing availability of sequenced genomes.'),
(93718, 2010, 'bmc', 'TagCleaner: Identification and removal of tag sequences from genomic and metagenomic datasets', 'Schmieder, Robert|Lim, Yan Wei|Rohwer, Forest|Edwards, Robert', 'Department of Computer Science, San Diego State University, CA, USA. rschmied@sciences.sdsu.edu.|Department of Computer Science, San Diego State University, CA, USA. rschmied@sciences.sdsu.edu.|Department of Computer Science, San Diego State University, CA, USA. rschmied@sciences.sdsu.edu.|Department of Computer Science, San Diego State University, CA, USA. rschmied@sciences.sdsu.edu.', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Culicidae|DNA Primers|Internet|Metagenomics|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1186/1471-2105-11-341', '20573248', 'Sequencing metagenomes that were pre-amplified with primer-based methods requires the removal of the additional tag sequences from the datasets. The sequenced reads can contain deletions or insertions due to sequencing limitations, and the primer sequence may contain ambiguous bases. Furthermore, the tag sequence may be unavailable or incorrectly reported. Because of the potential for downstream inaccuracies introduced by unwanted sequence contaminations, it is important to use reliable tools for pre-processing sequence data.'),
(93719, 2010, 'bmc', 'TabSQL: a MySQL tool to facilitate mapping user data to public databases', 'Xia, Xiao-Qin|McClelland, Michael|Wang, Yipeng', 'Vaccine Research Institute of San Diego, 10835 Road to the Cure, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute of San Diego, 10835 Road to the Cure, San Diego, CA 92121, USA. xqxia70@gmail.com|Vaccine Research Institute of San Diego, 10835 Road to the Cure, San Diego, CA 92121, USA. xqxia70@gmail.com', 'United States|United States|United States', 0, 'Databases, Genetic|Genomics|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-342', '20573251', 'With advances in high-throughput genomics and proteomics, it is challenging for biologists to deal with large data files and to map their data to annotations in public databases.'),
(93720, 2010, 'bmc', 'A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network', 'You, Zhu-Hong|Yin, Zheng|Han, Kyungsook|Huang, De-Shuang|Zhou, Xiaobo', 'Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China.|Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China.|Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China.|Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China.|Intelligent Computing Lab, Institute of Intelligent Machine, Chinese Academy of Science, P.O. Box 1130, Hefei, Anhui 230031, China.', 'China|China|China|China|China', 0, 'Artificial Intelligence|Computational Biology|Gene Regulatory Networks|Proteins|Saccharomyces cerevisiae|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-343', '20573270', 'Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.'),
(93721, 2010, 'bmc', 'Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays', 'Chain, Benjamin|Bowen, Helen|Hammond, John|Posch, Wilfried|Rasaiyaah, Jane|Tsang, Jhen|Noursadeghi, Mahdad', 'Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk|Division of infection and Immunity, UCL, London, UK. b.chain@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Arabidopsis|DNA, Complementary|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Humans|Linear Models|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-11-344', '20576120', 'Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.'),
(93722, 2010, 'bmc', 'SOPRA: Scaffolding algorithm for paired reads via statistical optimization', 'Dayarian, Adel|Michael, Todd P|Sengupta, Anirvan M', 'Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, USA.|Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, USA.|Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, USA.', 'Jersey|Jersey|Jersey', 95, 'Algorithms|Bacteria|Contig Mapping|Genome, Bacterial|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-345', '20576136', 'High throughput sequencing (HTS) platforms produce gigabases of short read (<100 bp) data per run. While these short reads are adequate for resequencing applications, de novo assembly of moderate size genomes from such reads remains a significant challenge. These limitations could be partially overcome by utilizing mate pair technology, which provides pairs of short reads separated by a known distance along the genome.'),
(93723, 2010, 'bmc', 'Predicting nucleosome positioning using a duration Hidden Markov Model', 'Xi, Liqun|Fondufe-Mittendorf, Yvonne|Xia, Lei|Flatow, Jared|Widom, Jonathan|Wang, Ji-Ping', 'Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.|Department of Statistics, Northwestern University, Evanston, IL 60208, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'DNA|Genome|Genomics|Markov Chains|Nucleosomes|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-11-346', '20576140', 'The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genomes nucleosome organization may facilitate genome function.'),
(93724, 2010, 'bmc', 'Local alignment of generalized k-base encoded DNA sequence', 'Homer, Nils|Nelson, Stanley F|Merriman, Barry', 'Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu', 'United States|United States|United States', 1, 'Algorithms|Computer Simulation|DNA|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-347', '20576157', 'DNA sequence comparison is a well-studied problem, in which two DNA sequences are compared using a weighted edit distance. Recent DNA sequencing technologies however observe an encoded form of the sequence, rather than each DNA base individually. The encoded DNA sequence may contain technical errors, and therefore encoded sequencing errors must be incorporated when comparing an encoded DNA sequence to a reference DNA sequence.'),
(93725, 2010, 'bmc', 'Metamotifs--a generative model for building families of nucleotide position weight matrices', 'Piipari, Matias|Down, Thomas A|Hubbard, Tim Jp', 'Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com|Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK. matias.piipari@gmail.com', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|DNA|Models, Statistical|Nucleotides|Position-Specific Scoring Matrices|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-348', '20579334', 'Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence.'),
(93726, 2010, 'bmc', 'Modular composition predicts kinase/substrate interactions', 'Liu, Yichuan|Tozeren, Aydin', 'Center for Integrated Bioinformatics, Drexel University, Philadelphia, PA 19104, USA.|Center for Integrated Bioinformatics, Drexel University, Philadelphia, PA 19104, USA.', 'United States|United States', 7, 'Humans|Phosphorylation|Phosphotransferases|Protein Binding|Protein Structure, Tertiary|Proteome|Sequence Analysis, Protein|Signal Transduction|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-349', '20579376', 'Phosphorylation events direct the flow of signals and metabolites along cellular protein networks. Current annotations of kinase-substrate binding events are far from complete. In this study, we scanned the entire human protein sequences using the PROSITE domain annotation tool to identify patterns of domain composition in kinases and their substrates. We identified statistically enriched pairs of strings of domains (signature pairs) in kinase-substrate couples presented in the 2006 version of the PTM database.'),
(93727, 2010, 'bmc', 'Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions', 'Ozawa, Yosuke|Saito, Rintaro|Fujimori, Shigeo|Kashima, Hisashi|Ishizaka, Masamichi|Yanagawa, Hiroshi|Miyamoto-Sato, Etsuko|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, 403-1, Daihoji, Tsuruoka, Yamagata 997-0017, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 31, 'Algorithms|Cluster Analysis|Multiprotein Complexes|Programming, Linear|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins|Two-Hybrid System Techniques', 'Journal Article', '10.1186/1471-2105-11-350', '20584269', 'High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions.'),
(93728, 2010, 'bmc', 'Identifying differentially regulated subnetworks from phosphoproteomic data', 'Klammer, Martin|Godl, Klaus|Tebbe, Andreas|Schaab, Christoph', 'KINAXO Biotechnologies GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany.|KINAXO Biotechnologies GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany.|KINAXO Biotechnologies GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany.|KINAXO Biotechnologies GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Bayes Theorem|Gene Expression Profiling|Models, Statistical|Phosphorylation|Proteomics|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-351', '20584295', 'Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrating these data with protein networks should make it possible to identify subnetworks that are significantly regulated. Furthermore, such integration can support identification of regulated entities from often noisy high throughput data. In particular, processing mass spectrometry-based phosphoproteomic data in this manner may expose signal transduction pathways and, in the case of experiments with drug-treated cells, reveal the drugs mode of action.'),
(93729, 2010, 'bmc', 'Protein docking by Rotation-Based Uniform Sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation', 'Solernou, Albert|Fernandez-Recio, Juan', 'Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain.|Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain.', 'Spain|Spain', 0, 'Algorithms|Molecular Conformation|Protein Binding|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-352', '20584304', 'Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test http://www.ebi.ac.uk/msd-srv/capri, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.'),
(93730, 2010, 'bmc', 'ScreenMill: a freely available software suite for growth measurement, analysis and visualization of high-throughput screen data', 'Dittmar, John C|Reid, Robert Jd|Rothstein, Rodney', 'Columbia University Medical Center, Dept, Genetics & Development, 701 West 168th Street, New York, NY 10032-2704, USA.|Columbia University Medical Center, Dept, Genetics & Development, 701 West 168th Street, New York, NY 10032-2704, USA.|Columbia University Medical Center, Dept, Genetics & Development, 701 West 168th Street, New York, NY 10032-2704, USA.', 'United States|United States|United States', 51, 'Algorithms|Genomics|Internet|Software', 'Journal Article', '10.1186/1471-2105-11-353', '20584323', 'Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments.'),
(93731, 2010, 'bmc', 'Nonparametric methods for the analysis of single-color pathogen microarrays', 'Jabado, Omar J|Conlan, Sean|Quan, Phenix-Lan|Hui, Jeffrey|Palacios, Gustavo|Hornig, Mady|Briese, Thomas|Lipkin, W Ian', 'Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.|Center for Infection and Immunity Mailman School of Public Health Columbia University New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Gene Expression Profiling|Humans|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Reference Standards|Viruses', 'Journal Article', '10.1186/1471-2105-11-354', '20584331', 'The analysis of oligonucleotide microarray data in pathogen surveillance and discovery is a challenging task. Target template concentration, nucleic acid integrity, and host nucleic acid composition can each have a profound effect on signal distribution. Exploratory analysis of fluorescent signal distribution in clinical samples has revealed deviations from normality, suggesting that distribution-free approaches should be applied.'),
(93732, 2010, 'bmc', 'Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach', 'Bailly-Bechet, Marc|Braunstein, Alfredo|Pagnani, Andrea|Weigt, Martin|Zecchina, Riccardo', 'ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.|ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.|ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.|ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.|ISI Foundation Viale Settimio Severo 65, Villa Gualino, I-10133 Torino, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Cell Cycle|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Models, Genetic|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-355', '20587029', 'Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.'),
(93733, 2010, 'bmc', 'Spatial normalization improves the quality of genotype calling for Affymetrix SNP 60 arrays', 'Chai, High Seng|Therneau, Terry M|Bailey, Kent R|Kocher, Jean-Pierre A', 'Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Mayo Clinic College of Medicine, Rochester, MN 55905, USA.|Mayo Clinic College of Medicine, Rochester, MN 55905, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Genotype|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-356', '20587065', 'Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms with similar design features. This procedure deals with both extreme and subtle biases and is intended to be applied before standard genotype calling algorithms.'),
(93734, 2010, 'bmc', 'Interaction prediction and classification of PDZ domains', 'Kalyoncu, Sibel|Keskin, Ozlem|Gursoy, Attila', 'Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.|Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.|Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu 34450 Sariyer, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 32, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Humans|Models, Molecular|Molecular Sequence Data|PDZ Domains|Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-357', '20591147', 'PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners.'),
(93735, 2010, 'bmc', 'MetaBar - a tool for consistent contextual data acquisition and standards compliant submission', 'Hankeln, Wolfgang|Buttigieg, Pier Luigi|Fink, Dennis|Kottmann, Renzo|Yilmaz, Pelin|Glöckner, Frank Oliver', 'Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.|Max Planck Institute for Marine Microbiology, Bremen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Base Sequence|Databases, Genetic|Genomics|Information Storage and Retrieval|Internet|Programming Languages|Software|User-Computer Interface|Workflow', 'Journal Article', '10.1186/1471-2105-11-358', '20591175', 'Environmental sequence datasets are increasing at an exponential rate, however, the vast majority of them lack appropriate descriptors like sampling location, time and depth/altitude: generally referred to as metadata or contextual data. The consistent capture and structured submission of these data is crucial for integrated data analysis and ecosystems modeling. The application MetaBar has been developed, to support consistent contextual data acquisition.'),
(93736, 2010, 'bmc', 'On the use of resampling tests for evaluating statistical significance of binding-site co-occurrence', 'Huen, David S|Russell, Steven', 'Department of Genetics, University of Cambridge, UK. david.s.huen@googlemail.com|Department of Genetics, University of Cambridge, UK. david.s.huen@googlemail.com', 'United Kingdom|United Kingdom', 0, 'Animals|Binding Sites|DNA-Binding Proteins|Drosophila|Genome-Wide Association Study|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-359', '20591178', 'In eukaryotes, most DNA-binding proteins exert their action as members of large effector complexes. The presence of these complexes are revealed in high-throughput genome-wide assays by the co-occurrence of the binding sites of different complex components. Resampling tests are one route by which the statistical significance of apparent co-occurrence can be assessed.');
