INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93989, 2010, 'bmc', 'TESTLoc: protein subcellular localization prediction from EST data', 'Shen, Yao-Qing|Burger, Gertraud', 'Robert-Cedergren Center for Bioinformatics and Genomics, Biochemistry Department, Université de Montréal, 2900 Edouard-Montpetit, Montreal, QC, H3T 1J4, Canada. yaoqing.shen@umontreal.ca|Robert-Cedergren Center for Bioinformatics and Genomics, Biochemistry Department, Université de Montréal, 2900 Edouard-Montpetit, Montreal, QC, H3T 1J4, Canada. yaoqing.shen@umontreal.ca', 'Canada|Canada', 10, 'Databases, Genetic|Databases, Protein|Expressed Sequence Tags|Proteins|Sequence Analysis, Protein|Software|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-11-563', '21078192', 'The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.'),
(93990, 2010, 'bmc', 'Empirical bayes analysis of sequencing-based transcriptional profiling without replicates', 'Wu, Zhijin|Jenkins, Bethany D|Rynearson, Tatiana A|Dyhrman, Sonya T|Saito, Mak A|Mercier, Melissa|Whitney, Leann P', 'Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu|Center for Statistical Sciences and Department of Community Health, Box G-121S-7, Brown University, Providence RI 02912, USA. zwu@stat.brown.edu', 'United States|United States|United States|United States|United States|United States|United States', 33, 'Bayes Theorem|Databases, Genetic|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-564', '21080965', 'Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses.'),
(93991, 2010, 'bmc', 'Reanalyze unassigned reads in Sanger based metagenomic data using conserved gene adjacency', 'Weng, Francis C|Su, Chien-Hao|Hsu, Ming-Tsung|Wang, Tse-Yi|Tsai, Huai-Kuang|Wang, Daryi', 'Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.|Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.', '-|-|-|-|-|-', 6, 'Algorithms|Base Sequence|Databases, Factual|Gene Order|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-565', '21083935', 'Investigation of metagenomes provides greater insight into uncultured microbial communities. The improvement in sequencing technology, which yields a large amount of sequence data, has led to major breakthroughs in the field. However, at present, taxonomic binning tools for metagenomes discard 30-40% of Sanger sequencing data due to the stringency of BLAST cut-offs. In an attempt to provide a comprehensive overview of metagenomic data, we re-analyzed the discarded metagenomes by using less stringent cut-offs. Additionally, we introduced a new criterion, namely, the evolutionary conservation of adjacency between neighboring genes. To evaluate the feasibility of our approach, we re-analyzed discarded contigs and singletons from several environments with different levels of complexity. We also compared the consistency between our taxonomic binning and those reported in the original studies.'),
(93992, 2010, 'bmc', 'Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study', 'Viti, Federica|Merelli, Ivan|Timmermans, Mieke|den Bakker, Michael|Beltrame, Francesco|Riegman, Peter|Milanesi, Luciano', 'Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it|Institute for Biomedical Technologies of the National Research Council, Segrate, Milan, Italy. federica.viti@dist.unige.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, 'Adenocarcinoma|Breast Neoplasms|Female|Humans|Pilot Projects|Tissue Array Analysis', 'Journal Article', '10.1186/1471-2105-11-566', '21087464', 'Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.'),
(93993, 2010, 'bmc', 'Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data', 'Bartenhagen, Christoph|Klein, Hans-Ulrich|Ruckert, Christian|Jiang, Xiaoyi|Dugas, Martin', 'Department of Medical Informatics and Biomathematics, University of Münster, Domagkstraße 9, 48149 Münster, Germany. Christoph.Bartenhagen@ukmuenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstraße 9, 48149 Münster, Germany. Christoph.Bartenhagen@ukmuenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstraße 9, 48149 Münster, Germany. Christoph.Bartenhagen@ukmuenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstraße 9, 48149 Münster, Germany. Christoph.Bartenhagen@ukmuenster.de|Department of Medical Informatics and Biomathematics, University of Münster, Domagkstraße 9, 48149 Münster, Germany. Christoph.Bartenhagen@ukmuenster.de', 'Germany|Germany|Germany|Germany|Germany', 43, 'Algorithms|Databases, Factual|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Principal Component Analysis', 'Comparative Study', '10.1186/1471-2105-11-567', '21087509', 'Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.'),
(93994, 2010, 'bmc', 'Peptide binding predictions for HLA DR, DP and DQ molecules', 'Wang, Peng|Sidney, John|Kim, Yohan|Sette, Alessandro|Lund, Ole|Nielsen, Morten|Peters, Bjoern', 'La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.|La Jolla Institute for Allergy and Immunology, La Jolla, USA.', 'United States|United States|United States|United States|United States|United States|United States', 296, 'Alleles|Binding Sites|Epitopes|Genes, MHC Class II|HLA-DP Antigens|HLA-DQ Antigens|HLA-DR Antigens|Humans|Peptides', 'Journal Article', '10.1186/1471-2105-11-568', '21092157', 'MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a significant gap in knowledge as HLA DP and DQ molecules are presumably equally important, and have only been studied less because they are more difficult to handle experimentally.'),
(93995, 2010, 'bmc', 'Knowledge-based biomedical word sense disambiguation: comparison of approaches', 'Jimeno-Yepes, Antonio J|Aronson, Alan R', 'National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com|National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA. antonio.jimeno@gmail.com', 'United States|United States', 34, 'Algorithms|Artificial Intelligence|Knowledge Bases|MEDLINE|Natural Language Processing|Semantics|United States|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-569', '21092226', 'Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain.'),
(93996, 2010, 'bmc', 'Reticular alignment: a progressive corner-cutting method for multiple sequence alignment', 'Szabó, Adrienn|Novák, Adám|Miklós, István|Hein, Jotun', 'Computer and Automation Research Institute, Hungarian Academy of Sciences, Lágymányosi u 11, 1111 Budapest, Hungary.|Computer and Automation Research Institute, Hungarian Academy of Sciences, Lágymányosi u 11, 1111 Budapest, Hungary.|Computer and Automation Research Institute, Hungarian Academy of Sciences, Lágymányosi u 11, 1111 Budapest, Hungary.|Computer and Automation Research Institute, Hungarian Academy of Sciences, Lágymányosi u 11, 1111 Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary', 12, 'Algorithms|Databases, Factual|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-570', '21092255', 'In this paper, we introduce a progressive corner cutting method called Reticular Alignment for multiple sequence alignment. Unlike previous corner-cutting methods, our approach does not define a compact part of the dynamic programming table. Instead, it defines a set of optimal and suboptimal alignments at each step during the progressive alignment. The set of alignments are represented with a network to store them and use them during the progressive alignment in an efficient way. The program contains a threshold parameter on which the size of the network depends. The larger the threshold parameter and thus the network, the deeper the search in the alignment space for better scored alignments.'),
(93997, 2010, 'bmc', 'Probabilistic principal component analysis for metabolomic data', 'Nyamundanda, Gift|Brennan, Lorraine|Gormley, Isobel Claire', 'School of Mathematical Sciences, University College Dublin, Ireland.|School of Mathematical Sciences, University College Dublin, Ireland.|School of Mathematical Sciences, University College Dublin, Ireland.', 'Ireland|Ireland|Ireland', 68, 'Algorithms|Databases, Factual|Metabolomics|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-11-571', '21092268', 'Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model.'),
(93998, 2010, 'bmc', 'Pash 30: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing', 'Coarfa, Cristian|Yu, Fuli|Miller, Christopher A|Chen, Zuozhou|Harris, R Alan|Milosavljevic, Aleksandar', 'Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu|Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza Houston, TX 77030, USA. coarfa@bcm.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Base Sequence|DNA|Databases, Genetic|Epigenomics|Genetic Variation|Genome|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-572', '21092284', 'Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.'),
(93999, 2010, 'bmc', 'DeltaProt: a software toolbox for comparative genomics', 'Thorvaldsen, Steinar|Flå, Tor|Willassen, Nils P', 'Department of Mathematics and Statistics, University of Tromsø, 9037 Tromsø, Norway. steinar.thorvaldsen@uit.no|Department of Mathematics and Statistics, University of Tromsø, 9037 Tromsø, Norway. steinar.thorvaldsen@uit.no|Department of Mathematics and Statistics, University of Tromsø, 9037 Tromsø, Norway. steinar.thorvaldsen@uit.no', 'Norway|Norway|Norway', 0, 'Databases, Protein|Genomics|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Comparative Study', '10.1186/1471-2105-11-573', '21092291', 'Statistical bioinformatics is the study of biological data sets obtained by new micro-technologies by means of proper statistical methods. For a better understanding of environmental adaptations of proteins, orthologous sequences from different habitats may be explored and compared. The main goal of the DeltaProt Toolbox is to provide users with important functionality that is needed for comparative screening and studies of extremophile proteins and protein classes. Visualization of the data sets is also the focus of this article, since visualizations can play a key role in making the various relationships transparent. This application paper is intended to inform the reader of the existence, functionality, and applicability of the toolbox.'),
(94000, 2010, 'bmc', 'iGTP: a software package for large-scale gene tree parsimony analysis', 'Chaudhary, Ruchi|Bansal, Mukul S|Wehe, André|Fernández-Baca, David|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.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Databases, Genetic|Evolution, Molecular|Gene Duplication|Genome|Genomics|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-11-574', '21092314', 'The ever-increasing wealth of genomic sequence information provides an unprecedented opportunity for large-scale phylogenetic analysis. However, species phylogeny inference is obfuscated by incongruence among gene trees due to evolutionary events such as gene duplication and loss, incomplete lineage sorting (deep coalescence), and horizontal gene transfer. Gene tree parsimony (GTP) addresses this issue by seeking a species tree that requires the minimum number of evolutionary events to reconcile a given set of incongruent gene trees. Despite its promise, the use of gene tree parsimony has been limited by the fact that existing software is either not fast enough to tackle large data sets or is restricted in the range of evolutionary events it can handle.'),
(94001, 2010, 'bmc', 'Sampling the conformation of protein surface residues for flexible protein docking', 'Francis-Lyon, Patricia|Gu, Shengyin|Hass, Joel|Amenta, Nina|Koehl, Patrice', 'Department of Computer Science and Genome Center, University of California, Davis, CA 95616, USA. koehl@cs.ucdavis.edu|Department of Computer Science and Genome Center, University of California, Davis, CA 95616, USA. koehl@cs.ucdavis.edu|Department of Computer Science and Genome Center, University of California, Davis, CA 95616, USA. koehl@cs.ucdavis.edu|Department of Computer Science and Genome Center, University of California, Davis, CA 95616, USA. koehl@cs.ucdavis.edu|Department of Computer Science and Genome Center, University of California, Davis, CA 95616, USA. koehl@cs.ucdavis.edu', 'United States|United States|United States|United States|United States', 0, 'Databases, Protein|Dimerization|Molecular Conformation|Protein Conformation|Proteins|Surface Properties', 'Journal Article', '10.1186/1471-2105-11-575', '21092317', 'The problem of determining the physical conformation of a protein dimer, given the structures of the two interacting proteins in their unbound state, is a difficult one. The location of the docking interface is determined largely by geometric complementarity, but finding complementary geometry is complicated by the flexibility of the backbone and side-chains of both proteins. We seek to generate candidates for docking that approximate the bound state well, even in cases where there is backbone and/or side-chain difference from unbound to bound states.'),
(94002, 2010, 'bmc', 'Validation and characterization of DNA microarray gene expression data distribution and associated moments', 'Thomas, Reuben|de la Torre, Luis|Chang, Xiaoqing|Mehrotra, Sanjay', 'Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA. mehrotra@iems.northwestern.edu|Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA. mehrotra@iems.northwestern.edu|Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA. mehrotra@iems.northwestern.edu|Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA. mehrotra@iems.northwestern.edu', 'United States|United States|United States|United States', 0, 'Databases, Genetic|Gene Expression|Gene Expression Profiling|Logistic Models|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-576', '21092329', 'The data from DNA microarrays are increasingly being used in order to understand effects of different conditions, exposures or diseases on the modulation of the expression of various genes in a biological system. This knowledge is then further used in order to generate molecular mechanistic hypotheses for an organism when it is exposed to different conditions. Several different methods have been proposed to analyze these data under different distributional assumptions on gene expression. However, the empirical validation of these assumptions is lacking.'),
(94003, 2010, 'bmc', 'InCoB2010 - 9th International Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010', 'Schönbach, Christian|Nakai, Kenta|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp|Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Fukuoka 820-8502, Japan. schoen@bio.kyutech.ac.jp', 'Japan|Japan|Japan|Japan', 1, 'Animals|Asia|Computational Biology|Databases, Factual|Humans|Japan|Publications|Sequence Analysis|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-11-S7-S1', '21106116', 'The International Conference on Bioinformatics (InCoB), the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted in one of countries of the Asia-Pacific region. The 2010 conference was awarded to Japan and has attracted more than one hundred high-quality research paper submissions. Thorough peer reviewing resulted in 47 (43.5%) accepted papers out of 108 submissions. Submissions from Japan, R.O. Korea, P.R. China, Australia, Singapore and U.S.A totaled 43.8% and contributed to 57.4% of accepted papers. Manuscripts originating from Taiwan and India added up to 42.8% of submissions and 28.3% of acceptances. The fifteen articles published in this BMC Bioinformatics supplement cover disease informatics, structural bioinformatics and drug design, biological databases and software tools, signaling pathways, gene regulatory and biochemical networks, evolution and sequence analysis.'),
(94004, 2010, 'bmc', 'Analysis and verification of the HMGB1 signaling pathway', 'Gong, Haijun|Zuliani, Paolo|Komuravelli, Anvesh|Faeder, James R|Clarke, Edmund M', 'Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu|Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. haijung@cs.cmu.edu', 'United States|United States|United States|United States|United States', 0, 'Apoptosis|Cell Proliferation|Computer Simulation|Gene Expression Regulation, Neoplastic|HMGB1 Protein|Models, Biological|Mutation|Neoplasms|Oncogene Proteins|Signal Transduction|Tumor Suppressor Proteins', 'Journal Article', '10.1186/1471-2105-11-S7-S10', '21106117', 'Recent studies have found that overexpression of the High-mobility group box-1 (HMGB1) protein, in conjunction with its receptors for advanced glycation end products (RAGEs) and toll-like receptors (TLRs), is associated with proliferation of various cancer types, including that of the breast and pancreatic.'),
(94005, 2010, 'bmc', 'Modularity of Escherichia coli sRNA regulation revealed by sRNA-target and protein network analysis', 'Wu, Timothy H|Chang, Ian Yi-Feng|Chu, Li-chieh Julie|Huang, Hsuan-Cheng|Ng, Wailap Victor', 'Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.', '-|-|-|-|-', 3, 'Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|RNA, Bacterial|RNA, Small Untranslated|Repressor Proteins|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-11-S7-S11', '21106118', 'sRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks.'),
(94006, 2010, 'bmc', 'Sensitivity analysis of dynamic biological systems with time-delays', 'Wu, Wu Hsiung|Wang, Feng Sheng|Chang, Maw Shang', 'Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw|Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw', '-|-|-', 5, 'Algorithms|Cardiovascular System|Computer Simulation|DNA Fragmentation|Humans|Models, Biological|NF-kappa B|Reproducibility of Results|Signal Transduction|Systems Biology|Time Factors|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1186/1471-2105-11-S7-S12', '21106119', 'Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary.'),
(94007, 2010, 'bmc', 'Modeling the competition between lung metastases and the immune system using agents', 'Pennisi, Marzio|Pappalardo, Francesco|Palladini, Ariannna|Nicoletti, Giordano|Nanni, Patrizia|Lollini, Pier-Luigi|Motta, Santo', 'Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it|Department of Mathematics and Computer Science, University of Catania, V,le A, Doria 6, Catania, Italy. mpennisi@dmi.unict.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 40, 'Animals|Cancer Vaccines|Computational Biology|Female|Immune System|Lung Neoplasms|Mammary Neoplasms, Experimental|Mice|Mice, Inbred BALB C|Mice, Transgenic|Models, Biological|Neoplasm Metastasis|Reproducibility of Results|Vaccination', 'Journal Article', '10.1186/1471-2105-11-S7-S13', '21106120', 'The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations.'),
(94008, 2010, 'bmc', 'DiScRIBinATE: a rapid method for accurate taxonomic classification of metagenomic sequences', 'Ghosh, Tarini Shankar|Monzoorul Haque, M|Mande, Sharmila S', 'Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India. tarini@atc.tcs.com|Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India. tarini@atc.tcs.com|Bio-Sciences Division, Innovation Labs, Tata Consultancy Services, 1 Software Units Layout, Hyderabad 500 081, Andhra Pradesh, India. tarini@atc.tcs.com', 'India|India|India', 27, 'Algorithms|Bacteria|Classification|Genome, Bacterial|Metagenomics|Reproducibility of Results|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1186/1471-2105-11-S7-S14', '21106121', 'In metagenomic sequence data, majority of sequences/reads originate from new or partially characterized genomes, the corresponding sequences of which are absent in existing reference databases. Since taxonomic assignment of reads is based on their similarity to sequences from known organisms, the presence of reads originating from new organisms poses a major challenge to taxonomic binning methods. The recently published SOrt-ITEMS algorithm uses an elaborate work-flow to assign reads originating from hitherto unknown genomes with significant accuracy and specificity. Nevertheless, a significant proportion of reads still get misclassified. Besides, the use of an alignment-based orthology step (for improving the specificity of assignments) increases the total binning time of SOrt-ITEMS.'),
(94009, 2010, 'bmc', 'IKKxcexb2 inhibitor identification: a multi-filter driven novel scaffold', 'Nagarajan, Shanthi|Choo, Hyunah|Cho, Yong Seo|Shin, Kye Jung|Oh, Kwang-Seok|Lee, Byung Ho|Pae, Ae Nim', 'Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com|Neuro-Medicine Center, Life/Health Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea. shanthi.bi@gmail.com', '-|-|-|-|-|-|-', 8, 'Computational Biology|Decision Trees|Drug Discovery|Drug Evaluation, Preclinical|Enzyme Activation|I-kappa B Kinase|Inhibitory Concentration 50|Models, Chemical|Protein Kinase Inhibitors|Quantitative Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-11-S7-S15', '21106122', 'Nuclear factor kappa B (NF-xcexbaB) is a chief nuclear transcription factor that controls the transcription of various genes, and its activation is tightly controlled by Inhibitor kappa B kinase (IKK). The irregular transcription of NF-xcexbaB has been linked to auto-immune disorders, cancer and other diseases. The IKK complex is composed of three units, IKKxcexb1, IKKxcexb2, and the regulatory domain NEMO, of which IKKxcexb2 is well understood in the canonical pathway. Therefore, the inhibition of IKKxcexb2 by drugs forms the molecular basis for anti-inflammatory drug research.'),
(94010, 2010, 'bmc', 'Identification of a better Homo sapiens Class II HDAC inhibitor through binding energy calculations and descriptor analysis', 'Tambunan, Usman Sumo Friend|Wulandari, Evi Kristin', 'Laboratory of Bioinformatics, Department of Chemistry, Faculty of Mathematics and Natural Science, University of Indonesia, Depok 16424, Indonesia. usman@ui.ac.id|Laboratory of Bioinformatics, Department of Chemistry, Faculty of Mathematics and Natural Science, University of Indonesia, Depok 16424, Indonesia. usman@ui.ac.id', 'Indonesia|Indonesia', 19, 'Amino Acid Sequence|Animals|Chromatography, Gel|Histone Deacetylase Inhibitors|Histone Deacetylases|Humans|Lethal Dose 50|Mice|Models, Chemical|Protein Binding|Rats|Sequence Alignment|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-S7-S16', '21106123', 'Human papillomaviruses (HPVs) are the most common on sexually transmitted viruses in the world. HPVs are responsible for a large spectrum of deseases, both benign and malignant. The certain types of HPV are involved in the development of cervical cancer. In attemps to find additional drugs in the treatment of cervical cancer, inhibitors of the histone deacetylases (HDAC) have received much attention due to their low cytotoxic profiles and the E6/E7 oncogene function of human papilomavirus can be completely by passed by HDAC inhibition. The histone deacetylase inhibitors can induce growth arrest, differentiation and apoptosis of cancer cells. HDAC class I and class II are considered the main targets for cancer. Therefore, the six HDACs class II was modeled and about two inhibitors (SAHA and TSA) were docked using AutoDock4.2, to each of the inhibitor in order to identify the pharmacological properties. Based on the results of docking, SAHA and TSA were able to bind with zinc ion in HDACs models as a drug target. SAHA was satisfied almost all the properties i.e., binding affinity, the Drug-Likeness value and Drug Score with 70% oral bioavailability and the carbonyl group of these compound fits well into the active site of the target where the zinc is present. Hence, SAHA could be developed as potential inhibitors of class II HDACs and valuable cervical cancer drug candidate.'),
(94011, 2010, 'bmc', 'Gradual transition from mosaic to global DNA methylation patterns during deuterostome evolution', 'Okamura, Kohji|Matsumoto, Kazuaki A|Nakai, Kenta', 'Human Genome Centre, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato Ward, Tokyo 108-8639, Japan.|Human Genome Centre, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato Ward, Tokyo 108-8639, Japan.|Human Genome Centre, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato Ward, Tokyo 108-8639, Japan.', 'Japan|Japan|Japan', 14, 'Animals|CpG Islands|DNA Methylation|Epigenomics|Fishes|Genome|Humans|Phylogeny|Urochordata|Vertebrates', 'Journal Article', '10.1186/1471-2105-11-S7-S2', '21106124', 'DNA methylation by the Dnmt family occurs in vertebrates and invertebrates, including ascidians, and is thought to play important roles in gene regulation and genome stability, especially in vertebrates. However, the global methylation patterns of vertebrates and invertebrates are distinctive. Whereas almost all CpG sites are methylated in vertebrates, with the exception of those in CpG islands, the ascidian genome contains approximately equal amounts of methylated and unmethylated regions. Curiously, methylation status can be reliably estimated from the local frequency of CpG dinucleotides in the ascidian genome. Methylated and unmethylated regions tend to have few and many CpG sites, respectively, consistent with our knowledge of the methylation status of CpG islands and other regions in mammals. However, DNA methylation patterns and levels in vertebrates and invertebrates have not been analyzed in the same way.'),
(94012, 2010, 'bmc', 'Robust and accurate prediction of noncoding RNAs from aligned sequences', 'Saito, Yutaka|Sato, Kengo|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.|Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.|Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.', 'Japan|Japan|Japan', 1, 'Algorithms|Base Sequence|Computational Biology|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Untranslated|Reproducibility of Results|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-S7-S3', '21106125', 'Computational prediction of noncoding RNAs (ncRNAs) is an important task in the post-genomic era. One common approach is to utilize the profile information contained in alignment data rather than single sequences. However, this strategy involves the possibility that the quality of input alignments can influence the performance of prediction methods. Therefore, the evaluation of the robustness against alignment errors is necessary as well as the development of accurate prediction methods.'),
(94013, 2010, 'bmc', 'T3SEdb: data warehousing of virulence effectors secreted by the bacterial Type III Secretion System', 'Tay, Daniel Ming Ming|Govindarajan, Kunde Ramamoorthy|Khan, Asif M|Ong, Terenze Yao Rui|Samad, Hanif M|Soh, Wei Wei|Tong, Minyan|Zhang, Fan|Tan, Tin Wee', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 28, 'Algorithms|Bacteria|Bacterial Secretion Systems|Databases, Protein|Genetic Variation|Virulence Factors', 'Journal Article', '10.1186/1471-2105-11-S7-S4', '21106126', 'Effectors of Type III Secretion System (T3SS) play a pivotal role in establishing and maintaining pathogenicity in the host and therefore the identification of these effectors is important in understanding virulence. However, the effectors display high level of sequence diversity, therefore making the identification a difficult process. There is a need to collate and annotate existing effector sequences in public databases to enable systematic analyses of these sequences for development of models for screening and selection of putative novel effectors from bacterial genomes that can be validated by a smaller number of key experiments.'),
(94014, 2010, 'bmc', 'A quantitatively-modeled homozygosity mapping algorithm, qHomozygosityMapping, utilizing whole genome single nucleotide polymorphism genotyping data', '-', '-', '-', 8, 'Algorithms|Consanguinity|Genes, Recessive|Genome|Genomics|Genotype|Homozygote|Humans|Japan|Models, Genetic|Polymorphism, Single Nucleotide|Reproducibility of Results|alpha 1-Antitrypsin Deficiency', 'Journal Article', '10.1186/1471-2105-11-S7-S5', '21106127', 'Homozygosity mapping is a powerful procedure that is capable of detecting recessive disease-causing genes in a few patients from families with a history of inbreeding. We report here a homozygosity mapping algorithm for high-density single nucleotide polymorphism arrays that is able to (i) correct genotyping errors, (ii) search for autozygous segments genome-wide through regions with runs of homozygous SNPs, (iii) check the validity of the inbreeding history, and (iv) calculate the probability of the disease-causing gene being located in the regions identified. The genotyping error correction restored an average of 94.2% of the total length of all regions with run of homozygous SNPs, and 99.9% of the total length of them that were longer than 2 cM. At the end of the analysis, we would know the probability that regions identified contain a disease-causing gene, and we would be able to determine how much effort should be devoted to scrutinizing the regions. We confirmed the power of this algorithm using 6 patients with Siiyama-type xcexb11-antitrypsin deficiency, a rare autosomal recessive disease in Japan. Our procedure will accelerate the identification of disease-causing genes using high-density SNP array data.'),
(94015, 2010, 'bmc', 'DODO: an efficient orthologous genes assignment tool based on domain architectures Domain based ortholog detection', 'Chen, Ting-wen|Wu, Timothy H|Ng, Wailap V|Lin, Wen-chang', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.', '-|-|-|-', 0, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Databases, Genetic|Genome|Humans|Mice|Molecular Sequence Annotation|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-11-S7-S6', '21106128', 'Orthologs are genes derived from the same ancestor gene loci after speciation events. Orthologous proteins usually have similar sequences and perform comparable biological functions. Therefore, ortholog identification is useful in annotations of newly sequenced genomes. With rapidly increasing number of sequenced genomes, constructing or updating ortholog relationship between all genomes requires lots of effort and computation time. In addition, elucidating ortholog relationships between distantly related genomes is challenging because of the lower sequence similarity. Therefore, an efficient ortholog detection method that can deal with large number of distantly related genomes is desired.'),
(94016, 2010, 'bmc', 'Intrinsically disordered domains deviate significantly from random sequences in mammalian proteins', 'Teraguchi, Shunsuke|Patil, Ashwini|Standley, Daron M', 'Laboratory of Host Defense, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan. teraguch@ifrec.osaka-u.ac.jp|Laboratory of Host Defense, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan. teraguch@ifrec.osaka-u.ac.jp|Laboratory of Host Defense, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan. teraguch@ifrec.osaka-u.ac.jp', 'Japan|Japan|Japan', 0, 'Amino Acid Motifs|Amino Acid Sequence|Amino Acids|Animals|Cluster Analysis|Humans|Immunity, Innate|Mice|Protein Structure, Tertiary|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-11-S7-S7', '21106129', 'In order to characterize mammalian intrinsically disordered domains (IDDs) we examined the patterns in their amino acid abundance as well as overrepresented local sequence motifs. We considered IDDs from mouse proteins associated with innate immune responses as well as a set of generic human genes. These sets were compared with artificially generated random sequences with the same overall amino acid abundance and length distributions. IDDs were then clustered by amino acid abundance, and further analyzed in terms of co-occurrence of clusters with functionally characterized Pfam domains.'),
(94017, 2010, 'bmc', 'Integrating diverse biological and computational sources for reliable protein-protein interactions', 'Wu, Min|Li, Xiaoli|Chua, Hon Nian|Kwoh, Chee-Keong|Ng, See-Kiong', 'School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Computational Biology|Protein Interaction Mapping|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-S7-S8', '21106130', 'Protein-protein interactions (PPIs) play important roles in various cellular processes. However, the low quality of current PPI data detected from high-throughput screening techniques has diminished the potential usefulness of the data. We need to develop a method to address the high data noise and incompleteness of PPI data, namely, to filter out inaccurate protein interactions (false positives) and predict putative protein interactions (false negatives).'),
(94018, 2010, 'bmc', 'Network analysis of human protein location', 'Kumar, Gaurav|Ranganathan, Shoba', 'ARC Centre of Excellence in Bioinformatics and Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney NSW, Australia. gaurav.kumar@mq.edu.au|ARC Centre of Excellence in Bioinformatics and Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney NSW, Australia. gaurav.kumar@mq.edu.au', 'Australia|Australia', 0, 'Amino Acid Sequence|Cells|Computational Biology|Databases, Genetic|Humans|Intracellular Space|Metabolic Networks and Pathways|Molecular Sequence Annotation|Proteins', 'Journal Article', '10.1186/1471-2105-11-S7-S9', '21106131', 'Understanding cellular systems requires the knowledge of a proteins subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94019, 2010, 'bmc', 'Speeding up tandem mass spectrometry-based database searching by longest common prefix', 'Zhou, Chen|Chi, Hao|Wang, Le-Heng|Li, You|Wu, Yan-Jie|Fu, Yan|Sun, Rui-Xiang|He, Si-Min', 'Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.|Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.', 'China|China|China|China|China|China|China|China', 5, 'Algorithms|Databases, Protein|Peptide Mapping|Peptides|Proteins|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-11-577', '21108792', 'Tandem mass spectrometry-based database searching has become an important technology for peptide and protein identification. One of the key challenges in database searching is the remarkable increase in computational demand, brought about by the expansion of protein databases, semi- or non-specific enzymatic digestion, post-translational modifications and other factors. Some software tools choose peptide indexing to accelerate processing. However, peptide indexing requires a large amount of time and space for construction, especially for the non-specific digestion. Additionally, it is not flexible to use.'),
(94020, 2010, 'bmc', 'Improvements to previous algorithms to predict gene structure and isoform concentrations using Affymetrix Exon arrays', 'Anton, Miguel A|Aramburu, Ander|Rubio, Angel', 'CEIT and TECNUN, University of Navarra, San Sebastián, Spain.|CEIT and TECNUN, University of Navarra, San Sebastián, Spain.|CEIT and TECNUN, University of Navarra, San Sebastián, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Alternative Splicing|Databases, Genetic|Exons|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Protein Isoforms|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-11-578', '21110835', 'Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Although the only biological analytes that can be properly assigned a concentration are transcripts, there are very few algorithms that focus on them. The reason is that previously developed summarization methods do not work well if applied to transcripts. In addition, gene structure prediction, i.e., the correspondence between probes and novel isoforms, is a field which is still unexplored.'),
(94021, 2010, 'bmc', 'webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser', 'Löytynoja, Ari|Goldman, Nick', 'EMBL-European Bioinformatics Institute, Hinxton, Cambridgeshire, UK. ari@ebi.ac.uk|EMBL-European Bioinformatics Institute, Hinxton, Cambridgeshire, UK. ari@ebi.ac.uk', 'United Kingdom|United Kingdom', 226, 'Algorithms|Codon|DNA|Databases, Genetic|Genome|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-579', '21110866', 'Phylogeny-aware progressive alignment has been found to perform well in phylogenetic alignment benchmarks and to produce superior alignments for the inference of selection on codon sequences. Its implementation in the PRANK alignment program package also allows modelling of complex evolutionary processes and inference of posterior probabilities for sequence sites evolving under each distinct scenario, either simultaneously with the alignment of sequences or as a post-processing step for an existing alignment. This has led to software with many advanced features, and users may find it difficult to generate optimal alignments, visualise the full information in their alignment results, or post-process these results, e.g. by objectively selecting subsets of alignment sites.'),
(94022, 2010, 'bmc', 'A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D', 'Santella, Anthony|Du, Zhuo|Nowotschin, Sonja|Hadjantonakis, Anna-Katerina|Bao, Zhirong', 'Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.|Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.|Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.|Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.|Developmental Biology, Sloan-Kettering Institute, 1275 York Avenue, New York, New York 10065, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Caenorhabditis elegans|Cell Nucleus|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Mice|Microscopy, Fluorescence|Single-Cell Analysis', 'Journal Article', '10.1186/1471-2105-11-580', '21114815', 'To exploit the flood of data from advances in high throughput imaging of optically sectioned nuclei, image analysis methods need to correctly detect thousands of nuclei, ideally in real time. Variability in nuclear appearance and undersampled volumetric data make this a challenge.'),
(94023, 2010, 'bmc', 'The INTERPRET Decision-Support System version 30 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses', 'Pérez-Ruiz, Alexander|Julià-Sapé, Margarida|Mercadal, Guillem|Olier, Iván|Majós, Carles|Arús, Carles', 'Centro de Investigación Biomédica en Red en Bioingeniería, Spain.|Centro de Investigación Biomédica en Red en Bioingeniería, Spain.|Centro de Investigación Biomédica en Red en Bioingeniería, Spain.|Centro de Investigación Biomédica en Red en Bioingeniería, Spain.|Centro de Investigación Biomédica en Red en Bioingeniería, Spain.|Centro de Investigación Biomédica en Red en Bioingeniería, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Brain|Brain Neoplasms|Databases, Factual|Decision Support Techniques|Humans|Magnetic Resonance Imaging|Magnetic Resonance Spectroscopy|Software', 'Journal Article', '10.1186/1471-2105-11-581', '21114820', 'Proton Magnetic Resonance (MR) Spectroscopy (MRS) is a widely available technique for those clinical centres equipped with MR scanners. Unlike the rest of MR-based techniques, MRS yields not images but spectra of metabolites in the tissues. In pathological situations, the MRS profile changes and this has been particularly described for brain tumours. However, radiologists are frequently not familiar to the interpretation of MRS data and for this reason, the usefulness of decision-support systems (DSS) in MRS data analysis has been explored.'),
(94024, 2010, 'bmc', 'Systematic integration of experimental data and models in systems biology', 'Li, Peter|Dada, Joseph O|Jameson, Daniel|Spasic, Irena|Swainston, Neil|Carroll, Kathleen|Dunn, Warwick|Khan, Farid|Malys, Naglis|Messiha, Hanan L|Simeonidis, Evangelos|Weichart, Dieter|Winder, Catherine|Wishart, Jill|Broomhead, David S|Goble, Carole A|Gaskell, Simon J|Kell, Douglas B|Westerhoff, Hans V|Mendes, Pedro|Paton, Norman W', 'School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk|School of Chemistry, The University of Manchester, Manchester M13 9PL, UK. peter.li@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|', 25, 'Databases, Factual|Metabolic Networks and Pathways|Models, Biological|Systems Biology', 'Journal Article', '10.1186/1471-2105-11-582', '21114840', 'The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.'),
(94025, 2010, 'bmc', 'Preprocessing of gene expression data by optimally robust estimators', 'Kohl, Matthias|Deigner, Hans-Peter', 'Department of Mechanical and Process Engineering, Furtwangen University, Jakob-Kienzle-Str, 17, 78054 Villingen-Schwenningen, Germany. Matthias.Kohl@stamats.de|Department of Mechanical and Process Engineering, Furtwangen University, Jakob-Kienzle-Str, 17, 78054 Villingen-Schwenningen, Germany. Matthias.Kohl@stamats.de', 'Germany|Germany', 12, 'Algorithms|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-583', '21118506', 'The preprocessing of gene expression data obtained from several platforms routinely includes the aggregation of multiple raw signal intensities to one expression value. Examples are the computation of a single expression measure based on the perfect match (PM) and mismatch (MM) probes for the Affymetrix technology, the summarization of bead level values to bead summary values for the Illumina technology or the aggregation of replicated measurements in the case of other technologies including real-time quantitative polymerase chain reaction (RT-qPCR) platforms. The summarization of technical replicates is also performed in other -omics disciplines like proteomics or metabolomics.Preprocessing methods like MAS 5.0, Illuminas default summarization method, RMA, or VSN show that the use of robust estimators is widely accepted in gene expression analysis. However, the selection of robust methods seems to be mainly driven by their high breakdown point and not by efficiency.'),
(94026, 2010, 'bmc', 'LTC: a novel algorithm to improve the efficiency of contig assembly for physical mapping in complex genomes', 'Frenkel, Zeev|Paux, Etienne|Mester, David|Feuillet, Catherine|Korol, Abraham', 'University of Haifa, Institute of Evolution, Haifa 31905, Israel. zvfrenkel@gmail.com|University of Haifa, Institute of Evolution, Haifa 31905, Israel. zvfrenkel@gmail.com|University of Haifa, Institute of Evolution, Haifa 31905, Israel. zvfrenkel@gmail.com|University of Haifa, Institute of Evolution, Haifa 31905, Israel. zvfrenkel@gmail.com|University of Haifa, Institute of Evolution, Haifa 31905, Israel. zvfrenkel@gmail.com', 'Israel|Israel|Israel|Israel|Israel', 29, 'Algorithms|Chromosomes, Artificial, Bacterial|Chromosomes, Plant|Cluster Analysis|Computational Biology|Contig Mapping|Gene Library|Genome, Plant|Oryza|Software|Triticum|Zea mays', 'Comparative Study', '10.1186/1471-2105-11-584', '21118513', 'Physical maps are the substrate of genome sequencing and map-based cloning and their construction relies on the accurate assembly of BAC clones into large contigs that are then anchored to genetic maps with molecular markers. High Information Content Fingerprinting has become the method of choice for large and repetitive genomes such as those of maize, barley, and wheat. However, the high level of repeated DNA present in these genomes requires the application of very stringent criteria to ensure a reliable assembly with the FingerPrinted Contig (FPC) software, which often results in short contig lengths (of 3-5 clones before merging) as well as an unreliable assembly in some difficult regions. Difficulties can originate from a non-linear topological structure of clone overlaps, low power of clone ordering algorithms, and the absence of tools to identify sources of gaps in Minimal Tiling Paths (MTPs).'),
(94027, 2010, 'bmc', 'Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation', 'Kowarsch, Andreas|Blöchl, Florian|Bohl, Sebastian|Saile, Maria|Gretz, Norbert|Klingmüller, Ursula|Theis, Fabian J', 'Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de|Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany. andreas.kowarsch@helmholtz-muenchen.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Gene Expression Profiling|Interleukin-6|Knowledge Bases|Mice|Protein Array Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-11-585', '21118515', 'External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function.'),
(94028, 2010, 'bmc', 'Prediction of RNA secondary structure by maximizing pseudo-expected accuracy', 'Hamada, Michiaki|Sato, Kengo|Asai, Kiyoshi', 'Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan. hamada-michiaki@aist.go.jp|Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan. hamada-michiaki@aist.go.jp|Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, Japan. hamada-michiaki@aist.go.jp', 'Japan|Japan|Japan', 0, 'Base Sequence|Computational Biology|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-586', '21118522', 'Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions, therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.'),
(94029, 2010, 'bmc', 'Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis', 'Du, Pan|Zhang, Xiao|Huang, Chiang-Ching|Jafari, Nadereh|Kibbe, Warren A|Hou, Lifang|Lin, Simon M', 'Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'CpG Islands|DNA Methylation|Data Interpretation, Statistical|Microarray Analysis', 'Comparative Study', '10.1186/1471-2105-11-587', '21118553', 'High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations.'),
(94030, 2010, 'bmc', 'IntelliGO: a new vector-based semantic similarity measure including annotation origin', 'Benabderrahmane, Sidahmed|Smail-Tabbone, Malika|Poch, Olivier|Napoli, Amedeo|Devignes, Marie-Dominique', 'LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr|LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr|LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr|LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr|LORIA (CNRS, INRIA, Nancy-Université), Équipe Orpailleur, Bâtiment B, Campus scientifique, 54506 Vandoeuvre-lès-Nancy Cedex, France. benabdsi@loria.fr', 'France|France|France|France|France', 0, 'Algorithms|Cluster Analysis|Computational Biology|Humans|Molecular Sequence Annotation|Proteins|Saccharomyces cerevisiae|Semantics|Software|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-588', '21122125', 'The Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes).'),
(94031, 2010, 'bmc', 'BrEPS: a flexible and automatic protocol to compute enzyme-specific sequence profiles for functional annotation', 'Bannert, C|Welfle, A|Aus dem Spring, C|Schomburg, D', 'Dept of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany.|Dept of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany.|Dept of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany.|Dept of Bioinformatics and Biochemistry, Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany.', 'Germany|Germany|Germany|Germany', 11, 'Base Sequence|Computational Biology|Databases, Factual|Enzymes|Genome|Metabolic Networks and Pathways|Molecular Sequence Annotation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-11-589', '21122127', 'Models for the simulation of metabolic networks require the accurate prediction of enzyme function. Based on a genomic sequence, enzymatic functions of gene products are today mainly predicted by sequence database searching and operon analysis. Other methods can support these techniques: We have developed an automatic method BrEPS that creates highly specific sequence patterns for the functional annotation of enzymes.'),
(94032, 2010, 'bmc', 'Merged consensus clustering to assess and improve class discovery with microarray data', 'Simpson, T Ian|Armstrong, J Douglas|Jarman, Andrew P', 'Genes and Development Group, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh, EH8 9XD, UK. ian.simpson@ed.ac.uk|Genes and Development Group, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh, EH8 9XD, UK. ian.simpson@ed.ac.uk|Genes and Development Group, Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh, EH8 9XD, UK. ian.simpson@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-11-590', '21129181', 'One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced.'),
(94033, 2010, 'bmc', 'An improved machine learning protocol for the identification of correct Sequest search results', 'Källberg, Morten|Lu, Hui', 'Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.|Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.', 'United States|United States', 0, 'Artificial Intelligence|Databases, Protein|Mass Spectrometry|Peptides|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-11-591', '21138573', 'Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS) techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols.'),
(94034, 2010, 'bmc', 'Haplotype association analyses in resources of mixed structure using Monte Carlo testing', 'Abo, Ryan|Wong, Jathine|Thomas, Alun|Camp, Nicola J', 'Department of Biomedical Informatics, University of Utah, Salt Lake City, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, Salt Lake City, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, Salt Lake City, USA. ryan.abo@hsc.utah.edu|Department of Biomedical Informatics, University of Utah, Salt Lake City, USA. ryan.abo@hsc.utah.edu', 'United States|United States|United States|United States', 1, 'Algorithms|Computer Simulation|Genetic Association Studies|Haplotypes|Humans|Monte Carlo Method|Pedigree|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-11-592', '21143908', 'Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.'),
(94035, 2010, 'bmc', 'Application of Wavelet Packet Transform to detect genetic polymorphisms by the analysis of inter-Alu PCR patterns', 'Cardelli, Maurizio|Nicoli, Matteo|Bazzani, Armando|Franceschi, Claudio', 'Italian National Research Centers on Aging (I.N.R.C.A.), Ancona, Italy.|Italian National Research Centers on Aging (I.N.R.C.A.), Ancona, Italy.|Italian National Research Centers on Aging (I.N.R.C.A.), Ancona, Italy.|Italian National Research Centers on Aging (I.N.R.C.A.), Ancona, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Alu Elements|Base Sequence|DNA|Polymerase Chain Reaction|Polymorphism, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-593', '21143911', 'The analysis of Inter-Alu PCR patterns obtained from human genomic DNA samples is a promising technique for a simultaneous analysis of many genomic loci flanked by Alu repetitive sequences in order to detect the presence of genetic polymorphisms. Inter-Alu PCR products may be separated and analyzed by capillary electrophoresis using an automatic sequencer that generates a complex pattern of peaks. We propose an algorithmic method based on the Haar-Walsh Wavelet Packet Transformation (WPT) for an efficient detection of fingerprint-type patterns generated by PCR-based methodologies. We have tested our algorithmic approach on inter-Alu patterns obtained from the genomic DNA of three couples of monozygotic twins, expecting that the inter-Alu patterns of each twins couple will show differences due to unavoidable experimental variability. On the contrary the differences among samples of different twins are supposed to originate from genetic variability. Our goal is to automatically detect regions in the inter-Alu pattern likely associated to the presence of genetic polymorphisms.'),
(94036, 2010, 'bmc', 'BIGSdb: Scalable analysis of bacterial genome variation at the population level', 'Jolley, Keith A|Maiden, Martin C J', 'Department of Zoology, University of Oxford, UK. keith.jolley@zoo.ox.ac.uk|Department of Zoology, University of Oxford, UK. keith.jolley@zoo.ox.ac.uk', 'United Kingdom|United Kingdom', 0, 'Base Sequence|Databases, Genetic|Genetic Variation|Genome, Bacterial|Genomics|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-595', '21143983', 'The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner.'),
(94037, 2010, 'bmc', 'PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus Signaling', 'Bebek, Gurkan|Patel, Vishal|Chance, Mark R', 'Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan@case.edu|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan@case.edu|Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan@case.edu', 'United States|United States|United States', 9, 'Algorithms|Animals|Colonic Neoplasms|Computational Biology|Genes, APC|Mice|Mutation|Protein Interaction Mapping|Proteomics|Signal Transduction|Two-Dimensional Difference Gel Electrophoresis', 'Journal Article', '10.1186/1471-2105-11-596', '21144021', 'Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apcs signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.'),
(94038, 2010, 'bmc', 'Extending pathways and processes using molecular interaction networks to analyse cancer genome data', 'Glaab, Enrico|Baudot, Anaïs|Krasnogor, Natalio|Valencia, Alfonso', 'Nottingham University, UK.|Nottingham University, UK.|Nottingham University, UK.|Nottingham University, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 36, 'Databases, Protein|Genes, Neoplasm|Genomics|Humans|Metabolic Networks and Pathways|Neoplasm Proteins|Neoplasms|Pancreatic Neoplasms|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-11-597', '21144022', 'Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.'),
(94039, 2010, 'bmc', 'Initial steps towards a production platform for DNA sequence analysis on the grid', 'Luyf, Angela C M|van Schaik, Barbera D C|de Vries, Michel|Baas, Frank|van Kampen, Antoine H C|Olabarriaga, Silvia D', 'Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.|Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Computational Biology|Computer Systems|High-Throughput Nucleotide Sequencing|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, DNA|Software|Workflow', 'Journal Article', '10.1186/1471-2105-11-598', '21156038', 'Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users.'),
(94040, 2010, 'bmc', 'A genome alignment algorithm based on compression', 'Cao, Minh Duc|Dix, Trevor I|Allison, Lloyd', 'Clayton School of Information Technology, Monash University, Clayton 3800, Australia. minhduc@monash.edu|Clayton School of Information Technology, Monash University, Clayton 3800, Australia. minhduc@monash.edu|Clayton School of Information Technology, Monash University, Clayton 3800, Australia. minhduc@monash.edu', 'Australia|Australia|Australia', 0, 'Algorithms|Base Sequence|Data Compression|Genomics|Models, Theoretical|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-11-599', '21159205', 'Traditional genome alignment methods consider sequence alignment as a variation of the string edit distance problem, and perform alignment by matching characters of the two sequences. They are often computationally expensive and unable to deal with low information regions. Furthermore, they lack a well-principled objective function to measure the performance of sets of parameters. Since genomic sequences carry genetic information, this article proposes that the information content of each nucleotide in a position should be considered in sequence alignment. An information-theoretic approach for pairwise genome local alignment, namely XMAligner, is presented. Instead of comparing sequences at the character level, XMAligner considers a pair of nucleotides from two sequences to be related if their mutual information in context is significant. The information content of nucleotides in sequences is measured by a lossless compression technique.'),
(94041, 2010, 'bmc', 'SNPexp - A web tool for calculating and visualizing correlation between HapMap genotypes and gene expression levels', 'Holm, Kristian|Melum, Espen|Franke, Andre|Karlsen, Tom H', 'Norwegian PSC Research Center, Clinic for Specialized Medicine and Surgery, Oslo University Hospital Rikshospitalet, Norway.|Norwegian PSC Research Center, Clinic for Specialized Medicine and Surgery, Oslo University Hospital Rikshospitalet, Norway.|Norwegian PSC Research Center, Clinic for Specialized Medicine and Surgery, Oslo University Hospital Rikshospitalet, Norway.|Norwegian PSC Research Center, Clinic for Specialized Medicine and Surgery, Oslo University Hospital Rikshospitalet, Norway.', 'Norway|Norway|Norway|Norway', 0, 'Gene Expression Profiling|Genome, Human|Genotype|Humans|Internet|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-600', '21167019', 'Expression levels for 47294 transcripts in lymphoblastoid cell lines from all 270 HapMap phase II individuals, and genotypes (both HapMap phase II and III) of 3.96 million single nucleotide polymorphisms (SNPs) in the same individuals are publicly available. We aimed to generate a user-friendly web based tool for visualization of the correlation between SNP genotypes within a specified genomic region and a gene of interest, which is also well-known as an expression quantitative trait locus (eQTL) analysis.'),
(94042, 2010, 'bmc', 'A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences', 'Russell, David J|Way, Samuel F|Benson, Andrew K|Sayood, Khalid', 'Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu|Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu|Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu|Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Cluster Analysis|RNA, Ribosomal, 16S|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-601', '21167044', 'We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. The proposed clustering algorithm uses a grammar-based distance metric to determine partitioning for a set of biological sequences. The algorithm performs clustering in which new sequences are compared with cluster-representative sequences to determine membership. If comparison fails to identify a suitable cluster, a new cluster is created.'),
(94043, 2010, 'bmc', 'Gene processing control loops suggested by sequencing, splicing, and RNA folding', 'Jeffries, Clark D|Perkins, Diana O|Guan, Xiaojun', 'Eshelman School of Pharmacy and Renaissance Computing Institute, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu|Eshelman School of Pharmacy and Renaissance Computing Institute, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu|Eshelman School of Pharmacy and Renaissance Computing Institute, University of North Carolina at Chapel Hill, NC, USA. clark_jeffries@med.unc.edu', 'United States|United States|United States', 0, 'Base Sequence|Gene Expression|Humans|MicroRNAs|Nucleic Acid Conformation|RNA Processing, Post-Transcriptional|RNA Splicing|RNA, Small Untranslated|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-602', '21167075', 'Small RNAs are known to regulate diverse gene expression processes including translation, transcription, and splicing. Among small RNAs, the microRNAs (miRNAs) of 17 to 27 nucleotides (nts) undergo biogeneses including primary transcription, RNA excision and folding, nuclear export, cytoplasmic processing, and then bioactivity as regulatory agents. We propose that analogous hairpins from RNA molecules that function as part of the spliceosome might also be the source of small, regulatory RNAs (somewhat smaller than miRNAs).'),
(94044, 2010, 'bmc', 'Content-based microarray search using differential expression profiles', 'Engreitz, Jesse M|Morgan, Alexander A|Dudley, Joel T|Chen, Rong|Thathoo, Rahul|Altman, Russ B|Butte, Atul J', 'Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.|Department of Bioengineering, Stanford University School of Medicine, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States', 33, 'Animals|Computational Biology|Databases, Genetic|Embryonic Stem Cells|Gene Expression Profiling|Humans|Lung Neoplasms|Mice|Neural Stem Cells|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-11-603', '21172034', 'With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.'),
(94045, 2010, 'bmc', 'Computing DNA duplex instability profiles efficiently with a two-state model: trends of promoters and binding sites', 'Kantorovitz, Miriam R|Rapti, Zoi|Gelev, Vladimir|Usheva, Anny', 'Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ruth@math.uiuc.edu|Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ruth@math.uiuc.edu|Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ruth@math.uiuc.edu|Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. ruth@math.uiuc.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Binding Sites|Computational Biology|DNA|Humans|Models, Statistical|Nucleic Acid Denaturation|Promoter Regions, Genetic|Transcription Initiation Site', 'Journal Article', '10.1186/1471-2105-11-604', '21172036', 'DNA instability profiles have been used recently for predicting the transcriptional start site and the location of core promoters, and to gain insight into promoter action. It was also shown that the use of these profiles can significantly improve the performance of motif finding programs.'),
(94046, 2010, 'bmc', 'Association of tissue lineage and gene expression: conservatively and differentially expressed genes define common and special functions of tissues', 'Yu, Yao|Xu, Tao|Yu, Yongtao|Hao, Pei|Li, Xuan', 'Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. yyu01@sibs.ac.cn|Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. yyu01@sibs.ac.cn|Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. yyu01@sibs.ac.cn|Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. yyu01@sibs.ac.cn|Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. yyu01@sibs.ac.cn', 'China|China|China|China|China', 0, 'Animals|Cell Differentiation|Ectoderm|Embryonic Development|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Developmental|Humans|Mesoderm|Mice|Microarray Analysis|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-11-S11-S1', '21172044', 'Embryogenesis is the process by which the embryo is formed, develops, and establishes developmental hierarchies of tissues. The recent advance in microarray technology made it possible to investigate the tissue specific patterns of gene expression and their relationship with tissue lineages. This study is focused on how tissue specific functions, tissue lineage, and cell differentiation are correlated, which is essential to understand embryonic development and organism complexity.'),
(94047, 2010, 'bmc', 'Accurate HLA type inference using a weighted similarity graph', 'Xie, Minzhu|Li, Jing|Jiang, Tao', 'Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. minzhux@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. minzhux@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. minzhux@cs.ucr.edu', 'United States|United States|United States', 0, 'Algorithms|Alleles|Genotype|HLA Antigens|HLA-A Antigens|HLA-B Antigens|HLA-C Antigens|HLA-DQ Antigens|HLA-DQ alpha-Chains|HLA-DR Antigens|HLA-DRB1 Chains|Haplotypes|Histocompatibility Testing|Humans|Polymorphism, Single Nucleotide', 'Evaluation Studies', '10.1186/1471-2105-11-S11-S10', '21172045', 'The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.'),
(94048, 2010, 'bmc', 'MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features', 'Ding, Jiandong|Zhou, Shuigeng|Guan, Jihong', 'Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China. jdding@fudan.edu.cn|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China. jdding@fudan.edu.cn|Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200433, China. jdding@fudan.edu.cn', 'China|China|China', 67, 'Animals|Anopheles|Artificial Intelligence|Classification|Computational Biology|Humans|MicroRNAs|RNA Precursors', 'Journal Article', '10.1186/1471-2105-11-S11-S11', '21172046', 'MicroRNAs (simply miRNAs) are derived from larger hairpin RNA precursors and play essential regular roles in both animals and plants. A number of computational methods for miRNA genes finding have been proposed in the past decade, yet the problem is far from being tackled, especially when considering the imbalance issue of known miRNAs and unidentified miRNAs, and the pre-miRNAs with multi-loops or higher minimum free energy (MFE). This paper presents a new computational approach, miRenSVM, for finding miRNA genes. Aiming at better prediction performance, an ensemble support vector machine (SVM) classifier is established to deal with the imbalance issue, and multi-loop features are included for identifying those pre-miRNAs with multi-loops.'),
(94049, 2010, 'bmc', 'Detection and reconstruction of tandemly organized de novo copy number variations', 'He, Dan|Furlotte, Nicholas|Eskin, Eleazar', 'Dept, of Comp, Sci, Univ, of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Dept, of Comp, Sci, Univ, of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu|Dept, of Comp, Sci, Univ, of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu', 'United States|United States|United States', 8, 'Algorithms|Animals|DNA Copy Number Variations|High-Throughput Nucleotide Sequencing|Mice|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S11-S12', '21172047', 'The characterization of structural variations (SV) such as insertions, deletions and copy number variations is a critical step in the process of understanding the full genetic architecture of organisms. Copy number variations (CNV) have attracted much recent attention due to their effects on gene expression and disease status.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94050, 2010, 'bmc', 'Revealing parasite influence in metabolic pathways in Apicomplexa infected patients', 'Xu, Tao|Ping, Jie|Yu, Yao|Yu, Fudong|Yu, Yongtao|Hao, Pei|Li, Xuan', 'College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com|College of life science and biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. xutaoseu@hotmail.com', 'China|China|China|China|China|China|China', 10, 'Animals|Cell Line|Cryptosporidium parvum|Down-Regulation|Gene Expression|Host-Parasite Interactions|Humans|Malaria, Falciparum|Metabolic Networks and Pathways|Plasmodium falciparum|Up-Regulation', 'Journal Article', '10.1186/1471-2105-11-S11-S13', '21172048', 'As an obligate intracellular parasite, Apicomplexa interacts with the host in the special living environment, competing for energy and nutrients from the host cells by manipulating the host metabolism. Previous studies of host-parasite interaction mainly focused on using cellular and biochemical methods to investigate molecular functions in metabolic pathways of parasite infected hosts. Computational approaches taking advantage of high-throughput biological data and topology of metabolic pathways have a great potential in revealing the details and mechanism of parasites-to-host interactions. A new analytical method was designed in this work to study host-parasite interactions in human cells infected with Plasmodium falciparum and Cryptosporidium parvum.'),
(94051, 2010, 'bmc', 'The utility of mass spectrometry-based proteomic data for validation of novel alternative splice forms reconstructed from RNA-Seq data: a preliminary assessment', 'Ning, Kang|Nesvizhskii, Alexey I', 'Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA. ningkang@qibebt.ac.cn|Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA. ningkang@qibebt.ac.cn', 'United States|United States', 47, 'Alternative Splicing|Animals|Databases, Factual|Databases, Protein|High-Throughput Nucleotide Sequencing|Mass Spectrometry|Mice|Peptides|Proteins|Proteomics|RNA, Messenger|Sequence Analysis, Protein|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-11-S11-S14', '21172049', 'Most mass spectrometry (MS) based proteomic studies depend on searching acquired tandem mass (MS/MS) spectra against databases of known protein sequences. In these experiments, however, a large number of high quality spectra remain unassigned. These spectra may correspond to novel peptides not present in the database, especially those corresponding to novel alternative splice (AS) forms. Recently, fast and comprehensive profiling of mammalian genomes using deep sequencing (i.e. RNA-Seq) has become possible. MS-based proteomics can potentially be used as an aid for protein-level validation of novel AS events observed in RNA-Seq data.'),
(94052, 2010, 'bmc', 'A novel parametric approach to mine gene regulatory relationship from microarray datasets', 'Liu, Wanlin|Li, Dong|Liu, Qijun|Zhu, Yunping|He, Fuchu', 'State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com|State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com', 'China|China|China|China|China', 4, 'Algorithms|Bayes Theorem|Data Mining|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Likelihood Functions|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-S11-S15', '21172050', 'Microarray has been widely used to measure the gene expression level on the genome scale in the current decade. Many algorithms have been developed to reconstruct gene regulatory networks based on microarray data. Unfortunately, most of these models and algorithms focus on global properties of the expression of genes in regulatory networks. And few of them are able to offer intuitive parameters. We wonder whether some simple but basic characteristics of microarray datasets can be found to identify the potential gene regulatory relationship.'),
(94053, 2010, 'bmc', 'Sorting out inherent features of head-to-head gene pairs by evolutionary conservation', 'Chen, Yun-Qin|Yu, Hui|Li, Yi-Xue|Li, Yuan-Yuan', 'School of Life Science and Technology, Tongji University, Shanghai 200092, PR China. yqchen@scbit.org|School of Life Science and Technology, Tongji University, Shanghai 200092, PR China. yqchen@scbit.org|School of Life Science and Technology, Tongji University, Shanghai 200092, PR China. yqchen@scbit.org|School of Life Science and Technology, Tongji University, Shanghai 200092, PR China. yqchen@scbit.org', 'China|China|China|China', 3, 'Evolution, Molecular|Gene Expression Regulation|Genetic Loci|Humans|Phylogeny|Promoter Regions, Genetic|Transcription Factors|Transcription Initiation Site|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-11-S11-S16', '21172051', 'A head-to-head (h2h) gene pair is defined as a genomic locus in which two adjacent genes are divergently transcribed from opposite strands of DNA. In our previous work, this gene organization was found to be ancient and conserved, which subjects functionally related genes to transcriptional co-regulation. However, some of the biological features of h2h pairs still need further clarification.'),
(94054, 2010, 'bmc', 'Improved protein surface comparison and application to low-resolution protein structure data', 'Sael, Lee|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. lee399@cs.purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. lee399@cs.purdue.edu', 'United States|United States', 17, 'Algorithms|Computational Biology|Databases, Protein|Models, Molecular|Protein Structure, Tertiary|Proteins|Surface Properties', 'Journal Article', '10.1186/1471-2105-11-S11-S2', '21172052', 'Recent advancements of experimental techniques for determining protein tertiary structures raise significant challenges for protein bioinformatics. With the number of known structures of unknown function expanding at a rapid pace, an urgent task is to provide reliable clues to their biological function on a large scale. Conventional approaches for structure comparison are not suitable for a real-time database search due to their slow speed. Moreover, a new challenge has arisen from recent techniques such as electron microscopy (EM), which provide low-resolution structure data. Previously, we have introduced a method for protein surface shape representation using the 3D Zernike descriptors (3DZDs). The 3DZD enables fast structure database searches, taking advantage of its rotation invariance and compact representation. The search results of protein surface represented with the 3DZD has showngood agreement with the existing structure classifications, but some discrepancies were also observed.'),
(94055, 2010, 'bmc', 'Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology', 'Jing, Liping|Ng, Michael K', 'School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China. lpjinghk@gmail.com|School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, PR China. lpjinghk@gmail.com', 'China|China', 4, 'Algorithms|Cluster Analysis|Data Mining|Fungal Proteins|Genes|Protein Interaction Mapping|Vocabulary, Controlled|Yeasts', 'Journal Article', '10.1186/1471-2105-11-S11-S3', '21172053', 'In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.'),
(94056, 2010, 'bmc', 'Integer programming-based method for grammar-based tree compression and its application to pattern extraction of glycan tree structures', 'Zhao, Yang|Hayashida, Morihiro|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.', 'Japan|Japan|Japan', 4, 'Algorithms|Computational Biology|Data Compression|Polysaccharides', 'Journal Article', '10.1186/1471-2105-11-S11-S4', '21172054', 'A bisection-type algorithm for the grammar-based compression of tree-structured data has been proposed recently. In this framework, an elementary ordered-tree grammar (EOTG) and an elementary unordered-tree grammar (EUTG) were defined, and an approximation algorithm was proposed.'),
(94057, 2010, 'bmc', 'TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information', 'Liu, Qi|Tan, Yejun|Huang, Tao|Ding, Guohui|Tu, Zhidong|Liu, Lei|Li, Yixue|Dai, Hongyue|Xie, Lu', 'School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn|School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. liuqi@sjtu.edu.cn', 'China|China|China|China|China|China|China|China|China', 7, 'DNA|Fermentation|Gene Deletion|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Glycerol|Models, Genetic|Protein Processing, Post-Translational|Transcription Factors', 'Journal Article', '10.1186/1471-2105-11-S11-S5', '21172055', 'Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.'),
(94058, 2010, 'bmc', 'Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae', 'Li, Shao|Zhang, Bo|Jiang, Duo|Wei, Yingying|Zhang, Ningbo', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, China. shaoli@mail.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, China. shaoli@mail.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, China. shaoli@mail.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, China. shaoli@mail.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST / Department of Automation, Tsinghua University, Beijing, China. shaoli@mail.tsinghua.edu.cn', 'China|China|China|China|China', 159, 'Angiogenesis Inhibitors|Drug Interactions|Drugs, Chinese Herbal|Medicine, Chinese Traditional', 'Journal Article', '10.1186/1471-2105-11-S11-S6', '21172056', 'Traditional Chinese Medicine (TCM) is characterized by the wide use of herbal formulae, which are capable of systematically treating diseases determined by interactions among various herbs. However, the combination rule of TCM herbal formulae remains a mystery due to the lack of appropriate methods.'),
(94059, 2010, 'bmc', 'Gene-centric association analysis for the correlation between the guanine-cytosine content levels and temperature range conditions of prokaryotic species', 'Zheng, Hao|Wu, Hongwei', 'School of Electrical and Computer Engineering, Georgia Institute of Technology, USA. hzheng7@gatech.edu|School of Electrical and Computer Engineering, Georgia Institute of Technology, USA. hzheng7@gatech.edu', 'Georgia|Georgia', 19, 'Base Composition|Cytosine|DNA, Archaeal|DNA, Bacterial|Genes, Archaeal|Genes, Bacterial|Genetic Association Studies|Genome, Archaeal|Genome, Bacterial|Genomics|Guanine|Temperature', 'Journal Article', '10.1186/1471-2105-11-S11-S7', '21172057', 'The environment has been playing an instrumental role in shaping and maintaining the morphological, physiological and biochemical diversities of prokaryotes. It has been debatable whether the whole-genome Guanine-Cytosine (GC) content levels of prokaryotic organisms are correlated with their optimal growth temperatures. Since the GC content is variable within a genome, we here focus on the correlation between the genic GC content levels and the temperature range conditions of prokaryotic organisms.'),
(94060, 2010, 'bmc', 'RecMotif: a novel fast algorithm for weak motif discovery', 'Sun, He Quan|Low, Malcolm Yoke Hean|Hsu, Wen Jing|Rajapakse, Jagath C', 'School of Computer Engineering, Nanyang Technological University, 639798, Singapore. Sunh0013@e.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, 639798, Singapore. Sunh0013@e.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, 639798, Singapore. Sunh0013@e.ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, 639798, Singapore. Sunh0013@e.ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 16, 'Algorithms|Computational Biology|Probability|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-S11-S8', '21172058', 'Weak motif discovery in DNA sequences is an important but unresolved problem in computational biology. Previous algorithms that aimed to solve the problem usually require a large amount of memory or execution time. In this paper, we proposed a fast and memory efficient algorithm, RecMotif, which guarantees to discover all motifs with specific (l, d) settings (where l is the motif length and d is the maximum number of mutations between a motif instance and the true motif).'),
(94061, 2010, 'bmc', 'Large number of phosphotransferase genes in the Clostridium beijerinckii NCIMB 8052 genome and the study on their evolution', 'Shi, Yixiang|Li, Yi-Xue|Li, Yuan-Yuan', 'Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yxshi@scbit.org|Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yxshi@scbit.org|Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yxshi@scbit.org', 'China|China|China', 0, 'Base Composition|Carbohydrate Metabolism|Clostridium beijerinckii|Evolution, Molecular|Genes, Bacterial|Genome, Bacterial|Multigene Family|Phosphotransferases|Phylogeny|Synteny', 'Journal Article', '10.1186/1471-2105-11-S11-S9', '21172059', 'Clostridium beijerinckii is a valuable bacteria species which has the ability of ABE (acetone, butanol and ethanol) production. It has been shown that Phosphotransferase (PTS) is an important and common system for both carbohydrate uptake and phosphorylation in bacteria, but detailed study of the system, especially its fructose/mannose/sorbose family is scant.'),
(94062, 2010, 'bmc', 'New insights into protein-protein interaction data lead to increased estimates of the S cerevisiae interactome size', 'Sambourg, Laure|Thierry-Mieg, Nicolas', 'Laboratoire TIMC-IMAG, BCM, CNRS UMR5525, Faculté de médecine, La Tronche, France.|Laboratoire TIMC-IMAG, BCM, CNRS UMR5525, Faculté de médecine, La Tronche, France.', 'France|France', 30, 'Databases, Protein|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-11-605', '21176124', 'As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation, yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.'),
(94063, 2010, 'bmc', 'Kernel based methods for accelerated failure time model with ultra-high dimensional data', 'Liu, Zhenqiu|Chen, Dechang|Tan, Ming|Jiang, Feng|Gartenhaus, Ronald B', 'University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu', 'United States|United States|United States|United States|United States', 3, 'Algorithms|Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Survival Analysis', 'Journal Article', '10.1186/1471-2105-11-606', '21176134', 'Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with Lxe2x82x81 and L(p) penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n < m (the number of genes), directly identifying a small subset of genes from ultra-high (m > 10, 000) dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds) of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes.'),
(94064, 2010, 'bmc', 'The complexity of gene expression dynamics revealed by permutation entropy', 'Sun, Xiaoliang|Zou, Yong|Nikiforova, Victoria|Kurths, Jürgen|Walther, Dirk', 'Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.|Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.|Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.|Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.|Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Arabidopsis|Computational Biology|Entropy|Gene Expression Profiling|Genes, Plant|Signal Transduction|Stress, Physiological', 'Journal Article', '10.1186/1471-2105-11-607', '21176199', 'High complexity is considered a hallmark of living systems. Here we investigate the complexity of temporal gene expression patterns using the concept of Permutation Entropy (PE) first introduced in dynamical systems theory. The analysis of gene expression data has so far focused primarily on the identification of differentially expressed genes, or on the elucidation of pathway and regulatory relationships. We aim to study gene expression time series data from the viewpoint of complexity.'),
(94065, 2010, 'bmc', 'MBAT: a scalable informatics system for unifying digital atlasing workflows', 'Lee, Daren|Ruffins, Seth|Ng, Queenie|Sane, Nikhil|Anderson, Steve|Toga, Arthur', 'UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.|UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.|UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.|UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.|UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.|UCLA Laboratory of NeuroImaging, David Geffin School of Medicine, Los Angeles, CA USA.', 'United States|United States|United States|United States|United States|United States', 10, 'Animals|Computational Biology|Genomics|Information Storage and Retrieval|Mice|Software|Workflow', 'Journal Article', '10.1186/1471-2105-11-608', '21176225', 'Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researchers own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data.'),
(94066, 2010, 'bmc', 'RISCI--Repeat Induced Sequence Changes Identifier: a comprehensive, comparative genomics-based, in silico subtractive hybridization pipeline to identify repeat induced sequence changes in closely related genomes', 'Singh, Vipin|Mishra, Rakesh K', 'Centre for Cellular and Molecular Biology, Hyderabad, India. vipin@ccmb.res.in|Centre for Cellular and Molecular Biology, Hyderabad, India. vipin@ccmb.res.in', 'India|India', 5, 'Animals|Comparative Genomic Hybridization|Data Mining|Genome, Human|Genomics|Humans|INDEL Mutation|Pan troglodytes|Polymorphism, Genetic|Retroelements|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-609', '21184688', 'The availability of multiple whole genome sequences has facilitated in silico identification of fixed and polymorphic transposable elements (TE). Whereas polymorphic loci serve as makers for phylogenetic and forensic analysis, fixed species-specific transposon insertions, when compared to orthologous loci in other closely related species, may give insights into their evolutionary significance. Besides, TE insertions are not isolated events and are frequently associated with subtle sequence changes concurrent with insertion or post insertion. These include duplication of target site, 3 and 5 flank transduction, deletion of the target locus, 5 truncation or partial deletion and inversion of the transposon, and post insertion changes like inter or intra element recombination, disruption etc. Although such changes have been studied independently, no automated platform to identify differential transposon insertions and the associated array of sequence changes in genomes of the same or closely related species is available till date. To this end, we have designed RISCI - Repeat Induced Sequence Changes Identifier - a comprehensive, comparative genomics-based, in silico subtractive hybridization pipeline to identify differential transposon insertions and associated sequence changes using specific alignment signatures, which may then be examined for their downstream effects.'),
(94067, 2010, 'bmc', 'BiologicalNetworks 20--an integrative view of genome biology data', 'Kozhenkov, Sergey|Dubinina, Yulia|Sedova, Mayya|Gupta, Amarnath|Ponomarenko, Julia|Baitaluk, Michael', 'San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.|San Diego Supercomputer Center, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.', 'United States|United States|United States|United States|United States|United States', 25, 'Computational Biology|Database Management Systems|Databases, Genetic|Genome|Genomics|Internet|Molecular Sequence Annotation|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-11-610', '21190573', 'A significant problem in the study of mechanisms of an organisms development is the elucidation of interrelated factors which are making an impact on the different levels of the organism, such as genes, biological molecules, cells, and cell systems. Numerous sources of heterogeneous data which exist for these subsystems are still not integrated sufficiently enough to give researchers a straightforward opportunity to analyze them together in the same frame of study. Systematic application of data integration methods is also hampered by a multitude of such factors as the orthogonal nature of the integrated data and naming problems.'),
(94068, 2010, 'bmc', 'SIDEKICK: Genomic data driven analysis and decision-making framework', 'Doderer, Mark S|Yoon, Kihoon|Robbins, Kay A', 'Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. mdoderer@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. mdoderer@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. mdoderer@cs.utsa.edu', 'United States|United States|United States', 2, 'Decision Making|Genomics|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/1471-2105-11-611', '21192813', 'Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.'),
(94069, 2010, 'bmc', 'A two-step site and mRNA-level model for predicting microRNA targets', 'Saito, Takaya|Sætrom, Pål', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, NO-7489 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, NO-7489 Trondheim, Norway.', 'Norway|Norway', 12, 'Algorithms|Binding Sites|Computational Biology|Humans|MicroRNAs|Models, Genetic|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-11-612', '21194446', 'Despite experiments showing that the number of microRNA (miRNA) target sites is critical for miRNA targeting, most existing methods focus on identifying individual miRNA target sites and do not model contributions of multiple target sites to miRNA regulation. To address this possible fault, we developed a miRNA target prediction model that recognizes the individual characteristics of functional binding sites and the global characteristics of miRNA-targeted mRNAs.'),
(94070, 2011, 'bmc', 'MTML-msBayes: approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity', 'Huang, Wen|Takebayashi, Naoki|Qi, Yan|Hickerson, Michael J', 'Biology Department, City University of New York, Queens College, 65-30 Kissena Blvd, Flushing, NY 11367-1597, USA. wenhuang19@yahoo.com|Biology Department, City University of New York, Queens College, 65-30 Kissena Blvd, Flushing, NY 11367-1597, USA. wenhuang19@yahoo.com|Biology Department, City University of New York, Queens College, 65-30 Kissena Blvd, Flushing, NY 11367-1597, USA. wenhuang19@yahoo.com|Biology Department, City University of New York, Queens College, 65-30 Kissena Blvd, Flushing, NY 11367-1597, USA. wenhuang19@yahoo.com', 'United States|United States|United States|United States', 71, 'Algorithms|Animals|Bayes Theorem|Birds|Demography|Evolution, Molecular|Gene Flow|Genetic Loci|Genetic Variation|Models, Statistical|Phylogeography|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-1', '21199577', 'MTML-msBayes uses hierarchical approximate Bayesian computation (HABC) under a coalescent model to infer temporal patterns of divergence and gene flow across codistributed taxon-pairs. Under a model of multiple codistributed taxa that diverge into taxon-pairs with subsequent gene flow or isolation, one can estimate hyper-parameters that quantify the mean and variability in divergence times or test models of migration and isolation. The software uses multi-locus DNA sequence data collected from multiple taxon-pairs and allows variation across taxa in demographic parameters as well as heterogeneity in DNA mutation rates across loci. The method also allows a flexible sampling scheme: different numbers of loci of varying length can be sampled from different taxon-pairs.'),
(94071, 2011, 'bmc', 'Clustering gene expression data with a penalized graph-based metric', 'Bayá, Ariel E|Granitto, Pablo M', 'CIFASIS French Argentine International Center for Information and Systems Sciences, UPCAM (France)/UNR-CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, República Argentina. baya@cifasis-conicet.gov.ar|CIFASIS French Argentine International Center for Information and Systems Sciences, UPCAM (France)/UNR-CONICET (Argentina), Bv 27 de Febrero 210 Bis, 2000 Rosario, República Argentina. baya@cifasis-conicet.gov.ar', 'Argentina|Argentina', 22, 'Algorithms|Cluster Analysis|Gene Expression Profiling', 'Comparative Study', '10.1186/1471-2105-12-2', '21205299', 'The search for cluster structure in microarray datasets is a base problem for the so-called -omic sciences. A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.'),
(94072, 2011, 'bmc', 'R2R--software to speed the depiction of aesthetic consensus RNA secondary structures', 'Weinberg, Zasha|Breaker, Ronald R', 'Howard Hughes Medical Institute, Yale University, Box 208103, New Haven, CT 06520-8103, USA. zasha.weinberg@yale.edu|Howard Hughes Medical Institute, Yale University, Box 208103, New Haven, CT 06520-8103, USA. zasha.weinberg@yale.edu', 'United States|United States', 105, 'Consensus Sequence|Models, Molecular|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-12-3', '21205310', 'With continuing identification of novel structured noncoding RNAs, there is an increasing need to create schematic diagrams showing the consensus features of these molecules. RNA structural diagrams are typically made either with general-purpose drawing programs like Adobe Illustrator, or with automated or interactive programs specific to RNA. Unfortunately, the use of applications like Illustrator is extremely time consuming, while existing RNA-specific programs produce figures that are useful, but usually not of the same aesthetic quality as those produced at great cost in Illustrator. Additionally, most existing RNA-specific applications are designed for drawing single RNA molecules, not consensus diagrams.'),
(94073, 2010, 'bmc', 'Addressing the challenge of defining valid proteomic biomarkers and classifiers', 'Dakna, Mohammed|Harris, Keith|Kalousis, Alexandros|Carpentier, Sebastien|Kolch, Walter|Schanstra, Joost P|Haubitz, Marion|Vlahou, Antonia|Mischak, Harald|Girolami, Mark', 'Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.|Mosaiques diagnostics and therapeutics, Hannover, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 84, 'Adult|Algorithms|Biomarkers|Electrophoresis, Capillary|Female|Humans|Male|Mass Spectrometry|Proteomics|Reference Values|Sample Size|Young Adult', 'Journal Article', '10.1186/1471-2105-11-594', '21208396', 'The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.'),
(94074, 2011, 'bmc', 'CNV-WebStore: online CNV analysis, storage and interpretation', 'Vandeweyer, Geert|Reyniers, Edwin|Wuyts, Wim|Rooms, Liesbeth|Kooy, R Frank', 'Department of Medical Genetics, University Hospital Antwerp, Antwerp, Belgium.|Department of Medical Genetics, University Hospital Antwerp, Antwerp, Belgium.|Department of Medical Genetics, University Hospital Antwerp, Antwerp, Belgium.|Department of Medical Genetics, University Hospital Antwerp, Antwerp, Belgium.|Department of Medical Genetics, University Hospital Antwerp, Antwerp, Belgium.', 'Belgium|Belgium|Belgium|Belgium|Belgium', 37, 'Database Management Systems|Humans|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-4', '21208430', 'Microarray technology allows the analysis of genomic aberrations at an ever increasing resolution, making functional interpretation of these vast amounts of data the main bottleneck in routine implementation of high resolution array platforms, and emphasising the need for a centralised and easy to use CNV data management and interpretation system.'),
(94075, 2011, 'bmc', 'Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing', 'Prosperi, Mattia C F|Prosperi, Luciano|Bruselles, Alessandro|Abbate, Isabella|Rozera, Gabriella|Vincenti, Donatella|Solmone, Maria Carmela|Capobianchi, Maria Rosaria|Ulivi, Giovanni', 'Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it|Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 55, 'Algorithms|Computer Simulation|Genetic Variation|Genome, Viral|Genomics|Hepatitis B virus|Humans|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-5', '21208435', 'Next-generation sequencing (NGS) offers a unique opportunity for high-throughput genomics and has potential to replace Sanger sequencing in many fields, including de-novo sequencing, re-sequencing, meta-genomics, and characterisation of infectious pathogens, such as viral quasispecies. Although methodologies and software for whole genome assembly and genome variation analysis have been developed and refined for NGS data, reconstructing a viral quasispecies using NGS data remains a challenge. This application would be useful for analysing intra-host evolutionary pathways in relation to immune responses and antiretroviral therapy exposures. Here we introduce a set of formulae for the combinatorial analysis of a quasispecies, given a NGS re-sequencing experiment and an algorithm for quasispecies reconstruction. We require that sequenced fragments are aligned against a reference genome, and that the reference genome is partitioned into a set of sliding windows (amplicons). The reconstruction algorithm is based on combinations of multinomial distributions and is designed to minimise the reconstruction of false variants, called in-silico recombinants.'),
(94076, 2011, 'bmc', 'Logical development of the cell ontology', 'Meehan, Terrence F|Masci, Anna Maria|Abdulla, Amina|Cowell, Lindsay G|Blake, Judith A|Mungall, Christopher J|Diehl, Alexander D', 'Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. tmeehan@informatics.jax.org', 'United States|United States|United States|United States|United States|United States|United States', 97, 'Blood Cells|Computational Biology|Databases, Factual|Information Storage and Retrieval|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-6', '21208450', 'The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.'),
(94077, 2010, 'bmc', 'An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics', 'Taylor, Ronald C', 'Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, Washington 99352, USA. ronald.taylor@pnl.gov', 'United States', 306, 'Algorithms|Cluster Analysis|Computational Biology|High-Throughput Nucleotide Sequencing|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S1', '21210976', 'Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce.'),
(94078, 2010, 'bmc', 'interPopula: a Python API to access the HapMap Project dataset', 'Antao, Tiago', 'Liverpool School of Tropical Medicine, L3 5QA, Liverpool, UK. tra@popgen.eu', 'United Kingdom', 0, 'Databases, Nucleic Acid|Humans|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Population Groups|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-11-S12-S10', '21210977', 'The HapMap project is a publicly available catalogue of common genetic variants that occur in humans, currently including several million SNPs across 1115 individuals spanning 11 different populations. This important database does not provide any programmatic access to the dataset, furthermore no standard relational database interface is provided.'),
(94079, 2010, 'bmc', 'An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators', 'Gautier, Laurent', 'DMAC, Centre for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Matematiktorvet, 2100 Lyngby, Denmark. laurent@cbs.dtu.dk', 'Denmark', 6, 'Algorithms|Computational Biology|High-Throughput Screening Assays|Libraries, Digital|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S12-S11', '21210978', 'Computer languages can be domain-related, and in the case of multidisciplinary projects, knowledge of several languages will be needed in order to quickly implements ideas. Moreover, each computer language has relative strong points, making some languages better suited than others for a given task to be implemented. The Bioconductor project, based on the R language, has become a reference for the numerical processing and statistical analysis of data coming from high-throughput biological assays, providing a rich selection of methods and algorithms to the research community. At the same time, Python has matured as a rich and reliable language for the agile development of prototypes or final implementations, as well as for handling large data sets.'),
(94080, 2010, 'bmc', 'The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button', 'Swertz, Morris A|Dijkstra, Martijn|Adamusiak, Tomasz|van der Velde, Joeri K|Kanterakis, Alexandros|Roos, Erik T|Lops, Joris|Thorisson, Gudmundur A|Arends, Danny|Byelas, George|Muilu, Juha|Brookes, Anthony J|de Brock, Engbert O|Jansen, Ritsert C|Parkinson, Helen', 'Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl|Genomics Coordination Center, Groningen Bioinformatics Center, University of Groningen & Department of Genetics, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands. m.a.swertz@rug.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 63, 'Computational Biology|Databases, Genetic|Genomics|Information Systems|Internet|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-11-S12-S12', '21210979', 'There is a huge demand on bioinformaticians to provide their biologists with user friendly and scalable software infrastructures to capture, exchange, and exploit the unprecedented amounts of new *omics data. We here present MOLGENIS, a generic, open source, software toolkit to quickly produce the bespoke MOLecular GENetics Information Systems needed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94081, 2010, 'bmc', 'IPRStats: visualization of the functional potential of an InterProScan run', 'Kelly, Ryan J|Vincent, David E|Friedberg, Iddo', 'Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA.|Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA.|Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA.', 'United States|United States|United States', 9, 'Genomics|Molecular Sequence Annotation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S13', '21210980', 'InterPro is a collection of protein signatures for the classification and automated annotation of proteins. Interproscan is a software tool that scans protein sequences against Interpro member databases using a variety of profile-based, hidden markov model and positional specific score matrix methods. It not only combines a set of analysis tools, but also performs data look-up from various sources, as well as some redundancy removal. Interproscan is robust and scalable, able to perform on any machine from a netbook to a large cluster. However, when performing whole-genome or metagenome analysis, there is a need for a fast statistical visualization of the results to have good initial grasp on the functional potential of the sequences in the analyzed data set. This is especially important when analyzing and comparing metagenomic or metaproteomic data-sets.'),
(94082, 2010, 'bmc', 'SeqWare Query Engine: storing and searching sequence data in the cloud', 'OConnor, Brian D|Merriman, Barry|Nelson, Stanley F', 'UNC Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.|UNC Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.|UNC Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.', 'United States|United States|United States', 75, 'Databases, Nucleic Acid|Genome, Human|Genomics|High-Throughput Nucleotide Sequencing|Humans|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S2', '21210981', 'Since the introduction of next-generation DNA sequencers the rapid increase in sequencer throughput, and associated drop in costs, has resulted in more than a dozen human genomes being resequenced over the last few years. These efforts are merely a prelude for a future in which genome resequencing will be commonplace for both biomedical research and clinical applications. The dramatic increase in sequencer output strains all facets of computational infrastructure, especially databases and query interfaces. The advent of cloud computing, and a variety of powerful tools designed to process petascale datasets, provide a compelling solution to these ever increasing demands.'),
(94083, 2010, 'bmc', 'Hybrid cloud and cluster computing paradigms for life science applications', 'Qiu, Judy|Ekanayake, Jaliya|Gunarathne, Thilina|Choi, Jong Youl|Bae, Seung-Hee|Li, Hui|Zhang, Bingjing|Wu, Tak-Lon|Ruan, Yang|Ekanayake, Saliya|Hughes, Adam|Fox, Geoffrey', 'School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu|School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu', 'India|India|India|India|India|India|India|India|India|India|India|India', 45, 'Biological Science Disciplines|Cluster Analysis|Computational Biology|Data Mining|Metagenomics|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S3', '21210982', 'Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister.'),
(94084, 2010, 'bmc', 'Galaxy CloudMan: delivering cloud compute clusters', 'Afgan, Enis|Baker, Dannon|Coraor, Nate|Chapman, Brad|Nekrutenko, Anton|Taylor, James', 'Department of Biology, Emory University, Atlanta, GA 30322, USA.|Department of Biology, Emory University, Atlanta, GA 30322, USA.|Department of Biology, Emory University, Atlanta, GA 30322, USA.|Department of Biology, Emory University, Atlanta, GA 30322, USA.|Department of Biology, Emory University, Atlanta, GA 30322, USA.|Department of Biology, Emory University, Atlanta, GA 30322, USA.', 'United States|United States|United States|United States|United States|United States', 125, 'Cluster Analysis|Computational Biology|Internet|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S4', '21210983', 'Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is cloud computing, which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate as is use by experimental biologists.'),
(94085, 2010, 'bmc', 'Community-driven computational biology with Debian Linux', 'Möller, Steffen|Krabbenhöft, Hajo Nils|Tille, Andreas|Paleino, David|Williams, Alan|Wolstencroft, Katy|Goble, Carole|Holland, Richard|Belhachemi, Dominique|Plessy, Charles', 'University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org|University Clinics of Schleswig-Holstein, Department of Dermatology, formerly University of Lübeck, Institute for Neuro- andBioinformatics, Ratzeburger Allee 160, 23530 Lübeck, Germany. moeller@debian.org', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 34, 'Computational Biology|Internet|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S5', '21210984', 'The Open Source movement and its technologies are popular in the bioinformatics community because they provide freely available tools and resources for research. In order to feed the steady demand for updates on software and associated data, a service infrastructure is required for sharing and providing these tools to heterogeneous computing environments.'),
(94086, 2010, 'bmc', 'WordSeeker: concurrent bioinformatics software for discovering genome-wide patterns and word-based genomic signatures', 'Lichtenberg, Jens|Kurz, Kyle|Liang, Xiaoyu|Al-ouran, Rami|Neiman, Lev|Nau, Lee J|Welch, Joshua D|Jacox, Edwin|Bitterman, Thomas|Ecker, Klaus|Elnitski, Laura|Drews, Frank|Lee, Stephen Sauchi|Welch, Lonnie R', 'Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.edu|Bioinformatics Laboratory, School of EECS, Ohio University, Athens, Ohio 45701, USA. lichtenj@ohio.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', 4, 'Algorithms|Arabidopsis|DNA|Genome, Plant|Genomics|Markov Chains|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-11-S12-S6', '21210985', 'An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets.'),
(94087, 2010, 'bmc', 'SADI, SHARE, and the in silico scientific method', 'Wilkinson, Mark D|McCarthy, Luke|Vandervalk, Benjamin|Withers, David|Kawas, Edward|Samadian, Soroush', 'Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com|Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com|Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com|Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com|Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com|Heart + Lung Institute at St. Pauls Hospital, University of British Columbia, Vancouver, BC, Canada. markw@illuminae.com', 'Canada|Canada|Canada|Canada|Canada|Canada', 32, 'Biological Science Disciplines|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Semantics|Software|Workflow', 'Journal Article', '10.1186/1471-2105-11-S12-S7', '21210986', 'The emergence and uptake of Semantic Web technologies by the Life Sciences provides exciting opportunities for exploring novel ways to conduct in silico science. Web Service Workflows are already becoming first-class objects in the new way, and serve as explicit, shareable, referenceable representations of how an experiment was done. In turn, Semantic Web Service projects aim to facilitate workflow construction by biological domain-experts such that workflows can be edited, re-purposed, and re-published by non-informaticians. However the aspects of the scientific method relating to explicit discourse, disagreement, and hypothesis generation have remained relatively impervious to new technologies.'),
(94088, 2010, 'bmc', 'ONTO-ToolKit: enabling bio-ontology engineering via Galaxy', 'Antezana, Erick|Venkatesan, Aravind|Mungall, Chris|Mironov, Vladimir|Kuiper, Martin', 'Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, N-7491 Trondheim, Norway. erick.antezana@bio.ntnu.no|Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, N-7491 Trondheim, Norway. erick.antezana@bio.ntnu.no|Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, N-7491 Trondheim, Norway. erick.antezana@bio.ntnu.no|Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, N-7491 Trondheim, Norway. erick.antezana@bio.ntnu.no|Department of Biology, Norwegian University of Science and Technology (NTNU), Høgskoleringen 5, N-7491 Trondheim, Norway. erick.antezana@bio.ntnu.no', 'Norway|Norway|Norway|Norway|Norway', 7, 'Biological Science Disciplines|Gene Expression|Internet|Proteins|Schizosaccharomyces|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-11-S12-S8', '21210987', 'The biosciences increasingly face the challenge of integrating a wide variety of available data, information and knowledge in order to gain an understanding of biological systems. Data integration is supported by a diverse series of tools, but the lack of a consistent terminology to label these data still presents significant hurdles. As a consequence, much of the available biological data remains disconnected or worse: becomes misconnected. The need to address this terminology problem has spawned the building of a large number of bio-ontologies. OBOF, RDF and OWL are among the most used ontology formats to capture terms and relationships in the Life Sciences, opening the potential to use the Semantic Web to support data integration and further exploitation of integrated resources via automated retrieval and reasoning procedures.'),
(94089, 2010, 'bmc', 'The Musite open-source framework for phosphorylation-site prediction', 'Gao, Jianjiong|Xu, Dong', 'Department of Computer Science, C.S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.|Department of Computer Science, C.S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.', 'United States|United States', 10, 'Artificial Intelligence|Phosphoproteins|Phosphorylation|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-11-S12-S9', '21210988', 'With the rapid accumulation of phosphoproteomics data, phosphorylation-site prediction is becoming an increasingly active research area. More than a dozen phosphorylation-site prediction tools have been released in the past decade. However, there is currently no open-source framework specifically designed for phosphorylation-site prediction except Musite.'),
(94090, 2011, 'bmc', 'Using stochastic causal trees to augment Bayesian networks for modeling eQTL datasets', 'Chipman, Kyle C|Singh, Ambuj K', 'Biomolecular Science and Engineering Program, UC Santa Barbara, Santa Barbara, CA, USA. chipman@lifesci.ucsb.edu|Biomolecular Science and Engineering Program, UC Santa Barbara, Santa Barbara, CA, USA. chipman@lifesci.ucsb.edu', 'United States|United States', 6, 'Bayes Theorem|Computer Simulation|Gene Regulatory Networks|Genetic Variation|Genotype|Models, Genetic|Quantitative Trait Loci|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-12-7', '21211042', 'The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.'),
(94091, 2011, 'bmc', 'Flexible taxonomic assignment of ambiguous sequencing reads', 'Clemente, José C|Jansson, Jesper|Valiente, Gabriel', 'Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima 411-8540, Japan.|Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima 411-8540, Japan.|Center for Information Biology and DNA Databank of Japan, National Institute of Genetics, Yata 1111, Mishima 411-8540, Japan.', 'Japan|Japan|Japan', 17, 'Algorithms|Bacteria|Computational Biology|DNA, Bacterial|Metagenomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-8', '21211059', 'To characterize the diversity of bacterial populations in metagenomic studies, sequencing reads need to be accurately assigned to taxonomic units in a given reference taxonomy. Reads that cannot be reliably assigned to a unique leaf in the taxonomy (ambiguous reads) are typically assigned to the lowest common ancestor of the set of species that match it. This introduces a potentially severe error in the estimation of bacteria present in the sample due to false positives, since all species in the subtree rooted at the ancestor are implicitly assigned to the read even though many of them may not match it.'),
(94092, 2011, 'bmc', 'Robust joint analysis allowing for model uncertainty in two-stage genetic association studies', 'Pan, Dongdong|Li, Qizhai|Jiang, Ningning|Liu, Aiyi|Yu, Kai', 'Department of Statistics, Yunnan University, Kunming 650091, PR China.|Department of Statistics, Yunnan University, Kunming 650091, PR China.|Department of Statistics, Yunnan University, Kunming 650091, PR China.|Department of Statistics, Yunnan University, Kunming 650091, PR China.|Department of Statistics, Yunnan University, Kunming 650091, PR China.', 'China|China|China|China|China', 6, 'Computational Biology|Gene Frequency|Genome-Wide Association Study|Humans|Models, Genetic|Models, Statistical|Monte Carlo Method|Polymorphism, Single Nucleotide|Uncertainty', 'Journal Article', '10.1186/1471-2105-12-9', '21211060', 'The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate.'),
(94093, 2011, 'bmc', 'PICS-Ord: unlimited coding of ambiguous regions by pairwise identity and cost scores ordination', 'Lücking, Robert|Hodkinson, Brendan P|Stamatakis, Alexandros|Cartwright, Reed A', 'Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA. rlucking@fieldmuseum.org|Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA. rlucking@fieldmuseum.org|Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA. rlucking@fieldmuseum.org|Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, IL 60605-2496, USA. rlucking@fieldmuseum.org', 'United States|United States|United States|United States', 16, 'Algorithms|Computational Biology|Computer Simulation|DNA|Lichens|Likelihood Functions|Phylogeny|Principal Component Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-12-10', '21214904', 'We present a novel method to encode ambiguously aligned regions in fixed multiple sequence alignments by Pairwise Identity and Cost Scores Ordination (PICS-Ord). The method works via ordination of sequence identity or cost scores matrices by means of Principal Coordinates Analysis (PCoA). After identification of ambiguous regions, the method computes pairwise distances as sequence identities or cost scores, ordinates the resulting distance matrix by means of PCoA, and encodes the principal coordinates as ordered integers. Three biological and 100 simulated datasets were used to assess the performance of the new method.'),
(94094, 2011, 'bmc', 'OrthoInspector: comprehensive orthology analysis and visual exploration', 'Linard, Benjamin|Thompson, Julie D|Poch, Olivier|Lecompte, Odile', 'Laboratoire de bioinformatique et genomique integratives, Département de Biologie et Génomique Structurales CNRS/INSERM/UDS, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 1 rue Laurent Fries, 67404, Illkirch, Cedex, France. linard@igbmc.fr|Laboratoire de bioinformatique et genomique integratives, Département de Biologie et Génomique Structurales CNRS/INSERM/UDS, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 1 rue Laurent Fries, 67404, Illkirch, Cedex, France. linard@igbmc.fr|Laboratoire de bioinformatique et genomique integratives, Département de Biologie et Génomique Structurales CNRS/INSERM/UDS, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 1 rue Laurent Fries, 67404, Illkirch, Cedex, France. linard@igbmc.fr|Laboratoire de bioinformatique et genomique integratives, Département de Biologie et Génomique Structurales CNRS/INSERM/UDS, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 1 rue Laurent Fries, 67404, Illkirch, Cedex, France. linard@igbmc.fr', 'France|France|France|France', 43, 'Algorithms|Molecular Sequence Annotation|Phylogeny|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-12-11', '21219603', 'The accurate determination of orthology and inparalogy relationships is essential for comparative sequence analysis, functional gene annotation and evolutionary studies. Various methods have been developed based on either simple blast all-versus-all pairwise comparisons and/or time-consuming phylogenetic tree analyses.'),
(94095, 2011, 'bmc', 'N-gram analysis of 970 microbial organisms reveals presence of biological language models', 'Osmanbeyoglu, Hatice Ulku|Ganapathiraju, Madhavi K', 'Department of Biomedical Informatics, University of Pittsburgh, 5150 Center Ave, Suite 301, Pittsburgh, PA 15232, USA.|Department of Biomedical Informatics, University of Pittsburgh, 5150 Center Ave, Suite 301, Pittsburgh, PA 15232, USA.', 'United States|United States', 12, 'Bacteria|Genome, Bacterial|Models, Biological|Models, Statistical|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-12-12', '21219653', 'It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as signature-style word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of biological language modeling, statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree.'),
(94096, 2011, 'bmc', 'A platform for processing expression of short time series (PESTS)', 'Sinha, Anshu|Markatou, Marianthi', 'Department of Biostatistics, Columbia University, New York, NY, USA.|Department of Biostatistics, Columbia University, New York, NY, USA.', 'United States|United States', 10, 'Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-13', '21223570', 'Time course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamically expressed under given conditions, and to determine the interaction between these genes. Because of cost and resource issues, most time series datasets contain less than 9 points and there are few tools available geared towards the analysis of this type of data.'),
(94097, 2011, 'bmc', 'Predicting disease-associated substitution of a single amino acid by analyzing residue interactions', 'Li, Yizhou|Wen, Zhining|Xiao, Jiamin|Yin, Hui|Yu, Lezheng|Yang, Li|Li, Menglong', 'Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.|Key Laboratory of Green Chemistry and Technology, Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.', 'China|China|China|China|China|China|China', 28, 'Algorithms|Amino Acid Substitution|Computational Biology|DNA Mutational Analysis|Databases, Protein|Genetic Association Studies|Humans|Models, Statistical|Mutation|Polymorphism, Single Nucleotide|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-12-14', '21223604', 'The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.'),
(94098, 2011, 'bmc', 'Shape-based peak identification for ChIP-Seq', 'Hower, Valerie|Evans, Steven N|Pachter, Lior', 'Department of Mathematics, University of California, Berkeley, California, USA. vhower@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, California, USA. vhower@math.berkeley.edu|Department of Mathematics, University of California, Berkeley, California, USA. vhower@math.berkeley.edu', 'United States|United States|United States', 31, 'Algorithms|Base Sequence|Binding Sites|Chromatin Immunoprecipitation|DNA|DNA-Binding Proteins|Genome|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-15', '21226895', 'The identification of binding targets for proteins using ChIP-Seq has gained popularity as an alternative to ChIP-chip. Sequencing can, in principle, eliminate artifacts associated with microarrays, and cheap sequencing offers the ability to sequence deeply and obtain a comprehensive survey of binding. A number of algorithms have been developed to call peaks representing bound regions from mapped reads. Most current algorithms incorporate multiple heuristics, and despite much work it remains difficult to accurately determine individual peaks corresponding to distinct binding events.'),
(94099, 2011, 'bmc', 'A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies', 'Mourad, Raphaël|Sinoquet, Christine|Leray, Philippe', 'LINA, UMR CNRS 6241, Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr|LINA, UMR CNRS 6241, Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr|LINA, UMR CNRS 6241, Ecole Polytechnique de lUniversité de Nantes, rue Christian Pauc, BP 50609, 44306 Nantes Cedex 3, France. raphael.mourad@univ-nantes.fr', 'France|France|France', 27, 'Algorithms|Bayes Theorem|Genetic Diseases, Inborn|Genetic Markers|Genome, Human|Genome-Wide Association Study|Humans|Linkage Disequilibrium|Models, Statistical|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-16', '21226914', 'Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task.'),
(94100, 2011, 'bmc', 'Pathway-based analysis using reduced gene subsets in genome-wide association studies', 'Zhao, Jingyuan|Gupta, Simone|Seielstad, Mark|Liu, Jianjun|Thalamuthu, Anbupalam', 'Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672 Singapore.|Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672 Singapore.|Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672 Singapore.|Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672 Singapore.|Human Genetics, 60 Biopolis Street 02-01, Genome Institute of Singapore, 138672 Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 22, 'Genes|Genetic Variation|Genome|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide|Psoriasis', 'Journal Article', '10.1186/1471-2105-12-17', '21226955', 'Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWASs of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study.'),
(94101, 2011, 'bmc', 'Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states', 'Yang, Jialiang|Li, Jun|Dong, Liuhuan|Grünewald, Stefan', 'Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, PR China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, PR China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, PR China.|Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, PR China.', 'China|China|China|China', 0, 'Algorithms|Evolution, Molecular|Phylogeny|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-12-18', '21226965', 'As one of the most widely used parsimony methods for ancestral reconstruction, the Fitch method minimizes the total number of hypothetical substitutions along all branches of a tree to explain the evolution of a character. Due to the extensive usage of this method, it has become a scientific endeavor in recent years to study the reconstruction accuracies of the Fitch method. However, most studies are restricted to 2-state evolutionary models and a study for higher-state models is needed since DNA sequences take the format of 4-state series and protein sequences even have 20 states.'),
(94102, 2011, 'bmc', 'Context-dependent transcriptional regulations between signal transduction pathways', 'Hwang, Sohyun|Kim, Sangwoo|Shin, Heesung|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Deajeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Deajeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Deajeon, Republic of Korea.|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Deajeon, Republic of Korea.', '-|-|-|-', 0, 'Algorithms|Computational Biology|Dendritic Cells|Gene Expression Regulation|Gene Regulatory Networks|Humans|Signal Transduction|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-12-19', '21232126', 'Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication.'),
(94103, 2011, 'bmc', 'Combining gene prediction methods to improve metagenomic gene annotation', 'Yok, Non G|Rosen, Gail L', 'Genomic Signal Processing Laboratory, Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA. ng39@drexel.edu|Genomic Signal Processing Laboratory, Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA. ng39@drexel.edu', 'United States|United States', 0, 'Algorithms|Base Sequence|Genes|Humans|Metagenomics|Molecular Sequence Annotation|Software', 'Evaluation Studies', '10.1186/1471-2105-12-20', '21232129', 'Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental) samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation.'),
(94104, 2011, 'bmc', 'NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins', 'Restrepo-Montoya, Daniel|Pino, Camilo|Nino, Luis F|Patarroyo, Manuel E|Patarroyo, Manuel A', 'School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.|School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.|School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.|School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.|School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No, 63C-69, Bogotá DC, Colombia.', 'Colombia|Colombia|Colombia|Colombia|Colombia', 9, 'Artificial Intelligence|Bacterial Proteins|Computational Biology|Databases, Protein|Gram-Positive Bacteria|Models, Theoretical|Software', 'Journal Article', '10.1186/1471-2105-12-21', '21235786', 'Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.'),
(94105, 2011, 'bmc', 'Revision history aware repositories of computational models of biological systems', 'Miller, Andrew K|Yu, Tommy|Britten, Randall|Cooling, Mike T|Lawson, James|Cowan, Dougal|Garny, Alan|Halstead, Matt D B|Hunter, Peter J|Nickerson, David P|Nunns, Geo|Wimalaratne, Sarala M|Nielsen, Poul M F', 'Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz|Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ. ak.miller@auckland.ac.nz', '-|-|-|-|-|-|-|-|-|-|-|-|-', 0, 'Computational Biology|Computer Simulation|Models, Biological|Software', 'Journal Article', '10.1186/1471-2105-12-22', '21235804', 'Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an ancestral model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems.'),
(94106, 2011, 'bmc', 'easyDAS: automatic creation of DAS servers', 'Gel Moreno, Bernat|Jenkinson, Andrew M|Jimenez, Rafael C|Messeguer Peypoch, Xavier|Hermjakob, Henning', 'Software Department, UPC-BarcelonaTech, Barcelona, Spain. bgel@lsi.upc.edu|Software Department, UPC-BarcelonaTech, Barcelona, Spain. bgel@lsi.upc.edu|Software Department, UPC-BarcelonaTech, Barcelona, Spain. bgel@lsi.upc.edu|Software Department, UPC-BarcelonaTech, Barcelona, Spain. bgel@lsi.upc.edu|Software Department, UPC-BarcelonaTech, Barcelona, Spain. bgel@lsi.upc.edu', 'Spain|Spain|Spain|Spain|Spain', 0, 'Computer Communication Networks|Internet|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/1471-2105-12-23', '21244646', 'The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process.'),
(94107, 2011, 'bmc', 'GraphCrunch 2: Software tool for network modeling, alignment and clustering', 'Kuchaiev, Oleksii|Stevanovixc4x87, Aleksandar|Hayes, Wayne|Prxc5xbeulj, Nataxc5xa1a', 'Department of Computer Science, University of California, Irvine, CA, USA.|Department of Computer Science, University of California, Irvine, CA, USA.|Department of Computer Science, University of California, Irvine, CA, USA.|Department of Computer Science, University of California, Irvine, CA, USA.', 'United States|United States|United States|United States', 54, 'Algorithms|Cluster Analysis|Humans|Models, Biological|Protein Interaction Mapping|Sequence Alignment|Software|Viral Proteins', 'Journal Article', '10.1186/1471-2105-12-24', '21244715', 'Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable network properties, such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the best mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype.'),
(94108, 2011, 'bmc', 'Systematic error detection in experimental high-throughput screening', 'Dragiev, Plamen|Nadon, Robert|Makarenkov, Vladimir', 'Dxc3xa9partement dinformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, Montreal (QC) H3C 3P8, Canada.|Dxc3xa9partement dinformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, Montreal (QC) H3C 3P8, Canada.|Dxc3xa9partement dinformatique, Universitxc3xa9 du Quxc3xa9bec xc3xa0 Montrxc3xa9al, Montreal (QC) H3C 3P8, Canada.', 'Canada|Canada|Canada', 25, 'Data Interpretation, Statistical|Drug Discovery|High-Throughput Screening Assays|Software', 'Evaluation Studies', '10.1186/1471-2105-12-25', '21247425', 'High-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measured in order to identify potential drug candidates (i.e., hits). Many technical, procedural or environmental factors can cause systematic measurement error or inequalities in the conditions in which the measurements are taken. Such systematic error has the potential to critically affect the hit selection process. Several error correction methods and software have been developed to address this issue in the context of experimental HTS 1234567. Despite their power to reduce the impact of systematic error when applied to error perturbed datasets, those methods also have one disadvantage - they introduce a bias when applied to data not containing any systematic error 6. Hence, we need first to assess the presence of systematic error in a given HTS assay and then carry out systematic error correction method if and only if the presence of systematic error has been confirmed by statistical tests.'),
(94109, 2011, 'bmc', 'Predicting functionally important SNP classes based on negative selection', 'Levenstien, Mark A|Klein, Robert J', 'Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.|Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.', 'United States|United States', 0, 'Epigenesis, Genetic|Gene Frequency|Genome, Human|Genome-Wide Association Study|Humans|Molecular Sequence Annotation|Polymorphism, Single Nucleotide|Selection, Genetic', 'Journal Article', '10.1186/1471-2105-12-26', '21247465', 'With the advent of cost-effective genotyping technologies, genome-wide association studies allow researchers to examine hundreds of thousands of single nucleotide polymorphisms (SNPs) for association with human disease. Recently, many researchers applying this strategy have detected strong associations to disease with SNP markers that are either not in linkage disequilibrium with any nonsynonymous SNP or large distances from any annotated gene. In such cases, no well-established standard practice for effective SNP selection for follow-up studies exists. We aim to identify and prioritize groups of SNPs that are more likely to affect phenotypes in order to facilitate efficient SNP selection for follow-up studies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94110, 2011, 'bmc', 'CODA (crossover distribution analyzer): quantitative characterization of crossover position patterns along chromosomes', 'Gauthier, Franck|Martin, Olivier C|Falque, Matthieu', 'UMR de Génétique Végétale, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Ferme du Moulon, F-91190 Gif-sur-Yvette, France.|UMR de Génétique Végétale, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Ferme du Moulon, F-91190 Gif-sur-Yvette, France.|UMR de Génétique Végétale, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Ferme du Moulon, F-91190 Gif-sur-Yvette, France.', 'France|France|France', 0, 'Animals|Chromosomes, Mammalian|Chromosomes, Plant|Crossing Over, Genetic|Mice|Models, Genetic|Software|Triticum|Zea mays', 'Journal Article', '10.1186/1471-2105-12-27', '21251248', 'During meiosis, homologous chromosomes exchange segments via the formation of crossovers. This phenomenon is highly regulated, in particular, crossovers are distributed heterogeneously along the physical map and rarely arise in close proximity, a property referred to as interference. Crossover positions form patterns that give clues about how crossovers are formed. In several organisms including yeast, tomato, Arabidopsis, and mouse, it is believed that crossovers form via at least two pathways, one interfering, the other not.'),
(94111, 2011, 'bmc', 'FASIMU: flexible software for flux-balance computation series in large metabolic networks', 'Hoppe, Andreas|Hoffmann, Sabrina|Gerasch, Andreas|Gille, Christoph|Holzhütter, Hermann-Georg', 'Institute of Biochemistry, University Medicine Charité Berlin, Seestr, 73, 13347 Berlin, Germany. hoppe@bioinformatics.org|Institute of Biochemistry, University Medicine Charité Berlin, Seestr, 73, 13347 Berlin, Germany. hoppe@bioinformatics.org|Institute of Biochemistry, University Medicine Charité Berlin, Seestr, 73, 13347 Berlin, Germany. hoppe@bioinformatics.org|Institute of Biochemistry, University Medicine Charité Berlin, Seestr, 73, 13347 Berlin, Germany. hoppe@bioinformatics.org|Institute of Biochemistry, University Medicine Charité Berlin, Seestr, 73, 13347 Berlin, Germany. hoppe@bioinformatics.org', 'Germany|Germany|Germany|Germany|Germany', 42, 'Algorithms|Computational Biology|Computer Simulation|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1186/1471-2105-12-28', '21255455', 'Flux-balance analysis based on linear optimization is widely used to compute metabolic fluxes in large metabolic networks and gains increasingly importance in network curation and structural analysis. Thus, a computational tool flexible enough to realize a wide variety of FBA algorithms and able to handle batch series of flux-balance optimizations is of great benefit.'),
(94112, 2011, 'bmc', 'Investigating the effect of paralogs on microarray gene-set analysis', 'Faure, Andre J|Seoighe, Cathal|Mulder, Nicola J', 'Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa. andrefau@ebi.ac.uk|Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa. andrefau@ebi.ac.uk|Computational Biology Group, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa. andrefau@ebi.ac.uk', 'South Africa|South Africa|South Africa', 0, 'Algorithms|Arabidopsis Proteins|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-29', '21261946', 'In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research.'),
(94113, 2011, 'bmc', 'iPhy: an integrated phylogenetic workbench for supermatrix analyses', 'Jones, Martin O|Koutsovoulos, Georgios D|Blaxter, Mark L', 'Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93JT, UK. martin.jones@ed.ac.uk|Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93JT, UK. martin.jones@ed.ac.uk|Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH93JT, UK. martin.jones@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Animals|Computational Biology|Consensus Sequence|Molecular Sequence Annotation|Nematoda|Phylogeny|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-30', '21261969', 'The increasing availability of molecular sequence data means that the accuracy of future phylogenetic studies is likely to by limited by systematic bias and taxon choice rather than by data. In order to take advantage of increasing datasets, user-friendly tools are required to facilitate phylogenetic analyses and to reduce duplication of dataset assembly efforts. Current phylogenetic pipelines are dependency-heavy and have significant technical barriers to use.'),
(94114, 2011, 'bmc', 'PileLine: a toolbox to handle genome position information in next-generation sequencing studies', 'Glez-Peña, Daniel|Gómez-López, Gonzalo|Reboiro-Jato, Miguel|Fdez-Riverola, Florentino|Pisano, David G', 'Higher Technical School of Computer Engineering, University of Vigo, Ourense, Spain. dgpena@uvigo.es|Higher Technical School of Computer Engineering, University of Vigo, Ourense, Spain. dgpena@uvigo.es|Higher Technical School of Computer Engineering, University of Vigo, Ourense, Spain. dgpena@uvigo.es|Higher Technical School of Computer Engineering, University of Vigo, Ourense, Spain. dgpena@uvigo.es|Higher Technical School of Computer Engineering, University of Vigo, Ourense, Spain. dgpena@uvigo.es', 'Spain|Spain|Spain|Spain|Spain', 0, 'Computational Biology|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-31', '21261974', 'Genomic position (GP) files currently used in next-generation sequencing (NGS) studies are always difficult to manipulate due to their huge size and the lack of appropriate tools to properly manage them. The structure of these flat files is based on representing one line per position that has been covered by at least one aligned read, imposing significant restrictions from a computational performance perspective.'),
(94115, 2011, 'bmc', 'Worm Phenotype Ontology: integrating phenotype data within and beyond the C elegans community', 'Schindelman, Gary|Fernandes, Jolene S|Bastiani, Carol A|Yook, Karen|Sternberg, Paul W', 'Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Caenorhabditis elegans|Information Storage and Retrieval|Phenotype|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-32', '21261995', 'Caenorhabditis elegans gene-based phenotype information dates back to the 1970s, beginning with Sydney Brenner and the characterization of behavioral and morphological mutant alleles via classical genetics in order to understand nervous system function. Since then C. elegans has become an important genetic model system for the study of basic biological and biomedical principles, largely through the use of phenotype analysis. Because of the growth of C. elegans as a genetically tractable model organism and the development of large-scale analyses, there has been a significant increase of phenotype data that needs to be managed and made accessible to the research community. To do so, a standardized vocabulary is necessary to integrate phenotype data from diverse sources, permit integration with other data types and render the data in a computable form.'),
(94116, 2011, 'bmc', 'A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species', 'Correa, Elon|Goodacre, Royston', 'School of Chemistry, The University of Manchester, 131 Princess Street, Manchester M17ND, UK.|School of Chemistry, The University of Manchester, 131 Princess Street, Manchester M17ND, UK.', 'United Kingdom|United Kingdom', 21, 'Algorithms|Bacillus|Bayes Theorem|Mass Spectrometry|Metabolomics|Models, Statistical|Principal Component Analysis|Spores, Bacterial', 'Journal Article', '10.1186/1471-2105-12-33', '21269434', 'The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS.'),
(94117, 2011, 'bmc', 'Gepoclu: a software tool for identifying and analyzing gene positional clusters in large-scale gene expression analysis', 'Dottorini, Tania|Senin, Nicola|Mazzoleni, Giorgio|Magnusson, Kalle|Crisanti, Andrea', 'Department of Experimental Medicine, University of Perugia, via del Giochetto, Perugia 06100, Italy.|Department of Experimental Medicine, University of Perugia, via del Giochetto, Perugia 06100, Italy.|Department of Experimental Medicine, University of Perugia, via del Giochetto, Perugia 06100, Italy.|Department of Experimental Medicine, University of Perugia, via del Giochetto, Perugia 06100, Italy.|Department of Experimental Medicine, University of Perugia, via del Giochetto, Perugia 06100, Italy.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Animals|Anopheles|Cluster Analysis|Computational Biology|Drosophila melanogaster|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction|Software', 'Journal Article', '10.1186/1471-2105-12-34', '21269436', 'The notion that genes are non-randomly organized within the chromosomes of eukaryotic organisms has recently received strong experimental support. Clusters of co-expressed and co-localized genes have been recognized as playing key roles in a number of functional pathways and adaptive responses including organism development, differentiation, disease states and aging. The identification of genes arranged in close proximity with each other within a particular temporal and spatial transcriptional program is anticipated to unravel possible functional links and reciprocal interactions.'),
(94118, 2011, 'bmc', 'VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R', 'Chen, Hanbo|Boutros, Paul C', 'Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, M5G 0A3, Canada.|Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, M5G 0A3, Canada.', 'Canada|Canada', 617, 'Computational Biology|Computer Graphics|Data Interpretation, Statistical|Software', 'Journal Article', '10.1186/1471-2105-12-35', '21269502', 'Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets.'),
(94119, 2011, 'bmc', 'ASPDock: protein-protein docking algorithm using atomic solvation parameters model', 'Li, Lin|Guo, Dachuan|Huang, Yangyu|Liu, Shiyong|Xiao, Yi', 'Biomolecular Physics and Modelling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.|Biomolecular Physics and Modelling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.|Biomolecular Physics and Modelling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.|Biomolecular Physics and Modelling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.|Biomolecular Physics and Modelling Group, Department of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.', 'China|China|China|China|China', 26, 'Algorithms|Protein Binding|Protein Structure, Tertiary|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-12-36', '21269517', 'Atomic Solvation Parameters (ASP) model has been proven to be a very successful method of calculating the binding free energy of protein complexes. This suggests that incorporating it into docking algorithms should improve the accuracy of prediction. In this paper we propose an FFT-based algorithm to calculate ASP scores of protein complexes and develop an ASP-based protein-protein docking method (ASPDock).'),
(94120, 2011, 'bmc', 'Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression', 'Heine, John J|Land, Walker H|Egan, Kathleen M', 'H, Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA. john.heine@moffitt.org|H, Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA. john.heine@moffitt.org|H, Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA. john.heine@moffitt.org', 'United States|United States|United States', 0, 'Artificial Intelligence|Case-Control Studies|Computer Simulation|Logistic Models|Models, Biological|Neural Networks (Computer)|Odds Ratio', 'Journal Article', '10.1186/1471-2105-12-37', '21272346', 'When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL) techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR) modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison.'),
(94121, 2011, 'bmc', 'Removing noise from pyrosequenced amplicons', 'Quince, Christopher|Lanzen, Anders|Davenport, Russell J|Turnbaugh, Peter J', 'Department of Civil Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. christopher.quince@glasgow.ac.uk|Department of Civil Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. christopher.quince@glasgow.ac.uk|Department of Civil Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. christopher.quince@glasgow.ac.uk|Department of Civil Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK. christopher.quince@glasgow.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|DNA|Logistic Models|Polymerase Chain Reaction|RNA, Ribosomal, 16S|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-38', '21276213', 'In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms.'),
(94122, 2011, 'bmc', 'ChIPseqR: analysis of ChIP-seq experiments', 'Humburg, Peter|Helliwell, Chris A|Bulger, David|Stone, Glenn', 'Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@well.ox.ac.uk|Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@well.ox.ac.uk|Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@well.ox.ac.uk|Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@well.ox.ac.uk', 'Australia|Australia|Australia|Australia', 11, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA, Plant|Genome, Plant|Histones|Models, Statistical|Nucleosomes|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-39', '21281468', 'The use of high-throughput sequencing in combination with chromatin immunoprecipitation (ChIP-seq) has enabled the study of genome-wide protein binding at high resolution. While the amount of data generated from such experiments is steadily increasing, the methods available for their analysis remain limited. Although several algorithms for the analysis of ChIP-seq data have been published they focus almost exclusively on transcription factor studies and are usually not well suited for the analysis of other types of experiments.'),
(94123, 2011, 'bmc', 'nocoRNAc: characterization of non-coding RNAs in prokaryotes', 'Herbig, Alexander|Nieselt, Kay', 'Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.', 'Germany|Germany', 0, 'Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|RNA, Antisense|RNA, Bacterial|RNA, Untranslated|Software|Streptomyces coelicolor|Terminator Regions, Genetic', 'Journal Article', '10.1186/1471-2105-12-40', '21281482', 'The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not.'),
(94124, 2011, 'bmc', 'RAIphy: phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles', 'Nalbantoglu, Ozkan U|Way, Samuel F|Hinrichs, Steven H|Sayood, Khalid', 'Department of Electrical Engineering, 209N WSEC University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA. nalbantoglu@huskers.unl.edu|Department of Electrical Engineering, 209N WSEC University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA. nalbantoglu@huskers.unl.edu|Department of Electrical Engineering, 209N WSEC University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA. nalbantoglu@huskers.unl.edu|Department of Electrical Engineering, 209N WSEC University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA. nalbantoglu@huskers.unl.edu', 'United States|United States|United States|United States', 49, 'Algorithms|Metagenomics|Phylogeny|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-12-41', '21281493', 'Computational analysis of metagenomes requires the taxonomical assignment of the genome contigs assembled from DNA reads of environmental samples. Because of the diverse nature of microbiomes, the length of the assemblies obtained can vary between a few hundred bp to a few hundred Kbp. Current taxonomic classification algorithms provide accurate classification for long contigs or for short fragments from organisms that have close relatives with annotated genomes. These are significant limitations for metagenome analysis because of the complexity of microbiomes and the paucity of existing annotated genomes.'),
(94125, 2011, 'bmc', 'Multivariate analysis of microarray data: differential expression and differential connection', 'Kiiveri, Harri T', 'CSIRO Mathematics Informatics and Statistics, The Leeuwin Centre, 65 Brockway Road, Floreat, Western Australia. harri.kiiveri@csiro.au', 'Australia', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Linear Models|Multivariate Analysis|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-42', '21281522', 'Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression.'),
(94126, 2011, 'bmc', 'DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning', 'Eickholt, Jesse|Deng, Xin|Cheng, Jianlin', 'Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.|Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.', 'United States|United States|United States', 0, 'Artificial Intelligence|Evolution, Molecular|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-12-43', '21284866', 'Accurate identification of protein domain boundaries is useful for protein structure determination and prediction. However, predicting protein domain boundaries from a sequence is still very challenging and largely unsolved.'),
(94127, 2011, 'bmc', 'Gene ontology based transfer learning for protein subcellular localization', 'Mei, Suyu|Fei, Wang|Zhou, Shuigeng', 'Software College, Shenyang Normal University, Shenyang, PR China. 061021053@fudan.edu.cn|Software College, Shenyang Normal University, Shenyang, PR China. 061021053@fudan.edu.cn|Software College, Shenyang Normal University, Shenyang, PR China. 061021053@fudan.edu.cn', 'China|China|China', 51, 'Computational Biology|Databases, Protein|Models, Statistical|Sequence Analysis, Protein|Software|Statistics, Nonparametric|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-44', '21284890', 'Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.'),
(94128, 2011, 'bmc', 'Hierarchical graphs for rule-based modeling of biochemical systems', 'Lemons, Nathan W|Hu, Bin|Hlavacek, William S', 'Department of Mathematics and its Applications, Central European University, H-1051 Budapest, Hungary.|Department of Mathematics and its Applications, Central European University, H-1051 Budapest, Hungary.|Department of Mathematics and its Applications, Central European University, H-1051 Budapest, Hungary.', 'Hungary|Hungary|Hungary', 12, 'Algorithms|Models, Biological|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-45', '21288338', 'In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system.'),
(94129, 2011, 'bmc', 'GCOD - GeneChip Oncology Database', 'Liu, Fenglong|White, Joseph A|Antonescu, Corina|Gusenleitner, Daniel|Quackenbush, John', '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', 0, 'Databases, Genetic|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-46', '21291543', 'DNA microarrays have become a nearly ubiquitous tool for the study of human disease, and nowhere is this more true than in cancer. With hundreds of studies and thousands of expression profiles representing the majority of human cancers completed and in public databases, the challenge has been effectively accessing and using this wealth of data.'),
(94130, 2011, 'bmc', 'Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling', 'Wolfsheimer, Stefan|Herms, Inke|Rahmann, Sven|Hartmann, Alexander K', 'Laboratoire MAP5 (UMR CNRS 8145), Université Paris Descartes, Paris, France. stefan.wolfsheimer@googlemail.com|Laboratoire MAP5 (UMR CNRS 8145), Université Paris Descartes, Paris, France. stefan.wolfsheimer@googlemail.com|Laboratoire MAP5 (UMR CNRS 8145), Université Paris Descartes, Paris, France. stefan.wolfsheimer@googlemail.com|Laboratoire MAP5 (UMR CNRS 8145), Université Paris Descartes, Paris, France. stefan.wolfsheimer@googlemail.com', 'France|France|France|France', 6, 'Algorithms|Computational Biology|Computer Simulation|Markov Chains|Models, Statistical|Monte Carlo Method|ROC Curve|Sensitivity and Specificity|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-12-47', '21291566', 'Molecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet.'),
(94131, 2011, 'bmc', 'A quality metric for homology modeling: the H-factor', 'di Luccio, Eric|Koehl, Patrice', 'Computer Science Department, Room 4337, Genome Center, GBSF University of California Davis 451 East Health Sciences Drive Davis, CA 95616, USA. eric.diluccio@gmail.com|Computer Science Department, Room 4337, Genome Center, GBSF University of California Davis 451 East Health Sciences Drive Davis, CA 95616, USA. eric.diluccio@gmail.com', 'United States|United States', 0, 'Computational Biology|Crystallography, X-Ray|Internet|Models, Molecular|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-12-48', '21291572', 'The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, in silico protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and in silico methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.'),
(94132, 2011, 'bmc', 'A novel approach to the clustering of microarray data via nonparametric density estimation', 'De Bin, Riccardo|Risso, Davide', 'Department of Statistical Sciences, University of Padova, Padova, Italy.|Department of Statistical Sciences, University of Padova, Padova, Italy.', 'Italy|Italy', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Software|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-12-49', '21303507', 'Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.'),
(94133, 2011, 'bmc', 'Dr PIAS: an integrative system for assessing the druggability of protein-protein interactions', 'Sugaya, Nobuyoshi|Furuya, Toshio', 'Drug Discovery Department, Research & Development Division, PharmaDesign, Inc,, Hatchobori 2-19-8, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp|Drug Discovery Department, Research & Development Division, PharmaDesign, Inc,, Hatchobori 2-19-8, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp', 'Japan|Japan', 17, 'Animals|Databases, Protein|Human Immunodeficiency Virus Proteins|Humans|Mice|Protein Interaction Mapping|Proteins|Rats|Software|Technology, Pharmaceutical', 'Journal Article', '10.1186/1471-2105-12-50', '21303559', 'The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.'),
(94134, 2011, 'bmc', 'Tree Pruner: An efficient tool for selecting data from a biased genetic database', 'Krishnamoorthy, Mohan|Patel, Pragneshkumar|Dimitrijevic, Mira|Dietrich, Jonathan|Green, Margaret|Macken, Catherine', 'Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Data Mining|Database Management Systems|Databases, Genetic|Phylogeny|Selection Bias|Software', 'Journal Article', '10.1186/1471-2105-12-51', '21306634', 'Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses.'),
(94135, 2011, 'bmc', 'RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis', 'Zeeberg, Barry R|Liu, Hongfang|Kahn, Ari B|Ehler, Martin|Rajapakse, Vinodh N|Bonner, Robert F|Brown, Jacob D|Brooks, Brian P|Larionov, Vladimir L|Reinhold, William|Weinstein, John N|Pommier, Yves G', 'Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Data Mining|Gene Expression Profiling|Mice|Oligonucleotide Array Sequence Analysis|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-12-52', '21310028', 'The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.'),
(94136, 2011, 'bmc', 'Estimating developmental states of tumors and normal tissues using a linear time-ordered model', 'Zhang, Bo|Chen, Beibei|Wu, Tao|Xuan, Zhenyu|Zhu, Xiaopeng|Chen, Runsheng', 'Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.|Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.|Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.|Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.|Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.|Laboratory of Bioinformatics and Noncoding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, PR China.', 'China|China|China|China|China|China', 15, 'Animals|Female|Gene Expression Profiling|Genes, Neoplasm|Humans|Linear Models|Liver|Liver Neoplasms|Lung|Mice|Neoplasms|Ovarian Neoplasms|Ovary|Principal Component Analysis|Time Factors', 'Journal Article', '10.1186/1471-2105-12-53', '21310084', 'Tumor cells are considered to have an aberrant cell state, and some evidence indicates different development states appearing in the tumorigenesis. Embryonic development and stem cell differentiation are ordered processes in which the sequence of events over time is highly conserved. The cancer attractor concept integrates normal developmental processes and tumorigenesis into a high-dimensional cell state space, and provides a reasonable explanation of the relationship between these two biological processes from theoretical viewpoint. However, it is hard to describe such relationship by using existed experimental data, moreover, the measurement of different development states is also difficult.'),
(94137, 2011, 'bmc', 'Identifying hypermethylated CpG islands using a quantile regression model', 'Sun, Shuying|Chen, Zhengyi|Yan, Pearlly S|Huang, Yi-Wen|Huang, Tim H M|Lin, Shili', 'Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu|Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA. shuying.sun@case.edu', 'United States|United States|United States|United States|United States|United States', 7, 'Breast Neoplasms|Cell Line, Tumor|CpG Islands|DNA Methylation|Female|Humans|Microarray Analysis|Models, Statistical|Nucleic Acid Hybridization|Ovarian Neoplasms|Regression Analysis', 'Comparative Study', '10.1186/1471-2105-12-54', '21324121', 'DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.'),
(94138, 2011, 'bmc', 'Support vector machines-based identification of alternative splicing in Arabidopsis thaliana from whole-genome tiling arrays', 'Eichner, Johannes|Zeller, Georg|Laubinger, Sascha|Rätsch, Gunnar', 'Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. johannes.eichner@uni-tuebingen.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. johannes.eichner@uni-tuebingen.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. johannes.eichner@uni-tuebingen.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr, 39, 72076 Tübingen, Germany. johannes.eichner@uni-tuebingen.de', 'Germany|Germany|Germany|Germany', 0, 'Alternative Splicing|Arabidopsis|DNA, Complementary|Exons|Expressed Sequence Tags|Genome, Plant|Introns|Oligonucleotide Array Sequence Analysis|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-12-55', '21324185', 'Alternative splicing (AS) is a process which generates several distinct mRNA isoforms from the same gene by splicing different portions out of the precursor transcript. Due to the (patho-)physiological importance of AS, a complete inventory of AS is of great interest. While this is in reach for human and mammalian model organisms, our knowledge of AS in plants has remained more incomplete. Experimental approaches for monitoring AS are either based on transcript sequencing or rely on hybridization to DNA microarrays. Among the microarray platforms facilitating the discovery of AS events, tiling arrays are well-suited for identifying intron retention, the most prevalent type of AS in plants. However, analyzing tiling array data is challenging, because of high noise levels and limited probe coverage.'),
(94139, 2011, 'bmc', 'Simulated evolution applied to study the genetic code optimality using a model of codon reassignments', 'Santos, José|Monteagudo, Angel', 'Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. santos@udc.es|Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. santos@udc.es', 'Spain|Spain', 0, 'Algorithms|Amino Acids|Codon|Evolution, Molecular|Genetic Code|Models, Genetic|Point Mutation', 'Journal Article', '10.1186/1471-2105-12-56', '21338505', 'As the canonical code is not universal, different theories about its origin and organization have appeared. The optimization or level of adaptation of the canonical genetic code was measured taking into account the harmful consequences resulting from point mutations leading to the replacement of one amino acid for another. There are two basic theories to measure the level of optimization: the statistical approach, which compares the canonical genetic code with many randomly generated alternative ones, and the engineering approach, which compares the canonical code with the best possible alternative.'),
(94140, 2011, 'bmc', 'Tiling array data analysis: a multiscale approach using wavelets', 'Karpikov, Alexander|Rozowsky, Joel|Gerstein, Mark', 'Diagnostic Radiology, Yale University, New Haven, CT, USA. alexander.karpikov@yale.edu|Diagnostic Radiology, Yale University, New Haven, CT, USA. alexander.karpikov@yale.edu|Diagnostic Radiology, Yale University, New Haven, CT, USA. alexander.karpikov@yale.edu', 'United States|United States|United States', 0, 'Algorithms|Chromatin Immunoprecipitation|Oligonucleotide Array Sequence Analysis|Wavelet Analysis', 'Journal Article', '10.1186/1471-2105-12-57', '21338513', 'Tiling array data is hard to interpret due to noise. The wavelet transformation is a widely used technique in signal processing for elucidating the true signal from noisy data. Consequently, we attempted to denoise representative tiling array datasets for ChIP-chip experiments using wavelets. In doing this, we used specific wavelet basis functions, Coiflets, since their triangular shape closely resembles the expected profiles of true ChIP-chip peaks.'),
(94141, 2011, 'bmc', 'Multiclass classification of microarray data samples with a reduced number of genes', 'Tapia, Elizabeth|Ornella, Leonardo|Bulacio, Pilar|Angelone, Laura', 'CIFASIS-Conicet Institute, Bv, 27 de Febrero 210 Bis, Rosario, Argentina. tapia@cifasis-conicet.gov.ar|CIFASIS-Conicet Institute, Bv, 27 de Febrero 210 Bis, Rosario, Argentina. tapia@cifasis-conicet.gov.ar|CIFASIS-Conicet Institute, Bv, 27 de Febrero 210 Bis, Rosario, Argentina. tapia@cifasis-conicet.gov.ar|CIFASIS-Conicet Institute, Bv, 27 de Febrero 210 Bis, Rosario, Argentina. tapia@cifasis-conicet.gov.ar', 'Argentina|Argentina|Argentina|Argentina', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-12-59', '21342522', 'Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94142, 2011, 'bmc', 'Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles', 'Tsai, Richard Tzong-Han|Lai, Po-Ting', 'Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw|Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan, R.O.C. thtsai@saturn.yzu.edu.tw', '-|-', 4, 'Algorithms|Area Under Curve|Computational Biology|Data Mining|Databases, Protein|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-12-60', '21342534', 'Experimentally verified protein-protein interactions (PPIs) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be facilitated by employing text-mining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers (interactor normalization task or INT) and then to return a list of interaction pairs for each article (interaction pair task or IPT). These two tasks are evaluated in terms of the area under curve of the interpolated precision/recall (AUC iP/R) score because the order of identifiers in the output list is important for ease of curation.'),
(94143, 2011, 'bmc', 'Genome-wide discovery of missing genes in biological pathways of prokaryotes', 'Chen, Yong|Mao, Fenglou|Li, Guojun|Xu, Ying', 'Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. yongchen@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. yongchen@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. yongchen@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. yongchen@csbl.bmb.uga.edu', 'Georgia|Georgia|Georgia|Georgia', 0, 'Algorithms|Chromosome Mapping|Computational Biology|Escherichia coli|Genome, Archaeal|Genome, Bacterial|Genomics|Operon|Phylogeny', 'Journal Article', '10.1186/1471-2105-12-S1-S1', '21342538', 'Reconstruction of biological pathways is typically done through mapping well-characterized pathways of model organisms to a target genome, through orthologous gene mapping. A limitation of such pathway-mapping approaches is that the mapped pathway models are constrained by the composition of the template pathways, e.g., some genes in a target pathway may not have corresponding genes in the template pathways, the so-called missing gene problem.'),
(94144, 2011, 'bmc', 'Gene-gene interaction filtering with ensemble of filters', 'Yang, Pengyi|Ho, Joshua Wk|Yang, Yee Hwa|Zhou, Bing B', 'School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au|School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy@it.usyd.edu.au', 'Australia|Australia|Australia|Australia', 33, 'Algorithms|Computational Biology|Computer Simulation|Genome-Wide Association Study|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S10', '21342539', 'Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the unstable results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance.'),
(94145, 2011, 'bmc', 'Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach', 'Zhang, Wangshu|Sun, Fengzhu|Jiang, Rui', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 10084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 10084, China.|MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 10084, China.', 'China|China|China', 0, 'Bayes Theorem|Computational Biology|Humans|Linear Models|Phenotype|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-12-S1-S11', '21342540', 'The identification of genes responsible for human inherited diseases is one of the most challenging tasks in human genetics. Recent studies based on phenotype similarity and gene proximity have demonstrated great success in prioritizing candidate genes for human diseases. However, most of these methods rely on a single protein-protein interaction (PPI) network to calculate similarities between genes, and thus greatly restrict the scope of application of such methods. Meanwhile, independently constructed and maintained PPI networks are usually quite diverse in coverage and quality, making the selection of a suitable PPI network inevitable but difficult.'),
(94146, 2011, 'bmc', 'An efficient algorithmic approach for mass spectrometry-based disulfide connectivity determination using multi-ion analysis', 'Murad, William|Singh, Rahul|Yen, Ten-Yang', 'Department of Computer Science, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. whemurad@sfsu.edu|Department of Computer Science, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. whemurad@sfsu.edu|Department of Computer Science, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA. whemurad@sfsu.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Disulfides|Glycosyltransferases|Ions|Mass Spectrometry|Proteins', 'Comparative Study', '10.1186/1471-2105-12-S1-S12', '21342541', 'Determining the disulfide (S-S) bond pattern in a protein is often crucial for understanding its structure and function. In recent research, mass spectrometry (MS) based analysis has been applied to this problem following protein digestion under both partial reduction and non-reduction conditions. However, this paradigm still awaits solutions to certain algorithmic problems fundamental amongst which is the efficient matching of an exponentially growing set of putative S-S bonded structural alternatives to the large amounts of experimental spectrometric data. Current methods circumvent this challenge primarily through simplifications, such as by assuming only the occurrence of certain ion-types (b-ions and y-ions) that predominate in the more popular dissociation methods, such as collision-induced dissociation (CID). Unfortunately, this can adversely impact the quality of results.'),
(94147, 2011, 'bmc', 'A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures', 'Fukagawa, Daiji|Tamura, Takeyuki|Takasu, Atsuhiro|Tomita, Etsuji|Akutsu, Tatsuya', 'Faculty of Culture and Information Science, Doshisha University, Kyoto 610-0394, Japan. dfukagaw@mail.doshisha.ac.jp|Faculty of Culture and Information Science, Doshisha University, Kyoto 610-0394, Japan. dfukagaw@mail.doshisha.ac.jp|Faculty of Culture and Information Science, Doshisha University, Kyoto 610-0394, Japan. dfukagaw@mail.doshisha.ac.jp|Faculty of Culture and Information Science, Doshisha University, Kyoto 610-0394, Japan. dfukagaw@mail.doshisha.ac.jp|Faculty of Culture and Information Science, Doshisha University, Kyoto 610-0394, Japan. dfukagaw@mail.doshisha.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Computational Biology|Polysaccharides|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S13', '21342542', 'Measuring similarities between tree structured data is important for analysis of RNA secondary structures, phylogenetic trees, glycan structures, and vascular trees. The edit distance is one of the most widely used measures for comparison of tree structured data. However, it is known that computation of the edit distance for rooted unordered trees is NP-hard. Furthermore, there is almost no available software tool that can compute the exact edit distance for unordered trees.'),
(94148, 2011, 'bmc', 'An ILP solution for the gene duplication problem', 'Chang, Wen-Chieh|Burleigh, Gordon J|Fernández-Baca, David F|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames 50011, USA. wcchang@iastate.edu|Department of Computer Science, Iowa State University, Ames 50011, USA. wcchang@iastate.edu|Department of Computer Science, Iowa State University, Ames 50011, USA. wcchang@iastate.edu|Department of Computer Science, Iowa State University, Ames 50011, USA. wcchang@iastate.edu', 'United States|United States|United States|United States', 14, 'Algorithms|Computer Simulation|Gene Duplication|Genome, Plant|Genomics|Phylogeny|Plants|Programming, Linear', 'Journal Article', '10.1186/1471-2105-12-S1-S14', '21342543', 'The gene duplication (GD) problem seeks a species tree that implies the fewest gene duplication events across a given collection of gene trees. Solving this problem makes it possible to use large gene families with complex histories of duplication and loss to infer phylogenetic trees. However, the GD problem is NP-hard, and therefore, most analyses use heuristics that lack any performance guarantee.'),
(94149, 2011, 'bmc', 'Maximum likelihood models and algorithms for gene tree evolution with duplications and losses', 'Górecki, Pawel|Burleigh, Gordon J|Eulenstein, Oliver', 'Institute of Informatics, Warsaw University, Warsaw 02-097, Poland. gorecki@mimuw.edu.pl|Institute of Informatics, Warsaw University, Warsaw 02-097, Poland. gorecki@mimuw.edu.pl|Institute of Informatics, Warsaw University, Warsaw 02-097, Poland. gorecki@mimuw.edu.pl', 'Poland|Poland|Poland', 0, 'Algorithms|Computer Simulation|Evolution, Molecular|Gene Duplication|Genetic Speciation|Genome|Genomics|Likelihood Functions|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S15', '21342544', 'The abundance of new genomic data provides the opportunity to map the location of gene duplication and loss events on a species phylogeny. The first methods for mapping gene duplications and losses were based on a parsimony criterion, finding the mapping that minimizes the number of duplication and loss events. Probabilistic modeling of gene duplication and loss is relatively new and has largely focused on birth-death processes.'),
(94150, 2011, 'bmc', 'SOMEA: self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model', 'Lee, Nung Kion|Wang, Dianhui', 'Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. n2lee@students.latrobe.edu.au|Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3086, Australia. n2lee@students.latrobe.edu.au', 'Australia|Australia', 14, 'Algorithms|Binding Sites|Cluster Analysis|DNA|Markov Chains|Models, Statistical|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-S1-S16', '21342545', 'Discrimination of transcription factor binding sites (TFBS) from background sequences plays a key role in computational motif discovery. Current clustering based algorithms employ homogeneous model for problem solving, which assumes that motifs and background signals can be equivalently characterized. This assumption has some limitations because both sequence signals have distinct properties.'),
(94151, 2011, 'bmc', 'A genetic algorithm-based Boolean delay model of intracellular signal transduction in inflammation', 'Kang, Chu Chun|Chuang, Yung Jen|Tung, Kai Che|Chao, Chun Cheih|Tang, Chuan Yi|Peng, Shih Chi|Wong, David Shan Hill', 'Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw|Department of Computer Science, National Tsing Hua University, Hsinchu, 30013 Taiwan, ROC. g9662584@oz.nthu.edu.tw', '-|-|-|-|-|-|-', 8, 'Algorithms|Inflammation|Models, Biological|NF-kappa B|Receptor Cross-Talk|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-S1-S17', '21342546', 'Signal transduction is the major mechanism through which cells transmit external stimuli to evoke intracellular biochemical responses. Understanding relationship between external stimuli and corresponding cellular responses, as well as the subsequent effects on downstream genes, is a major challenge in systems biology. Thus, a systematic approach to integrate experimental data and qualitative knowledge to identify the physiological consequences of environmental stimuli is needed.'),
(94152, 2011, 'bmc', 'Enhanced stochastic optimization algorithm for finding effective multi-target therapeutics', 'Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. bjyoon@ece.tamu.edu', 'United States', 18, 'Algorithms|Computational Biology|Drug Combinations|Stochastic Processes', 'Comparative Study', '10.1186/1471-2105-12-S1-S18', '21342547', 'For treating a complex disease such as cancer, we need effective means to control the biological network that underlies the disease. However, biological networks are typically robust to external perturbations, making it difficult to beneficially alter the network dynamics by controlling a single target. In fact, multi-target therapeutics is often more effective compared to monotherapies, and combinatory drugs are commonly used these days for treating various diseases. A practical challenge in combination therapy is that the number of possible drug combinations increases exponentially, which makes the prediction of the optimal drug combination a difficult combinatorial optimization problem. Recently, a stochastic optimization algorithm called the Gur Game algorithm was proposed for drug optimization, which was shown to be very efficient in finding potent drug combinations.'),
(94153, 2011, 'bmc', 'Comparative analysis of protein interaction networks reveals that conserved pathways are susceptible to HIV-1 interception', 'Qian, Xiaoning|Yoon, Byung-Jun', 'Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA. xqian@cse.usf.edu|Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA. xqian@cse.usf.edu', 'United States|United States', 6, 'Algorithms|Animals|HIV Infections|HIV-1|Humans|Markov Chains|Metabolic Networks and Pathways|Mice|Protein Interaction Mapping|Signal Transduction', 'Comparative Study', '10.1186/1471-2105-12-S1-S19', '21342548', 'Human immunodeficiency virus type one (HIV-1) is the major pathogen that causes the acquired immune deficiency syndrome (AIDS). With the availability of large-scale protein-protein interaction (PPI) measurements, comparative network analysis can provide a promising way to study the host-virus interactions and their functional significance in the pathogenesis of AIDS. Until now, there have been a large number of HIV studies based on various animal models. In this paper, we present a novel framework for studying the host-HIV interactions through comparative network analysis across different species.'),
(94154, 2011, 'bmc', 'Score regularization for peptide identification', 'He, Zengyou|Zhao, Hongyu|Yu, Weichuan', 'School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn', 'China|China|China', 53, 'Algorithms|Computational Biology|Databases, Protein|Peptides|Proteomics|Sequence Analysis, Protein|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-12-S1-S2', '21342549', 'Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.'),
(94155, 2011, 'bmc', 'Characterization and sequence prediction of structural variations in xcexb1-helix', 'Tendulkar, Ashish V|Wangikar, Pramod P', 'Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India. ashishvt@gmail.com|Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600 036, India. ashishvt@gmail.com', 'India|India', 2, 'Algorithms|Amino Acids|Computational Biology|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S20', '21342550', 'The structure conservation in various xcexb1-helix subclasses reveals the sequence and context dependent factors causing distortions in the xcexb1-helix. The sequence-structure relationship in these subclasses can be used to predict structural variations in xcexb1-helix purely based on its sequence. We train support vector machine(SVM) with dot product kernel function to discriminate between regular xcexb1-helix and non-regular xcexb1-helices purely based on the sequences, which are represented with various overall and position specific propensities of amino acids.'),
(94156, 2011, 'bmc', 'Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG', 'Mitra, Suparna|Rupek, Paul|Richter, Daniel C|Urich, Tim|Gilbert, Jack A|Meyer, Folker|Wilke, Andreas|Huson, Daniel H', 'Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.|Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen, Germany. mitra@informatik.uni-tuebingen.de.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 86, 'Computational Biology|Gene Expression Profiling|Metagenomics|Software', 'Comparative Study', '10.1186/1471-2105-12-S1-S21', '21342551', 'Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets.'),
(94157, 2011, 'bmc', 'Motif-All: discovering all phosphorylation motifs', 'He, Zengyou|Yang, Can|Guo, Guangyu|Li, Ning|Yu, Weichuan', 'School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn|School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn', 'China|China|China|China|China', 23, 'Algorithms|Amino Acid Motifs|Computational Biology|Data Mining|Phosphorylation|Protein Structure, Tertiary', 'Journal Article', '10.1186/1471-2105-12-S1-S22', '21342552', 'Phosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques.'),
(94158, 2011, 'bmc', 'Modeling allosteric signal propagation using protein structure networks', 'Park, Keunwan|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, S Korea.|Department of Bio and Brain Engineering, KAIST, S Korea.', '-|-', 20, 'Allosteric Regulation|Binding Sites|Linear Models|Markov Chains|Models, Molecular|Protein Binding|Protein Structure, Tertiary|Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-12-S1-S23', '21342553', 'Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains.'),
(94159, 2011, 'bmc', 'Strobe sequence design for haplotype assembly', 'Lo, Christine|Bashir, Ali|Bansal, Vikas|Bafna, Vineet', 'Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. cylo@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. cylo@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. cylo@cs.ucsd.edu|Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA. cylo@cs.ucsd.edu', 'United States|United States|United States|United States', 16, 'Algorithms|Computational Biology|Genome, Human|Genomics|Haplotypes|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-12-S1-S24', '21342554', 'Humans are diploid, carrying two copies of each chromosome, one from each parent. Separating the paternal and maternal chromosomes is an important component of genetic analyses such as determining genetic association, inferring evolutionary scenarios, computing recombination rates, and detecting cis-regulatory events. As the pair of chromosomes are mostly identical to each other, linking together of alleles at heterozygous sites is sufficient to phase, or separate the two chromosomes. In Haplotype Assembly, the linking is done by sequenced fragments that overlap two heterozygous sites. While there has been a lot of research on correcting errors to achieve accurate haplotypes via assembly, relatively little work has been done on designing sequencing experiments to get long haplotypes. Here, we describe the different design parameters that can be adjusted with next generation and upcoming sequencing technologies, and study the impact of design choice on the length of the haplotype.'),
(94160, 2011, 'bmc', 'GARNET--gene set analysis with exploration of annotation relations', 'Rho, Kyoohyoung|Kim, Bumjin|Jang, Youngjun|Lee, Sanghyun|Bae, Taejeong|Seo, Jihae|Seo, Chaehwa|Lee, Jihyun|Kang, Hyunjung|Yu, Ungsik|Kim, Sunghoon|Lee, Sanghyuk|Kim, Wan Kyu', 'Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org|Information Center for Bio-Pharmacological Network, Seoul National University, Suwon 443-270, Korea. krho@i-pharm.org', '-|-|-|-|-|-|-|-|-|-|-|-|-', 9, 'Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Annotation|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S25', '21342555', 'Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information.'),
(94161, 2011, 'bmc', 'The choice of null distributions for detecting gene-gene interactions in genome-wide association studies', 'Yang, Can|Wan, Xiang|He, Zengyou|Yang, Qiang|Xue, Hong|Yu, Weichuan', 'Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk|Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong. eeyang@ust.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong|Hong Kong', 6, 'Computer Simulation|Gene Expression|Genome-Wide Association Study|Humans|Macular Degeneration|Models, Statistical|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-S1-S26', '21342556', 'In genome-wide association studies (GWAS), the number of single-nucleotide polymorphisms (SNPs) typically ranges between 500,000 and 1,000,000. Accordingly, detecting gene-gene interactions in GWAS is computationally challenging because it involves hundreds of billions of SNP pairs. Stage-wise strategies are often used to overcome the computational difficulty. In the first stage, fast screening methods (e.g. Tuning ReliefF) are applied to reduce the whole SNP set to a small subset. In the second stage, sophisticated modeling methods (e.g., multifactor-dimensionality reduction (MDR)) are applied to the subset of SNPs to identify interesting interaction models and the corresponding interaction patterns. In the third stage, the significance of the identified interaction patterns is evaluated by hypothesis testing.'),
(94162, 2011, 'bmc', 'Reconstructing phylogeny from metabolic substrate-product relationships', 'Chang, Che-Wei|Lyu, Ping-Chiang|Arita, Masanori', 'Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Taiwan. freshrogerchang@gmail.com|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Taiwan. freshrogerchang@gmail.com|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Taiwan. freshrogerchang@gmail.com', '-|-|-', 15, 'Algorithms|Archaea|Bacteria|Cluster Analysis|Computational Biology|Eukaryota|Metabolic Networks and Pathways|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S27', '21342557', 'Many approaches utilize metabolic pathway information to reconstruct the phyletic tree of fully sequenced organisms, but how metabolic networks can add information to original genomic annotations has remained open.'),
(94163, 2011, 'bmc', 'Potent BACE-1 inhibitor design using pharmacophore modeling, in silico screening and molecular docking studies', 'John, Shalini|Thangapandian, Sundarapandian|Sakkiah, Sugunadevi|Lee, Keun Woo', 'Department of Biochemistry and Division of Applied Life Science, BK21 Program) Environmental Biotechnology National Core Research Center, Gyeongsang National University 900 Gazwa-dong, Jinju 660-701, Republic of Korea. shalini@bio.gnu.ac.kr|Department of Biochemistry and Division of Applied Life Science, BK21 Program) Environmental Biotechnology National Core Research Center, Gyeongsang National University 900 Gazwa-dong, Jinju 660-701, Republic of Korea. shalini@bio.gnu.ac.kr|Department of Biochemistry and Division of Applied Life Science, BK21 Program) Environmental Biotechnology National Core Research Center, Gyeongsang National University 900 Gazwa-dong, Jinju 660-701, Republic of Korea. shalini@bio.gnu.ac.kr|Department of Biochemistry and Division of Applied Life Science, BK21 Program) Environmental Biotechnology National Core Research Center, Gyeongsang National University 900 Gazwa-dong, Jinju 660-701, Republic of Korea. shalini@bio.gnu.ac.kr', '-|-|-|-', 69, 'Algorithms|Amyloid Precursor Protein Secretases|Amyloid beta-Protein Precursor|Aspartic Acid Endopeptidases|Computational Biology|Drug Design|Enzyme Inhibitors|Humans|Models, Biological|Models, Chemical|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-12-S1-S28', '21342558', 'Beta-site amyloid precursor protein cleaving enzyme (BACE-1) is a single-membrane protein belongs to the aspartyl protease class of catabolic enzymes. This enzyme involved in the processing of the amyloid precursor protein (APP). The cleavage of APP by BACE-1 is the rate-limiting step in the amyloid cascade leading to the production of two peptide fragments Axcexb240 and Axcexb242. Among two peptide fragments Axcexb242 is the primary species thought to be responsible for the neurotoxicity and amyloid plaque formation that lead to memory and cognitive defects in Alzheimers disease (AD). AD is a ravaging neurodegenerative disorder for which no disease-modifying treatment is currently available. Inhibition of BACE-1 is expected to stop amyloid plaque formation and emerged as an interesting and attractive therapeutic target for AD.'),
(94164, 2011, 'bmc', 'Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models', 'Costa, Ivan G|Roider, Helge G|do Rego, Thais G|de Carvalho, Francisco de A T', 'Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br|Center of Informatics, Federal University of Pernambuco, Recife, Brazil. igcf@cin.ufpe.br', 'Brazil|Brazil|Brazil|Brazil', 17, 'Bayes Theorem|CD4-Positive T-Lymphocytes|Cell Differentiation|DNA|Gene Expression Regulation|Histones|Linear Models|Protein Binding|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-S1-S29', '21342559', 'The differentiation process from stem cells to fully differentiated cell types is controlled by the interplay of chromatin modifications and transcription factor activity. Histone modifications or transcription factors frequently act in a multi-functional manner, with a given DNA motif or histone modification conveying both transcriptional repression and activation depending on its location in the promoter and other regulatory signals surrounding it.'),
(94165, 2011, 'bmc', 'The G protein-coupled receptors in the pufferfish Takifugu rubripes', 'Sarkar, Anita|Kumar, Sonu|Sundar, Durai', 'Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India.|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India.|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India.', 'India|India|India', 4, 'Animals|Chickens|Fish Proteins|Humans|Phylogeny|Receptors, G-Protein-Coupled|Takifugu', 'Journal Article', '10.1186/1471-2105-12-S1-S3', '21342560', 'Guanine protein-coupled receptors (GPCRs) constitute a eukaryotic transmembrane protein family and function as molecular switches in the second messenger cascades and are found in all organisms between yeast and humans. They form the single, biggest drug-target family due to their versatility of action and their role in several physiological functions, being active players in detecting the presence of light, a variety of smells and tastes, amino acids, nucleotides, lipids, chemicals etc. in the environment of the cell. Comparative genomic studies on model organisms provide information on target receptors in humans and their function. The Japanese teleost Fugu has been identified as one of the smallest vertebrate genomes and a compact model to study the human genome, owing to the great similarity in its gene repertoire with that of human and other vertebrates. Thus the characterization of the GPCRs of Fugu would provide insights to the evolution of the vertebrate genome.'),
(94166, 2011, 'bmc', 'Hsp90/Cdc37 chaperone/co-chaperone complex, a novel junction anticancer target elucidated by the mode of action of herbal drug Withaferin A', 'Grover, Abhinav|Shandilya, Ashutosh|Agrawal, Vibhuti|Pratik, Piyush|Bhasme, Divya|Bisaria, Virendra S|Sundar, Durai', 'Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com|Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. abhinavgr@gmail.com', 'India|India|India|India|India|India|India', 0, 'Algorithms|Antineoplastic Agents, Phytogenic|Cell Cycle Proteins|Chaperonins|Computational Biology|HSP90 Heat-Shock Proteins|Ligands|Molecular Dynamics Simulation|Neoplasms|Protein Binding|Protein Interaction Mapping|Withanolides', 'Journal Article', '10.1186/1471-2105-12-S1-S30', '21342561', 'HSPs (Heat shock proteins) are highly conserved ubiquitous proteins among species which are involved in maintaining appropriate folding and conformation of other proteins and are thus referred to as molecular chaperones. Hsp90 (Heat-shock protein 90 kDa) is one of a group of molecular chaperones responsible for managing protein folding and quality control in cell environment. However it is also involved in the maturation and stabilization of a wide range of oncogenic client proteins which are crucial for oncogenesis and malignant progression. Hsp90 requires a series of co-chaperones to assemble into a super-chaperone complex for its function. These co-chaperones bind and leave the complex at various stages to regulate the chaperoning process. Arresting the chaperone cycle at these stages by targeting different co-chaperone/Hsp90 interactions seems to be quite a viable alternative and is likely to achieve similar consequences as that of Hsp90 direct inhibition with added favors of high specificity and reduced side effect profile. The study conducted here is an attempt to explore the potential of Withania somniferas major constituent WA (Withaferin A) in attenuating the Hsp90/Cdc37 chaperone/co-chaperone interactions for enhanced tumor arresting activity and to elucidate the underlying mode of action using computational approaches.'),
(94167, 2011, 'bmc', 'Changed epitopes drive the antigenic drift for influenza A (H3N2) viruses', 'Huang, Jhang-Wei|Yang, Jinn-Moon', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.', '-|-', 0, 'Antibodies, Neutralizing|Antibodies, Viral|Antigens, Viral|Epitopes|Evolution, Molecular|Genetic Variation|Hemagglutination Inhibition Tests|Hemagglutinin Glycoproteins, Influenza Virus|Influenza A Virus, H3N2 Subtype|Models, Biological|Mutation', 'Journal Article', '10.1186/1471-2105-12-S1-S31', '21342562', 'In circulating influenza viruses, gradually accumulated mutations on the glycoprotein hemagglutinin (HA), which interacts with infectivity-neutralizing antibodies, lead to the escape of immune system (called antigenic drift). The antibody recognition is highly correlated to the conformation change on the antigenic sites (epitopes), which locate on HA surface. To quantify a changed epitope for escaping from neutralizing antibodies is the basis for the antigenic drift and vaccine development.'),
(94168, 2011, 'bmc', 'A study on the flexibility of enzyme active sites', 'Weng, Yi-Zhong|Chang, Darby Tien-Hao|Huang, Yu-Feng|Lin, Chih-Wei', 'Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. d95032@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. d95032@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. d95032@csie.ntu.edu.tw|Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan. d95032@csie.ntu.edu.tw', '-|-|-|-', 11, 'Algorithms|Binding Sites|Catalytic Domain|Computational Biology|Enzymes|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-12-S1-S32', '21342563', 'A common assumption about enzyme active sites is that their structures are highly conserved to specifically distinguish between closely similar compounds. However, with the discovery of distinct enzymes with similar reaction chemistries, more and more studies discussing the structural flexibility of the active site have been conducted.'),
(94169, 2011, 'bmc', 'iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis', 'Hsu, Kai-Cheng|Chen, Yen-Fu|Lin, Shen-Rong|Yang, Jinn-Moon', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan.', '-|-|-|-', 0, 'Binding Sites|Computer Graphics|Drug Discovery|Information Storage and Retrieval|Models, Molecular|Protein Binding|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S33', '21342564', 'Pharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.'),
(94170, 2011, 'bmc', 'Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories', 'Chikalov, Igor|Yao, Peggy|Moshkov, Mikhail|Latombe, Jean-Claude', 'Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. igor.chikalov@kaust.edu.sa|Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. igor.chikalov@kaust.edu.sa|Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. igor.chikalov@kaust.edu.sa|Mathematical and Computer Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. igor.chikalov@kaust.edu.sa', 'Saudi Arabia|Saudi Arabia|Saudi Arabia|Saudi Arabia', 0, 'Algorithms|Hydrogen Bonding|Models, Statistical|Molecular Dynamics Simulation|Protein Stability|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-12-S1-S34', '21342565', 'Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor.'),
(94171, 2011, 'bmc', 'Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites', 'Totrov, Maxim', 'Molsoft LLC,3366 N Torrey Pines Ct, La Jolla, CA 92037, USA. max@molsoft.com', 'United States', 26, 'Algorithms|Binding Sites|Cluster Analysis|Computational Biology|Databases, Protein|Ligands|Models, Molecular|Proteins', 'Journal Article', '10.1186/1471-2105-12-S1-S35', '21342566', 'A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB.'),
(94172, 2011, 'bmc', 'Integrated metabolome and transcriptome analysis of the NCI60 dataset', 'Su, Gang|Burant, Charles F|Beecher, Christopher W|Athey, Brian D|Meng, Fan', 'Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. sugang@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. sugang@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. sugang@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. sugang@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. sugang@umich.edu', 'United States|United States|United States|United States|United States', 0, 'Biomarkers|Cell Line, Tumor|DNA Copy Number Variations|Databases, Genetic|Gene Expression Profiling|Humans|Metabolome|Neoplasms|Organ Specificity', 'Journal Article', '10.1186/1471-2105-12-S1-S36', '21342567', 'Metabolite profiles can be used for identifying molecular signatures and mechanisms underlying diseases since they reflect the outcome of complex upstream genomic, transcriptomic, proteomic and environmental events. The scarcity of publicly accessible large scale metabolome datasets related to human disease has been a major obstacle for assessing the potential of metabolites as biomarkers as well as understanding the molecular events underlying disease-related metabolic changes. The availability of metabolite and gene expression profiles for the NCI-60 cell lines offers the possibility of identifying significant metabolome and transcriptome features and discovering unique molecular processes related to different cancer types.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94173, 2011, 'bmc', 'Query-based biclustering of gene expression data using Probabilistic Relational Models', 'Zhao, Hui|Cloots, Lore|Van den Bulcke, Tim|Wu, Yan|De Smet, Riet|Storms, Valerie|Meysman, Pieter|Engelen, Kristof|Marchal, Kathleen', 'Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be|Microbial and Molecular Systems, KU Leuven, Leuven 3001, Belgium. hui.zhao@biw.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Cluster Analysis|Databases, Genetic|Escherichia coli|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Regulon', 'Comparative Study', '10.1186/1471-2105-12-S1-S37', '21342568', 'With the availability of large scale expression compendia it is now possible to view own findings in the light of what is already available and retrieve genes with an expression profile similar to a set of genes of interest (i.e., a query or seed set) for a subset of conditions. To that end, a query-based strategy is needed that maximally exploits the coexpression behaviour of the seed genes to guide the biclustering, but that at the same time is robust against the presence of noisy genes in the seed set as seed genes are often assumed, but not guaranteed to be coexpressed in the queried compendium. Therefore, we developed ProBic, a query-based biclustering strategy based on Probabilistic Relational Models (PRMs) that exploits the use of prior distributions to extract the information contained within the seed set.'),
(94174, 2011, 'bmc', 'PicXAA-R: efficient structural alignment of multiple RNA sequences using a greedy approach', 'Sahraeian, Sayed Mohammad Ebrahim|Yoon, Byung-Jun', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. msahraeian@tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA. msahraeian@tamu.edu', 'United States|United States', 0, 'Algorithms|Base Pairing|Computational Biology|Models, Statistical|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-12-S1-S38', '21342569', 'Accurate and efficient structural alignment of non-coding RNAs (ncRNAs) has grasped more and more attentions as recent studies unveiled the significance of ncRNAs in living organisms. While the Sankoff style structural alignment algorithms cannot efficiently serve for multiple sequences, mostly progressive schemes are used to reduce the complexity. However, this idea tends to propagate the early stage errors throughout the entire process, thereby degrading the quality of the final alignment. For multiple protein sequence alignment, we have recently proposed PicXAA which constructs an accurate alignment in a non-progressive fashion.'),
(94175, 2011, 'bmc', 'Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks', 'Monji, Hiroyuki|Koizumi, Satoshi|Ozaki, Tomonobu|Ohkawa, Takenao', 'Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. h.monji@cs25.scitec.kobe-u.ac.jp', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Binding Sites|Cluster Analysis|Computational Biology|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-12-S1-S39', '21342570', 'Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored.'),
(94176, 2011, 'bmc', 'On the PATHGROUPS approach to rapid small phylogeny', 'Zheng, Chunfang|Sankoff, David', 'Département dinformatique et de recherche opérationnelle, Université de Montréal, Canada. chunfang313@gmail.com|Département dinformatique et de recherche opérationnelle, Université de Montréal, Canada. chunfang313@gmail.com', 'Canada|Canada', 0, 'Algorithms|Computational Biology|Genome, Fungal|Models, Genetic|Phylogeny|Yeasts', 'Journal Article', '10.1186/1471-2105-12-S1-S4', '21342571', 'We present a data structure enabling rapid heuristic solution to the ancestral genome reconstruction problem for given phylogenies under genomic rearrangement metrics. The efficiency of the greedy algorithm is due to fast updating of the structure during run time and a simple priority scheme for choosing the next step. Since accuracy deteriorates for sets of highly divergent genomes, we investigate strategies for improving accuracy and expanding the range of data sets where accurate reconstructions can be expected. This includes a more refined priority system, and a two-step look-ahead, as well as iterative local improvements based on a the median version of the problem, incorporating simulated annealing. We apply this to a set of yeast genomes to corroborate a recent gene sequence-based phylogeny.'),
(94177, 2011, 'bmc', 'GOChase-II: correcting semantic inconsistencies from Gene Ontology-based annotations for gene products', 'Park, Yu Rang|Kim, Jihun|Lee, Hye Won|Yoon, Young Jo|Kim, Ju Han', 'Seoul National University Biomedical Informatics, Div of Biomedical Informatics, Seoul National University College of medicine, Seoul 110799, Korea. kirarang@snu.ac.kr|Seoul National University Biomedical Informatics, Div of Biomedical Informatics, Seoul National University College of medicine, Seoul 110799, Korea. kirarang@snu.ac.kr|Seoul National University Biomedical Informatics, Div of Biomedical Informatics, Seoul National University College of medicine, Seoul 110799, Korea. kirarang@snu.ac.kr|Seoul National University Biomedical Informatics, Div of Biomedical Informatics, Seoul National University College of medicine, Seoul 110799, Korea. kirarang@snu.ac.kr|Seoul National University Biomedical Informatics, Div of Biomedical Informatics, Seoul National University College of medicine, Seoul 110799, Korea. kirarang@snu.ac.kr', '-|-|-|-|-', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Molecular Sequence Annotation|Proteins|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-12-S1-S40', '21342572', 'The Gene Ontology (GO) provides a controlled vocabulary for describing genes and gene products. In spite of the undoubted importance of GO, several drawbacks associated with GO and GO-based annotations have been introduced. We identified three types of semantic inconsistencies in GO-based annotations, semantically redundant, biological-domain inconsistent and taxonomy inconsistent annotations.'),
(94178, 2011, 'bmc', 'Coregulation of transcription factors and microRNAs in human transcriptional regulatory network', 'Chen, Cho-Yi|Chen, Shui-Tein|Fuh, Chiou-Shann|Juan, Hsueh-Fen|Huang, Hsuan-Cheng', 'Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan. joeychen@gate.sinica.edu.tw|Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan. joeychen@gate.sinica.edu.tw|Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan. joeychen@gate.sinica.edu.tw|Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan. joeychen@gate.sinica.edu.tw|Institute of Biomedical Informatics, Center for Systems and Synthetic Biology, National Yang-Ming University, Taipei, Taiwan. joeychen@gate.sinica.edu.tw', '-|-|-|-|-', 62, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|MicroRNAs|Molecular Sequence Annotation|RNA Processing, Post-Transcriptional|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-S1-S41', '21342573', 'MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level. Recent studies have suggested that miRNAs and transcription factors are primary metazoan gene regulators, however, the crosstalk between them still remains unclear.'),
(94179, 2011, 'bmc', 'A method for supporting retrieval of articles on protein structure analysis considering users intention', 'Kyogoku, Riku|Fujimoto, Ryo|Ozaki, Tomonobu|Ohkawa, Takenao', 'Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. kyogoku@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. kyogoku@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. kyogoku@cs25.scitec.kobe-u.ac.jp|Graduate School of System Informatics, Kobe University, Rokkodai, Nada, Kobe 657-8501, Japan. kyogoku@cs25.scitec.kobe-u.ac.jp', 'Japan|Japan|Japan|Japan', 0, 'Databases, Protein|Evaluation Studies as Topic|Information Storage and Retrieval|Periodicals as Topic|Protein Conformation|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-S1-S42', '21342574', 'In recent years, information about protein structure and function is described in a large amount of articles. However, a naive full-text search by specific keywords often fails to find desired articles, because the articles involve the ambiguous and complicated concepts that cannot be described with uniform representation. For retrieving articles on protein structure and function, it is important to consider the relevance between structural and/or functional concepts by identifying the users intention.'),
(94180, 2011, 'bmc', 'HDX-analyzer: a novel package for statistical analysis of protein structure dynamics', 'Liu, Sanmin|Liu, Lantao|Uzuner, Ugur|Zhou, Xin|Gu, Manxi|Shi, Weibing|Zhang, Yixiang|Dai, Susie Y|Yuan, Joshua S', 'Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com|Institute for Plant Genomics and Biotechnology, Texas A&M University, College Station, TX 77843, USA. sanminliu@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Data Interpretation, Statistical|Deuterium Exchange Measurement|Mass Spectrometry|Models, Statistical|Protein Conformation|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-S1-S43', '21342575', 'HDX mass spectrometry is a powerful platform to probe protein structure dynamics during ligand binding, protein folding, enzyme catalysis, and such. HDX mass spectrometry analysis derives the protein structure dynamics based on the mass increase of a protein of which the backbone protons exchanged with solvent deuterium. Coupled with enzyme digestion and MS/MS analysis, HDX mass spectrometry can be used to study the regional dynamics of protein based on the m/z value or percentage of deuterium incorporation for the digested peptides in the HDX experiments. Various software packages have been developed to analyze HDX mass spectrometry data. Despite the progresses, proper and explicit statistical treatment is still lacking in most of the current HDX mass spectrometry software. In order to address this issue, we have developed the HDXanalyzer for the statistical analysis of HDX mass spectrometry data using R, Python, and RPY2.'),
(94181, 2011, 'bmc', 'Resolving the structure of interactomes with hierarchical agglomerative clustering', 'Park, Yongjin|Bader, Joel S', 'Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ypark28@jhu.edu|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. ypark28@jhu.edu', 'United States|United States', 15, 'Algorithms|Bayes Theorem|Cluster Analysis|Computational Biology|Gene Regulatory Networks|Likelihood Functions|Models, Statistical|Protein Interaction Mapping|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S44', '21342576', 'Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters, over-splitting of bottom-level clusters, requirements to pre-define the number of clusters prior to analysis, and an inability to jointly cluster over multiple interaction types.'),
(94182, 2011, 'bmc', 'Evaluation of methods for detecting conversion events in gene clusters', 'Song, Giltae|Hsu, Chih-Hao|Riemer, Cathy|Miller, Webb', 'Center for Comparative Genomics and Bioinformatics, 506 Wartik Lab, Pennsylvania State University, University Park, PA 16802, USA. gsong@bx.psu.edu|Center for Comparative Genomics and Bioinformatics, 506 Wartik Lab, Pennsylvania State University, University Park, PA 16802, USA. gsong@bx.psu.edu|Center for Comparative Genomics and Bioinformatics, 506 Wartik Lab, Pennsylvania State University, University Park, PA 16802, USA. gsong@bx.psu.edu|Center for Comparative Genomics and Bioinformatics, 506 Wartik Lab, Pennsylvania State University, University Park, PA 16802, USA. gsong@bx.psu.edu', 'United States|United States|United States|United States', 7, 'Animals|Biological Evolution|Computational Biology|Computer Simulation|Gene Conversion|Humans|Multigene Family|Primates|Sequence Alignment', 'Comparative Study', '10.1186/1471-2105-12-S1-S45', '21342577', 'Gene clusters are genetically important, but their analysis poses significant computational challenges. One of the major reasons for these difficulties is gene conversion among the duplicated regions of the cluster, which can obscure their true relationships. Many computational methods for detecting gene conversion events have been released, but their performance has not been assessed for wide deployment in evolutionary history studies due to a lack of accurate evaluation methods.'),
(94183, 2011, 'bmc', 'High-throughput peptide quantification using mTRAQ reagent triplex', 'Yoon, Joo Young|Yeom, Jeonghun|Lee, Heebum|Kim, Kyutae|Na, Seungjin|Park, Kunsoo|Paek, Eunok|Lee, Cheolju', 'School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr|School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. jyyoon@theory.snu.ac.kr', '-|-|-|-|-|-|-|-', 0, 'Algorithms|Cluster Analysis|Isotope Labeling|Mass Spectrometry|Models, Statistical|Peptides|Protein Isoforms|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S46', '21342578', 'Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.'),
(94184, 2011, 'bmc', 'Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties', 'Huang, Hui-Lin|Lin, I-Che|Liou, Yi-Fan|Tsai, Chia-Ta|Hsu, Kai-Ti|Huang, Wen-Lin|Ho, Shinn-Jang|Ho, Shinn-Ying', 'Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. hlhuang@mail.nctu.edu.tw', '-|-|-|-|-|-|-|-', 33, 'Algorithms|Amino Acids|Cluster Analysis|Computational Biology|DNA-Binding Proteins|Databases, Protein|Protein Binding|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-12-S1-S47', '21342579', 'Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors.'),
(94185, 2011, 'bmc', 'Fast and accurate clustering of noncoding RNAs using ensembles of sequence alignments and secondary structures', 'Saito, Yutaka|Sato, Kengo|Sakakibara, Yasubumi', 'Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan. saito@dna.bio.keio.ac.jp|Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan. saito@dna.bio.keio.ac.jp|Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan. saito@dna.bio.keio.ac.jp', 'Japan|Japan|Japan', 9, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S48', '21342580', 'Clustering of unannotated transcripts is an important task to identify novel families of noncoding RNAs (ncRNAs). Several hierarchical clustering methods have been developed using similarity measures based on the scores of structural alignment. However, the high computational cost of exact structural alignment requires these methods to employ approximate algorithms. Such heuristics degrade the quality of clustering results, especially when the similarity among family members is not detectable at the primary sequence level.'),
(94186, 2011, 'bmc', 'Discriminative structural approaches for enzyme active-site prediction', 'Kato, Tsuyoshi|Nagano, Nozomi', 'Graduate school of Engineering, Gunma University, Tenjin-cho 1-5-1, Kiryu, Gunma 376-8515, Japan. kato-tsuyoshi@aist.go.jp|Graduate school of Engineering, Gunma University, Tenjin-cho 1-5-1, Kiryu, Gunma 376-8515, Japan. kato-tsuyoshi@aist.go.jp', 'Japan|Japan', 0, 'Algorithms|Artificial Intelligence|Catalytic Domain|Computational Biology|Enzymes|Protein Conformation', 'Journal Article', '10.1186/1471-2105-12-S1-S49', '21342581', 'Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.'),
(94187, 2011, 'bmc', 'FPGA accelerator for protein secondary structure prediction based on the GOR algorithm', 'Xia, Fei|Dou, Yong|Lei, Guoqing|Tan, Yusong', 'National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, China.|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, China.|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, China.|National Laboratory for Parallel&Distributed Processing, Department of Computer Science, National University of Defense Technology, ChangSha, 410073, China.', 'China|China|China|China', 83, 'Algorithms|Amino Acid Sequence|Computational Biology|Computers|Databases, Protein|Protein Folding|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S5', '21342582', 'Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design.'),
(94188, 2011, 'bmc', 'A regression analysis of gene expression in ES cells reveals two gene classes that are significantly different in epigenetic patterns', 'Park, Sung-Joon|Nakai, Kenta', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Japan. park@hgc.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Japan. park@hgc.jp', 'Japan|Japan', 0, 'Animals|Binding Sites|CpG Islands|DNA Methylation|Embryonic Stem Cells|Epigenomics|Gene Expression Regulation|Genome|Linear Models|Mice|Multivariate Analysis|Protein Binding|Regulatory Sequences, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-S1-S50', '21342583', 'To understand the gene regulatory system that governs the self-renewal and pluripotency of embryonic stem cells (ESCs) is an important step for promoting regenerative medicine. In it, the role of several core transcription factors (TFs), such as Oct4, Sox2 and Nanog, has been intensively investigated, details of their involvement in the genome-wide gene regulation are still not well clarified.'),
(94189, 2011, 'bmc', 'Semantically enabled and statistically supported biological hypothesis testing with tissue microarray databases', 'Song, Young Soo|Park, Chan Hee|Chung, Hee-Joon|Shin, Hyunjung|Kim, Jihun|Kim, Ju Han', 'Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.|Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.|Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.|Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.|Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.|Department of Industrial & Information Systems Engineering, Ajou University, Suwon 443-749, Korea.', '-|-|-|-|-|-', 0, 'Computational Biology|Databases, Factual|Humans|Information Storage and Retrieval|Internet|Microarray Analysis|Oligonucleotide Array Sequence Analysis|Semantics', 'Journal Article', '10.1186/1471-2105-12-S1-S51', '21342584', 'Although many biological databases are applying semantic web technologies, meaningful biological hypothesis testing cannot be easily achieved. Database-driven high throughput genomic hypothesis testing requires both of the capabilities of obtaining semantically relevant experimental data and of performing relevant statistical testing for the retrieved data. Tissue Microarray (TMA) data are semantically rich and contains many biologically important hypotheses waiting for high throughput conclusions.'),
(94190, 2011, 'bmc', 'Repeat-aware modeling and correction of short read errors', 'Yang, Xiao|Aluru, Srinivas|Dorman, Karin S', 'Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA. xyang@iastate.edu|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA. xyang@iastate.edu|Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA. xyang@iastate.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Genomics|High-Throughput Nucleotide Sequencing|Likelihood Functions|Models, Statistical|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S52', '21342585', 'High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of kmers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous kmer may be frequently observed if it has few nucleotide differences with valid kmers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content.'),
(94191, 2011, 'bmc', 'Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads', 'Duitama, Jorge|Kennedy, Justin|Dinakar, Sanjiv|Hernxc3xa1ndez, Yxc3xb6zen|Wu, Yufeng|Mxc4x83ndoiu, Ion I', 'Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu|Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu|Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu|Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu|Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu|Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT 06269-2155, USA. jduitama@engr.uconn.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Genome-Wide Association Study|Genomics|Genotype|Linkage Disequilibrium|Models, Statistical|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S53', '21342586', 'Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping.'),
(94192, 2011, 'bmc', 'Incorporating Ab Initio energy into threading approaches for protein structure prediction', 'Shao, Mingfu|Wang, Sheng|Wang, Chao|Yuan, Xiongying|Li, Shuai Cheng|Zheng, Weimou|Bu, Dongbo', 'Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn', 'China|China|China|China|China|China|China', 5, 'Algorithms|Computational Biology|Models, Statistical|Protein Conformation|Proteins|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-12-S1-S54', '21342587', 'Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches--threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure, while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.'),
(94193, 2011, 'bmc', 'Closest string with outliers', 'Boucher, Christina|Ma, Bin', 'David R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON. cabouche@cs.uwaterloo.ca|David R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON. cabouche@cs.uwaterloo.ca', '-|-', 0, 'Algorithms|Computational Biology|Models, Statistical', 'Journal Article', '10.1186/1471-2105-12-S1-S55', '21342588', 'Given n strings s1, xe2x80xa6, sn each of length xe2x84x93 and a nonnegative integer d, the CLOSEST STRING problem asks to find a center string s such that none of the input strings has Hamming distance greater than d from s. Finding a common pattern in many--but not necessarily all--input strings is an important task that plays a role in many applications in bioinformatics.'),
(94194, 2011, 'bmc', 'A minimal descriptor of an ancestral recombinations graph', 'Parida, Laxmi|Palamara, Pier Francesco|Javed, Asif', 'Computational Genomics, IBM T J Watson Research, Yorktown, New York, USA. parida@us.ibm.com|Computational Genomics, IBM T J Watson Research, Yorktown, New York, USA. parida@us.ibm.com|Computational Genomics, IBM T J Watson Research, Yorktown, New York, USA. parida@us.ibm.com', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Models, Genetic|Phylogeny|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-12-S1-S6', '21342589', 'Ancestral Recombinations Graph (ARG) is a phylogenetic structure that encodes both duplication events, such as mutations, as well as genetic exchange events, such as recombinations: this captures the (genetic) dynamics of a population evolving over generations.'),
(94195, 2011, 'bmc', 'Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery', 'Han, Henry|Li, Xiao-Li', 'Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.|Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States', 27, 'Algorithms|Biomarkers, Tumor|Discriminant Analysis|Gene Expression Profiling|Neoplasms|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Sensitivity and Specificity|Software', 'Comparative Study', '10.1186/1471-2105-12-S1-S7', '21342590', 'Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.'),
(94196, 2011, 'bmc', 'Ontology-based instance data validation for high-quality curated biological pathways', 'Jeong, Euna|Nagasaki, Masao|Ueno, Kazuko|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan. eajeong@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan', 2, 'Computer Simulation|Models, Biological|Systems Biology|Validation Studies as Topic', 'Journal Article', '10.1186/1471-2105-12-S1-S8', '21342591', 'Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback.'),
(94197, 2011, 'bmc', 'UMARS: Un-MAppable Reads Solution', 'Li, Sung-Chou|Chan, Wen-Ching|Lai, Chun-Hung|Tsai, Kuo-Wang|Hsu, Chun-Nan|Jou, Yuh-Shan|Chen, Hua-Chien|Chen, Chun-Hong|Lin, Wen-Chang', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.', '-|-|-|-|-|-|-|-|-', 0, 'Chromosome Mapping|DNA, Complementary|Exons|Genome, Viral|High-Throughput Nucleotide Sequencing|RNA Splicing|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-12-S1-S9', '21342592', 'Un-MAppable Reads Solution (UMARS) is a user-friendly web service focusing on retrieving valuable information from sequence reads that cannot be mapped back to reference genomes. Recently, next-generation sequencing (NGS) technology has emerged as a powerful tool for generating high-throughput sequencing data and has been applied to many kinds of biological research. In a typical analysis, adaptor-trimmed NGS reads were first mapped back to reference sequences, including genomes or transcripts. However, a fraction of NGS reads failed to be mapped back to the reference sequences. Such un-mappable reads are usually imputed to sequencing errors and discarded without further consideration.'),
(94198, 2010, 'bmc', 'Proceedings of the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference, February 19-20, 2010, Jonesboro, AR, USA', '-', '-', '-', 12, 'Computational Biology', 'Congress', '10.1186/1471-2105-12-S1-S9', '21351384', '-'),
(94199, 2010, 'bmc', 'Proceedings of the 2010 AMIA Summit on Translational Bioinformatics, March 10-12, 2010, San Francisco, CA, USA', '-', '-', '-', 0, 'Animals|Computational Biology|Humans|Translational Medical Research', 'Congress', '10.1186/1471-2105-12-S1-S9', '21351385', '-'),
(94200, 2011, 'bmc', 'Taxon ordering in phylogenetic trees: a workbench test', 'Cerutti, Francesco|Bertolotti, Luigi|Goldberg, Tony L|Giacobini, Mario', 'Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, Grugliasco, Italy.|Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, Grugliasco, Italy.|Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, Grugliasco, Italy.|Department of Animal Production, Epidemiology and Ecology, Faculty of Veterinary Medicine, University of Torino, Grugliasco, Italy.', 'Italy|Italy|Italy|Italy', 4, 'Algorithms|Biological Evolution|Geography|Phylogeny|Phylogeography|RNA Viruses', 'Journal Article', '10.1186/1471-2105-12-58', '21352514', 'Phylogenetic trees are an important tool for representing evolutionary relationships among organisms. In a phylogram or chronogram, the ordering of taxa is not considered meaningful, since complete topological information is given by the branching order and length of the branches, which are represented in the root-to-node direction. We apply a novel method based on a (xcexbb + xcexbc)-Evolutionary Algorithm to give meaning to the order of taxa in a phylogeny. This method applies random swaps between two taxa connected to the same node, without changing the topology of the tree. The evaluation of a new tree is based on different distance matrices, representing non-phylogenetic information such as other types of genetic distance, geographic distance, or combinations of these. To test our method we use published trees of Vesicular stomatitis virus, West Nile virus and Rice yellow mottle virus.'),
(94201, 2011, 'bmc', 'A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines', 'Ciexc5x9blik, Marcin|Mura, Cameron', 'Department of Chemistry, University of Virginia, Charlottesville, VA 22904-4319, USA.|Department of Chemistry, University of Virginia, Charlottesville, VA 22904-4319, USA.', 'United States|United States', 6, 'Computational Biology|Computing Methodologies|Programming Languages|Software|Workflow', 'Journal Article', '10.1186/1471-2105-12-61', '21352538', 'Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or workflow, is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts.'),
(94202, 2011, 'bmc', 'Novel computational analysis of protein binding array data identifies direct targets of Nkx22 in the pancreas', 'Hill, Jonathon T|Anderson, Keith R|Mastracci, Teresa L|Kaestner, Klaus H|Sussel, Lori', 'Department of Genetics and Development, Columbia University, New York, NY 10032, USA.|Department of Genetics and Development, Columbia University, New York, NY 10032, USA.|Department of Genetics and Development, Columbia University, New York, NY 10032, USA.|Department of Genetics and Development, Columbia University, New York, NY 10032, USA.|Department of Genetics and Development, Columbia University, New York, NY 10032, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Binding Sites|Computational Biology|Genome|Homeodomain Proteins|Mice|Pancreas|Promoter Regions, Genetic|Protein Array Analysis|Protein Binding|Regression Analysis|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-62', '21352540', 'The creation of a complete genome-wide map of transcription factor binding sites is essential for understanding gene regulatory networks in vivo. However, current prediction methods generally rely on statistical models that imperfectly model transcription factor binding. Generation of new prediction methods that are based on protein binding data, but do not rely on these models may improve prediction sensitivity and specificity.'),
(94203, 2011, 'bmc', 'BIO::Phylo-phyloinformatic analysis using perl', 'Vos, Rutger A|Caravas, Jason|Hartmann, Klaas|Jensen, Mark A|Miller, Chase', 'School of Biological Sciences, University of Reading, UK. R.A.Vos@reading.ac.uk|School of Biological Sciences, University of Reading, UK. R.A.Vos@reading.ac.uk|School of Biological Sciences, University of Reading, UK. R.A.Vos@reading.ac.uk|School of Biological Sciences, University of Reading, UK. R.A.Vos@reading.ac.uk|School of Biological Sciences, University of Reading, UK. R.A.Vos@reading.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 28, 'Computational Biology|Computer Systems|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-12-63', '21352572', 'Phyloinformatic analyses involve large amounts of data and metadata of complex structure. Collecting, processing, analyzing, visualizing and summarizing these data and metadata should be done in steps that can be automated and reproduced. This requires flexible, modular toolkits that can represent, manipulate and persist phylogenetic data and metadata as objects with programmable interfaces.'),
(94204, 2011, 'bmc', 'A computational framework for proteome-wide pursuit and prediction of metalloproteins using ICP-MS and MS/MS data', 'Lancaster, W Andrew|Praissman, Jeremy L|Poole, Farris L|Cvetkovic, Aleksandar|Menon, Angeli Lal|Scott, Joseph W|Jenney, Francis E|Thorgersen, Michael P|Kalisiak, Ewa|Apon, Junefredo V|Trauger, Sunia A|Siuzdak, Gary|Tainer, John A|Adams, Michael W W', 'Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.|Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Geor', 0, 'Amino Acid Sequence|Bacterial Proteins|Computational Biology|Databases, Protein|Electronic Data Processing|Metalloproteins|Metals|Molybdenum|Nickel|Protein Interaction Domains and Motifs|Proteome|Pyrococcus furiosus|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-12-64', '21356119', 'Metal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICP-MS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations and complement the metal-directed protein purification, we developed a computational infrastructure and statistical methodology to aid in the pursuit and identification of novel metalloproteins.'),
(94205, 2011, 'bmc', 'CSI-OMIM--Clinical Synopsis Search in OMIM', 'Cohen, Raphael|Gefen, Avitan|Elhadad, Michael|Birk, Ohad S', 'The Morris Kahn Laboratory of Human Genetics, National Institute for Biotechnology in the Negev, Ben-Gurion University, Beer-Sheva, Israel. cohenrap@bgu.ac.il|The Morris Kahn Laboratory of Human Genetics, National Institute for Biotechnology in the Negev, Ben-Gurion University, Beer-Sheva, Israel. cohenrap@bgu.ac.il|The Morris Kahn Laboratory of Human Genetics, National Institute for Biotechnology in the Negev, Ben-Gurion University, Beer-Sheva, Israel. cohenrap@bgu.ac.il|The Morris Kahn Laboratory of Human Genetics, National Institute for Biotechnology in the Negev, Ben-Gurion University, Beer-Sheva, Israel. cohenrap@bgu.ac.il', 'Israel|Israel|Israel|Israel', 8, 'Computational Biology|Data Mining|Databases, Factual|Databases, Genetic|Humans|Internet|Medical Subject Headings|Natural Language Processing|Phenotype|Syndrome|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-12-65', '21362185', 'The OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(94206, 2011, 'bmc', 'Bioinformatics analysis of disordered proteins in prokaryotes', 'Pavlovixc4x87-Laxc5xbeetixc4x87, Gordana M|Mitixc4x87, Nenad S|Kovaxc4x8devixc4x87, Jovana J|Obradovixc4x87, Zoran|Malkov, Saxc5xa1a N|Beljanski, Miloxc5xa1 V', 'Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs|Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs|Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs|Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs|Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs|Faculty of Mathematics, University of Belgrade, P.O.B. 550, Studentski trg 16, 11001 Belgrade, Serbia. gordana@matf.bg.ac.rs', 'Serbia|Serbia|Serbia|Serbia|Serbia|Serbia', 0, 'Amino Acids|Archaea|Archaeal Proteins|Bacteria|Bacterial Proteins|Base Composition|Cluster Analysis|Computational Biology|Databases, Protein|Genomics|Internet|Protein Conformation|Proteome', 'Journal Article', '10.1186/1471-2105-12-66', '21366926', 'A significant number of proteins have been shown to be intrinsically disordered, meaning that they lack a fixed 3 D structure or contain regions that do not posses a well defined 3 D structure. It has also been proven that a proteins disorder content is related to its function. We have performed an exhaustive analysis and comparison of the disorder content of proteins from prokaryotic organisms (i.e., superkingdoms Archaea and Bacteria) with respect to functional categories they belong to, i.e., Clusters of Orthologous Groups of proteins (COGs) and groups of COGs-Cellular processes (Cp), Information storage and processing (Isp), Metabolism (Me) and Poorly characterized (Pc). We also analyzed the disorder content of proteins with respect to various genomic, metabolic and ecological characteristics of the organism they belong to. We used correlations and association rule mining in order to identify the most confident associations between specific modalities of the characteristics considered and disorder content.'),
(94207, 2011, 'bmc', 'MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model', 'Le Béchec, Antony|Portales-Casamar, Elodie|Vetter, Guillaume|Moes, Michèle|Zindy, Pierre-Joachim|Saumet, Anne|Arenillas, David|Theillet, Charles|Wasserman, Wyeth W|Lecellier, Charles-Henri|Friederich, Evelyne', 'Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.|Cytoskeleton and Cell Plasticity Lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg.', 'Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|Luxembourg|L', 55, 'Amino Acid Motifs|Computational Biology|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Humans|Internet|MicroRNAs|RNA, Messenger|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-67', '21375730', 'To understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.'),
(94208, 2011, 'bmc', 'Comparing genotyping algorithms for Illuminas Infinium whole-genome SNP BeadChips', 'Ritchie, Matthew E|Liu, Ruijie|Carvalho, Benilton S|Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene)|Irizarry, Rafael A', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia', 32, 'Algorithms|Alleles|Cluster Analysis|Gene Frequency|Genome-Wide Association Study|Genotype|Humans|Models, Statistical|Polymorphism, Single Nucleotide|Sample Size', 'Comparative Study', '10.1186/1471-2105-12-68', '21385424', 'Illuminas Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensities from each SNP into genotype calls (AA, AB, BB). Various algorithms which make use of different statistical models are available for this task. We compare four methods (GenCall, Illuminus, GenoSNP and CRLMM) on data where the true genotypes are known in advance and data from a recently published genome-wide association study.'),
(94209, 2011, 'bmc', 'A comparison and user-based evaluation of models of textual information structure in the context of cancer risk assessment', 'Guo, Yufan|Korhonen, Anna|Liakata, Maria|Silins, Ilona|Hogberg, Johan|Stenius, Ulla', 'Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.|Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB30FD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Computational Biology|Data Mining|Electronic Data Processing|Humans|Neoplasms|Risk Assessment', 'Comparative Study', '10.1186/1471-2105-12-69', '21385430', 'Many practical tasks in biomedicine require accessing specific types of information in scientific literature, e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking.'),
(94210, 2011, 'bmc', 'compomics-utilities: an open-source Java library for computational proteomics', 'Barsnes, Harald|Vaudel, Marc|Colaert, Niklaas|Helsens, Kenny|Sickmann, Albert|Berven, Frode S|Martens, Lennart', 'Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.|Proteomics Unit, Department of Biomedicine, University of Bergen, Norway.', 'Norway|Norway|Norway|Norway|Norway|Norway|Norway', 70, 'Computational Biology|Databases, Protein|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-12-70', '21385435', 'The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.'),
(94211, 2011, 'bmc', 'LabKey Server: an open source platform for scientific data integration, analysis and collaboration', 'Nelson, Elizabeth K|Piehler, Britt|Eckels, Josh|Rauch, Adam|Bellew, Matthew|Hussey, Peter|Ramsay, Sarah|Nathe, Cory|Lum, Karl|Krouse, Kevin|Stearns, David|Connolly, Brian|Skillman, Tom|Igra, Mark', 'LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com|LabKey Software, Seattle, WA 98102, USA. eknelson@labkey.com', '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', 86, 'Computational Biology|Cooperative Behavior|Database Management Systems|Databases, Factual|Information Dissemination|Internet|Software', 'Journal Article', '10.1186/1471-2105-12-71', '21385461', 'Broad-based collaborations are becoming increasingly common among disease researchers. For example, the Global HIV Enterprise has united cross-disciplinary consortia to speed progress towards HIV vaccines through coordinated research across the boundaries of institutions, continents and specialties. New, end-to-end software tools for data and specimen management are necessary to achieve the ambitious goals of such alliances. These tools must enable researchers to organize and integrate heterogeneous data early in the discovery process, standardize processes, gain new insights into pooled data and collaborate securely.'),
(94212, 2011, 'bmc', 'The dChip survival analysis module for microarray data', 'Amin, Samir B|Shah, Parantu K|Yan, Aimin|Adamia, Sophia|Minvielle, Stéphane|Avet-Loiseau, Hervé|Munshi, Nikhil C|Li, Cheng', 'Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.|Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 8, 'Breast Neoplasms|Cluster Analysis|Gene Expression Profiling|Humans|Kaplan-Meier Estimate|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Proportional Hazards Models|Software', 'Journal Article', '10.1186/1471-2105-12-72', '21388547', 'Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters.'),
(94213, 2011, 'bmc', 'The impact of quantitative optimization of hybridization conditions on gene expression analysis', 'Sykacek, Peter|Kreil, David P|Meadows, Lisa A|Auburn, Richard P|Fischer, Bettina|Russell, Steven|Micklem, Gos', 'Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at|Department of Biotechnology, Boku University, Vienna, A-1190 Muthgasse 18, Austria. peter.sykacek@boku.ac.at', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Animals|Calibration|Drosophila melanogaster|Female|Gene Expression Profiling|Likelihood Functions|Linear Models|Male|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Software|Temperature', 'Journal Article', '10.1186/1471-2105-12-73', '21401920', 'With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.'),
(94214, 2011, 'bmc', 'MRCQuant- an accurate LC-MS relative isotopic quantification algorithm on TOF instruments', 'Haskins, William E|Petritis, Konstantinos|Zhang, Jianqiu', 'Pediatric Biochemistry Laboratory, University of Texas at San Antonio, 78249, USA.|Pediatric Biochemistry Laboratory, University of Texas at San Antonio, 78249, USA.|Pediatric Biochemistry Laboratory, University of Texas at San Antonio, 78249, USA.', 'United States|United States|United States', 7, 'Algorithms|Chromatography, Liquid|Isotopes|Mass Spectrometry|Peptides|Proteomics', 'Journal Article', '10.1186/1471-2105-12-74', '21406110', 'Relative isotope abundance quantification, which can be used for peptide identification and differential peptide quantification, plays an important role in liquid chromatography-mass spectrometry (LC-MS)-based proteomics. However, several major issues exist in the relative isotopic quantification of peptides on time-of-flight (TOF) instruments: LC peak boundary detection, thermal noise suppression, interference removal and mass drift correction. We propose to use the Maximum Ratio Combining (MRC) method to extract MS signal templates for interference detection/removal and LC peak boundary detection. In our method, MRCQuant, MS templates are extracted directly from experimental values, and the mass drift in each LC-MS run is automatically captured and compensated. We compared the quantification accuracy of MRCQuant to that of another representative LC-MS quantification algorithm (msInspect) using datasets downloaded from a public data repository.'),
(94215, 2010, 'bmc', 'Proceedings of the 9th International Conference on Bioinformatics, September 26-28, 2010, Tokyo, Japan', '-', '-', '-', 0, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-12-74', '21409779', '-'),
(94216, 2011, 'bmc', 'AnyExpress: integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm', 'Kim, Jihoon|Patel, Kiltesh|Jung, Hyunchul|Kuo, Winston P|Ohno-Machado, Lucila', 'Division of Biomedical Informatics, University of California, San Diego, CA, USA.|Division of Biomedical Informatics, University of California, San Diego, CA, USA.|Division of Biomedical Informatics, University of California, San Diego, CA, USA.|Division of Biomedical Informatics, University of California, San Diego, CA, USA.|Division of Biomedical Informatics, University of California, San Diego, CA, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-12-75', '21410990', 'Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough to support custom changes, which arise from the new statistical methods, updated versions of reference data, and better platforms released every month or year. Current tools are so tightly coupled with reference information, such as reference genome, transcriptome database, and SNP, which are often erroneous or outdated, that the output results are incorrect and misleading.'),
(94217, 2011, 'bmc', 'Outer membrane proteins can be simply identified using secondary structure element alignment', 'Yan, Ren-Xiang|Chen, Zhen|Zhang, Ziding', 'State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, PR China.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, PR China.|State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, PR China.', 'China|China|China', 17, 'Bacterial Outer Membrane Proteins|Escherichia coli Proteins|Membrane Proteins|Protein Structure, Secondary|Proteomics', 'Journal Article', '10.1186/1471-2105-12-76', '21414186', 'Outer membrane proteins (OMPs) are frequently found in the outer membranes of gram-negative bacteria, mitochondria and chloroplasts and have been found to play diverse functional roles. Computational discrimination of OMPs from globular proteins and other types of membrane proteins is helpful to accelerate new genome annotation and drug discovery.'),
(94218, 2011, 'bmc', 'pROC: an open-source package for R and S+ to analyze and compare ROC curves', 'Robin, Xavier|Turck, Natacha|Hainard, Alexandre|Tiberti, Natalia|Lisacek, Frédérique|Sanchez, Jean-Charles|Müller, Markus', 'Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch|Biomedical Proteomics Research Group, Department of Structural Biology and Bioinformatics, Medical University Centre, Geneva, Switzerland. Xavier.Robin@unige.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Biomarkers|Computational Biology|Confidence Intervals|Data Interpretation, Statistical|Humans|Programming Languages|ROC Curve|Software', 'Comparative Study', '10.1186/1471-2105-12-77', '21414208', 'Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.'),
(94219, 2011, 'bmc', 'ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry', 'Brusniak, Mi-Youn K|Kwok, Sung-Tat|Christiansen, Mark|Campbell, David|Reiter, Lukas|Picotti, Paola|Kusebauch, Ulrike|Ramos, Hector|Deutsch, Eric W|Chen, Jingchun|Moritz, Robert L|Aebersold, Ruedi', 'Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org|Institute for Systems Biology, Seattle, WA, USA. mbrusniak@systemsbiology.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Internet|Mass Spectrometry|Proteins|Proteomics|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-12-78', '21414234', 'Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology.'),
(94220, 2011, 'bmc', 'MicroSyn: a user friendly tool for detection of microsynteny in a gene family', 'Cai, Bin|Yang, Xiaohan|Tuskan, Gerald A|Cheng, Zong-Ming', 'College of Horticulture, Nanjing Agricultural University, China.|College of Horticulture, Nanjing Agricultural University, China.|College of Horticulture, Nanjing Agricultural University, China.|College of Horticulture, Nanjing Agricultural University, China.', 'China|China|China|China', 25, 'Genomics|MicroRNAs|Molecular Sequence Data|Phylogeny|Plants|Sequence Analysis, DNA|Software|Synteny', 'Journal Article', '10.1186/1471-2105-12-79', '21418570', 'The traditional phylogeny analysis within gene family is mainly based on DNA or amino acid sequence homologies. However, these phylogenetic tree analyses are not suitable for those non-traditional gene families like microRNA with very short sequences. For the normal protein-coding gene families, low bootstrap values are frequently encountered in some nodes, suggesting low confidence or likely inappropriateness of placement of those members in those nodes.'),
(94221, 2011, 'bmc', 'Network based transcription factor analysis of regenerating axolotl limbs', 'Jhamb, Deepali|Rao, Nandini|Milner, Derek J|Song, Fengyu|Cameron, Jo Ann|Stocum, David L|Palakal, Mathew J', 'School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.|School of Informatics, Indiana University-Purdue University Indianapolis, IN 46202, USA.', 'India|India|India|India|India|India|India', 16, 'Ambystoma mexicanum|Animals|DNA, Complementary|Extremities|Gene Expression Regulation, Developmental|Humans|Proteomics|Regeneration|Transcription Factors|Transforming Growth Factor beta1', 'Journal Article', '10.1186/1471-2105-12-80', '21418574', 'Studies on amphibian limb regeneration began in the early 1700s but we still do not completely understand the cellular and molecular events of this unique process. Understanding a complex biological process such as limb regeneration is more complicated than the knowledge of the individual genes or proteins involved. Here we followed a systems biology approach in an effort to construct the networks and pathways of protein interactions involved in formation of the accumulation blastema in regenerating axolotl limbs.'),
(94222, 2011, 'bmc', 'Appearance frequency modulated gene set enrichment testing', 'Ma, Jun|Sartor, Maureen A|Jagadish, H V', 'Department of EECS, University of Michigan, Ann Arbor, USA. majun@umich.edu|Department of EECS, University of Michigan, Ann Arbor, USA. majun@umich.edu|Department of EECS, University of Michigan, Ann Arbor, USA. majun@umich.edu', 'United States|United States|United States', 0, 'Gene Expression Profiling|Gene Expression Regulation|Gene Frequency|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-12-81', '21418606', 'Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results.'),
(94223, 2011, 'bmc', 'Motif-guided sparse decomposition of gene expression data for regulatory module identification', 'Gong, Ting|Xuan, Jianhua|Chen, Li|Riggins, Rebecca B|Li, Huai|Hoffman, Eric P|Clarke, Robert|Wang, Yue', 'Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.|Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Cell Line, Tumor|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Regression Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Transcription Factors', 'Journal Article', '10.1186/1471-2105-12-82', '21426557', 'Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data, for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.'),
(94224, 2011, 'bmc', 'A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models', 'Bernardes, Juliana S|Carbone, Alessandra|Zaverucha, Gerson', 'COPPE, Programa de Engenharia de Sistemas e Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. julibinho@gmail.com|COPPE, Programa de Engenharia de Sistemas e Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. julibinho@gmail.com|COPPE, Programa de Engenharia de Sistemas e Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. julibinho@gmail.com', 'Brazil|Brazil|Brazil', 9, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Computational Biology|Databases, Factual|Logic|Models, Genetic|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-12-83', '21429187', 'Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the twilight zone we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).'),
(94225, 2011, 'bmc', 'Meta-analysis of gene expression microarrays with missing replicates', 'Shi, Fan|Abraham, Gad|Leckie, Christopher|Haviv, Izhak|Kowalczyk, Adam', 'National ICT Australia, Victoria Research Laboratory, The University of Melbourne Victoria, Australia. shif@csse.unimelb.edu.au|National ICT Australia, Victoria Research Laboratory, The University of Melbourne Victoria, Australia. shif@csse.unimelb.edu.au|National ICT Australia, Victoria Research Laboratory, The University of Melbourne Victoria, Australia. shif@csse.unimelb.edu.au|National ICT Australia, Victoria Research Laboratory, The University of Melbourne Victoria, Australia. shif@csse.unimelb.edu.au|National ICT Australia, Victoria Research Laboratory, The University of Melbourne Victoria, Australia. shif@csse.unimelb.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Gene Expression Profiling|Linear Models|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-12-84', '21435268', 'Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combined using meta-analysis, in order to increase the overall sample size. However, some genes are not measured in all experiments, hence they cannot be included or their statistical significance cannot be appropriately estimated in traditional meta-analysis. Nonetheless, these genes, which we refer to as incomplete genes, may also be informative and useful.'),
(94226, 2011, 'bmc', 'DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI', 'Liu, Yongchao|Schmidt, Bertil|Maskell, Douglas L', 'School of Computer Engineering, Nanyang Technological University, 639798, Singapore. liuy0039@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, 639798, Singapore. liuy0039@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, 639798, Singapore. liuy0039@ntu.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Computer Simulation|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-12-85', '21447171', 'Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets.'),
(94227, 2011, 'bmc', 'Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems', 'Fronza, Raffaele|Tramonti, Michele|Atchley, William R|Nardini, Christine', 'Key Laboratory of Computational Biology, MPG-CAS PICB, Shanghai, PR China. christine@picb.ac.cn|Key Laboratory of Computational Biology, MPG-CAS PICB, Shanghai, PR China. christine@picb.ac.cn|Key Laboratory of Computational Biology, MPG-CAS PICB, Shanghai, PR China. christine@picb.ac.cn|Key Laboratory of Computational Biology, MPG-CAS PICB, Shanghai, PR China. christine@picb.ac.cn', 'China|China|China|China', 8, 'Algorithms|Brain Neoplasms|Cells, Cultured|Computational Biology|Databases, Genetic|Gene Expression Regulation, Neoplastic|Humans|MicroRNAs|RNA, Messenger|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-12-86', '21450054', 'Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.'),
(94228, 2011, 'bmc', 'An agent-based model to study the epidemiological and evolutionary dynamics of Influenza viruses', 'Roche, Benjamin|Drake, John M|Rohani, Pejman', 'Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr', 'United States|United States|United States', 39, 'Biological Evolution|Humans|Influenza A virus|Influenza, Human|Models, Biological|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-12-87', '21450071', 'Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.'),
(94229, 2011, 'bmc', 'Empirical Bayesian models for analysing molecular serotyping microarrays', 'Newton, Richard|Hinds, Jason|Wernisch, Lorenz', 'MRC Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, UK. richard.newton@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, UK. richard.newton@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, UK. richard.newton@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 17, 'Bacterial Typing Techniques|Bayes Theorem|Oligonucleotide Array Sequence Analysis|Probability|Reproducibility of Results|Serotyping|Streptococcus pneumoniae', 'Journal Article', '10.1186/1471-2105-12-88', '21453458', 'Microarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assays. In this study applied to infectious diseases, data from a microarray designed for molecular serotyping of Streptococcus pneumoniae was used, identifying the presence of any one of 91 known pneumococcal serotypes from DNA extracts. This microarray incorporated oligonucleotide probes for all known capsular polysaccharide synthesis genes and required a statistical analysis of the microarray intensity data to determine which serotype, or combination of serotypes, were present within a sample based on the combination of genes detected.'),
(94230, 2011, 'bmc', 'Learning genetic epistasis using Bayesian network scoring criteria', 'Jiang, Xia|Neapolitan, Richard E|Barmada, M Michael|Visweswaran, Shyam', 'Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. xij6@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. xij6@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. xij6@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. xij6@pitt.edu', 'United States|United States|United States|United States', 0, 'Bayes Theorem|Computational Biology|Epistasis, Genetic|Genotype|Humans|Models, Genetic|Multifactor Dimensionality Reduction|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-12-89', '21453508', 'Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model.'),
(94231, 2011, 'bmc', 'Using context to improve protein domain identification', 'Ochoa, Alejandro|Llinás, Manuel|Singh, Mona', 'Department of Molecular Biology, Princeton University, Princeton, NJ, USA.|Department of Molecular Biology, Princeton University, Princeton, NJ, USA.|Department of Molecular Biology, Princeton University, Princeton, NJ, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Animals|Genomics|Humans|Molecular Sequence Annotation|Plasmodium falciparum|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-12-90', '21453511', 'Identifying domains in protein sequences is an important step in protein structural and functional annotation. Existing domain recognition methods typically evaluate each domain prediction independently of the rest. However, the majority of proteins are multidomain, and pairwise domain co-occurrences are highly specific and non-transitive.'),
(94232, 2011, 'bmc', 'dbOGAP - an integrated bioinformatics resource for protein O-GlcNAcylation', 'Wang, Jinlian|Torii, Manabu|Liu, Hongfang|Hart, Gerald W|Hu, Zhang-Zhi', 'Department of Oncology, Georgetown University Medical Center, NW, Washington, DC 20007, USA.|Department of Oncology, Georgetown University Medical Center, NW, Washington, DC 20007, USA.|Department of Oncology, Georgetown University Medical Center, NW, Washington, DC 20007, USA.|Department of Oncology, Georgetown University Medical Center, NW, Washington, DC 20007, USA.|Department of Oncology, Georgetown University Medical Center, NW, Washington, DC 20007, USA.', 'United States|United States|United States|United States|United States', 71, 'Acetylglucosamine|Computational Biology|Glycosylation|Humans|Phosphorylation|Protein Processing, Post-Translational|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-12-91', '21466708', 'Protein O-GlcNAcylation (or O-GlcNAc-ylation) is an O-linked glycosylation involving the transfer of xcexb2-N-acetylglucosamine to the hydroxyl group of serine or threonine residues of proteins. Growing evidences suggest that protein O-GlcNAcylation is common and is analogous to phosphorylation in modulating broad ranges of biological processes. However, compared to phosphorylation, the amount of protein O-GlcNAcylation data is relatively limited and its annotation in databases is scarce. Furthermore, a bioinformatics resource for O-GlcNAcylation is lacking, and an O-GlcNAcylation site prediction tool is much needed.'),
(94233, 2011, 'bmc', 'Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies', 'Martini, Paolo|Risso, Davide|Sales, Gabriele|Romualdi, Chiara|Lanfranchi, Gerolamo|Cagnin, Stefano', 'CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.|CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.|CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.|CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.|CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.|CRIBI Biotechnology Centre, Department of Biology, University of Padova, via U, Bassi 58/B, 35121 Padova, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 4, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Genome|Histone-Lysine N-Methyltransferase|Humans|Leukemia|Leukemia, Myeloid, Acute|Meta-Analysis as Topic|Muscular Diseases|Muscular Dystrophies, Limb-Girdle|Myeloid-Lymphoid Leukemia Protein|Oligonucleotide Array Sequence Analysis|Translocation, Genetic', 'Journal Article', '10.1186/1471-2105-12-92', '21481242', 'In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes. In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets.'),
(94234, 2011, 'bmc', 'Detection of viral sequence fragments of HIV-1 subfamilies yet unknown', 'Unterthiner, Thomas|Schultz, Anne-Kathrin|Bulla, Jan|Morgenstern, Burkhard|Stanke, Mario|Bulla, Ingo', 'Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. ingobulla@gmail.com', 'Germany|Germany|Germany|Germany|Germany|Germany', 1, 'Algorithms|Computational Biology|Computer Simulation|Genetic Variation|HIV-1|Models, Genetic', 'Journal Article', '10.1186/1471-2105-12-93', '21481263', 'Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part - from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI), has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments.');
