INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93737, 2010, 'bmc', 'Flexible network reconstruction from relational databases with Cytoscape and CytoSQL', 'Laukens, Kris|Hollunder, Jens|Dang, Thanh Hai|De Jaeger, Geert|Kuiper, Martin|Witters, Erwin|Verschoren, Alain|Van Leemput, Koenraad', 'Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be|Department of Mathematics and Computer Science, University of Antwerp, Antwerpen, Belgium. kris.laukens@ua.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Animals|Cell Physiological Phenomena|Databases, Genetic|Genomics|Humans|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-360', '20594316', 'Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented.'),
(93738, 2010, 'bmc', 'Scanner calibration revisited', 'Pozhitkov, Alexander E', 'Max Plank Institute for Evolutionary Biology, Ploen, Germany. alexander.pozhitkov@evolbio.mpg.de', 'Germany', 2, 'Calibration|Fluorescence|Microarray Analysis|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-11-361', '20594322', 'Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al.'),
(93739, 2010, 'bmc', 'Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse', 'Carlsson, Lars|Spjuth, Ola|Adams, Samuel|Glen, Robert C|Boyer, Scott', 'Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. lars.a.carlsson@astrazeneca.com|Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. lars.a.carlsson@astrazeneca.com|Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. lars.a.carlsson@astrazeneca.com|Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. lars.a.carlsson@astrazeneca.com|Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. lars.a.carlsson@astrazeneca.com', 'Sweden|Sweden|Sweden|Sweden|Sweden', 62, 'Biotransformation|Drug Discovery|Internet|Ligands|Software', 'Evaluation Studies', '10.1186/1471-2105-11-362', '20594327', 'Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive.'),
(93740, 2010, 'bmc', 'Robust probabilistic superposition and comparison of protein structures', 'Mechelke, Martin|Habeck, Michael', 'Department of Protein Evolution, Max-Planck-Institute for Developmental Biology, Spemannstr 35, 72076 Tübingen, Germany.|Department of Protein Evolution, Max-Planck-Institute for Developmental Biology, Spemannstr 35, 72076 Tübingen, Germany.', 'Germany|Germany', 9, 'Algorithms|Normal Distribution|Nuclear Magnetic Resonance, Biomolecular|Probability|Protein Conformation|Proteins|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-11-363', '20594332', 'Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as alpha-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely--a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions.'),
(93741, 2010, 'bmc', 'Automatic structure classification of small proteins using random forest', 'Jain, Pooja|Hirst, Jonathan D', 'School of Chemistry, The University of Nottingham, University Park, Nottingham, NG7 2RD, UK.|School of Chemistry, The University of Nottingham, University Park, Nottingham, NG7 2RD, UK.', 'United Kingdom|United Kingdom', 17, 'Algorithms|Artificial Intelligence|Databases, Protein|Models, Molecular|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-11-364', '20594334', 'Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.'),
(93742, 2010, 'bmc', 'Knowledge-based annotation of small molecule binding sites in proteins', 'Thangudu, Ratna R|Tyagi, Manoj|Shoemaker, Benjamin A|Bryant, Stephen H|Panchenko, Anna R|Madej, Thomas', 'National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, 8600 Rockville Pike, Building 38A, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Cluster Analysis|Knowledge Bases|Protein Binding|Proteins|Sequence Analysis, Protein|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-11-365', '20594344', 'The study of protein-small molecule interactions is vital for understanding protein function and for practical applications in drug discovery. To benefit from the rapidly increasing structural data, it is essential to improve the tools that enable large scale binding site prediction with greater emphasis on their biological validity.'),
(93743, 2010, 'bmc', 'Dual-functioning transcription factors in the developmental gene network of Drosophila melanogaster', 'Bauer, Denis C|Buske, Fabian A|Bailey, Timothy L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072 Australia. d.bauer@uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072 Australia. d.bauer@uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072 Australia. d.bauer@uq.edu.au', 'Australia|Australia|Australia', 13, 'Amino Acid Motifs|Animals|Drosophila Proteins|Drosophila melanogaster|Evolution, Molecular|Gene Expression Regulation, Developmental|Gene Regulatory Networks|Genes, Developmental|Models, Genetic|Repressor Proteins|Small Ubiquitin-Related Modifier Proteins|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-366', '20594356', 'Quantitative models for transcriptional regulation have shown great promise for advancing our understanding of the biological mechanisms underlying gene regulation. However, all of the models to date assume a transcription factor (TF) to have either activating or repressing function towards all the genes it is regulating.'),
(93744, 2010, 'bmc', 'A flexible R package for nonnegative matrix factorization', 'Gaujoux, Renaud|Seoighe, Cathal', 'Computational Biology Group, Department of Clinical Laboratory Sciences, Faculty of Health Sciences, University of Cape Town, South Africa.|Computational Biology Group, Department of Clinical Laboratory Sciences, Faculty of Health Sciences, University of Cape Town, South Africa.', 'South Africa|South Africa', 0, 'Algorithms|Computational Biology|Humans|Leukemia, Myeloid, Acute|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Software', 'Journal Article', '10.1186/1471-2105-11-367', '20598126', 'Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community.'),
(93745, 2010, 'bmc', 'A factor model to analyze heterogeneity in gene expression', 'Blum, Yuna|Le Mignon, Guillaume|Lagarrigue, Sandrine|Causeur, David', 'Agrocampus Ouest, UMR598, Animal Genetics, 35000 Rennes, France. Yuna.blum@rennes.inra.fr|Agrocampus Ouest, UMR598, Animal Genetics, 35000 Rennes, France. Yuna.blum@rennes.inra.fr|Agrocampus Ouest, UMR598, Animal Genetics, 35000 Rennes, France. Yuna.blum@rennes.inra.fr|Agrocampus Ouest, UMR598, Animal Genetics, 35000 Rennes, France. Yuna.blum@rennes.inra.fr', 'France|France|France|France', 0, 'Abdominal Fat|Algorithms|Animals|Chickens|Crosses, Genetic|Female|Gene Expression Profiling|Male|Models, Genetic|Oligonucleotide Array Sequence Analysis|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-11-368', '20598132', 'Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes.'),
(93746, 2010, 'bmc', 'HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data', 'Qin, Zhaohui S|Yu, Jianjun|Shen, Jincheng|Maher, Christopher A|Hu, Ming|Kalyana-Sundaram, Shanker|Yu, Jindan|Chinnaiyan, Arul M', 'Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu|Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 85, 'Algorithms|Base Sequence|Chromatin Immunoprecipitation|Computational Biology|Genome, Human|Histones|Humans|Protein Binding|Repressor Proteins|STAT1 Transcription Factor|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-369', '20598134', 'Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method.'),
(93747, 2010, 'bmc', 'Predicting changes in protein thermostability brought about by single- or multi-site mutations', 'Tian, Jian|Wu, Ningfeng|Chu, Xiaoyu|Fan, Yunliu', 'Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.|Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.', 'China|China|China|China', 0, 'Artificial Intelligence|Mutation|Protein Stability|Proteins|Software|Temperature', 'Journal Article', '10.1186/1471-2105-11-370', '20598148', 'An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (DeltaDeltaG) of thermal denaturation.'),
(93748, 2010, 'bmc', 'Visualisation tool for peptide fractionation data in proteomics: application to OFFGEL isoelectric focussing', 'Azulay, David-Olivier D|Neubert, Hendrik|Ocaña, Mireia Fernández', 'Pfizer Global Research and Development, Sandwich, UK. david-olivier.azulay@pfizer.com|Pfizer Global Research and Development, Sandwich, UK. david-olivier.azulay@pfizer.com|Pfizer Global Research and Development, Sandwich, UK. david-olivier.azulay@pfizer.com', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Cell Extracts|Cell Line|Chemical Fractionation|Data Mining|Humans|Isoelectric Focusing|Peptides|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-371', '20602788', 'OFFGEL isoelectric focussing (IEF) has become a popular tool in proteomics to fractionate peptides or proteins. As a consequence there is a need for software solutions supporting data mining, interpretation and characterisation of experimental quality.'),
(93749, 2010, 'bmc', 'Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images', 'Helmuth, Jo A|Paul, Grégory|Sbalzarini, Ivo F', 'Institute of Theoretical Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland.|Institute of Theoretical Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland.|Institute of Theoretical Computer Science, ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Adenoviridae|Cell Line|Endosomes|Host-Pathogen Interactions|Humans|Image Processing, Computer-Assisted|Microscopy|Microscopy, Confocal|Microscopy, Fluorescence', 'Journal Article', '10.1186/1471-2105-11-372', '20609242', 'Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on spatial proximity, other interactions typically result in spatial correlations between the interacting structures. Such correlations are the target of microscopy-based co-localization analysis, which can provide hints of potential interactions. Two complementary approaches to co-localization analysis can be distinguished: intensity correlation methods capitalize on pattern discovery, whereas object-based methods emphasize detection power.'),
(93750, 2010, 'bmc', 'Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images', 'Kimori, Yoshitaka|Baba, Norio|Morone, Nobuhiro', 'Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi-cho 4-1-1, Kodaira, Tokyo 187-8502, Japan.|Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi-cho 4-1-1, Kodaira, Tokyo 187-8502, Japan.|Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi-cho 4-1-1, Kodaira, Tokyo 187-8502, Japan.', 'Japan|Japan|Japan', 0, 'Algorithms|Biomarkers|Image Processing, Computer-Assisted|Microscopy|Software', 'Journal Article', '10.1186/1471-2105-11-373', '20615231', 'A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.'),
(93751, 2010, 'bmc', 'Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions', 'Potapov, Vladimir|Cohen, Mati|Inbar, Yuval|Schreiber, Gideon', 'Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel|Israel|Israel', 16, 'Caspase 7|Caspase 8|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-11-374', '20624289', 'Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.'),
(93752, 2010, 'bmc', 'KID--an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes', 'Heinen, Stephanie|Thielen, Bernhard|Schomburg, Dietmar', 'Dpt. of Bioinformatics & Biochemistry, University of Braunschweig, Institute of Technology, Langer Kamp 19B, 38106 Braunschweig, Germany.|Dpt. of Bioinformatics & Biochemistry, University of Braunschweig, Institute of Technology, Langer Kamp 19B, 38106 Braunschweig, Germany.|Dpt. of Bioinformatics & Biochemistry, University of Braunschweig, Institute of Technology, Langer Kamp 19B, 38106 Braunschweig, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Data Mining|Databases, Protein|Dictionaries as Topic|Enzymes|Internet|Kinetics', 'Journal Article', '10.1186/1471-2105-11-375', '20626859', 'The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed.'),
(93753, 2010, 'bmc', 'The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets', 'Carroll, Adam J|Badger, Murray R|Harvey Millar, A', 'University of Western Australia, Perth, Australia. adam.carroll@anu.edu.au|University of Western Australia, Perth, Australia. adam.carroll@anu.edu.au|University of Western Australia, Perth, Australia. adam.carroll@anu.edu.au', 'Australia|Australia|Australia', 82, 'Cluster Analysis|Gas Chromatography-Mass Spectrometry|Information Dissemination|Internet|Metabolomics', 'Journal Article', '10.1186/1471-2105-11-376', '20626915', 'Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.'),
(93754, 2010, 'bmc', 'SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments', 'Burdick, David B|Cavnor, Chris C|Handcock, Jeremy|Killcoyne, Sarah|Lin, Jake|Marzolf, Bruz|Ramsey, Stephen A|Rovira, Hector|Bressler, Ryan|Shmulevich, Ilya|Boyle, John', 'Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org|Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. dburdick@systemsbiology.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Computational Biology|Database Management Systems|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-377', '20630057', 'High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.'),
(93755, 2010, 'bmc', 'Graph-based clustering and characterization of repetitive sequences in next-generation sequencing data', 'Novák, Petr|Neumann, Pavel|Macas, Jirí', 'Biology Centre ASCR, Institute of Plant Molecular Biology, Branisovska 31, Ceske Budejovice, CZ-37005, Czech Republic.|Biology Centre ASCR, Institute of Plant Molecular Biology, Branisovska 31, Ceske Budejovice, CZ-37005, Czech Republic.|Biology Centre ASCR, Institute of Plant Molecular Biology, Branisovska 31, Ceske Budejovice, CZ-37005, Czech Republic.', '-|-|-', 229, 'Chromosome Mapping|Cluster Analysis|DNA, Plant|Genome, Plant|Peas|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Soybeans', 'Journal Article', '10.1186/1471-2105-11-378', '20633259', 'The investigation of plant genome structure and evolution requires comprehensive characterization of repetitive sequences that make up the majority of higher plant nuclear DNA. Since genome-wide characterization of repetitive elements is complicated by their high abundance and diversity, novel approaches based on massively-parallel sequencing are being adapted to facilitate the analysis. It has recently been demonstrated that the low-pass genome sequencing provided by a single 454 sequencing reaction is sufficient to capture information about all major repeat families, thus providing the opportunity for efficient repeat investigation in a wide range of species. However, the development of appropriate data mining tools is required in order to fully utilize this sequencing data for repeat characterization.'),
(93756, 2010, 'bmc', 'MetaPIGA v20: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics', 'Helaers, Raphaël|Milinkovitch, Michel C', 'Department of Biology of Namur University, Belgium.|Department of Biology of Namur University, Belgium.', 'Belgium|Belgium', 82, 'Algorithms|Artificial Intelligence|Computational Biology|Internet|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-379', '20633263', 'The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities.'),
(93757, 2010, 'bmc', 'R-Gada: a fast and flexible pipeline for copy number analysis in association studies', 'Pique-Regi, Roger|Cáceres, Alejandro|González, Juan R', 'Signal and Image Processing Institute, Viterbi School of Engineering, University of Southern California, EEB 400, 3740 McClintock Ave, Los Angeles, CA 90089-2564, USA. rpique@ieee.org|Signal and Image Processing Institute, Viterbi School of Engineering, University of Southern California, EEB 400, 3740 McClintock Ave, Los Angeles, CA 90089-2564, USA. rpique@ieee.org|Signal and Image Processing Institute, Viterbi School of Engineering, University of Southern California, EEB 400, 3740 McClintock Ave, Los Angeles, CA 90089-2564, USA. rpique@ieee.org', 'United States|United States|United States', 39, 'Algorithms|Bayes Theorem|DNA Copy Number Variations|Genome-Wide Association Study|Humans|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Population Groups|Software', 'Journal Article', '10.1186/1471-2105-11-380', '20637081', 'Genome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.'),
(93758, 2010, 'bmc', 'EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results', 'Liang, Shide|Zheng, Dandan|Standley, Daron M|Yao, Bo|Zacharias, Martin|Zhang, Chi', 'Jacobs University Bremen, Germany.|Jacobs University Bremen, Germany.|Jacobs University Bremen, Germany.|Jacobs University Bremen, Germany.|Jacobs University Bremen, Germany.|Jacobs University Bremen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 84, 'Algorithms|Antigen-Antibody Complex|Computational Biology|Epitope Mapping|Epitopes, B-Lymphocyte|Humans|ROC Curve|Regression Analysis', 'Journal Article', '10.1186/1471-2105-11-381', '20637083', 'Accurate prediction of antigenic epitopes is important for immunologic research and medical applications, but it is still an open problem in bioinformatics. The case for discontinuous epitopes is even worse - currently there are only a few discontinuous epitope prediction servers available, though discontinuous peptides constitute the majority of all B-cell antigenic epitopes. The small number of structures for antigen-antibody complexes limits the development of reliable discontinuous epitope prediction methods and an unbiased benchmark to evaluate developed methods.'),
(93759, 2010, 'bmc', 'Integration and visualization of systems biology data in context of the genome', 'Bare, J Christopher|Koide, Tie|Reiss, David J|Tenenbaum, Dan|Baliga, Nitin S', 'Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA.', 'United States|United States|United States|United States|United States', 21, 'Bacillus anthracis|Gene Expression Profiling|Gene Expression Regulation|Genome, Archaeal|Genomics|Halobacterium salinarum|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-382', '20642854', 'High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment.'),
(93760, 2010, 'bmc', 'The oligodeoxynucleotide sequences corresponding to never-expressed peptide motifs are mainly located in the non-coding strand', 'Capone, Giovanni|Novello, Giuseppe|Fasano, Candida|Trost, Brett|Bickis, Mik|Kusalik, Anthony|Kanduc, Darja', 'Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.|Department of Biochemistry and Molecular Biology Ernesto Quagliariello, University of Bari, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 13, 'Amino Acid Motifs|Codon|DNA|Gene Expression|Oligopeptides|Proteome', 'Journal Article', '10.1186/1471-2105-11-383', '20646284', 'We study the usage of specific peptide platforms in protein composition. Using the pentapeptide as a unit of length, we find that in the universal proteome many pentapeptides are heavily repeated (even thousands of times), whereas some are quite rare, and a small number do not appear at all. To understand the physico-chemical-biological basis underlying peptide usage at the proteomic level, in this study we analyse the energetic costs for the synthesis of rare and never-expressed versus frequent pentapeptides. In addition, we explore residue bulkiness, hydrophobicity, and codon number as factors able to modulate specific peptide frequencies. Then, the possible influence of amino acid composition is investigated in zero- and high-frequency pentapeptide sets by analysing the frequencies of the corresponding inverse-sequence pentapeptides. As a final step, we analyse the pentadecamer oligodeoxynucleotide sequences corresponding to the never-expressed pentapeptides.'),
(93761, 2010, 'bmc', 'VIPR: A probabilistic algorithm for analysis of microbial detection microarrays', 'Allred, Adam F|Wu, Guang|Wulan, Tuya|Fischer, Kael F|Holbrook, Michael R|Tesh, Robert B|Wang, David', 'Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA.', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Bayes Theorem|Hemorrhagic Fevers, Viral|Humans|Oligonucleotide Array Sequence Analysis|RNA Viruses', 'Journal Article', '10.1186/1471-2105-11-384', '20646301', 'All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance.'),
(93762, 2010, 'bmc', 'Applications of a formal approach to decipher discrete genetic networks', 'Corblin, Fabien|Fanchon, Eric|Trilling, Laurent', 'Laboratoire TIMC-IMAG, UMR CNRS/UJF 5525, Domaine de la Merci, 38710 La Tronche, France. fabien.corblin@irisa.fr|Laboratoire TIMC-IMAG, UMR CNRS/UJF 5525, Domaine de la Merci, 38710 La Tronche, France. fabien.corblin@irisa.fr|Laboratoire TIMC-IMAG, UMR CNRS/UJF 5525, Domaine de la Merci, 38710 La Tronche, France. fabien.corblin@irisa.fr', 'France|France|France', 36, 'Algorithms|Gene Regulatory Networks|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-385', '20646302', 'A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language.'),
(93763, 2010, 'bmc', 'Efficiency clustering for low-density microarrays and its application to QPCR', 'Lock, Eric F|Ziemiecke, Ryan|Marron, Js|Dittmer, Dirk P', 'Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA.', 'United States|United States|United States|United States', 6, 'Algorithms|Cluster Analysis|DNA Primers|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Regression Analysis', 'Journal Article', '10.1186/1471-2105-11-386', '20646303', 'Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency.'),
(93764, 2010, 'bmc', 'S3DB core: a framework for RDF generation and management in bioinformatics infrastructures', 'Almeida, Jonas S|Deus, Helena F|Maass, Wolfgang', 'Department of Bioinformatics and Computational Biology, The University of Texas M D Anderson Cancer Center, 1515 Holcombe Blvd Houston, TX 77030, USA. jalmeida@mathbiol.org|Department of Bioinformatics and Computational Biology, The University of Texas M D Anderson Cancer Center, 1515 Holcombe Blvd Houston, TX 77030, USA. jalmeida@mathbiol.org|Department of Bioinformatics and Computational Biology, The University of Texas M D Anderson Cancer Center, 1515 Holcombe Blvd Houston, TX 77030, USA. jalmeida@mathbiol.org', 'United States|United States|United States', 12, 'Computational Biology|Computers|Internet|Markov Chains|Models, Biological', 'Journal Article', '10.1186/1471-2105-11-387', '20646315', 'Biomedical research is set to greatly benefit from the use of semantic web technologies in the design of computational infrastructure. However, beyond well defined research initiatives, substantial issues of data heterogeneity, source distribution, and privacy currently stand in the way towards the personalization of Medicine.'),
(93765, 2010, 'bmc', 'Deriving enzymatic and taxonomic signatures of metagenomes from short read data', 'Weingart, Uri|Persi, Erez|Gophna, Uri|Horn, David', 'School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.|School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.|School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.|School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel', 0, 'Bacteria|Bacterial Proteins|Databases, Protein|Escherichia coli|Genome, Bacterial|Metagenome|Metagenomics|Peptides', 'Journal Article', '10.1186/1471-2105-11-390', '20649951', 'We propose a method for deriving enzymatic signatures from short read metagenomic data of unknown species. The short read data are converted to six pseudo-peptide candidates. We search for occurrences of Specific Peptides (SPs) on the latter. SPs are peptides that are indicative of enzymatic function as defined by the Enzyme Commission (EC) nomenclature. The number of SP hits on an ensemble of short reads is counted and then converted to estimates of numbers of enzymatic genes associated with different EC categories in the studied metagenome. Relative amounts of different EC categories define the enzymatic spectrum, without the need to perform genomic assemblies of short reads.'),
(93766, 2010, 'bmc', 'Robust test method for time-course microarray experiments', 'Sohn, Insuk|Owzar, Kouros|George, Stephen L|Kim, Sujong|Jung, Sin-Ho', 'Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com|Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com|Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com|Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com|Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 1, 'Adult|Aged|Aged, 80 and over|Algorithms|Animals|Arthritis, Rheumatoid|Caenorhabditis elegans|Computer Simulation|Gene Expression Profiling|Humans|Middle Aged|Oligonucleotide Array Sequence Analysis|Regression Analysis|Time Factors|Young Adult', 'Journal Article', '10.1186/1471-2105-11-391', '20649954', 'In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For many of these experiments, the scientific aim is the identification of genes for which the trajectories depend on an experimental or phenotypic factor. There is an extensive recent body of literature on statistical methodology for addressing this analytical problem. Most of the existing methods are based on estimating the time-course trajectories using parametric or non-parametric mean regression methods. The sensitivity of these regression methods to outliers, an issue that is well documented in the statistical literature, should be of concern when analyzing microarray data.'),
(93767, 2010, 'bmc', 'Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks', 'Chen, Lina|Wang, Hong|Zhang, Liangcai|Li, Wan|Wang, Qian|Shang, Yukui|He, Yuehan|He, Weiming|Li, Xu|Tai, Jingxie|Li, Xia', 'College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn|College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China. chenlina_2004@yahoo.com.cn', 'China|China|China|China|China|China|China|China|China|China|China', 0, 'Databases, Genetic|Gene Regulatory Networks|Humans|Metabolic Networks and Pathways|Models, Genetic|Models, Statistical|Neoplasms|Proteins', 'Journal Article', '10.1186/1471-2105-11-392', '20649980', 'Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.'),
(93768, 2010, 'bmc', 'Compartmentalization of the Edinburgh Human Metabolic Network', 'Hao, Tong|Ma, Hong-Wu|Zhao, Xue-Ming|Goryanin, Igor', 'Department of Biochemical Engineering, School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China.|Department of Biochemical Engineering, School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China.|Department of Biochemical Engineering, School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China.|Department of Biochemical Engineering, School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, China.', 'China|China|China|China', 74, 'Biological Transport|Cell Physiological Phenomena|Computational Biology|Computer Simulation|Databases, Protein|Humans|Internet|Metabolic Networks and Pathways|Models, Biological|Organelles', 'Journal Article', '10.1186/1471-2105-11-393', '20649990', 'Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles. Diverse micro-environments in different organelles may lead to distinct functions of the same protein and the use of different enzymes for the same metabolic reaction. To better understand the complexity in the human metabolism, a compartmentalized human metabolic network with integrated sub-cellular location information is required.'),
(93769, 2010, 'bmc', 'A comparison of internal validation techniques for multifactor dimensionality reduction', 'Winham, Stacey J|Slater, Andrew J|Motsinger-Reif, Alison A', 'Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.|Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.|Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.', 'United States|United States|United States', 15, 'Algorithms|Causality|Computer Simulation|Epistasis, Genetic|HIV Infections|Humans|Models, Genetic|Validation Studies as Topic', 'Comparative Study', '10.1186/1471-2105-11-394', '20650002', 'It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.'),
(93770, 2010, 'bmc', 'MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data', 'Pluskal, Tomás|Castillo, Sandra|Villar-Briones, Alejandro|Oresic, Matej', 'G0 Cell Unit, Okinawa Institute of Science and Technology, Onna, Okinawa, Japan. pluskal@oist.jp|G0 Cell Unit, Okinawa Institute of Science and Technology, Onna, Okinawa, Japan. pluskal@oist.jp|G0 Cell Unit, Okinawa Institute of Science and Technology, Onna, Okinawa, Japan. pluskal@oist.jp|G0 Cell Unit, Okinawa Institute of Science and Technology, Onna, Okinawa, Japan. pluskal@oist.jp', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Mass Spectrometry|Proteomics|Software|Software Design', 'Journal Article', '10.1186/1471-2105-11-395', '20650010', 'Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93771, 2010, 'bmc', 'Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression', 'Xu, Xiaojiang|Hoang, Stephen|Mayo, Marty W|Bekiranov, Stefan', 'Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, Virginia, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, Virginia, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, Virginia, USA.|Department of Biochemistry and Molecular Genetics, University of Virginia Health System, Charlottesville, Virginia, USA.', 'United States|United States|United States|United States', 62, 'Arginine|Artificial Intelligence|Base Sequence|CD4-Positive T-Lymphocytes|Epigenesis, Genetic|Gene Expression|Histones|Humans|Lysine|Methylation|Oligonucleotide Array Sequence Analysis|Protein Methyltransferases|Protein Processing, Post-Translational', 'Journal Article', '10.1186/1471-2105-11-396', '20653935', 'In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets.'),
(93772, 2010, 'bmc', 'Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes', 'Zhang, Shaoqiang|Li, Shan|Pham, Phuc T|Su, Zhengchang', 'Department of Bioinformatics and Genomics, Center for Bioinformatics Research, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, Center for Bioinformatics Research, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, Center for Bioinformatics Research, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, Center for Bioinformatics Research, the University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Binding Sites|Chromosome Mapping|Escherichia coli|Genome, Bacterial|Genome-Wide Association Study|Phylogeny|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-397', '20653963', 'Our current understanding of transcription factor binding sites (TFBSs) in sequenced prokaryotic genomes is very limited due to the lack of an accurate and efficient computational method for the prediction of TFBSs at a genome scale. In an attempt to change this situation, we have recently developed a comparative genomics based algorithm called GLECLUBS for de novo genome-wide prediction of TFBSs in a target genome. Although GLECLUBS has achieved rather high prediction accuracy of TFBSs in a target genome, it is still not efficient enough to be applied to all the sequenced prokaryotic genomes.'),
(93773, 2010, 'bmc', 'A comparative study of conservation and variation scores', 'Johansson, Fredrik|Toh, Hiroyuki', 'Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. fredjoha@gmail.com|Division of Bioinformatics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. fredjoha@gmail.com', 'Japan|Japan', 33, 'Amino Acid Sequence|Area Under Curve|Catalytic Domain|Cluster Analysis|Phylogeny|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-388', '20663120', 'Conservation and variation scores are used when evaluating sites in a multiple sequence alignment, in order to identify residues critical for structure or function. A variety of scores are available today but it is not clear how different scores relate to each other.'),
(93774, 2010, 'bmc', 'GeneBrowser 2: an application to explore and identify common biological traits in a set of genes', 'Arrais, Joel P|Fernandes, João|Pereira, João|Oliveira, José Luís', 'Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.|Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.', 'Portugal|Portugal|Portugal|Portugal', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genomics|Information Storage and Retrieval|Internet|Saccharomyces cerevisiae|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-389', '20663121', 'The development of high-throughput laboratory techniques created a demand for computer-assisted result analysis tools. Many of these techniques return lists of genes whose interpretation requires finding relevant biological roles for the problem at hand. The required information is typically available in public databases, and usually, this information must be manually retrieved to complement the analysis. This process is a very time-consuming task that should be automated as much as possible.'),
(93775, 2010, 'bmc', 'CIG-DB: the database for human or mouse immunoglobulin and T cell receptor genes available for cancer studies', 'Nakamura, Yoji|Komiyama, Tomoyoshi|Furue, Motoki|Gojobori, Takashi|Akiyama, Yasuto', 'Immunotherapy Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.|Immunotherapy Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.|Immunotherapy Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.|Immunotherapy Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.|Immunotherapy Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan.', 'Japan|Japan|Japan|Japan|Japan', 0, 'Animals|Base Sequence|Databases, Genetic|Epitopes|Genes, Neoplasm|Genes, T-Cell Receptor|Humans|Immunoglobulins|Mice|Models, Molecular|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-398', '20663186', 'Immunoglobulin (IG or antibody) and the T-cell receptor (TR) are pivotal proteins in the immune system of higher organisms. In cancer immunotherapy, the immune responses mediated by tumor-epitope-binding IG or TR play important roles in anticancer effects. Although there are public databases specific for immunological genes, their contents have not been associated with clinical studies. Therefore, we developed an integrated database of IG/TR data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]).'),
(93776, 2010, 'bmc', 'Quantized correlation coefficient for measuring reproducibility of ChIP-chip data', 'Peng, Shouyong|Kuroda, Mitzi I|Park, Peter J', 'Department of Medicine, Brigham and Womens Hospital, Boston, MA 02115, USA.|Department of Medicine, Brigham and Womens Hospital, Boston, MA 02115, USA.|Department of Medicine, Brigham and Womens Hospital, Boston, MA 02115, USA.', 'United States|United States|United States', 1, 'Animals|Chromatin Immunoprecipitation|Drosophila|Female|Gene Expression Profiling|Humans|Male|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1186/1471-2105-11-399', '20663215', 'Chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) is used to study protein-DNA interactions and histone modifications on a genome-scale. To ensure data quality, these experiments are usually performed in replicates, and a correlation coefficient between replicates is used often to assess reproducibility. However, the correlation coefficient can be misleading because it is affected not only by the reproducibility of the signal but also by the amount of binding signal present in the data.'),
(93777, 2010, 'bmc', 'Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes', 'Zeisel, Amit|Amir, Amnon|Köstler, Wolfgang J|Domany, Eytan', 'Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel.|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel.|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel.|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel|Israel|Israel', 21, 'Gene Expression Profiling|Gene Expression Regulation|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-11-400', '20663218', 'In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one searches for differentially expressed genes, the small number of samples gives rise to an inaccurate estimation of the experimental noise. This, in turn, leads to loss of statistical power.'),
(93778, 2010, 'bmc', 'Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v10)', 'Cornuet, Jean-Marie|Ravigné, Virgine|Estoup, Arnaud', 'INRA, UMR CBGP (INRA/IRD/Cirad/Montpellier SupAgro), Campus international de Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez cedex, France.|INRA, UMR CBGP (INRA/IRD/Cirad/Montpellier SupAgro), Campus international de Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez cedex, France.|INRA, UMR CBGP (INRA/IRD/Cirad/Montpellier SupAgro), Campus international de Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez cedex, France.', 'France|France|France', 0, 'Algorithms|Base Sequence|Bayes Theorem|Biological Evolution|Cell Nucleus|Computer Simulation|DNA, Mitochondrial|Genetics, Population|Humans|Microsatellite Repeats|Models, Genetic|Software', 'Journal Article', '10.1186/1471-2105-11-401', '20667077', 'Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations. The software DIYABC offers a user-friendly interface allowing non-expert users to consider population histories involving any combination of population divergences, admixtures and population size changes. We here describe and illustrate new developments of this software that mainly include (i) inference from DNA sequence data in addition or separately to microsatellite data, (ii) the possibility to analyze five categories of loci considering balanced or non balanced sex ratios: autosomal diploid, autosomal haploid, X-linked, Y-linked and mitochondrial, and (iii) the possibility to perform model checking computation to assess the goodness-of-fit of a model, a feature of ABC analysis that has been so far neglected.'),
(93779, 2010, 'bmc', 'Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information', 'Chen, Peng|Li, Jinyan', 'Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, 639798 Singapore.|Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, 639798 Singapore.', 'Singapore|Singapore', 34, 'Algorithms|Amino Acid Sequence|Evolution, Molecular|Humans|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-402', '20667087', 'Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.'),
(93780, 2010, 'bmc', 'Data reduction for spectral clustering to analyze high throughput flow cytometry data', 'Zare, Habil|Shooshtari, Parisa|Gupta, Arvind|Brinkman, Ryan R', 'Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada.|Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada.|Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada.|Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada.', 'Canada|Canada|Canada|Canada', 0, 'Algorithms|Cluster Analysis|Flow Cytometry', 'Journal Article', '10.1186/1471-2105-11-403', '20667133', 'Recent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.'),
(93781, 2010, 'bmc', 'RuleMonkey: software for stochastic simulation of rule-based models', 'Colvin, Joshua|Monine, Michael I|Gutenkunst, Ryan N|Hlavacek, William S|Von Hoff, Daniel D|Posner, Richard G', 'Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.|Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.|Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.|Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.|Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.|Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computer Simulation|Internet|Models, Biological|Proteins|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-404', '20673321', 'The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these network-free simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.'),
(93782, 2010, 'bmc', 'OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data', 'Wenig, Philip|Odermatt, Juergen', 'Department of Wood Science, University of Hamburg, Leuschnerstrasse 91, 21031 Hamburg, Germany. philip.wenig@gmx.net|Department of Wood Science, University of Hamburg, Leuschnerstrasse 91, 21031 Hamburg, Germany. philip.wenig@gmx.net', 'Germany|Germany', 74, 'Algorithms|Chromatography|Mass Spectrometry|Software', 'Journal Article', '10.1186/1471-2105-11-405', '20673335', 'Today, data evaluation has become a bottleneck in chromatographic science. Analytical instruments equipped with automated samplers yield large amounts of measurement data, which needs to be verified and analyzed. Since nearly every GC/MS instrument vendor offers its own data format and software tools, the consequences are problems with data exchange and a lack of comparability between the analytical results. To challenge this situation a number of either commercial or non-profit software applications have been developed. These applications provide functionalities to import and analyze several data formats but have shortcomings in terms of the transparency of the implemented analytical algorithms and/or are restricted to a specific computer platform.'),
(93783, 2010, 'bmc', 'MS4--Multi-Scale Selector of Sequence Signatures: an alignment-free method for classification of biological sequences', 'Corel, Eduardo|Pitschi, Florian|Laprevotte, Ivan|Grasseau, Gilles|Didier, Gilles|Devauchelle, Claudine', 'Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.|Georg-August-Universität, Institut für Mikrobiologie und Genetik, Göttingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 6, 'Algorithms|Amino Acid Sequence|Base Sequence|Classification|Computational Biology|Genes, nef|Genome, Viral|HIV|HIV Long Terminal Repeat|Simian Immunodeficiency Virus|Software', 'Journal Article', '10.1186/1471-2105-11-406', '20673356', 'While multiple alignment is the first step of usual classification schemes for biological sequences, alignment-free methods are being increasingly used as alternatives when multiple alignments fail. Subword-based combinatorial methods are popular for their low algorithmic complexity (suffix trees ...) or exhaustivity (motif search), in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the N-local decoding) based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some good values of N, sufficiently relevant to form the basis of a reliable alignment-free classification. The aim of this paper is to develop a method that uses the similarities detected by N-local decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool.'),
(93784, 2010, 'bmc', 'Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures', 'Kountouris, Petros|Hirst, Jonathan D', 'School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK.|School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-407', '20673368', 'Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.'),
(93785, 2010, 'bmc', 'Comparison study of microarray meta-analysis methods', 'Campain, Anna|Yang, Yee Hwa', 'School of Mathematics and Statistics, Center of Mathematical Biology, University of Sydney, F07 Sydney, NSW 2006, Australia. anna.campain@sydney.edu.au|School of Mathematics and Statistics, Center of Mathematical Biology, University of Sydney, F07 Sydney, NSW 2006, Australia. anna.campain@sydney.edu.au', 'Australia|Australia', 62, 'Algorithms|Breast Neoplasms|Databases, Genetic|Female|Gene Expression Profiling|Humans|Lymphoma|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|ROC Curve', 'Comparative Study', '10.1186/1471-2105-11-408', '20678237', 'Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.'),
(93786, 2010, 'bmc', 'Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape', 'Theys, Kristof|Deforche, Koen|Beheydt, Gertjan|Moreau, Yves|van Laethem, Kristel|Lemey, Philippe|Camacho, Ricardo J|Rhee, Soo-Yon|Shafer, Robert W|Van Wijngaerden, Eric|Vandamme, Anne-Mieke', 'Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven, Leuven, Belgium. kristof.theys@uz.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 12, 'Anti-HIV Agents|Drug Resistance, Viral|HIV Infections|HIV Protease|HIV-1|Humans|Mutation|Nelfinavir|Treatment Failure', 'Journal Article', '10.1186/1471-2105-11-409', '20682040', 'Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.'),
(93787, 2010, 'bmc', 'A method for automatically extracting infectious disease-related primers and probes from the literature', 'García-Remesal, Miguel|Cuevas, Alejandro|López-Alonso, Victoria|López-Campos, Guillermo|de la Calle, Guillermo|de la Iglesia, Diana|Pérez-Rey, David|Crespo, José|Martín-Sánchez, Fernando|Maojo, Víctor', 'Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es|Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Base Sequence|DNA Primers|DNA Probes|Data Mining|Databases, Genetic|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-11-410', '20682041', 'Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information.'),
(93788, 2010, 'bmc', 'Extracting the abstraction pyramid from complex networks', 'Cheng, Chia-Ying|Hu, Yuh-Jyh', 'Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.|Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.', '-|-', 9, 'Algorithms|Cluster Analysis|Escherichia coli|Information Storage and Retrieval|Metabolic Networks and Pathways|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-11-411', '20682075', 'At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the vertical relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the horizontal relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships.'),
(93789, 2010, 'bmc', 'The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based high-throughput data', 'Wang, Junbai', 'Department of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello 0310 Oslo, Norway. junbai.wang@rr-research.no', 'Norway', 5, 'Algorithms|Bayes Theorem|Chromatin Immunoprecipitation|DNA|DNA-Binding Proteins|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-412', '20684785', 'To further understand the implementation of hyperparameters re-estimation technique in Bayesian hierarchical model, we added two more prior assumptions over the weight in BayesPI, namely Laplace prior and Cauchy prior, by using the evidence approximation method. In addition, we divided hyperparameter (regularization constants alpha of the model) into multiple distinct classes based on either the structure of the neural networks or the property of the weights.'),
(93790, 2010, 'bmc', 'Nonparametric identification of regulatory interactions from spatial and temporal gene expression data', 'Aswani, Anil|Keränen, Soile V E|Brown, James|Fowlkes, Charless C|Knowles, David W|Biggin, Mark D|Bickel, Peter|Tomlin, Claire J', 'Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu|Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA. aaswani@eecs.berkeley.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Animals|Blastoderm|Drosophila Proteins|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Gene Regulatory Networks|Homeodomain Proteins|Models, Biological|Proteins|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-413', '20684787', 'The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.'),
(93791, 2010, 'bmc', 'Global parameter estimation methods for stochastic biochemical systems', 'Poovathingal, Suresh Kumar|Gunawan, Rudiyanto', 'Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117576, Singapore.|Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117576, Singapore.', 'Singapore|Singapore', 42, 'Escherichia coli|Galactose|Models, Biological|Monte Carlo Method|RNA, Bacterial|RNA, Messenger|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-11-414', '20691037', 'The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.'),
(93792, 2010, 'bmc', 'PeakAnalyzer: genome-wide annotation of chromatin binding and modification loci', 'Salmon-Divon, Mali|Dvinge, Heidi|Tammoja, Kairi|Bertone, Paul', 'EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 140, 'Base Sequence|Binding Sites|Chromatin|Chromatin Assembly and Disassembly|Chromatin Immunoprecipitation|Gene Expression Regulation|Genome-Wide Association Study|Oligonucleotide Array Sequence Analysis|Programming Languages|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|Transcription Initiation Site', 'Journal Article', '10.1186/1471-2105-11-415', '20691053', 'Functional genomic studies involving high-throughput sequencing and tiling array applications, such as ChIP-seq and ChIP-chip, generate large numbers of experimentally-derived signal peaks across the genome under study. In analyzing these loci to determine their potential regulatory functions, areas of signal enrichment must be considered relative to proximal genes and regulatory elements annotated throughout the target genome Regions of chromatin association by transcriptional regulators should be distinguished as individual binding sites in order to enhance downstream analyses, such as the identification of known and novel consensus motifs.'),
(93793, 2010, 'bmc', 'Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data', 'Beretta, Lorenzo|Santaniello, Alessandro|van Riel, Piet L C M|Coenen, Marieke J H|Scorza, Raffaella', 'Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy. lorberimm@hotmail.com|Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy. lorberimm@hotmail.com|Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy. lorberimm@hotmail.com|Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy. lorberimm@hotmail.com|Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca Granda Ospedale Maggiore Policlinico and University of Milan, Milan, Italy. lorberimm@hotmail.com', 'Italy|Italy|Italy|Italy|Italy', 12, 'Adult|Algorithms|Antirheumatic Agents|Arthritis, Rheumatoid|Case-Control Studies|Computer Simulation|Epistasis, Genetic|Gene Expression Profiling|Humans|Middle Aged|Polymorphism, Single Nucleotide|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1186/1471-2105-11-416', '20691091', 'Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets.'),
(93794, 2010, 'bmc', 'FACT: functional annotation transfer between proteins with similar feature architectures', 'Koestler, Tina|von Haeseler, Arndt|Ebersberger, Ingo', 'Center for Integrative Bioinformatics Vienna, Max F Perutz Laboratories, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F Perutz Laboratories, Dr Bohrgasse 9, A-1030 Vienna, Austria.|Center for Integrative Bioinformatics Vienna, Max F Perutz Laboratories, Dr Bohrgasse 9, A-1030 Vienna, Austria.', 'Austria|Austria|Austria', 18, 'Chromosome Mapping|Glutathione|Glutathione Transferase|Golgi Matrix Proteins|Humans|Membrane Proteins|Proteins|Proteomics|Protozoan Proteins|Trypanosoma brucei brucei|Yeasts', 'Journal Article', '10.1186/1471-2105-11-417', '20696036', 'The increasing number of sequenced genomes provides the basis for exploring the genetic and functional diversity within the tree of life. Only a tiny fraction of the encoded proteins undergoes a thorough experimental characterization. For the remainder, bioinformatics annotation tools are the only means to infer their function. Exploiting significant sequence similarities to already characterized proteins, commonly taken as evidence for homology, is the prevalent method to deduce functional equivalence. Such methods fail when homologs are too diverged, or when they have assumed a different function. Finally, due to convergent evolution, functional equivalence is not necessarily linked to common ancestry. Therefore complementary approaches are required to identify functional equivalents.'),
(93795, 2010, 'bmc', 'eGIFT: mining gene information from the literature', 'Tudor, Catalina O|Schmidt, Carl J|Vijay-Shanker, K', 'Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA. tudor@cis.udel.edu|Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA. tudor@cis.udel.edu|Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA. tudor@cis.udel.edu', 'United States|United States|United States', 22, 'Data Mining|Genes|Internet|Periodicals as Topic|Software|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-11-418', '20696046', 'With the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.'),
(93796, 2010, 'bmc', 'TAM: a method for enrichment and depletion analysis of a microRNA category in a list of microRNAs', 'Lu, Ming|Shi, Bing|Wang, Juan|Cao, Qun|Cui, Qinghua', 'Department of Biomedical Informatics, Peking University Health Science Center, Beijing, 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, Beijing, 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, Beijing, 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, Beijing, 100191, China.|Department of Biomedical Informatics, Peking University Health Science Center, Beijing, 100191, China.', 'China|China|China|China|China', 102, 'Gene Expression Profiling|Genome, Human|Humans|Internet|MicroRNAs|Myocardial Infarction|Software', 'Journal Article', '10.1186/1471-2105-11-419', '20696049', 'MicroRNAs (miRNAs) are a class of important gene regulators. The number of identified miRNAs has been increasing dramatically in recent years. An emerging major challenge is the interpretation of the genome-scale miRNA datasets, including those derived from microarray and deep-sequencing. It is interesting and important to know the common rules or patterns behind a list of miRNAs, (i.e. the deregulated miRNAs resulted from an experiment of miRNA microarray or deep-sequencing).'),
(93797, 2010, 'bmc', 'An improved classification of G-protein-coupled receptors using sequence-derived features', 'Peng, Zhen-Ling|Yang, Jian-Yi|Chen, Xin', '1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada.|1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada.|1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada.', 'Canada|Canada|Canada', 30, 'Algorithms|Amino Acids|Principal Component Analysis|Receptors, G-Protein-Coupled|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-420', '20696050', 'G-protein-coupled receptors (GPCRs) play a key role in diverse physiological processes and are the targets of almost two-thirds of the marketed drugs. The 3 D structures of GPCRs are largely unavailable, however, a large number of GPCR primary sequences are known. To facilitate the identification and characterization of novel receptors, it is therefore very valuable to develop a computational method to accurately predict GPCRs from the protein primary sequences.'),
(93798, 2010, 'bmc', 'Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database', 'Cheng, Wei-Chung|Tsai, Min-Lung|Chang, Cheng-Wei|Huang, Ching-Lung|Chen, Chaang-Ray|Shu, Wun-Yi|Lee, Yun-Shien|Wang, Tzu-Hao|Hong, Ji-Hong|Li, Chia-Yang|Hsu, Ian C', 'Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.|Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.', '-|-|-|-|-|-|-|-|-|-|-', 25, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Humans|Internet|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Quality Control|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-421', '20698961', 'Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.'),
(93799, 2010, 'bmc', 'baySeq: empirical Bayesian methods for identifying differential expression in sequence count data', 'Hardcastle, Thomas J|Kelly, Krystyna A', 'Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK. tjh48@cam.ac.uk|Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK. tjh48@cam.ac.uk', 'United Kingdom|United Kingdom', 453, 'Algorithms|Arabidopsis|Base Sequence|Bayes Theorem|Gene Expression Profiling|RNA, Plant|Research Design', 'Journal Article', '10.1186/1471-2105-11-422', '20698981', 'High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation.'),
(93800, 2010, 'bmc', 'Ranked retrieval of Computational Biology models', 'Henkel, Ron|Endler, Lukas|Peters, Andre|Le Novère, Nicolas|Waltemath, Dagmar', 'Database and Information Systems, University of Rostock, Rostock, Germany.|Database and Information Systems, University of Rostock, Rostock, Germany.|Database and Information Systems, University of Rostock, Rostock, Germany.|Database and Information Systems, University of Rostock, Rostock, Germany.|Database and Information Systems, University of Rostock, Rostock, Germany.', 'Germany|Germany|Germany|Germany|Germany', 27, 'Computational Biology|Computer Simulation|Information Storage and Retrieval|Models, Biological|Search Engine|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-423', '20701772', 'The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind.'),
(93801, 2010, 'bmc', 'Computing H/D-exchange rates of single residues from data of proteolytic fragments', 'Althaus, Ernst|Canzar, Stefan|Ehrler, Carsten|Emmett, Mark R|Karrenbauer, Andreas|Marshall, Alan G|Meyer-Bäse, Anke|Tipton, Jeremiah D|Zhang, Hui-Min', 'Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de|Institut für Informatik, Fachbereich 08, Staudingerweg 9, 55099 Mainz, Germany. ernst.althaus@uni-mainz.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Deuterium Exchange Measurement|Hydrolysis|Mass Spectrometry|Models, Molecular|Pepsin A|Peptide Hydrolases|Peptides|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-11-424', '20701784', 'Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein complex. In sHDX experiments mutant proteins are compared to wild-type proteins or a ligand is added to the protein and compared to the wild-type protein (or mutant). The number of deuteriums incorporated into the polypeptides generated from the protease digest of the protein is related to the solvent accessibility of amide protons within the original protein construct.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93802, 2010, 'bmc', 'TF-finder: a software package for identifying transcription factors involved in biological processes using microarray data and existing knowledge base', 'Cui, Xiaoqi|Wang, Tong|Chen, Huann-Sheng|Busov, Victor|Wei, Hairong', 'School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.|School of Forest Resources and Environmental Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA.', 'United States|United States|United States|United States|United States', 9, 'Arabidopsis|Arabidopsis Proteins|Conserved Sequence|Gene Regulatory Networks|Knowledge Bases|Software|Stress, Physiological|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-425', '20704747', 'Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a large number of genes and complicated interactions in a gene regulatory network (GRN), identification of the TFs involved in a biology process remains to be very challenging. In reality, the recognition of TFs for a given a biological process can be further complicated by the fact that most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation except for small conserved domains. This poses a significant challenge for identification of the exact TFs involved or ranking the importance of a set of TFs to a process of interest. Therefore, new methods for recognizing novel TFs are desperately needed. Although a plethora of methods have been developed to infer regulatory genes using microarray data, it is still rare to find the methods that use existing knowledge base in particular the validated genes known to be involved in a process to bait/guide discovery of novel TFs. Such methods can replace the sometimes-arbitrary process of selection of candidate genes for experimental validation and significantly advance our knowledge and understanding of the regulation of a process.'),
(93803, 2010, 'bmc', 'Proceedings from IEEE International Conference on Bioinformatics and Biomedicine 2009, Washington, DC, USA  1-4 November 2009', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1186/1471-2105-11-425', '20715302', '-'),
(93804, 2010, 'bmc', 'TOPSAN: a collaborative annotation environment for structural genomics', 'Weekes, Dana|Krishna, S Sri|Bakolitsa, Constantina|Wilson, Ian A|Godzik, Adam|Wooley, John', 'Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.|Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.|Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.|Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.|Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.|Joint Center for Structural Genomics, Bioinformatics Core, Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Cooperative Behavior|Databases, Genetic|Genomics|Internet|Microarray Analysis|Proteins', 'Journal Article', '10.1186/1471-2105-11-426', '20716366', 'Many protein structures determined in high-throughput structural genomics centers, despite their significant novelty and importance, are available only as PDB depositions and are not accompanied by a peer-reviewed manuscript. Because of this they are not accessible by the standard tools of literature searches, remaining underutilized by the broad biological community.'),
(93805, 2010, 'bmc', 'An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data', 'Datta, Susmita|Pihur, Vasyl|Datta, Somnath', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.', 'United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Breast Neoplasms|Computer Simulation|Female|Humans|Male|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-11-427', '20716381', 'Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers.'),
(93806, 2010, 'bmc', 'Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets', 'Albayrak, Aydin|Otu, Hasan H|Sezerman, Ugur O', 'Biological Sciences and Bioengineering, Sabanci University, Orhanli, Tuzla, Istanbul, Turkey.|Biological Sciences and Bioengineering, Sabanci University, Orhanli, Tuzla, Istanbul, Turkey.|Biological Sciences and Bioengineering, Sabanci University, Orhanli, Tuzla, Istanbul, Turkey.', 'Turkey|Turkey|Turkey', 16, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computer Simulation|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-428', '20718947', 'Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.'),
(93807, 2010, 'bmc', 'Calculation of accurate small angle X-ray scattering curves from coarse-grained protein models', 'Stovgaard, Kasper|Andreetta, Christian|Ferkinghoff-Borg, Jesper|Hamelryck, Thomas', 'Department of Biology, University of Copenhagen, The Bioinformatics Centre, Denmark.|Department of Biology, University of Copenhagen, The Bioinformatics Centre, Denmark.|Department of Biology, University of Copenhagen, The Bioinformatics Centre, Denmark.|Department of Biology, University of Copenhagen, The Bioinformatics Centre, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 34, 'Amino Acid Sequence|Models, Molecular|Models, Statistical|Protein Conformation|Proteins|Scattering, Small Angle|Solutions|X-Ray Diffraction', 'Journal Article', '10.1186/1471-2105-11-429', '20718956', 'Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure determination methods make it unlikely that this gap will disappear in the near future. Small angle X-ray scattering (SAXS) is an established low resolution method for routinely determining the structure of proteins in solution. The purpose of this study is to develop a method for the efficient calculation of accurate SAXS curves from coarse-grained protein models. Such a method can for example be used to construct a likelihood function, which is paramount for structure determination based on statistical inference.'),
(93808, 2010, 'bmc', 'Coverage statistics for sequence census methods', 'Evans, Steven N|Hower, Valerie|Pachter, Lior', 'Department of Mathematics, University of California, Berkeley, California, USA.|Department of Mathematics, University of California, Berkeley, California, USA.|Department of Mathematics, University of California, Berkeley, California, USA.', 'United States|United States|United States', 8, 'Base Sequence|DNA Fragmentation|Models, Statistical|Monte Carlo Method|Sequence Analysis, DNA|Statistical Distributions', 'Journal Article', '10.1186/1471-2105-11-430', '20718980', 'We study the statistical properties of fragment coverage in genome sequencing experiments. In an extension of the classic Lander-Waterman model, we consider the effect of the length distribution of fragments. We also introduce a coding of the shape of the coverage depth function as a tree and explain how this can be used to detect regions with anomalous coverage. This modeling perspective is especially germane to current high-throughput sequencing experiments, where both sample preparation protocols and sequencing technology particulars can affect fragment length distributions.'),
(93809, 2010, 'bmc', 'Hidden Markov model speed heuristic and iterative HMM search procedure', 'Johnson, L Steven|Eddy, Sean R|Portugaly, Elon', 'Department of Immunology and Pathology, Washington University School of Medicine, St Louis, Missouri, USA. stevej@pathology.wustl.edu|Department of Immunology and Pathology, Washington University School of Medicine, St Louis, Missouri, USA. stevej@pathology.wustl.edu|Department of Immunology and Pathology, Washington University School of Medicine, St Louis, Missouri, USA. stevej@pathology.wustl.edu', 'United States|United States|United States', 349, 'Algorithms|Artificial Intelligence|Base Sequence|Databases, Protein|Markov Chains|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-431', '20718988', 'Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases.'),
(93810, 2010, 'bmc', 'rSW-seq: algorithm for detection of copy number alterations in deep sequencing data', 'Kim, Tae-Min|Luquette, Lovelace J|Xi, Ruibin|Park, Peter J', 'Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.|Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.', 'United States|United States|United States|United States', 41, 'Algorithms|Base Sequence|Computer Simulation|DNA Copy Number Variations|DNA, Neoplasm|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Tumor Cells, Cultured', 'Comparative Study', '10.1186/1471-2105-11-432', '20718989', 'Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy.'),
(93811, 2010, 'bmc', 'Gene Expression Browser: large-scale and cross-experiment microarray data integration, management, search & visualization', 'Zhang, Ming|Zhang, Yudong|Liu, Li|Yu, Lijuan|Tsang, Shirley|Tan, Jing|Yao, Wenhua|Kang, Manjit S|An, Yongqiang|Fan, Xingming', 'GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.|GeneExp, 310 South Third Street, San Jose, CA 95112, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Arabidopsis|Computational Biology|Database Management Systems|Databases, Genetic|Electronic Data Processing|Gene Expression|Information Storage and Retrieval|Internet|Microarray Analysis|Search Engine|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-433', '20727159', 'In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for exploring gene functions, gene networks and biological pathways. Effectively utilizing these invaluable microarray data remains challenging due to a lack of powerful tools to integrate large-scale gene-expression information across diverse experiments and to search and visualize a large number of gene-expression data points.'),
(93812, 2010, 'bmc', 'A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis', 'Kim, Jiwoong|Ahn, Yongju|Lee, Kichan|Park, Sung Hee|Kim, Sangsoo', 'Department of Bioinformatics & Life Sciences, Soongsil University, Seoul, Korea.|Department of Bioinformatics & Life Sciences, Soongsil University, Seoul, Korea.|Department of Bioinformatics & Life Sciences, Soongsil University, Seoul, Korea.|Department of Bioinformatics & Life Sciences, Soongsil University, Seoul, Korea.|Department of Bioinformatics & Life Sciences, Soongsil University, Seoul, Korea.', '-|-|-|-|-', 8, 'Algorithms|Base Sequence|Classification|Discriminant Analysis|HIV-1|Hepacivirus|Models, Genetic|Models, Statistical|Multivariate Analysis|Phylogeny|Sequence Alignment|Viruses', 'Journal Article', '10.1186/1471-2105-11-434', '20727194', 'Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment.'),
(93813, 2010, 'bmc', 'CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse', 'Friard, Olivier|Re, Angela|Taverna, Daniela|De Bortoli, Michele|Corá, Davide', 'Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy.|Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy.|Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy.|Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy.|Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy.', 'Italy|Italy|Italy|Italy|Italy', 86, 'Animals|Base Sequence|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Genome|Humans|Internet|Mice|MicroRNAs|Neoplasms|Sequence Alignment|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-435', '20731828', 'Transcription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them.'),
(93814, 2010, 'bmc', 'A dynamic noise level algorithm for spectral screening of peptide MS/MS spectra', 'Xu, Hua|Freitas, Michael A', 'Proteomics and Informatics Services Facility, University of Illinois at Chicago, IL 60612, USA. huaxu@uic.edu|Proteomics and Informatics Services Facility, University of Illinois at Chicago, IL 60612, USA. huaxu@uic.edu', 'United States|United States', 15, 'Algorithms|Animals|Cattle|Computer Simulation|Databases, Factual|Humans|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Evaluation Studies', '10.1186/1471-2105-11-436', '20731867', 'High-throughput shotgun proteomics data contain a significant number of spectra from non-peptide ions or spectra of too poor quality to obtain highly confident peptide identifications. These spectra cannot be identified with any positive peptide matches in some database search programs or are identified with false positives in others. Removing these spectra can improve the database search results and lower computational expense.'),
(93815, 2010, 'bmc', 'Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics', 'Al-Shahib, Ali|Misra, Raju|Ahmod, Nadia|Fang, Min|Shah, Haroun|Gharbia, Saheer', 'Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk|Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk|Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk|Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk|Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk|Health Protection Agency, Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK. ali.al-shahib@hpa.org.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Bacterial Proteins|Biomarkers|Clostridium botulinum|Computational Biology|Conserved Sequence|Databases, Factual|Mass Spectrometry|Proteomics|Sensitivity and Specificity|Species Specificity', 'Journal Article', '10.1186/1471-2105-11-437', '20796299', 'Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.'),
(93816, 2010, 'bmc', '*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants', 'Milone, Diego H|Stegmayer, Georgina S|Kamenetzky, Laura|López, Mariana|Lee, Je Min|Giovannoni, James J|Carrari, Fernando', 'Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina|Research Center for Signals, Systems and Computational Intelligence, FICH-UNL, CONICET, Ciudad Universitaria UNL, Santa Fe, (3000), Argentina', 'Argentina|Argentina|Argentina|Argentina|Argentina|Argentina|Argentina', 25, 'Cluster Analysis|Crops, Agricultural|Crosses, Genetic|Data Mining|Databases, Factual|Genome, Plant|Metabolic Networks and Pathways|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-438', '20796314', 'modern biology uses experimental systems that involve the exploration of phenotypic variation as a result of the recombination of several genomes. Such systems are useful to investigate the functional evolution of metabolic networks. One such approach is the analysis of transcript and metabolite profiles. These kinds of studies generate a large amount of data, which require dedicated computational tools for their analysis.'),
(93817, 2010, 'bmc', 'CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation', 'Li, Gong-Hua|Huang, Jing-Fei', 'State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.|State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.', 'China|China', 15, 'Algorithms|Catalytic Domain|Computational Biology|Databases, Protein|Enzymes|Proteins', 'Journal Article', '10.1186/1471-2105-11-439', '20796320', 'The rapid development of structural genomics has resulted in many unknown function proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.'),
(93818, 2010, 'bmc', 'An exploratory data analysis method to reveal modular latent structures in high-throughput data', 'Yu, Tianwei', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, USA. tyu8@emory.edu', 'United States', 10, 'Algorithms|Carcinoma, Squamous Cell|Cell Cycle|Cell Line, Tumor|Computer Simulation|Gene Regulatory Networks|Humans|Information Systems|Lung Neoplasms|Models, Biological|Models, Statistical|Systems Biology|Systems Theory|Yeasts', 'Journal Article', '10.1186/1471-2105-11-440', '20799972', 'Modular structures are ubiquitous across various types of biological networks. The study of network modularity can help reveal regulatory mechanisms in systems biology, evolutionary biology and developmental biology. Identifying putative modular latent structures from high-throughput data using exploratory analysis can help better interpret the data and generate new hypotheses. Unsupervised learning methods designed for global dimension reduction or clustering fall short of identifying modules with factors acting in linear combinations.'),
(93819, 2010, 'bmc', 'Relations as patterns: bridging the gap between OBO and OWL', 'Hoehndorf, Robert|Oellrich, Anika|Dumontier, Michel|Kelso, Janet|Rebholz-Schuhmann, Dietrich|Herre, Heinrich', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. rh497@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 44, 'Computational Biology|Information Storage and Retrieval|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-441', '20807438', 'most biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately.'),
(93820, 2010, 'bmc', 'Forward-time simulation of realistic samples for genome-wide association studies', 'Peng, Bo|Amos, Christopher I', 'Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. bpeng@mdanderson.org|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. bpeng@mdanderson.org', 'United States|United States', 23, 'Algorithms|Biological Evolution|Computer Simulation|Genetic Markers|Genetic Predisposition to Disease|Genetics, Population|Genome, Human|Genome-Wide Association Study|Humans|Models, Genetic', 'Journal Article', '10.1186/1471-2105-11-442', '20809983', 'Forward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods.'),
(93821, 2010, 'bmc', 'Efficient Bayesian approach for multilocus association mapping including gene-gene interactions', 'Marttinen, Pekka|Corander, Jukka', 'Department of Biomedical Engineering and Computational Science, FI-02015 Helsinki University of Technology, Finland. pekka.marttinen@hut.fi|Department of Biomedical Engineering and Computational Science, FI-02015 Helsinki University of Technology, Finland. pekka.marttinen@hut.fi', 'Finland|Finland', 6, 'Algorithms|Bayes Theorem|Chromosome Mapping|Epistasis, Genetic|Genetic Loci|Genome|Genome-Wide Association Study|Genotype|Phenotype', 'Journal Article', '10.1186/1471-2105-11-443', '20809988', 'since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature.'),
(93822, 2010, 'bmc', 'Genome3D: a viewer-model framework for integrating and visualizing multi-scale epigenomic information within a three-dimensional genome', 'Asbury, Thomas M|Mitman, Matt|Tang, Jijun|Zheng, W Jim', 'Department of Biochemistry & Molecular Biology, Medical University of South Carolina, 135 Cannon Street, Suite 303E, Charleston, SC 29425, USA.|Department of Biochemistry & Molecular Biology, Medical University of South Carolina, 135 Cannon Street, Suite 303E, Charleston, SC 29425, USA.|Department of Biochemistry & Molecular Biology, Medical University of South Carolina, 135 Cannon Street, Suite 303E, Charleston, SC 29425, USA.|Department of Biochemistry & Molecular Biology, Medical University of South Carolina, 135 Cannon Street, Suite 303E, Charleston, SC 29425, USA.', 'United States|United States|United States|United States', 23, 'Algorithms|Computer Simulation|Database Management Systems|Epigenesis, Genetic|Genome, Human|Genomics|Humans|Imaging, Three-Dimensional|Models, Genetic|Molecular Conformation|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-444', '20813045', 'New technologies are enabling the measurement of many types of genomic and epigenomic information at scales ranging from the atomic to nuclear. Much of this new data is increasingly structural in nature, and is often difficult to coordinate with other data sets. There is a legitimate need for integrating and visualizing these disparate data sets to reveal structural relationships not apparent when looking at these data in isolation.'),
(93823, 2010, 'bmc', 'Automatic detection of anchor points for multiple sequence alignment', 'Pitschi, Florian|Devauchelle, Claudine|Corel, Eduardo', 'Partner Institute for Computational Biology, CAS-MPG, 320 Yue Yang Rd, 200031 Shanghai, China.|Partner Institute for Computational Biology, CAS-MPG, 320 Yue Yang Rd, 200031 Shanghai, China.|Partner Institute for Computational Biology, CAS-MPG, 320 Yue Yang Rd, 200031 Shanghai, China.', 'China|China|China', 3, 'Algorithms|Databases, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-445', '20813050', 'determining beforehand specific positions to align (anchor points) has proved valuable for the accuracy of automated multiple sequence alignment (MSA) software. This feature can be used manually to include biological expertise, or automatically, usually by pairwise similarity searches. Multiple local similarities are be expected to be more adequate, as more biologically relevant. However, even good multiple local similarities can prove incompatible with the ordering of an alignment.'),
(93824, 2010, 'bmc', 'Fast and accurate protein substructure searching with simulated annealing and GPUs', 'Stivala, Alex D|Stuckey, Peter J|Wirth, Anthony I', 'Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. a.stivala@pgrad.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. a.stivala@pgrad.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia. a.stivala@pgrad.unimelb.edu.au', 'Australia|Australia|Australia', 31, 'Computer Graphics|Computer Simulation|Databases, Protein|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-11-446', '20813068', 'Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.'),
(93825, 2010, 'bmc', 'Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms', 'Guo, Yu|Graber, Armin|McBurney, Robert N|Balasubramanian, Raji', 'BG Medicine, Inc., 610 Lincoln St., Waltham, MA 02451, USA.|BG Medicine, Inc., 610 Lincoln St., Waltham, MA 02451, USA.|BG Medicine, Inc., 610 Lincoln St., Waltham, MA 02451, USA.|BG Medicine, Inc., 610 Lincoln St., Waltham, MA 02451, USA.', 'United States|United States|United States|United States', 38, 'Algorithms|Animals|Classification|Databases, Factual|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sample Size', 'Comparative Study', '10.1186/1471-2105-11-447', '20815881', 'data generated using omics technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of subjects in the study. In this paper, we consider issues relevant in the design of biomedical studies in which the goal is the discovery of a subset of features and an associated algorithm that can predict a binary outcome, such as disease status. We compare the performance of four commonly used classifiers (K-Nearest Neighbors, Prediction Analysis for Microarrays, Random Forests and Support Vector Machines) in high-dimensionality data settings. We evaluate the effects of varying levels of signal-to-noise ratio in the dataset, imbalance in class distribution and choice of metric for quantifying performance of the classifier. To guide study design, we present a summary of the key characteristics of omics data profiled in several human or animal model experiments utilizing high-content mass spectrometry and multiplexed immunoassay based techniques.'),
(93826, 2010, 'bmc', 'Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration', 'McHugh, Leo C|Arthur, Jonathan W', 'Discipline of Medicine, Sydney Medical School, University of Sydney, Sydney, Australia.|Discipline of Medicine, Sydney Medical School, University of Sydney, Sydney, Australia.', 'Australia|Australia', 0, 'Algorithms|Electronic Data Processing|Peptide Fragments|Peptides|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-11-448', '20815925', 'Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying metrics that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset.'),
(93827, 2010, 'bmc', 'Consistency, comprehensiveness, and compatibility of pathway databases', 'Soh, Donny|Dong, Difeng|Guo, Yike|Wong, Limsoon', 'School of Computing, National University of Singapore, Building COM1, 117417 Singapore. donnysoh@gmail.com|School of Computing, National University of Singapore, Building COM1, 117417 Singapore. donnysoh@gmail.com|School of Computing, National University of Singapore, Building COM1, 117417 Singapore. donnysoh@gmail.com|School of Computing, National University of Singapore, Building COM1, 117417 Singapore. donnysoh@gmail.com', 'Singapore|Singapore|Singapore|Singapore', 0, 'Computational Biology|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-449', '20819233', 'It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.'),
(93828, 2010, 'bmc', 'Filtering, FDR and power', 'van Iterson, Maarten|Boer, Judith M|Menezes, Renée X', 'Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007 MB, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Leukemia', 'Journal Article', '10.1186/1471-2105-11-450', '20822518', 'In high-dimensional data analysis such as differential gene expression analysis, people often use filtering methods like fold-change or variance filters in an attempt to reduce the multiple testing penalty and improve power. However, filtering may introduce a bias on the multiple testing correction. The precise amount of bias depends on many quantities, such as fraction of probes filtered out, filter statistic and test statistic used.'),
(93829, 2010, 'bmc', 'VIGOR, an annotation program for small viral genomes', 'Wang, Shiliang|Sundaram, Jaideep P|Spiro, David', 'J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. shiliang@jcvi.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. shiliang@jcvi.org|J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. shiliang@jcvi.org', 'United States|United States|United States', 28, 'Codon, Initiator|Codon, Terminator|Computational Biology|Databases, Genetic|Genome, Viral|Molecular Sequence Annotation|Open Reading Frames|Orthomyxoviridae|Software', 'Journal Article', '10.1186/1471-2105-11-451', '20822531', 'the decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.'),
(93830, 2010, 'bmc', 'Simple and flexible classification of gene expression microarrays via Swirls and Ripples', 'Baker, Stuart G', 'Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, EPN 3131, 6130 Executive Blvd MSC 7354, Bethesda, MD 20892-7354, USA. sb16i@nih.gov', 'United States', 0, 'Computer Simulation|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-11-452', '20825641', 'A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagonal discriminant analysis, yields linear or curved classification boundaries, called Ripples, that are optimal when gene expression levels are normally distributed with the appropriate variance, but may yield poor classification in other situations.'),
(93831, 2010, 'bmc', 'A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context', 'Valavanis, Ioannis K|Mougiakakou, Stavroula G|Grimaldi, Keith A|Nikita, Konstantina S', 'School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15780 Zografos, Athens, Greece. ivalavan@biosim.ntua.gr|School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15780 Zografos, Athens, Greece. ivalavan@biosim.ntua.gr|School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15780 Zografos, Athens, Greece. ivalavan@biosim.ntua.gr|School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15780 Zografos, Athens, Greece. ivalavan@biosim.ntua.gr', 'Greece|Greece|Greece|Greece', 22, 'Adult|Aged|Body Mass Index|Cardiovascular Diseases|Energy Intake|European Continental Ancestry Group|Female|Genetic Variation|Humans|Male|Middle Aged|Neural Networks (Computer)|Nutrigenomics|Nutrition Assessment|Obesity|Predictive Value of Tests|ROC Curve|Risk Factors|Young Adult', 'Comparative Study', '10.1186/1471-2105-11-453', '20825661', 'Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI xe2x89xa4 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.'),
(93832, 2010, 'bmc', 'On the choice and number of microarrays for transcriptional regulatory network inference', 'Cosgrove, Elissa J|Gardner, Timothy S|Kolaczyk, Eric D', 'Department of Mathematics and Statistics, Boston University, Boston, MA, USA.|Department of Mathematics and Statistics, Boston University, Boston, MA, USA.|Department of Mathematics and Statistics, Boston University, Boston, MA, USA.', 'United States|United States|United States', 10, 'Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Regulatory Elements, Transcriptional|Sample Size|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-454', '20825684', 'Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI.'),
(93833, 2010, 'bmc', 'Next generation tools for genomic data generation, distribution, and visualization', 'Nix, David A|Di Sera, Tonya L|Dalley, Brian K|Milash, Brett A|Cundick, Robert M|Quinn, Kevin S|Courdy, Samir J', 'Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu|Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, USA. david.nix@hci.utah.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computer Graphics|Databases, Factual|Genome|Genomics|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-455', '20828407', 'With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data.'),
(93834, 2010, 'bmc', 'Multivariate Hawkes process models of the occurrence of regulatory elements', 'Carstensen, Lisbeth|Sandelin, Albin|Winther, Ole|Hansen, Niels R', 'Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark.|Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark.|Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark.|Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 23, 'Algorithms|Gene Expression Regulation|Genome|Models, Genetic|Regulatory Elements, Transcriptional', 'Journal Article', '10.1186/1471-2105-11-456', '20828413', 'A central question in molecular biology is how transcriptional regulatory elements (TREs) act in combination. Recent high-throughput data provide us with the location of multiple regulatory regions for multiple regulators, and thus with the possibility of analyzing the multivariate distribution of the occurrences of these TREs along the genome.'),
(93835, 2010, 'bmc', 'An algorithm for automated closure during assembly', 'Koren, Sergey|Miller, Jason R|Walenz, Brian P|Sutton, Granger', 'The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. skoren@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. skoren@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. skoren@jcvi.org|The J. Craig Venter Institute, 9712 Medical Center Drive, Rockville MD 20850, USA. skoren@jcvi.org', 'United States|United States|United States|United States', 10, 'Algorithms|Databases, Factual|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-457', '20831800', 'Finishing is the process of improving the quality and utility of draft genome sequences generated by shotgun sequencing and computational assembly. Finishing can involve targeted sequencing. Finishing reads may be incorporated by manual or automated means. One automated method uses targeted addition by local re-assembly of gap regions. An obvious alternative uses de novo assembly of all the reads.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93836, 2010, 'bmc', 'The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems', 'Geifman, Nophar|Monsonego, Alon|Rubin, Eitan', 'Department of Microbiology and Immunology, Faculty of Medical Sciences and The National Institute of Biotechnology in the Negev, Ben Gurion University, Beer Sheva, Israel.|Department of Microbiology and Immunology, Faculty of Medical Sciences and The National Institute of Biotechnology in the Negev, Ben Gurion University, Beer Sheva, Israel.|Department of Microbiology and Immunology, Faculty of Medical Sciences and The National Institute of Biotechnology in the Negev, Ben Gurion University, Beer Sheva, Israel.', 'Israel|Israel|Israel', 0, 'Computational Biology|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Immune System|Nervous System|Oligonucleotide Array Sequence Analysis|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-11-458', '20831831', 'The Gene Ontology (GO) is used to describe genes and gene products from many organisms. When used for functional annotation of microarray data, GO is often slimmed by editing so that only higher level terms remain. This practice is designed to improve the summarizing of experimental results by grouping high level terms and the statistical power of GO term enrichment analysis. Here, we propose a new approach to editing the gene ontology, clipping, which is the editing of GO according to biological relevance. Creation of a GO subset by clipping is achieved by removing terms (from all hierarchal levels) if they are not functionally relevant to a given domain of interest. Terms that are located in levels higher to relevant terms are kept, thus, biologically irrelevant terms are only removed if they are not parental to terms that are relevant.'),
(93837, 2010, 'bmc', 'Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks', 'Swain, Martin T|Mandel, Johannes J|Dubitzky, Werner', 'University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine BT52 1SA, Co, Londonderry, UK.|University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine BT52 1SA, Co, Londonderry, UK.|University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine BT52 1SA, Co, Londonderry, UK.', 'United Kingdom|United Kingdom|United Kingdom', 17, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Models, Genetic|Transcription, Genetic', 'Comparative Study', '10.1186/1471-2105-11-459', '20840745', 'A gene-regulatory network (GRN) refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS), artificial neural networks (ANNs), and the general rate law of transcription (GRLOT) method. These were thoroughly evaluated in terms of their ability to replicate the reference models regulatory structure and dynamic gene expression behavior under varying conditions.'),
(93838, 2010, 'bmc', 'Candidate gene prioritization by network analysis of differential expression using machine learning approaches', 'Nitsch, Daniela|Gonçalves, Joana P|Ojeda, Fabian|de Moor, Bart|Moreau, Yves', 'Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven, 3001 Leuven, Belgium. Daniela.Nitsch@esat.kuleuven.be|Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven, 3001 Leuven, Belgium. Daniela.Nitsch@esat.kuleuven.be|Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven, 3001 Leuven, Belgium. Daniela.Nitsch@esat.kuleuven.be|Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven, 3001 Leuven, Belgium. Daniela.Nitsch@esat.kuleuven.be|Department of Electrical Engineering (ESAT-SCD) Katholieke Universiteit Leuven, 3001 Leuven, Belgium. Daniela.Nitsch@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 77, 'Animals|Artificial Intelligence|Databases, Genetic|Gene Expression Profiling|Mice|Phenotype', 'Journal Article', '10.1186/1471-2105-11-460', '20840752', 'Discovering novel disease genes is still challenging for diseases for which no prior knowledge--such as known disease genes or disease-related pathways--is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.'),
(93839, 2010, 'bmc', 'Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions', 'Laing, Chad|Buchanan, Cody|Taboada, Eduardo N|Zhang, Yongxiang|Kropinski, Andrew|Villegas, Andre|Thomas, James E|Gannon, Victor P J', 'Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.|Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, AB, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 151, 'DNA, Bacterial|Escherichia coli|Escherichia coli O157|Genome, Bacterial|Phylogeny|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-461', '20843356', 'The pan-genome of a bacterial species consists of a core and an accessory gene pool. The accessory genome is thought to be an important source of genetic variability in bacterial populations and is gained through lateral gene transfer, allowing subpopulations of bacteria to better adapt to specific niches. Low-cost and high-throughput sequencing platforms have created an exponential increase in genome sequence data and an opportunity to study the pan-genomes of many bacterial species. In this study, we describe a new online pan-genome sequence analysis program, Panseq.'),
(93840, 2010, 'bmc', 'Context dependent substitution biases vary within the human genome', 'Nevarez, P Andrew|DeBoever, Christopher M|Freeland, Benjamin J|Quitt, Marissa A|Bush, Eliot C', 'Department of Biology, Harvey Mudd College, Claremont, CA, USA.|Department of Biology, Harvey Mudd College, Claremont, CA, USA.|Department of Biology, Harvey Mudd College, Claremont, CA, USA.|Department of Biology, Harvey Mudd College, Claremont, CA, USA.|Department of Biology, Harvey Mudd College, Claremont, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Base Composition|Base Sequence|Computational Biology|DNA Transposable Elements|Genome, Human|Humans|Nucleotides|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-462', '20843365', 'Models of sequence evolution typically assume that different nucleotide positions evolve independently. This assumption is widely appreciated to be an over-simplification. The best known violations involve biases due to adjacent nucleotides. There have also been suggestions that biases exist at larger scales, however this possibility has not been systematically explored.'),
(93841, 2010, 'bmc', 'Sigma-2: Multiple sequence alignment of non-coding DNA via an evolutionary model', 'Jayaraman, Gayathri|Siddharthan, Rahul', 'The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India. rsidd@imsc.res.in.|The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India. rsidd@imsc.res.in.', 'India|India', 2, 'DNA, Intergenic|Evolution, Molecular|Genomics|Likelihood Functions|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-464', '20846408', 'While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of homology and not similarity, we incorporate an evolutionary model into a previously published multiple sequence alignment program for non-coding DNA, Sigma, as a sensitive likelihood-based way to assess the significance of alignments. Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the null hypothesis of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence.'),
(93842, 2010, 'bmc', 'Parallel multiplicity and error discovery rate (EDR) in microarray experiments', 'Xu, Wayne Wenzhong|Carter, Clay J', 'Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, MN 55455, USA. wxu@msi.umn.edu|Minnesota Supercomputing Institute for Advanced Computational Research, University of Minnesota, Minneapolis, MN 55455, USA. wxu@msi.umn.edu', 'United States|United States', 3, 'Computer Simulation|Gene Expression Profiling|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-11-465', '20846437', 'In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.'),
(93843, 2010, 'bmc', 'Investigating the validity of current network analysis on static conglomerate networks by protein network stratification', 'Zhang, Minlu|Lu, Long J', 'Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA.', 'United States|United States', 0, 'Arabidopsis|Databases, Protein|Genome, Plant|Humans|Protein Interaction Mapping|Proteins|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-11-466', '20846443', 'A molecular network perspective forms the foundation of systems biology. A common practice in analyzing protein-protein interaction (PPI) networks is to perform network analysis on a conglomerate network that is an assembly of all available binary interactions in a given organism from diverse data sources. Recent studies on network dynamics suggested that this approach might have ignored the dynamic nature of context-dependent molecular systems.'),
(93844, 2010, 'bmc', 'ClustalXeed: a GUI-based grid computation version for high performance and terabyte size multiple sequence alignment', 'Kim, Taeho|Joo, Hyun', 'Department of Physiology and Integrated Biosystems, College of Medicine, Inje University, Busan 614-735, South Korea.|Department of Physiology and Integrated Biosystems, College of Medicine, Inje University, Busan 614-735, South Korea.', '-|-', 11, 'Algorithms|Cluster Analysis|Computing Methodologies|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-467', '20849574', 'There is an increasing demand to assemble and align large-scale biological sequence data sets. The commonly used multiple sequence alignment programs are still limited in their ability to handle very large amounts of sequences because the system lacks a scalable high-performance computing (HPC) environment with a greatly extended data storage capacity.'),
(93845, 2010, 'bmc', 'Multivariate meta-analysis of proteomics data from human prostate and colon tumours', 'Rosenberg, Lina Hultin|Franzén, Bo|Auer, Gert|Lehtiö, Janne|Forshed, Jenny', 'Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden.|Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden.|Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden.|Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden.|Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 12, 'Biomarkers, Tumor|Colonic Neoplasms|Electrophoresis, Gel, Two-Dimensional|Humans|Least-Squares Analysis|Male|Prostatic Neoplasms|Proteomics', 'Journal Article', '10.1186/1471-2105-11-468', '20849579', 'There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.'),
(93846, 2010, 'bmc', 'MetNetGE: interactive views of biological networks and ontologies', 'Jia, Ming|Choi, Suh-Yeon|Reiners, Dirk|Wurtele, Eve S|Dickerson, Julie A', 'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. julied@iastate.edu.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. julied@iastate.edu.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. julied@iastate.edu.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. julied@iastate.edu.|Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA. julied@iastate.edu.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Information Storage and Retrieval|Internet|Metabolic Networks and Pathways|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-469', '20849585', 'Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability.'),
(93847, 2010, 'bmc', 'Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins', 'Kelly, William P|Stumpf, Michael P H', 'Centre for Bioinformatics, Imperial College, London, UK.|Centre for Bioinformatics, Imperial College, London, UK.', 'United Kingdom|United Kingdom', 3, 'Biological Phenomena|Classification|Databases, Protein|Evolution, Molecular|Models, Statistical|Phylogeny|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-11-470', '20854660', 'Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in terms of the phylogenic tree. In a similar way it has been argued that biological networks also induce correlations among sets of interacting genes or their protein products.'),
(93848, 2010, 'bmc', 'SeqAnt: a web service to rapidly identify and annotate DNA sequence variations', 'Shetty, Amol Carl|Athri, Prashanth|Mondal, Kajari|Horner, Vanessa L|Steinberg, Karyn Meltz|Patel, Viren|Caspary, Tamara|Cutler, David J|Zwick, Michael E', 'Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.|Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 32, 'Animals|Base Sequence|Databases, Genetic|Genetic Variation|Genomics|Humans|Internet|Mice|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-471', '20854673', 'The enormous throughput and low cost of second-generation sequencing platforms now allow research and clinical geneticists to routinely perform single experiments that identify tens of thousands to millions of variant sites. Existing methods to annotate variant sites using information from publicly available databases via web browsers are too slow to be useful for the large sequencing datasets being routinely generated by geneticists. Because sequence annotation of variant sites is required before functional characterization can proceed, the lack of a high-throughput pipeline to efficiently annotate variant sites can act as a significant bottleneck in genetics research.'),
(93849, 2010, 'bmc', 'GPFrontend and GPGraphics: graphical analysis tools for genetic association studies', 'Uebe, Steffen|Pasutto, Francesca|Krumbiegel, Mandy|Schanze, Denny|Ekici, Arif B|Reis, André', 'Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.|Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.|Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.|Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.|Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.|Institute of Human Genetics, University of Erlangen-Nuremberg, Schwabachanlage 10, 91054 Erlangen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Computer Graphics|Genome|Genome-Wide Association Study|Microarray Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-472', '20858257', 'Most software packages for whole genome association studies are non-graphical, purely text based programs originally designed to run with UNIX-like operating systems. Graphical output is often not intended or supposed to be performed with other command line tools, e.g. gnuplot.'),
(93850, 2010, 'bmc', 'PeptideMine--a webserver for the design of peptides for protein-peptide binding studies derived from protein-protein interactomes', 'Shameer, Khader|Madan, Lalima L|Veeranna, Shivamurthy|Gopal, Balasubramanian|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.|National Centre for Biological Sciences (TIFR), GKVK Campus, Bellary Road, Bangalore, 560065, India.', 'India|India|India|India|India', 0, 'Binding Sites|Databases, Protein|Internet|Peptides|Protein Interaction Mapping|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-473', '20858292', 'Signal transduction events often involve transient, yet specific, interactions between structurally conserved protein domains and polypeptide sequences in target proteins. The identification and validation of these associating domains is crucial to understand signal transduction pathways that modulate different cellular or developmental processes. Bioinformatics strategies to extract and integrate information from diverse sources have been shown to facilitate the experimental design to understand complex biological events. These methods, primarily based on information from high-throughput experiments, have also led to the identification of new connections thus providing hypothetical models for cellular events. Such models, in turn, provide a framework for directing experimental efforts for validating the predicted molecular rationale for complex cellular processes. In this context, it is envisaged that the rational design of peptides for protein-peptide binding studies could substantially facilitate the experimental strategies to evaluate a predicted interaction. This rational design procedure involves the integration of protein-protein interaction data, gene ontology, physico-chemical calculations, domain-domain interaction data and information on functional sites or critical residues.'),
(93851, 2010, 'bmc', 'ModuleOrganizer: detecting modules in families of transposable elements', 'Tempel, Sebastien|Rousseau, Christine|Tahi, Fariza|Nicolas, Jacques', 'IBISC, Tour Evry 2, 523 Place des Terrasses delAgora, 91000 Evry, France.|IBISC, Tour Evry 2, 523 Place des Terrasses delAgora, 91000 Evry, France.|IBISC, Tour Evry 2, 523 Place des Terrasses delAgora, 91000 Evry, France.|IBISC, Tour Evry 2, 523 Place des Terrasses delAgora, 91000 Evry, France.', 'France|France|France|France', 6, 'Animals|Base Sequence|DNA Transposable Elements|Drosophila melanogaster|Genetic Variation|Genome|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-474', '20860790', 'Most known eukaryotic genomes contain mobile copied elements called transposable elements. In some species, these elements account for the majority of the genome sequence. They have been subject to many mutations and other genomic events (copies, deletions, captures) during transposition. The identification of these transformations remains a difficult issue. The study of families of transposable elements is generally founded on a multiple alignment of their sequences, a critical step that is adapted to transposons containing mostly localized nucleotide mutations. Many transposons that have lost their protein-coding capacity have undergone more complex rearrangements, needing the development of more complex methods in order to characterize the architecture of sequence variations.'),
(93852, 2010, 'bmc', 'FragViz: visualization of fragmented networks', 'Stajdohar, Miha|Mramor, Minca|Zupan, Blaž|Demšar, Janez', 'Faculty of Computer and Information Science, University of Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Slovenia.|Faculty of Computer and Information Science, University of Ljubljana, Slovenia.', 'Slovenia|Slovenia|Slovenia|Slovenia', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Regulatory Networks|Humans|Leukemia|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-475', '20860802', 'Researchers in systems biology use network visualization to summarize the results of their analysis. Such networks often include unconnected components, which popular network alignment algorithms place arbitrarily with respect to the rest of the network. This can lead to misinterpretations due to the proximity of otherwise unrelated elements.'),
(93853, 2010, 'bmc', 'Improving performance of mammalian microRNA target prediction', 'Liu, Hui|Yue, Dong|Chen, Yidong|Gao, Shou-Jiang|Huang, Yufei', 'SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China.|SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China.|SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China.|SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China.|SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China.', 'China|China|China|China|China', 61, '3 Untranslated Regions|5 Untranslated Regions|Algorithms|Animals|Gene Silencing|Genome|Humans|Mammals|MicroRNAs|Proteomics|RNA, Untranslated', 'Evaluation Studies', '10.1186/1471-2105-11-476', '20860840', 'MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.'),
(93854, 2010, 'bmc', 'A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer', 'Shi, Fan|Leckie, Christopher|MacIntyre, Geoff|Haviv, Izhak|Boussioutas, Alex|Kowalczyk, Adam', 'National ICT Australia. shif@csse.unimelb.edu.au|National ICT Australia. shif@csse.unimelb.edu.au|National ICT Australia. shif@csse.unimelb.edu.au|National ICT Australia. shif@csse.unimelb.edu.au|National ICT Australia. shif@csse.unimelb.edu.au|National ICT Australia. shif@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia', 4, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Lymphoma|Molecular Sequence Annotation|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Stomach Neoplasms', 'Journal Article', '10.1186/1471-2105-11-477', '20860844', 'In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries.'),
(93855, 2010, 'bmc', 'Detecting variants with Metabolic Design, a new software tool to design probes for explorative functional DNA microarray development', 'Terrat, Sébastien|Peyretaillade, Eric|Gonçalves, Olivier|Dugat-Bony, Eric|Gravelat, Fabrice|Moné, Anne|Biderre-Petit, Corinne|Boucher, Delphine|Troquet, Julien|Peyret, Pierre', 'Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.|Clermont Université, Université dAuvergne, Laboratoire: Microorganismes Génome et Environnement, BP 10448, F-63000 Clermont-Ferrand, France.', 'France|France|France|France|France|France|France|France|France|France', 10, 'Algorithms|Biodegradation, Environmental|DNA Primers|DNA Probes|Genetic Variation|Microarray Analysis|Oligonucleotide Array Sequence Analysis|Polycyclic Aromatic Hydrocarbons|Software|Soil Microbiology|Soil Pollutants|Sphingomonas', 'Journal Article', '10.1186/1471-2105-11-478', '20860850', 'Microorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes.'),
(93856, 2010, 'bmc', 'Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome', 'Diez-Rivero, Carmen M|Lafuente, Esther M|Reche, Pedro A', 'Laboratory of Immunomedicine, Department of Microbiology I-Immunology, Facultad de Medicina, Universidad Complutense de Madrid, Ave Complutense S/N, Madrid 28040, Spain.|Laboratory of Immunomedicine, Department of Microbiology I-Immunology, Facultad de Medicina, Universidad Complutense de Madrid, Ave Complutense S/N, Madrid 28040, Spain.|Laboratory of Immunomedicine, Department of Microbiology I-Immunology, Facultad de Medicina, Universidad Complutense de Madrid, Ave Complutense S/N, Madrid 28040, Spain.', 'Spain|Spain|Spain', 29, 'Animals|CD8 Antigens|Computational Biology|Databases, Genetic|Epitopes, T-Lymphocyte|Genes, MHC Class I|Humans|Proteasome Endopeptidase Complex', 'Journal Article', '10.1186/1471-2105-11-479', '20863374', 'Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases.'),
(93857, 2010, 'bmc', 'Using diffusion distances for flexible molecular shape comparison', 'Liu, Yu-Shen|Li, Qi|Zheng, Guo-Qin|Ramani, Karthik|Benjamin, William', 'School of Software, Tsinghua University, Beijing 100084, China. liuyushen00@gmail.com|School of Software, Tsinghua University, Beijing 100084, China. liuyushen00@gmail.com|School of Software, Tsinghua University, Beijing 100084, China. liuyushen00@gmail.com|School of Software, Tsinghua University, Beijing 100084, China. liuyushen00@gmail.com|School of Software, Tsinghua University, Beijing 100084, China. liuyushen00@gmail.com', 'China|China|China|China|China', 25, 'Algorithms|Computer Simulation|Databases, Factual|Image Interpretation, Computer-Assisted|Models, Molecular|Molecular Conformation|Pattern Recognition, Automated|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-11-480', '20868474', 'Many molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition.'),
(93858, 2010, 'bmc', 'Predicting phenotypic traits of prokaryotes from protein domain frequencies', 'Lingner, Thomas|Mühlhausen, Stefanie|Gabaldón, Toni|Notredame, Cedric|Meinicke, Peter', 'Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. thomas@gobics.de', 'Germany|Germany|Germany|Germany|Germany', 10, 'Algorithms|Bacterial Proteins|Genome, Archaeal|Genome, Bacterial|Molecular Sequence Annotation|Phenotype|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-11-481', '20868492', 'Establishing the relationship between an organisms genome sequence and its phenotype is a fundamental challenge that remains largely unsolved. Accurately predicting microbial phenotypes solely based on genomic features will allow us to infer relevant phenotypic characteristics when the availability of a genome sequence precedes experimental characterization, a scenario that is favored by the advent of novel high-throughput and single cell sequencing techniques.'),
(93859, 2010, 'bmc', 'MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes', 'Bordner, Andrew J|Mittelmann, Hans D', 'Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu|Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu', 'United States|United States', 31, 'Algorithms|Alleles|Binding Sites|Databases, Protein|HLA-DP Antigens|HLA-DR Antigens|Histocompatibility Antigens Class II|Peptides|Software', 'Journal Article', '10.1186/1471-2105-11-482', '20868497', 'The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.'),
(93860, 2010, 'bmc', 'Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types', 'Dawany, Noor B|Tozeren, Aydin', 'Center for Integrated Bioinformatics, Drexel University, Bossone Research Building 711, 3102 Market Street, Philadelphia, PA 19104, USA.|Center for Integrated Bioinformatics, Drexel University, Bossone Research Building 711, 3102 Market Street, Philadelphia, PA 19104, USA.', 'United States|United States', 11, 'Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-483', '20875095', 'Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.'),
(93861, 2010, 'bmc', 'Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection', 'Flórez, Andrés F|Park, Daeui|Bhak, Jong|Kim, Byoung-Chul|Kuchinsky, Allan|Morris, John H|Espinosa, Jairo|Muskus, Carlos', 'Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.|Programa de Estudio y Control de Enfermedades Tropicales-PECET, Universidad de Antioquia, Calle 62 No 52-59, Lab. 632, Medellín, Colombia.', 'Colombia|Colombia|Colombia|Colombia|Colombia|Colombia|Colombia|Colombia', 60, 'Antiprotozoal Agents|Computational Biology|Drug Discovery|Humans|Leishmania major|Leishmaniasis|Protein Interaction Mapping|Proteome|Protozoan Proteins', 'Journal Article', '10.1186/1471-2105-11-484', '20875130', 'Leishmaniasis is a virulent parasitic infection that causes a worldwide disease burden. Most treatments have toxic side-effects and efficacy has decreased due to the emergence of resistant strains. The outlook is worsened by the absence of promising drug targets for this disease. We have taken a computational approach to the detection of new drug targets, which may become an effective strategy for the discovery of new drugs for this tropical disease.'),
(93862, 2010, 'bmc', 'SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data', 'Cox, Murray P|Peterson, Daniel A|Biggs, Patrick J', 'Institute of Molecular BioSciences, Massey University, Palmerston North 4442, New Zealand. m.p.cox@massey.ac.nz|Institute of Molecular BioSciences, Massey University, Palmerston North 4442, New Zealand. m.p.cox@massey.ac.nz|Institute of Molecular BioSciences, Massey University, Palmerston North 4442, New Zealand. m.p.cox@massey.ac.nz', 'New Zealand|New Zealand|New Zealand', 765, 'Base Sequence|Internet|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-485', '20875133', 'Illuminas second-generation sequencing platform is playing an increasingly prominent role in modern DNA and RNA sequencing efforts. However, rapid, simple, standardized and independent measures of run quality are currently lacking, as are tools to process sequences for use in downstream applications based on read-level quality data.'),
(93863, 2010, 'bmc', 'A semi-nonparametric mixture model for selecting functionally consistent proteins', 'Yu, Lianbo|Doerge, Rw', 'Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. doerge@purdue.edu|Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. doerge@purdue.edu', 'United States|United States', 0, 'Databases, Protein|Models, Statistical|Protein Array Analysis|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-11-486', '20920204', 'High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins.'),
(93864, 2010, 'bmc', 'Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks', 'Aussem, Alex|Tchernof, André|de Morais, Sérgio Rodrigues|Rome, Sophie', 'University of Lyon, F-69000, Lyon, France. aaussem@univ-lyon1.dfr|University of Lyon, F-69000, Lyon, France. aaussem@univ-lyon1.dfr|University of Lyon, F-69000, Lyon, France. aaussem@univ-lyon1.dfr|University of Lyon, F-69000, Lyon, France. aaussem@univ-lyon1.dfr', 'France|France|France|France', 10, 'Adipose Tissue|Algorithms|Bayes Theorem|Female|Gene Expression Profiling|Humans|Life Style|Obesity, Abdominal', 'Journal Article', '10.1186/1471-2105-11-487', '20920208', 'The aim of this study was to provide a framework for the analysis of visceral obesity and its determinants in women, where complex inter-relationships are observed among lifestyle, nutritional and metabolic predictors. Thirty-four predictors related to lifestyle, adiposity, body fat distribution, blood lipids and adipocyte sizes have been considered as potential correlates of visceral obesity in women. To properly address the difficulties in managing such interactions given our limited sample of 150 women, bootstrapped Bayesian networks were constructed based on novel constraint-based learning methods that appeared recently in the statistical learning community. Statistical significance of edge strengths was evaluated and the less reliable edges were pruned to increase the network robustness. To allow accessible interpretation and integrate biological knowledge into the final network, several undirected edges were afterwards directed with physiological expertise according to relevant literature.'),
(93865, 2010, 'bmc', 'A global optimization algorithm for protein surface alignment', 'Bertolazzi, Paola|Guerra, Concettina|Liuzzi, Giampaolo', 'Istituto di Analisi dei Sistemi ed Informatica A. Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni, 30, 00185 Rome, Italy.|Istituto di Analisi dei Sistemi ed Informatica A. Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni, 30, 00185 Rome, Italy.|Istituto di Analisi dei Sistemi ed Informatica A. Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni, 30, 00185 Rome, Italy.', 'Italy|Italy|Italy', 15, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Drug Design|Protein Conformation|Proteins|Surface Properties', 'Journal Article', '10.1186/1471-2105-11-488', '20920230', 'A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved.'),
(93866, 2010, 'bmc', 'Computationally efficient flux variability analysis', 'Gudmundsson, Steinn|Thiele, Ines', 'Center for Systems Biology, University of Iceland, Reykjavik, Iceland.|Center for Systems Biology, University of Iceland, Reykjavik, Iceland.', 'Iceland|Iceland', 123, 'Computer Simulation|Metabolic Networks and Pathways|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-489', '20920235', 'Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.'),
(93867, 2010, 'bmc', 'BPDA - a Bayesian peptide detection algorithm for mass spectrometry', 'Sun, Youting|Zhang, Jianqiu|Braga-Neto, Ulisses|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States', 13, 'Algorithms|Databases, Protein|Mass Spectrometry|Peptides|Proteome|Software', 'Journal Article', '10.1186/1471-2105-11-490', '20920238', 'Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides.'),
(93868, 2010, 'bmc', 'Non-coding RNA detection methods combined to improve usability, reproducibility and precision', 'Raasch, Peter|Schmitz, Ulf|Patenge, Nadja|Vera, Julio|Kreikemeyer, Bernd|Wolkenhauer, Olaf', 'Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 17, 'Databases, Genetic|Genome, Bacterial|RNA, Bacterial|RNA, Untranslated|Reproducibility of Results|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-491', '20920260', 'Non-coding RNAs gain more attention as their diverse roles in many cellular processes are discovered. At the same time, the need for efficient computational prediction of ncRNAs increases with the pace of sequencing technology. Existing tools are based on various approaches and techniques, but none of them provides a reliable ncRNA detector yet. Consequently, a natural approach is to combine existing tools. Due to a lack of standard input and output formats combination and comparison of existing tools is difficult. Also, for genomic scans they often need to be incorporated in detection workflows using custom scripts, which decreases transparency and reproducibility.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93869, 2010, 'bmc', 'The structural and content aspects of abstracts versus bodies of full text journal articles are different', 'Cohen, K Bretonnel|Johnson, Helen L|Verspoor, Karin|Roeder, Christophe|Hunter, Lawrence E', 'Department of Pharmacology, Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com|Department of Pharmacology, Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com|Department of Pharmacology, Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com|Department of Pharmacology, Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com|Department of Pharmacology, Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com', 'United States|United States|United States|United States|United States', 76, 'Abstracting and Indexing as Topic|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-11-492', '20920264', 'An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research.'),
(93870, 2010, 'bmc', 'A systematic study of genome context methods: calibration, normalization and combination', 'Ferrer, Luciana|Dale, Joseph M|Karp, Peter D', 'Artificial Intelligence Center, SRI International, Menlo Park, California, USA.|Artificial Intelligence Center, SRI International, Menlo Park, California, USA.|Artificial Intelligence Center, SRI International, Menlo Park, California, USA.', 'United States|United States|United States', 23, 'Calibration|Databases, Genetic|Gene Expression Profiling|Genome|Genomics|Multigene Family|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-493', '20920312', 'Genome context methods have been introduced in the last decade as automatic methods to predict functional relatedness between genes in a target genome using the patterns of existence and relative locations of the homologs of those genes in a set of reference genomes. Much work has been done in the application of these methods to different bioinformatics tasks, but few papers present a systematic study of the methods and their combination necessary for their optimal use.'),
(93871, 2010, 'bmc', 'Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage', 'Masso, Majid|Vaisman, Iosif I', 'Laboratory for Structural Bioinformatics, Department of Bioinformatics and Computational Biology, George Mason University, 10900 University Blvd, MS 5B3, Manassas, VA 20110, USA.|Laboratory for Structural Bioinformatics, Department of Bioinformatics and Computational Biology, George Mason University, 10900 University Blvd, MS 5B3, Manassas, VA 20110, USA.', 'United States|United States', 14, 'Algorithms|Computer Simulation|Databases, Genetic|HIV Envelope Protein gp120|HIV-1|Models, Molecular|Receptors, CCR5|Receptors, CXCR4', 'Journal Article', '10.1186/1471-2105-11-494', '20923564', 'HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An in silico mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.'),
(93872, 2010, 'bmc', 'An integrated analysis of molecular aberrations in NCI-60 cell lines', 'Yeang, Chen-Hsiang', 'Institute of Statistical Science, Academia Sinica, Taipei, Taiwan. chyeang@webmail.stat.sinica.edu.tw', '-', 5, 'Cell Line, Tumor|Computational Biology|DNA Methylation|Databases, Genetic|Genes, p16|Humans|Neoplasms|RNA, Messenger|United States', 'Journal Article', '10.1186/1471-2105-11-495', '20925909', 'Cancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.'),
(93873, 2010, 'bmc', 'MetAssimulo: simulation of realistic NMR metabolic profiles', 'Muncey, Harriet J|Jones, Rebecca|De Iorio, Maria|Ebbels, Timothy M D', 'Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.|Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Algorithms|Databases, Factual|Magnetic Resonance Spectroscopy|Metabolome|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-11-496', '20925910', 'Probing the complex fusion of genetic and environmental interactions, metabolic profiling (or metabolomics/metabonomics), the study of small molecules involved in metabolic reactions, is a rapidly expanding omics field. A major technique for capturing metabolite data is 1H-NMR spectroscopy and this yields highly complex profiles that require sophisticated statistical analysis methods. However, experimental data is difficult to control and expensive to obtain. Thus data simulation is a productive route to aid algorithm development.'),
(93874, 2010, 'bmc', 'DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules', 'Tesson, Bruno M|Breitling, Rainer|Jansen, Ritsert C', 'Groningen Bioinformatics Center, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.|Groningen Bioinformatics Center, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.|Groningen Bioinformatics Center, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.', 'Netherlands|Netherlands|Netherlands', 100, 'Algorithms|Animals|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Rats|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-11-497', '20925918', 'Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.'),
(93875, 2010, 'bmc', 'Automatic, context-specific generation of Gene Ontology slims', 'Davis, Melissa J|Sehgal, Muhammad Shoaib B|Ragan, Mark A', 'The University of Queensland, Brisbane, QLD 4072, Australia. m.ragan@imb.uq.edu.au|The University of Queensland, Brisbane, QLD 4072, Australia. m.ragan@imb.uq.edu.au|The University of Queensland, Brisbane, QLD 4072, Australia. m.ragan@imb.uq.edu.au', 'Australia|Australia|Australia', 29, 'Computational Biology|Databases, Factual|Genome|Molecular Sequence Annotation|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-498', '20929524', 'The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual.'),
(93876, 2010, 'bmc', 'Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome', 'Morine, Melissa J|McMonagle, Jolene|Toomey, Sinead|Reynolds, Clare M|Moloney, Aidan P|Gormley, Isobel C|Gaora, Peadar O|Roche, Helen M', 'Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.|Nutrigenomics Research Group, School of Public Health, UCD Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 18, 'Animals|Biomarkers|Computational Biology|Databases, Genetic|Diet|Gene Expression|Gene Expression Profiling|Metabolic Syndrome|Mice|Obesity', 'Journal Article', '10.1186/1471-2105-11-499', '20929581', 'Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets.'),
(93877, 2010, 'bmc', 'Analysis of X-ray structures of matrix metalloproteinases via chaotic map clustering', 'Giangreco, Ilenia|Nicolotti, Orazio|Carotti, Angelo|De Carlo, Francesco|Gargano, Gianfranco|Bellotti, Roberto', 'Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.|Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.|Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.|Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.|Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.|Dipartimento Farmaco-Chimico, University of Bari, via Orabona 4, Bari, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 9, 'Algorithms|Cluster Analysis|Crystallography, X-Ray|Ligands|Matrix Metalloproteinases|Models, Molecular|Protein Conformation|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-500', '20932281', 'Matrix metalloproteinases (MMPs) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. With this in mind, the perception of the intimate relationships among diverse MMPs could be a solid basis for accelerated learning in designing new selective MMP inhibitors. In this regard, decrypting the latent molecular reasons in order to elucidate similarity among MMPs is a key challenge.'),
(93878, 2010, 'bmc', 'Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines', 'Li, Limin|Zhou, Xiaobo|Ching, Wai-Ki|Wang, Ping', 'Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.', 'United States|United States|United States|United States', 41, 'Antineoplastic Agents|Cell Line, Tumor|Computational Biology|Enzyme Inhibitors|Humans|Metabolic Networks and Pathways|Neoplasms', 'Journal Article', '10.1186/1471-2105-11-501', '20932284', 'Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.'),
(93879, 2010, 'bmc', 'Misty Mountain clustering: application to fast unsupervised flow cytometry gating', 'Sugár, István P|Sealfon, Stuart C', 'Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, NY, USA. istvan.sugar@mssm.edu|Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, NY, USA. istvan.sugar@mssm.edu', 'United States|United States', 0, 'Algorithms|Cluster Analysis|Databases, Factual|Flow Cytometry|Software', 'Journal Article', '10.1186/1471-2105-11-502', '20932336', 'There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments.'),
(93880, 2010, 'bmc', 'Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering', 'Freyhult, Eva|Landfors, Mattias|Önskog, Jenny|Hvidsten, Torgeir R|Rydén, Patrik', 'Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se|Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se|Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se|Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se|Department of Clinical Microbiology, Division of Clinical Bacteriology, Umeå University, Umeå, Sweden. eva.freyhult@medsci.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-503', '20937082', 'Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to, handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.'),
(93881, 2010, 'bmc', 'MCL-CAw: a refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure', 'Srihari, Sriganesh|Ning, Kang|Leong, Hon Wai', 'Department of Computer Science, National University of Singapore, Singapore. srigsri@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore. srigsri@comp.nus.edu.sg|Department of Computer Science, National University of Singapore, Singapore. srigsri@comp.nus.edu.sg', 'Singapore|Singapore|Singapore', 45, 'Cluster Analysis|Databases, Protein|Markov Chains|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-504', '20939868', 'The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes.'),
(93882, 2010, 'bmc', 'Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology', 'Ning, Kang|Ng, Hoong Kee|Srihari, Sriganesh|Leong, Hon Wai|Nesvizhskii, Alexey I', 'Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States', 52, 'Cluster Analysis|Computational Biology|Databases, Protein|Genes, Essential|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-505', '20939873', 'In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the centrality-lethality rule, which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).'),
(93883, 2010, 'bmc', 'Geoseq: a tool for dissecting deep-sequencing datasets', 'Gurtowski, James|Cancio, Anthony|Shah, Hardik|Levovitz, Chaya|George, Ajish|Homann, Robert|Sachidanandam, Ravi', 'Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.|Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, NY 10029, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Base Sequence|Databases, Genetic|Gene Expression Profiling|Gene Library|High-Throughput Nucleotide Sequencing|MicroRNAs|RNA, Messenger|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-11-506', '20939882', 'Datasets generated on deep-sequencing platforms have been deposited in various public repositories such as the Gene Expression Omnibus (GEO), Sequence Read Archive (SRA) hosted by the NCBI, or the DNA Data Bank of Japan (ddbj). Despite being rich data sources, they have not been used much due to the difficulty in locating and analyzing datasets of interest.'),
(93884, 2010, 'bmc', 'Proteome scanning to predict PDZ domain interactions using support vector machines', 'Hui, Shirley|Bader, Gary D', 'Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada.|Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada.', 'Canada|Canada', 0, 'Animals|Artificial Intelligence|Binding Sites|Humans|Mice|PDZ Domains|Protein Array Analysis|Protein Interaction Mapping|Proteins|Proteome', 'Journal Article', '10.1186/1471-2105-11-507', '20939902', 'PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Two recent independent studies have used protein microarray or phage display technology to detect PDZ domain interactions with peptide ligands on a large scale. Several computational predictors of PDZ domain interactions have been developed, however they are trained using only protein microarray data and focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.'),
(93885, 2010, 'bmc', 'GenHtr: a tool for comparative assessment of genetic heterogeneity in microbial genomes generated by massive short-read sequencing', 'Yu, Gongxin', 'Department of Biological Science, Boise State University, 1910 University Drive, Boise, Idaho 83725, USA. gongxinyu@boisestate.edu', 'United States', 0, 'Base Sequence|Genetic Heterogeneity|Genome, Bacterial|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Comparative Study', '10.1186/1471-2105-11-508', '20939910', 'Microevolution is the study of short-term changes of alleles within a population and their effects on the phenotype of organisms. The result of the below-species-level evolution is heterogeneity, where populations consist of subpopulations with a large number of structural variations. Heterogeneity analysis is thus essential to our understanding of how selective and neutral forces shape bacterial populations over a short period of time. The Solexa Genome Analyzer, a next-generation sequencing platform, allows millions of short sequencing reads to be obtained with great accuracy, allowing for the ability to study the dynamics of the bacterial population at the whole genome level. The tool referred to as GenHtr was developed for genome-wide heterogeneity analysis.'),
(93886, 2010, 'bmc', 'Confronting two-pair primer design for enzyme-free SNP genotyping based on a genetic algorithm', 'Yang, Cheng-Hong|Cheng, Yu-Huei|Chuang, Li-Yeh|Chang, Hsueh-Wei', '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.', '-|-|-|-', 0, 'Algorithms|DNA Primers|Genotype|Polymerase Chain Reaction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-509', '20942913', 'Polymerase chain reaction with confronting two-pair primers (PCR-CTPP) method produces allele-specific DNA bands of different lengths by adding four designed primers and it achieves the single nucleotide polymorphism (SNP) genotyping by electrophoresis without further steps. It is a time- and cost-effective SNP genotyping method that has the advantage of simplicity. However, computation of feasible CTPP primers is still challenging.'),
(93887, 2010, 'bmc', 'Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets', 'Berger, Fabrice|De Meulder, Bertrand|Gaigneaux, Anthoula|Depiereux, Sophie|Bareke, Eric|Pierre, Michael|De Hertogh, Benoît|Delorenzi, Mauro|Depiereux, Eric', 'Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be|Bioinformatics and Biostatistics Laboratory, Molecular Biology Research Unit (URBM), FUNDP University of Namur, Namur, Belgium. fabrice.berger@fundp.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Analysis of Variance|Databases, Factual|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1186/1471-2105-11-510', '20942918', 'Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms.'),
(93888, 2010, 'bmc', 'Trimming of mammalian transcriptional networks using network component analysis', 'Tran, Linh M|Hyduke, Daniel R|Liao, James C', 'Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.|Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095-1592, USA.', 'United States|United States|United States', 10, 'Algorithms|Animals|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Humans|Mice|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-511', '20942926', 'Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies.'),
(93889, 2010, 'bmc', 'Quadratic variance models for adaptively preprocessing SELDI-TOF mass spectrometry data', 'Emanuele, Vincent A|Gurbaxani, Brian M', 'School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. VEmanueleII@cdc.gov|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. VEmanueleII@cdc.gov', 'Georgia|Georgia', 2, 'Algorithms|Data Interpretation, Statistical|Proteins|Proteome|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-11-512', '20942945', 'Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) is a proteomics tool for biomarker discovery and other high throughput applications. Previous studies have identified various areas for improvement in preprocessing algorithms used for protein peak detection. Bottom-up approaches to preprocessing that emphasize modeling SELDI data acquisition are promising avenues of research to find the needed improvements in reproducibility.'),
(93890, 2010, 'bmc', 'Booly: a new data integration platform', 'Do, Long H|Esteves, Francisco F|Karten, Harvey J|Bier, Ethan', 'Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu', 'United States|United States|United States|United States', 4, 'Computational Biology|Databases, Factual|Databases, Genetic|Drug Discovery|Gene Expression Profiling|Information Storage and Retrieval|Software', 'Journal Article', '10.1186/1471-2105-11-513', '20942966', 'Data integration is an escalating problem in bioinformatics. We have developed a web tool and warehousing system, Booly, that features a simple yet flexible data model coupled with the ability to perform powerful comparative analysis, including the use of Boolean logic to merge datasets together, and an integrated aliasing system to decipher differing names of the same gene or protein. Furthermore, Booly features a collaborative sharing system and a public repository so that users can retrieve new datasets while contributors can easily disseminate new content.'),
(93891, 2010, 'bmc', 'Proceedings of the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference', 'Wren, Jonathan D|Kupfer, Doris M|Perkins, Edward J|Bridges, Susan|Berleant, Daniel', 'Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu|Section of Cell and Developmental Biology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0349, USA. lhdo@ucsd.edu', 'United States|United States|United States|United States|United States', 0, 'Computational Biology|Genomics|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-11-S6-S1', '20946592', '-'),
(93892, 2010, 'bmc', 'Evaluation of gene expression data generated from expired Affymetrix GeneChip® microarrays using MAQC reference RNA samples', 'Wen, Zhining|Wang, Charles|Shi, Quan|Huang, Ying|Su, Zhenqiang|Hong, Huixiao|Tong, Weida|Shi, Leming', 'Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.|Division of Systems Biology, National Center for Toxicological Research (NCTR), US Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Quality Control|RNA|Reference Standards|Sensitivity and Specificity|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-11-S6-S10', '20946593', 'The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturers expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions.'),
(93893, 2010, 'bmc', 'Mixed reality simulation of rasping procedure in artificial cervical disc replacement (ACDR) surgery', 'Halic, Tansel|Kockara, Sinan|Bayrak, Coskun|Rowe, Richard', 'Mechanical, Aerospace and Nuclear Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA. halict@rpi.edu|Mechanical, Aerospace and Nuclear Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA. halict@rpi.edu|Mechanical, Aerospace and Nuclear Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA. halict@rpi.edu|Mechanical, Aerospace and Nuclear Engineering Department, Rensselaer Polytechnic Institute, Troy, New York, USA. halict@rpi.edu', 'United States|United States|United States|United States', 0, 'Cervical Vertebrae|Computer Simulation|Humans|Intervertebral Disc|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S6-S11', '20946594', 'Until quite recently spinal disorder problems in the U.S. have been operated by fusing cervical vertebrae instead of replacement of the cervical disc with an artificial disc. Cervical disc replacement is a recently approved procedure in the U.S. It is one of the most challenging surgical procedures in the medical field due to the deficiencies in available diagnostic tools and insufficient number of surgical practices For physicians and surgical instrument developers, it is critical to understand how to successfully deploy the new artificial disc replacement systems. Without proper understanding and practice of the deployment procedure, it is possible to injure the vertebral body. Mixed reality (MR) and virtual reality (VR) surgical simulators are becoming an indispensable part of physicians training, since they offer a risk free training environment. In this study, MR simulation framework and intricacies involved in the development of a MR simulator for the rasping procedure in artificial cervical disc replacement (ACDR) surgery are investigated. The major components that make up the MR surgical simulator with motion tracking system are addressed.'),
(93894, 2010, 'bmc', 'Enzyme structure dynamics of xylanase I from Trichoderma longibrachiatum', 'Uzuner, Ugur|Shi, Weibing|Liu, Lantao|Liu, Sanmin|Dai, Susie Y|Yuan, Joshua S', 'Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Deuterium|Deuterium Exchange Measurement|Fungal Proteins|Mass Spectrometry|Protein Conformation|Substrate Specificity|Trichoderma|Xylosidases', 'Journal Article', '10.1186/1471-2105-11-S6-S12', '20946595', 'Enzyme dynamics has recently been shown to be crucial for structure-function relationship. Among various structure dynamics analysis platforms, HDX (hydrogen deuterium exchange) mass spectrometry stands out as an efficient and high-throughput way to analyze protein dynamics upon ligand binding. Despite the potential, limited research has employed the HDX mass spec platform to probe regional structure dynamics of enzymes. In particular, the technique has never been used for analyzing cell wall degrading enzymes. We hereby used xylanase as a model to explore the potential of HDX mass spectrometry for studying cell wall degrading enzymes.'),
(93895, 2010, 'bmc', 'Quail Genomics: a knowledgebase for Northern bobwhite', 'Rawat, Arun|Gust, Kurt A|Elasri, Mohamed O|Perkins, Edward J', 'University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, MS, USA.|University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, MS, USA.|University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, MS, USA.|University of Southern Mississippi, Department of Biological Sciences, Hattiesburg, MS, USA.', 'United States|United States|United States|United States', 0, 'Animals|Colinus|Coturnix|Databases, Genetic|Expressed Sequence Tags|Gene Library|Genome|Genomics|Knowledge Bases|Quail', 'Journal Article', '10.1186/1471-2105-11-S6-S13', '20946596', 'The Quail Genomics knowledgebase (http://www.quailgenomics.info) has been initiated to share and develop functional genomic data for Northern bobwhite (Colinus virginianus). This web-based platform has been designed to allow researchers to perform analysis and curate genomic information for this non-model species that has little supporting information in GenBank.'),
(93896, 2010, 'bmc', 'Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)', 'Tjioe, Elina|Berry, Michael W|Homayouni, Ramin', 'Department of Electrical Engineering and Computer Science and Graduate School of Genome Science and Techonology, University of Tennessee, Knoxville, TN 37996, USA.|Department of Electrical Engineering and Computer Science and Graduate School of Genome Science and Techonology, University of Tennessee, Knoxville, TN 37996, USA.|Department of Electrical Engineering and Computer Science and Graduate School of Genome Science and Techonology, University of Tennessee, Knoxville, TN 37996, USA.', 'United States|United States|United States', 0, 'Databases, Factual|Gene Expression Profiling|Genome|Genomics|Knowledge Bases|Molecular Sequence Annotation', 'Journal Article', '10.1186/1471-2105-11-S6-S14', '20946597', 'Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments.'),
(93897, 2010, 'bmc', 'Next generation models for storage and representation of microbial biological annotation', 'Quest, Daniel J|Land, Miriam L|Brettin, Thomas S|Cottingham, Robert W', 'Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6420, USA. questdj@ornl.gov|Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6420, USA. questdj@ornl.gov|Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6420, USA. questdj@ornl.gov|Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6420, USA. questdj@ornl.gov', 'United States|United States|United States|United States', 0, 'Base Sequence|Databases, Genetic|Genome|Genome, Bacterial|Genomics|Molecular Sequence Annotation|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S6-S15', '20946598', 'Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software system to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way.'),
(93898, 2010, 'bmc', 'HPIDB--a unified resource for host-pathogen interactions', 'Kumar, Ranjit|Nanduri, Bindu', 'College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. rkumar@cvm.msstate.edu|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. rkumar@cvm.msstate.edu', 'United States|United States', 0, 'Databases, Protein|Host-Pathogen Interactions|Internet|Protein Interaction Mapping|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S6-S16', '20946599', 'Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.'),
(93899, 2010, 'bmc', 'Promoter prediction in E coli based on SIDD profiles and Artificial Neural Networks', 'Bland, Charles|Newsome, Abigail S|Markovets, Aleksandra A', 'Department Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Hwy 82 West, Itta Bena, Mississippi 38941, USA.|Department Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Hwy 82 West, Itta Bena, Mississippi 38941, USA.|Department Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Hwy 82 West, Itta Bena, Mississippi 38941, USA.', 'United States|United States|United States', 0, 'DNA, Bacterial|Escherichia coli|Genomics|Neural Networks (Computer)|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-11-S6-S17', '20946600', 'One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93900, 2010, 'bmc', 'Phylometrics: a pipeline for inferring phylogenetic trees from a sequence relationship network perspective', 'Smits, Samuel A|Ouverney, Cleber C', 'Department of Biological Sciences, San José State University, One Washington Square, San José, California 95192-0100, USA. samsmits@gmail.com|Department of Biological Sciences, San José State University, One Washington Square, San José, California 95192-0100, USA. samsmits@gmail.com', 'United States|United States', 0, 'Genomics|Phylogeny|RNA, Ribosomal, 16S|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-S6-S18', '20946601', 'Comparative sequence analysis of the 16S rRNA gene is frequently used to characterize the microbial diversity of environmental samples. However, sequence similarities do not always imply functional or evolutionary relatedness due to many factors, including unequal rates of change and convergence. Thus, relying on top BLASTN hits for phylogenetic studies may misrepresent the diversity of these constituents. Furthermore, attempts to circumvent this issue by including a large number of BLASTN hits per sequence in one tree to explore their relatedness presents other problems. For instance, the multiple sequence alignment will be poor and computationally costly if not relying on manual alignment, and it may be difficult to derive meaningful relationships from the resulting tree. Analyzing sequence relationship networks within collective BLASTN results, however, reveal sequences that are closely related despite low rank.'),
(93901, 2010, 'bmc', 'Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks', 'Chaitankar, Vijender|Ghosh, Preetam|Perkins, Edward J|Gong, Ping|Zhang, Chaoyang', 'School of Computing, The University of Southern Mississippi, MS 39402, USA.|School of Computing, The University of Southern Mississippi, MS 39402, USA.|School of Computing, The University of Southern Mississippi, MS 39402, USA.|School of Computing, The University of Southern Mississippi, MS 39402, USA.|School of Computing, The University of Southern Mississippi, MS 39402, USA.', 'United States|United States|United States|United States|United States', 0, 'Bayes Theorem|Databases, Genetic|Gene Regulatory Networks|Models, Genetic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-S6-S19', '20946602', 'A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference, however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric, hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters.'),
(93902, 2010, 'bmc', 'Applying internal coordinate mechanics to model the interactions between 8R-lipoxygenase and its substrate', 'Bai, Shuju|Du, Tianchuan|Khosravi, Ebrahim', 'Department of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USA. shuju_bai@subr.edu|Department of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USA. shuju_bai@subr.edu|Department of Computer Science, Southern University and A&M College, Baton Rouge, LA 70813, USA. shuju_bai@subr.edu', 'United States|United States|United States', 0, 'Arachidonate Lipoxygenases|Arachidonic Acid|Binding Sites|Computational Biology|Models, Molecular|Mutation|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-11-S6-S2', '20946603', 'Lipoxygenases (LOX) play pivotal roles in the biosynthesis of leukotrienes and other biologically active potent signalling compounds. Developing inhibitors for LOX is of high interest to researchers. Modelling the interactions between LOX and its substrate arachidonic acid is critical for developing LOX specific inhibitors. Currently, there are no LOX-substrate structures. Recently, the structure of a coral LOX, 8R-LOX, which is 41% sequence identical to the human 5-LOX was solved to 1.85Å resolution. This structure provides a foundation for modelling enzyme-substrate interactions.'),
(93903, 2010, 'bmc', 'A framework for automated enrichment of functionally significant inverted repeats in whole genomes', 'Kandoth, Cyriac|Ercal, Fikret|Frank, Ronald L', 'Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA. ckandoth@wustl.edu|Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA. ckandoth@wustl.edu|Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA. ckandoth@wustl.edu', 'United States|United States|United States', 0, 'Arabidopsis|Base Composition|Base Sequence|Genome|Genomics|Inverted Repeat Sequences|MicroRNAs|RNA, Small Interfering', 'Journal Article', '10.1186/1471-2105-11-S6-S20', '20946604', 'RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional.'),
(93904, 2010, 'bmc', 'A predictive model for secondary RNA structure using graph theory and a neural network', 'Koessler, Denise R|Knisley, Debra J|Knisley, Jeff|Haynes, Teresa', 'Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA.|Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA.|Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA.|Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Neural Networks (Computer)|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1186/1471-2105-11-S6-S21', '20946605', 'Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like.'),
(93905, 2010, 'bmc', 'Comparative genome analysis of PHB gene family reveals deep evolutionary origins and diverse gene function', 'Di, Chao|Xu, Wenying|Su, Zhen|Yuan, Joshua S', 'College of Biological Sciences, China Agricultural University, Beijing, China.|College of Biological Sciences, China Agricultural University, Beijing, China.|College of Biological Sciences, China Agricultural University, Beijing, China.|College of Biological Sciences, China Agricultural University, Beijing, China.', 'China|China|China|China', 0, 'Animals|Arabidopsis|Evolution, Molecular|Exons|Gene Duplication|Genome|Genomics|Humans|Introns|Repressor Proteins', 'Comparative Study', '10.1186/1471-2105-11-S6-S22', '20946606', 'PHB (Prohibitin) gene family is involved in a variety of functions important for different biological processes. PHB genes are ubiquitously present in divergent species from prokaryotes to eukaryotes. Human PHB genes have been found to be associated with various diseases. Recent studies by our group and others have shown diverse function of PHB genes in plants for development, senescence, defence, and others. Despite the importance of the PHB gene family, no comprehensive gene family analysis has been carried to evaluate the relatedness of PHB genes across different species. In order to better guide the gene function analysis and understand the evolution of the PHB gene family, we therefore carried out the comparative genome analysis of the PHB genes across different kingdoms.'),
(93906, 2010, 'bmc', 'Lesion detection in demoscopy images with novel density-based and active contour approaches', 'Mete, Mutlu|Sirakov, Nikolay Metodiev', 'Department of Computer Science, Texas A&M University-Commerce, Commerce, Texas, USA. Mutlu_Mete@tamu-commerce.edu|Department of Computer Science, Texas A&M University-Commerce, Commerce, Texas, USA. Mutlu_Mete@tamu-commerce.edu', 'United States|United States', 0, 'Dermoscopy|Humans|Image Enhancement|Image Interpretation, Computer-Assisted|Melanoma|Pattern Recognition, Automated|Skin Neoplasms', 'Journal Article', '10.1186/1471-2105-11-S6-S23', '20946607', 'Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion.'),
(93907, 2010, 'bmc', 'Dynamics of protofibril elongation and association involved in Axcexb242 peptide aggregation in Alzheimers disease', 'Ghosh, Preetam|Kumar, Amit|Datta, Bhaswati|Rangachari, Vijayaraghavan', 'School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA. preetam.ghosh@usm.edu|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA. preetam.ghosh@usm.edu|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA. preetam.ghosh@usm.edu|School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA. preetam.ghosh@usm.edu', 'United States|United States|United States|United States', 0, 'Alzheimer Disease|Amyloid|Amyloid beta-Peptides|Humans|Kinetics|Models, Molecular|Molecular Dynamics Simulation|Scattering, Radiation', 'Journal Article', '10.1186/1471-2105-11-S6-S24', '20946608', 'The aggregates of a protein called, Axcexb2 found in brains of Alzheimers patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Axcexb2 aggregates, smaller aggregates called soluble oligomers are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Axcexb2 aggregation is a nucleation dependent process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the on-pathway products. Modeling of Axcexb2 aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways.'),
(93908, 2010, 'bmc', 'Integrated database for identifying candidate genes for Aspergillus flavus resistance in maize', 'Kelley, Rowena Y|Gresham, Cathy|Harper, Jonathan|Bridges, Susan M|Warburton, Marilyn L|Hawkins, Leigh K|Pechanova, Olga|Peethambaran, Bela|Pechan, Tibor|Luthe, Dawn S|Mylroie, J E|Ankala, Arunkanth|Ozkan, Seval|Henry, W B|Williams, W P', 'Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.edu|Department of Biochemistry and Molecular Biology, Mississippi State University, MS, USA. rkelley@bch.msstate.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|United States|United States|United States', 0, 'Aspergillus flavus|Databases, Genetic|Genomics|Polymorphism, Single Nucleotide|Proteomics|Quantitative Trait Loci|Zea mays', 'Journal Article', '10.1186/1471-2105-11-S6-S25', '20946609', 'Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin.'),
(93909, 2010, 'bmc', 'Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images', 'Kockara, Sinan|Mete, Mutlu|Chen, Bernard|Aydin, Kemal', 'Computer Science Department, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu|Computer Science Department, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu|Computer Science Department, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu|Computer Science Department, University of Central Arkansas, Conway, AR, USA. skockara@uca.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Dermoscopy|Fuzzy Logic|Humans|Image Interpretation, Computer-Assisted|Melanoma|Pattern Recognition, Automated|Skin Neoplasms', 'Journal Article', '10.1186/1471-2105-11-S6-S26', '20946610', 'Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density--greater than certain number of points--around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.'),
(93910, 2010, 'bmc', 'Constructing non-stationary Dynamic Bayesian Networks with a flexible lag choosing mechanism', 'Jia, Yi|Huan, Jun', 'Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS 66045, USA.|Department of Electrical Engineering & Computer Science, University of Kansas, Lawrence, KS 66045, USA.', 'United States|United States', 0, 'Animals|Arabidopsis|Bayes Theorem|Databases, Genetic|Drosophila|Gene Regulatory Networks|Genomics|Macrophages', 'Journal Article', '10.1186/1471-2105-11-S6-S27', '20946611', 'Dynamic Bayesian Networks (DBNs) are widely used in regulatory network structure inference with gene expression data. Current methods assumed that the underlying stochastic processes that generate the gene expression data are stationary. The assumption is not realistic in certain applications where the intrinsic regulatory networks are subject to changes for adapting to internal or external stimuli.'),
(93911, 2010, 'bmc', 'IView: introgression library visualization and query tool', 'Bottoms, Christopher A|Flint-Garcia, Sherry|McMullen, Michael D', 'Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA.|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA.|Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 0, 'Genetic Markers|Genomic Library|Genomics|Genotype|Phenotype|Quantitative Trait Loci|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S6-S28', '20946612', 'An introgression library is a family of near-isogenic lines in a common genetic background, each of which carries one or more genomic regions contributed by a donor genome. Near-isogenic lines are powerful genetic resources for the analysis of phenotypic variation and are important for map-base cloning genes underlying mutations and traits. With many thousands of distinct genotypes, querying introgression libraries for lines of interest is an issue.'),
(93912, 2010, 'bmc', 'GOModeler--a tool for hypothesis-testing of functional genomics datasets', 'Manda, Prashanti|Freeman, McKinley G|Bridges, Susan M|Jankun-Kelly, T J|Nanduri, Bindu|McCarthy, Fiona M|Burgess, Shane C', 'Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.|Department of Computer Science and Engineering, Mississippi State University, MS, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Genome|Genomics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S6-S29', '20946613', 'Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available discovery-based computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing.'),
(93913, 2010, 'bmc', 'Assessing constancy of substitution rates in viruses over evolutionary time', 'Melcher, Ulrich', 'Department of Biochemistry & Molecular Biology, Oklahoma State University, Stillwater, OK 74078, USA. u-melcher-4@alumni.uchicago.edu', 'United States', 0, 'Base Sequence|Databases, Genetic|Evolution, Molecular|Genomics|Phylogeny|Viruses', 'Journal Article', '10.1186/1471-2105-11-S6-S3', '20946614', 'Phylogenetic analyses reveal probable patterns of divergence of present day organisms from common ancestors. The points of divergence of lineages can be dated if a corresponding historical or fossil record exists. For many species, in particular viruses, such records are rare. Recently, Bayesian phylogenetic analysis using sequences from closely related organisms isolated at different times have been used to calibrate divergences. Phylogenetic analyses depend on the assumption that the average substitution rates that can be calculated from the data apply throughout the course of evolution.'),
(93914, 2010, 'bmc', 'An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays', 'Fang, Hong|Xu, Joshua|Ding, Don|Jackson, Scott A|Patel, Isha R|Frye, Jonathan G|Zou, Wen|Nayak, Rajesh|Foley, Steven|Chen, James|Su, Zhenqiang|Ye, Yanbin|Turner, Steve|Harris, Steve|Zhou, Guangxu|Cerniglia, Carl|Tong, Weida', 'Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.|Z-Tech Corporation, ICF International Company at National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Databases, Protein|Foodborne Diseases|Genome, Bacterial|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Software|United States|United States Food and Drug Administration', 'Journal Article', '10.1186/1471-2105-11-S6-S4', '20946615', 'Advances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrack™, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrack™ has been widely adopted by the research community and used for pharmacogenomics data review in the FDAs Voluntary Genomics Data Submission program.'),
(93915, 2010, 'bmc', 'The EDKB: an established knowledge base for endocrine disrupting chemicals', 'Ding, Don|Xu, Lei|Fang, Hong|Hong, Huixiao|Perkins, Roger|Harris, Steve|Bearden, Edward D|Shi, Leming|Tong, Weida', 'Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.|Division of Bioinformatics, Z-Tech Corporation, ICF International Company at NCTR/FDA, Jefferson, AR 72079, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Factual|Endocrine Disruptors|Food Contamination|Food Packaging|Government Regulation|Humans|Knowledge Bases|Search Engine', 'Journal Article', '10.1186/1471-2105-11-S6-S5', '20946616', 'Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990s by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing.'),
(93916, 2010, 'bmc', 'Two new ArrayTrack libraries for personalized biomedical research', 'Xu, Joshua|Wise, Carolyn|Varma, Vijayalakshmi|Fang, Hong|Ning, Baitang|Hong, Huixiao|Tong, Weida|Kaput, Jim', 'Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov|Z-Tech Corporation, ICF International company at NCTR, National Center for Toxicological Research, 3900 NCTR Rd, Jefferson, AR 72079, USA. Zhihua.xu@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Biomedical Research|Databases, Genetic|Genomics|Humans|Mice|Microarray Analysis|Polymorphism, Single Nucleotide|Precision Medicine|Quantitative Trait Loci|Rats|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S6-S6', '20946617', 'Recent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait. Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies.'),
(93917, 2010, 'bmc', 'PEPPI: a peptidomic database of human protein isoforms for proteomics experiments', 'Zhou, Ao|Zhang, Fan|Chen, Jake Y', 'School of Informatics, Indiana University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University, Indianapolis, IN 46202, USA.', 'India|India|India', 0, 'Databases, Factual|Databases, Protein|Humans|Peptides|Protein Isoforms|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-11-S6-S7', '20946618', 'Protein isoform generation, which may derive from alternative splicing, genetic polymorphism, and posttranslational modification, is an essential source of achieving molecular diversity by eukaryotic cells. Previous studies have shown that protein isoforms play critical roles in disease diagnosis, risk assessment, sub-typing, prognosis, and treatment outcome predictions. Understanding the types, presence, and abundance of different protein isoforms in different cellular and physiological conditions is a major task in functional proteomics, and may pave ways to molecular biomarker discovery of human diseases. In tandem mass spectrometry (MS/MS) based proteomics analysis, peptide peaks with exact matches to protein sequence records in the proteomics database may be identified with mass spectrometry (MS) search software. However, due to limited annotation and poor coverage of protein isoforms in proteomics databases, high throughput protein isoform identifications, particularly those arising from alternative splicing and genetic polymorphism, have not been possible.'),
(93918, 2010, 'bmc', 'Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network', 'Su, Junjie|Yoon, Byung-Jun|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.', 'United States|United States|United States', 0, 'Biomarkers, Tumor|Breast Neoplasms|Female|Gene Expression|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-11-S6-S8', '20946619', 'Finding reliable gene markers for accurate disease classification is very challenging due to a number of reasons, including the small sample size of typical clinical data, high noise in gene expression measurements, and the heterogeneity across patients. In fact, gene markers identified in independent studies often do not coincide with each other, suggesting that many of the predicted markers may have no biological significance and may be simply artifacts of the analyzed dataset. To find more reliable and reproducible diagnostic markers, several studies proposed to analyze the gene expression data at the level of groups of functionally related genes, such as pathways. Studies have shown that pathway markers tend to be more robust and yield more accurate classification results. One practical problem of the pathway-based approach is the limited coverage of genes by currently known pathways. As a result, potentially important genes that play critical roles in cancer development may be excluded. To overcome this problem, we propose a novel method for identifying reliable subnetwork markers in a human protein-protein interaction (PPI) network.'),
(93919, 2010, 'bmc', 'Analysis of Bovine Viral Diarrhea Viruses-infected monocytes: identification of cytopathic and non-cytopathic biotype differences', 'Ammari, Mais|McCarthy, Fiona M|Nanduri, Bindu|Pinchuk, Lesya M', 'Department of Basic Sciences, Mississippi State University, Mississippi State, MS 39762, USA.|Department of Basic Sciences, Mississippi State University, Mississippi State, MS 39762, USA.|Department of Basic Sciences, Mississippi State University, Mississippi State, MS 39762, USA.|Department of Basic Sciences, Mississippi State University, Mississippi State, MS 39762, USA.', 'United States|United States|United States|United States', 0, 'Animals|Cattle|Cells, Cultured|Cytopathogenic Effect, Viral|Diarrhea Viruses, Bovine Viral|Host-Pathogen Interactions|Molecular Sequence Annotation|Monocytes', 'Journal Article', '10.1186/1471-2105-11-S6-S9', '20946620', 'Bovine Viral Diarrhea Virus (BVDV) infection is widespread in cattle worldwide, causing important economic losses. Pathogenesis of the disease caused by BVDV is complex, as each BVDV strain has two biotypes: non-cytopathic (ncp) and cytopathic (cp). BVDV can cause a persistent latent infection and immune suppression if animals are infected with an ncp biotype during early gestation, followed by a subsequent infection of the cp biotype. The molecular mechanisms that underscore the complex disease etiology leading to immune suppression in cattle caused by BVDV are not well understood.'),
(93920, 2010, 'bmc', 'Data structures and compression algorithms for high-throughput sequencing technologies', 'Daily, Kenny|Rigor, Paul|Christley, Scott|Xie, Xiaohui|Baldi, Pierre', 'Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA.|Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA.', 'United States|United States|United States|United States|United States', 38, 'Algorithms|Computational Biology|Data Compression|Databases, Factual|Entropy|High-Throughput Nucleotide Sequencing', 'Journal Article', '10.1186/1471-2105-11-514', '20946637', 'High-throughput sequencing (HTS) technologies play important roles in the life sciences by allowing the rapid parallel sequencing of very large numbers of relatively short nucleotide sequences, in applications ranging from genome sequencing and resequencing to digital microarrays and ChIP-Seq experiments. As experiments scale up, HTS technologies create new bioinformatics challenges for the storage and sharing of HTS data.'),
(93921, 2010, 'bmc', 'Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: transcriptional dynamics and regulatory structures', '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', 12, 'Animals|Gene Expression Profiling|Gene Expression Regulation|Glucocorticoids|Liver|Pharmacogenetics|Rats|Transcription Factors|Transcription, Genetic', 'Comparative Study', '10.1186/1471-2105-11-515', '20946642', 'Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich in vivo datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.'),
(93922, 2010, 'bmc', 'QTLminer: identifying genes regulating quantitative traits', 'Alberts, Rudi|Schughart, Klaus', 'Department of Infection Genetics, Helmholtz Centre for Infection Research and University of Veterinary Medicine Hannover, Braunschweig, Germany.|Department of Infection Genetics, Helmholtz Centre for Infection Research and University of Veterinary Medicine Hannover, Braunschweig, Germany.', 'Germany|Germany', 21, 'Computational Biology|Genome|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-11-516', '20950438', 'Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.'),
(93923, 2010, 'bmc', 'Inferring gene regression networks with model trees', 'Nepomuceno-Chamorro, Isabel A|Aguilar-Ruiz, Jesus S|Riquelme, Jose C', 'Dpt Lenguajes y Sistemas Informaticos, Universidad de Sevilla, Seville, Spain. inepomuceno@us.es|Dpt Lenguajes y Sistemas Informaticos, Universidad de Sevilla, Seville, Spain. inepomuceno@us.es|Dpt Lenguajes y Sistemas Informaticos, Universidad de Sevilla, Seville, Spain. inepomuceno@us.es', 'Spain|Spain|Spain', 23, 'Computational Biology|Databases, Genetic|Escherichia coli|Gene Regulatory Networks|Linear Models|Saccharomyces cerevisiae|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-517', '20950452', 'Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.'),
(93924, 2010, 'bmc', 'Accurate peak list extraction from proteomic mass spectra for identification and profiling studies', 'Barbarini, Nicola|Magni, Paolo', 'Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. nicola.barbarini@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. nicola.barbarini@unipv.it', 'Italy|Italy', 0, 'Algorithms|Databases, Protein|Mass Spectrometry|Proteins|Proteomics', 'Evaluation Studies', '10.1186/1471-2105-11-518', '20950483', 'Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which contains the mass, the charge and the intensity of every detected biomolecule. The main steps of the peak list extraction procedure are usually preprocessing, peak detection, peak selection, charge determination and monoisotoping operation.'),
(93925, 2010, 'bmc', 'Workshop on advances in Bio Text Mining, Ghent, Belgium 10-11 May 2010 Abstracts', '-', '-', '-', 0, 'Data Mining', 'Congress', '10.1186/1471-2105-11-518', '20955542', '-'),
(93926, 2010, 'bmc', 'Statistical analysis and molecular dynamics simulations of ambivalent xcexb1-helices', 'Bhattacharjee, Nicholus|Biswas, Parbati', 'Department of Chemistry, University of Delhi, Delhi-110007, India.|Department of Chemistry, University of Delhi, Delhi-110007, India.', 'India|India', 0, 'Amino Acid Sequence|Data Interpretation, Statistical|Databases, Protein|Molecular Dynamics Simulation|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-11-519', '20955581', 'Analysis of known protein structures reveals that identical sequence fragments in proteins can adopt different secondary structure conformations. The extent of this conformational diversity is influenced by various factors like the intrinsic sequence propensity, sequence context and other environmental factors such as pH, site directed mutations or alteration of the binding ligands. Understanding the mechanism by which the environment affects the structural ambivalence of these peptides has potential implications for protein design and reliable local structure prediction algorithms. Identification of the structurally ambivalent sequence fragments and determining the rules which dictate their conformational preferences play an important role in understanding the conformational changes observed in misfolding diseases. However, a systematic classification of their intrinsic sequence patterns or a statistical analysis of their properties and sequence context in relation to the origin of their structural diversity have largely remained unexplored.'),
(93927, 2010, 'bmc', 'Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks', 'Li, Yong|Liu, Lili|Bai, Xi|Cai, Hua|Ji, Wei|Guo, Dianjing|Zhu, Yanming', 'Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.|Plant Bioengineering Laboratory, Northeast Agricultural University, Harbin, China.', 'China|China|China|China|China|China|China', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-11-520', '20955620', 'Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data.'),
(93928, 2010, 'bmc', 'Discover protein complexes in protein-protein interaction networks using parametric local modularity', 'Kim, Jongkwang|Tan, Kai', 'Department of Internal Medicine, The University of Iowa, 2294 CBRB, Iowa City, IA 52242, USA.|Department of Internal Medicine, The University of Iowa, 2294 CBRB, Iowa City, IA 52242, USA.', 'United States|United States', 10, 'Algorithms|Multiprotein Complexes|Protein Interaction Mapping|Proteomics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-11-521', '20958996', 'Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.'),
(93929, 2010, 'bmc', 'Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs', 'Hartsperger, Mara L|Blöchl, Florian|Stümpflen, Volker|Theis, Fabian J', 'Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. mara.hartsperger@helmholtz-muenchen.de|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. mara.hartsperger@helmholtz-muenchen.de|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. mara.hartsperger@helmholtz-muenchen.de|Institute of Bioinformatics and Systems Biology (MIPS), Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany. mara.hartsperger@helmholtz-muenchen.de', 'Germany|Germany|Germany|Germany', 12, 'Algorithms|Cluster Analysis|Computational Biology|Models, Biological|Pattern Recognition, Automated|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-522', '20961418', 'Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93930, 2010, 'bmc', 'Class prediction for high-dimensional class-imbalanced data', 'Blagus, Rok|Lusa, Lara', 'Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia.|Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia.', 'Slovenia|Slovenia', 0, 'Algorithms|Breast Neoplasms|Classification|Databases, Factual|Female|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-523', '20961420', 'The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class, the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.'),
(93931, 2010, 'bmc', 'A genetic ensemble approach for gene-gene interaction identification', 'Yang, Pengyi|Ho, Joshua W K|Zomaya, Albert Y|Zhou, Bing B', 'School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy7@gmail.com|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy7@gmail.com|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy7@gmail.com|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy7@gmail.com', 'Australia|Australia|Australia|Australia', 20, 'Algorithms|Computer Simulation|Genes|Models, Genetic|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-11-524', '20961462', 'It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.'),
(93932, 2010, 'bmc', 'solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database', 'Tecle, Isaak Y|Menda, Naama|Buels, Robert M|van der Knaap, Esther|Mueller, Lukas A', 'Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.|Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.|Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.|Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.|Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Databases, Factual|Databases, Genetic|Genome, Plant|Genomics|Phenotype|Quantitative Trait Loci|Software|Solanaceae', 'Journal Article', '10.1186/1471-2105-11-525', '20964836', 'A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.'),
(93933, 2010, 'bmc', 'A statistical approach to finding overlooked genetic associations', 'Rider, Andrew K|Siwo, Geoffrey|Chawla, Nitesh V|Ferdig, Michael|Emrich, Scott J', 'Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.|Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.|Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.|Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.|Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.', 'India|India|India|India|India', 0, 'Genetic Association Studies|Genome|Models, Statistical|Plasmodium falciparum|Quantitative Trait Loci|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-11-526', '20964847', 'Complexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.'),
(93934, 2010, 'bmc', 'CCRaVAT and QuTie-enabling analysis of rare variants in large-scale case control and quantitative trait association studies', 'Lawrence, Robert|Day-Williams, Aaron G|Elliott, Katherine S|Morris, Andrew P|Zeggini, Eleftheria', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Case-Control Studies|Genetic Variation|Genetics, Population|Genome-Wide Association Study|Humans|Phenotype|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-11-527', '20964851', 'Genome-wide association studies have been successful in finding common variants influencing common traits. However, these associations only account for a fraction of trait heritability. There has been a shift in the field towards studying low frequency and rare variants, which are now widely recognised as putative complex trait determinants. Despite this increasing focus on examining the role of low frequency and rare variants in complex disease susceptibility, there is a lack of user-friendly analytical packages implementing powerful association tests for the analysis of rare variants.'),
(93935, 2010, 'bmc', 'PathEx: a novel multi factors based datasets selector web tool', 'Bareke, Eric|Pierre, Michael|Gaigneaux, Anthoula|De Meulder, Bertrand|Depiereux, Sophie|Berger, Fabrice|Habra, Naji|Depiereux, Eric', 'Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be|Molecular Biology Research Unit, University of Namur - FUNDP, Namur, Belgium. eric.bareke@fundp.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 5, 'Databases, Factual|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-528', '20969778', 'Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.'),
(93936, 2010, 'bmc', 'Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers', 'Shepherd, Ross K|Meuwissen, Theo H E|Woolliams, John A', 'School of Information and Communication Technology, CQUniversity, Rockhampton 4702, Australia. r.shepherd@cqu.edu.au|School of Information and Communication Technology, CQUniversity, Rockhampton 4702, Australia. r.shepherd@cqu.edu.au|School of Information and Communication Technology, CQUniversity, Rockhampton 4702, Australia. r.shepherd@cqu.edu.au', 'Australia|Australia|Australia', 0, 'Algorithms|Animals|Genetic Markers|Genome|Genome-Wide Association Study|Genomics|Genotype|Humans|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-11-529', '20969788', 'The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved.'),
(93937, 2010, 'bmc', 'Formalization of taxon-based constraints to detect inconsistencies in annotation and ontology development', 'Deegan née Clark, Jennifer I|Dimmer, Emily C|Mungall, Christopher J', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. jdeegan@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. jdeegan@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. jdeegan@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Classification|Databases, Genetic|Databases, Protein|Molecular Sequence Annotation|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-530', '20973947', 'The Gene Ontology project supports categorization of gene products according to their location of action, the molecular functions that they carry out, and the processes that they are involved in. Although the ontologies are intentionally developed to be taxon neutral, and to cover all species, there are inherent taxon specificities in some branches. For example, the process lactation is specific to mammals and the location mitochondrion is specific to eukaryotes. The lack of an explicit formalization of these constraints can lead to errors and inconsistencies in automated and manual annotation.'),
(93938, 2010, 'bmc', 'BALL--biochemical algorithms library 13', 'Hildebrandt, Andreas|Dehof, Anna Katharina|Rurainski, Alexander|Bertsch, Andreas|Schumann, Marcel|Toussaint, Nora C|Moll, Andreas|Stöckel, Daniel|Nickels, Stefan|Mueller, Sabine C|Lenhof, Hans-Peter|Kohlbacher, Oliver', 'Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de|Center for Bioinformatics Saar, Saarland University, Saarbrücken, Germany. anhi@bioinf.uni-sb.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 60, 'Algorithms|Computational Biology|Databases, Factual|Software', 'Journal Article', '10.1186/1471-2105-11-531', '20973958', 'The Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements.'),
(93939, 2010, 'bmc', 'Estimating time since infection in early homogeneous HIV-1 samples using a poisson model', 'Giorgi, Elena E|Funkhouser, Bob|Athreya, Gayathri|Perelson, Alan S|Korber, Bette T|Bhattacharya, Tanmoy', 'Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov|Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov|Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov|Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov|Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov|Los Alamos National Laboratory, Los Alamos, NM 87545, USA. egiorgi@lanl.gov', 'United States|United States|United States|United States|United States|United States', 0, 'Evolution, Molecular|Genetic Variation|HIV Infections|HIV-1|Humans|Phylogeny|Poisson Distribution', 'Journal Article', '10.1186/1471-2105-11-532', '20973976', 'The occurrence of a genetic bottleneck in HIV sexual or mother-to-infant transmission has been well documented. This results in a majority of new infections being homogeneous, i.e., initiated by a single genetic strain. Early after infection, prior to the onset of the host immune response, the viral population grows exponentially. In this simple setting, an approach for estimating evolutionary and demographic parameters based on comparison of diversity measures is a feasible alternative to the existing Bayesian methods (e.g., BEAST), which are instead based on the simulation of genealogies.'),
(93940, 2010, 'bmc', 'Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins', 'Ahmad, Shandar|Singh, Yumlembam Hemajit|Paudel, Yogesh|Mori, Takaharu|Sugita, Yuji|Mizuguchi, Kenji', 'National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp|National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp|National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp|National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp|National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp|National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan. shandar@nibio.go.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 3, 'Binding Sites|Computational Biology|Databases, Protein|Membrane Proteins|Models, Molecular|Protein Structure, Secondary', 'Journal Article', '10.1186/1471-2105-11-533', '20977780', 'Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.'),
(93941, 2010, 'bmc', 'Subdivision of the MDR superfamily of medium-chain dehydrogenases/reductases through iterative hidden Markov model refinement', 'Hedlund, Joel|Jörnvall, Hans|Persson, Bengt', 'IFM Bioinformatics, Linköping University, S-581 83 Linköping, Sweden. yohell@ifm.liu.se|IFM Bioinformatics, Linköping University, S-581 83 Linköping, Sweden. yohell@ifm.liu.se|IFM Bioinformatics, Linköping University, S-581 83 Linköping, Sweden. yohell@ifm.liu.se', 'Sweden|Sweden|Sweden', 28, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Markov Chains|Oxidoreductases|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-534', '20979641', 'The Medium-chain Dehydrogenases/Reductases (MDR) form a protein superfamily whose size and complexity defeats traditional means of subclassification, it currently has over 15000 members in the databases, the pairwise sequence identity is typically around 25%, there are members from all kingdoms of life, the chain-lengths vary as does the oligomericity, and the members are partaking in a multitude of biological processes. There are profile hidden Markov models (HMMs) available for detecting MDR superfamily members, but none for determining which MDR family each protein belongs to. The current torrential influx of new sequence data enables elucidation of more and more protein families, and at an increasingly fine granularity. However, gathering good quality training data usually requires manual attention by experts and has therefore been the rate limiting step for expanding the number of available models.'),
(93942, 2010, 'bmc', 'Optimizing structural modeling for a specific protein scaffold: knottins or inhibitor cystine knots', 'Gracy, Jérôme|Chiche, Laurent', 'CNRS, UMR5048, Université Montpellier 1 et 2, Centre de Biochimie Structurale, 34090 Montpellier, France. Jerome.Gracy@cbs.cnrs.fr|CNRS, UMR5048, Université Montpellier 1 et 2, Centre de Biochimie Structurale, 34090 Montpellier, France. Jerome.Gracy@cbs.cnrs.fr', 'France|France', 6, 'Cysteine|Cystine-Knot Miniproteins|Databases, Protein|Models, Molecular|Protein Folding|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-11-535', '21029427', 'Knottins are small, diverse and stable proteins with important drug design potential. They can be classified in 30 families which cover a wide range of sequences (1621 sequenced), three-dimensional structures (155 solved) and functions (> 10). Inter knottin similarity lies mainly between 15% and 40% sequence identity and 1.5 to 4.5 Å backbone deviations although they all share a tightly knotted disulfide core. This important variability is likely to arise from the highly diverse loops which connect the successive knotted cysteines. The prediction of structural models for all knottin sequences would open new directions for the analysis of interaction sites and to provide a better understanding of the structural and functional organization of proteins sharing this scaffold.'),
(93943, 2010, 'bmc', 'Efficient motif finding algorithms for large-alphabet inputs', 'Kuksa, Pavel P|Pavlovic, Vladimir', 'Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu|Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu', 'United States|United States', 19, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Binding Sites|Computational Biology|DNA|Databases, Genetic|Nucleic Acid Conformation|Pattern Recognition, Automated|Protein Conformation|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-S8-S1', '21034426', 'We consider the problem of identifying motifs, recurring or conserved patterns, in the biological sequence data sets. To solve this task, we present a new deterministic algorithm for finding patterns that are embedded as exact or inexact instances in all or most of the input strings.'),
(93944, 2010, 'bmc', 'Graphical models for inferring single molecule dynamics', 'Bronson, Jonathan E|Hofman, Jake M|Fei, Jingyi|Gonzalez, Ruben L|Wiggins, Chris H', 'Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu|Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu|Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu|Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu|Department of Chemistry, Columbia University, New York, NY 10027, USA. jeb2126@columbia.edu', 'United States|United States|United States|United States|United States', 15, 'Algorithms|Bayes Theorem|Computer Graphics|DNA|Databases, Factual|Fluorescence Resonance Energy Transfer|Inverted Repeat Sequences|Markov Chains|Models, Theoretical|Molecular Dynamics Simulation|Software', 'Journal Article', '10.1186/1471-2105-11-S8-S2', '21034427', 'The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.'),
(93945, 2010, 'bmc', 'A machine learning pipeline for quantitative phenotype prediction from genotype data', 'Guzzetta, Giorgio|Jurman, Giuseppe|Furlanello, Cesare', 'Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.|Fondazione Bruno Kessler, Trento, Italy.', 'Italy|Italy|Italy', 9, 'Algorithms|Animals|Artificial Intelligence|CD8 Antigens|Databases, Genetic|Genome-Wide Association Study|Genotype|Linkage Disequilibrium|Mice|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Polymorphism, Single Nucleotide|Regression Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S8-S3', '21034428', 'Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.'),
(93946, 2010, 'bmc', 'Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization', 'Ma, Eddie Y T|Cameron, Christopher J F|Kremer, Stefan C', 'Department of Biology at the University of Waterloo, Waterloo, Ontario, Canada. e3ma@uwaterloo.ca|Department of Biology at the University of Waterloo, Waterloo, Ontario, Canada. e3ma@uwaterloo.ca|Department of Biology at the University of Waterloo, Waterloo, Ontario, Canada. e3ma@uwaterloo.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Alkaloids|Animals|Artificial Intelligence|Computational Biology|Databases, Factual|Mice|Pattern Recognition, Automated|Proteins|Quantitative Structure-Activity Relationship|Rats|Regression Analysis|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-11-S8-S4', '21034429', 'This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.'),
(93947, 2010, 'bmc', 'Inferring latent task structure for Multitask Learning by Multiple Kernel Learning', 'Widmer, Christian|Toussaint, Nora C|Altun, Yasemin|Rätsch, Gunnar', 'Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. cwidmer@tuebingen.mpg.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. cwidmer@tuebingen.mpg.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. cwidmer@tuebingen.mpg.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. cwidmer@tuebingen.mpg.de', 'Germany|Germany|Germany|Germany', 23, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein', 'Journal Article', '10.1186/1471-2105-11-S8-S5', '21034430', 'The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm.'),
(93948, 2010, 'bmc', 'Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models', 'Caragea, Cornelia|Caragea, Doina|Silvescu, Adrian|Honavar, Vasant', 'Artificial Intelligence Research Laboratory, Department of Computer Science,Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Department of Computer Science,Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Department of Computer Science,Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Department of Computer Science,Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu', 'United States|United States|United States|United States', 13, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Databases, Protein|Markov Chains|Models, Biological|Plant Proteins|Proteins|Reproducibility of Results|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-11-S8-S6', '21034431', 'Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data.'),
(93949, 2010, 'bmc', 'Exploiting physico-chemical properties in string kernels', 'Toussaint, Nora C|Widmer, Christian|Kohlbacher, Oliver|Rätsch, Gunnar', 'Center for Bioinformatics, Eberhard-Karls-Universität, Sand 14, 72076 Tübingen, Germany. nora.toussaint@uni-tuebingen.de|Center for Bioinformatics, Eberhard-Karls-Universität, Sand 14, 72076 Tübingen, Germany. nora.toussaint@uni-tuebingen.de|Center for Bioinformatics, Eberhard-Karls-Universität, Sand 14, 72076 Tübingen, Germany. nora.toussaint@uni-tuebingen.de|Center for Bioinformatics, Eberhard-Karls-Universität, Sand 14, 72076 Tübingen, Germany. nora.toussaint@uni-tuebingen.de', 'Germany|Germany|Germany|Germany', 20, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Chemical Phenomena|Computational Biology|Humans|Major Histocompatibility Complex|Proteins|ROC Curve', 'Journal Article', '10.1186/1471-2105-11-S8-S7', '21034432', 'String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas.'),
(93950, 2010, 'bmc', 'Infinite mixture-of-experts model for sparse survival regression with application to breast cancer', 'Raman, Sudhir|Fuchs, Thomas J|Wild, Peter J|Dahl, Edgar|Buhmann, Joachim M|Roth, Volker', 'Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch|Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch|Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch|Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch|Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch|Department of Computer Science, University of Basel, Bernoullistr, 16, CH-4056 Basel, Switzerland. sudhir.raman@unibas.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 7, 'Bayes Theorem|Breast Neoplasms|Cluster Analysis|Cohort Studies|Computer Simulation|Databases, Factual|Female|Humans|Kaplan-Meier Estimate|Markov Chains|Models, Statistical|Monte Carlo Method|Prognosis|Proportional Hazards Models|Regression Analysis|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-11-S8-S8', '21034433', 'We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Coxs proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.'),
(93951, 2010, 'bmc', 'Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins', 'Chen, Shu-An|Lee, Tzong-Yi|Ou, Yu-Yen', 'Department of Computer Science and Engineering, Yuan Ze University, Chungli 320, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Chungli 320, Taiwan.|Department of Computer Science and Engineering, Yuan Ze University, Chungli 320, Taiwan.', '-|-|-', 28, 'Algorithms|Amino Acid Motifs|Amino Acids|Glycoproteins|Glycosylation|Membrane Glycoproteins|Protein Folding', 'Journal Article', '10.1186/1471-2105-11-536', '21034461', 'While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites.'),
(93952, 2010, 'bmc', 'Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines', 'González, Alvaro J|Liao, Li', 'Department of Computer and Information Sciences, University of Delaware 421 Smith Hall, Newark, DE 19716, USA.|Department of Computer and Information Sciences, University of Delaware 421 Smith Hall, Newark, DE 19716, USA.', 'United States|United States', 17, 'Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-11-537', '21034480', 'Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles.'),
(93953, 2010, 'bmc', 'pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree', 'Matsen, Frederick A|Kodner, Robin B|Armbrust, E Virginia', 'Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. matsen@fhcrc.org|Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. matsen@fhcrc.org|Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. matsen@fhcrc.org', 'United States|United States|United States', 429, 'Base Sequence|Bayes Theorem|Computational Biology|Likelihood Functions|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-538', '21034504', 'Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. Phylogenetic placement, where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets.'),
(93954, 2010, 'bmc', 'Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model', 'Zhang, Zhengdong D|Gerstein, Mark B', 'Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA. zhengdong.zhang@einstein.yu.edu|Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA. zhengdong.zhang@einstein.yu.edu', 'United States|United States', 5, 'Bayes Theorem|Comparative Genomic Hybridization|DNA Copy Number Variations|Databases, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-539', '21034510', 'Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variation in human populations. Array-based comparative genomic hybridization (array-CGH) and newly developed read-depth approach through ultrahigh throughput genomic sequencing both provide rapid, robust, and comprehensive methods to identify CNVs on a whole-genome scale.'),
(93955, 2010, 'bmc', 'Circadian signatures in rat liver: from gene expression to pathways', 'Ovacik, Meric A|Sukumaran, Siddharth|Almon, Richard R|DuBois, Debra C|Jusko, William J|Androulakis, Ioannis P', 'Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.|Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.|Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.|Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.|Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.|Chemical and Biochemical Engineering Department, Rutgers University Piscataway, NJ 08854, USA.', 'United States|United States|United States|United States|United States|United States', 14, 'Animals|Circadian Clocks|DNA Replication|Gene Expression|Liver|Rats|Signal Transduction|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-540', '21040584', 'Circadian rhythms are 24 hour oscillations in many behavioural, physiological, cellular and molecular processes that are controlled by an endogenous clock which is entrained to environmental factors including light, food and stress. Transcriptional analyses of circadian patterns demonstrate that genes showing circadian rhythms are part of a wide variety of biological pathways.Pathway activity method can identify the significant pattern of the gene expression levels within a pathway. In this method, the overall gene expression levels are translated to a reduced form, pathway activity levels, via singular value decomposition (SVD). A given pathway represented by pathway activity levels can then be as analyzed using the same approaches used for analyzing gene expression levels. We propose to use pathway activity method across time to identify underlying circadian pattern of pathways.'),
(93956, 2010, 'bmc', 'CaGrid Workflow Toolkit: a Taverna based workflow tool for cancer grid', 'Tan, Wei|Madduri, Ravi|Nenadic, Alexandra|Soiland-Reyes, Stian|Sulakhe, Dinanath|Foster, Ian|Goble, Carole A', 'Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov|Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA. wtan@mcs.anl.gov', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Internet|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-11-542', '21044328', 'In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows.'),
(93957, 2010, 'bmc', 'Predicting success of oligomerized pool engineering (OPEN) for zinc finger target site sequences', 'Sander, Jeffry D|Reyon, Deepak|Maeder, Morgan L|Foley, Jonathan E|Thibodeau-Beganny, Stacey|Li, Xiaohong|Regan, Maureen R|Dahlborg, Elizabeth J|Goodwin, Mathew J|Fu, Fengli|Voytas, Daniel F|Joung, J Keith|Dobbs, Drena', 'Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org|Molecular Pathology Unit, Center for Cancer Research, and Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA 02129, USA. jsander@partners.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Artificial Intelligence|Base Sequence|Binding Sites|DNA-Binding Proteins|Gene Targeting|Protein Engineering|Sequence Analysis, DNA|Zinc Fingers', 'Journal Article', '10.1186/1471-2105-11-543', '21044337', 'Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable.'),
(93958, 2010, 'bmc', 'Clustering metagenomic sequences with interpolated Markov models', 'Kelley, David R|Salzberg, Steven L', 'Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, College Park, MD 20742, USA. dakelley@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, College Park, MD 20742, USA. dakelley@umiacs.umd.edu', 'United States|United States', 61, 'Cluster Analysis|Databases, Factual|Markov Chains|Metagenomics|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-544', '21044341', 'Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.'),
(93959, 2010, 'bmc', 'Selected proceedings of the 2010 Summit on Translational Bioinformatics', 'Mendonça, Eneida A|Tarczy-Hornoch, Peter', 'Department of Pediatrics and Computation Institute, The University of Chicago, Chicago, IL, USA. emendonca@uchicago.edu|Department of Pediatrics and Computation Institute, The University of Chicago, Chicago, IL, USA. emendonca@uchicago.edu', 'United States|United States', 0, 'Computational Biology|Congresses as Topic|Translational Medical Research', 'Introductory Journal Article', '10.1186/1471-2105-11-S9-S1', '21044356', '-');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93960, 2010, 'bmc', 'Feasibility of incorporating genomic knowledge into electronic medical records for pharmacogenomic clinical decision support', 'Overby, Casey Lynnette|Tarczy-Hornoch, Peter|Hoath, James I|Kalet, Ira J|Veenstra, David L', 'Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu', 'United States|United States|United States|United States|United States', 39, 'Databases, Factual|Decision Support Systems, Clinical|Electronic Health Records|Genome, Human|Humans|Pharmacogenetics|Precision Medicine', 'Journal Article', '10.1186/1471-2105-11-S9-S10', '21044357', 'In pursuing personalized medicine, pharmacogenomic (PGx) knowledge may help guide prescribing drugs based on a persons genotype. Here we evaluate the feasibility of incorporating PGx knowledge, combined with clinical data, to support clinical decision-making by: 1) analyzing clinically relevant knowledge contained in PGx knowledge resources, 2) evaluating the feasibility of a rule-based framework to support formal representation of clinically relevant knowledge contained in PGx knowledge resources, and, 3) evaluating the ability of an electronic medical record/electronic health record (EMR/EHR) to provide computable forms of clinical data needed for PGx clinical decision support. Findings suggest that the PharmGKB is a good source for PGx knowledge to supplement information contained in FDA approved drug labels. Furthermore, we found that with supporting knowledge (e.g. IF age <18 THEN patient is a child), sufficient clinical data exists in University of Washingtons EMR systems to support 50% of PGx knowledge contained in drug labels that could be expressed as rules.'),
(93961, 2010, 'bmc', 'Stromal microenvironment processes unveiled by biological component analysis of gene expression in xenograft tumor models', 'Yang, Xinan|Lee, Younghee|Huang, Yong|Chen, James L|Xing, Rosie H|Lussier, Yves A', 'Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu|Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu|Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu|Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu|Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu|Section of Genetic Medicine, Dept, of Medicine, University of Chicago, IL, USA. xyang2@uchicago.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Mice|Oligonucleotide Array Sequence Analysis|Stromal Cells|Transplantation, Heterologous|Tumor Microenvironment|Xenograft Model Antitumor Assays', 'Journal Article', '10.1186/1471-2105-11-S9-S11', '21044358', 'Mouse xenograft models, in which human cancer cells are implanted in immune-suppressed mice, have been popular for studying the mechanisms of novel therapeutic targets, tumor progression and metastasis. We hypothesized that we could exploit the interspecies genetic differences in these experiments. Our purpose is to elucidate stromal microenvironment signals from probes on human arrays unintentionally cross-hybridizing with mouse homologous genes in xenograft tumor models.'),
(93962, 2010, 'bmc', 'Assessment of genetic variation for the LINE-1 retrotransposon from next generation sequence data', 'Rouchka, Eric|Montoya-Durango, Diego E|Stribinskis, Vilius|Ramos, Kenneth|Kalbfleisch, Ted', 'Computer Engineering and Computer Science Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Computer Engineering and Computer Science Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Computer Engineering and Computer Science Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Computer Engineering and Computer Science Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Computer Engineering and Computer Science Department, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu', 'United States|United States|United States|United States|United States', 4, 'Base Sequence|Databases, Genetic|Genetic Variation|Genome|Humans|Long Interspersed Nucleotide Elements|Molecular Sequence Data|Phylogeny', 'Journal Article', '10.1186/1471-2105-11-S9-S12', '21044359', 'In humans, copies of the Long Interspersed Nuclear Element 1 (LINE-1) retrotransposon comprise 21% of the reference genome, and have been shown to modulate expression and produce novel splice isoforms of transcripts from genes that span or neighbor the LINE-1 insertion site.'),
(93963, 2010, 'bmc', 'Mapping transcription mechanisms from multimodal genomic data', 'Chang, Hsun-Hsien|McGeachie, Michael|Alterovitz, Gil|Ramoni, Marco F', 'Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu', 'United States|United States|United States|United States', 5, 'Computational Biology|Gene Expression|Gene Expression Profiling|Genetic Variation|Genome|Humans|Leukemia|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-S9-S2', '21044360', 'Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data.'),
(93964, 2010, 'bmc', 'Multi-dimensional discovery of biomarker and phenotype complexes', 'Payne, Philip R O|Huang, Kun|Keen-Circle, Kristin|Kundu, Abhisek|Zhang, Jie|Borlawsky, Tara B', 'Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu|Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu|Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu|Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu|Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu|Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA. philip.payne@osumc.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Biomarkers|Computational Biology|Gene Regulatory Networks|National Cancer Institute (U.S.)|Phenotype|Translational Medical Research|United States', 'Journal Article', '10.1186/1471-2105-11-S9-S3', '21044361', 'Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.'),
(93965, 2010, 'bmc', 'Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays', 'Chen, David P|Dudley, Joel T|Butte, Atul J', 'Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA. dpchen@stanford.edu|Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA. dpchen@stanford.edu|Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA. dpchen@stanford.edu', 'United States|United States|United States', 7, 'Biomarkers|Clinical Laboratory Techniques|Cystic Fibrosis|Diabetes Mellitus, Type 2|Down Syndrome|Humans', 'Journal Article', '10.1186/1471-2105-11-S9-S4', '21044362', 'Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.'),
(93966, 2010, 'bmc', 'Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia', 'Zhang, Jie|Xiang, Yang|Ding, Liya|Keen-Circle, Kristin|Borlawsky, Tara B|Ozer, Hatice Gulcin|Jin, Ruoming|Payne, Philip|Huang, Kun', 'Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu|Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biomarkers|Databases, Genetic|Gene Expression|Gene Regulatory Networks|Humans|Immunoglobulin Heavy Chains|Immunoglobulin Variable Region|Leukemia, Lymphocytic, Chronic, B-Cell|ZAP-70 Protein-Tyrosine Kinase', 'Journal Article', '10.1186/1471-2105-11-S9-S5', '21044363', 'Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered.'),
(93967, 2010, 'bmc', 'Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants', 'Morgan, Alexander A|Khatri, Purvesh|Jones, Richard Hayden|Sarwal, Minnie M|Butte, Atul J', 'Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.|Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA.', 'United States|United States|United States|United States|United States', 18, 'Gene Expression|Gene Expression Profiling|Graft Rejection|Meta-Analysis as Topic|Organ Transplantation', 'Comparative Study', '10.1186/1471-2105-11-S9-S6', '21044364', 'Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated by a need for a deeper understanding of organ transplant rejection, we combine the data from five separate studies to compare acute rejection versus stability after solid organ transplantation, and use this data to examine approaches to multiplex meta analysis.'),
(93968, 2010, 'bmc', 'Mining multi-item drug adverse effect associations in spontaneous reporting systems', 'Harpaz, Rave|Chase, Herbert S|Friedman, Carol', 'Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA. rave.harpaz@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA. rave.harpaz@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA. rave.harpaz@dbmi.columbia.edu', 'United States|United States|United States', 0, 'Adverse Drug Reaction Reporting Systems|Algorithms|Data Mining|Databases, Factual|Drug Synergism|Drug-Related Side Effects and Adverse Reactions|United States|United States Food and Drug Administration', 'Journal Article', '10.1186/1471-2105-11-S9-S7', '21044365', 'Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.'),
(93969, 2010, 'bmc', 'Assessing the quality of annotations in asthma gene expression experiments', 'Lacson, Ronilda|Mbagwu, Michael|Yousif, Hisham|Ohno-Machado, Lucila', 'Decision Systems Group, Brigham & Womens Hospital, Harvard Medical School, Boston, MA, USA. rlacson@dsg.harvard.edu|Decision Systems Group, Brigham & Womens Hospital, Harvard Medical School, Boston, MA, USA. rlacson@dsg.harvard.edu|Decision Systems Group, Brigham & Womens Hospital, Harvard Medical School, Boston, MA, USA. rlacson@dsg.harvard.edu|Decision Systems Group, Brigham & Womens Hospital, Harvard Medical School, Boston, MA, USA. rlacson@dsg.harvard.edu', 'United States|United States|United States|United States', 1, 'Asthma|Databases, Genetic|Gene Expression|Gene Expression Profiling|Humans|Molecular Sequence Annotation|Research Design', 'Journal Article', '10.1186/1471-2105-11-S9-S8', '21044366', 'The amount of data deposited in the Gene Expression Omnibus (GEO) has expanded significantly. It is important to ensure that these data are properly annotated with clinical data and descriptions of experimental conditions so that they can be useful for future analysis. This study assesses the adequacy of documented asthma markers in GEO. Three objective measures (coverage, consistency and association) were used for evaluation of annotations contained in 17 asthma studies.'),
(93970, 2010, 'bmc', 'An integrative method for scoring candidate genes from association studies: application to warfarin dosing', 'Tatonetti, Nicholas P|Dudley, Joel T|Sagreiya, Hersh|Butte, Atul J|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu|Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu|Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu|Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu|Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA. nick.tatonetti@stanford.edu', 'United States|United States|United States|United States|United States', 0, 'Anticoagulants|Cytochrome P-450 Enzyme System|Dose-Response Relationship, Drug|Gene Frequency|Genome-Wide Association Study|Genotype|Logistic Models|Mixed Function Oxygenases|Pharmacogenetics|Polymorphism, Single Nucleotide|Vitamin K Epoxide Reductases|Warfarin', 'Journal Article', '10.1186/1471-2105-11-S9-S9', '21044367', 'A key challenge in pharmacogenomics is the identification of genes whose variants contribute to drug response phenotypes, which can include severe adverse effects. Pharmacogenomics GWAS attempt to elucidate genotypes predictive of drug response. However, the size of these studies has severely limited their power and potential application. We propose a novel knowledge integration and SNP aggregation approach for identifying genes impacting drug response. Our SNP aggregation method characterizes the degree to which uncommon alleles of a gene are associated with drug response. We first use pre-existing knowledge sources to rank pharmacogenes by their likelihood to affect drug response. We then define a summary score for each gene based on allele frequencies and train linear and logistic regression classifiers to predict drug response phenotypes.'),
(93971, 2010, 'bmc', 'Bioactive conformational generation of small molecules: a comparative analysis between force-field and multiple empirical criteria based methods', 'Bai, Fang|Liu, Xiaofeng|Li, Jiabo|Zhang, Haoyun|Jiang, Hualiang|Wang, Xicheng|Li, Honglin', 'Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.|Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116023, PR China. guixum@dlut.edu.cn.', 'China|China|China|China|China|China|China', 15, 'Algorithms|Computer Simulation|Drug Discovery|Ligands|Models, Molecular|Molecular Conformation', 'Comparative Study', '10.1186/1471-2105-11-545', '21050454', 'Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.'),
(93972, 2010, 'bmc', 'Optimizing transformations for automated, high throughput analysis of flow cytometry data', 'Finak, Greg|Perez, Juan-Manuel|Weng, Andrew|Gottardo, Raphael', 'Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fariview Ave N, Seattle, WA 98109, USA. gfinak@fhcrc.org|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fariview Ave N, Seattle, WA 98109, USA. gfinak@fhcrc.org|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fariview Ave N, Seattle, WA 98109, USA. gfinak@fhcrc.org|Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fariview Ave N, Seattle, WA 98109, USA. gfinak@fhcrc.org', 'United States|United States|United States|United States', 46, 'Algorithms|Cells|Data Interpretation, Statistical|Databases, Factual|Flow Cytometry', 'Journal Article', '10.1186/1471-2105-11-546', '21050468', 'In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.'),
(93973, 2010, 'bmc', 'Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems', 'Jenkinson, Garrett|Zhong, Xiaogang|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.|Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA.', 'United States|United States|United States', 4, 'Algorithms|Bayes Theorem|Biochemical Phenomena|Computational Biology|Kinetics|Signal Transduction|Thermodynamics', 'Journal Article', '10.1186/1471-2105-11-547', '21054868', 'Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable.'),
(93974, 2010, 'bmc', 'Protein structure analysis of mutations causing inheritable diseases An e-Science approach with life scientist friendly interfaces', 'Venselaar, Hanka|Te Beek, Tim A H|Kuipers, Remko K P|Hekkelman, Maarten L|Vriend, Gert', 'CMBI, NCMLS, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, Netherlands. h.venselaar@cmbi.ru.nl|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, Netherlands. h.venselaar@cmbi.ru.nl|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, Netherlands. h.venselaar@cmbi.ru.nl|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, Netherlands. h.venselaar@cmbi.ru.nl|CMBI, NCMLS, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, Netherlands. h.venselaar@cmbi.ru.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 399, 'Computational Biology|Databases, Protein|Genetic Diseases, Inborn|Genome, Human|Humans|Mutation|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-548', '21059217', 'Many newly detected point mutations are located in protein-coding regions of the human genome. Knowledge of their effects on the proteins 3D structure provides insight into the proteins mechanism, can aid the design of further experiments, and eventually can lead to the development of new medicines and diagnostic tools.'),
(93975, 2010, 'bmc', 'The Text-mining based PubChem Bioassay neighboring analysis', 'Han, Lianyi|Suzek, Tugba O|Wang, Yanli|Bryant, Steve H', 'National Center for Biotechnology Information, US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, US National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 0, 'Biological Assay|Data Mining|Databases, Factual|High-Throughput Screening Assays', 'Journal Article', '10.1186/1471-2105-11-549', '21059237', 'In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e. molecular structure, bioactivities) and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database.'),
(93976, 2010, 'bmc', 'Localizing triplet periodicity in DNA and cDNA sequences', 'Wang, Liya|Stein, Lincoln D', 'Cold Spring Harbor Laboratory, Williams #5, Cold Spring Harbor, NY 11724, USA. wangli@cshl.edu|Cold Spring Harbor Laboratory, Williams #5, Cold Spring Harbor, NY 11724, USA. wangli@cshl.edu', 'United States|United States', 26, 'Animals|Base Sequence|Caenorhabditis elegans|DNA|DNA, Complementary|Fourier Analysis|Introns|RNA Splicing|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-550', '21059240', 'The protein-coding regions (coding exons) of a DNA sequence exhibit a triplet periodicity (TP) due to fact that coding exons contain a series of three nucleotide codons that encode specific amino acid residues. Such periodicity is usually not observed in introns and intergenic regions. If a DNA sequence is divided into small segments and a Fourier Transform is applied on each segment, a strong peak at frequency 1/3 is typically observed in the Fourier spectrum of coding segments, but not in non-coding regions. This property has been used in identifying the locations of protein-coding genes in unannotated sequence. The method is fast and requires no training. However, the need to compute the Fourier Transform across a segment (window) of arbitrary size affects the accuracy with which one can localize TP boundaries. Here, we report a technique that provides higher-resolution identification of these boundaries, and use the technique to explore the biological correlates of TP regions in the genome of the model organism C. elegans.'),
(93977, 2010, 'bmc', 'An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs', 'Garcia-Alcalde, Fernando|Blanco, Armando|Shepherd, Adrian J', 'Bionformatics and Genomics Department, Centro de Investigación Príncipe Felipe , Valencia 46013, Spain. fdgarcia@cipf.es|Bionformatics and Genomics Department, Centro de Investigación Príncipe Felipe , Valencia 46013, Spain. fdgarcia@cipf.es|Bionformatics and Genomics Department, Centro de Investigación Príncipe Felipe , Valencia 46013, Spain. fdgarcia@cipf.es', 'Spain|Spain|Spain', 6, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|DNA|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-551', '21059262', 'Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty.'),
(93978, 2010, 'bmc', 'Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies', 'Chen, Bo|Chen, Minhua|Paisley, John|Zaas, Aimee|Woods, Christopher|Ginsburg, Geoffrey S|Hero, Alfred|Lucas, Joseph|Dunson, David|Carin, Lawrence', 'Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.|Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 25, 'Bayes Theorem|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Orthomyxoviridae|Respiratory Syncytial Viruses|Rhinovirus|Virus Physiological Phenomena', 'Journal Article', '10.1186/1471-2105-11-552', '21062443', 'Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis.'),
(93979, 2010, 'bmc', 'Algorithm-driven artifacts in median polish summarization of microarray data', 'Giorgi, Federico M|Bolger, Anthony M|Lohse, Marc|Usadel, Bjoern', 'Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Golm, Germany.|Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Golm, Germany.|Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Golm, Germany.|Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Golm, Germany.', 'Germany|Germany|Germany|Germany', 39, 'Algorithms|Artifacts|Databases, Factual|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-553', '21070630', 'High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification.'),
(93980, 2010, 'bmc', 'FITBAR: a web tool for the robust prediction of prokaryotic regulons', 'Oberto, Jacques', 'Université Paris-Sud 11, Centre National de la Recherche Scientifique, UMR 8621, Institut de Génétique et Microbiologie, Orsay, France. jacques.oberto@igmors.u-psud.fr', 'France', 21, 'Algorithms|Binding Sites|Genome, Archaeal|Genome, Bacterial|Internet|Regulon|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-554', '21070640', 'The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets, however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.'),
(93981, 2010, 'bmc', 'The LabelHash algorithm for substructure matching', 'Moll, Mark|Bryant, Drew H|Kavraki, Lydia E', 'Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu|Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu|Department of Computer Science, Rice University, Houston, TX 77005, USA. mmoll@rice.edu', 'United States|United States|United States', 26, 'Algorithms|Computational Biology|Databases, Protein|Protein Conformation|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-555', '21070651', 'There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity.'),
(93982, 2010, 'bmc', 'Assessment and optimisation of normalisation methods for dual-colour antibody microarrays', 'Sill, Martin|Schröder, Christoph|Hoheisel, Jörg D|Benner, Axel|Zucknick, Manuela', 'Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany. m.sill@dkfz.de|Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany. m.sill@dkfz.de', 'Germany|Germany|Germany|Germany|Germany', 13, 'Algorithms|Antibodies|Computer Simulation|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Protein Array Analysis', 'Journal Article', '10.1186/1471-2105-11-556', '21073702', 'Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.'),
(93983, 2010, 'bmc', 'Epigenetic domains found in mouse embryonic stem cells via a hidden Markov model', 'Larson, Jessica L|Yuan, Guo-Cheng', 'Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.', 'United States|United States', 14, 'Animals|DNA Methylation|Embryonic Stem Cells|Epigenesis, Genetic|Genes, Homeobox|Histones|Markov Chains|Mice', 'Journal Article', '10.1186/1471-2105-11-557', '21073706', 'Epigenetics is an important layer of transcriptional control necessary for cell-type specific gene regulation. Recent studies have shown significant epigenetic patterns associated with developmental stages and diseases. However, previous studies have been mostly limited to focal epigenetic patterns, whereas methods for analyzing large-scale organizations are still lacking.'),
(93984, 2010, 'bmc', 'Leukocyte nucleus segmentation and nucleus lobe counting', 'Chan, Yung-Kuan|Tsai, Meng-Hsiun|Huang, Der-Chen|Zheng, Zong-Han|Hung, Kun-Ding', 'Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.|Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.|Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.|Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.|Department of Management Information Systems, National Chung Hsing University, Taichung, ROC, Taiwan.', '-|-|-|-|-', 42, 'Cell Nucleus|Humans|Image Processing, Computer-Assisted|Leukocyte Count|Leukocytes|Neutrophils', 'Journal Article', '10.1186/1471-2105-11-558', '21073711', 'Leukocytes play an important role in the human immune system. The family of leukocytes is comprised of lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Any infection or acute stress may increase or decrease the number of leukocytes. An increased percentage of neutrophils may be caused by an acute infection, while an increased percentage of lymphocytes can be caused by a chronic bacterial infection. It is important to realize an abnormal variation in the leukocytes. The five types of leukocytes can be distinguished by their cytoplasmic granules, staining properties of the granules, size of cell, the proportion of the nuclear to the cytoplasmic material, and the type of nucleolar lobes. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.Biomedical technologists can currently recognize abnormal leukocytes using human eyes. However, the quality and efficiency of diagnosis may be compromised due to the limitations of the biomedical technologists eyesight, strength, and medical knowledge. Therefore, the development of an automatic leukocyte recognition system is feasible and necessary. It is essential to extract the leukocyte region from a blood smear image in order to develop an automatic leukocyte recognition system. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.'),
(93985, 2010, 'bmc', 'Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection', 'Yu, Tianwei|Peng, Hesen', 'Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@emory.edu|Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@emory.edu', 'United States|United States', 28, 'Chromatography, Liquid|Databases, Factual|Mass Spectrometry|Models, Statistical', 'Journal Article', '10.1186/1471-2105-11-559', '21073736', 'Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. Peak modeling is one of the key components in LC-MS data pre-processing.'),
(93986, 2010, 'bmc', 'Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs', 'Kundeti, Vamsi K|Rajasekaran, Sanguthevar|Dinh, Hieu|Vaughn, Matthew|Thapar, Vishal', 'Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu|Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, U-2155, Storrs, CT 06269, USA. vamsik@engr.uconn.edu', 'United States|United States|United States|United States|United States', 18, 'Algorithms|Base Sequence|Computational Biology|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-560', '21078174', 'Assembling genomic sequences from a set of overlapping reads is one of the most fundamental problems in computational biology. Algorithms addressing the assembly problem fall into two broad categories - based on the data structures which they employ. The first class uses an overlap/string graph and the second type uses a de Bruijn graph. However with the recent advances in short read sequencing technology, de Bruijn graph based algorithms seem to play a vital role in practice. Efficient algorithms for building these massive de Bruijn graphs are very essential in large sequencing projects based on short reads. In an earlier work, an O(n/p) time parallel algorithm has been given for this problem. Here n is the size of the input and p is the number of processors. This algorithm enumerates all possible bi-directed edges which can overlap with a node and ends up generating xcex98(nxcexa3) messages (xcexa3 being the size of the alphabet).'),
(93987, 2010, 'bmc', 'A factorization method for the classification of infrared spectra', 'Henneges, Carsten|Laskov, Pavel|Darmawan, Endang|Backhaus, Jürgen|Kammerer, Bernd|Zell, Andreas', 'Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de|Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de|Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de|Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de|Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de|Zentrum für Bioinformatik Tübingen, Eberhard Karls Universität Tübingen, Sand 13, Tübingen, Germany. carsten.henneges@uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, 'Algorithms|Computational Biology|Databases, Factual|Models, Theoretical|Sensitivity and Specificity|Signal Processing, Computer-Assisted|Spectrophotometry, Infrared', 'Journal Article', '10.1186/1471-2105-11-561', '21078178', 'Bioinformatics data analysis often deals with additive mixtures of signals for which only class labels are known. Then, the overall goal is to estimate class related signals for data mining purposes. A convenient application is metabolic monitoring of patients using infrared spectroscopy. Within an infrared spectrum each single compound contributes quantitatively to the measurement.'),
(93988, 2010, 'bmc', 'An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology', 'Jain, Shobhit|Bader, Gary D', 'Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario M5S3G4, Canada.|Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario M5S3G4, Canada.', 'Canada|Canada', 84, 'Algorithms|Databases, Protein|Humans|Molecular Sequence Annotation|Protein Interaction Mapping|Proteins|Semantics', 'Journal Article', '10.1186/1471-2105-11-562', '21078182', 'Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.');
