INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95008, 2012, 'bmc', 'PollenCALC: software for estimation of pollen compatibility of self-incompatible allo- and autotetraploid species', 'Aguirre, Andrea Arias|Wollenweber, Bernd|Frei, Ursula K|Lübberstedt, Thomas', 'Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, USA. andreasa@iastate.edu|Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, USA. andreasa@iastate.edu|Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, USA. andreasa@iastate.edu|Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, USA. andreasa@iastate.edu', 'United States|United States|United States|United States', 0, 'Alleles|Breeding|Genotype|Haplotypes|Meiosis|Models, Genetic|Plants|Pollen|Self-Incompatibility in Flowering Plants|Software|Tetraploidy', 'Journal Article', '10.1186/1471-2105-13-125', '22676372', 'Self-incompatibility (SI) is a biological mechanism to avoid inbreeding in allogamous plants. In grasses, this mechanism is controlled by a two-locus system (S-Z). Calculation of male and female gamete frequencies is complex for tetraploid species. We are not aware of any software available for predicting pollen haplotype frequencies and pollen compatibility in tetraploid species.'),
(95009, 2012, 'bmc', 'Identifying dysregulated pathways in cancers from pathway interaction networks', 'Liu, Ke-Qin|Liu, Zhi-Ping|Hao, Jin-Kao|Chen, Luonan|Zhao, Xing-Ming', 'Institute of Systems Biology, Shanghai University, Shanghai 200444, China.|Institute of Systems Biology, Shanghai University, Shanghai 200444, China.|Institute of Systems Biology, Shanghai University, Shanghai 200444, China.|Institute of Systems Biology, Shanghai University, Shanghai 200444, China.|Institute of Systems Biology, Shanghai University, Shanghai 200444, China.', 'China|China|China|China|China', 88, 'Biomarkers, Tumor|Computational Biology|Gene Expression|Humans|Metabolic Networks and Pathways|Neoplasms|Protein Interaction Maps', 'Journal Article', '10.1186/1471-2105-13-126', '22676414', 'Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes.'),
(95010, 2012, 'bmc', 'The language of gene ontology: a Zipfs law analysis', 'Kalankesh, Leila Ranandeh|Stevens, Robert|Brass, Andy', 'School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Databases, Genetic|Genes|Genomics|Molecular Sequence Annotation|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-13-127', '22676436', 'Most major genome projects and sequence databases provide a GO annotation of their data, either automatically or through human annotators, creating a large corpus of data written in the language of GO. Texts written in natural language show a statistical power law behaviour, Zipfs law, the exponent of which can provide useful information on the nature of the language being used. We have therefore explored the hypothesis that collections of GO annotations will show similar statistical behaviours to natural language.'),
(95011, 2012, 'bmc', 'ROCS: a reproducibility index and confidence score for interaction proteomics studies', 'Dazard, Jean-Eudes|Saha, Sudipto|Ewing, Rob M', 'Division of Bioinformatics, Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. jxd101@case.edu|Division of Bioinformatics, Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. jxd101@case.edu|Division of Bioinformatics, Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. jxd101@case.edu', 'United States|United States|United States', 0, 'Chromatography, Affinity|Confidence Intervals|Mass Spectrometry|Probability|Protein Interaction Mapping|Proteins|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-13-128', '22682516', 'Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.'),
(95012, 2012, 'bmc', 'Fast and robust multiple sequence alignment with phylogeny-aware gap placement', 'Szalkowski, Adam M', 'Department of Computer Science, ETH Zürich, Universitätstrasse, Switzerland. adam.szalkowski@inf.ethz.ch', 'Switzerland', 0, 'Algorithms|Alternative Splicing|Phylogeny|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-129', '22694311', 'ProGraphMSA is a state-of-the-art multiple sequence alignment tool which produces phylogenetically sensible gap patterns while maintaining robustness by allowing alternative splicings and errors in the branching pattern of the guide tree.'),
(95013, 2012, 'bmc', 'Bayesian model to detect phenotype-specific genes for copy number data', 'González, Juan R|Abellán, Carlos|Abellán, Juan J', 'Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat', 'Spain|Spain|Spain', 0, 'Algorithms|Bayes Theorem|Computer Simulation|Female|Gene Dosage|Genetic Predisposition to Disease|Genetic Testing|Genotype|HapMap Project|Humans|Male|Models, Genetic|Models, Statistical|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-13-130', '22694346', 'An important question in genetic studies is to determine those genetic variants, in particular CNVs, that are specific to different groups of individuals. This could help in elucidating differences in disease predisposition and response to pharmaceutical treatments. We propose a Bayesian model designed to analyze thousands of copy number variants (CNVs) where only few of them are expected to be associated with a specific phenotype.'),
(95014, 2012, 'bmc', 'Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network', 'Xuan, Nguyen Vinh|Chetty, Madhu|Coppel, Ross|Wangikar, Pramod P', 'Gippsland School of Information Technology, Monash University, Melbourne, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Melbourne, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Melbourne, Australia. vinh.nguyen@monash.edu|Gippsland School of Information Technology, Monash University, Melbourne, Australia. vinh.nguyen@monash.edu', 'Australia|Australia|Australia|Australia', 36, 'Algorithms|Bayes Theorem|Computational Biology|Cyanobacteria|Gene Expression Profiling|Gene Regulatory Networks', 'Journal Article', '10.1186/1471-2105-13-131', '22694481', 'Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks.'),
(95015, 2012, 'bmc', 'Simulating gene-gene and gene-environment interactions in complex diseases: Gene-Environment iNteraction Simulator 2', 'Pinelli, Michele|Scala, Giovanni|Amato, Roberto|Cocozza, Sergio|Miele, Gennaro', 'Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II - Università di Salerno, Italy. michele.pinelli@unina.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II - Università di Salerno, Italy. michele.pinelli@unina.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II - Università di Salerno, Italy. michele.pinelli@unina.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II - Università di Salerno, Italy. michele.pinelli@unina.it|Gruppo Interdipartimentale di Bioinformatica e Biologia Computazionale, Università di Napoli Federico II - Università di Salerno, Italy. michele.pinelli@unina.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Computer Simulation|Disease|Environment|Gene-Environment Interaction|Humans|Linkage Disequilibrium|Risk Factors|Software', 'Journal Article', '10.1186/1471-2105-13-132', '22698142', 'The analysis of complex diseases is an important problem in human genetics. Because multifactoriality is expected to play a pivotal role, many studies are currently focused on collecting information on the genetic and environmental factors that potentially influence these diseases. However, there is still a lack of efficient and thoroughly tested statistical models that can be used to identify implicated features and their interactions. Simulations using large biologically realistic data sets with known gene-gene and gene-environment interactions that influence the risk of a complex disease are a convenient and useful way to assess the performance of statistical methods.'),
(95016, 2012, 'bmc', 'Bios2mds: an R package for comparing orthologous protein families by metric multidimensional scaling', 'Pelé, Julien|Bécu, Jean-Michel|Abdi, Hervé|Chabbert, Marie', 'CNRS UMR 6214 - INSERM 1083, Faculté de Médecine, 3 rue Haute de Reculée, Angers, 49045, France.|CNRS UMR 6214 - INSERM 1083, Faculté de Médecine, 3 rue Haute de Reculée, Angers, 49045, France.|CNRS UMR 6214 - INSERM 1083, Faculté de Médecine, 3 rue Haute de Reculée, Angers, 49045, France.|CNRS UMR 6214 - INSERM 1083, Faculté de Médecine, 3 rue Haute de Reculée, Angers, 49045, France.', 'France|France|France|France', 17, 'Algorithms|Animals|Cluster Analysis|Humans|Multivariate Analysis|Phylogeny|Receptors, G-Protein-Coupled|Sea Anemones|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-133', '22702410', 'The distance matrix computed from multiple alignments of homologous sequences is widely used by distance-based phylogenetic methods to provide information on the evolution of protein families. This matrix can also be visualized in a low dimensional space by metric multidimensional scaling (MDS). Applied to protein families, MDS provides information complementary to the information derived from tree-based methods. Moreover, MDS gives a unique opportunity to compare orthologous sequence sets because it can add supplementary elements to a reference space.'),
(95017, 2012, 'bmc', 'Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction', 'Ye, Jian|Coulouris, George|Zaretskaya, Irena|Cutcutache, Ioana|Rozen, Steve|Madden, Thomas L', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA. jianye@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Carrier Proteins|DNA Primers|Humans|Introns|Molecular Sequence Data|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-13-134', '22708584', 'Choosing appropriate primers is probably the single most important factor affecting the polymerase chain reaction (PCR). Specific amplification of the intended target requires that primers do not have matches to other targets in certain orientations and within certain distances that allow undesired amplification. The process of designing specific primers typically involves two stages. First, the primers flanking regions of interest are generated either manually or using software tools, then they are searched against an appropriate nucleotide sequence database using tools such as BLAST to examine the potential targets. However, the latter is not an easy process as one needs to examine many details between primers and targets, such as the number and the positions of matched bases, the primer orientations and distance between forward and reverse primers. The complexity of such analysis usually makes this a time-consuming and very difficult task for users, especially when the primers have a large number of hits. Furthermore, although the BLAST program has been widely used for primer target detection, it is in fact not an ideal tool for this purpose as BLAST is a local alignment algorithm and does not necessarily return complete match information over the entire primer range.'),
(95018, 2012, 'bmc', 'A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels', 'Wu, Steven H|Black, Michael A|North, Robyn A|Rodrigo, Allen G', 'Bioinformatics Institute, University of Auckland, Private Bag, 92019, Auckland, New Zealand. steven.wu@duke.edu|Bioinformatics Institute, University of Auckland, Private Bag, 92019, Auckland, New Zealand. steven.wu@duke.edu|Bioinformatics Institute, University of Auckland, Private Bag, 92019, Auckland, New Zealand. steven.wu@duke.edu|Bioinformatics Institute, University of Auckland, Private Bag, 92019, Auckland, New Zealand. steven.wu@duke.edu', 'New Zealand|New Zealand|New Zealand|New Zealand', 1, 'Bayes Theorem|Computer Simulation|Electrophoresis, Gel, Two-Dimensional|Likelihood Functions|Markov Chains|Models, Biological|Models, Statistical|Monte Carlo Method|Probability|Protein Biosynthesis|Proteomics', 'Journal Article', '10.1186/1471-2105-13-137', '22712439', 'Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein, or (2) a level of expression that falls below the limit of detection.'),
(95019, 2012, 'bmc', 'xcexb2-empirical Bayes inference and model diagnosis of microarray data', 'Mollah, Mohammad Manir Hossain|Mollah, M Nurul Haque|Kishino, Hirohisa', 'Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. mollah@lbm.ab.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. mollah@lbm.ab.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. mollah@lbm.ab.a.u-tokyo.ac.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Arabidopsis|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Head and Neck Neoplasms|Humans|Likelihood Functions|Lung Neoplasms|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-135', '22713095', 'Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level, however, he data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models.'),
(95020, 2012, 'bmc', 'Down-weighting overlapping genes improves gene set analysis', 'Tarca, Adi Laurentiu|Draghici, Sorin|Bhatti, Gaurav|Romero, Roberto', 'Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Maryland, USA. atarca@med.wayne.edu|Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Maryland, USA. atarca@med.wayne.edu|Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Maryland, USA. atarca@med.wayne.edu|Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Maryland, USA. atarca@med.wayne.edu', 'United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Genes, Overlapping|Humans|Metabolic Networks and Pathways|Oligonucleotide Array Sequence Analysis|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-13-136', '22713124', 'The identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.'),
(95021, 2012, 'bmc', 'EGNAS: an exhaustive DNA sequence design algorithm', 'Kick, Alfred|Bönsch, Martin|Mertig, Michael', 'Technische Universität Dresden, Germany.|Technische Universität Dresden, Germany.|Technische Universität Dresden, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Base Pairing|Base Sequence|DNA|Genotype|Nucleic Acid Conformation|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-138', '22716030', 'The molecular recognition based on the complementary base pairing of deoxyribonucleic acid (DNA) is the fundamental principle in the fields of genetics, DNA nanotechnology and DNA computing. We present an exhaustive DNA sequence design algorithm that allows to generate sets containing a maximum number of sequences with defined properties. EGNAS (Exhaustive Generation of Nucleic Acid Sequences) offers the possibility of controlling both interstrand and intrastrand properties. The guanine-cytosine content can be adjusted. Sequences can be forced to start and end with guanine or cytosine. This option reduces the risk of fraying of DNA strands. It is possible to limit cross hybridizations of a defined length, and to adjust the uniqueness of sequences. Self-complementarity and hairpin structures of certain length can be avoided. Sequences and subsequences can optionally be forbidden. Furthermore, sequences can be designed to have minimum interactions with predefined strands and neighboring sequences.'),
(95022, 2012, 'bmc', 'Comparative study of classification algorithms for immunosignaturing data', 'Kukreja, Muskan|Johnston, Stephen Albert|Stafford, Phillip', 'Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.|Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.|Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA.', 'United States|United States|United States', 0, 'Algorithms|Bayes Theorem|Computational Biology|Data Mining|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-13-139', '22720696', 'High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.'),
(95023, 2012, 'bmc', 'miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments', 'Wen, Ming|Shen, Yang|Shi, Suhua|Tang, Tian', 'State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, Peoples Republic of China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, Peoples Republic of China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, Peoples Republic of China.|State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, 510275, Guangdong, Peoples Republic of China.', 'China|China|China|China', 0, 'Algorithms|Animals|Arabidopsis|Drosophila melanogaster|Evolution, Molecular|High-Throughput Nucleotide Sequencing|MicroRNAs|RNA, Untranslated|Sequence Analysis, RNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-140', '22720726', 'MicroRNAs (miRNAs) are small (~19-24nt) non-coding RNAs that play important roles in various biological processes. To date, the next-generation sequencing (NGS) technology has been widely used to discover miRNAs in plants and animals. Although evolutionary analysis is important to reveal the functional dynamics of miRNAs, few computational tools have been developed to analyze the evolution of miRNA sequence and expression across species, especially the newly emerged ones,'),
(95024, 2012, 'bmc', 'The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools', 'Wilke, Andreas|Harrison, Travis|Wilkening, Jared|Field, Dawn|Glass, Elizabeth M|Kyrpides, Nikos|Mavrommatis, Konstantinos|Meyer, Folker', 'Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.|Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 143, 'Computational Biology|Databases, Nucleic Acid|Databases, Protein|Metagenomics|Proteins|Software', 'Journal Article', '10.1186/1471-2105-13-141', '22720753', 'Computing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference.'),
(95025, 2012, 'bmc', 'Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in proteins structural organization', 'Sengupta, Dhriti|Kundu, Sudip', 'Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92 APC Road, Kolkata-700009, India.|Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92 APC Road, Kolkata-700009, India.', 'India|India', 0, 'Amino Acids|Cluster Analysis|Hydrophobic and Hydrophilic Interactions|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-13-142', '22720789', 'The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein.'),
(95026, 2012, 'bmc', 'QuartetS-DB: a large-scale orthology database for prokaryotes and eukaryotes inferred by evolutionary evidence', 'Yu, Chenggang|Desai, Valmik|Cheng, Li|Reifman, Jaques', 'United States Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|United States Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|United States Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.|United States Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Archaea|Bacteria|Biological Evolution|Computational Biology|Databases, Genetic|Eukaryota|Gene Duplication|Genomics|Proteins', 'Journal Article', '10.1186/1471-2105-13-143', '22726705', 'The concept of orthology is key to decoding evolutionary relationships among genes across different species using comparative genomics. QuartetS is a recently reported algorithm for large-scale orthology detection. Based on the well-established evolutionary principle that gene duplication events discriminate paralogous from orthologous genes, QuartetS has been shown to improve orthology detection accuracy while maintaining computational efficiency.'),
(95027, 2012, 'bmc', 'Automated hierarchical classification of protein domain subfamilies based on functionally-divergent residue signatures', 'Neuwald, Andrew F|Lanczycki, Christopher J|Marchler-Bauer, Aron', 'Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, 801 West Baltimore St, Baltimore, MD 21201, USA. aneuwald@som.umaryland.edu|Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, 801 West Baltimore St, Baltimore, MD 21201, USA. aneuwald@som.umaryland.edu|Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, 801 West Baltimore St, Baltimore, MD 21201, USA. aneuwald@som.umaryland.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Databases, Protein|Markov Chains|Monte Carlo Method|Phylogeny|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-13-144', '22726767', 'The NCBI Conserved Domain Database (CDD) consists of a collection of multiple sequence alignments of protein domains that are at various stages of being manually curated into evolutionary hierarchies based on conserved and divergent sequence and structural features. These domain models are annotated to provide insights into the relationships between sequence, structure and function via web-based BLAST searches.'),
(95028, 2012, 'bmc', 'Error-correcting properties of the SOLiD Exact Call Chemistry', 'Massingham, Tim|Goldman, Nick', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK. tim.massingham@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK. tim.massingham@ebi.ac.uk', 'United Kingdom|United Kingdom', 4, 'Algorithms|Computational Biology|Computer Simulation|Fluorescent Dyes|Nucleic Acid Probes|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-145', '22726842', 'The Exact Call Chemistry for the SOLiD Next-Generation Sequencing platform augments the two-base-encoding chemistry with an additional round of ligation, using an alternative set of probes, that allows some mistakes made when reading the first set of probes to be corrected. Additionally, the Exact Call Chemistry allows reads produced by the platform to be decoded directly into nucleotide sequence rather than its two-base color encoding.'),
(95029, 2012, 'bmc', 'Identifying mutation regions for closely related individuals without a known pedigree', 'Cui, Wenjuan|Wang, Lusheng', 'Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong', 2, 'Algorithms|DNA Mutational Analysis|Genetic Linkage|Genotype|HapMap Project|Haplotypes|Humans|Mutation|Pedigree|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-13-146', '22731852', 'Linkage analysis is the first step in the search for a disease gene. Linkage studies have facilitated the identification of several hundred human genes that can harbor mutations leading to a disease phenotype. In this paper, we study a very important case, where the sampled individuals are closely related, but the pedigree is not given. This situation happens very often when the individuals share a common ancestor 6 or more generations ago. To our knowledge, no algorithm can give good results for this case.'),
(95030, 2012, 'bmc', 'Arrow plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups', 'Silva-Fortes, Carina|Amaral Turkman, Maria Antónia|Sousa, Lisete', 'Natural and Exact Sciences Department, Higher School of Health Technology of Lisbon of Polytechnic Institute of Lisbon and Center of Statistics and Applications of University of Lisbon, Lisbon, Portugal. carina.silva@estesl.ipl.pt|Natural and Exact Sciences Department, Higher School of Health Technology of Lisbon of Polytechnic Institute of Lisbon and Center of Statistics and Applications of University of Lisbon, Lisbon, Portugal. carina.silva@estesl.ipl.pt|Natural and Exact Sciences Department, Higher School of Health Technology of Lisbon of Polytechnic Institute of Lisbon and Center of Statistics and Applications of University of Lisbon, Lisbon, Portugal. carina.silva@estesl.ipl.pt', 'Portugal|Portugal|Portugal', 12, 'Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|ROC Curve|Software', 'Journal Article', '10.1186/1471-2105-13-147', '22734592', 'A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software.'),
(95031, 2012, 'bmc', 'Surprising results on phylogenetic tree building methods based on molecular sequences', 'Gonnet, Gaston H', 'Department of Computer Science, ETH Zurich, Zurich, Switzerland. gonnet@inf.ethz.ch', 'Switzerland', 0, 'Evolution, Molecular|Likelihood Functions|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-148', '22738078', 'We analyze phylogenetic tree building methods from molecular sequences (PTMS). These are methods which base their construction solely on sequences, coding DNA or amino acids.'),
(95032, 2012, 'bmc', 'PREMIM and EMIM: tools for estimation of maternal, imprinting and interaction effects using multinomial modelling', 'Howey, Richard|Cordell, Heather J', 'Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK.|Institute of Genetic Medicine, Newcastle University, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK.', 'United Kingdom|United Kingdom', 0, 'Computer Simulation|Female|Genomic Imprinting|Genotype|Haplotypes|Humans|Likelihood Functions|Male|Models, Genetic|Pedigree|Software', 'Comparative Study', '10.1186/1471-2105-13-149', '22738121', 'Here we present two new computer tools, PREMIM and EMIM, for the estimation of parental and child genetic effects, based on genotype data from a variety of different child-parent configurations. PREMIM allows the extraction of child-parent genotype data from standard-format pedigree data files, while EMIM uses the extracted genotype data to perform subsequent statistical analysis. The use of genotype data from the parents as well as from the child in question allows the estimation of complex genetic effects such as maternal genotype effects, maternal-foetal interactions and parent-of-origin (imprinting) effects. These effects are estimated by EMIM, incorporating chosen assumptions such as Hardy-Weinberg equilibrium or exchangeability of parental matings as required.'),
(95033, 2012, 'bmc', 'A statistical approach to selecting and confirming validation targets in -omics experiments', 'Leek, Jeffrey T|Taub, Margaret A|Rasgon, Jason L', 'Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA. jleek@jhsph.edu|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA. jleek@jhsph.edu|Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205-2179, USA. jleek@jhsph.edu', 'United States|United States|United States', 0, 'Data Interpretation, Statistical|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-13-150', '22738145', 'Genomic technologies are, by their very nature, designed for hypothesis generation. In some cases, the hypotheses that are generated require that genome scientists confirm findings about specific genes or proteins. But one major advantage of high-throughput technology is that global genetic, genomic, transcriptomic, and proteomic behaviors can be observed. Manual confirmation of every statistically significant genomic result is prohibitively expensive. This has led researchers in genomics to adopt the strategy of confirming only a handful of the most statistically significant results, a small subset chosen for biological interest, or a small random subset. But there is no standard approach for selecting and quantitatively evaluating validation targets.'),
(95034, 2012, 'bmc', 'Towards a theoretical understanding of false positives in DNA motif finding', 'Zia, Amin|Moses, Alan M', 'Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.|Department of Cell & Systems Biology, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada', 14, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Nucleotide Motifs|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-151', '22738169', 'Detection of false-positive motifs is one of the main causes of low performance in de novo DNA motif-finding methods. Despite the substantial algorithm development effort in this area, recent comprehensive benchmark studies revealed that the performance of DNA motif-finders leaves room for improvement in realistic scenarios.'),
(95035, 2012, 'bmc', 'Detecting sequence dependent transcriptional pauses from RNA and protein number time series', 'Emmert-Streib, Frank|Häkkinen, Antti|Ribeiro, Andre S', 'Computational Biology and Machine Learning Lab, Center for CancerResearch and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, UK.|Computational Biology and Machine Learning Lab, Center for CancerResearch and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, UK.|Computational Biology and Machine Learning Lab, Center for CancerResearch and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queens University Belfast, Belfast, UK.', 'United Kingdom|United Kingdom|United Kingdom', 1, 'Codon|Data Interpretation, Statistical|Gene Expression|Gene Regulatory Networks|Models, Genetic|Models, Statistical|Prokaryotic Cells|Protein Biosynthesis|RNA|Ribosomes|Stochastic Processes|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-13-152', '22741547', 'Evidence suggests that in prokaryotes sequence-dependent transcriptional pauses affect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identified from in vitro measurements of transcription elongation kinetics.'),
(95036, 2012, 'bmc', 'Molecular evolution of dihydrouridine synthases', 'Kasprzak, Joanna M|Czerwoniec, Anna|Bujnicki, Janusz M', 'Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Umultowska 89, PL-61-614 Poznan, Poland.|Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Umultowska 89, PL-61-614 Poznan, Poland.|Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Umultowska 89, PL-61-614 Poznan, Poland.', 'Poland|Poland|Poland', 20, 'Amino Acid Sequence|Animals|Archaea|Bacteria|Eukaryota|Evolution, Molecular|Humans|Molecular Sequence Data|Oxidoreductases|Phylogeny|RNA, Transfer|Sequence Alignment|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-13-153', '22741570', 'Dihydrouridine (D) is a modified base found in conserved positions in the D-loop of tRNA in Bacteria, Eukaryota, and some Archaea. Despite the abundant occurrence of D, little is known about its biochemical roles in mediating tRNA function. It is assumed that D may destabilize the structure of tRNA and thus enhance its conformational flexibility. D is generated post-transcriptionally by the reduction of the 5,6-double bond of a uridine residue in RNA transcripts. The reaction is carried out by dihydrouridine synthases (DUS). DUS constitute a conserved family of enzymes encoded by the orthologous gene family COG0042. In protein sequence databases, members of COG0042 are typically annotated as predicted TIM-barrel enzymes, possibly dehydrogenases, nifR3 family.'),
(95037, 2012, 'bmc', 'TAPDANCE: an automated tool to identify and annotate transposon insertion CISs and associations between CISs from next generation sequence data', 'Sarver, Aaron L|Erdman, Jesse|Starr, Tim|Largaespada, David A|Silverstein, Kevin A T', 'Biostatistics and Bioinformatics Masonic Cancer Center, University of Minnesota, Minneapolis, USA. sarver@umn.edu|Biostatistics and Bioinformatics Masonic Cancer Center, University of Minnesota, Minneapolis, USA. sarver@umn.edu|Biostatistics and Bioinformatics Masonic Cancer Center, University of Minnesota, Minneapolis, USA. sarver@umn.edu|Biostatistics and Bioinformatics Masonic Cancer Center, University of Minnesota, Minneapolis, USA. sarver@umn.edu|Biostatistics and Bioinformatics Masonic Cancer Center, University of Minnesota, Minneapolis, USA. sarver@umn.edu', 'United States|United States|United States|United States|United States', 0, 'Animals|Base Sequence|Chromosome Mapping|DNA Transposable Elements|Electronic Data Processing|Genomic Library|Genomics|Humans|Mice|Mutagenesis, Insertional|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-13-154', '22748055', 'Next generation sequencing approaches applied to the analyses of transposon insertion junction fragments generated in high throughput forward genetic screens has created the need for clear informatics and statistical approaches to deal with the massive amount of data currently being generated. Previous approaches utilized to 1) map junction fragments within the genome and 2) identify Common Insertion Sites (CISs) within the genome are not practical due to the volume of data generated by current sequencing technologies. Previous approaches applied to this problem also required significant manual annotation.'),
(95038, 2012, 'bmc', 'A fast tool for minimum hybridization networks', 'Chen, Zhi-Zhong|Wang, Lusheng|Yamanaka, Satoshi', 'Division of Information System Design, Tokyo Denki University, Hatoyama, Hiki, Saitama, Japan. zzchen@mail.dendai.ac.jp|Division of Information System Design, Tokyo Denki University, Hatoyama, Hiki, Saitama, Japan. zzchen@mail.dendai.ac.jp|Division of Information System Design, Tokyo Denki University, Hatoyama, Hiki, Saitama, Japan. zzchen@mail.dendai.ac.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Computational Biology|Computer Simulation|Hybridization, Genetic|Phylogeny|Poaceae|Software', 'Journal Article', '10.1186/1471-2105-13-155', '22748099', 'Due to hybridization events in evolution, studying two different genes of a set of species may yield two related but different phylogenetic trees for the set of species. In this case, we want to combine the two phylogenetic trees into a hybridization network with the fewest hybridization events. This leads to three computational problems, namely, the problem of computing the minimum size of a hybridization network, the problem of constructing one minimum hybridization network, and the problem of enumerating a representative set of minimum hybridization networks. The previously best software tools for these problems (namely, Chen and Wangs HybridNet and Albrecht et al.s Dendroscope 3) run very slowly for large instances that cannot be reduced to relatively small instances. Indeed, when the minimum size of a hybridization network of two given trees is larger than 23 and the problem for the trees cannot be reduced to relatively smaller independent subproblems, then HybridNet almost always takes longer than 1 day and Dendroscope 3 often fails to complete. Thus, a faster software tool for the problems is in need.'),
(95039, 2012, 'bmc', 'Genes2FANs: connecting genes through functional association networks', 'Dannenfelser, Ruth|Clark, Neil R|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York, Mount Sinai School of Medicine, New York, NY 10029, USA.|Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York, Mount Sinai School of Medicine, New York, NY 10029, USA.', 'United States|United States|United States', 0, 'Animals|Disease|Gene Regulatory Networks|Genes|Humans|Internet|Mice|Protein Interaction Mapping|PubMed|Software', 'Journal Article', '10.1186/1471-2105-13-156', '22748121', 'Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.'),
(95040, 2012, 'bmc', 'Minimalist ensemble algorithms for genome-wide protein localization prediction', 'Lin, Jhih-Rong|Mondal, Ananda Mohan|Liu, Rong|Hu, Jianjun', 'Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.', 'United States|United States|United States|United States', 16, 'Algorithms|Area Under Curve|Computational Biology|Genome, Fungal|Genome, Human|Humans|Internet|Logistic Models|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-13-157', '22759391', 'Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95041, 2012, 'bmc', 'Guest editors introduction', 'Chen, Jianer|Mxc4x83ndoiu, Ion|Sunderraman, Raj|Wang, Jianxin|Zelikovsky, Alexander', 'Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.|Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Congresses as Topic|Evolution, Molecular|Phylogeny|Protein Binding|Protein Interaction Maps|Sequence Alignment|Sequence Analysis, DNA|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-13-S10-S1', '22759414', '-'),
(95042, 2012, 'bmc', 'Guided evolution of in silico microbial populations in complex environments accelerates evolutionary rates through a step-wise adaptation', 'Mozhayskiy, Vadim|Tagkopoulos, Ilias', 'Department of Computer Science and UC Davis Genome Center, University of California Davis, Davis, California 95616, USA.|Department of Computer Science and UC Davis Genome Center, University of California Davis, Davis, California 95616, USA.', 'United States|United States', 0, 'Adaptation, Physiological|Bacteria|Biological Evolution|Computer Simulation|Directed Molecular Evolution|Environment|Gene Transfer, Horizontal|Genetic Fitness|Mutation|Phenotype', 'Journal Article', '10.1186/1471-2105-13-S10-S10', '22759415', 'During their lifetime, microbes are exposed to environmental variations, each with its distinct spatio-temporal dynamics. Microbial communities display a remarkable degree of phenotypic plasticity, and highly-fit individuals emerge quite rapidly during microbial adaptation to novel environments. However, there exists a high variability when it comes to adaptation potential, and while adaptation occurs rapidly in certain environmental transitions, in others organisms struggle to adapt. Here, we investigate the hypothesis that the rate of evolution can both increase or decrease, depending on the similarity and complexity of the intermediate and final environments. Elucidating such dependencies paves the way towards controlling the rate and direction of evolution, which is of interest to industrial and medical applications.'),
(95043, 2012, 'bmc', 'Efficient error correction algorithms for gene tree reconciliation based on duplication, duplication and loss, and deep coalescence', 'Chaudhary, Ruchi|Burleigh, J Gordon|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States', 18, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Duplication|Genome|Genomics|Models, Theoretical|Phylogeny|Yeasts', 'Journal Article', '10.1186/1471-2105-13-S10-S11', '22759416', 'Gene tree - species tree reconciliation problems infer the patterns and processes of gene evolution within a species tree. Gene tree parsimony approaches seek the evolutionary scenario that implies the fewest gene duplications, duplications and losses, or deep coalescence (incomplete lineage sorting) events needed to reconcile a gene tree and a species tree. While a gene tree parsimony approach can be informative about genome evolution and phylogenetics, error in gene trees can profoundly bias the results.'),
(95044, 2012, 'bmc', 'Consensus properties for the deep coalescence problem and their application for scalable tree search', 'Lin, Harris T|Burleigh, J Gordon|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA, USA.|Department of Computer Science, Iowa State University, Ames, IA, USA.|Department of Computer Science, Iowa State University, Ames, IA, USA.', 'United States|United States|United States', 22, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Genomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-13-S10-S12', '22759417', 'To infer a species phylogeny from unlinked genes, phylogenetic inference methods must confront the biological processes that create incongruence between gene trees and the species phylogeny. Intra-specific gene variation in ancestral species can result in deep coalescence, also known as incomplete lineage sorting, which creates incongruence between gene trees and the species tree. One approach to account for deep coalescence in phylogenetic analyses is the deep coalescence problem, which takes a collection of gene trees and seeks the species tree that implies the fewest deep coalescence events. Although this approach is promising for phylogenetics, the consensus properties of this problem are mostly unknown and analyses of large data sets may be computationally prohibitive.'),
(95045, 2012, 'bmc', 'Horizontal gene transfer dynamics and distribution of fitness effects during microbial in silico evolution', 'Mozhayskiy, Vadim|Tagkopoulos, Ilias', 'Department of Computer Science and UC Davis Genome Center, University of California Davis, Davis, California 95616, USA.|Department of Computer Science and UC Davis Genome Center, University of California Davis, Davis, California 95616, USA.', 'United States|United States', 0, 'Adaptation, Biological|Algorithms|Bacteria|Computer Simulation|Directed Molecular Evolution|Environment|Gene Regulatory Networks|Gene Transfer, Horizontal|Genetic Fitness|Metabolic Networks and Pathways|Models, Theoretical|Phenotype|Phylogeny', 'Journal Article', '10.1186/1471-2105-13-S10-S13', '22759418', 'Horizontal gene transfer (HGT) is a process that facilitates the transfer of genetic material between organisms that are not directly related, and thus can affect both the rate of evolution and emergence of traits. Recent phylogenetic studies reveal HGT events are likely ubiquitous in the Tree of Life. However, our knowledge of HGTs role in evolution and biological organization is very limited, mainly due to the lack of ancestral evolutionary signatures and the difficulty to observe complex evolutionary dynamics in a laboratory setting. Here, we utilize a multi-scale microbial evolution model to comprehensively study the effect of HGT on the evolution of complex traits and organization of gene regulatory networks.'),
(95046, 2012, 'bmc', 'Algorithms: simultaneous error-correction and rooting for gene tree reconciliation and the gene duplication problem', 'Górecki, Pawel|Eulenstein, Oliver', 'Institute of Informatics, University of Warsaw, Warsaw, 02-097, Poland. gorecki@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, 02-097, Poland. gorecki@mimuw.edu.pl', 'Poland|Poland', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Gene Duplication|Genomics|Models, Genetic|Phylogeny|Software|Yeasts', 'Journal Article', '10.1186/1471-2105-13-S10-S14', '22759419', 'Evolutionary methods are increasingly challenged by the wealth of fast growing resources of genomic sequence information. Evolutionary events, like gene duplication, loss, and deep coalescence, account more then ever for incongruence between gene trees and the actual species tree. Gene tree reconciliation is addressing this fundamental problem by invoking the minimum number of gene duplication and losses that reconcile a rooted gene tree with a rooted species tree. However, the reconciliation process is highly sensitive to topological error or wrong rooting of the gene tree, a condition that is not met by most gene trees in practice. Thus, despite the promises of gene tree reconciliation, its applicability in practice is severely limited.'),
(95047, 2012, 'bmc', 'Comparative evaluation of set-level techniques in predictive classification of gene expression samples', 'Holec, Matxc4x9bj|Klxc3xa9ma, Jixc5x99xc3xad|Zeleznxc3xbd, Filip|Tolar, Jakub', 'Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic.|Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic.|Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic.|Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic.', '-|-|-|-', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computational Biology|Decision Trees|Gene Expression Profiling|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-S10-S15', '22759420', 'Analysis of gene expression data in terms of a priori-defined gene sets has recently received significant attention as this approach typically yields more compact and interpretable results than those produced by traditional methods that rely on individual genes. The set-level strategy can also be adopted with similar benefits in predictive classification tasks accomplished with machine learning algorithms. Initial studies into the predictive performance of set-level classifiers have yielded rather controversial results. The goal of this study is to provide a more conclusive evaluation by testing various components of the set-level framework within a large collection of machine learning experiments.'),
(95048, 2012, 'bmc', 'Exploring biological interaction networks with tailored weighted quasi-bicliques', 'Chang, Wen-Chieh|Vakati, Sudheer|Krause, Roland|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|Computational Biology|Computer Simulation|Data Mining|Gene Regulatory Networks|Metabolic Networks and Pathways|Models, Theoretical|Systems Biology', 'Journal Article', '10.1186/1471-2105-13-S10-S16', '22759421', 'Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges.'),
(95049, 2012, 'bmc', 'Gene network modular-based classification of microarray samples', 'Hu, Pingzhao|Bull, Shelley B|Jiang, Hui', 'Department of Computer Science and Engineering, York University, Toronto, M3J 1P3, Canada. phu@cse.yorku.ca|Department of Computer Science and Engineering, York University, Toronto, M3J 1P3, Canada. phu@cse.yorku.ca|Department of Computer Science and Engineering, York University, Toronto, M3J 1P3, Canada. phu@cse.yorku.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Discriminant Analysis|Gene Expression Profiling|Gene Regulatory Networks|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-S10-S17', '22759422', 'Molecular predictor is a new tool for disease diagnosis, which uses gene expression to classify diagnostic category of a patient. The statistical challenge for constructing such a predictor is that there are thousands of genes to predict for the disease categories, but only a small number of samples are available.'),
(95050, 2012, 'bmc', 'A methodology for detecting the orthology signal in a PPI network at a functional complex level', 'Jancura, Pavol|Mavridou, Eleftheria|Carrillo-de Santa Pau, Enrique|Marchiori, Elena', 'Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, 6500 GL, The Netherlands. pavol.jancura@gmail.com|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, 6500 GL, The Netherlands. pavol.jancura@gmail.com|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, 6500 GL, The Netherlands. pavol.jancura@gmail.com|Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, 6500 GL, The Netherlands. pavol.jancura@gmail.com', 'Netherlands|Netherlands|Netherlands|Netherlands', 9, 'Algorithms|Animals|Caenorhabditis elegans|Cluster Analysis|Computational Biology|Computer Simulation|Drosophila melanogaster|Escherichia coli|Evolution, Molecular|Humans|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-13-S10-S18', '22759423', 'Stable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal.'),
(95051, 2012, 'bmc', 'Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks', 'He, Jieyue|Li, Chaojun|Ye, Baoliu|Zhong, Wei', 'School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China. jieyuehe@seu.edu.cn|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China. jieyuehe@seu.edu.cn|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China. jieyuehe@seu.edu.cn|School of Computer Science and Engineering, Key Lab of Computer Network & Information Integration, MOE, Southeast University, Nanjing, 210018, China. jieyuehe@seu.edu.cn', 'China|China|China|China', 5, 'Algorithms|Cluster Analysis|Computational Biology|Data Mining|Protein Interaction Mapping|Protein Interaction Maps|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-S10-S19', '22759424', 'Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.'),
(95052, 2012, 'bmc', 'Computing the protein binding sites', 'Guo, Fei|Wang, Lusheng', 'Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong', 0, 'Algorithms|Binding Sites|Databases, Protein|Drug Design|Protein Binding|Protein Structure, Tertiary|Proteins|Sequence Alignment|Software', 'Comparative Study', '10.1186/1471-2105-13-S10-S2', '22759425', 'Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification and comparison of functional sites. Structural genomic projects are beginning to produce protein structures with unknown functions. Therefore, efficient methods are required if all these structures are to be properly annotated. Lots of methods for finding binding sites involve 3D structure comparison. Here we design a method to find protein binding sites by direct comparison of protein 3D structures.'),
(95053, 2012, 'bmc', 'Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study', 'Jiang, Jonathan Q|Wu, Maoying', 'School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.|School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.', 'China|China', 15, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Protein Interaction Maps|Proteins|Proteome|Saccharomyces cerevisiae', 'Comparative Study', '10.1186/1471-2105-13-S10-S20', '22759426', 'Proteins that interact in vivo tend to reside within the same or adjacent subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.'),
(95054, 2012, 'bmc', 'Prediction of DNA-binding propensity of proteins by the ball-histogram method using automatic template search', 'Szabxc3xb3ovxc3xa1, Andrea|Kuxc5xbeelka, Ondxc5x99ej|Zeleznxc3xbd, Filip|Tolar, Jakub', 'Czech Technical University, Department of Cybernetics, Prague, 166 27, Czech Republic. szaboand@fel.cvut.cz|Czech Technical University, Department of Cybernetics, Prague, 166 27, Czech Republic. szaboand@fel.cvut.cz|Czech Technical University, Department of Cybernetics, Prague, 166 27, Czech Republic. szaboand@fel.cvut.cz|Czech Technical University, Department of Cybernetics, Prague, 166 27, Czech Republic. szaboand@fel.cvut.cz', '-|-|-|-', 0, 'Algorithms|Amino Acids|Computational Biology|DNA|Models, Theoretical|Monte Carlo Method|Protein Binding|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-13-S10-S3', '22759427', 'We contribute a novel, ball-histogram approach to DNA-binding propensity prediction of proteins. Unlike state-of-the-art methods based on constructing an ad-hoc set of features describing physicochemical properties of the proteins, the ball-histogram technique enables a systematic, Monte-Carlo exploration of the spatial distribution of amino acids complying with automatically selected properties. This exploration yields a model for the prediction of DNA binding propensity. We validate our method in prediction experiments, improving on state-of-the-art accuracies. Moreover, our method also provides interpretable features involving spatial distributions of selected amino acids.'),
(95055, 2012, 'bmc', 'High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs', 'Wan, Xiaohua|Zhang, Fa|Chu, Qi|Liu, Zhiyong', 'Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China.|Institute of Computing Technology and Key Lab of Intelligent Information Processing, Beijing, China.', 'China|China|China|China', 4, 'Algorithms|Electron Microscope Tomography|Image Processing, Computer-Assisted', 'Journal Article', '10.1186/1471-2105-13-S10-S4', '22759428', 'Three-dimensional (3D) reconstruction in electron tomography (ET) has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs) offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs.'),
(95056, 2012, 'bmc', 'The maximum clique enumeration problem: algorithms, applications, and implementations', 'Eblen, John D|Phillips, Charles A|Rogers, Gary L|Langston, Michael A', 'Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Software', 'Journal Article', '10.1186/1471-2105-13-S10-S5', '22759429', 'The maximum clique enumeration (MCE) problem asks that we identify all maximum cliques in a finite, simple graph. MCE is closely related to two other well-known and widely-studied problems: the maximum clique optimization problem, which asks us to determine the size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a listing of all maximal cliques. Naturally, these three problems are NP-hard, given that they subsume the classic version of the NP-complete clique decision problem. MCE can be solved in principle with standard enumeration methods due to Bron, Kerbosch, Kose and others. Unfortunately, these techniques are ill-suited to graphs encountered in our applications. We must solve MCE on instances deeply seeded in data mining and computational biology, where high-throughput data capture often creates graphs of extreme size and density. MCE can also be solved in principle using more modern algorithms based in part on vertex cover and the theory of fixed-parameter tractability (FPT). While FPT is an improvement, these algorithms too can fail to scale sufficiently well as the sizes and densities of our datasets grow.'),
(95057, 2012, 'bmc', 'Efficient error correction for next-generation sequencing of viral amplicons', 'Skums, Pavel|Dimitrova, Zoya|Campo, David S|Vaughan, Gilberto|Rossi, Livia|Forbi, Joseph C|Yokosawa, Jonny|Zelikovsky, Alex|Khudyakov, Yury', 'Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov|Laboratory of Molecular Epidemiology and Bioinformatics, Division of Viral Hepatitis, Centers for Disease Control and Prevention, 1600 Clifton Road NE, Atlanta, GA 30333, USA. kki8@cdc.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 49, 'Algorithms|Cluster Analysis|Computational Biology|DNA, Viral|Haplotypes|Sequence Analysis, DNA|Viruses', 'Comparative Study', '10.1186/1471-2105-13-S10-S6', '22759430', 'Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.'),
(95058, 2012, 'bmc', 'A systematic comparison of genome-scale clustering algorithms', 'Jay, Jeremy J|Eblen, John D|Zhang, Yun|Benson, Mikael|Perkins, Andy D|Saxton, Arnold M|Voy, Brynn H|Chesler, Elissa J|Langston, Michael A', 'The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Genome, Fungal|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Comparative Study', '10.1186/1471-2105-13-S10-S7', '22759431', 'A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae.'),
(95059, 2012, 'bmc', 'Genome plasticity and systems evolution in Streptomyces', 'Zhou, Zhan|Gu, Jianying|Li, Yong-Quan|Wang, Yufeng', 'College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.|College of Life Sciences, Zhejiang University, Hangzhou 310058, PR China.', 'China|China|China|China', 0, 'DNA, Bacterial|Evolution, Molecular|Gene Duplication|Gene Transfer, Horizontal|Genome, Bacterial|Molecular Sequence Annotation|Multigene Family|Phylogeny|Sequence Analysis, DNA|Streptomyces', 'Journal Article', '10.1186/1471-2105-13-S10-S8', '22759432', 'Streptomycetes are filamentous soil-dwelling bacteria. They are best known as the producers of a great variety of natural products such as antibiotics, antifungals, antiparasitics, and anticancer agents and the decomposers of organic substances for carbon recycling. They are also model organisms for the studies of gene regulatory networks, morphological differentiation, and stress response. The availability of sets of genomes from closely related Streptomyces strains makes it possible to assess the mechanisms underlying genome plasticity and systems adaptation.'),
(95060, 2012, 'bmc', 'Gene order in rosid phylogeny, inferred from pairwise syntenies among extant genomes', 'Zheng, Chunfang|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, Canada.|Department of Mathematics and Statistics, University of Ottawa, Canada.', 'Canada|Canada', 0, 'Base Sequence|Computational Biology|DNA, Plant|Evolution, Molecular|Gene Order|Genome, Plant|Genomics|Magnoliopsida|Phylogeny|Sequence Analysis, DNA|Software|Synteny', 'Journal Article', '10.1186/1471-2105-13-S10-S9', '22759433', 'Ancestral gene order reconstruction for flowering plants has lagged behind developments in yeasts, insects and higher animals, because of the recency of widespread plant genome sequencing, sequencers embargoes on public data use, paralogies due to whole genome duplication (WGD) and fractionation of undeleted duplicates, extensive paralogy from other sources, and the computational cost of existing methods.'),
(95061, 2012, 'bmc', 'The Genia Event and Protein Coreference tasks of the BioNLP Shared Task 2011', 'Kim, Jin-Dong|Nguyen, Ngan|Wang, Yue|Tsujii, Junichi|Takagi, Toshihisa|Yonezawa, Akinori', 'Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp|Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp|Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp|Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp|Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp|Database Center for Life Science, Research Organization of Information and Science, 2-11-16 Yayoi, Bunkyo-ku, Tokyo, Japan. jdkim@dbcls.rois.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Congresses as Topic|Gene Expression|Information Systems|Natural Language Processing|Proteins', 'Journal Article', '10.1186/1471-2105-13-S11-S1', '22759455', 'The Genia task, when it was introduced in 2009, was the first community-wide effort to address a fine-grained, structural information extraction from biomedical literature. Arranged for the second time as one of the main tasks of BioNLP Shared Task 2011, it aimed to measure the progress of the community since 2009, and to evaluate generalization of the technology to full text papers. The Protein Coreference task was arranged as one of the supporting tasks, motivated from one of the lessons of the 2009 task that the abundance of coreference structures in natural language text hinders further improvement with the Genia task.'),
(95062, 2012, 'bmc', 'Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011', 'Pyysalo, Sampo|Ohta, Tomoko|Rak, Rafal|Sullivan, Dan|Mao, Chunhong|Wang, Chunxia|Sobral, Bruno|Tsujii, Junichi|Ananiadou, Sophia', 'School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com|School of Computer Science, University of Manchester, Manchester, UK. sampo.pyysalo@gmail.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Communicable Diseases|DNA Methylation|Epigenomics|Histone Code|Information Storage and Retrieval|Lipoproteins|Natural Language Processing|Protein Processing, Post-Translational|Proteins', 'Journal Article', '10.1186/1471-2105-13-S11-S2', '22759456', 'We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties.'),
(95063, 2012, 'bmc', 'BioNLP Shared Task--The Bacteria Track', 'Bossy, Robert|Jourde, Julien|Manine, Alain-Pierre|Veber, Philippe|Alphonse, Erick|van de Guchte, Maarten|Bessières, Philippe|Nédellec, Claire', 'Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.|Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352 Jouy-en-Josas, France.', 'France|France|France|France|France|France|France|France', 0, 'Bacteria|Epistasis, Genetic|Genes, Bacterial|Humans|Information Storage and Retrieval|PubMed|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-13-S11-S3', '22759457', 'We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles. We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants submissions.'),
(95064, 2012, 'bmc', 'University of Turku in the BioNLP11 Shared Task', 'Björne, Jari|Ginter, Filip|Salakoski, Tapio', 'Department of Information Technology, University of Turku, Turku Centre for Computer Science (TUCS), Joukahaisenkatu 3-5, 20520 Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku Centre for Computer Science (TUCS), Joukahaisenkatu 3-5, 20520 Turku, Finland. jari.bjorne@utu.fi|Department of Information Technology, University of Turku, Turku Centre for Computer Science (TUCS), Joukahaisenkatu 3-5, 20520 Turku, Finland. jari.bjorne@utu.fi', 'Finland|Finland|Finland', 0, 'Artificial Intelligence|Bacteria|Communicable Diseases|Data Mining|Ecosystem|Epigenomics|Epistasis, Genetic|Genes, Bacterial|Humans|Natural Language Processing|Protein Processing, Post-Translational|Proteins|Support Vector Machine|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-13-S11-S4', '22759458', 'We present a system for extracting biomedical events (detailed descriptions of biomolecular interactions) from research articles, developed for the BioNLP11 Shared Task. Our goal is to develop a system easily adaptable to different event schemes, following the theme of the BioNLP11 Shared Task: generalization, the extension of event extraction to varied biomedical domains. Our system extends our BioNLP09 Shared Task winning Turku Event Extraction System, which uses support vector machines to first detect event-defining words, followed by detection of their relationships.'),
(95065, 2012, 'bmc', 'Biomedical event extraction from abstracts and full papers using search-based structured prediction', 'Vlachos, Andreas|Craven, Mark', 'Computer Laboratory, University of Cambridge, UK. andreas.vlachos@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, UK. andreas.vlachos@cl.cam.ac.uk', 'United Kingdom|United Kingdom', 18, 'Algorithms|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-13-S11-S5', '22759459', 'Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task.'),
(95066, 2012, 'bmc', 'Semantically linking molecular entities in literature through entity relationships', 'Van Landeghem, Sofie|Björne, Jari|Abeel, Thomas|De Baets, Bernard|Salakoski, Tapio|Van de Peer, Yves', 'Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be|Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be|Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be|Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be|Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be|Department of Plant Systems Biology, Flanders Institute for Biotechnology (VIB), B-9052 Gent, Belgium. sofie.vanlandeghem@psb.ugent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Artificial Intelligence|Data Mining|Databases, Factual|Genes|PubMed', 'Journal Article', '10.1186/1471-2105-13-S11-S6', '22759460', 'Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts.'),
(95067, 2012, 'bmc', 'Biological event composition', 'Kilicoglu, Halil|Bergler, Sabine', 'Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Blvd, West, Montréal, Canada. h_kilico@cse.concordia.ca|Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Blvd, West, Montréal, Canada. h_kilico@cse.concordia.ca', 'Canada|Canada', 13, 'Algorithms|Blood Cells|Data Mining|Gene Expression Regulation|Humans|MEDLINE|Natural Language Processing|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-S11-S7', '22759461', 'In recent years, biological event extraction has emerged as a key natural language processing task, aiming to address the information overload problem in accessing the molecular biology literature. The BioNLP shared task competitions have contributed to this recent interest considerably. The first competition (BioNLP09) focused on extracting biological events from Medline abstracts from a narrow domain, while the theme of the latest competition (BioNLP-ST11) was generalization and a wider range of text types, event types, and subject domains were considered. We view event extraction as a building block in larger discourse interpretation and propose a two-phase, linguistically-grounded, rule-based methodology. In the first phase, a general, underspecified semantic interpretation is composed from syntactic dependency relations in a bottom-up manner. The notion of embedding underpins this phase and it is informed by a trigger dictionary and argument identification rules. Coreference resolution is also performed at this step, allowing extraction of inter-sentential relations. The second phase is concerned with constraining the resulting semantic interpretation by shared task specifications. We evaluated our general methodology on core biological event extraction and speculation/negation tasks in three main tracks of BioNLP-ST11 (GENIA, EPI, and ID).'),
(95068, 2012, 'bmc', 'Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach', 'Ratkovic, Zorana|Golik, Wiktoria|Warnier, Pierre', 'MIG INRA UR1077 Domaine de Vilvert, F-78352 Jouy-en-Josas, France. zorana.ratkovic@jouy.inra.fr|MIG INRA UR1077 Domaine de Vilvert, F-78352 Jouy-en-Josas, France. zorana.ratkovic@jouy.inra.fr|MIG INRA UR1077 Domaine de Vilvert, F-78352 Jouy-en-Josas, France. zorana.ratkovic@jouy.inra.fr', 'France|France|France', 13, 'Bacteria|Ecosystem|Environmental Microbiology|Humans|Information Storage and Retrieval|Knowledge Bases|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-13-S11-S8', '22759462', 'Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field.'),
(95069, 2012, 'bmc', 'Combining joint models for biomedical event extraction', 'McClosky, David|Riedel, Sebastian|Surdeanu, Mihai|McCallum, Andrew|Manning, Christopher D', 'Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA, USA. mcclosky@stanford.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Communicable Diseases|Data Mining|Epigenomics|Humans|Information Storage and Retrieval|Models, Theoretical|Natural Language Processing|Protein Processing, Post-Translational', 'Journal Article', '10.1186/1471-2105-13-S11-S9', '22759463', 'We explore techniques for performing model combination between the UMass and Stanford biomedical event extraction systems. Both sub-components address event extraction as a structured prediction problem, and use dual decomposition (UMass) and parsing algorithms (Stanford) to find the best scoring event structure. Our primary focus is on stacking where the predictions from the Stanford system are used as features in the UMass system. For comparison, we look at simpler model combination techniques such as intersection and union which require only the outputs from each system and combine them directly.'),
(95070, 2012, 'bmc', 'Protein-protein binding site identification by enumerating the configurations', 'Guo, Fei|Li, Shuai Cheng|Wang, Lusheng|Zhu, Daming', 'Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 0, 'Algorithms|Binding Sites|Models, Molecular|Molecular Docking Simulation|Multiprotein Complexes|Protein Binding|Software', 'Journal Article', '10.1186/1471-2105-13-158', '22768846', 'The ability to predict protein-protein binding sites has a wide range of applications, including signal transduction studies, de novo drug design, structure identification and comparison of functional sites. The interface in a complex involves two structurally matched protein subunits, and the binding sites can be predicted by identifying structural matches at protein surfaces.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95071, 2012, 'bmc', 'Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures', 'Scheid, Anika|Nebel, Markus E', 'Department of Computer Science, University of Kaiserslautern, D-67653 Kaiserslautern, P.O. Box 3049, Germany. a_scheid@cs.uni-kl.de|Department of Computer Science, University of Kaiserslautern, D-67653 Kaiserslautern, P.O. Box 3049, Germany. a_scheid@cs.uni-kl.de', 'Germany|Germany', 0, 'Algorithms|Bayes Theorem|Models, Statistical|Nucleic Acid Conformation|Probability|RNA|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-13-159', '22776037', 'Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent) stochastic context-free grammar (SCFG) that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF) approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples), where neither of these two competing approaches generally outperforms the other.'),
(95072, 2012, 'bmc', 'ParticleCall: a particle filter for base calling in next-generation sequencing systems', 'Shen, Xiaohu|Vikalo, Haris', 'Department of Electrical and Computer Engineering, University of Texas, Austin, TX, US.|Department of Electrical and Computer Engineering, University of Texas, Austin, TX, US.', '-|-', 9, 'Algorithms|Markov Chains|Monte Carlo Method|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-160', '22776067', 'Next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. Accuracy and lengths of their reads, however, are yet to surpass those provided by the conventional Sanger sequencing method. This motivates the search for computationally efficient algorithms capable of reliable and accurate detection of the order of nucleotides in short DNA fragments from the acquired data.'),
(95073, 2012, 'bmc', 'Concept annotation in the CRAFT corpus', 'Bada, Michael|Eckert, Miriam|Evans, Donald|Garcia, Kristin|Shipley, Krista|Sitnikov, Dmitry|Baumgartner, William A|Cohen, K Bretonnel|Verspoor, Karin|Blake, Judith A|Hunter, Lawrence E', 'Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu|Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. mike.bada@ucdenver.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Data Mining|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-13-161', '22776079', 'Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text.'),
(95074, 2012, 'bmc', 'Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study', 'A Santos, Jose C|Nassif, Houssam|Page, David|Muggleton, Stephen H|E Sternberg, Michael J', 'Computational Bioinformatics Laboratory, Department of Computer Science, Imperial College London, London, SW7 2BZ, UK. jcs06@doc.ic.ac.uk|Computational Bioinformatics Laboratory, Department of Computer Science, Imperial College London, London, SW7 2BZ, UK. jcs06@doc.ic.ac.uk|Computational Bioinformatics Laboratory, Department of Computer Science, Imperial College London, London, SW7 2BZ, UK. jcs06@doc.ic.ac.uk|Computational Bioinformatics Laboratory, Department of Computer Science, Imperial College London, London, SW7 2BZ, UK. jcs06@doc.ic.ac.uk|Computational Bioinformatics Laboratory, Department of Computer Science, Imperial College London, London, SW7 2BZ, UK. jcs06@doc.ic.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Artificial Intelligence|Hexoses|Ligands|Protein Binding|Proteins', 'Journal Article', '10.1186/1471-2105-13-162', '22783946', 'There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions.'),
(95075, 2012, 'bmc', 'A graph-based approach for designing extensible pipelines', 'Rodrigues, Maíra R|Magalhães, Wagner C S|Machado, Moara|Tarazona-Santos, Eduardo', 'Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. maira.r.rodrigues@gmail.com|Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. maira.r.rodrigues@gmail.com|Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. maira.r.rodrigues@gmail.com|Departamento de Biologia Geral, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. maira.r.rodrigues@gmail.com', 'Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Computational Biology|Genome|Proteomics|Software|Workflow', 'Journal Article', '10.1186/1471-2105-13-163', '22788675', 'In bioinformatics, it is important to build extensible and low-maintenance systems that are able to deal with the new tools and data formats that are constantly being developed. The traditional and simplest implementation of pipelines involves hardcoding the execution steps into programs or scripts. This approach can lead to problems when a pipeline is expanding because the incorporation of new tools is often error prone and time consuming. Current approaches to pipeline development such as workflow management systems focus on analysis tasks that are systematically repeated without significant changes in their course of execution, such as genome annotation. However, more dynamism on the pipeline composition is necessary when each execution requires a different combination of steps.'),
(95076, 2012, 'bmc', 'SNP interaction detection with Random Forests in high-dimensional genetic data', 'Winham, Stacey J|Colby, Colin L|Freimuth, Robert R|Wang, Xin|de Andrade, Mariza|Huebner, Marianne|Biernacka, Joanna M', 'Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu|Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Data Mining|Genes|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Logistic Models|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-13-164', '22793366', 'Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.'),
(95077, 2012, 'bmc', 'Network-enabled gene expression analysis', 'Edwards, David|Wang, Lei|Sørensen, Peter', 'Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark. David.Edwards@agrsci.dk|Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark. David.Edwards@agrsci.dk|Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark. David.Edwards@agrsci.dk', 'Denmark|Denmark|Denmark', 12, 'Algorithms|Animals|Bayes Theorem|Computational Biology|Gene Expression|Gene Expression Profiling|Liver|Male|Metabolic Networks and Pathways|Peroxisome Proliferator-Activated Receptors|Rats|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-167', '22799258', 'Although genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the expression of each gene, one at a time, between treatment groups. This implicitly treats the gene expression levels as independent, but they are in fact highly interdependent, and exploiting this enables substantial power gains to be realized.'),
(95078, 2012, 'bmc', 'Integrative prescreening in analysis of multiple cancer genomic studies', 'Song, Rui|Huang, Jian|Ma, Shuangge', 'Department of Statistics, Colorado State University, Fort Collins, USA. song@stat.colostate.edu|Department of Statistics, Colorado State University, Fort Collins, USA. song@stat.colostate.edu|Department of Statistics, Colorado State University, Fort Collins, USA. song@stat.colostate.edu', 'United States|United States|United States', 6, 'Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Genomics|Humans|Liver Neoplasms|Neoplasms|Pancreatic Neoplasms|Reproducibility of Results|Sample Size', 'Journal Article', '10.1186/1471-2105-13-168', '22799431', 'In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost.'),
(95079, 2012, 'bmc', 'Re-visiting protein-centric two-tier classification of existing DNA-protein complexes', 'Malhotra, Sony|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences, UAS-GKVK Campus, Bangalore 560 065, India.|National Centre for Biological Sciences, UAS-GKVK Campus, Bangalore 560 065, India.', 'India|India', 0, 'Algorithms|Classification|DNA|DNA-Binding Proteins|Databases, Protein|Models, Molecular|Protein Conformation|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-165', '22800292', 'Precise DNA-protein interactions play most important and vital role in maintaining the normal physiological functioning of the cell, as it controls many high fidelity cellular processes. Detailed study of the nature of these interactions has paved the way for understanding the mechanisms behind the biological processes in which they are involved. Earlier in 2000, a systematic classification of DNA-protein complexes based on the structural analysis of the proteins was proposed at two tiers, namely groups and families. With the advancement in the number and resolution of structures of DNA-protein complexes deposited in the Protein Data Bank, it is important to revisit the existing classification.'),
(95080, 2012, 'bmc', 'Slim-filter: an interactive Windows-based application for illumina genome analyzer data assessment and manipulation', 'Golovko, Georgiy|Khanipov, Kamil|Rojas, Mark|Martinez-Alcántara, Antonio|Howard, Jesse J|Ballesteros, Efren|Gupta, Sharu|Widger, William|Fofanov, Yuriy', 'Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu|Center for BioMedical and Environmental Genomics, University of Houston, Houston, TX, USA. ggolovko@bioinfo.uh.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Computational Biology|DNA Primers|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-166', '22800377', 'The emergence of Next Generation Sequencing technologies has made it possible for individual investigators to generate gigabases of sequencing data per week. Effective analysis and manipulation of these data is limited due to large file sizes, so even simple tasks such as data filtration and quality assessment have to be performed in several steps. This requires (potentially problematic) interaction between the investigator and a bioinformatics/computational service provider. Furthermore, such services are often performed using specialized computational facilities.'),
(95081, 2012, 'bmc', 'Detecting pore-lining regions in transmembrane protein sequences', 'Nugent, Timothy|Jones, David T', 'Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.|Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.', 'United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Membrane Proteins|Protein Structure, Secondary|Sequence Analysis, Protein|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-169', '22805427', 'Alpha-helical transmembrane channel and transporter proteins play vital roles in a diverse range of essential biological processes and are crucial in facilitating the passage of ions and molecules across the lipid bilayer. However, the experimental difficulties associated with obtaining high quality crystals has led to their significant under-representation in structural databases. Computational methods that can identify structural features from sequence alone are therefore of high importance.'),
(95082, 2012, 'bmc', 'Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms', 'Haznedaroglu, Berat Z|Reeves, Darryl|Rismani-Yazdi, Hamid|Peccia, Jordan', 'Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.|Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.|Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.|Department of Chemical and Environmental Engineering, Yale University, New Haven, CT 06511, USA.', 'United States|United States|United States|United States', 23, 'Algorithms|Gene Expression Profiling|Genome|High-Throughput Nucleotide Sequencing|Molecular Sequence Annotation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-170', '22808927', 'The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly.'),
(95083, 2012, 'bmc', 'An image classification approach to analyze the suppression of plant immunity by the human pathogen Salmonella Typhimurium', 'Schikora, Marek|Neupane, Balram|Madhogaria, Satish|Koch, Wolfgang|Cremers, Daniel|Hirt, Heribert|Kogel, Karl-Heinz|Schikora, Adam', 'Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.|Department Sensor Data and Information Fusion, Fraunhofer FKIE, 53343 Wachtberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 19, 'Algorithms|Animals|Arabidopsis|Bacterial Secretion Systems|Escherichia coli|Host-Pathogen Interactions|Humans|Image Processing, Computer-Assisted|Plant Diseases|Plant Immunity|Plant Leaves|Pseudomonas syringae|Salmonella typhimurium', 'Journal Article', '10.1186/1471-2105-13-171', '22812426', 'The enteric pathogen Salmonella is the causative agent of the majority of food-borne bacterial poisonings. Resent research revealed that colonization of plants by Salmonella is an active infection process. Salmonella changes the metabolism and adjust the plant host by suppressing the defense mechanisms. In this report we developed an automatic algorithm to quantify the symptoms caused by Salmonella infection on Arabidopsis.'),
(95084, 2012, 'bmc', 'A text-mining system for extracting metabolic reactions from full-text articles', 'Czarnecki, Jan|Nobeli, Irene|Smith, Adrian M|Shepherd, Adrian J', 'Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, London, UK.|Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, London, UK.|Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, London, UK.|Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 22, 'Algorithms|Data Mining|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-13-172', '22823282', 'Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions.'),
(95085, 2012, 'bmc', 'Defining and searching for structural motifs using DeepView/Swiss-PdbViewer', 'Johansson, Maria U|Zoete, Vincent|Michielin, Olivier|Guex, Nicolas', 'Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.|Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 142, 'Algorithms|Amino Acid Motifs|Databases, Protein|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-13-173', '22823337', 'Today, recognition and classification of sequence motifs and protein folds is a mature field, thanks to the availability of numerous comprehensive and easy to use software packages and web-based services. Recognition of structural motifs, by comparison, is less well developed and much less frequently used, possibly due to a lack of easily accessible and easy to use software.'),
(95086, 2012, 'bmc', 'A novel hierarchical clustering algorithm for gene sequences', 'Wei, Dan|Jiang, Qingshan|Wei, Yanjie|Wang, Shengrui', 'Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China.|Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China.|Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China.|Cognitive Science Department & Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China.', 'China|China|China|China', 40, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Phylogeny|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-13-174', '22823405', 'Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors.'),
(95087, 2012, 'bmc', 'The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding', 'Kornacker, Karl|Rye, Morten Beck|Håndstad, Tony|Drabløs, Finn', 'Division of Sensory Biophysics, Ohio State University, Columbus, OH, USA.|Division of Sensory Biophysics, Ohio State University, Columbus, OH, USA.|Division of Sensory Biophysics, Ohio State University, Columbus, OH, USA.|Division of Sensory Biophysics, Ohio State University, Columbus, OH, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|High-Throughput Nucleotide Sequencing|Sensitivity and Specificity|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-176', '22827163', 'Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen as peaks in enrichment profiles when the sequencing reads are mapped to a reference genome. However, the profiles are normally noisy, making it challenging to identify all significantly enriched regions in a reliable way and with an acceptable false discovery rate.'),
(95088, 2012, 'bmc', 'Analysis of high dimensional data using pre-defined set and subset information, with applications to genomic data', 'Guo, Wenge|Yang, Mingan|Xing, Chuanhua|Peddada, Shyamal D', 'Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.|Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.', 'Jersey|Jersey|Jersey|Jersey', 3, 'Computational Biology|Computer Simulation|Gene Expression Profiling|Genomics|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-177', '22827252', 'Based on available biological information, genomic data can often be partitioned into pre-defined sets (e.g. pathways) and subsets within sets. Biologists are often interested in determining whether some pre-defined sets of variables (e.g. genes) are differentially expressed under varying experimental conditions. Several procedures are available in the literature for making such determinations, however, they do not take into account information regarding the subsets within each set. Secondly, variables (e.g. genes) belonging to a set or a subset are potentially correlated, yet such information is often ignored and univariate methods are used. This may result in loss of power and/or inflated false positive rate.'),
(95089, 2012, 'bmc', 'Seqcrawler: biological data indexing and browsing platform', 'Sallou, Olivier|Bretaudeau, Anthony|Roult, Aurelien', 'Campus de Beaulieu, University of Rennes 1, Rennes, France. olivier.sallou@irisa.fr|Campus de Beaulieu, University of Rennes 1, Rennes, France. olivier.sallou@irisa.fr|Campus de Beaulieu, University of Rennes 1, Rennes, France. olivier.sallou@irisa.fr', 'France|France|France', 0, 'Abstracting and Indexing as Topic|Internet|Search Engine|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-175', '22827839', 'Seqcrawler takes its roots in software like SRS or Lucegene. It provides an indexing platform to ease the search of data and meta-data in biological banks and it can scale to face the current flow of data. While many biological bank search tools are available on the Internet, mainly provided by large organizations to search their data, there is a lack of free and open source solutions to browse ones own set of data with a flexible query system and able to scale from a single computer to a cloud system. A personal index platform will help labs and bioinformaticians to search their meta-data but also to build a larger information system with custom subsets of data.'),
(95090, 2012, 'bmc', 'Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification', 'Wang, Shu-Lin|Li, Xue-Ling|Fang, Jianwen', 'Applied Bioinformatics Laboratory, University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.|Applied Bioinformatics Laboratory, University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA.', 'United States|United States|United States', 25, 'Algorithms|Gene Expression Profiling|Genes|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-13-178', '22830977', 'Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development.'),
(95091, 2012, 'bmc', 'Inferring transcript phylogenies', 'Christinat, Yann|Moret, Bernard M E', 'Laboratory of Computational Biology and Bioinformatics, EPFL, 1015 Lausanne, Switzerland. yann.christinat@epfl.ch|Laboratory of Computational Biology and Bioinformatics, EPFL, 1015 Lausanne, Switzerland. yann.christinat@epfl.ch', 'Switzerland|Switzerland', 25, 'Algorithms|Alternative Splicing|Animals|Evolution, Molecular|Exons|Eye Proteins|Homeodomain Proteins|Humans|Introns|Models, Genetic|Myelin-Associated Glycoprotein|PAX6 Transcription Factor|Paired Box Transcription Factors|Phylogeny|Repressor Proteins', 'Journal Article', '10.1186/1471-2105-13-178', '22831154', 'Alternative splicing, an unknown mechanism 20 years ago, is now recognized as a major mechanism for proteome and transcriptome diversity, particularly in mammals–some researchers conjecture that up to 90% of human genes are alternatively spliced. Despite much research on exon and intron evolution, little is known about the evolution of transcripts. In this paper, we present a model of transcript evolution and an associated algorithm to reconstruct transcript phylogenies. The evolution of the gene structure–exons and introns–is used as basis for the reconstruction of transcript phylogenies. We apply our model and reconstruction algorithm on two well-studied genes, MAG and PAX6, obtaining results consistent with current knowledge and thereby providing evidence that a phylogenetic analysis of transcripts is feasible and likely to be informative.'),
(95092, 2012, 'bmc', 'A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard', 'Keith, Jonathan M|Davey, Christian M|Boyd, Sarah E', 'School of Mathematical Sciences, Monash University, Victoria 3800, Australia. jonathan.keith@monash.edu|School of Mathematical Sciences, Monash University, Victoria 3800, Australia. jonathan.keith@monash.edu|School of Mathematical Sciences, Monash University, Victoria 3800, Australia. jonathan.keith@monash.edu', 'Australia|Australia|Australia', 3, 'Algorithms|Bayes Theorem|Classification|Computational Biology|Genome-Wide Association Study|Humans|Macular Degeneration|Polymorphism, Single Nucleotide|Proteins|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-13-179', '22838505', 'Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers.'),
(95093, 2012, 'bmc', 'Optimal experiment selection for parameter estimation in biological differential equation models', 'Transtrum, Mark K|Qiu, Peng', 'Department of Bioinformatics and Computational Biology, University of Texas M,D, Anderson Cancer Cneter, Houston, Texas, USA.|Department of Bioinformatics and Computational Biology, University of Texas M,D, Anderson Cancer Cneter, Houston, Texas, USA.', 'United States|United States', 18, 'Algorithms|Gene Regulatory Networks|Mathematical Computing|Models, Genetic|Research Design', 'Journal Article', '10.1186/1471-2105-13-181', '22838836', 'Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection.'),
(95094, 2012, 'bmc', 'Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes', 'Wu, Chao|Zhu, Jun|Zhang, Xuegong', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, PR China. wuchao1984@gmail.com|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, PR China. wuchao1984@gmail.com|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, PR China. wuchao1984@gmail.com', 'China|China|China', 85, 'Breast Neoplasms|Carcinoma, Non-Small-Cell Lung|Female|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Genes, Neoplasm|Humans|Lung Neoplasms|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-13-182', '22838965', 'To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved.'),
(95095, 2012, 'bmc', 'Short-read reading-frame predictors are not created equal: sequence error causes loss of signal', 'Trimble, William L|Keegan, Kevin P|DSouza, Mark|Wilke, Andreas|Wilkening, Jared|Gilbert, Jack|Meyer, Folker', 'Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov|Computation Institute, University of Chicago, Chicago, IL 60637, USA. trimble@anl.gov', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Metagenomics|Molecular Sequence Annotation|Reading Frames|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-183', '22839106', 'Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines, it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms-FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal-for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and real metagenomic datasets.'),
(95096, 2012, 'bmc', 'Computational modeling of the bHLH domain of the transcription factor TWIST1 and R118C, S144R and K145E mutants', 'Maia, Amanda M|da Silva, João Hm|Mencalha, André L|Caffarena, Ernesto R|Abdelhay, Eliana', 'Laboratório de Célula-tronco-CEMO/INCA, Praça da Cruz Vermelha 236 Andar, Centro, Rio de Janeiro/RJ, Brasil.|Laboratório de Célula-tronco-CEMO/INCA, Praça da Cruz Vermelha 236 Andar, Centro, Rio de Janeiro/RJ, Brasil.|Laboratório de Célula-tronco-CEMO/INCA, Praça da Cruz Vermelha 236 Andar, Centro, Rio de Janeiro/RJ, Brasil.|Laboratório de Célula-tronco-CEMO/INCA, Praça da Cruz Vermelha 236 Andar, Centro, Rio de Janeiro/RJ, Brasil.|Laboratório de Célula-tronco-CEMO/INCA, Praça da Cruz Vermelha 236 Andar, Centro, Rio de Janeiro/RJ, Brasil.', '-|-|-|-|-', 19, 'Acrocephalosyndactylia|Amino Acid Sequence|Amino Acid Substitution|Basic Helix-Loop-Helix Transcription Factors|Computer Simulation|Female|Helix-Loop-Helix Motifs|Humans|Models, Chemical|Molecular Dynamics Simulation|Molecular Sequence Data|Mutation|Nuclear Proteins|Protein Multimerization|Protein Structure, Secondary|Twist-Related Protein 1', 'Journal Article', '10.1186/1471-2105-13-184', '22839202', 'Human TWIST1 is a highly conserved member of the regulatory basic helix-loop-helix (bHLH) transcription factors. TWIST1 forms homo- or heterodimers with E-box proteins, such as E2A (isoforms E12 and E47), MYOD and HAND2. Haploinsufficiency germ-line mutations of the twist1 gene in humans are the main cause of Saethre-Chotzen syndrome (SCS), which is characterized by limb abnormalities and premature fusion of cranial sutures. Because of the importance of TWIST1 in the regulation of embryonic development and its relationship with SCS, along with the lack of an experimentally solved 3D structure, we performed comparative modeling for the TWIST1 bHLH region arranged into wild-type homodimers and heterodimers with E47. In addition, three mutations that promote DNA binding failure (R118C, S144R and K145E) were studied on the TWIST1 monomer. We also explored the behavior of the mutant forms in aqueous solution using molecular dynamics (MD) simulations, focusing on the structural changes of the wild-type versus mutant dimers.'),
(95097, 2012, 'bmc', 'Estimation of sequencing error rates in short reads', 'Wang, Xin Victoria|Blades, Natalie|Ding, Jie|Sultana, Razvan|Parmigiani, Giovanni', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.', 'United States|United States|United States|United States|United States', 37, 'DNA Mutational Analysis|Gene Expression Profiling|Genome|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-185', '22846331', 'Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments.'),
(95098, 2012, 'bmc', 'Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method', 'Gao, Jun|Che, Dongsheng|Zheng, Vincent W|Zhu, Ruixin|Liu, Qi', 'College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.|College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.|College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.|College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.|College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.', 'China|China|China|China|China', 7, 'Antineoplastic Agents|Cell Line, Tumor|Cell Proliferation|Female|Hedgehog Proteins|Humans|Models, Chemical|Quantitative Structure-Activity Relationship|Receptors, G-Protein-Coupled|Signal Transduction|Smoothened Receptor|Veratrum Alkaloids', 'Journal Article', '10.1186/1471-2105-13-186', '22849868', 'The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway.'),
(95099, 2012, 'bmc', 'ParsEval: parallel comparison and analysis of gene structure annotations', 'Standage, Daniel S|Brendel, Volker P', 'Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50011, USA.|Department of Genetics, Development, and Cell Biology, Iowa State University, Ames, Iowa 50011, USA.', 'United States|United States', 0, 'Computational Biology|Databases, Genetic|Genomics|Humans|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/1471-2105-13-187', '22852583', 'Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation. In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows. Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources. We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations. ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided. These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlapping, gene-model-centric genomic loci. Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations. Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis.'),
(95100, 2012, 'bmc', 'A resource for benchmarking the usefulness of protein structure models', 'Carbajo, Daniel|Tramontano, Anna', 'Department of Physics, Sapienza University of Rome, P,le A, Moro, 5, 00185 Rome, Italy.|Department of Physics, Sapienza University of Rome, P,le A, Moro, 5, 00185 Rome, Italy.', 'Italy|Italy', 2, 'Benchmarking|Databases, Protein|Models, Molecular|Protein Conformation|Software', 'Journal Article', '10.1186/1471-2105-13-188', '22856649', 'Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application.'),
(95101, 2012, 'bmc', 'AGORA: Assembly Guided by Optical Restriction Alignment', 'Lin, Henry C|Goldstein, Steve|Mendelowitz, Lee|Zhou, Shiguo|Wetzel, Joshua|Schwartz, David C|Pop, Mihai', 'Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.|Center for Bioinformatics and Computational Biology, University of Maryland-College Park, College Park, MD, USA.', 'United States|United States|United States|United States|United States|United States|United States', 29, 'Algorithms|Chromosome Mapping|Computational Biology|Computer Simulation|Genome, Bacterial|Genomics|Yersinia pestis', 'Journal Article', '10.1186/1471-2105-13-189', '22856673', 'Genome assembly is difficult due to repeated sequences within the genome, which create ambiguities and cause the final assembly to be broken up into many separate sequences (contigs). Long range linking information, such as mate-pairs or mapping data, is necessary to help assembly software resolve repeats, thereby leading to a more complete reconstruction of genomes. Prior work has used optical maps for validating assemblies and scaffolding contigs, after an initial assembly has been produced. However, optical maps have not previously been used within the genome assembly process. Here, we use optical map information within the popular de Bruijn graph assembly paradigm to eliminate paths in the de Bruijn graph which are not consistent with the optical map and help determine the correct reconstruction of the genome.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95102, 2012, 'bmc', 'A web-based multi-genome synteny viewer for customized data', 'Revanna, Kashi V|Munro, Daniel|Gao, Alvin|Chiu, Chi-Chen|Pathak, Anil|Dong, Qunfeng', 'Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.|Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.|Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.|Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.|Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.|Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.', 'United States|United States|United States|United States|United States|United States', 12, 'Computational Biology|Genomics|Internet|Software|Synteny|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-190', '22856879', 'Web-based synteny visualization tools are important for sharing data and revealing patterns of complicated genome conservation and rearrangements. Such tools should allow biologists to upload genomic data for their own analysis. This requirement is critical because individual biologists are generating large amounts of genomic sequences that quickly overwhelm any centralized web resources to collect and display all those data. Recently, we published a web-based synteny viewer, GSV, which was designed to satisfy the above requirement. However, GSV can only compare two genomes at a given time. Extending the functionality of GSV to visualize multiple genomes is important to meet the increasing demand of the research community.'),
(95103, 2011, 'bmc', 'Proceedings of the Ninth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics, October 8-10, 2011, Galway, Ireland', '-', '-', '-', 12, 'Animals|Computational Biology|Genomics|Humans', 'Congress', '10.1186/1471-2105-13-190', '22860254', '-'),
(95104, 2012, 'bmc', 'Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis', 'Kaur, Parminder|Schlatzer, Daniela|Cooke, Kenneth|Chance, Mark R', 'Case Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. pkhatra@gmail.com|Case Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. pkhatra@gmail.com|Case Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. pkhatra@gmail.com|Case Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. pkhatra@gmail.com', 'United States|United States|United States|United States', 5, 'Algorithms|Biomarkers|Classification|Disease Progression|Early Diagnosis|Humans|Mass Spectrometry|Peptides|Pneumonia|Prognosis|Proteins|Proteomics|Syndrome', 'Journal Article', '10.1186/1471-2105-13-191', '22870920', 'An approach to molecular classification based on the comparative expression of protein pairs is presented. The method overcomes some of the present limitations in using peptide intensity data for class prediction for problems such as the detection of a disease, disease prognosis, or for predicting treatment response. Data analysis is particularly challenging in these situations due to sample size (typically tens) being much smaller than the large number of peptides (typically thousands). Methods based upon high dimensional statistical models, machine learning or other complex classifiers generate decisions which may be very accurate but can be complex and difficult to interpret in simple or biologically meaningful terms. A classification scheme, called ProtPair, is presented that generates simple decision rules leading to accurate classification which is based on measurement of very few proteins and requires only relative expression values, providing specific targeted hypotheses suitable for straightforward validation.'),
(95105, 2012, 'bmc', 'Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data', 'Mosén-Ansorena, David|Aransay, Ana María|Rodríguez-Ezpeleta, Naiara', 'Genome Analysis Platform, CIC bioGUNE-CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es|Genome Analysis Platform, CIC bioGUNE-CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es|Genome Analysis Platform, CIC bioGUNE-CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es', 'Spain|Spain|Spain', 12, 'Cell Line, Tumor|DNA Copy Number Variations|Genomics|Genotype|Genotyping Techniques|Humans|Loss of Heterozygosity|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Comparative Study', '10.1186/1471-2105-13-192', '22870940', 'The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.'),
(95106, 2012, 'bmc', 'Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data', 'Tintle, Nathan L|Sitarik, Alexandra|Boerema, Benjamin|Young, Kylie|Best, Aaron A|Dejongh, Matthew', 'Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu|Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu|Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu|Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu|Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu|Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA 51250, USA. ntintle@dordt.edu', 'United States|United States|United States|United States|United States|United States', 7, 'Arginine|Bacteria|Data Interpretation, Statistical|Gene Expression Profiling|Genes, Bacterial|Genome, Bacterial|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Transcriptome', 'Evaluation Studies', '10.1186/1471-2105-13-193', '22873695', 'Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed.'),
(95107, 2012, 'bmc', 'Early classification of multivariate temporal observations by extraction of interpretable shapelets', 'Ghalwash, Mohamed F|Obradovic, Zoran', 'Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, USA. zoran.obradovic@temple.edu.|Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, USA. zoran.obradovic@temple.edu.', 'United States|United States', 57, 'Algorithms|Classification|Gene Expression|Humans|Influenza, Human|Medical Informatics|Multiple Sclerosis|Multivariate Analysis', 'Evaluation Studies', '10.1186/1471-2105-13-195', '22873729', '-'),
(95108, 2012, 'bmc', 'Abstracts of the 11th Annual UT-ORNL-KBRIN Bioinformatics Summit 2012 Louisville, Kentucky, USA March 30-April 1, 2012', '-', '-', '-', 57, 'Computational Biology', 'Congress', '10.1186/1471-2105-13-195', '22873757', '-'),
(95109, 2012, 'bmc', 'Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel', 'Alachiotis, Nikolaos|Berger, Simon A|Stamatakis, Alexandros', 'The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Germany. Nikolaos.Alachiotis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Germany. Nikolaos.Alachiotis@h-its.org|The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Germany. Nikolaos.Alachiotis@h-its.org', 'Germany|Germany|Germany', 9, 'Algorithms|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-196', '22876807', 'Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles that correspond to the branches of such a reference tree. The algorithm needs to perform an immense number of pairwise alignments. Therefore, we explore vector intrinsics and GPUs to accelerate the PaPaRa alignment kernel.'),
(95110, 2012, 'bmc', 'GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network', 'Jacobson, Dan|Emerton, Guy', 'Institute for Wine Biotechnology, Stellenbosch University, Stellenbosch, 7600, South Africa. jacobson@sun.ac.za|Institute for Wine Biotechnology, Stellenbosch University, Stellenbosch, 7600, South Africa. jacobson@sun.ac.za', 'South Africa|South Africa', 0, 'Gene Expression Profiling|Genome, Fungal|Metabolic Networks and Pathways|Oligonucleotide Array Sequence Analysis|Passive Cutaneous Anaphylaxis|Principal Component Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-197', '22876834', 'Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets.'),
(95111, 2012, 'bmc', 'GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions', 'Ko, Junsu|Park, Hahnbeom|Seok, Chaok', 'Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea. chaok@snu.ac.kr|Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea. chaok@snu.ac.kr|Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea. chaok@snu.ac.kr', '-|-|-', 38, 'Models, Molecular|Proteins|Sequence Alignment|Software|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-13-198', '22883815', 'Protein structures can be reliably predicted by template-based modeling (TBM) when experimental structures of homologous proteins are available. However, it is challenging to obtain structures more accurate than the single best templates by either combining information from multiple templates or by modeling regions that vary among templates or are not covered by any templates.'),
(95112, 2012, 'bmc', 'Normalization of ChIP-seq data with control', 'Liang, Kun|Kelexc5x9f, Sxc3xbcndxc3xbcz', 'Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. k22liang@uwaterloo.ca|Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. k22liang@uwaterloo.ca', 'Canada|Canada', 44, 'Binding Sites|Chromatin Immunoprecipitation|Data Interpretation, Statistical|Genome|Humans|NF-kappa B|Saccharomyces cerevisiae Proteins|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-199', '22883957', 'ChIP-seq has become an important tool for identifying genome-wide protein-DNA interactions, including transcription factor binding and histone modifications. In ChIP-seq experiments, ChIP samples are usually coupled with their matching control samples. Proper normalization between the ChIP and control samples is an essential aspect of ChIP-seq data analysis.'),
(95113, 2012, 'bmc', 'Cloudgene: a graphical execution platform for MapReduce programs on private and public clouds', 'Schönherr, Sebastian|Forer, Lukas|Weißensteiner, Hansi|Kronenberg, Florian|Specht, Günther|Kloss-Brandstätter, Anita', 'Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.|Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 35, 'Computational Biology|Computer Communication Networks|Genome|Polymorphism, Single Nucleotide|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-200', '22888776', 'The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster. In Bioinformatics, MapReduce has already been adopted to various case scenarios such as mapping next generation sequencing data to a reference genome, finding SNPs from short read data or matching strings in genotype files. Nevertheless, tasks like installing and maintaining MapReduce on a cluster system, importing data into its distributed file system or executing MapReduce programs require advanced knowledge in computer science and could thus prevent scientists from usage of currently available and useful software solutions.'),
(95114, 2012, 'bmc', 'SDAR: a practical tool for graphical analysis of two-dimensional data', 'Weeratunga, Saroja|Hu, Nien-Jen|Simon, Anne|Hofmann, Andreas', 'Structural Chemistry Program, Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane, Qld 4111, Australia.|Structural Chemistry Program, Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane, Qld 4111, Australia.|Structural Chemistry Program, Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane, Qld 4111, Australia.|Structural Chemistry Program, Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane, Qld 4111, Australia.', 'Australia|Australia|Australia|Australia', 19, 'Computer Graphics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-201', '22892030', 'Two-dimensional data needs to be processed and analysed in almost any experimental laboratory. Some tasks in this context may be performed with generic software such as spreadsheet programs which are available ubiquitously, others may require more specialised software that requires paid licences. Additionally, more complex software packages typically require more time by the individual user to understand and operate. Practical and convenient graphical data analysis software in Java with a user-friendly interface are rare.'),
(95115, 2011, 'bmc', 'Proceedings of the Asia Pacific Bioinformatics Network (APBioNet) Tenth International Conference on Bioinformatics First ISCB Asia Joint Conference 2011, November 30-December 2, 2011, Kuala Lumpur, Malaysia', '-', '-', '-', 19, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-13-201', '22893921', '-'),
(95116, 2012, 'bmc', 'An integrative computational approach to effectively guide experimental identification of regulatory elements in promoters', 'Deyneko, Igor V|Weiss, Siegfried|Leschner, Sara', 'Molecular Immunology, Helmholtz Centre for Infection Research, Inhoffenstr, 7, 38124 Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Molecular Immunology, Helmholtz Centre for Infection Research, Inhoffenstr, 7, 38124 Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de|Molecular Immunology, Helmholtz Centre for Infection Research, Inhoffenstr, 7, 38124 Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.de', 'Germany|Germany|Germany', 5, 'Algorithms|Computational Biology|Gene Expression|Neoplasms|Nucleotide Motifs|Promoter Regions, Genetic|Salmonella|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-202', '22897887', 'Transcriptional activity of genes depends on many factors like DNA motifs, conformational characteristics of DNA, melting etc. and there are computational approaches for their identification. However, in real applications, the number of predicted, for example, DNA motifs may be considerably large. In cases when various computational programs are applied, systematic experimental knock out of each of the potential elements obviously becomes nonproductive. Hence, one needs an approach that is able to integrate many heterogeneous computational methods and upon that suggest selected regulatory elements for experimental verification.'),
(95117, 2012, 'bmc', 'Stepwise kinetic equilibrium models of quantitative polymerase chain reaction', 'Cobbs, Gary', 'Department of Biology, University of Louisville, Louisville, KY 40292, USA. gacobb01@louisville.edu', 'United States', 7, 'DNA|DNA Primers|Fluorescence|Kinetics|Models, Chemical|Real-Time Polymerase Chain Reaction|Regression Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-13-203', '22897900', 'Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented.'),
(95118, 2012, 'bmc', 'Enabling dynamic network analysis through visualization in TVNViewer', 'Curtis, Ross E|Xiang, Jing|Parikh, Ankur|Kinnaird, Peter|Xing, Eric P', 'Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.|Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.|Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.|Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.|Joint Carnegie Mellon, University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA.', 'United States|United States|United States|United States|United States', 1, 'Breast Neoplasms|Cell Cycle|Computer Graphics|Female|Gene Regulatory Networks|Humans|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-13-204', '22897913', 'Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.'),
(95119, 2012, 'bmc', 'Reconstructing DNA copy number by joint segmentation of multiple sequences', 'Zhang, Zhongyang|Lange, Kenneth|Sabatti, Chiara', 'Department of Statistics, University of California, Los Angeles, CA, USA.|Department of Statistics, University of California, Los Angeles, CA, USA.|Department of Statistics, University of California, Los Angeles, CA, USA.', 'United States|United States|United States', 12, 'Algorithms|Bipolar Disorder|Contig Mapping|DNA|Gene Dosage|Gene Duplication|Genes, Tumor Suppressor|Genetic Variation|Genome|Genomics|Humans|Neoplasms|Pedigree|Regression Analysis|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-205', '22897923', 'Variations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual.'),
(95120, 2012, 'bmc', 'CaPSID: a bioinformatics platform for computational pathogen sequence identification in human genomes and transcriptomes', 'Borozan, Ivan|Wilson, Shane|Blanchette, Paola|Laflamme, Philippe|Watt, Stuart N|Krzyzanowski, Paul M|Sircoulomb, Fabrice|Rottapel, Robert|Branton, Philip E|Ferretti, Vincent', 'Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.|Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 30, 'Algorithms|Cell Line, Tumor|Computational Biology|Computer Simulation|Databases, Genetic|Female|Genome, Human|Humans|Internet|Oncogenic Viruses|Ovarian Neoplasms|Sensitivity and Specificity|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-206', '22901030', 'It is now well established that nearly 20% of human cancers are caused by infectious agents, and the list of human oncogenic pathogens will grow in the future for a variety of cancer types. Whole tumor transcriptome and genome sequencing by next-generation sequencing technologies presents an unparalleled opportunity for pathogen detection and discovery in human tissues but requires development of new genome-wide bioinformatics tools.'),
(95121, 2012, 'bmc', 'A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools', 'Verspoor, Karin|Cohen, Kevin Bretonnel|Lanfranchi, Arrick|Warner, Colin|Johnson, Helen L|Roeder, Christophe|Choi, Jinho D|Funk, Christopher|Malenkiy, Yuriy|Eckert, Miriam|Xue, Nianwen|Baumgartner, William A|Bada, Michael|Palmer, Martha|Hunter, Lawrence E', 'Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.edu|Computational Bioscience Program, U, Colorado School of Medicine, 12801 E 17th Ave, Aurora, MS 8303, CO 80045, USA. karin.verspoor@ucdenver.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', 51, 'Data Mining|Natural Language Processing|Software', 'Journal Article', '10.1186/1471-2105-13-207', '22901054', 'We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus.'),
(95122, 2012, 'bmc', 'Modeling and mining term association for improving biomedical information retrieval performance', 'Hu, Qinmin|Huang, Jimmy Xiangji|Hu, Xiaohua', 'Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.|Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.|Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada.', 'Canada|Canada|Canada', 7, 'Algorithms|Computational Biology|Data Mining|Genomics|Information Storage and Retrieval|Semantics', 'Journal Article', '10.1186/1471-2105-13-S9-S2', '22901087', 'The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance.'),
(95123, 2012, 'bmc', 'Inference of gene regulatory subnetworks from time course gene expression data', 'Liang, Xi-Jun|Xia, Zhonghang|Zhang, Li-Wei|Wu, Fang-Xiang', 'School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.|School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.|School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.|School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.', 'China|China|China|China', 4, 'Algorithms|Computational Biology|Gene Expression|Gene Regulatory Networks', 'Journal Article', '10.1186/1471-2105-13-S9-S3', '22901088', 'Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN reconstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to others in the sub-networks.'),
(95124, 2012, 'bmc', 'Discovering patterns in drug-protein interactions based on their fingerprints', 'Luo, Weimin|Chan, Keith C C', 'Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China. cswluo@comp.polyu.edu.hk|Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China. cswluo@comp.polyu.edu.hk', 'China|China', 2, 'Data Mining|Databases, Factual|Ligands|Pharmaceutical Preparations|Protein Structure, Tertiary|Proteins|ROC Curve', 'Journal Article', '10.1186/1471-2105-13-S9-S4', '22901089', 'The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called Drug-Protein Interaction Analysis (D-PIA) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.'),
(95125, 2012, 'bmc', 'A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR', 'Oh, Sohee|Lee, Jaehoon|Kwon, Min-Seok|Weir, Bruce|Ha, Kyooseob|Park, Taesung', 'Department of Statistics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, Seoul, South Korea.|Department of Statistics, Seoul National University, Seoul, South Korea.', '-|-|-|-|-|-', 43, 'Algorithms|Bipolar Disorder|Computational Biology|Gene-Environment Interaction|Genetic Predisposition to Disease|Genome-Wide Association Study|Humans|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-13-S9-S5', '22901090', 'Because common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism (SNP) chips, the study of gene-gene interaction becomes a next challenge. Multifactor dimensionality reduction (MDR) analysis has been widely used for the gene-gene interaction analysis. In practice, however, it is not easy to perform high order gene-gene interaction analyses via MDR in genome-wide level because it requires exploring a huge search space and suffers from a computational burden due to high dimensionality.'),
(95126, 2012, 'bmc', 'Bayesian probabilistic network modeling from multiple independent replicates', 'Patton, Kristopher L|John, David J|Norris, James L', 'Department of Mathematics, Wake Forest University, Winston-Salem, North Carolina 27109, USA.|Department of Mathematics, Wake Forest University, Winston-Salem, North Carolina 27109, USA.|Department of Mathematics, Wake Forest University, Winston-Salem, North Carolina 27109, USA.', 'United States|United States|United States', 4, 'Algorithms|Bayes Theorem|Computational Biology|Computer Simulation|Markov Chains|Monte Carlo Method|Phosphorylation|Probability|Proteins', 'Journal Article', '10.1186/1471-2105-13-S9-S6', '22901091', 'Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edges partial correlation between two proteins is at least moderate, then the composites posterior probability is large.'),
(95127, 2012, 'bmc', 'DTome: a web-based tool for drug-target interactome construction', 'Sun, Jingchun|Wu, Yonghui|Xu, Hua|Zhao, Zhongming', 'Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.|Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.', 'United States|United States|United States|United States', 25, 'Algorithms|Clozapine|Computational Biology|Databases, Factual|Drug Discovery|Drug Interactions|Humans|Internet|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-S9-S7', '22901092', 'Understanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process.'),
(95128, 2012, 'bmc', 'Generalized adjacency and the conservation of gene clusters in genetic networks defined by synthetic lethals', 'Yang, Zhenyu|Sankoff, David', 'Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5.|Department of Mathematics and Statistics, University of Ottawa, 585 King Edward Avenue, Ottawa, Canada K1N 6N5.', 'Canada|Canada', 0, 'Algorithms|Conserved Sequence|Gene Regulatory Networks|Genes, Lethal|Genome, Fungal|Models, Genetic|Multigene Family|Saccharomyces cerevisiae|Schizosaccharomyces', 'Journal Article', '10.1186/1471-2105-13-S9-S8', '22901093', 'Given genetic networks derived from two genomes, it may be difficult to decide if their local structures are similar enough in both genomes to infer some ancestral configuration or some conserved functional relationships. Current methods all depend on searching for identical substructures.'),
(95129, 2012, 'bmc', 'FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry', 'Lun, Aaron Tl|Wong, Jason Wh|Downard, Kevin M', 'School of Molecular Bioscience G-08, The University of Sydney, Sydney, NSW, 2006, Australia.|School of Molecular Bioscience G-08, The University of Sydney, Sydney, NSW, 2006, Australia.|School of Molecular Bioscience G-08, The University of Sydney, Sydney, NSW, 2006, Australia.', 'Australia|Australia|Australia', 0, 'Algorithms|Animals|Humans|Influenza Vaccines|Influenza, Human|Mass Spectrometry|Orthomyxoviridae|Orthomyxoviridae Infections|Pandemics|Phylogeny|Reassortant Viruses|Sequence Alignment|Sequence Analysis, Protein|Software|Viral Proteins', 'Journal Article', '10.1186/1471-2105-13-208', '22906155', 'Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.'),
(95130, 2012, 'bmc', 'BioPhylo: a unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython', 'Talevich, Eric|Invergo, Brandon M|Cock, Peter J A|Chapman, Brad A', 'Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, USA. etal@uga.edu|Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, USA. etal@uga.edu|Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, USA. etal@uga.edu|Institute of Bioinformatics, University of Georgia, 120 Green Street, Athens, GA 30602, USA. etal@uga.edu', 'Georgia|Georgia|Georgia|Georgia', 53, 'Computational Biology|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-13-209', '22909249', 'Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. This brings with it the challenge of integrating phylogenetic and other related biological data found in a wide variety of formats, and underlines the need for reusable software that can read, manipulate and transform this information into the various forms required to build computational pipelines.'),
(95131, 2012, 'bmc', 'A support vector machine based test for incongruence between sets of trees in tree space', 'Haws, David C|Huggins, Peter|ONeill, Eric M|Weisrock, David W|Yoshida, Ruriko', 'Department of Statistics, University of Kentucky, 725 Rose Street, Lexington, KY 40536-0082, USA.|Department of Statistics, University of Kentucky, 725 Rose Street, Lexington, KY 40536-0082, USA.|Department of Statistics, University of Kentucky, 725 Rose Street, Lexington, KY 40536-0082, USA.|Department of Statistics, University of Kentucky, 725 Rose Street, Lexington, KY 40536-0082, USA.|Department of Statistics, University of Kentucky, 725 Rose Street, Lexington, KY 40536-0082, USA.', 'United States|United States|United States|United States|United States', 0, 'Base Sequence|Gene Duplication|Gene Transfer, Horizontal|Genes|Phylogeny|Sequence Analysis, DNA|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-210', '22909268', 'The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need to determine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes. Such unusual gene trees may have been influenced by other evolutionary processes such as selection, gene duplication, or horizontal gene transfer.'),
(95132, 2012, 'bmc', 'NetiNeti: discovery of scientific names from text using machine learning methods', 'Akella, Lakshmi Manohar|Norton, Catherine N|Miller, Holly', 'MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA, USA. manohar.akella@gmail.com|MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA, USA. manohar.akella@gmail.com|MBLWHOI Library, Marine Biological Laboratory, Woods Hole, MA, USA. manohar.akella@gmail.com', 'United States|United States|United States', 15, 'Animals|Artificial Intelligence|Classification|Data Mining|MEDLINE|PubMed', 'Journal Article', '10.1186/1471-2105-13-211', '22913485', 'A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.'),
(95133, 2012, 'bmc', 'Screening of selective histone deacetylase inhibitors by proteochemometric modeling', 'Wu, Dingfeng|Huang, Qi|Zhang, Yida|Zhang, Qingchen|Liu, Qi|Gao, Jun|Cao, Zhiwei|Zhu, Ruixin', 'School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.|School of Life Sciences and Technology, Tongji University, Shanghai, 200092, PR China.', 'China|China|China|China|China|China|China|China', 24, 'Antineoplastic Agents|Drug Screening Assays, Antitumor|Histone Deacetylase Inhibitors|Histone Deacetylases|Humans|Ligands|Models, Molecular|Neoplasms|Protein Isoforms', 'Journal Article', '10.1186/1471-2105-13-212', '22913517', 'Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study.'),
(95134, 2012, 'bmc', 'Sampling issues in quantitative analysis of dendritic spines morphology', 'Ruszczycki, Bxc5x82axc5xbcej|Szepesi, Zsuzsanna|Wilczynski, Grzegorz M|Bijata, Monika|Kalita, Katarzyna|Kaczmarek, Leszek|Wlodarczyk, Jakub', 'Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.|Nencki Institute of Experimental Biology, Polish Academy of Sciences, Pasteura 3, Warszawa, Poland.', 'Poland|Poland|Poland|Poland|Poland|Poland|Poland', 0, 'Animals|Dendritic Spines|Hippocampus|Microscopy, Confocal|Monte Carlo Method|Rats|Rats, Wistar', 'Journal Article', '10.1186/1471-2105-13-213', '22920322', 'Quantitative analysis of changes in dendritic spine morphology has become an interesting issue in contemporary neuroscience. However, the diversity in dendritic spine population might seriously influence the result of measurements in which their morphology is studied. The detection of differences in spine morphology between control and test group is often compromised by the number of dendritic spines taken for analysis. In order to estimate the impact of dendritic spine diversity we performed Monte Carlo simulations examining various experimental setups and statistical approaches. The confocal images of dendritic spines from hippocampal dissociated cultures have been used to create a set of variables exploited as the simulation resources.'),
(95135, 2012, 'bmc', 'Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets', 'Hoffmann, Nils|Keck, Matthias|Neuweger, Heiko|Wilhelm, Mathias|Högy, Petra|Niehaus, Karsten|Stoye, Jens', 'Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de|Genome Informatics Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany. nils.hoffmann@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 26, 'Algorithms|Gas Chromatography-Mass Spectrometry|Leishmania|Metabolomics|Proteomics|Software|Triticum', 'Journal Article', '10.1186/1471-2105-13-214', '22920415', 'Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.'),
(95136, 2012, 'bmc', 'GenNon-h: generating multiple sequence alignments on nonhomogeneous phylogenetic trees', 'Kedzierska, Anna M|Casanellas, Marta', 'Centre for Genomic Regulation, Dr. Aiguader 88, 08003 Barcelona, Spain.|Centre for Genomic Regulation, Dr. Aiguader 88, 08003 Barcelona, Spain.', 'Spain|Spain', 0, 'Algorithms|Evolution, Molecular|Markov Chains|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-216', '22928840', 'A number of software packages are available to generate DNA multiple sequence alignments (MSAs) evolved under continuous-time Markov processes on phylogenetic trees. On the other hand, methods of simulating the DNA MSA directly from the transition matrices do not exist. Moreover, existing software restricts to the time-reversible models and it is not optimized to generate nonhomogeneous data (i.e. placing distinct substitution rates at different lineages).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95137, 2012, 'bmc', 'Process attributes in bio-ontologies', 'Andrade, André Q|Blondé, Ward|Hastings, Janna|Schulz, Stefan', 'Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.|Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.|Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.|Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.', 'Austria|Austria|Austria|Austria', 2, 'Biomedical Research|Language|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-13-217', '22928880', 'Biomedical processes can provide essential information about the (mal-) functioning of an organism and are thus frequently represented in biomedical terminologies and ontologies, including the GO Biological Process branch. These processes often need to be described and categorised in terms of their attributes, such as rates or regularities. The adequate representation of such process attributes has been a contentious issue in bio-ontologies recently, and domain ontologies have correspondingly developed ad hoc workarounds that compromise interoperability and logical consistency.'),
(95138, 2012, 'bmc', 'Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: incorporating EGFR signaling pathway and angiogenesis', 'Sun, Xiaoqiang|Zhang, Le|Tan, Hua|Bao, Jiguang|Strouthos, Costas|Zhou, Xiaobo', 'Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.|Department of Radiology, The Methodist Hospital Research Institute, Weil Cornell Medical College, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 28, 'Antineoplastic Agents|Brain Neoplasms|Computational Biology|Computer Simulation|ErbB Receptors|Humans|Models, Biological|Neovascularization, Pathologic|Protein Kinase Inhibitors|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-13-218', '22935054', 'The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment.'),
(95139, 2012, 'bmc', 'CerealsDB 20: an integrated resource for plant breeders and scientists', 'Wilkinson, Paul A|Winfield, Mark O|Barker, Gary L A|Allen, Alexandra M|Burridge, Amanda|Coghill, Jane A|Edwards, Keith J', 'School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk|School of Biological Sciences, University of Bristol, Bristol, BS8 1UG, UK. Paul.Wilkinson@bristol.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Breeding|Databases, Nucleic Acid|Expressed Sequence Tags|Genomics|Humans|Internet|Polymorphism, Single Nucleotide|Software|Triticum|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-219', '22943283', 'Food security is an issue that has come under renewed scrutiny amidst concerns that substantial yield increases in cereal crops are required to feed the worlds booming population. Wheat is of fundamental importance in this regard being one of the three most important crops for both human consumption and livestock feed, however, increase in crop yields have not kept pace with the demands of a growing world population. In order to address this issue, plant breeders require new molecular tools to help them identify genes for important agronomic traits that can be introduced into elite varieties. Studies of the genome using next-generation sequencing enable the identification of molecular markers such as single nucleotide polymorphisms that may be used by breeders to identify and follow genes when breeding new varieties. The development and application of next-generation sequencing technologies has made the characterisation of SNP markers in wheat relatively cheap and straightforward. There is a growing need for the widespread dissemination of this information to plant breeders.'),
(95140, 2012, 'bmc', 'TFinDit: transcription factor-DNA interaction data depository', 'Turner, Daniel|Kim, RyangGuk|Guo, Jun-tao', 'Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States|United States', 6, 'Algorithms|Binding Sites|Computational Biology|DNA|Databases, Genetic|Internet|Software|Thermodynamics|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-220', '22943312', 'One of the crucial steps in regulation of gene expression is the binding of transcription factor(s) to specific DNA sequences. Knowledge of the binding affinity and specificity at a structural level between transcription factors and their target sites has important implications in our understanding of the mechanism of gene regulation. Due to their unique functions and binding specificity, there is a need for a transcription factor-specific, structure-based database and corresponding web service to facilitate structural bioinformatics studies of transcription factor-DNA interactions, such as development of knowledge-based interaction potential, transcription factor-DNA docking, binding induced conformational changes, and the thermodynamics of protein-DNA interactions.'),
(95141, 2012, 'bmc', 'ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data', 'Cabanski, Christopher R|Cavin, Keary|Bizon, Chris|Wilkerson, Matthew D|Parker, Joel S|Wilhelmsen, Kirk C|Perou, Charles M|Marron, J S|Hayes, D Neil', 'Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.|Department of Statistics and Operations Research, Chapel Hill, NC, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 12, 'Calibration|Genome|High-Throughput Nucleotide Sequencing|Logistic Models|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-13-221', '22946927', 'Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results.'),
(95142, 2012, 'bmc', 'Wavelet-based detection of transcriptional activity on a novel Staphylococcus aureus tiling microarray', 'Segura, Víctor|Toledo-Arana, Alejandro|Uzqueda, Maite|Lasa, Iñigo|Muñoz-Barrutia, Arrate', 'Genomics, Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain. vsegura@unav.es|Genomics, Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain. vsegura@unav.es|Genomics, Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain. vsegura@unav.es|Genomics, Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain. vsegura@unav.es|Genomics, Proteomics and Bioinformatics Unit, Center for Applied Medical Research, University of Navarra, Pamplona, Spain. vsegura@unav.es', 'Spain|Spain|Spain|Spain|Spain', 3, 'Algorithms|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Staphylococcus aureus|Transcription, Genetic|Wavelet Analysis', 'Evaluation Studies', '10.1186/1471-2105-13-222', '22950634', 'High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification.'),
(95143, 2012, 'bmc', 'ANCAC: amino acid, nucleotide, and codon analysis of COGs--a tool for sequence bias analysis in microbial orthologs', 'Meiler, Arno|Klinger, Claudia|Kaufmann, Michael', 'The Protein Chemistry Group, Institute for Medical Biochemistry, Centre for Biomedical Education and Research, School of Medicine, Faculty of Health, Witten/Herdecke University, Stockumer Str. 10, Witten 58448, Germany.|The Protein Chemistry Group, Institute for Medical Biochemistry, Centre for Biomedical Education and Research, School of Medicine, Faculty of Health, Witten/Herdecke University, Stockumer Str. 10, Witten 58448, Germany.|The Protein Chemistry Group, Institute for Medical Biochemistry, Centre for Biomedical Education and Research, School of Medicine, Faculty of Health, Witten/Herdecke University, Stockumer Str. 10, Witten 58448, Germany.', 'Germany|Germany|Germany', 3, 'Amino Acids|Archaea|Archaeal Proteins|Base Composition|Codon|Databases, Protein|Nucleotides|Phylogeny|Proteins|Sequence Analysis|Software|Temperature', 'Journal Article', '10.1186/1471-2105-13-223', '22958836', 'The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins.'),
(95144, 2012, 'bmc', 'Improved model quality assessment using ProQ2', 'Ray, Arjun|Lindahl, Erik|Wallner, Björn', 'Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.|Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.|Department of Theoretical Physics & Swedish eScience Research Center, Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 113, 'Algorithms|Caspase 8|Computational Biology|Humans|Models, Molecular|Protein Conformation|Proteins|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-224', '22963006', 'Employing methods to assess the quality of modeled protein structures is now standard practice in bioinformatics. In a broad sense, the techniques can be divided into methods relying on consensus prediction on the one hand, and single-model methods on the other. Consensus methods frequently perform very well when there is a clear consensus, but this is not always the case. In particular, they frequently fail in selecting the best possible model in the hard cases (lacking consensus) or in the easy cases where models are very similar. In contrast, single-model methods do not suffer from these drawbacks and could potentially be applied on any protein of interest to assess quality or as a scoring function for sampling-based refinement.'),
(95145, 2012, 'bmc', 'Coupled mutation finder: a new entropy-based method quantifying phylogenetic noise for the detection of compensatory mutations', 'Gültas, Mehmet|Haubrock, Martin|Tüysüz, Nesrin|Waack, Stephan', 'Institute of Computer Science, University of Göttingen, Goldschmidtstr. 7, Göttingen, 37077, Germany. gueltas@cs.uni-goettingen.de|Institute of Computer Science, University of Göttingen, Goldschmidtstr. 7, Göttingen, 37077, Germany. gueltas@cs.uni-goettingen.de|Institute of Computer Science, University of Göttingen, Goldschmidtstr. 7, Göttingen, 37077, Germany. gueltas@cs.uni-goettingen.de|Institute of Computer Science, University of Göttingen, Goldschmidtstr. 7, Göttingen, 37077, Germany. gueltas@cs.uni-goettingen.de', 'Germany|Germany|Germany|Germany', 8, 'Amino Acid Sequence|Computational Biology|Entropy|ErbB Receptors|Glucokinase|Humans|Mutation|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-225', '22963049', 'The detection of significant compensatory mutation signals in multiple sequence alignments (MSAs) is often complicated by noise. A challenging problem in bioinformatics is remains the separation of significant signals between two or more non-conserved residue sites from the phylogenetic noise and unrelated pair signals. Determination of these non-conserved residue sites is as important as the recognition of strictly conserved positions for understanding of the structural basis of protein functions and identification of functionally important residue regions. In this study, we developed a new method, the Coupled Mutation Finder (CMF) quantifying the phylogenetic noise for the detection of compensatory mutations.'),
(95146, 2012, 'bmc', 'Network enrichment analysis: extension of gene-set enrichment analysis to gene networks', 'Alexeyenko, Andrey|Lee, Woojoo|Pernemalm, Maria|Guegan, Justin|Dessen, Philippe|Lazar, Vladimir|Lehtiö, Janne|Pawitan, Yudi', 'School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.|School of Biotechnology, Royal Institute of Technology, Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 63, 'Algorithms|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|Lung Neoplasms|Protein Biosynthesis|Proteomics', 'Journal Article', '10.1186/1471-2105-13-226', '22966941', 'Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set, the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.'),
(95147, 2012, 'bmc', 'The top-scoring N algorithm: a generalized relative expression classification method from small numbers of biomolecules', 'Magis, Andrew T|Price, Nathan D', 'Institute for Systems Biology, Seattle, WA 98109, USA.|Institute for Systems Biology, Seattle, WA 98109, USA.', 'United States|United States', 17, 'Algorithms|Classification|Gene Expression|Humans|Neoplasms|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-227', '22966958', 'Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring N (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification.'),
(95148, 2012, 'bmc', 'Protein-DNA docking with a coarse-grained force field', 'Setny, Piotr|Bahadur, Ranjit Prasad|Zacharias, Martin', 'Physics Department T38, Technical University Munich, James Franck Str. 1, 85748 Garching, Germany. piotr.setny@tum.de|Physics Department T38, Technical University Munich, James Franck Str. 1, 85748 Garching, Germany. piotr.setny@tum.de|Physics Department T38, Technical University Munich, James Franck Str. 1, 85748 Garching, Germany. piotr.setny@tum.de', 'Germany|Germany|Germany', 29, 'Algorithms|Binding Sites|Crystallography, X-Ray|DNA|HSP40 Heat-Shock Proteins|Molecular Docking Simulation|Protein Binding|Proteins', 'Journal Article', '10.1186/1471-2105-13-228', '22966980', 'Protein-DNA interactions are important for many cellular processes, however structural knowledge for a large fraction of known and putative complexes is still lacking. Computational docking methods aim at the prediction of complex architecture given detailed structures of its constituents. They are becoming an increasingly important tool in the field of macromolecular assemblies, complementing particularly demanding protein-nucleic acids X ray crystallography and providing means for the refinement and integration of low resolution data coming from rapidly advancing methods such as cryoelectron microscopy.'),
(95149, 2012, 'bmc', 'AbsIDconvert: an absolute approach for converting genetic identifiers at different granularities', 'Mohammad, Fahim|Flight, Robert M|Harrison, Benjamin J|Petruska, Jeffrey C|Rouchka, Eric C', 'Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA.|Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA.|Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA.|Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA.|Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA.', 'United States|United States|United States|United States|United States', 6, 'Animals|Chromosome Mapping|Computational Biology|Culicidae|Genome|Genomics|Humans|Internet|Molecular Sequence Annotation|Plasmodium', 'Journal Article', '10.1186/1471-2105-13-229', '22967011', 'High-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.'),
(95150, 2012, 'bmc', 'Highly improved homopolymer aware nucleotide-protein alignments with 454 data', 'Lysholm, Fredrik', 'IFM Bioinformatics and SeRC (Swedish e-Science Research Centre), Linköping University, S-581 83, Linköping, Sweden. frely@ifm.liu.se', 'Sweden', 4, 'Algorithms|Amino Acid Sequence|Base Sequence|Frameshift Mutation|Nucleotides|Phylogeny|Protein Biosynthesis|Proteins|Search Engine|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-230', '22971057', 'Roche 454 sequencing is the leading sequencing technology for producing long read high throughput sequence data. Unlike most methods where sequencing errors translate to base uncertainties, 454 sequencing inaccuracies create nucleotide gaps. These gaps are particularly troublesome for translated search tools such as BLASTx where they introduce frame-shifts and result in regions of decreased identity and/or terminated alignments, which affect further analysis.'),
(95151, 2012, 'bmc', 'Quick, imputation-free meta-analysis with proxy-SNPs', 'Meesters, Christian|Leber, Markus|Herold, Christine|Angisch, Marina|Mattheisen, Manuel|Drichel, Dmitriy|Lacour, André|Becker, Tim', 'German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.|German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 2, 'Algorithms|Alleles|Diabetes Mellitus, Type 2|Genome, Human|Genome-Wide Association Study|Genotype|HapMap Project|Humans|Linkage Disequilibrium|Meta-Analysis as Topic|Polymorphism, Single Nucleotide|Software', 'Evaluation Studies', '10.1186/1471-2105-13-231', '22971100', 'Meta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming.'),
(95152, 2012, 'bmc', 'Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning', 'Cukierski, William J|Nandy, Kaustav|Gudla, Prabhakar|Meaburn, Karen J|Misteli, Tom|Foran, David J|Lockett, Stephen J', 'Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu|Rutgers University, New Brunswick, NJ 08903, USA. wcuk@eden.rutgers.edu', 'United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Breast Neoplasms|Cell Nucleus|Female|Genes, Neoplasm|Humans|Image Processing, Computer-Assisted|In Situ Hybridization, Fluorescence|Logistic Models|ROC Curve', 'Journal Article', '10.1186/1471-2105-13-232', '22971117', 'Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task.'),
(95153, 2012, 'bmc', 'Automatic classification of protein structures relying on similarities between alignments', 'Santini, Guillaume|Soldano, Henry|Pothier, Joël', 'Université Paris 13, Sorbonne Paris Cité, Laboratoire dInformatique de Paris-Nord (LIPN), CNRS(, UMR 7030), Villetaneuse, F-93430, France. guillaume.santini@lipn.univ-paris13.fr|Université Paris 13, Sorbonne Paris Cité, Laboratoire dInformatique de Paris-Nord (LIPN), CNRS(, UMR 7030), Villetaneuse, F-93430, France. guillaume.santini@lipn.univ-paris13.fr|Université Paris 13, Sorbonne Paris Cité, Laboratoire dInformatique de Paris-Nord (LIPN), CNRS(, UMR 7030), Villetaneuse, F-93430, France. guillaume.santini@lipn.univ-paris13.fr', 'France|France|France', 4, 'Algorithms|Cluster Analysis|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-233', '22974051', 'Identification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins.'),
(95154, 2012, 'bmc', 'Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling)', 'De Beuf, Kristof|Pipelers, Peter|Andriankaja, Megan|Thas, Olivier|Inzé, Dirk|Crainiceanu, Ciprian|Clement, Lieven', 'Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, B9000 Ghent, Belgium. kristof.debeuf@ugent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Data Interpretation, Statistical|Exons|Gene Expression Profiling|Genome|Genomics|Oligonucleotide Array Sequence Analysis|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-234', '22974078', 'Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential.'),
(95155, 2012, 'bmc', 'Disentangling evolutionary signals: conservation, specificity determining positions and coevolution Implication for catalytic residue prediction', 'Teppa, Elin|Wilkins, Angela D|Nielsen, Morten|Buslje, Cristina Marino', 'Fundación Instituto Leloir, Avda, Patricias Argentinas 435, CABA, C1405BWE, Argentina.|Fundación Instituto Leloir, Avda, Patricias Argentinas 435, CABA, C1405BWE, Argentina.|Fundación Instituto Leloir, Avda, Patricias Argentinas 435, CABA, C1405BWE, Argentina.|Fundación Instituto Leloir, Avda, Patricias Argentinas 435, CABA, C1405BWE, Argentina.', 'Argentina|Argentina|Argentina|Argentina', 22, 'Catalysis|Catalytic Domain|Conserved Sequence|Enzymes|Evolution, Molecular|Models, Molecular|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-235', '22978315', 'A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.'),
(95156, 2012, 'bmc', 'Correlation analysis of the side-chains conformational distribution in bound and unbound proteins', 'Kirys, Tatsiana|Ruvinsky, Anatoly M|Tuzikov, Alexander V|Vakser, Ilya A', 'Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047, USA.|Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047, USA.|Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047, USA.|Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047, USA.', 'United States|United States|United States|United States', 6, 'Amino Acids|Models, Molecular|Multiprotein Complexes|Protein Binding|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-13-236', '22984947', 'Protein interactions play a key role in life processes. Characterization of conformational properties of protein-protein interactions is important for understanding the mechanisms of protein association. The rapidly increasing amount of experimentally determined structures of proteins and protein-protein complexes provides foundation for research on protein interactions and complex formation. The knowledge of the conformations of the surface side chains is essential for modeling of protein complexes. The purpose of this study was to analyze and compare dihedral angle distribution functions of the side chains at the interface and non-interface areas in bound and unbound proteins.'),
(95157, 2012, 'bmc', 'A pipeline for automated annotation of yeast genome sequences by a conserved-synteny approach', 'Proux-Wxc3xa9ra, Estelle|Armisxc3xa9n, David|Byrne, Kevin P|Wolfe, Kenneth H', 'Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.|Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.|Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.|Smurfit Institute of Genetics, Trinity College Dublin, Dublin 2, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 33, 'Base Sequence|Conserved Sequence|Gene Order|Genome, Fungal|Genomics|Molecular Sequence Annotation|Saccharomyces cerevisiae|Software|Synteny|Yeasts', 'Journal Article', '10.1186/1471-2105-13-237', '22984983', 'Yeasts are a model system for exploring eukaryotic genome evolution. Next-generation sequencing technologies are poised to vastly increase the number of yeast genome sequences, both from resequencing projects (population studies) and from de novo sequencing projects (new species). However, the annotation of genomes presents a major bottleneck for de novo projects, because it still relies on a process that is largely manual.'),
(95158, 2012, 'bmc', 'Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory', 'Chaisson, Mark J|Tesler, Glenn', 'Department of Mathematics, University of California, San Diego, 9500 Gilman Dr, CA, La Jolla, USA.|Department of Mathematics, University of California, San Diego, 9500 Gilman Dr, CA, La Jolla, USA.', 'United States|United States', 538, 'Algorithms|Base Sequence|DNA|Genome|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-238', '22988817', 'Recent methods have been developed to perform high-throughput sequencing of DNA by Single Molecule Sequencing (SMS). While Next-Generation sequencing methods may produce reads up to several hundred bases long, SMS sequencing produces reads up to tens of kilobases long. Existing alignment methods are either too inefficient for high-throughput datasets, or not sensitive enough to align SMS reads, which have a higher error rate than Next-Generation sequencing.'),
(95159, 2012, 'bmc', 'SNP calling by sequencing pooled samples', 'Raineri, Emanuele|Ferretti, Luca|Esteve-Codina, Anna|Nevado, Bruno|Heath, Simon|Pxc3xa9rez-Enciso, Miguel', 'Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com|Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com|Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com|Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com|Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com|Centro Nacional de Anxc3xa1lisis Genxc3xb3mico, Parc Cientxc3xadfic de Barcelona, Barcelona, 08028, Spain. emanuele.raineri@gmail.com', 'Spain|Spain|Spain|Spain|Spain|Spain', 36, 'Alleles|Bayes Theorem|Gene Frequency|Genome|High-Throughput Nucleotide Sequencing|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-239', '22992255', 'Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) xe2x88x9d 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read - or, more likely, none - from a true singleton.'),
(95160, 2012, 'bmc', 'The Ruby UCSC API: accessing the UCSC genome database using Ruby', 'Mishima, Hiroyuki|Aerts, Jan|Katayama, Toshiaki|Bonnal, Raoul J P|Yoshiura, Koh-ichiro', 'Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan. hmishima@nagasaki-u.ac.jp|Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan. hmishima@nagasaki-u.ac.jp|Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan. hmishima@nagasaki-u.ac.jp|Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan. hmishima@nagasaki-u.ac.jp|Department of Human Genetics, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki, Nagasaki, 852-8523, Japan. hmishima@nagasaki-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Animals|Databases, Genetic|Genome|Genomics|Humans|Internet|Invertebrates|Software|Vertebrates', 'Journal Article', '10.1186/1471-2105-13-240', '22994508', 'The University of California, Santa Cruz (UCSC) genome database is among the most used sources of genomic annotation in human and other organisms. The database offers an excellent web-based graphical user interface (the UCSC genome browser) and several means for programmatic queries. A simple application programming interface (API) in a scripting language aimed at the biologist was however not yet available. Here, we present the Ruby UCSC API, a library to access the UCSC genome database using Ruby.'),
(95161, 2012, 'bmc', 'Graph rigidity reveals well-constrained regions of chromosome conformation embeddings', 'Duggal, Geet|Kingsford, Carl', 'Department of Computer Science and Institute for Advanced Computer Studies University of Maryland, College Park, MD 20742, USA.|Department of Computer Science and Institute for Advanced Computer Studies University of Maryland, College Park, MD 20742, USA.', 'United States|United States', 0, 'Algorithms|Chromatin|Chromosomes|Humans|Models, Molecular|Nucleic Acid Conformation', 'Journal Article', '10.1186/1471-2105-13-241', '22998471', 'Chromosome conformation capture experiments result in pairwise proximity measurements between chromosome locations in a genome, and they have been used to construct three-dimensional models of genomic regions, chromosomes, and entire genomes. These models can be used to understand long-range gene regulation, chromosome rearrangements, and the relationships between sequence and spatial location. However, it is unclear whether these pairwise distance constraints provide sufficient information to embed chromatin in three dimensions. A priori, it is possible that an infinite number of embeddings are consistent with the measurements due to a lack of constraints between some regions. It is therefore necessary to separate regions of the chromatin structure that are sufficiently constrained from regions with measurements that do not provide enough information to reconstruct the embedding.'),
(95162, 2012, 'bmc', 'Quality assessment of protein model-structures based on structural and functional similarities', 'Konopka, Bogumil M|Nebel, Jean-Christophe|Kotulska, Malgorzata', 'Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland.|Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland.|Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland.', 'Poland|Poland|Poland', 0, 'Algorithms|Amino Acid Sequence|Caspase 8|Caspase 9|Computational Biology|Humans|Models, Molecular|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-13-242', '22998498', 'Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology.'),
(95163, 2012, 'bmc', 'Regulatory network operations in the Pathway Tools software', 'Paley, Suzanne M|Latendresse, Mario|Karp, Peter D', 'Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025, USA.|Bioinformatics Research Group, SRI International 333 Ravenswood Ave, Menlo Park, CA 94025, USA.', 'United States|United States|United States', 7, 'Algorithms|Bacillus subtilis|Enzymes|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Genome|Metabolic Networks and Pathways|Protein Biosynthesis|Protein Processing, Post-Translational|Shewanella|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-243', '22998532', 'Biologists are elucidating complex collections of genetic regulatory data for multiple organisms. Software is needed for such regulatory network data.'),
(95164, 2012, 'bmc', 'The LO-BaFL method and ALS microarray expression analysis', 'Baciu, Cristina|Thompson, Kevin J|Mougeot, Jean-Luc|Brooks, Benjamin R|Weller, Jennifer W', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.', 'United States|United States|United States|United States|United States', 0, 'Amyotrophic Lateral Sclerosis|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Leukocytes|Microarray Analysis|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-13-244', '23006766', 'Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays.'),
(95165, 2012, 'bmc', 'An algorithm for chemical genomic profiling that minimizes batch effects: bucket evaluations', 'Shabtai, Daniel|Giaever, Guri|Nislow, Corey', 'Department of Cell and Systems Biology and the Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Department of Cell and Systems Biology and the Donnelly Centre, University of Toronto, Toronto, ON, Canada.|Department of Cell and Systems Biology and the Donnelly Centre, University of Toronto, Toronto, ON, Canada.', 'Canada|Canada|Canada', 2, 'Algorithms|Animals|Cluster Analysis|Gene Expression Profiling|Gene Knockdown Techniques|Genome-Wide Association Study|High-Throughput Nucleotide Sequencing|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-245', '23009392', 'Chemical genomics is an interdisciplinary field that combines small molecule perturbation with traditional genomics to understand gene function and to study the mode(s) of drug action. A benefit of chemical genomic screens is their breadth, each screen can capture the sensitivity of comprehensive collections of mutants or, in the case of mammalian cells, gene knock-downs, simultaneously. As with other large-scale experimental platforms, to compare and contrast such profiles, e.g. for clustering known compounds with uncharacterized compounds, a robust means to compare a large cohort of profiles is required. Existing methods for correlating different chemical profiles include diverse statistical discriminant analysis-based methods and specific gene filtering or normalization methods. Though powerful, none are ideal because they typically require one to define the disrupting effects, commonly known as batch effects, to detect true signal from experimental variation. These effects are not always known, and they can mask true biological differences. We present a method, Bucket Evaluations (BE) that surmounts many of these problems and is extensible to other datasets such as those obtained via gene expression profiling and which is platform independent.'),
(95166, 2012, 'bmc', 'ncRNAclassifier: a tool for detection and classification of transposable element sequences in RNA hairpins', 'Tempel, Sébastien|Pollet, Nicolas|Tahi, Fariza', 'Laboratoire IBISC-IBGBI EA 4526, Université dEvry-Val dEssonne, Genopole, 91034 EVRY, France.|Laboratoire IBISC-IBGBI EA 4526, Université dEvry-Val dEssonne, Genopole, 91034 EVRY, France.|Laboratoire IBISC-IBGBI EA 4526, Université dEvry-Val dEssonne, Genopole, 91034 EVRY, France.', 'France|France|France', 9, 'Animals|Genome|High-Throughput Nucleotide Sequencing|Humans|Interspersed Repetitive Sequences|Inverted Repeat Sequences|Mice|MicroRNAs|RNA Precursors|RNA, Small Untranslated|Rats|Software', 'Journal Article', '10.1186/1471-2105-13-246', '23009561', 'Inverted repeat genes encode precursor RNAs characterized by hairpin structures. These RNA hairpins are then metabolized by biosynthetic pathways to produce functional small RNAs. In eukaryotic genomes, short non-autonomous transposable elements can have similar size and hairpin structures as non-coding precursor RNAs. This resemblance leads to problems annotating small RNAs.'),
(95167, 2012, 'bmc', 'ESTclean: a cleaning tool for next-gen transcriptome shotgun sequencing', 'Tae, Hongseok|Ryu, Dongsung|Sureshchandra, Suhas|Choi, Jeong-Hyeon', 'The Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47401, USA.|The Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47401, USA.|The Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47401, USA.|The Center for Genomics and Bioinformatics, Indiana University, Bloomington, IN 47401, USA.', 'India|India|India|India', 0, 'Animals|DNA Primers|DNA, Complementary|Drosophila melanogaster|Expressed Sequence Tags|High-Throughput Nucleotide Sequencing|Sequence Analysis, DNA|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-247', '23009593', 'With the advent of next-generation sequencing (NGS) technologies, full cDNA shotgun sequencing has become a major approach in the study of transcriptomes, and several different protocols in 454 sequencing have been invented. As each protocol uses its own short DNA tags or adapters attached to the ends of cDNA fragments for labeling or sequencing, different contaminants may lead to mis-assembly and inaccurate sequence products.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95168, 2012, 'bmc', 'The simulation of meiosis in diploid and tetraploid organisms using various genetic models', 'Voorrips, Roeland E|Maliepaard, Chris A', 'Department of Plant Breeding, Wageningen University and Research Center, P,O, Box 16, 6700 AA, Wageningen, the Netherlands. roeland.voorrips@wur.nl|Department of Plant Breeding, Wageningen University and Research Center, P,O, Box 16, 6700 AA, Wageningen, the Netherlands. roeland.voorrips@wur.nl', 'Netherlands|Netherlands', 0, 'Chromosome Mapping|Chromosomes|Diploidy|Genetics, Population|Meiosis|Models, Genetic|Pedigree|Plants|Quantitative Trait Loci|Software|Tetraploidy', 'Journal Article', '10.1186/1471-2105-13-248', '23013469', 'While the genetics of diploid inheritance are well studied and software for linkage mapping, haplotyping and QTL analysis are available, for tetraploids the available tools are limited. In order to develop such tools it would be helpful if simulated populations based on a variety of models of the tetraploid meiosis would be available.'),
(95169, 2012, 'bmc', 'HIDEN: Hierarchical decomposition of regulatory networks', 'Gülsoy, Günhan|Bandhyopadhyay, Nirmalya|Kahveci, Tamer', 'Computer and Information Sciences and Engineering, University of Florida, Gainesville, FL 32611, USA. ggulsoy@cise.ufl.edu|Computer and Information Sciences and Engineering, University of Florida, Gainesville, FL 32611, USA. ggulsoy@cise.ufl.edu|Computer and Information Sciences and Engineering, University of Florida, Gainesville, FL 32611, USA. ggulsoy@cise.ufl.edu', 'United States|United States|United States', 8, 'Computational Biology|Escherichia coli|Gene Expression Regulation|Gene Regulatory Networks|Humans|Saccharomyces cerevisiae|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-250', '23016513', 'Transcription factors regulate numerous cellular processes by controlling the rate of production of each gene. The regulatory relations are modeled using transcriptional regulatory networks. Recent studies have shown that such networks have an underlying hierarchical organization. We consider the problem of discovering the underlying hierarchy in transcriptional regulatory networks.'),
(95170, 2012, 'bmc', 'Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs)', 'Cheung, Warren A|Ouellette, B F Francis|Wasserman, Wyeth W', 'Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.|Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.|Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.', 'Canada|Canada|Canada', 21, 'Biomedical Research|Cluster Analysis|Genes|Humans|MEDLINE|Medical Subject Headings|Molecular Sequence Annotation|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-13-249', '23017167', 'MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships.'),
(95171, 2012, 'bmc', 'The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail', 'Kolczyk, Katrin|Samaga, Regina|Conzelmann, Holger|Mirschel, Sebastian|Conradi, Carsten', 'Max Planck Institute Magdeburg, 39106 Magdeburg, Sandtorstr. 1, Germany. kolczyk@mpi-magdeburg.mpg.de|Max Planck Institute Magdeburg, 39106 Magdeburg, Sandtorstr. 1, Germany. kolczyk@mpi-magdeburg.mpg.de|Max Planck Institute Magdeburg, 39106 Magdeburg, Sandtorstr. 1, Germany. kolczyk@mpi-magdeburg.mpg.de|Max Planck Institute Magdeburg, 39106 Magdeburg, Sandtorstr. 1, Germany. kolczyk@mpi-magdeburg.mpg.de|Max Planck Institute Magdeburg, 39106 Magdeburg, Sandtorstr. 1, Germany. kolczyk@mpi-magdeburg.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Cell Physiological Phenomena|Models, Biological|Protein Processing, Post-Translational|Protein Structure, Tertiary|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-13-251', '23020215', 'Signaling systems typically involve large, structured molecules each consisting of a large number of subunits called molecule domains. In modeling such systems these domains can be considered as the main players. In order to handle the resulting combinatorial complexity, rule-based modeling has been established as the tool of choice. In contrast to the detailed quantitative rule-based modeling, qualitative modeling approaches like logical modeling rely solely on the network structure and are particularly useful for analyzing structural and functional properties of signaling systems.'),
(95172, 2012, 'bmc', 'QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data', 'Finak, Greg|Jiang, Wenxin|Pardo, Jorge|Asare, Adam|Gottardo, Raphael', 'Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.|Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98109, USA.', 'United States|United States|United States|United States|United States', 0, 'Data Interpretation, Statistical|Flow Cytometry|Humans|Software', 'Journal Article', '10.1186/1471-2105-13-252', '23020243', 'Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non-obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata. Consequently, a systematic and integrated approach to quality assessment of flow cytometry data is necessary to effectively identify technical errors that impact multiple samples over time. Gated cell populations and their statistics must be monitored within the context of the experimental run, assay, and the overall study.'),
(95173, 2012, 'bmc', 'G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes', 'Lemay, Danielle G|Martin, William F|Hinrichs, Angie S|Rijnkels, Monique|German, J Bruce|Korf, Ian|Pollard, Katherine S', 'Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu|Genome Center, University of California Davis, 451 Health Science Dr, Davis, CA, 95616, United States of America. dglemay@ucdavis.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Animals|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Gene Duplication|Gene Expression|Gene Order|Genome|Genomics|Humans|Mice|Multigene Family|Software', 'Journal Article', '10.1186/1471-2105-13-253', '23020263', 'In previous studies, gene neighborhoods-spatial clusters of co-expressed genes in the genome-have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST) which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes simultaneously.'),
(95174, 2012, 'bmc', 'VarioML framework for comprehensive variation data representation and exchange', 'Byrne, Myles|Fokkema, Ivo Fac|Lancaster, Owen|Adamusiak, Tomasz|Ahonen-Bishopp, Anni|Atlan, David|Béroud, Christophe|Cornell, Michael|Dalgleish, Raymond|Devereau, Andrew|Patrinos, George P|Swertz, Morris A|Taschner, Peter Em|Thorisson, Gudmundur A|Vihinen, Mauno|Brookes, Anthony J|Muilu, Juha', 'Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi|Institute for Molecular Medicine Finland-FIMM, University of Helsinki, Helsinki, Finland. juha.muilu@helsinki.fi', 'Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finland|Finl', 13, 'Computer Systems|Databases, Genetic|Disease|Genetic Variation|Humans|Information Dissemination', 'Journal Article', '10.1186/1471-2105-13-254', '23031277', 'Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement.'),
(95175, 2012, 'bmc', 'Normalized N50 assembly metric using gap-restricted co-linear chaining', 'Mäkinen, Veli|Salmela, Leena|Ylinen, Johannes', 'Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P,O, Box 68 (Gustaf Hällstromin katu 2b), Helsinki, 00014, Finland. veli.makinen@cs.helsinki.fi|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P,O, Box 68 (Gustaf Hällstromin katu 2b), Helsinki, 00014, Finland. veli.makinen@cs.helsinki.fi|Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P,O, Box 68 (Gustaf Hällstromin katu 2b), Helsinki, 00014, Finland. veli.makinen@cs.helsinki.fi', 'Finland|Finland|Finland', 0, 'Contig Mapping|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-255', '23031320', 'For the development of genome assembly tools, some comprehensive and efficiently computable validation measures are required to assess the quality of the assembly. The mostly used N50 measure summarizes the assembly results by the length of the scaffold (or contig) overlapping the midpoint of the length-order concatenation of scaffolds (contigs). Especially for scaffold assemblies it is non-trivial to combine a correctness measure to the N50 values, and the current methods for doing this are rather involved.'),
(95176, 2012, 'bmc', 'A scalable method for identifying frequent subtrees in sets of large phylogenetic trees', 'Ramu, Avinash|Kahveci, Tamer|Burleigh, J Gordon', 'Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.|Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.|Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.', 'United States|United States|United States', 3, 'Algorithms|Biological Evolution|Classification|Phylogeny', 'Journal Article', '10.1186/1471-2105-13-256', '23033843', 'We consider the problem of finding the maximum frequent agreement subtrees (MFASTs) in a collection of phylogenetic trees. Existing methods for this problem often do not scale beyond datasets with around 100 taxa. Our goal is to address this problem for datasets with over a thousand taxa and hundreds of trees.'),
(95177, 2012, 'bmc', 'Coalescent: an open-source and scalable framework for exact calculations in coalescent theory', 'Tewari, Susanta|Spouge, John L', 'National Center for Biotechnology Information, Bethesda, MD 20894, USA. tewaris@ncbi.nlm.nih.gov|National Center for Biotechnology Information, Bethesda, MD 20894, USA. tewaris@ncbi.nlm.nih.gov', 'United States|United States', 0, 'Algorithms|Alleles|Genetic Drift|Genetics, Population|Models, Genetic|Phylogeny|Probability|Software', 'Journal Article', '10.1186/1471-2105-13-257', '23033878', 'Currently, there is no open-source, cross-platform and scalable framework for coalescent analysis in population genetics. There is no scalable GUI based user application either. Such a framework and application would not only drive the creation of more complex and realistic models but also make them truly accessible.'),
(95178, 2012, 'bmc', 'DMET-analyzer: automatic analysis of Affymetrix DMET data', 'Guzzi, Pietro Hiram|Agapito, Giuseppe|Di Martino, Maria Teresa|Arbitrio, Mariamena|Tassone, Pierfrancesco|Tagliaferri, Pierosandro|Cannataro, Mario', 'Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it|Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Italy. hguzzi@unicz.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Computational Biology|Genome, Human|Humans|Pharmaceutical Preparations|Pharmacogenetics|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-13-258', '23035929', 'Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs.'),
(95179, 2012, 'bmc', 'Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment', 'Daniels, Noah M|Nadimpalli, Shilpa|Cowen, Lenore J', 'Department of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155, USA.|Department of Computer Science, Tufts University, 161 College Ave, Medford, MA 02155, USA.', 'United States|United States|United States', 11, 'Algorithms|Amino Acid Sequence|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-259', '23039758', 'The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult.'),
(95180, 2012, 'bmc', 'Frnakenstein: multiple target inverse RNA folding', 'Lyngsø, Rune B|Anderson, James W J|Sizikova, Elena|Badugu, Amarendra|Hyland, Tomas|Hein, Jotun', 'Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.|Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 43, 'Algorithms|Base Pairing|Base Sequence|Computational Biology|Computer Simulation|RNA|RNA Folding|Riboswitch|Software', 'Journal Article', '10.1186/1471-2105-13-260', '23043260', 'RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest. With a growing appreciation and understanding of the functional and structural properties of RNA motifs, and a growing interest in utilising biomolecules in nano-scale designs, the interest in the inverse RNA folding problem is bound to increase. However, whereas the RNA folding problem from an algorithmic viewpoint has an elegant and efficient solution, the inverse RNA folding problem appears to be hard.'),
(95181, 2012, 'bmc', 'Semantic similarity in the biomedical domain: an evaluation across knowledge sources', 'Garla, Vijay N|Brandt, Cynthia', 'Yale Center for Medical Informatics, Yale University, New Haven, CT 06520-8009, USA. vijay.garla@yale.edu|Yale Center for Medical Informatics, Yale University, New Haven, CT 06520-8009, USA. vijay.garla@yale.edu', 'United States|United States', 58, 'Knowledge Bases|Medical Subject Headings|Natural Language Processing|Semantics|Systematized Nomenclature of Medicine|Unified Medical Language System', 'Comparative Study', '10.1186/1471-2105-13-261', '23046094', 'Semantic similarity measures estimate the similarity between concepts, and play an important role in many text processing tasks. Approaches to semantic similarity in the biomedical domain can be roughly divided into knowledge based and distributional based methods. Knowledge based approaches utilize knowledge sources such as dictionaries, taxonomies, and semantic networks, and include path finding measures and intrinsic information content (IC) measures. Distributional measures utilize, in addition to a knowledge source, the distribution of concepts within a corpus to compute similarity, these include corpus IC and context vector methods. Prior evaluations of these measures in the biomedical domain showed that distributional measures outperform knowledge based path finding methods, but more recent studies suggested that intrinsic IC based measures exceed the accuracy of distributional approaches. Limitations of previous evaluations of similarity measures in the biomedical domain include their focus on the SNOMED CT ontology, and their reliance on small benchmarks not powered to detect significant differences between measure accuracy. There have been few evaluations of the relative performance of these measures on other biomedical knowledge sources such as the UMLS, and on larger, recently developed semantic similarity benchmarks.'),
(95182, 2012, 'bmc', 'Proceedings of the NETTAB 2011 Workshop on Clinical Bioinformatics Pavia, Italy 12-14 October 2011', '-', '-', '-', 58, 'Computational Biology', 'Journal Article', '10.1186/1471-2105-13-261', '23046162', '-'),
(95183, 2012, 'bmc', 'Proceedings of the 2012 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference Introduction', 'Wren, Jonathan D|Dozmorov, Mikhail G|Burian, Dennis|Kaundal, Rakesh|Bridges, Susan|Kupfer, Doris M', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com', 'United States|United States|United States|United States|United States|United States', 1, 'Computational Biology|Congresses as Topic|Genomics|Proteomics|Sequence Analysis, RNA|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-13-S15-S1', '23046182', '-'),
(95184, 2012, 'bmc', 'Comparative genomic analysis of NAC transcriptional factors to dissect the regulatory mechanisms for cell wall biosynthesis', 'Yao, Dongxia|Wei, Qiang|Xu, Wenying|Syrenne, Ryan D|Yuan, Joshua S|Su, Zhen', 'State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.', 'China|China|China|China|China|China', 18, 'Arabidopsis|Arabidopsis Proteins|Cell Wall|Cluster Analysis|Comparative Genomic Hybridization|Computational Biology|DNA, Plant|Evolution, Molecular|Gene Expression Regulation, Plant|Gene Regulatory Networks|Genome, Plant|Genomics|Oryza|Phylogeny|Plant Proteins|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-13-S15-S10', '23046216', 'NAC domain transcription factors are important transcriptional regulators involved in plant growth, development and stress responses. Recent studies have revealed several classes of NAC transcriptional factors crucial for controlling secondary cell wall biosynthesis. These transcriptional factors mainly include three classes, SND, NST and VND. Despite progress, most current analysis is carried out in the model plant Arabidopsis. Moreover, many downstream genes regulated by these transcriptional factors are still not clear.'),
(95185, 2012, 'bmc', 'BirdsEyeView (BEV): graphical overviews of experimental data', 'Zhang, Lifeng|Berleant, Daniel|Wang, Yi|Li, Ling|Cook, Diane|Wurtele, Eve Syrkin', 'Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Gene Expression Profiling|Genomics|Metabolic Networks and Pathways|Software|Transcriptome|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-S15-S11', '23046276', 'Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe.'),
(95186, 2012, 'bmc', 'Inferences from structural comparison: flexibility, secondary structure wobble and sequence alignment optimization', 'Zhang, Gaihua|Su, Zhen', 'State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100094, Peoples Republic of China.|State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100094, Peoples Republic of China.', 'China|China', 2, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Models, Molecular|Pliability|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-13-S15-S12', '23046301', 'Work on protein structure prediction is very useful in biological research. To evaluate their accuracy, experimental protein structures or their derived data are used as the gold standard. However, as proteins are dynamic molecular machines with structural flexibility such a standard may be unreliable.'),
(95187, 2012, 'bmc', 'Calculations of relative intensities of fragment ions in the MSMS spectra of a doubly charged penta-peptide', 'Pechan, Tibor|Gwaltney, Steven R', 'Institute for Genomics, Biocomputing and Biotechnology, Mississippi Agricultural and Forestry Experiment Station, High Performance Computing Collaboratory, Mississippi State University, Mississippi State, MS 39762, USA. pechan@ra.msstate.edu|Institute for Genomics, Biocomputing and Biotechnology, Mississippi Agricultural and Forestry Experiment Station, High Performance Computing Collaboratory, Mississippi State University, Mississippi State, MS 39762, USA. pechan@ra.msstate.edu', 'United States|United States', 7, 'Algorithms|Amino Acid Sequence|Computational Biology|Ions|Peptides|Tandem Mass Spectrometry|Thermodynamics', 'Journal Article', '10.1186/1471-2105-13-S15-S13', '23046347', 'Currently, the tandem mass spectrometry (MSMS) of peptides is a dominant technique used to identify peptides and consequently proteins. The peptide fragmentation inside the mass analyzer typically offers a spectrum containing several different groups of ions. The mass to charge (m/z) values of these ions can be exactly calculated following simple rules based on the possible peptide fragmentation reactions. But the (relative) intensities of the particular ions cannot be simply predicted from the amino-acid sequence of the peptide. This study presents initial work towards developing a theoretical fundamental approach to ion intensity elucidation by utilizing quantum mechanical computations.'),
(95188, 2012, 'bmc', 'Empirical evaluation of scoring functions for Bayesian network model selection', 'Liu, Zhifa|Malone, Brandon|Yuan, Changhe', 'Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS 39762, USA.|Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS 39762, USA.|Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS 39762, USA.', 'United States|United States|United States', 37, 'Algorithms|Bayes Theorem|Computational Biology|Likelihood Functions|Models, Statistical|Reproducibility of Results|Sample Size', 'Comparative Study', '10.1186/1471-2105-13-S15-S14', '23046392', 'In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaikes information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure, the BDeu score is sensitive to the parameter settings, and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm.'),
(95189, 2012, 'bmc', 'PAGED: a pathway and gene-set enrichment database to enable molecular phenotype discoveries', 'Huang, Hui|Wu, Xiaogang|Sonachalam, Madhankumar|Mandape, Sammed N|Pandey, Ragini|MacDorman, Karl F|Wan, Ping|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.|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.|School of Informatics, Indiana University, Indianapolis, IN 46202, USA.|School of Informatics, Indiana University, Indianapolis, IN 46202, USA.', 'India|India|India|India|India|India|India|India', 0, 'Algorithms|Colorectal Neoplasms|Computational Biology|Databases, Genetic|Gene Regulatory Networks|Genome, Human|Genomics|Humans|Proteome|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-S15-S2', '23046413', 'Over the past decade, pathway and gene-set enrichment analysis has evolved into the study of high-throughput functional genomics. Owing to poorly annotated and incomplete pathway data, researchers have begun to combine pathway and gene-set enrichment analysis as well as network module-based approaches to identify crucial relationships between different molecular mechanisms.'),
(95190, 2012, 'bmc', 'Implementation of multiple-instance learning in drug activity prediction', 'Fu, Gang|Nan, Xiaofei|Liu, Haining|Patel, Ronak Y|Daga, Pankaj R|Chen, Yixin|Wilkins, Dawn E|Doerksen, Robert J', 'Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.|Department of Medicinal Chemistry, School of Pharmacy, University of Mississippi, University 38677, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 16, 'Artificial Intelligence|Computational Biology|Drug Discovery|Models, Theoretical|Molecular Conformation|Quantitative Structure-Activity Relationship', 'Comparative Study', '10.1186/1471-2105-13-S15-S3', '23046442', 'In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity, and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule.'),
(95191, 2012, 'bmc', 'IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis', 'Zhang, Fan|Drabier, Renee', 'Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA.|Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA.', 'United States|United States', 33, 'Brain|Breast Neoplasms|Databases, Factual|Female|Genome-Wide Association Study|Humans|Internet|Male|Organ Specificity|Prostatic Neoplasms|Signal Transduction|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-S15-S7', '23046449', 'Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).'),
(95192, 2012, 'bmc', 'Transcriptome profile of a bovine respiratory disease pathogen: Mannheimia haemolytica PHL213', 'Reddy, Joseph S|Kumar, Ranjit|Watt, James M|Lawrence, Mark L|Burgess, Shane C|Nanduri, Bindu', 'College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.|College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Animals|Cattle|Cattle Diseases|Computational Biology|Gene Expression Profiling|Genome, Bacterial|Mannheimia haemolytica|Molecular Sequence Annotation|Open Reading Frames|Operon|RNA, Bacterial|Respiratory Tract Diseases|Sequence Alignment|Sequence Analysis, RNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-S15-S4', '23046475', 'Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen.'),
(95193, 2012, 'bmc', 'Integration of shot-gun proteomics and bioinformatics analysis to explore plant hormone responses', 'Zhang, Yixiang|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.', 'United States|United States|United States|United States', 12, 'Brassinosteroids|Chloroplasts|Computational Biology|Metabolic Networks and Pathways|Mitochondria|Plant Growth Regulators|Plant Proteins|Proteomics|Software|Zeatin', 'Journal Article', '10.1186/1471-2105-13-S15-S8', '23046480', 'Multidimensional protein identification technology (MudPIT)-based shot-gun proteomics has been proven to be an effective platform for functional proteomics. In particular, the various sample preparation methods and bioinformatics tools can be integrated to improve the proteomics platform for applications like target organelle proteomics. We have recently integrated a rapid sample preparation method and bioinformatics classification system for comparative analysis of plant responses to two plant hormones, zeatin and brassinosteroid (BR). These hormones belong to two distinct classes of plant growth regulators, yet both can promote cell elongation and growth. An understanding of the differences and the cross-talk between the two types of hormone responses will allow us to better understand the molecular mechanisms and to identify new candidate genes for plant engineering.'),
(95194, 2012, 'bmc', 'Computational workflow for analysis of gain and loss of genes in distantly related genomes', 'Ptitsyn, Andrey|Moroz, Leonid L', 'Whitney Laboratory for Marine Biosciences, University of Florida, 9505 Ocean Shore Blvd, Saint Augustine, FL 32080, USA. andrey.ptitsyn@whitney.ufl.edu|Whitney Laboratory for Marine Biosciences, University of Florida, 9505 Ocean Shore Blvd, Saint Augustine, FL 32080, USA. andrey.ptitsyn@whitney.ufl.edu', 'United States|United States', 5, 'Animals|Cluster Analysis|Computational Biology|Evolution, Molecular|Genome|Genomics|Molecular Sequence Annotation|Multigene Family|Phylogeny|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/1471-2105-13-S15-S5', '23046496', 'Early evolution of animals led to profound changes in body plan organization, symmetry and the rise of tissue complexity including formation of muscular and nervous systems. This process was associated with massive restructuring of animal genomes as well as deletion, acquisition and rapid differentiation of genes from a common metazoan ancestor. Here, we present a simple but efficient workflow for elucidation of gene gain and gene loss within major branches of the animal kingdom.'),
(95195, 2012, 'bmc', 'A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins', 'Verma, Ruchi|Melcher, Ulrich', 'Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK 74078, USA.|Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK 74078, USA.', 'United States|United States', 0, 'Amino Acid Sequence|Bacterial Proteins|Databases, Protein|Models, Theoretical|Plant Proteins|Proteobacteria|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-S15-S9', '23046503', 'Members of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM).'),
(95196, 2012, 'bmc', 'Investigating drug repositioning opportunities in FDA drug labels through topic modeling', 'Bisgin, Halil|Liu, Zhichao|Kelly, Reagan|Fang, Hong|Xu, Xiaowei|Tong, Weida', 'Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.|Department of Information Science, University of Arkansas at Little Rock, 2801 S, University Ave, Little Rock, AR 72204-1099, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Drug Labeling|Drug Repositioning|Drug-Related Side Effects and Adverse Reactions|Models, Theoretical|United States|United States Food and Drug Administration', 'Journal Article', '10.1186/1471-2105-13-S15-S6', '23046522', 'Drug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach.'),
(95197, 2012, 'bmc', 'Cluster-based assessment of protein-protein interaction confidence', 'Kamburov, Atanas|Grossmann, Arndt|Herwig, Ralf|Stelzl, Ulrich', 'Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany. kamburov@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany. kamburov@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany. kamburov@molgen.mpg.de|Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany. kamburov@molgen.mpg.de', 'Germany|Germany|Germany|Germany', 17, 'Algorithms|Cluster Analysis|Confidence Intervals|Humans|Protein Interaction Mapping|Protein Interaction Maps|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-13-262', '23050565', 'Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.'),
(95198, 2012, 'bmc', 'Improving the thermostability of alpha-amylase by combinatorial coevolving-site saturation mutagenesis', 'Wang, Chenghua|Huang, Ribo|He, Bingfang|Du, Qishi', 'Nanjing University of Technology, Nanjing, Jiangsu, China.|Nanjing University of Technology, Nanjing, Jiangsu, China.|Nanjing University of Technology, Nanjing, Jiangsu, China.|Nanjing University of Technology, Nanjing, Jiangsu, China.', 'China|China|China|China', 0, 'Bacillus subtilis|Directed Molecular Evolution|Enzyme Stability|Models, Molecular|Mutagenesis|Mutation|Protein Engineering|Temperature|alpha-Amylases', 'Journal Article', '10.1186/1471-2105-13-263', '23057711', 'The generation of focused mutant libraries at hotspot residues is an important strategy in directed protein evolution. Existing methods, such as combinatorial active site testing and residual coupling analysis, depend primarily on the evolutionary conserved information to find the hotspot residues. Hardly any attention has been paid to another important functional and structural determinants, the functionally correlated variation information--coevolution.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95199, 2012, 'bmc', 'Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource', 'Sharpton, Thomas J|Jospin, Guillaume|Wu, Dongying|Langille, Morgan G I|Pollard, Katherine S|Eisen, Jonathan A', 'The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu|The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu|The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu|The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu|The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu|The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA 94158, USA. thomas.sharpton@gladstone.ucsf.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Cluster Analysis|Databases, Protein|Genomics|Molecular Sequence Annotation|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-13-264', '23061897', 'New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences.'),
(95200, 2012, 'bmc', 'Supervised segmentation of phenotype descriptions for the human skeletal phenome using hybrid methods', 'Groza, Tudor|Hunter, Jane|Zankl, Andreas', 'School of ITEE, The University of Queensland, Brisbane, Australia. tudor.groza@uq.edu.au|School of ITEE, The University of Queensland, Brisbane, Australia. tudor.groza@uq.edu.au|School of ITEE, The University of Queensland, Brisbane, Australia. tudor.groza@uq.edu.au', 'Australia|Australia|Australia', 0, 'Algorithms|Bone and Bones|Data Interpretation, Statistical|Humans|Phenotype', 'Journal Article', '10.1186/1471-2105-13-265', '23061930', 'Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. In order to fully capture the intrinsic value and knowledge expressed within them, we need to take advantage of their inner structure, which implicitly combines qualities and anatomical entities. The first step in this process is the segmentation of the phenotype descriptions into their atomic elements.'),
(95201, 2012, 'bmc', 'Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets', 'Scheubert, Lena|Luštrek, Mitja|Schmidt, Rainer|Repsilber, Dirk|Fuellen, Georg', 'Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.|Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.|Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.|Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.|Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Alzheimer Disease|Artificial Intelligence|Gene Expression|Gene Expression Profiling|Genetic Markers|Humans|Oligonucleotide Array Sequence Analysis|Support Vector Machine|Tissue Array Analysis', 'Comparative Study', '10.1186/1471-2105-13-266', '23066814', 'Alzheimers disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.'),
(95202, 2012, 'bmc', 'MetNet Online: a novel integrated resource for plant systems biology', 'Sucaet, Yves|Wang, Yi|Li, Jie|Wurtele, Eve Syrkin', 'Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA.|Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA.|Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA.|Dept of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA.', 'United States|United States|United States|United States', 13, 'Arabidopsis|Databases, Factual|Gene Regulatory Networks|Genes, Plant|Internet|Metabolic Networks and Pathways|Plants|Signal Transduction|Soybeans|Systems Biology|Vitis', 'Journal Article', '10.1186/1471-2105-13-267', '23066841', 'Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets.'),
(95203, 2012, 'bmc', 'Simple regression models as a threshold for selecting AFLP loci with reduced error rates', 'Price, David L|Casler, Michael D', 'Department of Agronomy, University of Wisconsin-Madison, 53706, USA. DLPrice2@wisc.edu|Department of Agronomy, University of Wisconsin-Madison, 53706, USA. DLPrice2@wisc.edu', 'United States|United States', 0, 'Alleles|Amplified Fragment Length Polymorphism Analysis|Computational Biology|Genetic Loci|Genetic Markers|Genome|Polymorphism, Genetic|Reproducibility of Results|Research Design', 'Journal Article', '10.1186/1471-2105-13-268', '23072295', 'Amplified fragment length polymorphism is a popular DNA marker technique that has applications in multiple fields of study. Technological improvements and decreasing costs have dramatically increased the number of markers that can be generated in an amplified fragment length polymorphism experiment. As datasets increase in size, the number of genotyping errors also increases. Error within a DNA marker dataset can result in reduced statistical power, incorrect conclusions, and decreased reproducibility. It is essential that error within a dataset be recognized and reduced where possible, while still balancing the need for genomic diversity.'),
(95204, 2012, 'bmc', 'Gene Fusion Markup Language: a prototype for exchanging gene fusion data', 'Kalyana-Sundaram, Shanker|Shanmugam, Achiraman|Chinnaiyan, Arul M', 'Michigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Michigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Michigan Center for Translational Pathology, Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 0, 'Databases, Genetic|Gene Fusion|Genetic Markers|Humans|Internet|Male|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-13-269', '23072312', 'An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future.'),
(95205, 2012, 'bmc', 'Combining multiple hypothesis testing and affinity propagation clustering leads to accurate, robust and sample size independent classification on gene expression data', 'Sakellariou, Argiris|Sanoudou, Despina|Spyrou, George', 'Biomedical Informatics Unit, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.|Biomedical Informatics Unit, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.|Biomedical Informatics Unit, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.', 'Greece|Greece|Greece', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Humans|Multigene Family|Neoplasms|Neuromuscular Diseases|Oligonucleotide Array Sequence Analysis|Sample Size|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-270', '23075381', 'A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list, there should be clusters of genes that share similar biological functions related to the investigated disease. Thus, instead of keeping N top ranked genes, it would be more appropriate to define and keep a number of gene cluster exemplars.'),
(95206, 2012, 'bmc', 'dsPIG: a tool to predict imprinted genes from the deep sequencing of whole transcriptomes', 'Li, Hua|Su, Xiao|Gallegos, Juan|Lu, Yue|Ji, Yuan|Molldrem, Jeffrey J|Liang, Shoudan', 'Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.|Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China.', 'China|China|China|China|China|China|China', 0, 'Alleles|Allelic Imbalance|Base Sequence|Bayes Theorem|Gene Expression|Gene Expression Profiling|Gene Frequency|Genome, Human|Genomic Imprinting|High-Throughput Nucleotide Sequencing|Humans|Models, Genetic|Polymorphism, Single Nucleotide|RNA, Messenger|Sequence Analysis, DNA|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-271', '23083219', 'Dysregulation of imprinted genes, which are expressed in a parent-of-origin-specific manner, plays an important role in various human diseases, such as cancer and behavioral disorder. To date, however, fewer than 100 imprinted genes have been identified in the human genome. The recent availability of high-throughput technology makes it possible to have large-scale prediction of imprinted genes. Here we propose a Bayesian model (dsPIG) to predict imprinted genes on the basis of allelic expression observed in mRNA-Seq data of independent human tissues.'),
(95207, 2012, 'bmc', 'Gene expression anti-profiles as a basis for accurate universal cancer signatures', 'Bravo, Héctor Corrada|Pihur, Vasyl|McCall, Matthew|Irizarry, Rafael A|Leek, Jeffrey T', 'Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. hcorrada@umiacs.umd.edu|Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. hcorrada@umiacs.umd.edu|Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. hcorrada@umiacs.umd.edu|Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. hcorrada@umiacs.umd.edu|Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. hcorrada@umiacs.umd.edu', 'United States|United States|United States|United States|United States', 0, 'Area Under Curve|Biomarkers, Tumor|Colonic Neoplasms|Gene Expression Profiling|Genetic Variation|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Prognosis|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-272', '23088656', 'Early screening for cancer is arguably one of the greatest public health advances over the last fifty years. However, many cancer screening tests are invasive (digital rectal exams), expensive (mammograms, imaging) or both (colonoscopies). This has spurred growing interest in developing genomic signatures that can be used for cancer diagnosis and prognosis. However, progress has been slowed by heterogeneity in cancer profiles and the lack of effective computational prediction tools for this type of data.'),
(95208, 2012, 'bmc', 'Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data', 'Sambo, Francesco|Trifoglio, Emanuele|Di Camillo, Barbara|Toffolo, Gianna M|Cobelli, Claudio', 'Department of Information Engineering, University of Padova, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Bayes Theorem|Case-Control Studies|Diabetes Mellitus, Type 1|Female|Genetic Markers|Genome-Wide Association Study|Humans|Logistic Models|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-13-S14-S2', '23095127', 'Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously.'),
(95209, 2012, 'bmc', 'Cancer module genes ranking using kernelized score functions', 'Re, Matteo|Valentini, Giorgio', 'Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano MI, Italia.|Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano MI, Italia.', '-|-', 17, 'Algorithms|Gene Expression Profiling|Gene Regulatory Networks|Genes, Neoplasm|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-13-S14-S3', '23095178', 'Co-expression based Cancer Modules (CMs) are sets of genes that act in concert to carry out specific functions in different cancer types, and are constructed by exploiting gene expression profiles related to specific clinical conditions or expression signatures associated to specific processes altered in cancer. Unfortunately, genes involved in cancer are not always detectable using only expression signatures or co-expressed sets of genes, and in principle other types of functional interactions should be exploited to obtain a comprehensive picture of the molecular mechanisms underlying the onset and progression of cancer.'),
(95210, 2012, 'bmc', 'Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA', 'Yang, Xian|Han, Rui|Guo, Yike|Bradley, Jeremy|Cox, Benita|Dickinson, Robert|Kitney, Richard', 'Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.|Department of Computing, Imperial College London, London, SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Critical Pathways|Hospitals|Humans|Quality of Health Care|Stochastic Processes|Stroke', 'Journal Article', '10.1186/1471-2105-13-S14-S4', '23095226', 'Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions cannot fully capture the complexities accurately in clinical pathways and hinders the effective management and further optimization of clinical pathways.'),
(95211, 2012, 'bmc', 'IBDsite: a Galaxy-interacting, integrative database for supporting inflammatory bowel disease high throughput data analysis', 'Merelli, I|Viti, F|Milanesi, L', 'Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), Italy. ivan.merelli@itb.cnr.it|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), Italy. ivan.merelli@itb.cnr.it|Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), Italy. ivan.merelli@itb.cnr.it', 'Italy|Italy|Italy', 0, 'Colon|Databases, Genetic|Humans|Inflammatory Bowel Diseases|Internet|Intestine, Small|Metagenome|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-13-S14-S5', '23095257', 'Inflammatory bowel diseases (IBD) refer to a group of inflammatory conditions concerning colon and small intestine, which cause socially uncomfortable symptoms and often are associated with an increased risk of colon cancer. IBD are complex disorders, which rely on genetic susceptibility, environmental factors, deregulation of the immune system, and host relationship with commensal microbiota. The complexity of these pathologies makes difficult to clearly understand the mechanisms of their onset. Therefore, the study of IBD must be faced exploiting an integrated and multilevel approach, ranging from genes, transcripts and proteins to pathways altered in affected tissues, and carefully considering their regulatory mechanisms, which may intervene in the pathology onset. It is also crucial to have a knowledge base about the symbiotic bacteria that are hosted in the human gut. To date, much data exist regarding IBD and human commensal bacteria, but this information is sparse in literature and no free resource provides a homogeneously and rationally integrated view of biomolecular data related to these pathologies.'),
(95212, 2012, 'bmc', 'Exploiting protein flexibility to predict the location of allosteric sites', 'Panjkovich, Alejandro|Daura, Xavier', 'Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.|Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.', 'Spain|Spain', 48, 'Allosteric Regulation|Allosteric Site|Catalytic Domain|Computational Biology|Drug Discovery|Humans|Ligands|Pliability|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-273', '23095452', 'Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure.'),
(95213, 2012, 'bmc', 'Hierarchical Naive Bayes for genetic association studies', 'Malovini, Alberto|Barbarini, Nicola|Bellazzi, Riccardo|de Michelis, Francesca', 'Department of Industrial and Information Engineering, University of Pavia, Pavia, 27100, Italy. alberto.malovini@unipv.it|Department of Industrial and Information Engineering, University of Pavia, Pavia, 27100, Italy. alberto.malovini@unipv.it|Department of Industrial and Information Engineering, University of Pavia, Pavia, 27100, Italy. alberto.malovini@unipv.it|Department of Industrial and Information Engineering, University of Pavia, Pavia, 27100, Italy. alberto.malovini@unipv.it', 'Italy|Italy|Italy|Italy', 9, 'Bayes Theorem|Case-Control Studies|Computer Simulation|Diabetes Mellitus, Type 1|Diabetes Mellitus, Type 2|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-13-S14-S6', '23095471', 'Genome Wide Association Studies represent powerful approaches that aim at disentangling the genetic and molecular mechanisms underlying complex traits. The usual one-SNP-at-the-time testing strategy cannot capture the multi-factorial nature of this kind of disorders. We propose a Hierarchical Naïve Bayes classification model for taking into account associations in SNPs data characterized by Linkage Disequilibrium. Validation shows that our model reaches classification performances superior to those obtained by the standard Naïve Bayes classifier for simulated and real datasets.'),
(95214, 2012, 'bmc', 'Clinical Bioinformatics: challenges and opportunities', 'Bellazzi, Riccardo|Masseroli, Marco|Murphy, Shawn|Shabo, Amnon|Romano, Paolo', 'Dipartimento di Ingegneria Industriale e dellInformazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy. riccardo.bellazzi@unipv.it|Dipartimento di Ingegneria Industriale e dellInformazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy. riccardo.bellazzi@unipv.it|Dipartimento di Ingegneria Industriale e dellInformazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy. riccardo.bellazzi@unipv.it|Dipartimento di Ingegneria Industriale e dellInformazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy. riccardo.bellazzi@unipv.it|Dipartimento di Ingegneria Industriale e dellInformazione, Università di Pavia, Via Ferrata 1, 27100, Pavia, Italy. riccardo.bellazzi@unipv.it', 'Italy|Italy|Italy|Italy|Italy', 8, 'Algorithms|Biomedical Research|Computational Biology|Congresses as Topic|Genomics|Humans|Information Storage and Retrieval|Precision Medicine', 'Editorial', '10.1186/1471-2105-13-S14-S1', '23095472', 'Network Tools and Applications in Biology (NETTAB) Workshops are a series of meetings focused on the most promising and innovative ICT tools and to their usefulness in Bioinformatics. The NETTAB 2011 workshop, held in Pavia, Italy, in October 2011 was aimed at presenting some of the most relevant methods, tools and infrastructures that are nowadays available for Clinical Bioinformatics (CBI), the research field that deals with clinical applications of bioinformatics.'),
(95215, 2012, 'bmc', 'The human magnesome: detecting magnesium binding sites on human proteins', 'Piovesan, Damiano|Profiti, Giuseppe|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, Department of Biology, University of Bologna, Bologna, 40126, Italy.|Biocomputing Group, Department of Biology, University of Bologna, Bologna, 40126, Italy.|Biocomputing Group, Department of Biology, University of Bologna, Bologna, 40126, Italy.|Biocomputing Group, Department of Biology, University of Bologna, Bologna, 40126, Italy.', 'Italy|Italy|Italy|Italy', 13, 'Binding Sites|Cluster Analysis|Humans|Magnesium|Models, Molecular|Molecular Sequence Annotation|Nuclear Receptor Subfamily 4, Group A, Member 2|Proteins|Proteome', 'Journal Article', '10.1186/1471-2105-13-S14-S10', '23095498', 'Magnesium research is increasing in molecular medicine due to the relevance of this ion in several important biological processes and associated molecular pathogeneses. It is still difficult to predict from the protein covalent structure whether a human chain is or not involved in magnesium binding. This is mainly due to little information on the structural characteristics of magnesium binding sites in proteins and protein complexes. Magnesium binding features, differently from those of other divalent cations such as calcium and zinc, are elusive. Here we address a question that is relevant in protein annotation: how many human proteins can bind Mg2+? Our analysis is performed taking advantage of the recently implemented Bologna Annotation Resource (BAR-PLUS), a non hierarchical clustering method that relies on the pair wise sequence comparison of about 14 millions proteins from over 300.000 species and their grouping into clusters where annotation can safely be inherited after statistical validation.'),
(95216, 2012, 'bmc', 'Imputation reliability on DNA biallelic markers for drug metabolism studies', 'Mijatovic, Vladan|Iacobucci, Ilaria|Sazzini, Marco|Xumerle, Luciano|Mori, Antonio|Pignatti, Pier Franco|Martinelli, Giovanni|Malerba, Giovanni', 'Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.|Dep, of Life and Reproductions Sciences, Sec, of Biology and Genetics - University of Verona, strada Le grazie 8, 37134 Verona, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 3, 'Economics, Pharmaceutical|Genetic Markers|Genome, Human|Genome-Wide Association Study|HapMap Project|Humans|Leukemia, Myeloid, Acute|Pharmacogenetics|Polymorphism, Single Nucleotide|Reproducibility of Results|Software|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-13-S14-S7', '23095502', 'Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies.'),
(95217, 2012, 'bmc', 'Matching health information seekers queries to medical terms', 'Soualmia, Lina F|Prieur-Gaston, Elise|Moalla, Zied|Lecroq, Thierry|Darmoni, Stéfan J', 'LIM & Bio EA 3969, Université Paris XIII, Sorbonne Paris Cité, 93017 Bobigny, France. lina.soualmia@chu-rouen.fr|LIM & Bio EA 3969, Université Paris XIII, Sorbonne Paris Cité, 93017 Bobigny, France. lina.soualmia@chu-rouen.fr|LIM & Bio EA 3969, Université Paris XIII, Sorbonne Paris Cité, 93017 Bobigny, France. lina.soualmia@chu-rouen.fr|LIM & Bio EA 3969, Université Paris XIII, Sorbonne Paris Cité, 93017 Bobigny, France. lina.soualmia@chu-rouen.fr|LIM & Bio EA 3969, Université Paris XIII, Sorbonne Paris Cité, 93017 Bobigny, France. lina.soualmia@chu-rouen.fr', 'France|France|France|France|France', 5, 'Algorithms|Humans|Information Storage and Retrieval|Internet|Language|Medical Informatics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-13-S14-S11', '23095521', 'The Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool.'),
(95218, 2012, 'bmc', 'GapFiller: a de novo assembly approach to fill the gap within paired reads', 'Nadalin, Francesca|Vezzi, Francesco|Policriti, Alberto', 'Department of Mathematics and Computer Science, University of Udine, Udine 33100, Italy. fnadalin@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, Udine 33100, Italy. fnadalin@appliedgenomics.org|Department of Mathematics and Computer Science, University of Udine, Udine 33100, Italy. fnadalin@appliedgenomics.org', 'Italy|Italy|Italy', 171, 'Algorithms|Contig Mapping|Genome|Genome, Human|High-Throughput Nucleotide Sequencing|Humans|Rhodobacter sphaeroides|Sequence Analysis, DNA|Software|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-13-S14-S8', '23095524', 'Next Generation Sequencing technologies are able to provide high genome coverages at a relatively low cost. However, due to limited reads length (from 30 bp up to 200 bp), specific bioinformatics problems have become even more difficult to solve. De novo assembly with short reads, for example, is more complicated at least for two reasons: first, the overall amount of noisy data to cope with increased and, second, as the reads length decreases the number of unsolvable repeats grows. Our works aim is to go at the root of the problem by providing a pre-processing tool capable to produce (in-silico) longer and highly accurate sequences from a collection of Next Generation Sequencing reads.'),
(95219, 2012, 'bmc', 'Modelling osteomyelitis', 'Liò, Pietro|Paoletti, Nicola|Moni, Mohammad Ali|Atwell, Kathryn|Merelli, Emanuela|Viceconti, Marco', 'Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.|Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.|Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.|Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.|Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.|Computer Laboratory, Cambridge University, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Bone Density|Bone Remodeling|Bone and Bones|Humans|Models, Biological|NF-kappa B|Osteomyelitis|Osteoporosis|Receptors, Tumor Necrosis Factor|Signal Transduction|Staphylococcal Infections|Staphylococcus aureus|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-S14-S12', '23095605', 'This work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure.'),
(95220, 2012, 'bmc', 'EVA: Exome Variation Analyzer, an efficient and versatile tool for filtering strategies in medical genomics', 'Coutant, Sophie|Cabot, Chloé|Lefebvre, Arnaud|Léonard, Martine|Prieur-Gaston, Elise|Campion, Dominique|Lecroq, Thierry|Dauchel, Hélène', 'University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.|University of Rouen, INSERM U1079 Molecular genetics of cancer and neuropsychiatric diseases, 76183 Rouen cedex, France.', 'France|France|France|France|France|France|France|France', 13, 'Algorithms|Alzheimer Disease|Databases, Genetic|Exome|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-S14-S9', '23095660', 'Whole exome sequencing (WES) has become the strategy of choice to identify a coding allelic variant for a rare human monogenic disorder. This approach is a revolution in medical genetics history, impacting both fundamental research, and diagnostic methods leading to personalized medicine. A plethora of efficient algorithms has been developed to ensure the variant discovery. They generally lead to ~20,000 variations that have to be narrow down to find the potential pathogenic allelic variant(s) and the affected gene(s). For this purpose, commonly adopted procedures which implicate various filtering strategies have emerged: exclusion of common variations, type of the allelics variants, pathogenicity effect prediction, modes of inheritance and multiple individuals for exome comparison. To deal with the expansion of WES in medical genomics individual laboratories, new convivial and versatile software tools have to implement these filtering steps. Non-programmer biologists have to be autonomous combining themselves different filtering criteria and conduct a personal strategy depending on their assumptions and study design.'),
(95221, 2012, 'bmc', 'High-Throughput parallel blind Virtual Screening using BINDSURF', 'Sánchez-Linares, Irene|Pérez-Sánchez, Horacio|Cecilia, José M|García, José M', 'Computer Engineering Department, School of Computer Science, University of Murcia, Spain.|Computer Engineering Department, School of Computer Science, University of Murcia, Spain.|Computer Engineering Department, School of Computer Science, University of Murcia, Spain.|Computer Engineering Department, School of Computer Science, University of Murcia, Spain.', 'Spain|Spain|Spain|Spain', 98, 'Binding Sites|Databases, Factual|Drug Discovery|Drug Evaluation, Preclinical|Humans|Ligands|Models, Chemical|Protein Binding|Proteins|Software', 'Journal Article', '10.1186/1471-2105-13-S14-S13', '23095663', 'Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, usually derived from the interpretation of the protein crystal structure. However, it has been demonstrated that in many cases, diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact.'),
(95222, 2012, 'bmc', 'Modelling non-homogeneous stochastic reaction-diffusion systems: the case study of gemcitabine-treated non-small cell lung cancer growth', 'Lecca, Paola|Morpurgo, Daniele', 'The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy. lecca@cosbi.eu|The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy. lecca@cosbi.eu', 'Italy|Italy', 3, 'Algorithms|Carcinoma, Non-Small-Cell Lung|Deoxycytidine|Diffusion|Humans|Lung Neoplasms|Models, Biological|Viscosity', 'Journal Article', '10.1186/1471-2105-13-S14-S14', '23095709', 'Reaction-diffusion based models have been widely used in the literature for modeling the growth of solid tumors. Many of the current models treat both diffusion/consumption of nutrients and cell proliferation. The majority of these models use classical transport/mass conservation equations for describing the distribution of molecular species in tumor spheroids, and the Ficks law for describing the flux of uncharged molecules (i.e oxygen, glucose). Commonly, the equations for the cell movement and proliferation are first order differential equations describing the rate of change of the velocity of the cells with respect to the spatial coordinates as a function of the nutrients gradient. Several modifications of these equations have been developed in the last decade to explicitly indicate that the tumor includes cells, interstitial fluids and extracellular matrix: these variants provided a model of tumor as a multiphase material with these as the different phases. Most of the current reaction-diffusion tumor models are deterministic and do not model the diffusion as a local state-dependent process in a non-homogeneous medium at the micro- and meso-scale of the intra- and inter-cellular processes, respectively. Furthermore, a stochastic reaction-diffusion model in which diffusive transport of the molecular species of nutrients and chemotherapy drugs as well as the interactions of the tumor cells with these species is a novel approach. The application of this approach to he scase of non-small cell lung cancer treated with gemcitabine is also novel.'),
(95223, 2012, 'bmc', 'A quantitative genetic and epigenetic model of complex traits', 'Wang, Zhong|Wang, Zuoheng|Wang, Jianxin|Sui, Yihan|Zhang, Jian|Liao, Duanping|Wu, Rongling', 'Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.|Siyang Science and Technology Station, Yuanpeng Institute of Genome, Nantong, Jiangsu 226019, China.', 'China|China|China|China|China|China|China', 9, 'Computer Simulation|Epigenesis, Genetic|Genetic Variation|Genome|Humans|Models, Genetic|Multifactorial Inheritance', 'Journal Article', '10.1186/1471-2105-13-274', '23102025', 'Despite our increasing recognition of the mechanisms that specify and propagate epigenetic states of gene expression, the pattern of how epigenetic modifications contribute to the overall genetic variation of a phenotypic trait remains largely elusive.'),
(95224, 2012, 'bmc', 'Combing the hairball with BioFabric: a new approach for visualization of large networks', 'Longabaugh, William J R', 'Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109-5234, USA. wlongabaugh@systemsbiology.org', 'United States', 0, 'Computer Graphics|Gene Regulatory Networks|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-13-275', '23102059', 'The analysis of large, complex networks is an important aspect of ongoing biological research. Yet there is a need for entirely new, scalable approaches for network visualization that can provide more insight into the structure and function of these complex networks.'),
(95225, 2012, 'bmc', 'Coral: an integrated suite of visualizations for comparing clusterings', 'Filippova, Darya|Gadani, Aashish|Kingsford, Carl', 'Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. dfilippo@cs.CMU.edu|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. dfilippo@cs.CMU.edu|Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. dfilippo@cs.CMU.edu', 'United States|United States|United States', 0, 'Algorithms|Arabidopsis|Cluster Analysis|Computer Graphics|Gene Expression|Gene Regulatory Networks|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1186/1471-2105-13-276', '23102108', 'Clustering has become a standard analysis for many types of biological data (e.g interaction networks, gene expression, metagenomic abundance). In practice, it is possible to obtain a large number of contradictory clusterings by varying which clustering algorithm is used, which data attributes are considered, how algorithmic parameters are set, and which near-optimal clusterings are chosen. It is a difficult task to sift though such a large collection of varied clusterings to determine which clustering features are affected by parameter settings or are artifacts of particular algorithms and which represent meaningful patterns. Knowing which items are often clustered together helps to improve our understanding of the underlying data and to increase our confidence about generated modules.'),
(95226, 2012, 'bmc', 'Simple binary segmentation frameworks for identifying variation in DNA copy number', 'Yang, Tae Young', 'Department of Mathematics, Myongji University, Yongin, Kyonggi, 449-728, Korea. tyang@mju.ac.kr', '-', 0, 'Algorithms|Bayes Theorem|DNA|DNA Copy Number Variations|DNA, Complementary|Genetic Variation|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-277', '23107320', 'Variation in DNA copy number, due to gains and losses of chromosome segments, is common. A first step for analyzing DNA copy number data is to identify amplified or deleted regions in individuals. To locate such regions, we propose a circular binary segmentation procedure, which is based on a sequence of nested hypothesis tests, each using the Bayesian information criterion.'),
(95227, 2012, 'bmc', 'Bayesian semiparametric regression models to characterize molecular evolution', 'Datta, Saheli|Rodriguez, Abel|Prado, Raquel', 'Fred Hutchinson Cancer Research Center, Seattle, WA, USA. sdatta2@fhcrc.org|Fred Hutchinson Cancer Research Center, Seattle, WA, USA. sdatta2@fhcrc.org|Fred Hutchinson Cancer Research Center, Seattle, WA, USA. sdatta2@fhcrc.org', 'United States|United States|United States', 0, 'Amino Acids|Bayes Theorem|Computer Simulation|Evolution, Molecular|Models, Statistical|Open Reading Frames|Proteins|Selection, Genetic|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-13-278', '23107360', 'Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments.'),
(95228, 2012, 'bmc', 'ChopSticks: High-resolution analysis of homozygous deletions by exploiting concordant read pairs', 'Yasuda, Tomohiro|Suzuki, Shin|Nagasaki, Masao|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. tyasuda@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. tyasuda@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. tyasuda@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. tyasuda@hgc.jp', 'Japan|Japan|Japan|Japan', 3, 'Animals|Base Sequence|Chromosome Mapping|Genome|Homozygote|Mice|Sequence Analysis, DNA|Sequence Deletion', 'Journal Article', '10.1186/1471-2105-13-279', '23110596', 'Structural variations (SVs) in genomes are commonly observed even in healthy individuals and play key roles in biological functions. To understand their functional impact or to infer molecular mechanisms of SVs, they have to be characterized with the maximum resolution. However, high-resolution analysis is a difficult task because it requires investigation of the complex structures involved in an enormous number of alignments of next-generation sequencing (NGS) reads and genome sequences that contain errors.'),
(95229, 2012, 'bmc', 'Multi-scale RNA comparison based on RNA triple vector curve representation', 'Li, Ying|Duan, Ming|Liang, Yanchun', 'College of Computer Science and Technology, Symbol Computation and Knowledge Engineering Lab of Ministry of Education, Jilin University, Changchun, China.|College of Computer Science and Technology, Symbol Computation and Knowledge Engineering Lab of Ministry of Education, Jilin University, Changchun, China.|College of Computer Science and Technology, Symbol Computation and Knowledge Engineering Lab of Ministry of Education, Jilin University, Changchun, China.', 'China|China|China', 5, 'Algorithms|Models, Molecular|Nucleic Acid Conformation|Protein Structure, Secondary|RNA|RNA, Untranslated|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-13-280', '23110635', 'In recent years, the important functional roles of RNAs in biological processes have been repeatedly demonstrated. Computing the similarity between two RNAs contributes to better understanding the functional relationship between them. But due to the long-range correlations of RNA, many efficient methods of detecting protein similarity do not work well. In order to comprehensively understand the RNAs function, the better similarity measure among RNAs should be designed to consider their structure features (base pairs). Current methods for RNA comparison could be generally classified into alignment-based and alignment-free.'),
(95230, 2012, 'bmc', 'Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors', 'Boxc5xa1naxc4x8dki, Dragan|Odenbrett, Maximilian R|Wijs, Anton|Ligtenberg, Willem|Hilbers, Peter', 'Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. D.Bosnacki@tue.nl|Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. D.Bosnacki@tue.nl|Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. D.Bosnacki@tue.nl|Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. D.Bosnacki@tue.nl|Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. D.Bosnacki@tue.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Computer Graphics|Gene Regulatory Networks|Metabolic Networks and Pathways', 'Journal Article', '10.1186/1471-2105-13-281', '23110660', 'Techniques for reconstruction of biological networks which are based on perturbation experiments often predict direct interactions between nodes that do not exist. Transitive reduction removes such relations if they can be explained by an indirect path of influences. The existing algorithms for transitive reduction are sequential and might suffer from too long run times for large networks. They also exhibit the anomaly that some existing direct interactions are also removed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(95231, 2012, 'bmc', 'Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer', 'Doyle, Scott|Feldman, Michael D|Shih, Natalie|Tomaszewski, John|Madabhushi, Anant', 'Ibris, Inc., Monmouth Junction, New Jersey, USA. scottydoyle@ibrisinc.com|Ibris, Inc., Monmouth Junction, New Jersey, USA. scottydoyle@ibrisinc.com|Ibris, Inc., Monmouth Junction, New Jersey, USA. scottydoyle@ibrisinc.com|Ibris, Inc., Monmouth Junction, New Jersey, USA. scottydoyle@ibrisinc.com|Ibris, Inc., Monmouth Junction, New Jersey, USA. scottydoyle@ibrisinc.com', 'Jersey|Jersey|Jersey|Jersey|Jersey', 0, 'Epithelium|Humans|Male|Neoplasm Grading|Prostate|Prostatic Intraepithelial Neoplasia|Prostatic Neoplasms', 'Journal Article', '10.1186/1471-2105-13-282', '23110677', 'Automated classification of histopathology involves identification of multiple classes, including benign, cancerous, and confounder categories. The confounder tissue classes can often mimic and share attributes with both the diseased and normal tissue classes, and can be particularly difficult to identify, both manually and by automated classifiers. In the case of prostate cancer, they may be several confounding tissue types present in a biopsy sample, posing as major sources of diagnostic error for pathologists. Two common multi-class approaches are one-shot classification (OSC), where all classes are identified simultaneously, and one-versus-all (OVA), where a target class is distinguished from all non-target classes. OSC is typically unable to handle discrimination of classes of varying similarity (e.g. with images of prostate atrophy and high grade cancer), while OVA forces several heterogeneous classes into a single non-target class. In this work, we present a cascaded (CAS) approach to classifying prostate biopsy tissue samples, where images from different classes are grouped to maximize intra-group homogeneity while maximizing inter-group heterogeneity.'),
(95232, 2012, 'bmc', 'Denoising PCR-amplified metagenome data', 'Rosen, Michael J|Callahan, Benjamin J|Fisher, Daniel S|Holmes, Susan P', 'Department of Applied Physics, Stanford University, CA, USA. mjrosen@stanford.edu|Department of Applied Physics, Stanford University, CA, USA. mjrosen@stanford.edu|Department of Applied Physics, Stanford University, CA, USA. mjrosen@stanford.edu|Department of Applied Physics, Stanford University, CA, USA. mjrosen@stanford.edu', 'United States|United States|United States|United States', 34, 'Algorithms|Metagenome|Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-13-283', '23113967', 'PCR amplification and high-throughput sequencing theoretically enable the characterization of the finest-scale diversity in natural microbial and viral populations, but each of these methods introduces random errors that are difficult to distinguish from genuine biological diversity. Several approaches have been proposed to denoise these data but lack either speed or accuracy.'),
(95233, 2012, 'bmc', 'A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies', 'Zuber, Verena|Duarte Silva, A Pedro|Strimmer, Korbinian', 'Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany. vzuber@uni-leipzig.de|Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany. vzuber@uni-leipzig.de|Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany. vzuber@uni-leipzig.de', 'Germany|Germany|Germany', 14, 'Algorithms|Genome-Wide Association Study|Humans|Polymorphism, Single Nucleotide|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-13-284', '23113980', 'Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that needs to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs.'),
(95234, 2012, 'bmc', 'Dynamic changes in the secondary structure of ECE-1 and XCE account for their different substrate specificities', 'Ul-Haq, Zaheer|Iqbal, Sadaf|Moin, Syed Tarique', 'Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan. zaheer.qasmi@iccs.edu|Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan. zaheer.qasmi@iccs.edu|Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan. zaheer.qasmi@iccs.edu', 'Pakistan|Pakistan|Pakistan', 5, 'Amino Acid Sequence|Aspartic Acid Endopeptidases|Binding Sites|Catalytic Domain|Endothelin-Converting Enzymes|Glycopeptides|Humans|Metalloendopeptidases|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-13-285', '23113990', 'X-converting enzyme (XCE) involved in nervous control of respiration, is a member of the M13 family of zinc peptidases, for which no natural substrate has been identified yet. In contrast, its well characterized homologue endothelin-converting enzyme-1 (ECE-1) showed broad substrate specificity and acts as endopeptidase as well as dipeptidase. To explore the structural differences between XCE and ECE-1, homology model of XCE was built using the complex structure of ECE-1 with phosphoramidon (pdb-id: 3DWB) as template. Phosphoramidon was docked into the binding site of XCE whereas phosphate oxygen of the inhibitor was used as water molecule to design the apo forms of both enzymes. Molecular dynamics simulation of both enzymes was performed to analyze the dynamic nature of their active site residues in the absence and presence of the inhibitor.'),
(95235, 2012, 'bmc', 'Identifying structural domains of proteins using clustering', 'Feldman, Howard J', 'Chemical Computing Group, Inc., Montreal, Quebec, Canada. feldman@chemcomp.com', 'Canada', 9, 'Algorithms|Cluster Analysis|Computational Biology|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-13-286', '23116496', 'Protein structures are comprised of modular elements known as domains. These units are used and re-used over and over in nature, and usually serve some particular function in the structure. Thus it is useful to be able to break up a protein of interest into its component domains, prior to similarity searching for example. Numerous computational methods exist for doing so, but most operate only on a single protein chain and many are limited to making a series of cuts to the sequence, while domains can and do span multiple chains.'),
(95236, 2012, 'bmc', 'MCMC-ODPR: primer design optimization using Markov Chain Monte Carlo sampling', 'Kitchen, James L|Moore, Jonathan D|Palmer, Sarah A|Allaby, Robin G', 'School of Life Sciences, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, UK.|School of Life Sciences, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, UK.|School of Life Sciences, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, UK.|School of Life Sciences, University of Warwick, Gibbet Hill, Coventry, CV4 7AL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|DNA Primers|Markov Chains|Monte Carlo Method|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Software|Transition Temperature', 'Journal Article', '10.1186/1471-2105-13-287', '23126469', 'Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly. We aimed to develop a system that would implement primer reuse to design degenerate primers that could be designed around SNPs, thus find the fewest necessary primers and the lowest cost whilst maintaining an acceptable coverage and provide a cost effective solution. We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse. We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reuse (MCMC-ODPR) algorithm.'),
(95237, 2012, 'bmc', 'PAnalyzer: a software tool for protein inference in shotgun proteomics', 'Prieto, Gorka|Aloria, Kerman|Osinalde, Nerea|Fullaondo, Asier|Arizmendi, Jesus M|Matthiesen, Rune', 'Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es|Department of Communications Engineering, University of the Basque Country (UPV/EHU), Alda, Urquijo s/n, Bilbao, 48013, Spain. gorka.prieto@ehu.es', 'Spain|Spain|Spain|Spain|Spain|Spain', 22, 'Databases, Protein|HEK293 Cells|Humans|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-13-288', '23126499', 'Protein inference from peptide identifications in shotgun proteomics must deal with ambiguities that arise due to the presence of peptides shared between different proteins, which is common in higher eukaryotes. Recently data independent acquisition (DIA) approaches have emerged as an alternative to the traditional data dependent acquisition (DDA) in shotgun proteomics experiments. MSE is the term used to name one of the DIA approaches used in QTOF instruments. MSE data require specialized software to process acquired spectra and to perform peptide and protein identifications. However the software available at the moment does not group the identified proteins in a transparent way by taking into account peptide evidence categories. Furthermore the inspection, comparison and report of the obtained results require tedious manual intervention. Here we report a software tool to address these limitations for MSE data.'),
(95238, 2012, 'bmc', 'The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement', 'Pawlowski, Marcin|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland. marcinp@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Warsaw, Poland. marcinp@genesilico.pl', 'Poland|Poland', 5, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Crystallography, X-Ray|Models, Molecular|Peptides|Proteins', 'Journal Article', '10.1186/1471-2105-13-289', '23126528', 'Computational models of protein structures were proved to be useful as search models in Molecular Replacement (MR), a common method to solve the phase problem faced by macromolecular crystallography. The success of MR depends on the accuracy of a search model. Unfortunately, this parameter remains unknown until the final structure of the target protein is determined. During the last few years, several Model Quality Assessment Programs (MQAPs) that predict the local accuracy of theoretical models have been developed. In this article, we analyze whether the application of MQAPs improves the utility of theoretical models in MR.'),
(95239, 2012, 'bmc', 'mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines', 'Wan, Shibiao|Mak, Man-Wai|Kung, Sun-Yuan', 'Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.|Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.|Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China.', 'China|China|China', 66, 'Algorithms|Computational Biology|Databases, Protein|Genes|Intracellular Space|Proteins|Software|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-290', '23130999', 'Although many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery.'),
(95240, 2012, 'bmc', 'Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching', 'Slawski, Martin|Hussong, Rene|Tholey, Andreas|Jakoby, Thomas|Gregorius, Barbara|Hildebrandt, Andreas|Hein, Matthias', 'Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de|Department of Computer Science, Saarland University, Saarbrücken, Germany. ms@cs.uni-saarland.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Algorithms|Artifacts|Humans|Isotopes|Least-Squares Analysis|Mass Spectrometry|Peptides|Protein Processing, Post-Translational|Proteomics', 'Journal Article', '10.1186/1471-2105-13-291', '23137144', 'The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either noise or signal lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context.'),
(95241, 2012, 'bmc', 'Effective inter-residue contact definitions for accurate protein fold recognition', 'Yuan, Chao|Chen, Hao|Kihara, Daisuke', 'Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.|Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.', 'United States|United States|United States', 20, 'Models, Molecular|Protein Conformation|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-13-292', '23140471', 'Effective encoding of residue contact information is crucial for protein structure prediction since it has a unique role to capture long-range residue interactions compared to other commonly used scoring terms. The residue contact information can be incorporated in structure prediction in several different ways: It can be incorporated as statistical potentials or it can be also used as constraints in ab initio structure prediction. To seek the most effective definition of residue contacts for template-based protein structure prediction, we evaluated 45 different contact definitions, varying bases of contacts and distance cutoffs, in terms of their ability to identify proteins of the same fold.'),
(95242, 2012, 'bmc', 'The tree alignment problem', 'Varón, Andrés|Wheeler, Ward C', 'Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA.|Division of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA.', 'United States|United States', 0, 'Algorithms|Evolution, Molecular|Genome|Molecular Sequence Data|Phylogeny|Programming, Linear|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-293', '23140486', 'The inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred.'),
(95243, 2012, 'bmc', 'Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction', 'Mora, Antonio|Donaldson, Ian M', 'Department for Molecular Biosciences, University of Oslo, Norway.|Department for Molecular Biosciences, University of Oslo, Norway.', 'Norway|Norway', 16, 'Drug Delivery Systems|Humans|Protein Interaction Maps|Proteins', 'Journal Article', '10.1186/1471-2105-13-294', '23146171', 'Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction.'),
(95244, 2012, 'bmc', 'iMS2Flux--a high-throughput processing tool for stable isotope labeled mass spectrometric data used for metabolic flux analysis', 'Poskar, C Hart|Huege, Jan|Krach, Christian|Franke, Mathias|Shachar-Hill, Yair|Junker, Björn H', 'Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.|Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.|Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.|Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.|Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.|Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research-IPK, 06466 Gatersleben, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 29, 'Isotope Labeling|Mass Spectrometry|Metabolic Networks and Pathways|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-13-295', '23146204', 'Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary.'),
(95245, 2012, 'bmc', 'Quantitative fluorescence loss in photobleaching for analysis of protein transport and aggregation', 'Wüstner, Daniel|Solanko, Lukasz M|Lund, Frederik W|Sage, Daniel|Schroll, Hans J|Lomholt, Michael A', 'Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk|Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark. wuestner@bmb.sdu.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 24, 'Cytoplasm|Diffusion|Fluorescence|Fluorescence Recovery After Photobleaching|Green Fluorescent Proteins|Humans|Huntingtin Protein|Kinetics|Nerve Tissue Proteins|Neurodegenerative Diseases|Photobleaching|Protein Transport|Proteins', 'Journal Article', '10.1186/1471-2105-13-296', '23148417', 'Fluorescence loss in photobleaching (FLIP) is a widely used imaging technique, which provides information about protein dynamics in various cellular regions. In FLIP, a small cellular region is repeatedly illuminated by an intense laser pulse, while images are taken with reduced laser power with a time lag between the bleaches. Despite its popularity, tools are lacking for quantitative analysis of FLIP experiments. Typically, the user defines regions of interest (ROIs) for further analysis which is subjective and does not allow for comparing different cells and experimental settings.'),
(95246, 2012, 'bmc', 'Oculus: faster sequence alignment by streaming read compression', 'Veeneman, Brendan A|Iyer, Matthew K|Chinnaiyan, Arul M', 'Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.|Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.', 'United States|United States|United States', 2, 'Algorithms|Data Compression|Genome|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-13-297', '23148484', 'Despite significant advancement in alignment algorithms, the exponential growth of nucleotide sequencing throughput threatens to outpace bioinformatic analysis. Computation may become the bottleneck of genome analysis if growing alignment costs are not mitigated by further improvement in algorithms. Much gain has been gleaned from indexing and compressing alignment databases, but many widely used alignment tools process input reads sequentially and are oblivious to any underlying redundancy in the reads themselves.'),
(95247, 2012, 'bmc', 'Improving accuracy for cancer classification with a new algorithm for genes selection', 'Zhang, Hongyan|Wang, Haiyan|Dai, Zhijun|Chen, Ming-shun|Yuan, Zheming', 'Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China.|Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China.|Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China.|Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China.|Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Changsha 410128, China.', 'China|China|China|China|China', 42, 'Algorithms|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Neoplasms|Support Vector Machine', 'Journal Article', '10.1186/1471-2105-13-298', '23148517', 'Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.'),
(95248, 2012, 'bmc', 'Human-specific protein isoforms produced by novel splice sites in the human genome after the human-chimpanzee divergence', 'Kim, Dong Seon|Hahn, Yoonsoo', 'Department of Life Science, Research Center for Biomolecules and Biosystems, Chung-Ang University, Seoul 156-756, Korea.|Department of Life Science, Research Center for Biomolecules and Biosystems, Chung-Ang University, Seoul 156-756, Korea.', '-|-', 5, 'Animals|Base Sequence|Chromosome Mapping|Evolution, Molecular|Exons|Female|Genome, Human|Humans|Molecular Sequence Annotation|Mutagenesis, Insertional|Nuclear Proteins|Pan troglodytes|Protein Isoforms|Proteomics|RNA Splice Sites|RNA Splicing|Transcription Factors|Ubiquitins', 'Journal Article', '10.1186/1471-2105-13-299', '23148531', 'Evolution of splice sites is a well-known phenomenon that results in transcript diversity during human evolution. Many novel splice sites are derived from repetitive elements and may not contribute to protein products. Here, we analyzed annotated human protein-coding exons and identified human-specific splice sites that arose after the human-chimpanzee divergence.'),
(95249, 2012, 'bmc', 'Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects', 'Wang, Kui|Ng, Shu Kay|McLachlan, Geoffrey J', 'Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia', 16, 'Algorithms|Cell Cycle|Cluster Analysis|Databases, Factual|Gene Expression|Gene Expression Profiling|Models, Genetic|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Software|Transcriptome', 'Journal Article', '10.1186/1471-2105-13-300', '23151154', 'Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models.'),
(95250, 2012, 'bmc', 'NRE: a tool for exploring neutral loci in the human genome', 'Arbiza, Leonardo|Zhong, Elaine|Keinan, Alon', 'Department of Biological Statistics and Computational Biology, Cornell University, 102 Weill Hall, Ithaca 14853, USA.|Department of Biological Statistics and Computational Biology, Cornell University, 102 Weill Hall, Ithaca 14853, USA.|Department of Biological Statistics and Computational Biology, Cornell University, 102 Weill Hall, Ithaca 14853, USA.', 'United States|United States|United States', 17, 'Animals|Genetic Variation|Genetics, Population|Genome, Human|Genomics|Humans|Mammals|Polymorphism, Single Nucleotide|Selection, Genetic|Software', 'Journal Article', '10.1186/1471-2105-13-301', '23151206', 'Analyzing regions of the genome where genetic variation is free from the confounding effects of natural selection is essential for many population genetic studies. Several recent studies in humans have stressed the large effect of natural selection at linked neutral sites and have shown that the choice of putatively neutral regions can have a marked effect on estimates of demographic history.'),
(95251, 2012, 'bmc', 'PolySac3DB: an annotated data base of 3 dimensional structures of polysaccharides', 'Sarkar, Anita|Pérez, Serge', 'Centre de Recherches sur les Macromolécules Végétales (CERMAV*) Centre National de la Recherche Scientifique, Grenoble Cedex 9, BP 53X, F-38041, France.|Centre de Recherches sur les Macromolécules Végétales (CERMAV*) Centre National de la Recherche Scientifique, Grenoble Cedex 9, BP 53X, F-38041, France.', 'France|France', 14, 'Carbohydrate Sequence|Databases, Factual|Internet|Molecular Sequence Annotation|Polysaccharides|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-13-302', '23151233', 'Polysaccharides are ubiquitously present in the living world. Their structural versatility makes them important and interesting components in numerous biological and technological processes ranging from structural stabilization to a variety of immunologically important molecular recognition events. The knowledge of polysaccharide three-dimensional (3D) structure is important in studying carbohydrate-mediated host-pathogen interactions, interactions with other bio-macromolecules, drug design and vaccine development as well as material science applications or production of bio-ethanol.'),
(95252, 2012, 'bmc', 'Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model', 'Beuf, Kristof De|Schrijver, Joachim De|Thas, Olivier|Criekinge, Wim Van|Irizarry, Rafael A|Clement, Lieven', 'Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be|Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Belgium. kristof.debeuf@ugent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 13, 'Escherichia coli K12|Poisson Distribution|Sequence Analysis, DNA|Sequence Deletion', 'Journal Article', '10.1186/1471-2105-13-303', '23151247', '454 pyrosequencing is a commonly used massively parallel DNA sequencing technology with a wide variety of application fields such as epigenetics, metagenomics and transcriptomics. A well-known problem of this platform is its sensitivity to base-calling insertion and deletion errors, particularly in the presence of long homopolymers. In addition, the base-call quality scores are not informative with respect to whether an insertion or a deletion error is more likely. Surprisingly, not much effort has been devoted to the development of improved base-calling methods and more intuitive quality scores for this platform.'),
(95253, 2012, 'bmc', 'Improving protein coreference resolution by simple semantic classification', 'Nguyen, Ngan|Kim, Jin-Dong|Miwa, Makoto|Matsuzaki, Takuya|Tsujii, Junichi', 'National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp|National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp|National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp|National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp|National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 10, 'Computational Biology|Proteins|Semantics', 'Journal Article', '10.1186/1471-2105-13-304', '23157272', 'Current research has shown that major difficulties in event extraction for the biomedical domain are traceable to coreference. Therefore, coreference resolution is believed to be useful for improving event extraction. To address coreference resolution in molecular biology literature, the Protein Coreference (COREF) task was arranged in the BioNLP Shared Task (BioNLP-ST, hereafter) 2011, as a supporting task. However, the shared task results indicated that transferring coreference resolution methods developed for other domains to the biological domain was not a straight-forward task, due to the domain differences in the coreference phenomena.'),
(95254, 2012, 'bmc', 'Copy Number Variation detection from 1000 Genomes Project exon capture sequencing data', 'Wu, Jiantao|Grzeda, Krzysztof R|Stewart, Chip|Grubert, Fabian|Urban, Alexander E|Snyder, Michael P|Marth, Gabor T', 'Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.|Boston College, Boston, Chestnut Hill, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 21, 'Bayes Theorem|DNA Copy Number Variations|Exome|Exons|Genome|Genotype|High-Throughput Nucleotide Sequencing|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-13-305', '23157288', 'DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.'),
(95255, 2012, 'bmc', 'Gains of ubiquitylation sites in highly conserved proteins in the human lineage', 'Kim, Dong Seon|Hahn, Yoonsoo', 'Department of Life Science, Research Center for Biomolecules and Biosystems, Chung-Ang University, Seoul, 156-756, Korea. yoonsoo.hahn@gmail.com|Department of Life Science, Research Center for Biomolecules and Biosystems, Chung-Ang University, Seoul, 156-756, Korea. yoonsoo.hahn@gmail.com', '-|-', 11, 'Amino Acid Sequence|Animals|Conserved Sequence|Evolution, Molecular|Humans|Lysine|Molecular Sequence Data|Pan troglodytes|Protein Interaction Maps|Proteins|Proteolysis|Ubiquitin|Ubiquitination', 'Journal Article', '10.1186/1471-2105-13-306', '23157318', 'Post-translational modification of lysine residues of specific proteins by ubiquitin modulates the degradation, localization, and activity of these target proteins. Here, we identified gains of ubiquitylation sites in highly conserved regions of human proteins that occurred during human evolution.'),
(95256, 2012, 'bmc', 'Multiple graph regularized protein domain ranking', 'Wang, Jim Jing-Yan|Bensmail, Halima|Gao, Xin', 'Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. xin.gao@kaust.edu.sa|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. xin.gao@kaust.edu.sa|Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia. xin.gao@kaust.edu.sa', 'Saudi Arabia|Saudi Arabia|Saudi Arabia', 38, 'Algorithms|Databases, Protein|Protein Structure, Tertiary|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-13-307', '23157331', 'Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.'),
(95257, 2012, 'bmc', 'Estimating relative abundances of proteins from shotgun proteomics data', 'McIlwain, Sean|Mathews, Michael|Bereman, Michael S|Rubel, Edwin W|MacCoss, Michael J|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States|United States', 48, 'Mass Spectrometry|Peptides|Proteins|Proteomics|Reproducibility of Results|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-13-308', '23164367', 'Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SI(N)), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).'),
(95258, 2012, 'bmc', 'An assessment on epitope prediction methods for protozoa genomes', 'Resende, Daniela M|Rezende, Antônio M|Oliveira, Nesley J D|Batista, Izabella C A|Corrêa-Oliveira, Rodrigo|Reis, Alexandre B|Ruiz, Jeronimo C', 'Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.|Programa de Pós-Graduação em Ciências Farmacêuticas-CiPharma, Laboratório de Pesquisas Clínicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto, MG 35400-000, Brazil.', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 19, 'Algorithms|Antigens, Protozoan|Area Under Curve|Computer Simulation|Epitopes, B-Lymphocyte|Epitopes, T-Lymphocyte|Genome, Protozoan|Leishmania|Protozoan Vaccines|Software', 'Journal Article', '10.1186/1471-2105-13-309', '23170965', 'Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a) evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve) performance and a threshold dependent method that employs a confusion matrix, b) integrating data from experimentally validated and in silico predicted epitopes, and c) integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes.'),
(95259, 2012, 'bmc', 'Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data', 'Barros, Rodrigo C|Winck, Ana T|Machado, Karina S|Basgalupp, Márcio P|de Carvalho, André C P L F|Ruiz, Duncan D|de Souza, Osmar Norberto', 'University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br|University of São Paulo, São Carlos, Brazil. rcbarros@icmc.usp.br', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 16, 'Algorithms|Antitubercular Agents|Bacterial Proteins|Computational Biology|Decision Trees|Directed Molecular Evolution|Drug Design|Entropy|Ligands|Molecular Conformation|Molecular Docking Simulation|Mycobacterium tuberculosis|Oxidoreductases|Protein Binding', 'Journal Article', '10.1186/1471-2105-13-310', '23171000', 'This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance.'),
(95260, 2012, 'bmc', 'Bioinformatics Resource Manager v23: an integrated software environment for systems biology with microRNA and cross-species analysis tools', 'Tilton, Susan C|Tal, Tamara L|Scroggins, Sheena M|Franzosa, Jill A|Peterson, Elena S|Tanguay, Robert L|Waters, Katrina M', 'Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov|Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA, USA. susan.tilton@pnnl.gov', 'United States|United States|United States|United States|United States|United States|United States', 17, 'Animals|Computational Biology|Gene Expression Regulation|Humans|MicroRNAs|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Sequence Analysis, RNA|Software|Systems Biology|Zebrafish', 'Journal Article', '10.1186/1471-2105-13-311', '23174015', 'MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets.'),
(95261, 2012, 'bmc', 'Core Hunter II: fast core subset selection based on multiple genetic diversity measures using Mixed Replica search', 'De Beukelaer, Herman|Smýkal, Petr|Davenport, Guy F|Fack, Veerle', 'Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be|Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be|Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be|Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium. herman.debeukelaer@ugent.be', 'Belgium|Belgium|Belgium|Belgium', 29, 'Algorithms|Alleles|Genetic Variation|Monte Carlo Method|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-13-312', '23174036', 'Sampling core subsets from genetic resources while maintaining as much as possible the genetic diversity of the original collection is an important but computationally complex task for gene bank managers. The Core Hunter computer program was developed as a tool to generate such subsets based on multiple genetic measures, including both distance measures and allelic diversity indices. At first we investigate the effect of minimum (instead of the default mean) distance measures on the performance of Core Hunter. Secondly, we try to gain more insight into the performance of the original Core Hunter search algorithm through comparison with several other heuristics working with several realistic datasets of varying size and allelic composition. Finally, we propose a new algorithm (Mixed Replica search) for Core Hunter II with the aim of improving the diversity of the constructed core sets and their corresponding generation times.');
