INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92962, 2009, 'bmc', 'TableButler - a Windows based tool for processing large data tables generated with high-throughput methods', 'Schwager, Christian|Wirkner, Ute|Abdollahi, Amir|Huber, Peter E', 'Department of Radiation Oncology, German Cancer Research Center (DKFZ) and University of Heidelberg Medical School, Heidelberg, Germany. christian.schwager@med.uni-heidelberg.de|Department of Radiation Oncology, German Cancer Research Center (DKFZ) and University of Heidelberg Medical School, Heidelberg, Germany. christian.schwager@med.uni-heidelberg.de|Department of Radiation Oncology, German Cancer Research Center (DKFZ) and University of Heidelberg Medical School, Heidelberg, Germany. christian.schwager@med.uni-heidelberg.de|Department of Radiation Oncology, German Cancer Research Center (DKFZ) and University of Heidelberg Medical School, Heidelberg, Germany. christian.schwager@med.uni-heidelberg.de', 'Germany|Germany|Germany|Germany', 8, 'Computational Biology|Gene Expression|Humans|Software Design', 'Journal Article', '10.1186/1471-2105-10-235', '19640300', 'High-throughput omics based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these methods. Depending on the study, dozens to hundreds of these data tables are generated. Before the actual statistical or cluster analysis, these data tables have to be combined and merged to expression matrices (e.g., in case of gene expression analysis). Gene annotations as well as information concerning the samples analyzed may be appended, renewed or extended. Often additional data values shall be computed or certain features must be filtered out.'),
(92963, 2009, 'bmc', 'sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures', 'Zhang, Shu-Dong|Gant, Timothy W', 'MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK. sdz1@le.ac.uk|MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK. sdz1@le.ac.uk', 'United Kingdom|United Kingdom', 0, 'Animals|Cell Line|Combinatorial Chemistry Techniques|Estrogens|Gene Expression|Humans|Mice|Rats|Small Molecule Libraries|Software Design', 'Journal Article', '10.1186/1471-2105-10-236', '19646231', 'Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures.'),
(92964, 2009, 'bmc', 'Resolving deconvolution ambiguity in gene alternative splicing', 'She, Yiyuan|Hubbell, Earl|Wang, Hui', 'Affymetrix Inc, Santa Clara, CA 95051, USA. yshe@stat.fsu.edu|Affymetrix Inc, Santa Clara, CA 95051, USA. yshe@stat.fsu.edu|Affymetrix Inc, Santa Clara, CA 95051, USA. yshe@stat.fsu.edu', 'United States|United States|United States', 5, 'Algorithms|Alternative Splicing|Computational Biology|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-237', '19653895', 'For many gene structures it is impossible to resolve intensity data uniquely to establish abundances of splice variants. This was empirically noted by Wang et al. in which it was called a degeneracy problem. The ambiguity results from an ill-posed problem where additional information is needed in order to obtain an unique answer in splice variant deconvolution.'),
(92965, 2009, 'bmc', 'Generating rate equations for complex enzyme systems by a computer-assisted systematic method', 'Qi, Feng|Dash, Ranjan K|Han, Yu|Beard, Daniel A', 'Biotechnology and Bioengineering Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA. fqi@mcw.edu|Biotechnology and Bioengineering Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA. fqi@mcw.edu|Biotechnology and Bioengineering Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA. fqi@mcw.edu|Biotechnology and Bioengineering Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA. fqi@mcw.edu', 'United States|United States|United States|United States', 31, 'Algorithms|Biocatalysis|Fumarate Hydratase|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-238', '19653903', 'While the theory of enzyme kinetics is fundamental to analyzing and simulating biochemical systems, the derivation of rate equations for complex mechanisms for enzyme-catalyzed reactions is cumbersome and error prone. Therefore, a number of algorithms and related computer programs have been developed to assist in such derivations. Yet although a number of algorithms, programs, and software packages are reported in the literature, one or more significant limitation is associated with each of these tools. Furthermore, none is freely available for download and use by the community.'),
(92966, 2009, 'bmc', 'Screening non-coding RNAs in transcriptomes from neglected species using PORTRAIT: case study of the pathogenic fungus Paracoccidioides brasiliensis', 'Arrial, Roberto T|Togawa, Roberto C|Brigido, Marcelo de M', 'Biology Institute, University of Brasília, Brazil. rtarrial@gmail.com|Biology Institute, University of Brasília, Brazil. rtarrial@gmail.com|Biology Institute, University of Brasília, Brazil. rtarrial@gmail.com', 'Brazil|Brazil|Brazil', 60, 'Databases, Genetic|Gene Expression Profiling|Genetic Vectors|Paracoccidioides|RNA, Fungal|RNA, Untranslated|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-239', '19653905', 'Transcriptome sequences provide a complement to structural genomic information and provide snapshots of an organisms transcriptional profile. Such sequences also represent an alternative method for characterizing neglected species that are not expected to undergo whole-genome sequencing. One difficulty for transcriptome sequencing of these organisms is the low quality of reads and incomplete coverage of transcripts, both of which compromise further bioinformatics analyses. Another complicating factor is the lack of known protein homologs, which frustrates searches against established protein databases. This lack of homologs may be caused by divergence from well-characterized and over-represented model organisms. Another explanation is that non-coding RNAs (ncRNAs) may be caught during sequencing. NcRNAs are RNA sequences that, unlike messenger RNAs, do not code for protein products and instead perform unique functions by folding into higher order structural conformations. There is ncRNA screening software available that is specific for transcriptome sequences, but their analyses are optimized for those transcriptomes that are well represented in protein databases, and also assume that input ESTs are full-length and high quality.'),
(92967, 2009, 'bmc', 'Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology', 'Xu, Tao|Gu, JianLei|Zhou, Yan|Du, LinFang', 'College of Life Sciences, Sichuan University, Chengdu, PR China. xutao@chgc.sh.cn|College of Life Sciences, Sichuan University, Chengdu, PR China. xutao@chgc.sh.cn|College of Life Sciences, Sichuan University, Chengdu, PR China. xutao@chgc.sh.cn|College of Life Sciences, Sichuan University, Chengdu, PR China. xutao@chgc.sh.cn', 'China|China|China|China', 11, 'Cell Cycle|Cluster Analysis|Computational Biology|Gene Expression|Gene Expression Profiling|Leukemia, Myeloid, Acute|Phenotype|Precursor Cell Lymphoblastic Leukemia-Lymphoma', 'Evaluation Studies', '10.1186/1471-2105-10-240', '19653916', 'Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.'),
(92968, 2009, 'bmc', 'Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels', 'Yip, Kevin Y|Kim, Philip M|McDermott, Drew|Gerstein, Mark', 'Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA. yuklap.yip@yale.edu|Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA. yuklap.yip@yale.edu|Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA. yuklap.yip@yale.edu|Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA. yuklap.yip@yale.edu', 'United States|United States|United States|United States', 7, 'Computational Biology|Models, Molecular|Protein Multimerization|Protein Structure, Quaternary|Protein Structure, Tertiary|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-241', '19656385', 'Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and residues. Each level offers a distinct and complementary set of features for computationally predicting interactions, including functional genomic features of whole proteins, evolutionary features of domain families and physical-chemical features of individual residues. The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity.'),
(92969, 2009, 'bmc', 'R/BHC: fast Bayesian hierarchical clustering for microarray data', 'Savage, Richard S|Heller, Katherine|Xu, Yang|Ghahramani, Zoubin|Truman, William M|Grant, Murray|Denby, Katherine J|Wild, David L', 'Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk|Systems Biology Centre, University of Warwick, Coventry House, Coventry CV47AL, UK. r.s.savage@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Algorithms|Arabidopsis|Bayes Theorem|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software Design|Time Factors', 'Journal Article', '10.1186/1471-2105-10-242', '19660130', 'Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained.'),
(92970, 2009, 'bmc', 'Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures', 'Thachuk, Chris|Crossa, José|Franco, Jorge|Dreisigacker, Susanne|Warburton, Marilyn|Davenport, Guy F', 'Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC V6T1Z4, Canada. cthachuk@cs.ubc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 53, 'Algorithms|Alleles|Genetic Techniques', 'Journal Article', '10.1186/1471-2105-10-243', '19660135', 'Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeders preference) or allele richness (taxonomists preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.'),
(92971, 2009, 'bmc', 'ETISEQ--an algorithm for automated elution time ion sequencing of concurrently fragmented peptides for mass spectrometry-based proteomics', 'Wong, Jason W H|Schwahn, Alexander B|Downard, Kevin M', 'UNSW Cancer Research Centre, University of New South Wales, Sydney, NSW 2052, Australia. jason.wong@unsw.edu.au|UNSW Cancer Research Centre, University of New South Wales, Sydney, NSW 2052, Australia. jason.wong@unsw.edu.au|UNSW Cancer Research Centre, University of New South Wales, Sydney, NSW 2052, Australia. jason.wong@unsw.edu.au', 'Australia|Australia|Australia', 9, 'Algorithms|Amino Acid Sequence|Animals|Avian Proteins|Chickens|Influenza A Virus, H1N1 Subtype|Ions|Molecular Sequence Data|Peptide Fragments|Proteomics|Tandem Mass Spectrometry|Time Factors|Viral Proteins', 'Journal Article', '10.1186/1471-2105-10-244', '19664259', 'Concurrent peptide fragmentation (i.e. shotgun CID, parallel CID or MSE) has emerged as an alternative to data-dependent acquisition in generating peptide fragmentation data in LC-MS/MS proteomics experiments. Concurrent peptide fragmentation data acquisition has been shown to be advantageous over data-dependent acquisition by providing greater detection dynamic range and providing more accurate quantitative information. Nevertheless, concurrent peptide fragmentation data acquisition remains to be widely adopted due to the lack of published algorithms designed specifically to process or interpret such data acquired on any mass spectrometer.'),
(92972, 2009, 'bmc', 'Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity', 'Quintus, Flavien|Sperandio, Olivier|Grynberg, Julien|Petitjean, Michel|Tuffery, Pierre', 'MTi, RPBS, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France. flavien.quintus@etu.univ-paris-diderot.fr|MTi, RPBS, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France. flavien.quintus@etu.univ-paris-diderot.fr|MTi, RPBS, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France. flavien.quintus@etu.univ-paris-diderot.fr|MTi, RPBS, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France. flavien.quintus@etu.univ-paris-diderot.fr|MTi, RPBS, INSERM UMR-S973, Université Paris Diderot-Paris 7, Paris, France. flavien.quintus@etu.univ-paris-diderot.fr', 'France|France|France|France|France', 19, 'Computational Biology|Drug Design|Drug Discovery|Ligands|Models, Molecular|Molecular Structure|Small Molecule Libraries', 'Journal Article', '10.1186/1471-2105-10-245', '19671127', 'Virtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of 3D knowledge can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.'),
(92973, 2009, 'bmc', 'A structured overview of simultaneous component based data integration', 'Van Deun, Katrijn|Smilde, Age K|van der Werf, Mariët J|Kiers, Henk A L|Van Mechelen, Iven', 'SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 71, 'Algorithms|Computational Biology|Electronic Data Processing|Metabolomics|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-10-246', '19671149', 'Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules) with the different pieces stemming, for example, from different measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. An integrative analysis of such coupled data should be based on a simultaneous analysis of all data arrays. In this respect, the family of simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P) is a natural choice. Yet, different simultaneous component methods may lead to quite different results.'),
(92974, 2009, 'bmc', 'Accurate and fast methods to estimate the population mutation rate from error prone sequences', 'Knudsen, Bjarne|Miyamoto, Michael M', 'CLC bio, 8200 Arhus N, Denmark. bknudsen@clcbio.com|CLC bio, 8200 Arhus N, Denmark. bknudsen@clcbio.com', 'Denmark|Denmark', 13, 'Algorithms|Computational Biology|Genetics, Population|Mutation|Population Density|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-10-247', '19671163', 'The population mutation rate (theta) remains one of the most fundamental parameters in genetics, ecology, and evolutionary biology. However, its accurate estimation can be seriously compromised when working with error prone data such as expressed sequence tags, low coverage draft sequences, and other such unfinished products. This study is premised on the simple idea that a random sequence error due to a chance accident during data collection or recording will be distributed within a population dataset as a singleton (i.e., as a polymorphic site where one sampled sequence exhibits a unique base relative to the common nucleotide of the others). Thus, one can avoid these random errors by ignoring the singletons within a dataset.'),
(92975, 2009, 'bmc', 'Supervised learning for the automated transcription of spacer classification from spoligotype films', 'Jeffries, David J|Abernethy, Neil|de Jong, Bouke C', 'MRC Laboratories, Fajara, The Gambia. djeffries@mrc.gm|MRC Laboratories, Fajara, The Gambia. djeffries@mrc.gm|MRC Laboratories, Fajara, The Gambia. djeffries@mrc.gm', 'Gambia|Gambia|Gambia', 4, 'Algorithms|Bacterial Typing Techniques|Gambia|Humans|Mycobacterium tuberculosis|Pattern Recognition, Automated|Software|Tuberculosis', 'Journal Article', '10.1186/1471-2105-10-248', '19674444', 'Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing.'),
(92976, 2009, 'bmc', 'EvoRSR: an integrated system for exploring evolution of RNA structural robustness', 'Shu, Wenjie|Ni, Ming|Bo, Xiaochen|Zheng, Zhiqiang|Wang, Shengqi', 'Beijing Institute of Radiation Medicine, Beijing, PR China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing, PR China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing, PR China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing, PR China. shuwj@bmi.ac.cn|Beijing Institute of Radiation Medicine, Beijing, PR China. shuwj@bmi.ac.cn', 'China|China|China|China|China', 0, 'Animals|Base Sequence|Caenorhabditis elegans|Computational Biology|Evolution, Molecular|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1186/1471-2105-10-249', '19674478', 'Robustness, maintaining a constant phenotype despite perturbations, is a fundamental property of biological systems that is incorporated at various levels of biological complexity. Although robustness has been frequently observed in nature, its evolutionary origin remains unknown. Current hypotheses suggest that robustness originated as a direct consequence of natural selection, as an intrinsic property of adaptations, or as a congruent correlate of environment robustness. To elucidate the evolutionary origins of robustness, a convenient computational package is strongly needed.'),
(92977, 2009, 'bmc', 'OnEX: Exploring changes in life science ontologies', 'Hartung, Michael|Kirsten, Toralf|Gross, Anika|Rahm, Erhard', 'Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, 04107 Leipzig, Germany. hartung@izbi.uni-leipzig.de', 'Germany|Germany|Germany|Germany', 23, 'Computational Biology|Databases, Factual|Gene Expression Profiling|Internet|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-250', '19678926', 'Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.'),
(92978, 2009, 'bmc', 'SNAD: Sequence Name Annotation-based Designer', 'Sidorov, Igor A|Reshetov, Denis A|Gorbalenya, Alexander E', 'Molecular Virology Laboratory, Department of Medical Microbiology, Center of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands. i.a.sidorov@lumc.nl|Molecular Virology Laboratory, Department of Medical Microbiology, Center of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands. i.a.sidorov@lumc.nl|Molecular Virology Laboratory, Department of Medical Microbiology, Center of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands. i.a.sidorov@lumc.nl', 'Netherlands|Netherlands|Netherlands', 6, 'Computational Biology|Databases, Factual|Internet|Sequence Analysis|Sequence Analysis, DNA|Software|Terminology as Topic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-251', '19682364', 'A growing diversity of biological data is tagged with unique identifiers (UIDs) associated with polynucleotides and proteins to ensure efficient computer-mediated data storage, maintenance, and processing. These identifiers, which are not informative for most people, are often substituted by biologically meaningful names in various presentations to facilitate utilization and dissemination of sequence-based knowledge. This substitution is commonly done manually that may be a tedious exercise prone to mistakes and omissions.'),
(92979, 2009, 'bmc', 'An XML transfer schema for exchange of genomic and genetic mapping data: implementation as a web service in a Taverna workflow', 'Paterson, Trevor|Law, Andy', 'Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK. trevor.paterson@roslin.ed.ac.uk|Division of Genetics and Genomics, The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Edinburgh, UK. trevor.paterson@roslin.ed.ac.uk', 'United Kingdom|United Kingdom', 4, 'Computational Biology|Database Management Systems|Databases, Genetic|Genome|Information Storage and Retrieval|Internet|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-10-252', '19682365', 'Genomic analysis, particularly for less well-characterized organisms, is greatly assisted by performing comparative analyses between different types of genome maps and across species boundaries. Various providers publish a plethora of on-line resources collating genome mapping data from a multitude of species. Datasources range in scale and scope from small bespoke resources for particular organisms, through larger web-resources containing data from multiple species, to large-scale bioinformatics resources providing access to data derived from genome projects for model and non-model organisms. The heterogeneity of information held in these resources reflects both the technologies used to generate the data and the target users of each resource. Currently there is no common information exchange standard or protocol to enable access and integration of these disparate resources. Consequently data integration and comparison must be performed in an ad hoc manner.'),
(92980, 2009, 'bmc', 'A fast and efficient gene-network reconstruction method from multiple over-expression experiments', 'Stokixc4x87, Dejan|Hanel, Rudolf|Thurner, Stefan', 'Complex Systems Research Group, Medical University of Vienna, A-1090, Austria. stokic@swissquant.ch|Complex Systems Research Group, Medical University of Vienna, A-1090, Austria. stokic@swissquant.ch|Complex Systems Research Group, Medical University of Vienna, A-1090, Austria. stokic@swissquant.ch', 'Austria|Austria|Austria', 9, 'Algorithms|Computational Biology|Gene Regulatory Networks|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-253', '19686586', 'Reverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series.'),
(92981, 2009, 'bmc', 'Ultra-Structure database design methodology for managing systems biology data and analyses', 'Maier, Christopher W|Long, Jeffrey G|Hemminger, Bradley M|Giddings, Morgan C', 'Department of Microbiology and Immunology, UNC Chapel Hill, NC, USA. maier@med.unc.edu|Department of Microbiology and Immunology, UNC Chapel Hill, NC, USA. maier@med.unc.edu|Department of Microbiology and Immunology, UNC Chapel Hill, NC, USA. maier@med.unc.edu|Department of Microbiology and Immunology, UNC Chapel Hill, NC, USA. maier@med.unc.edu', 'United States|United States|United States|United States', 6, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-254', '19691849', 'Modern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the systems capabilities without programmer intervention, simply by altering database contents, no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping).'),
(92982, 2009, 'bmc', 'Extracting biologically significant patterns from short time series gene expression data', 'Tchagang, Alain B|Bui, Kevin V|McGinnis, Thomas|Benos, Panayiotis V', 'Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA. abt10@pitt.edu|Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA. abt10@pitt.edu|Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA. abt10@pitt.edu|Department of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA. abt10@pitt.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Information Storage and Retrieval|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-255', '19695084', 'Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.'),
(92983, 2009, 'bmc', 'The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations', 'Lin, Xue|Afsari, Bahman|Marchionni, Luigi|Cope, Leslie|Parmigiani, Giovanni|Naiman, Daniel|Geman, Donald', 'Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu|Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, Maryland, USA. xlin@ams.jhu.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, BRCA1|Humans', 'Journal Article', '10.1186/1471-2105-10-256', '19695104', 'A major challenge in computational biology is to extract knowledge about the genetic nature of disease from high-throughput data. However, an important obstacle to both biological understanding and clinical applications is the black box nature of the decision rules provided by most machine learning approaches, which usually involve many genes combined in a highly complex fashion. Achieving biologically relevant results argues for a different strategy. A promising alternative is to base prediction entirely upon the relative expression ordering of a small number of genes.'),
(92984, 2009, 'bmc', 'Structure-based substrate screening for an enzyme', 'Xu, Tao|Zhang, Lujia|Wang, Xuedong|Wei, Dongzhi|Li, Tianbi', 'State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. robertxutao@mail.ecust.edu.cn|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. robertxutao@mail.ecust.edu.cn|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. robertxutao@mail.ecust.edu.cn|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. robertxutao@mail.ecust.edu.cn|State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, PR China. robertxutao@mail.ecust.edu.cn', 'China|China|China|China|China', 6, 'Binding Sites|Computational Biology|Drug Design|Ligands|Models, Molecular|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-10-257', '19695105', 'Nowadays, more and more novel enzymes can be easily found in the whole enzyme pool with the rapid development of genetic operation. However, experimental work for substrate screening of a new enzyme is laborious, time consuming and costly. On the other hand, many computational methods have been widely used in lead screening of drug design. Seeing that the ligand-target protein system in drug design and the substrate-enzyme system in enzyme applications share the similar molecular recognition mechanism, we aim to fulfill the goal of substrate screening by in silico means in the present study.'),
(92985, 2009, 'bmc', 'p3d--Python module for structural bioinformatics', 'Fufezan, Christian|Specht, Michael', 'Westfälische Wilhelms-Universität Münster, Institute for Evolution and Biodiversity, Münster, Germany. christian@fufezan.net|Westfälische Wilhelms-Universität Münster, Institute for Evolution and Biodiversity, Münster, Germany. christian@fufezan.net', 'Germany|Germany', 10, 'Computational Biology|Databases, Protein|Programming Languages|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-258', '19698094', 'High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code.'),
(92986, 2009, 'bmc', 'Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines', 'Guan, Wei|Zhou, Manshui|Hampton, Christina Y|Benigno, Benedict B|Walker, L Deette|Gray, Alexander|McDonald, John F|Fernández, Facundo M', 'College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu|College of Computing, Georgia Institute of Technology, Atlanta GA 30332, USA. wguan@cc.gatech.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 79, 'Adult|Aged|Aged, 80 and over|Algorithms|Chromatography, Liquid|Computational Biology|Female|Humans|Mass Spectrometry|Metabolomics|Middle Aged|Ovarian Neoplasms|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-259', '19698113', 'The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.'),
(92987, 2009, 'bmc', 'MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering', 'Kim, Eun-Youn|Kim, Seon-Young|Ashlock, Daniel|Nam, Dougu', 'National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea. eunykim@nims.re.kr|National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea. eunykim@nims.re.kr|National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea. eunykim@nims.re.kr|National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea. eunykim@nims.re.kr', '-|-|-|-', 48, 'Cluster Analysis|Computational Biology|Databases, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-10-260', '19698124', 'Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.'),
(92988, 2009, 'bmc', 'Computing power of quantitative trait locus association mapping for haploid loci', 'Gordon, Derek|Zinn, Andrew R', 'Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA. gordon@biology.rutgers.edu|Department of Genetics, Rutgers University, 145 Bevier Road, Piscataway, NJ 08854, USA. gordon@biology.rutgers.edu', 'United States|United States', 0, 'Chromosome Mapping|Computer Simulation|Haploidy|Humans|Male|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-10-261', '19698182', 'Statistical power calculations are a critical part of any study design for gene mapping. Most calculations assume that the locus of interest is biallelic. However, there are common situations in human genetics such as X-linked loci in males where the locus is haploid. The purpose of this work is to mathematically derive the biometric model for haploid loci, and to compute power for QTL mapping when the loci are haploid.'),
(92989, 2009, 'bmc', 'Integrative modeling of transcriptional regulation in response to antirheumatic therapy', 'Hecker, Michael|Goertsches, Robert Hermann|Engelmann, Robby|Thiesen, Hans-Juergen|Guthke, Reinhard', 'Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany. michael.hecker@hki-jena.de|Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany. michael.hecker@hki-jena.de|Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany. michael.hecker@hki-jena.de|Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany. michael.hecker@hki-jena.de|Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute, Jena, Germany. michael.hecker@hki-jena.de', 'Germany|Germany|Germany|Germany|Germany', 34, 'Antirheumatic Agents|Computational Biology|Etanercept|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Humans|Immunoglobulin G|Oligonucleotide Array Sequence Analysis|Receptors, Tumor Necrosis Factor|Rheumatic Diseases|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-262', '19703281', 'The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.'),
(92990, 2009, 'bmc', 'Assessing the druggability of protein-protein interactions by a supervised machine-learning method', 'Sugaya, Nobuyoshi|Ikeda, Kazuyoshi', 'Drug Discovery Department, Research & Development Division, PharmaDesign, Inc, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp|Drug Discovery Department, Research & Development Division, PharmaDesign, Inc, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp', 'Japan|Japan', 31, 'Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Pharmaceutical Preparations|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-263', '19703312', 'Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).'),
(92991, 2009, 'bmc', 'GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction', 'Yu, Yao|Tu, Kang|Zheng, Siyuan|Li, Yun|Ding, Guohui|Ping, Jie|Hao, Pei|Li, Yixue', 'Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn|Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China. yyu01@sibs.ac.cn', 'China|China|China|China|China|China|China|China', 5, 'Computational Biology|Databases, Genetic|Gene Expression|Phenotype|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-264', '19703314', 'In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.'),
(92992, 2009, 'bmc', 'dictyExpress: a Dictyostelium discoideum gene expression database with an explorative data analysis web-based interface', 'Rot, Gregor|Parikh, Anup|Curk, Tomaz|Kuspa, Adam|Shaulsky, Gad|Zupan, Blaz', 'Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si|Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia. gregor.rot@fri.uni-lj.si', 'Slovenia|Slovenia|Slovenia|Slovenia|Slovenia|Slovenia', 54, 'Animals|Computational Biology|Databases, Genetic|Dictyostelium|Gene Expression Profiling|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-265', '19706156', 'Bioinformatics often leverages on recent advancements in computer science to support biologists in their scientific discovery process. Such efforts include the development of easy-to-use web interfaces to biomedical databases. Recent advancements in interactive web technologies require us to rethink the standard submit-and-wait paradigm, and craft bioinformatics web applications that share analytical and interactive power with their desktop relatives, while retaining simplicity and availability.'),
(92993, 2009, 'bmc', 'Genoviz Software Development Kit: Java tool kit for building genomics visualization applications', 'Helt, Gregg A|Nicol, John W|Erwin, Ed|Blossom, Eric|Blanchard, Steven G|Chervitz, Stephen A|Harmon, Cyrus|Loraine, Ann E', 'Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com|Genomancer Consulting, Healdsburg, CA 95448, USA. gregghelt@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 19, 'Computer Graphics|Databases, Factual|Genomics|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-266', '19706180', 'Visualization software can expose previously undiscovered patterns in genomic data and advance biological science.'),
(92994, 2009, 'bmc', 'Enhanced protein fold recognition through a novel data integration approach', 'Ying, Yiming|Huang, Kaizhu|Campbell, Colin', 'Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK. mathying@gmail.com|Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK. mathying@gmail.com|Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1TR, UK. mathying@gmail.com', 'United Kingdom|United Kingdom|United Kingdom', 37, 'Algorithms|Computational Biology|Pattern Recognition, Automated|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-10-267', '19709406', 'Protein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92995, 2009, 'bmc', 'QPCR: Application for real-time PCR data management and analysis', 'Pabinger, Stephan|Thallinger, Gerhard G|Snajder, René|Eichhorn, Heiko|Rader, Robert|Trajanoski, Zlatko', 'Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. stephan.pabinger@tugraz.at', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Genetic|Polymerase Chain Reaction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-268', '19712446', 'Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline.'),
(92996, 2009, 'bmc', 'Global haplotype partitioning for maximal associated SNP pairs', 'Katanforoush, Ali|Sadeghi, Mehdi|Pezeshk, Hamid|Elahi, Elahe', 'Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. katanfor@ibb.ut.ac.ir|Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. katanfor@ibb.ut.ac.ir|Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. katanfor@ibb.ut.ac.ir|Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. katanfor@ibb.ut.ac.ir', '-|-|-|-', 10, 'Algorithms|Alleles|Computational Biology|Genetic Variation|Genome-Wide Association Study|Haplotypes|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-269', '19712447', 'Global partitioning based on pairwise associations of SNPs has not previously been used to define haplotype blocks within genomes. Here, we define an association index based on LD between SNP pairs. We use the Fishers exact test to assess the statistical significance of the LD estimator. By this test, each SNP pair is characterized as associated, independent, or not-statistically-significant. We set limits on the maximum acceptable proportion of independent pairs within all blocks and search for the partitioning with maximal proportion of associated SNP pairs. Essentially, this model is reduced to a constrained optimization problem, the solution of which is obtained by iterating a dynamic programming algorithm.'),
(92997, 2009, 'bmc', 'A reliable measure of similarity based on dependency for short time series: an application to gene expression networks', 'Campiteli, Mônica G|Soriani, Frederico M|Malavazi, Iran|Kinouchi, Osame|Pereira, Carlos A B|Goldman, Gustavo H', 'Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com|Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com|Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com|Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com|Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com|Departamento de Ciencias Farmacêuticas - Faculdade de Ciências Farmacêuticas de Ribeirão Preto Universidade de São Paulo, Ribeirão Preto, SP, Brazil. mocampiteli@gmail.com', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-270', '19712487', 'Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges.'),
(92998, 2009, 'bmc', 'An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis sigma66 promoters', 'Mallios, Ronna R|Ojcius, David M|Ardell, David H', 'School of Natural Sciences, University of California, Merced, CA 95344, USA. rmallios@fresno.ucsf.edu|School of Natural Sciences, University of California, Merced, CA 95344, USA. rmallios@fresno.ucsf.edu|School of Natural Sciences, University of California, Merced, CA 95344, USA. rmallios@fresno.ucsf.edu', 'United States|United States|United States', 11, 'Algorithms|Bacterial Proteins|Biophysics|Chlamydia trachomatis|Computational Biology|Genes, Bacterial|Genome, Bacterial|Markov Chains|Promoter Regions, Genetic|Sigma Factor', 'Journal Article', '10.1186/1471-2105-10-271', '19715597', 'Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase sigma-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort.'),
(92999, 2009, 'bmc', 'Correlating gene and protein expression data using Correlated Factor Analysis', 'Tan, Chuen Seng|Salim, Agus|Ploner, Alexander|Lehtiö, Janne|Chia, Kee Seng|Pawitan, Yudi', 'Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu|Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu|Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu|Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu|Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu|Lewis-Sigler Institute, Princeton University, New Jersey, USA. chuentan@princeton.edu', 'Jersey|Jersey|Jersey|Jersey|Jersey|Jersey', 0, 'Algorithms|Computational Biology|Gene Expression|Gene Expression Profiling|Proteins', 'Journal Article', '10.1186/1471-2105-10-272', '19723309', 'Joint analysis of transcriptomic and proteomic data taken from the same samples has the potential to elucidate complex biological mechanisms. Most current methods that integrate these datasets allow for the computation of the correlation between a gene and protein but only after a one-to-one matching of genes and proteins is done. However, genes and proteins are connected via biological pathways and their relationship is not necessarily one-to-one. In this paper, we investigate the use of Correlated Factor Analysis (CFA) for modeling the correlation of genome-scale gene and protein data. Unlike existing approaches, CFA considers all possible gene-protein pairs and utilizes all gene and protein information in its modeling framework. The Generalized Singular Value Decomposition (gSVD) is another method which takes into account all available transcriptomic and proteomic data. Comparison is made between CFA and gSVD.'),
(93000, 2009, 'bmc', 'The B6 database: a tool for the description and classification of vitamin B6-dependent enzymatic activities and of the corresponding protein families', 'Percudani, Riccardo|Peracchi, Alessio', 'Department of Biochemistry and Molecular Biology, University of Parma, 43100 Parma, Italy. riccardo.percudani@unipr.it|Department of Biochemistry and Molecular Biology, University of Parma, 43100 Parma, Italy. riccardo.percudani@unipr.it', 'Italy|Italy', 141, 'Binding Sites|Computational Biology|Databases, Factual|Databases, Protein|Proteins|Substrate Specificity|Vitamin B 6', 'Journal Article', '10.1186/1471-2105-10-273', '19723314', 'Enzymes that depend on vitamin B6 (and in particular on its metabolically active form, pyridoxal 5-phosphate, PLP) are of great relevance to biology and medicine, as they catalyze a wide variety of biochemical reactions mainly involving amino acid substrates. Although PLP-dependent enzymes belong to a small number of independent evolutionary lineages, they encompass more than 160 distinct catalytic functions, thus representing a striking example of divergent evolution. The importance and remarkable versatility of these enzymes, as well as the difficulties in their functional classification, create a need for an integrated source of information about them.'),
(93001, 2009, 'bmc', 'MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction', 'Blum, Torsten|Briesemeister, Sebastian|Kohlbacher, Oliver', 'Division for Simulation of Biological Systems, ZBIT/WSI, Eberhard-Karls-Universität Tübingen, Germany. blum@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, ZBIT/WSI, Eberhard-Karls-Universität Tübingen, Germany. blum@informatik.uni-tuebingen.de|Division for Simulation of Biological Systems, ZBIT/WSI, Eberhard-Karls-Universität Tübingen, Germany. blum@informatik.uni-tuebingen.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Databases, Protein|Phylogeny|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-10-274', '19723330', 'Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy.'),
(93002, 2009, 'bmc', 'Comparative study of gene set enrichment methods', 'Abatangelo, Luca|Maglietta, Rosalia|Distaso, Angela|DAddabbo, Annarita|Creanza, Teresa Maria|Mukherjee, Sayan|Ancona, Nicola', 'Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Bari, Italy. abatangelo@ba.issia.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 72, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Phenotype', 'Comparative Study', '10.1186/1471-2105-10-275', '19725948', 'The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fishers exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fishers exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.'),
(93003, 2009, 'bmc', 'Regression applied to protein binding site prediction and comparison with classification', 'Giard, Joachim|Ambroise, Jérôme|Gala, Jean-Luc|Macq, Benoît', 'Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. joachim.giard@uclouvain.be|Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. joachim.giard@uclouvain.be|Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. joachim.giard@uclouvain.be|Communications and Remote Sensing Laboratory, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium. joachim.giard@uclouvain.be', 'Belgium|Belgium|Belgium|Belgium', 8, 'Algorithms|Binding Sites|Classification|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Regression Analysis', 'Comparative Study', '10.1186/1471-2105-10-276', '19728868', 'The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools.'),
(93004, 2009, 'bmc', 'Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach', 'Wang, Wenhui|Nunez-Iglesias, Juan|Luan, Yihui|Sun, Fengzhu', 'School of Mathematics, Shandong University, Jinan, Shandong 250100, PR China. wwhlazio@mail.sdu.edu.cn|School of Mathematics, Shandong University, Jinan, Shandong 250100, PR China. wwhlazio@mail.sdu.edu.cn|School of Mathematics, Shandong University, Jinan, Shandong 250100, PR China. wwhlazio@mail.sdu.edu.cn|School of Mathematics, Shandong University, Jinan, Shandong 250100, PR China. wwhlazio@mail.sdu.edu.cn', 'China|China|China|China', 0, 'Computational Biology|Gene Regulatory Networks|Models, Statistical|Protein Interaction Mapping', 'Journal Article', '10.1186/1471-2105-10-277', '19728875', 'Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks.'),
(93005, 2009, 'bmc', 'Genephony: a knowledge management tool for genome-wide research', 'Nuzzo, Angelo|Riva, Alberto', 'Centre for Tissue Engineering, University of Pavia, Pavia, Italy. angelo.nuzzo@unipv.it|Centre for Tissue Engineering, University of Pavia, Pavia, Italy. angelo.nuzzo@unipv.it', 'Italy|Italy', 5, 'Computational Biology|Database Management Systems|Genome|Genomics|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-278', '19728881', 'One of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges.'),
(93006, 2009, 'bmc', 'XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services', 'Wagener, Johannes|Spjuth, Ola|Willighagen, Egon L|Wikberg, Jarl E S', 'Max von Pettenkofer-Institut, Ludwig-Maximilians-Universität, Munich, Germany. johannes.wagener@med.uni-muenchen.de|Max von Pettenkofer-Institut, Ludwig-Maximilians-Universität, Munich, Germany. johannes.wagener@med.uni-muenchen.de|Max von Pettenkofer-Institut, Ludwig-Maximilians-Universität, Munich, Germany. johannes.wagener@med.uni-muenchen.de|Max von Pettenkofer-Institut, Ludwig-Maximilians-Universität, Munich, Germany. johannes.wagener@med.uni-muenchen.de', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|Computer Communication Networks|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-279', '19732427', 'Life sciences make heavily use of the web for both data provision and analysis. However, the increasing amount of available data and the diversity of analysis tools call for machine accessible interfaces in order to be effective. HTTP-based Web service technologies, like the Simple Object Access Protocol (SOAP) and REpresentational State Transfer (REST) services, are today the most common technologies for this in bioinformatics. However, these methods have severe drawbacks, including lack of discoverability, and the inability for services to send status notifications. Several complementary workarounds have been proposed, but the results are ad-hoc solutions of varying quality that can be difficult to use.'),
(93007, 2009, 'bmc', 'EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments', 'Vollrath, Aaron L|Smith, Adam A|Craven, Mark|Bradfield, Christopher A', 'McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA. alvollra@wisc.edu|McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA. alvollra@wisc.edu|McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA. alvollra@wisc.edu|McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706, USA. alvollra@wisc.edu', 'United States|United States|United States|United States', 23, 'Computational Biology|Database Management Systems|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-280', '19732451', 'The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users.'),
(93008, 2008, 'bmc', 'Abstracts of UT-ORNL-KBRIN (University of Tennessee-Oak Ridge National Laboratory-Kentucky Bioinformatics Network) Bioinformatics Summit 2008 Cadiz, Kentucky, USA March 28-30, 2008', '-', '-', '-', 23, 'Computational Biology', 'Congress', '10.1186/1471-2105-10-280', '19732469', '-'),
(93009, 2009, 'bmc', 'Context-driven discovery of gene cassettes in mobile integrons using a computational grammar', 'Tsafnat, Guy|Coiera, Enrico|Partridge, Sally R|Schaeffer, Jaron|Iredell, Jon R', 'Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au|Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au|Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au|Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au|Centre for Health Informatics, Univ. of New South Wales, Sydney, NSW 2052, Australia. guyt@unsw.edu.au', 'Australia|Australia|Australia|Australia|Australia', 0, 'Computational Biology|DNA|Drug Resistance, Microbial|Genes|Integrons', 'Journal Article', '10.1186/1471-2105-10-281', '19735578', 'Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies.'),
(93010, 2009, 'bmc', 'Abstracts of UT-ORNL-KBRIN (University of Tennessee-Oak Ridge National Laboratory-Kentucky Bioinformatics Network) Bioinformatics Summit 2009 Pikeville, Tennessee, USA March 20-22, 2009', '-', '-', '-', 14, 'Computational Biology', 'Congress', '10.1186/1471-2105-10-281', '19735588', '-'),
(93011, 2009, 'bmc', 'Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics', 'Rè, Matteo|Pesole, Graziano|Horner, David S', 'Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano, Via Celoria 26, 20133 Milano, Italia. matteo.re@unimi.it|Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano, Via Celoria 26, 20133 Milano, Italia. matteo.re@unimi.it|Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano, Via Celoria 26, 20133 Milano, Italia. matteo.re@unimi.it', '-|-|-', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Conserved Sequence|Databases, Genetic|Evolution, Molecular|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-10-282', '19737408', 'The conservation of sequences between related genomes has long been recognised as an indication of functional significance and recognition of sequence homology is one of the principal approaches used in the annotation of newly sequenced genomes. In the context of recent findings that the number non-coding transcripts in higher organisms is likely to be much higher than previously imagined, discrimination between conserved coding and non-coding sequences is a topic of considerable interest. Additionally, it should be considered desirable to discriminate between coding and non-coding conserved sequences without recourse to the use of sequence similarity searches of protein databases as such approaches exclude the identification of novel conserved proteins without characterized homologs and may be influenced by the presence in databases of sequences which are erroneously annotated as coding.'),
(93012, 2009, 'bmc', 'RRW: repeated random walks on genome-scale protein networks for local cluster discovery', 'Macropol, Kathy|Can, Tolga|Singh, Ambuj K', 'Department of Computer Science, University of California, Santa Barbara, CA 93106, USA. kpm@cs.ucsb.edu|Department of Computer Science, University of California, Santa Barbara, CA 93106, USA. kpm@cs.ucsb.edu|Department of Computer Science, University of California, Santa Barbara, CA 93106, USA. kpm@cs.ucsb.edu', 'United States|United States|United States', 131, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Genome|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-283', '19740439', 'We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins.'),
(93013, 2009, 'bmc', 'CASSIOPE: an expert system for conserved regions searches', 'Rascol, Virginie Lopez|Levasseur, Anthony|Chabrol, Olivier|Grusea, Simona|Gouret, Philippe|Danchin, Etienne G J|Pontarotti, Pierre', 'EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr|EBM UMR 6632 LATP, 3 place V Hugo - 13 331 Marseille cedex 03 France. virginie.lopez-rascol@univ-provence.fr', 'France|France|France|France|France|France|France', 0, 'Computational Biology|Conserved Sequence|Genome|Genomics|Phylogeny|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-10-284', '19740451', 'Understanding genome evolution provides insight into biological mechanisms. For many years comparative genomics and analysis of conserved chromosomal regions have helped to unravel the mechanisms involved in genome evolution and their implications for the study of biological systems. Detection of conserved regions (descending from a common ancestor) not only helps clarify genome evolution but also makes it possible to identify quantitative trait loci (QTLs) and investigate gene function.The identification and comparison of conserved regions on a genome scale is computationally intensive, making process automation essential. Three key requirements are necessary: consideration of phylogeny to identify orthologs between multiple species, frequent updating of the annotation and panel of compared genomes and computation of statistical tests to assess the significance of identified conserved gene clusters.'),
(93014, 2009, 'bmc', 'ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites', 'Li, Chao|Li, Yuhua|Zhang, Xiangmin|Stafford, Phillip|Dinu, Valentin', 'Department of Biomedical Informatics, Ira A Fulton School Engineering, Arizona State University, Phoenix, AZ 85004, USA. Charles.Li@asu.edu|Department of Biomedical Informatics, Ira A Fulton School Engineering, Arizona State University, Phoenix, AZ 85004, USA. Charles.Li@asu.edu|Department of Biomedical Informatics, Ira A Fulton School Engineering, Arizona State University, Phoenix, AZ 85004, USA. Charles.Li@asu.edu|Department of Biomedical Informatics, Ira A Fulton School Engineering, Arizona State University, Phoenix, AZ 85004, USA. Charles.Li@asu.edu|Department of Biomedical Informatics, Ira A Fulton School Engineering, Arizona State University, Phoenix, AZ 85004, USA. Charles.Li@asu.edu', 'United States|United States|United States|United States|United States', 2, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|DNA|DNA Restriction Enzymes|Databases, Protein|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-286', '19747395', 'Restriction enzymes can produce easily definable segments from DNA sequences by using a variety of cut patterns. There are, however, no software tools that can aid in gene building -- that is, modifying wild-type DNA sequences to express the same wild-type amino acid sequences but with enhanced codons, specific cut sites, unique post-translational modifications, and other engineered-in components for recombinant applications. A fast DNA pattern design algorithm, ICRPfinder, is provided in this paper and applied to find or create potential recognition sites in target coding sequences.'),
(93015, 2009, 'bmc', 'Epitopia: a web-server for predicting B-cell epitopes', 'Rubinstein, Nimrod D|Mayrose, Itay|Martz, Eric|Pupko, Tal', 'Department of Cell Research and Immunology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rubi@post.tau.ac.il|Department of Cell Research and Immunology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rubi@post.tau.ac.il|Department of Cell Research and Immunology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rubi@post.tau.ac.il|Department of Cell Research and Immunology, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel. rubi@post.tau.ac.il', 'Israel|Israel|Israel|Israel', 122, 'Algorithms|Artificial Intelligence|Computational Biology|Epitopes, B-Lymphocyte|Internet|Software', 'Journal Article', '10.1186/1471-2105-10-287', '19751513', 'Detecting candidate B-cell epitopes in a protein is a basic and fundamental step in many immunological applications. Due to the impracticality of experimental approaches to systematically scan the entire protein, a computational tool that predicts the most probable epitope regions is desirable.'),
(93016, 2009, 'bmc', 'Exploratory and inferential analysis of gene cluster neighborhood graphs', 'Scharl, Theresa|Voglhuber, Ingo|Leisch, Friedrich', 'Department of Statistics and Probability Theory, Vienna University of Technology, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at|Department of Statistics and Probability Theory, Vienna University of Technology, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at|Department of Statistics and Probability Theory, Vienna University of Technology, A-1040 Vienna, Austria. theresa.scharl@ci.tuwien.ac.at', 'Austria|Austria|Austria', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Multigene Family|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-10-288', '19751523', 'Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.'),
(93017, 2009, 'bmc', 'In Silico screening for functional candidates amongst hypothetical proteins', 'Desler, Claus|Suravajhala, Prashanth|Sanderhoff, May|Rasmussen, Merete|Rasmussen, Lene Juel', 'Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark. cdesler@ruc.dk|Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark. cdesler@ruc.dk|Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark. cdesler@ruc.dk|Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark. cdesler@ruc.dk|Department of Science, Systems and Models, Roskilde University, DK-4000 Roskilde, Denmark. cdesler@ruc.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 26, 'Computational Biology|Databases, Protein|Humans|Open Reading Frames|Proteins|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-10-289', '19754976', 'The definition of a hypothetical protein is a protein that is predicted to be expressed from an open reading frame, but for which there is no experimental evidence of translation. Hypothetical proteins constitute a substantial fraction of proteomes of human as well as of other eukaryotes. With the general belief that the majority of hypothetical proteins are the product of pseudogenes, it is essential to have a tool with the ability of pinpointing the minority of hypothetical proteins with a high probability of being expressed.'),
(93018, 2009, 'bmc', 'Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information', 'Acencio, Marcio L|Lemke, Ney', 'Department of Physics and Biophysics, São Paulo State University, Botucatu, São Paulo, Brazil. mlacencio@ibb.unesp.br|Department of Physics and Biophysics, São Paulo State University, Botucatu, São Paulo, Brazil. mlacencio@ibb.unesp.br', 'Brazil|Brazil', 119, 'Algorithms|Computational Biology|Gene Regulatory Networks|Genes, Essential', 'Journal Article', '10.1186/1471-2105-10-290', '19758426', 'The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.'),
(93019, 2009, 'bmc', 'Automated seeding of specialised wiki knowledgebases with BioKb', 'Manning, Jonathan R|Hedley, Ann|Mullins, John J|Dunbar, Donald R', 'Centres for Cardiovascular Science and Inflammation Research, Queens Medical Research Institute, University of Edinburgh, EH16 4TJ, UK. jmanning@staffmail.ed.ac.uk|Centres for Cardiovascular Science and Inflammation Research, Queens Medical Research Institute, University of Edinburgh, EH16 4TJ, UK. jmanning@staffmail.ed.ac.uk|Centres for Cardiovascular Science and Inflammation Research, Queens Medical Research Institute, University of Edinburgh, EH16 4TJ, UK. jmanning@staffmail.ed.ac.uk|Centres for Cardiovascular Science and Inflammation Research, Queens Medical Research Institute, University of Edinburgh, EH16 4TJ, UK. jmanning@staffmail.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Knowledge Bases|Software', 'Journal Article', '10.1186/1471-2105-10-291', '19758431', 'Wiki technology has become a ubiquitous mechanism for dissemination of information, and places strong emphasis on collaboration. We aimed to leverage wiki technology to allow small groups of researchers to collaborate around a specific domain, for example a biological pathway. Automatically gathered seed data could be modified by the group and enriched with domain specific information.'),
(93020, 2009, 'bmc', 'A first principles approach to differential expression in microarray data analysis', 'Rubin, Robert A', 'Mathematics Department, Whittier College, 13406 E. Philadelphia St., Whittier, CA 90608, USA. brubin698@earthlink.net', 'United States', 3, 'Computational Biology|Gene Expression Profiling|Microarray Analysis', 'Journal Article', '10.1186/1471-2105-10-292', '19758448', 'The disparate results from the methods commonly used to determine differential expression in Affymetrix microarray experiments may well result from the wide variety of probe set and probe level models employed. Here we take the approach of making the fewest assumptions about the structure of the microarray data. Specifically, we only require that, under the null hypothesis that a gene is not differentially expressed for specified conditions, for any probe position in the genes probe set: a) the probe amplitudes are independent and identically distributed over the conditions, and b) the distributions of the replicated probe amplitudes are amenable to classical analysis of variance (ANOVA). Log-amplitudes that have been standardized within-chip meet these conditions well enough for our approach, which is to perform ANOVA across conditions for each probe position, and then take the median of the resulting (1 - p) values as a gene-level measure of differential expression.'),
(93021, 2009, 'bmc', 'Efficient oligonucleotide probe selection for pan-genomic tiling arrays', 'Phillippy, Adam M|Deng, Xiangyu|Zhang, Wei|Salzberg, Steven L', 'Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. amp@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. amp@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. amp@umiacs.umd.edu|Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. amp@umiacs.umd.edu', 'United States|United States|United States|United States', 19, 'Algorithms|Computational Biology|Genome|Listeria monocytogenes|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes', 'Journal Article', '10.1186/1471-2105-10-293', '19758451', 'Array comparative genomic hybridization is a fast and cost-effective method for detecting, genotyping, and comparing the genomic sequence of unknown bacterial isolates. This method, as with all microarray applications, requires adequate coverage of probes targeting the regions of interest. An unbiased tiling of probes across the entire length of the genome is the most flexible design approach. However, such a whole-genome tiling requires that the genome sequence is known in advance. For the accurate analysis of uncharacterized bacteria, an array must query a fully representative set of sequences from the species pan-genome. Prior microarrays have included only a single strain per array or the conserved sequences of gene families. These arrays omit potentially important genes and sequence variants from the pan-genome.'),
(93022, 2009, 'bmc', 'Between proteins and phenotypes: annotation and interpretation of mutations', 'Baker, Christopher J O|Rebholz-Schuhmann, Dietrich', 'University of New Brunswick, Saint John, Canada. bakerc@unb.ca|University of New Brunswick, Saint John, Canada. bakerc@unb.ca', 'Canada|Canada', 0, 'Computational Biology|Databases, Protein|Documentation|Genomics|Mutation|Phenotype|Proteins|Terminology as Topic', 'Introductory Journal Article', '10.1186/1471-2105-10-S8-I1', '19758463', '-'),
(93023, 2009, 'bmc', 'Extraction of human kinase mutations from literature, databases and genotyping studies', 'Krallinger, Martin|Izarzugaza, Jose M G|Rodriguez-Penagos, Carlos|Valencia, Alfonso', 'Spanish National Cancer Research Centre, Madrid, Spain. mkrallinger@cnio.es|Spanish National Cancer Research Centre, Madrid, Spain. mkrallinger@cnio.es|Spanish National Cancer Research Centre, Madrid, Spain. mkrallinger@cnio.es|Spanish National Cancer Research Centre, Madrid, Spain. mkrallinger@cnio.es', 'Spain|Spain|Spain|Spain', 0, 'Databases, Genetic|ErbB Receptors|Genomics|Genotype|Humans|Information Storage and Retrieval|Mutation|Periodicals as Topic|Protein Kinases|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S8-S1', '19758464', 'There is a considerable interest in characterizing the biological role of specific protein residue substitutions through mutagenesis experiments. Additionally, recent efforts related to the detection of disease-associated SNPs motivated both the manual annotation, as well as the automatic extraction, of naturally occurring sequence variations from the literature, especially for protein families that play a significant role in signaling processes such as kinases. Systematic integration and comparison of kinase mutation information from multiple sources, covering literature, manual annotation databases and large-scale experiments can result in a more comprehensive view of functional, structural and disease associated aspects of protein sequence variants. Previously published mutation extraction approaches did not sufficiently distinguish between two fundamentally different variation origin categories, namely natural occurring and induced mutations generated through in vitro experiments.'),
(93024, 2009, 'bmc', 'Pairwise and higher-order correlations among drug-resistance mutations in HIV-1 subtype B protease', 'Haq, Omar|Levy, Ronald M|Morozov, Alexandre V|Andrec, Michael', 'BioMaPS Institute for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, 08854, USA. omarhaq@rci.rutgers.edu|BioMaPS Institute for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, 08854, USA. omarhaq@rci.rutgers.edu|BioMaPS Institute for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, 08854, USA. omarhaq@rci.rutgers.edu|BioMaPS Institute for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, 08854, USA. omarhaq@rci.rutgers.edu', 'Jersey|Jersey|Jersey|Jersey', 23, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Drug Resistance, Viral|HIV Protease|HIV Protease Inhibitors|HIV-1|Linear Models|Models, Molecular|Mutation|Sequence Alignment|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-S8-S10', '19758465', 'The reaction of HIV protease to inhibitor therapy is characterized by the emergence of complex mutational patterns which confer drug resistance. The response of HIV protease to drugs often involves both primary mutations that directly inhibit the action of the drug, and a host of accessory resistance mutations that may occur far from the active site but may contribute to restoring the fitness or stability of the enzyme. Here we develop a probabilistic approach based on connected information that allows us to study residue, pair level and higher-order correlations within the same framework.'),
(93025, 2009, 'bmc', 'EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts', 'Yeniterzi, Süveyda|Sezerman, Ugur', 'Sabanci University, Istanbul, Turkey. suveyday@su.sabanciuniv.edu|Sabanci University, Istanbul, Turkey. suveyday@su.sabanciuniv.edu', 'Turkey|Turkey', 18, 'Algorithms|Amino Acid Sequence|Amylases|Computational Biology|Enzymes|Genomics|Information Storage and Retrieval|Lipase|Mutation|Pattern Recognition, Automated|Periodicals as Topic|Proteins|PubMed|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-S8-S2', '19758466', 'A better understanding of the mechanisms of an enzymes functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme.'),
(93026, 2009, 'bmc', 'Improved mutation tagging with gene identifiers applied to membrane protein stability prediction', 'Winnenburg, Rainer|Plake, Conrad|Schroeder, Michael', 'Biotechnology Center, Technische Universität Dresden, Tatzberg, Germany. rainer.winnenburg@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Tatzberg, Germany. rainer.winnenburg@biotec.tu-dresden.de|Biotechnology Center, Technische Universität Dresden, Tatzberg, Germany. rainer.winnenburg@biotec.tu-dresden.de', 'Germany|Germany|Germany', 17, 'Algorithms|Amino Acid Substitution|Animals|Computational Biology|Databases, Genetic|Genes|Genomics|Humans|Information Storage and Retrieval|Membrane Proteins|Models, Genetic|Mutation|Pattern Recognition, Automated|Periodicals as Topic|Phenotype|Point Mutation|Protein Stability|PubMed|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S8-S3', '19758467', 'The automated retrieval and integration of information about protein point mutations in combination with structure, domain and interaction data from literature and databases promises to be a valuable approach to study structure-function relationships in biomedical data sets.'),
(93027, 2009, 'bmc', 'Annotation of protein residues based on a literature analysis: cross-validation against UniProtKb', 'Nagel, Kevin|Jimeno-Yepes, Antonio|Rebholz-Schuhmann, Dietrich', 'European Bioinformatics Institute, Hinxton, Cambridge, UK. auyeung@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, UK. auyeung@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, UK. auyeung@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 19, 'Amino Acids|Computational Biology|Databases, Protein|Fuzzy Logic|Information Storage and Retrieval|Pattern Recognition, Automated|Proteins|Proteomics|Reproducibility of Results|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-10-S8-S4', '19758468', 'A protein annotation database, such as the Universal Protein Resource knowledge base (UniProtKb), is a valuable resource for the validation and interpretation of predicted 3D structure patterns in proteins. Existing studies have focussed on point mutation extraction methods from biomedical literature which can be used to support the time consuming work of manual database curation. However, these methods were limited to point mutation extraction and do not extract features for the annotation of proteins at the residue level.'),
(93028, 2009, 'bmc', 'An integrated approach to the interpretation of single amino acid polymorphisms within the framework of CATH and Gene3D', 'Izarzugaza, Jose M G|Baresic, Anja|McMillan, Lisa E M|Yeats, Corin|Clegg, Andrew B|Orengo, Christine A|Martin, Andrew C R|Valencia, Alfonso', 'Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es|Institute of Structural and Molecular Biology, University College London, UK. jmgonzalez@cnio.es', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Amino Acid Substitution|Databases, Protein|Information Storage and Retrieval|Internet|Models, Molecular|Mutation|Phenotype|Proteins', 'Journal Article', '10.1186/1471-2105-10-S8-S5', '19758469', 'The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93029, 2009, 'bmc', 'From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways', 'Bauer-Mehren, Anna|Furlong, Laura I|Rautschka, Michael|Sanz, Ferran', 'IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona, Spain. anna.bauer-mehren@upf.edu|IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona, Spain. anna.bauer-mehren@upf.edu|IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona, Spain. anna.bauer-mehren@upf.edu|IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona, Spain. anna.bauer-mehren@upf.edu', 'Spain|Spain|Spain|Spain', 21, 'Computer Simulation|ErbB Receptors|Information Storage and Retrieval|Models, Biological|Phenotype|Polymorphism, Single Nucleotide|Proteins|Sequence Analysis, Protein|Signal Transduction|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S8-S6', '19758470', 'Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks.'),
(93030, 2009, 'bmc', 'MtSNPscore: a combined evidence approach for assessing cumulative impact of mitochondrial variations in disease', 'Bhardwaj, Anshu|Mukerji, Mitali|Sharma, Shipra|Paul, Jinny|Gokhale, Chaitanya S|Srivastava, Achal K|Tiwari, Shrish', 'Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in|Institute of Genomics and Integrative Biology, CSIR, Delhi, India. anshu@igib.res.in', 'India|India|India|India|India|India|India', 19, 'Alzheimer Disease|Ataxia|Computational Biology|Computer Simulation|DNA, Mitochondrial|Diabetes Mellitus, Type 2|Disease|Genes, Mitochondrial|Genetic Predisposition to Disease|Humans|Male|Mutation|Obesity|Parkinson Disease|Phenotype|Polymorphism, Single Nucleotide|Predictive Value of Tests', 'Journal Article', '10.1186/1471-2105-10-S8-S7', '19758471', 'Human mitochondrial DNA (mtDNA) variations have been implicated in a broad spectrum of diseases. With over 3000 mtDNA variations reported across databases, establishing pathogenicity of variations in mtDNA is a major challenge. We have designed and developed a comprehensive weighted scoring system (MtSNPscore) for identification of mtDNA variations that can impact pathogenicity and would likely be associated with disease. The criteria for pathogenicity include information available in the literature, predictions made by various in silico tools and frequency of variation in normal and patient datasets. The scoring scheme also assigns scores to patients and normal individuals to estimate the cumulative impact of variations. The method has been implemented in an automated pipeline and has been tested on Indian ataxia dataset (92 individuals), sequenced in this study, and other publicly available mtSNP dataset comprising of 576 mitochondrial genomes of Japanese individuals from six different groups, namely, patients with Parkinsons disease, patients with Alzheimers disease, young obese males, young non-obese males, and type-2 diabetes patients with or without severe vascular involvement. MtSNPscore, for analysis can extract information from variation data or from mitochondrial DNA sequences. It has a web-interface http://bioinformatics.ccmb.res.in/cgi-bin/snpscore/Mtsnpscore.pl that provides flexibility to update/modify the parameters for estimating pathogenicity.'),
(93031, 2009, 'bmc', 'Correlating protein function and stability through the analysis of single amino acid substitutions', 'Bromberg, Yana|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. bromberg@rostlab.org|Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA. bromberg@rostlab.org', 'United States|United States', 40, 'Amino Acid Substitution|Computational Biology|Databases, Protein|Information Storage and Retrieval|Polymorphism, Single Nucleotide|Protein Stability|Proteins|Sequence Alignment|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-10-S8-S8', '19758472', 'Mutations resulting in the disruption of protein function are the underlying causes of many genetic diseases. Some mutations affect the number of expressed proteins while others alter the activity on a per-molecule basis. Single amino acid substitutions as caused by non-synonymous Single Nucleotide Polymorphisms (nsSNPs) often disrupt function by altering protein structure and/or stability, but can also wreak havoc by directly impacting functional binding sites. Given the experimental three-dimensional (3D) structure of a protein, we can try to differentiate between the effect on structure/stability and the effect on binding. However, experimental 3D structures are available for only 1% of all known proteins, the magnitude of stability change caused by a given mutation is more widely available.'),
(93032, 2009, 'bmc', 'Using structural bioinformatics to investigate the impact of non synonymous SNPs and disease mutations: scope and limitations', 'Reumers, Joke|Schymkowitz, Joost|Rousseau, Fréderic', 'VIB, Vrije Universiteit Brussel, Brussels, Belgium. joke.reumers@switch.vib-vub.be|VIB, Vrije Universiteit Brussel, Brussels, Belgium. joke.reumers@switch.vib-vub.be|VIB, Vrije Universiteit Brussel, Brussels, Belgium. joke.reumers@switch.vib-vub.be', 'Belgium|Belgium|Belgium', 21, 'Algorithms|Computational Biology|Databases, Genetic|Disease|Humans|Mutation|Polymorphism, Single Nucleotide|Protein Multimerization|Proteins|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-S8-S9', '19758473', 'Linking structural effects of mutations to functional outcomes is a major issue in structural bioinformatics, and many tools and studies have shown that specific structural properties such as stability and residue burial can be used to distinguish neutral variations and disease associated mutations.'),
(93033, 2009, 'bmc', 'Selected proceedings of the 2009 Summit on Translational Bioinformatics', 'Lussier, Yves A|Sarkar, Indra Neil', 'Center for Biomedical Informatics and Section of Genetic Medicine of Department of Medicine, Institute for Translational Medicine, and Ludwig Center for Metastasis Research, Cancer Research Center, University of Chicago, Chicago, IL 60637, USA. lussier@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine of Department of Medicine, Institute for Translational Medicine, and Ludwig Center for Metastasis Research, Cancer Research Center, University of Chicago, Chicago, IL 60637, USA. lussier@uchicago.edu', 'United States|United States', 0, 'Computational Biology|Congresses as Topic|Genome', 'Introductory Journal Article', '10.1186/1471-2105-10-S9-I1', '19761562', '-'),
(93034, 2009, 'bmc', 'Transcriptional network classifiers', 'Chang, Hsun-Hsien|Ramoni, Marco F', 'Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. hsun-hsien.chang@childrens.harvard.edu', 'United States|United States', 5, 'Aortic Aneurysm, Thoracic|Bayes Theorem|Gene Regulatory Networks|Humans|Lung Neoplasms|Systems Biology|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-S9-S1', '19761563', 'Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods.'),
(93035, 2009, 'bmc', 'Evaluation of a large-scale biomedical data annotation initiative', 'Lacson, Ronilda|Pitzer, Erik|Hinske, Christian|Galante, Pedro|Ohno-Machado, Lucila', 'Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. rlacson@dsg.harvard.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. rlacson@dsg.harvard.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. rlacson@dsg.harvard.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. rlacson@dsg.harvard.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. rlacson@dsg.harvard.edu', 'United States|United States|United States|United States|United States', 8, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-10-S9-S10', '19761564', 'This study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is described for creating an annotation scheme for various diseases that is flexible, comprehensive, and scalable. The annotation structure is evaluated by measuring coverage and agreement between annotators.'),
(93036, 2009, 'bmc', 'Evaluating the accuracy of a functional SNP annotation system', 'Shen, Terry H|Carlson, Christopher S|Tarczy-Hornoch, Peter', 'Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. hyshen@u.washington.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. hyshen@u.washington.edu|Departments of Biomedical & Health Informatics, University of Washington, Seattle, WA, USA. hyshen@u.washington.edu', 'United States|United States|United States', 1, 'Computational Biology|Databases, Genetic|Genetic Variation|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-10-S9-S11', '19761565', 'Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results.'),
(93037, 2009, 'bmc', 'Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease', 'South, Brett R|Shen, Shuying|Jones, Makoto|Garvin, Jennifer|Samore, Matthew H|Chapman, Wendy W|Gundlapalli, Adi V', 'VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu|VA Salt Lake City Health Care System, IDEAS Center, Salt Lake City, Utah 84148, USA. brett.south@hsc.utah.edu', 'United States|United States|United States|United States|United States|United States|United States', 19, 'Computational Biology|Humans|Inflammatory Bowel Diseases|Phenotype|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S9-S12', '19761566', 'Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.'),
(93038, 2009, 'bmc', 'Characterizing environmental and phenotypic associations using information theory and electronic health records', 'Wang, Xiaoyan|Hripcsak, George|Friedman, Carol', 'Dept of Biomedical Informatics, Columbia University, New York 10032, USA. xiaoyan.wang@dbmi.columbia.edu|Dept of Biomedical Informatics, Columbia University, New York 10032, USA. xiaoyan.wang@dbmi.columbia.edu|Dept of Biomedical Informatics, Columbia University, New York 10032, USA. xiaoyan.wang@dbmi.columbia.edu', 'United States|United States|United States', 25, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Information Theory|Medical Records Systems, Computerized|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-10-S9-S13', '19761567', 'The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data.'),
(93039, 2009, 'bmc', 'Comparison of concept recognizers for building the Open Biomedical Annotator', 'Shah, Nigam H|Bhatia, Nipun|Jonquet, Clement|Rubin, Daniel|Chiang, Annie P|Musen, Mark A', 'Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Centre for Biomedical Informatics, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu', 'United States|United States|United States|United States|United States|United States', 85, 'Abstracting and Indexing as Topic|Computational Biology|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|Medical Informatics|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-10-S9-S14', '19761568', 'The National Center for Biomedical Ontology (NCBO) is developing a system for automated, ontology-based access to online biomedical resources (Shah NH, et al.: Ontology-driven indexing of public datasets for translational bioinformatics. BMC Bioinformatics 2009, 10(Suppl 2):S1). The systems indexing workflow processes the text metadata of diverse resources such as datasets from GEO and ArrayExpress to annotate and index them with concepts from appropriate ontologies. This indexing requires the use of a concept-recognition tool to identify ontology concepts in the resources textual metadata. In this paper, we present a comparison of two concept recognizers - NLMs MetaMap and the University of Michigans Mgrep. We utilize a number of data sources and dictionaries to evaluate the concept recognizers in terms of precision, recall, speed of execution, scalability and customizability. Our evaluations demonstrate that Mgrep has a clear edge over MetaMap for large-scale service oriented applications. Based on our analysis we also suggest areas of potential improvements for Mgrep. We have subsequently used Mgrep to build the Open Biomedical Annotator service. The Annotator service has access to a large dictionary of biomedical terms derived from the United Medical Language System (UMLS) and NCBO ontologies. The Annotator also leverages the hierarchical structure of the ontologies and their mappings to expand annotations. The Annotator service is available to the community as a REST Web service for creating ontology-based annotations of their data.'),
(93040, 2009, 'bmc', 'Feature selection for fMRI-based deception detection', 'Jin, Bo|Strasburger, Alvin|Laken, Steven J|Kozel, F Andrew|Johnson, Kevin A|George, Mark S|Lu, Xinghua', 'Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu|Medical University of South Carolina, Charleston, SC, USA. jinbo@musc.edu', 'United States|United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Artificial Intelligence|Brain|Brain Mapping|Computational Biology|Magnetic Resonance Imaging', 'Journal Article', '10.1186/1471-2105-10-S9-S15', '19761569', 'Functional magnetic resonance imaging (fMRI) is a technology used to detect brain activity. Patterns of brain activation have been utilized as biomarkers for various neuropsychiatric applications. Detecting deception based on the pattern of brain activation characterized with fMRI is getting attention - with machine learning algorithms being applied to this field in recent years. The high dimensionality of fMRI data makes it a difficult task to directly utilize the original data as input for classification algorithms in detecting deception. In this paper, we investigated the procedures of feature selection to enhance fMRI-based deception detection.'),
(93041, 2009, 'bmc', 'Knowledge-based variable selection for learning rules from proteomic data', 'Lustgarten, Jonathan L|Visweswaran, Shyam|Bowser, Robert P|Hogan, William R|Gopalakrishnan, Vanathi', 'Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave, Parkvale M-183, Pittsburgh, PA, USA. JLL47@pitt.edu', 'United States|United States|United States|United States|United States', 3, 'Algorithms|Computational Biology|Pattern Recognition, Automated|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-10-S9-S16', '19761570', 'The incorporation of biological knowledge can enhance the analysis of biomedical data. We present a novel method that uses a proteomic knowledge base to enhance the performance of a rule-learning algorithm in identifying putative biomarkers of disease from high-dimensional proteomic mass spectral data. In particular, we use the Empirical Proteomics Ontology Knowledge Base (EPO-KB) that contains previously identified and validated proteomic biomarkers to select m/zs in a proteomic dataset prior to analysis to increase performance.'),
(93042, 2009, 'bmc', 'Structural similarity assessment for drug sensitivity prediction in cancer', 'Shivakumar, Pavithra|Krauthammer, Michael', 'Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. pavithra.shivakumar@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. pavithra.shivakumar@yale.edu', 'United States|United States', 0, 'Computational Biology|Databases, Factual|Drug Screening Assays, Antitumor|Humans|National Cancer Institute (U.S.)|Neoplasms|United States', 'Journal Article', '10.1186/1471-2105-10-S9-S17', '19761571', 'The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.'),
(93043, 2009, 'bmc', 'Analysis of AML genes in dysregulated molecular networks', 'Lee, Eunjung|Jung, Hyunchul|Radivojac, Predrag|Kim, Jong-Won|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea. ejlee@biosoft.kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea. ejlee@biosoft.kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea. ejlee@biosoft.kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea. ejlee@biosoft.kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea. ejlee@biosoft.kaist.ac.kr', '-|-|-|-|-', 0, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Leukemia, Myeloid, Acute|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-10-S9-S2', '19761572', 'Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.'),
(93044, 2009, 'bmc', 'Network analysis of genes regulated in renal diseases: implications for a molecular-based classification', 'Bhavnani, Suresh K|Eichinger, Felix|Martini, Sebastian|Saxman, Paul|Jagadish, H V|Kretzler, Matthias', 'Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu|Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu|Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu|Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu|Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu|Center for Computational Medicine & Bioinformatics, 24 Frank Lloyd Wright Dr, Dominos Farm, Lobby L, Ann Arbor, MI 48109-0738, USA. bhavnani@umich.edu', 'United States|United States|United States|United States|United States|United States', 22, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Kidney Diseases|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S9-S3', '19761573', 'Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases.'),
(93045, 2009, 'bmc', 'Infection in the intensive care unit alters physiological networks', 'Grossman, Adam D|Cohen, Mitchell J|Manley, Geoffrey T|Butte, Atul J', 'Department of Bioengineering, Stanford University, Stanford, California 94305, USA. agrossma@stanford.edu|Department of Bioengineering, Stanford University, Stanford, California 94305, USA. agrossma@stanford.edu|Department of Bioengineering, Stanford University, Stanford, California 94305, USA. agrossma@stanford.edu|Department of Bioengineering, Stanford University, Stanford, California 94305, USA. agrossma@stanford.edu', 'United States|United States|United States|United States', 3, 'Computational Biology|Health Plan Implementation|Humans|Infection|Intensive Care Units|Monitoring, Physiologic|Treatment Outcome', 'Journal Article', '10.1186/1471-2105-10-S9-S4', '19761574', 'Physicians use clinical and physiological data to treat patients every day, and it is essential for treating a patient appropriately. However, medical sources of clinical physiological data are only now starting to find use in bioinformatics research.'),
(93046, 2009, 'bmc', 'A systems biology approach to the identification and analysis of transcriptional regulatory networks in osteocytes', 'Dean, Angela K|Harris, Stephen E|Kalajzic, Ivo|Ruan, Jianhua', 'Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. adean@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. adean@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. adean@cs.utsa.edu|Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. adean@cs.utsa.edu', 'United States|United States|United States|United States', 0, 'Cell Differentiation|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Osteocytes|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S9-S5', '19761575', 'The osteocyte is a type of cell that appears to be one of the key endocrine regulators of bone metabolism and a key responder to initiate bone formation and remodeling. Identifying the regulatory networks in osteocytes may lead to new therapies for osteoporosis and loss of bone.'),
(93047, 2009, 'bmc', 'Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia', 'Yang, Xinan|Huang, Yong|Chen, James L|Xie, Jianming|Sun, Xiao|Lussier, Yves A', 'Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu|Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA. xyang2@uchicago.edu', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Epigenesis, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma', 'Journal Article', '10.1186/1471-2105-10-S9-S6', '19761576', 'Current outcome predictors based on molecular profiling rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients - a paradigm shift towards accurate mechanism-anchored profiling. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.'),
(93048, 2009, 'bmc', 'PAPAyA: a platform for breast cancer biomarker signature discovery, evaluation and assessment', 'Janevski, Angel|Kamalakaran, Sitharthan|Banerjee, Nilanjana|Varadan, Vinay|Dimitrova, Nevenka', 'Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com|Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com|Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com|Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com|Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY10510, USA. angel.janevski@philips.com', 'United States|United States|United States|United States|United States', 0, 'Biomarkers|Breast Neoplasms|Computational Biology|Databases, Genetic|Decision Support Techniques|Gene Expression Profiling|Humans|Software', 'Journal Article', '10.1186/1471-2105-10-S9-S7', '19761577', 'The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions.'),
(93049, 2009, 'bmc', 'The potential for automated question answering in the context of genomic medicine: an assessment of existing resources and properties of answers', 'Overby, Casey Lynnette|Tarczy-Hornoch, Peter|Demner-Fushman, Dina', 'Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu|Department of Medical Education & Biomedical Informatics, University of Washington, Seattle, WA, USA. overbyc@uw.edu', 'United States|United States|United States', 6, 'Algorithms|Databases, Factual|Electronic Data Processing|Genomics|Information Storage and Retrieval|Medical Informatics|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S9-S8', '19761578', 'Knowledge gained in studies of genetic disorders is reported in a growing body of biomedical literature containing reports of genetic variation in individuals that map to medical conditions and/or response to therapy. These scientific discoveries need to be translated into practical applications to optimize patient care. Translating research into practice can be facilitated by supplying clinicians with research evidence. We assessed the role of existing tools in extracting answers to translational research questions in the area of genomic medicine. We: evaluate the coverage of translational research terms in the Unified Medical Language Systems (UMLS) Metathesaurus, determine where answers are most often found in full-text articles, and determine common answer patterns. Findings suggest that we will be able to leverage the UMLS in development of natural language processing algorithms for automated extraction of answers to translational research questions from biomedical text in the area of genomic medicine.'),
(93050, 2009, 'bmc', 'Towards large-scale sample annotation in gene expression repositories', 'Pitzer, Erik|Lacson, Ronilda|Hinske, Christian|Kim, Jihoon|Galante, Pedro Af|Ohno-Machado, Lucila', 'Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at|Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at|Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at|Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at|Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at|Decision Systems Group, Brigham and Womens Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S9-S9', '19761579', 'Large repositories of biomedical research data are most useful to translational researchers if their data can be aggregated for efficient queries and analyses. However, inconsistent or non-existent annotations describing important sample details such as name of tissue or cell line, histopathological type, and subject characteristics like demographics, treatment, and survival are seldom present in data repositories, making it difficult to aggregate data.'),
(93051, 2009, 'bmc', 'Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses', 'Wongseree, Waranyu|Assawamakin, Anunchai|Piroonratana, Theera|Sinsomros, Saravudh|Limwongse, Chanin|Chaiyaratana, Nachol', 'Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com|Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com|Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com|Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com|Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com|Department of Electrical Engineering, Faculty of Engineering, King Mongkuts University of Technology North Bangkok, Bangkok, Thailand. waranyu.wongseree@gmail.com', 'Thailand|Thailand|Thailand|Thailand|Thailand|Thailand', 27, 'Case-Control Studies|Computational Biology|Epistasis, Genetic|Genetic Predisposition to Disease|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-294', '19761607', 'Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.'),
(93052, 2009, 'bmc', 'Accurate microRNA target prediction correlates with protein repression levels', 'Maragkakis, Manolis|Alexiou, Panagiotis|Papadopoulos, Giorgio L|Reczko, Martin|Dalamagas, Theodore|Giannopoulos, George|Goumas, George|Koukis, Evangelos|Kourtis, Kornilios|Simossis, Victor A|Sethupathy, Praveen|Vergoulis, Thanasis|Koziris, Nectarios|Sellis, Timos|Tsanakas, Panagiotis|Hatzigeorgiou, Artemis G', 'Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr|Institute of Molecular Oncology, Biomedical Sciences Research Center Alexander Fleming, Vari, Greece. maragkakis@fleming.gr', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Greece|Gr', 273, 'Algorithms|Binding Sites|Computational Biology|MicroRNAs|Proteins|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-10-295', '19765283', 'MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.'),
(93053, 2009, 'bmc', 'NN-align An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction', 'Nielsen, Morten|Lund, Ole', 'Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark. mniel@cbs.dtu.dk|Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark. mniel@cbs.dtu.dk', 'Denmark|Denmark', 283, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Histocompatibility Antigens Class II|Neural Networks (Computer)|Peptides|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-296', '19765293', 'The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.'),
(93054, 2009, 'bmc', 'Finding local communities in protein networks', 'Voevodski, Konstantin|Teng, Shang-Hua|Xia, Yu', 'Department of Computer Science, Boston University, Boston, MA 02215, USA. kvodski@bu.edu|Department of Computer Science, Boston University, Boston, MA 02215, USA. kvodski@bu.edu|Department of Computer Science, Boston University, Boston, MA 02215, USA. kvodski@bu.edu', 'United States|United States|United States', 46, 'Algorithms|Computational Biology|Databases, Protein|Proteins', 'Journal Article', '10.1186/1471-2105-10-297', '19765306', 'Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks.'),
(93055, 2009, 'bmc', 'PhyloPattern: regular expressions to identify complex patterns in phylogenetic trees', 'Gouret, Philippe|Thompson, Julie D|Pontarotti, Pierre', 'UMR 6632, Evolutionary Biology and Modeling, University of Provence, Marseille, France. philippe.gouret@univ-provence.fr|UMR 6632, Evolutionary Biology and Modeling, University of Provence, Marseille, France. philippe.gouret@univ-provence.fr|UMR 6632, Evolutionary Biology and Modeling, University of Provence, Marseille, France. philippe.gouret@univ-provence.fr', 'France|France|France', 0, 'Algorithms|Computational Biology|Evolution, Molecular|Phylogeny|Software', 'Journal Article', '10.1186/1471-2105-10-298', '19765311', 'To effectively apply evolutionary concepts in genome-scale studies, large numbers of phylogenetic trees have to be automatically analysed, at a level approaching human expertise. Complex architectures must be recognized within the trees, so that associated information can be extracted.'),
(93056, 2009, 'bmc', 'BayesPeak: Bayesian analysis of ChIP-seq data', 'Spyrou, Christiana|Stark, Rory|Lynch, Andy G|Tavaré, Simon', 'Statistical Laboratory, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, UK. C.Spyrou@statslab.cam.ac.uk|Statistical Laboratory, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, UK. C.Spyrou@statslab.cam.ac.uk|Statistical Laboratory, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, UK. C.Spyrou@statslab.cam.ac.uk|Statistical Laboratory, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, UK. C.Spyrou@statslab.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Binding Sites|Chromatin Immunoprecipitation|Computational Biology|DNA|Proteins', 'Journal Article', '10.1186/1471-2105-10-299', '19772557', 'High-throughput sequencing technology has become popular and widely used to study protein and DNA interactions. Chromatin immunoprecipitation, followed by sequencing of the resulting samples, produces large amounts of data that can be used to map genomic features such as transcription factor binding sites and histone modifications.'),
(93057, 2009, 'bmc', 'RCMAT: a regularized covariance matrix approach to testing gene sets', 'Yates, Phillip D|Reimers, Mark A', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298, USA. yatespd@vcu.edu|Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298, USA. yatespd@vcu.edu', 'United States|United States', 0, 'Computational Biology|Databases, Genetic|False Positive Reactions|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-300', '19772589', 'Gene sets are widely used to interpret genome-scale data. Analysis techniques that make better use of the correlation structure of microarray data while addressing practical n<p concerns could provide a real increase in power. However correlation structure is hard to estimate with typical genomics sample sizes. In this paper we present an extension of a classical multivariate procedure that confronts this challenge by the use of a regularized covariance matrix.'),
(93058, 2009, 'bmc', 'Deep proteogenomics, high throughput gene validation by multidimensional liquid chromatography and mass spectrometry of proteins from the fungal wheat pathogen Stagonospora nodorum', 'Bringans, Scott|Hane, James K|Casey, Tammy|Tan, Kar-Chun|Lipscombe, Richard|Solomon, Peter S|Oliver, Richard P', 'Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au|Proteomics International, Perth, WA, Australia. scott@proteomics.com.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 26, 'Ascomycota|Chromatography, Liquid|Computational Biology|Fungal Proteins|Genome, Fungal|Genomics|Mass Spectrometry|Proteomics|Triticum', 'Journal Article', '10.1186/1471-2105-10-301', '19772613', 'Stagonospora nodorum, a fungal ascomycete in the class dothideomycetes, is a damaging pathogen of wheat. It is a model for necrotrophic fungi that cause necrotic symptoms via the interaction of multiple effector proteins with cultivar-specific receptors. A draft genome sequence and annotation was published in 2007. A second-pass gene prediction using a training set of 795 fully EST-supported genes predicted a total of 10762 version 2 nuclear-encoded genes, with an additional 5354 less reliable version 1 genes also retained.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93059, 2009, 'bmc', 'Prediction of antigenic epitopes on protein surfaces by consensus scoring', 'Liang, Shide|Zheng, Dandan|Zhang, Chi|Zacharias, Martin', 'School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.|School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Epitopes|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-302', '19772615', 'Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance.'),
(93060, 2009, 'bmc', 'The first step in the development of Text Mining technology for Cancer Risk Assessment: identifying and organizing scientific evidence in risk assessment literature', 'Korhonen, Anna|Silins, Ilona|Sun, Lin|Stenius, Ulla', 'Computer Laboratory, University of Cambridge, Cambridge, UK. alk23@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, Cambridge, UK. alk23@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, Cambridge, UK. alk23@cl.cam.ac.uk|Computer Laboratory, University of Cambridge, Cambridge, UK. alk23@cl.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 18, 'Abstracting and Indexing as Topic|Computational Biology|Databases, Factual|Neoplasms|PubMed|Risk Assessment|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-303', '19772619', 'One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature.'),
(93061, 2009, 'bmc', 'MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs', 'Sacan, Ahmet|Ferhatosmanoglu, Nilgun|Ferhatosmanoglu, Hakan', 'Computer Engineering Department, Middle East Technical University, Ankara, Turkey. ahmet@ceng.metu.edu.tr|Computer Engineering Department, Middle East Technical University, Ankara, Turkey. ahmet@ceng.metu.edu.tr|Computer Engineering Department, Middle East Technical University, Ankara, Turkey. ahmet@ceng.metu.edu.tr', 'Turkey|Turkey|Turkey', 0, 'Algorithms|Computational Biology|Internet|Oligonucleotide Array Sequence Analysis|Research Design|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-304', '19772626', 'Dual-channel microarray experiments are commonly employed for inference of differential gene expressions across varying organisms and experimental conditions. The design of dual-channel microarray experiments that can help minimize the errors in the resulting inferences has recently received increasing attention. However, a general and scalable search tool and a corresponding database of optimal designs were still missing.'),
(93062, 2009, 'bmc', 'Strategies for analyzing highly enriched IP-chip datasets', 'Knott, Simon R V|Viggiani, Christopher J|Aparicio, Oscar M|Tavaré, Simon', 'Molecular and Computational Biology Program, University of Southern California, Ray Irani Hall, University Park Campus, Los Angeles, CA 90089-2910, USA. knott@usc.edu|Molecular and Computational Biology Program, University of Southern California, Ray Irani Hall, University Park Campus, Los Angeles, CA 90089-2910, USA. knott@usc.edu|Molecular and Computational Biology Program, University of Southern California, Ray Irani Hall, University Park Campus, Los Angeles, CA 90089-2910, USA. knott@usc.edu|Molecular and Computational Biology Program, University of Southern California, Ray Irani Hall, University Park Campus, Los Angeles, CA 90089-2910, USA. knott@usc.edu', 'United States|United States|United States|United States', 0, 'Chromatin Immunoprecipitation|Computational Biology|Gene Expression Profiling|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-10-305', '19772646', 'Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.'),
(93063, 2009, 'bmc', 'A methodology for the analysis of differential coexpression across the human lifespan', 'Gillis, Jesse|Pavlidis, Paul', 'Department of Psychiatry, University of British Columbia, Vancouver, Canada. jesse.gillis@gmail.com|Department of Psychiatry, University of British Columbia, Vancouver, Canada. jesse.gillis@gmail.com', 'Canada|Canada', 18, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-306', '19772654', 'Differential coexpression is a change in coexpression between genes that may reflect rewiring of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time) and extensively test its validity and usefulness.'),
(93064, 2009, 'bmc', 'Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function', 'Törönen, Petri|Ojala, Pauli J|Marttinen, Pekka|Holm, Liisa', 'The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi|The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi|The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi|The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi', 'Finland|Finland|Finland|Finland', 26, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Software', 'Journal Article', '10.1186/1471-2105-10-307', '19775443', 'A central task in contemporary biosciences is the identification of biological processes showing response in genome-wide differential gene expression experiments. Two types of analysis are common. Either, one generates an ordered list based on the differential expression values of the probed genes and examines the tail areas of the list for over-representation of various functional classes. Alternatively, one monitors the average differential expression level of genes belonging to a given functional class. So far these two types of method have not been combined.'),
(93065, 2009, 'bmc', 'Detection of recurrent copy number alterations in the genome: taking among-subject heterogeneity seriously', 'Rueda, Oscar M|Diaz-Uriarte, Ramon', 'Structural and Computational Biology Programme, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain. rueda.om@gmail.com|Structural and Computational Biology Programme, Spanish National Cancer Centre (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain. rueda.om@gmail.com', 'Spain|Spain', 0, 'Algorithms|Comparative Genomic Hybridization|Gene Dosage|Genome|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-308', '19775444', 'Alterations in the number of copies of genomic DNA that are common or recurrent among diseased individuals are likely to contain disease-critical genes. Unfortunately, defining common or recurrent copy number alteration (CNA) regions remains a challenge. Moreover, the heterogeneous nature of many diseases requires that we search for common or recurrent CNA regions that affect only some subsets of the samples (without knowledge of the regions and subsets affected), but this is neglected by most methods.'),
(93066, 2009, 'bmc', 'SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services', 'Gessler, Damian D G|Schiltz, Gary S|May, Greg D|Avraham, Shulamit|Town, Christopher D|Grant, David|Nelson, Rex T', 'University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org|University of Arizona, Tucson, AZ 85721, USA. dgessler@iplantcollaborative.org', 'United States|United States|United States|United States|United States|United States|United States', 46, 'Computational Biology|Databases, Factual|Information Dissemination|Information Storage and Retrieval|Internet|Semantics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-309', '19775460', 'SSWAP (Simple Semantic Web Architecture and Protocol, pronounced swap) is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies.'),
(93067, 2009, 'bmc', 'Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays', 'Tino, Peter', 'School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK. P.Tino@cs.bham.ac.uk', 'United Kingdom', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Poisson Distribution', 'Journal Article', '10.1186/1471-2105-10-310', '19775462', 'The Audic-Claverie method 1 has been and still continues to be a popular approach for detection of differentially expressed genes in the SAGE framework. The method is based on the assumption that under the null hypothesis tag counts of the same gene in two libraries come from the same but unknown Poisson distribution. The problem is that each SAGE library represents only a single measurement. We ask: Given that the tag count samples from SAGE libraries are extremely limited, how useful actually is the Audic-Claverie methodology? We rigorously analyze the A-C statistic that forms a backbone of the methodology and represents our knowledge of the underlying tag generating process based on one observation.'),
(93068, 2009, 'bmc', 'Challenges for automatically extracting molecular interactions from full-text articles', 'McIntosh, Tara|Curran, James R', 'School of Information Technology, Faculty of Engineering and IT, University of Sydney, Sydney, Australia. tara@it.usyd.edu.au|School of Information Technology, Faculty of Engineering and IT, University of Sydney, Sydney, Australia. tara@it.usyd.edu.au', 'Australia|Australia', 14, 'Abstracting and Indexing as Topic|Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Pattern Recognition, Automated|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-311', '19778419', 'The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles.'),
(93069, 2009, 'bmc', 'Predicting protein-protein binding sites in membrane proteins', 'Bordner, Andrew J', 'Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA. bordner.andrew@mayo.edu', 'United States', 22, 'Binding Sites|Computational Biology|Databases, Protein|Membrane Proteins|Models, Molecular|Protein Conformation', 'Journal Article', '10.1186/1471-2105-10-312', '19778442', 'Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.'),
(93070, 2009, 'bmc', 'Social tagging in the life sciences: characterizing a new metadata resource for bioinformatics', 'Good, Benjamin M|Tennis, Joseph T|Wilkinson, Mark D', 'Heart + Lung Institute at St, Pauls Hospital, University of British Columbia, Vancouver, Canada. goodb@interchange.ubc.ca|Heart + Lung Institute at St, Pauls Hospital, University of British Columbia, Vancouver, Canada. goodb@interchange.ubc.ca|Heart + Lung Institute at St, Pauls Hospital, University of British Columbia, Vancouver, Canada. goodb@interchange.ubc.ca', 'Canada|Canada|Canada', 0, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Information Systems|Internet|Medical Subject Headings|PubMed', 'Journal Article', '10.1186/1471-2105-10-313', '19781082', 'Academic social tagging systems, such as Connotea and CiteULike, provide researchers with a means to organize personal collections of online references with keywords (tags) and to share these collections with others. One of the side-effects of the operation of these systems is the generation of large, publicly accessible metadata repositories describing the resources in the collections. In light of the well-known expansion of information in the life sciences and the need for metadata to enhance its value, these repositories present a potentially valuable new resource for application developers. Here we characterize the current contents of two scientifically relevant metadata repositories created through social tagging. This investigation helps to establish how such socially constructed metadata might be used as it stands currently and to suggest ways that new social tagging systems might be designed that would yield better aggregate products.'),
(93071, 2009, 'bmc', 'MetaTM - a consensus method for transmembrane protein topology prediction', 'Klammer, Martin|Messina, David N|Schmitt, Thomas|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Centre, Albanova, Stockholm University, 10691 Stockholm, Sweden. klammer@sbc.su.se|Stockholm Bioinformatics Centre, Albanova, Stockholm University, 10691 Stockholm, Sweden. klammer@sbc.su.se|Stockholm Bioinformatics Centre, Albanova, Stockholm University, 10691 Stockholm, Sweden. klammer@sbc.su.se|Stockholm Bioinformatics Centre, Albanova, Stockholm University, 10691 Stockholm, Sweden. klammer@sbc.su.se', 'Sweden|Sweden|Sweden|Sweden', 22, 'Algorithms|Computational Biology|Databases, Protein|Membrane Proteins|Protein Conformation|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-314', '19785723', 'Transmembrane (TM) proteins are proteins that span a biological membrane one or more times. As their 3-D structures are hard to determine, experiments focus on identifying their topology (i. e. which parts of the amino acid sequence are buried in the membrane and which are located on either side of the membrane), but only a few topologies are known. Consequently, various computational TM topology predictors have been developed, but their accuracies are far from perfect. The prediction quality can be improved by applying a consensus approach, which combines results of several predictors to yield a more reliable result.'),
(93072, 2009, 'bmc', 'Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks', 'Waaijenborg, Sandra|Zwinderman, Aeilko H', 'Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.|Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.', 'Netherlands|Netherlands', 0, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Glioblastoma|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-315', '19785734', 'We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wolds method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes.'),
(93073, 2009, 'bmc', 'Semantic Web Applications and Tools for Life Sciences, 2008--preface', 'Burger, Albert|Romano, Paolo|Paschke, Adrian|Splendiani, Andrea', 'Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.|Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.|Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.|Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands', 3, 'Biological Science Disciplines|Internet|Medical Informatics|Semantics|Software', 'Introductory Journal Article', '10.1186/1471-2105-10-S10-S1', '19796393', '-'),
(93074, 2009, 'bmc', 'A journey to Semantic Web query federation in the life sciences', 'Cheung, Kei-Hoi|Frost, H Robert|Marshall, M Scott|Prudhommeaux, Eric|Samwald, Matthias|Zhao, Jun|Paschke, Adrian', 'Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06511, USA. kei.cheung@yale.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Biological Science Disciplines|Computational Biology|Databases, Factual|Information Dissemination|Information Storage and Retrieval|Internet|Semantics', 'Journal Article', '10.1186/1471-2105-10-S10-S10', '19796394', 'As interest in adopting the Semantic Web in the biomedical domain continues to grow, Semantic Web technology has been evolving and maturing. A variety of technological approaches including triplestore technologies, SPARQL endpoints, Linked Data, and Vocabulary of Interlinked Datasets have emerged in recent years. In addition to the data warehouse construction, these technological approaches can be used to support dynamic query federation. As a community effort, the BioRDF task force, within the Semantic Web for Health Care and Life Sciences Interest Group, is exploring how these emerging approaches can be utilized to execute distributed queries across different neuroscience data sources.'),
(93075, 2009, 'bmc', 'BioGateway: a semantic systems biology tool for the life sciences', 'Antezana, Erick|Blondé, Ward|Egaña, Mikel|Rutherford, Alistair|Stevens, Robert|De Baets, Bernard|Mironov, Vladimir|Kuiper, Martin', 'Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com|Department of Plant Systems Biology, VIB, Gent, Belgium. erick.antezana@gmail.com', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 49, 'Biological Science Disciplines|Computational Biology|Database Management Systems|Databases, Factual|Internet|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S10-S11', '19796395', 'Life scientists need help in coping with the plethora of fast growing and scattered knowledge resources. Ideally, this knowledge should be integrated in a form that allows them to pose complex questions that address the properties of biological systems, independently from the origin of the knowledge. Semantic Web technologies prove to be well suited for knowledge integration, knowledge production (hypothesis formulation), knowledge querying and knowledge maintenance.'),
(93076, 2009, 'bmc', 'Knowledge-driven enhancements for task composition in bioinformatics', 'Sutherland, Karen|McLeod, Kenneth|Ferguson, Gus|Burger, Albert', 'Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kjs1@hw.ac.uk|Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kjs1@hw.ac.uk|Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kjs1@hw.ac.uk|Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kjs1@hw.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S10-S12', '19796396', 'A key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user.'),
(93077, 2009, 'bmc', 'OGO: an ontological approach for integrating knowledge about orthology', 'Miñarro-Gimenez, Jose Antonio|Madrid, Marisa|Fernandez-Breis, Jesualdo Tomas', 'Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, 30100, Spain. jose.minyarro@um.es|Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, 30100, Spain. jose.minyarro@um.es|Departamento de Informática y Sistemas, Universidad de Murcia, Murcia, 30100, Spain. jose.minyarro@um.es', 'Spain|Spain|Spain', 0, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Software', 'Journal Article', '10.1186/1471-2105-10-S10-S13', '19796397', 'There exist several information resources about orthology of genes and proteins, and there are also systems for querying those resources in an integrated way. However, caveats with current approaches include lack of integration, since results are shown sequentially by resource, meaning that there is redundant information and the users are required to combine the results obtained manually.'),
(93078, 2009, 'bmc', 'A user-centred evaluation framework for the Sealife semantic web browsers', 'Oliver, Helen|Diallo, Gayo|de Quincey, Ed|Alexopoulou, Dimitra|Habermann, Bianca|Kostkova, Patty|Schroeder, Michael|Jupp, Simon|Khelif, Khaled|Stevens, Robert|Jawaheer, Gawesh|Madle, Gemma', 'City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.ac.uk|City eHealth Research Centre, City University, London, EC1V 0HB, UK. Helen.oliver.1@city.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', 6, 'Computational Biology|Databases, Factual|Internet|Semantics|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S10-S14', '19796398', 'Semantically-enriched browsing has enhanced the browsing experience by providing contextualized dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealifes main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure.'),
(93079, 2009, 'bmc', 'Semantic web data warehousing for caGrid', 'McCusker, James P|Phillips, Joshua A|González Beltrán, Alejandra|Finkelstein, Anthony|Krauthammer, Michael', 'Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. james.mccusker@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. james.mccusker@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. james.mccusker@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. james.mccusker@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. james.mccusker@yale.edu', 'United States|United States|United States|United States|United States', 27, 'Biomarkers, Tumor|Computational Biology|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Neoplasm Proteins|Neoplasms|Semantics|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S10-S2', '19796399', 'The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges.'),
(93080, 2009, 'bmc', 'Francisella tularensis novicida proteomic and transcriptomic data integration and annotation based on semantic web technologies', 'Anwar, Nadia|Hunt, Ela', 'Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK. n.anwar@bio.gla.ac.uk|Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK. n.anwar@bio.gla.ac.uk', 'United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Protein|Francisella tularensis|Gene Expression Profiling|Internet|Proteomics', 'Journal Article', '10.1186/1471-2105-10-S10-S3', '19796400', 'This paper summarises the lessons and experiences gained from a case study of the application of semantic web technologies to the integration of data from the bacterial species Francisella tularensis novicida (Fn). Fn data sources are disparate and heterogeneous, as multiple laboratories across the world, using multiple technologies, perform experiments to understand the mechanism of virulence. It is hard to integrate these data sources in a flexible manner that allows new experimental data to be added and compared when required.'),
(93081, 2009, 'bmc', 'Reuse of terminological resources for efficient ontological engineering in Life Sciences', 'Jimeno-Yepes, Antonio|Jiménez-Ruiz, Ernesto|Berlanga-Llavori, Rafael|Rebholz-Schuhmann, Dietrich', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. yepes@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Natural Language Processing|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S10-S4', '19796401', 'This paper is intended to explore how to use terminological resources for ontology engineering. Nowadays there are several biomedical ontologies describing overlapping domains, but there is not a clear correspondence between the concepts that are supposed to be equivalent or just similar. These resources are quite precious but their integration and further development are expensive. Terminologies may support the ontological development in several stages of the lifecycle of the ontology, e.g. ontology integration. In this paper we investigate the use of terminological resources during the ontology lifecycle. We claim that the proper creation and use of a shared thesaurus is a cornerstone for the successful application of the Semantic Web technology within life sciences. Moreover, we have applied our approach to a real scenario, the Health-e-Child (HeC) project, and we have evaluated the impact of filtering and re-organizing several resources. As a result, we have created a reference thesaurus for this project, named HeCTh.'),
(93082, 2009, 'bmc', 'KA-SB: from data integration to large scale reasoning', 'Roldán-García, María del Mar|Navas-Delgado, Ismael|Kerzazi, Amine|Chniber, Othmane|Molina-Castro, Joaquín|Aldana-Montes, José F', 'Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es|Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es|Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es|Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es|Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es|Computer Languages and Computing Science Department, Higher Technical School of Computer Science Engineering, University of Málaga, Málaga, 29071, Spain. mmar@lcc.uma.es', 'Spain|Spain|Spain|Spain|Spain|Spain', 6, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/1471-2105-10-S10-S5', '19796402', 'The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of the information is not possible without the integration of such data.'),
(93083, 2009, 'bmc', 'Service-based analysis of biological pathways', 'Zheng, George|Bouguettaya, Athman', 'Virginia Tech, Blacksburg, Virginia, USA. gzheng@vt.edu|Virginia Tech, Blacksburg, Virginia, USA. gzheng@vt.edu', 'United States|United States', 4, 'Algorithms|Computational Biology|Databases, Factual|Internet|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S10-S6', '19796403', 'Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach.'),
(93084, 2009, 'bmc', 'GoWeb: a semantic search engine for the life science web', 'Dietze, Heiko|Schroeder, Michael', 'Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. heiko.dietze@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01062, Dresden, Germany. heiko.dietze@biotec.tu-dresden.de', 'Germany|Germany', 34, 'Algorithms|Biological Science Disciplines|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Search Engine|Software', 'Journal Article', '10.1186/1471-2105-10-S10-S7', '19796404', 'Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason.'),
(93085, 2009, 'bmc', 'Bio-jETI: a framework for semantics-based service composition', 'Lamprecht, Anna-Lena|Margaria, Tiziana|Steffen, Bernhard', 'Chair for Programming Systems, Dortmund University of Technology, Dortmund, D-44227, Germany. anna-lena.lamprecht@cs.tu-dortmund.de|Chair for Programming Systems, Dortmund University of Technology, Dortmund, D-44227, Germany. anna-lena.lamprecht@cs.tu-dortmund.de|Chair for Programming Systems, Dortmund University of Technology, Dortmund, D-44227, Germany. anna-lena.lamprecht@cs.tu-dortmund.de', 'Germany|Germany|Germany', 55, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Natural Language Processing|Semantics|Software', 'Journal Article', '10.1186/1471-2105-10-S10-S8', '19796405', 'The development of bioinformatics databases, algorithms, and tools throughout the last years has lead to a highly distributed world of bioinformatics services. Without adequate management and development support, in silico researchers are hardly able to exploit the potential of building complex, specialized analysis processes from these services. The Semantic Web aims at thoroughly equipping individual data and services with machine-processable meta-information, while workflow systems support the construction of service compositions. However, even in this combination, in silico researchers currently would have to deal manually with the service interfaces, the adequacy of the semantic annotations, type incompatibilities, and the consistency of service compositions.'),
(93086, 2009, 'bmc', 'Structuring and extracting knowledge for the support of hypothesis generation in molecular biology', 'Roos, Marco|Marshall, M Scott|Gibson, Andrew P|Schuemie, Martijn|Meij, Edgar|Katrenko, Sophia|van Hage, Willem Robert|Krommydas, Konstantinos|Adriaans, Pieter W', 'Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl|Informatics Institute, University of Amsterdam, Amsterdam, 1098 SJ, The Netherlands. roos@science.uva.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 16, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Molecular Biology|PubMed|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S10-S9', '19796406', 'Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes.'),
(93087, 2009, 'bmc', 'Unsupervised statistical clustering of environmental shotgun sequences', 'Kislyuk, Andrey|Bhatnagar, Srijak|Dushoff, Jonathan|Weitz, Joshua S', 'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. kislyuk@gatech.edu|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. kislyuk@gatech.edu|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. kislyuk@gatech.edu|School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. kislyuk@gatech.edu', 'Georgia|Georgia|Georgia|Georgia', 55, 'Algorithms|Cluster Analysis|Genome|Genomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-316', '19799776', 'The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed.'),
(93088, 2009, 'bmc', 'Enhancing navigation in biomedical databases by community voting and database-driven text classification', 'Duchrow, Timo|Shtatland, Timur|Guettler, Daniel|Pivovarov, Misha|Kramer, Stefan|Weissleder, Ralph', 'Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA. timo.duchrow@dfki.de', 'United States|United States|United States|United States|United States|United States', 21, 'Classification|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet', 'Journal Article', '10.1186/1471-2105-10-317', '19799796', 'The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them.'),
(93089, 2009, 'bmc', 'Kavosh: a new algorithm for finding network motifs', 'Kashani, Zahra Razaghi Moghadam|Ahrabian, Hayedeh|Elahi, Elahe|Nowzari-Dalini, Abbas|Ansari, Elnaz Saberi|Asadi, Sahar|Mohammadi, Shahin|Schreiber, Falk|Masoudi-Nejad, Ali', 'Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir|Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. razaghi@ibb.ut.ac.ir', '-|-|-|-|-|-|-|-|-', 140, 'Algorithms|Computational Biology|Escherichia coli|Neural Networks (Computer)|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-10-318', '19799800', 'Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.'),
(93090, 2009, 'bmc', 'Generation of Gene Ontology benchmark datasets with various types of positive signal', 'Törönen, Petri|Pehkonen, Petri|Holm, Liisa', 'The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi|The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi|The Holm Group, Biocenter II, Institute of Biotechnology, PO Box 56, 00014 University of Helsinki, Finland. petri.toronen@helsinki.fi', 'Finland|Finland|Finland', 4, 'Algorithms|Computational Biology|Databases, Genetic|Information Storage and Retrieval|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-319', '19811632', 'The analysis of over-represented functional classes in a list of genes is one of the most essential bioinformatics research topics. Typical examples of such lists are the differentially expressed genes from transcriptional analysis which need to be linked to functional information represented in the Gene Ontology (GO). Despite the importance of this procedure, there is a little work on consistent evaluation of various GO analysis methods. Especially, there is no literature on creating benchmark datasets for GO analysis tools.'),
(93091, 2009, 'bmc', 'BIRI: a new approach for automatically discovering and indexing available public bioinformatics resources from the literature', 'de la Calle, Guillermo|García-Remesal, Miguel|Chiesa, Stefano|de la Iglesia, Diana|Maojo, Victor', 'Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. gcalle@infomed.dia.fi.upm.es|Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. gcalle@infomed.dia.fi.upm.es|Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. gcalle@infomed.dia.fi.upm.es|Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. gcalle@infomed.dia.fi.upm.es|Dept Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. gcalle@infomed.dia.fi.upm.es', 'Spain|Spain|Spain|Spain|Spain', 29, 'Abstracting and Indexing as Topic|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-320', '19811635', 'The rapid evolution of Internet technologies and the collaborative approaches that dominate the field have stimulated the development of numerous bioinformatics resources. To address this new framework, several initiatives have tried to organize these services and resources. In this paper, we present the BioInformatics Resource Inventory (BIRI), a new approach for automatically discovering and indexing available public bioinformatics resources using information extracted from the scientific literature. The index generated can be automatically updated by adding additional manuscripts describing new resources. We have developed web services and applications to test and validate our approach. It has not been designed to replace current indexes but to extend their capabilities with richer functionalities.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93092, 2009, 'bmc', 'Discovering multiple realistic TFBS motifs based on a generalized model', 'Chan, Tak-Ming|Li, Gang|Leung, Kwong-Sak|Lee, Kin-Hong', 'Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N, T,, Hong Kong. tmchan@cse.cuhk.edu.hk|Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N, T,, Hong Kong. tmchan@cse.cuhk.edu.hk|Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N, T,, Hong Kong. tmchan@cse.cuhk.edu.hk|Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin, N, T,, Hong Kong. tmchan@cse.cuhk.edu.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 18, 'Algorithms|Binding Sites|Computational Biology|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-321', '19811641', 'Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range. Deciding a single motif width by existing models may be biased and misleading. Additionally, multiple, possibly overlapping, candidate motifs are desired and necessary for biological verification in practice. However, current techniques either prohibit overlapping TFBSs or lack explicit control of different motifs.'),
(93093, 2009, 'bmc', 'Proceedings of the 2009 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) conference Introduction', 'Wren, Jonathan D|Gusev, Yuriy|Isokpehi, Raphael D|Berleant, Daniel|Braga-Neto, Ulisses|Wilkins, Dawn|Bridges, Susan', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, OK 73104-5005, USA. jdwren@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Genomics|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Introductory Journal Article', '10.1186/1471-2105-10-S11-S1', '19811674', '-'),
(93094, 2009, 'bmc', 'Analysis and modeling of time-course gene-expression profiles from nanomaterial-exposed primary human epidermal keratinocytes', 'Zollanvari, Amin|Cunningham, Mary Jane|Braga-Neto, Ulisses|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States|United States', 9, 'Epidermal Cells|Epidermis|Gene Expression Profiling|Humans|Keratinocytes|Nanostructures|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S11-S10', '19811675', 'Nanomaterials are being manufactured on a commercial scale for use in medical, diagnostic, energy, component and communications industries. However, concerns over the safety of engineered nanomaterials have surfaced. Humans can be exposed to nanomaterials in different ways such as inhalation or exposure through the integumentary system.'),
(93095, 2009, 'bmc', 'Site-specific impacts on gene expression and behavior in fathead minnows (Pimephales promelas) exposed in situ to streams adjacent to sewage treatment plants', 'Garcia-Reyero, Natàlia|Adelman, Ira R|Martinovic, Dalma|Liu, Li|Denslow, Nancy D', 'Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611, USA.|Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611, USA.|Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611, USA.|Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611, USA.|Department of Physiological Sciences and Center for Environmental and Human Toxicology, University of Florida, Gainesville, FL 32611, USA.', 'United States|United States|United States|United States|United States', 33, 'Animals|Behavior, Animal|Cyprinidae|Environmental Exposure|Gene Expression|Gonads|Oligonucleotide Array Sequence Analysis|Rivers|Sewage', 'Journal Article', '10.1186/1471-2105-10-S11-S11', '19811676', 'Environmental monitoring for pharmaceuticals and endocrine disruptors in the aquatic environment traditionally employs a variety of methods including analytical chemistry, as well as a variety of histological and biochemical endpoints that correlate with the fish fitness. It is now clear that analytical chemistry alone is insufficient to identify aquatic environments that are compromised because these measurements do not identify the biologically available dose. The biological endpoints that are measured are important because they relate to known impairments, however, they are not specific to the contaminants and often focus on only a few known endpoints. These studies can be enhanced by looking more broadly at changes in gene expression, especially if the analysis focuses on biochemical pathways. The present study was designed to obtain additional information for well-characterized sites adjacent to sewage treatment plants in MN that are thought to be impacted by endocrine disruptors.'),
(93096, 2009, 'bmc', 'Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles', 'Li, Zhiguang|Su, Zhenqiang|Wen, Zhining|Shi, Leming|Chen, Tao', 'Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U,S, Food and Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas 72079, USA.|Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U,S, Food and Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas 72079, USA.|Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U,S, Food and Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas 72079, USA.|Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U,S, Food and Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas 72079, USA.|Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research, U,S, Food and Drug Administration, 3900 NCTR Rd, Jefferson, Arkansas 72079, USA.', 'United States|United States|United States|United States|United States', 0, 'Animals|Aristolochic Acids|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Rats', 'Journal Article', '10.1186/1471-2105-10-S11-S12', '19811677', 'Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra- and inter-platform consistency of these platforms. Both high and low consistencies were demonstrated across different platforms in terms of genes with significantly differential expression. Array studies for gene expression are used to explore biological causes and effects. Therefore, consistency should eventually be evaluated in a biological setting to reveal the functional differences between the examined samples, not just a list of differentially expressed genes (DEG). In this study, we investigated whether different platforms had a high consistency from the biologically functional perspective.'),
(93097, 2009, 'bmc', 'Automatic identification of angiogenesis in double stained images of liver tissue', 'Mete, Mutlu|Hennings, Leah|Spencer, Horace J|Topaloglu, Umit', 'Information Technology Research, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA. MMete@uams.edu|Information Technology Research, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA. MMete@uams.edu|Information Technology Research, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA. MMete@uams.edu|Information Technology Research, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA. MMete@uams.edu', 'United States|United States|United States|United States', 14, 'Animals|Diagnostic Imaging|Humans|Liver|Liver Neoplasms|Neovascularization, Pathologic', 'Journal Article', '10.1186/1471-2105-10-S11-S13', '19811678', 'To grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms.'),
(93098, 2009, 'bmc', 'NATbox: a network analysis toolbox in R', 'Chavan, Shweta S|Bauer, Michael A|Scutari, Marco|Nagarajan, Radhakrishnan', 'UALR/UAMS Joint Bioinformatics Program, University of Arkansas at Little Rock, Arkansas, USA. schavan@uams.edu|UALR/UAMS Joint Bioinformatics Program, University of Arkansas at Little Rock, Arkansas, USA. schavan@uams.edu|UALR/UAMS Joint Bioinformatics Program, University of Arkansas at Little Rock, Arkansas, USA. schavan@uams.edu|UALR/UAMS Joint Bioinformatics Program, University of Arkansas at Little Rock, Arkansas, USA. schavan@uams.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S11-S14', '19811679', 'There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis.'),
(93099, 2009, 'bmc', 'Protein local 3D structure prediction by Super Granule Support Vector Machines (Super GSVM)', 'Chen, Bernard|Johnson, Matthew', 'Department of Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, AR 72035, USA. bchen@uca.edu|Department of Computer Science, University of Central Arkansas, 201 Donaghey Avenue, Conway, AR 72035, USA. bchen@uca.edu', 'United States|United States', 5, 'Artificial Intelligence|Computational Biology|Databases, Protein|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S11-S15', '19811680', 'Understanding the relationship between the protein sequence and the 3D structure is a major research area in bioinformatics. The prediction of complete protein tertiary structure based only on sequence information is still an impractical work. This paper aims at revealing the hidden knowledge of the sequence motifs and the local tertiary structure.'),
(93100, 2009, 'bmc', 'Novel software package for cross-platform transcriptome analysis (CPTRA)', 'Zhou, Xin|Su, Zhen|Sammons, R Douglas|Peng, Yanhui|Tranel, Patrick J|Stewart, C Neal|Yuan, Joshua S', 'Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.|Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States', 17, 'Computational Biology|Expressed Sequence Tags|Gene Expression Profiling|Genome|Oligonucleotide Array Sequence Analysis|RNA|Software', 'Journal Article', '10.1186/1471-2105-10-S11-S16', '19811681', 'Next-generation sequencing techniques enable several novel transcriptome profiling approaches. Recent studies indicated that digital gene expression profiling based on short sequence tags has superior performance as compared to other transcriptome analysis platforms including microarrays. However, the transcriptomic analysis with tag-based methods often depends on available genome sequence. The use of tag-based methods in species without genome sequence should be complemented by other methods such as cDNA library sequencing. The combination of different next generation sequencing techniques like 454 pyrosequencing and Illumina Genome Analyzer (Solexa) will enable high-throughput and accurate global gene expression profiling in species with limited genome information. The combination of transcriptome data acquisition methods requires cross-platform transcriptome data analysis platforms, including a new software package for data processing.'),
(93101, 2009, 'bmc', 'An automated proteomic data analysis workflow for mass spectrometry', 'Pendarvis, Ken|Kumar, Ranjit|Burgess, Shane C|Nanduri, Bindu', 'Institute for Digital Biology, Mississippi State University, Mississippi State, MS 39762, USA.|Institute for Digital Biology, Mississippi State University, Mississippi State, MS 39762, USA.|Institute for Digital Biology, Mississippi State University, Mississippi State, MS 39762, USA.|Institute for Digital Biology, Mississippi State University, Mississippi State, MS 39762, USA.', 'United States|United States|United States|United States', 13, 'Databases, Protein|Mass Spectrometry|Proteins|Proteomics|Software|Statistics as Topic|Workflow', 'Journal Article', '10.1186/1471-2105-10-S11-S17', '19811682', 'Mass spectrometry-based protein identification methods are fundamental to proteomics. Biological experiments are usually performed in replicates and proteomic analyses generate huge datasets which need to be integrated and quantitatively analyzed. The Sequest search algorithm is a commonly used algorithm for identifying peptides and proteins from two dimensional liquid chromatography electrospray ionization tandem mass spectrometry (2-D LC ESI MS(2)) data. A number of proteomic pipelines that facilitate high throughput post data acquisition analysis are described in the literature. However, these pipelines need to be updated to accommodate the rapidly evolving data analysis methods. Here, we describe a proteomic data analysis pipeline that specifically addresses two main issues pertinent to protein identification and differential expression analysis: 1) estimation of the probability of peptide and protein identifications and 2) non-parametric statistics for protein differential expression analysis. Our proteomic analysis workflow analyzes replicate datasets from a single experimental paradigm to generate a list of identified proteins with their probabilities and significant changes in protein expression using parametric and non-parametric statistics.'),
(93102, 2009, 'bmc', 'PathBinder--text empirics and automatic extraction of biomolecular interactions', 'Zhang, Lifeng|Berleant, Daniel|Ding, Jing|Cao, Tuan|Syrkin Wurtele, Eve', 'Iowa State University, Ames, Iowa, USA.|Iowa State University, Ames, Iowa, USA.|Iowa State University, Ames, Iowa, USA.|Iowa State University, Ames, Iowa, USA.|Iowa State University, Ames, Iowa, USA.', 'United States|United States|United States|United States|United States', 11, 'Artificial Intelligence|Computational Biology|Data Mining|Database Management Systems|PubMed|Software', 'Journal Article', '10.1186/1471-2105-10-S11-S18', '19811683', 'The increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe.'),
(93103, 2009, 'bmc', 'Graph ranking for exploratory gene data analysis', 'Gao, Cuilan|Dang, Xin|Chen, Yixin|Wilkins, Dawn', 'Department of Mathematics, The University of Mississippi, University, MS 38677, USA.|Department of Mathematics, The University of Mississippi, University, MS 38677, USA.|Department of Mathematics, The University of Mississippi, University, MS 38677, USA.|Department of Mathematics, The University of Mississippi, University, MS 38677, USA.', 'United States|United States|United States|United States', 13, 'Algorithms|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S11-S19', '19811684', 'Microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes in a single experiment. However, the large number of genes greatly increases the challenges of analyzing, comprehending and interpreting the resulting mass of data. Selecting a subset of important genes is inevitable to address the challenge. Gene selection has been investigated extensively over the last decade. Most selection procedures, however, are not sufficient for accurate inference of underlying biology, because biological significance does not necessarily have to be statistically significant. Additional biological knowledge needs to be integrated into the gene selection procedure.'),
(93104, 2009, 'bmc', 'Facilitating functional annotation of chicken microarray data', 'Buza, Teresia J|Kumar, Ranjit|Gresham, Cathy R|Burgess, Shane C|McCarthy, Fiona M', 'Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. tbuza@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. tbuza@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. tbuza@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. tbuza@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA. tbuza@cvm.msstate.edu', 'United States|United States|United States|United States|United States', 8, 'Animals|Chickens|Databases, Genetic|Genome|Genomics|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S11-S2', '19811685', 'Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information.'),
(93105, 2009, 'bmc', 'Integrating phenotype and gene expression data for predicting gene function', 'Malone, Brandon M|Perkins, Andy D|Bridges, Susan M', 'Department of Computer Science and Engineering, Box 9637, Mississippi State University, Mississippi State, MS 39762, USA. bm542@msstate.edu|Department of Computer Science and Engineering, Box 9637, Mississippi State University, Mississippi State, MS 39762, USA. bm542@msstate.edu|Department of Computer Science and Engineering, Box 9637, Mississippi State University, Mississippi State, MS 39762, USA. bm542@msstate.edu', 'United States|United States|United States', 7, 'Algorithms|Computational Biology|Gene Expression|Genomics|Phenotype|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-S11-S20', '19811686', 'This paper presents a framework for integrating disparate data sets to predict gene function. The algorithm constructs a graph, called an integrated similarity graph, by computing similarities based upon both gene expression and textual phenotype data. This integrated graph is then used to make predictions about whether individual genes should be assigned a particular annotation from the Gene Ontology.'),
(93106, 2009, 'bmc', 'Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom', 'Xu, Zhanyou|Zhang, Dandan|Hu, Jun|Zhou, Xin|Ye, Xia|Reichel, Kristen L|Stewart, Nathan R|Syrenne, Ryan D|Yang, Xiaohan|Gao, Peng|Shi, Weibing|Doeppke, Crissa|Sykes, Robert W|Burris, Jason N|Bozell, Joseph J|Cheng, Max Zong-Ming|Hayes, Douglas G|Labbe, Nicole|Davis, Mark|Stewart, C Neal|Yuan, Joshua S', 'Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.|Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 143, 'Arabidopsis|Evolution, Molecular|Gene Duplication|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|Lignin|Oryza|Phylogeny|Plant Proteins|Plants|Poaceae', 'Comparative Study', '10.1186/1471-2105-10-S11-S3', '19811687', 'As a major component of plant cell wall, lignin plays important roles in mechanical support, water transport, and stress responses. As the main cause for the recalcitrance of plant cell wall, lignin modification has been a major task for bioenergy feedstock improvement. The study of the evolution and function of lignin biosynthesis genes thus has two-fold implications. First, the lignin biosynthesis pathway provides an excellent model to study the coordinative evolution of a biochemical pathway in plants. Second, understanding the function and evolution of lignin biosynthesis genes will guide us to develop better strategies for bioenergy feedstock improvement.'),
(93107, 2009, 'bmc', 'Threshold selection in gene co-expression networks using spectral graph theory techniques', 'Perkins, Andy D|Langston, Michael A', 'Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, USA. perkins@cse.msstate.edu|Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, USA. perkins@cse.msstate.edu', 'United States|United States', 49, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-S11-S4', '19811688', 'Gene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff.'),
(93108, 2009, 'bmc', 'HPD: an online integrated human pathway database enabling systems biology studies', 'Chowbina, Sudhir R|Wu, Xiaogang|Zhang, Fan|Li, Peter M|Pandey, Ragini|Kasamsetty, Harini N|Chen, Jake Y', 'Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.|Indiana University School of Informatics, Indianapolis, IN 46202, USA.', 'India|India|India|India|India|India|India', 36, 'Computational Biology|Databases, Factual|Databases, Protein|Humans|Internet|Proteins|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S11-S5', '19811689', 'Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database, however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.'),
(93109, 2009, 'bmc', 'Computational analysis of gene expression space associated with metastatic cancer', 'Ptitsyn, Andrey', 'Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Campus delivery 1682 Fort Collins, CO 80523, USA. Andrey.Ptitsyn@colostate.edu', 'United States', 3, 'Computational Biology|Gene Expression|Gene Expression Profiling|Humans|Neoplasm Metastasis', 'Journal Article', '10.1186/1471-2105-10-S11-S6', '19811690', 'Prostate carcinoma is among the most common types of cancer affecting hundreds of thousands people every year. Once the metastatic form of prostate carcinoma is documented, the majority of patients die from their tumors as opposed to other causes. The key to successful treatment is in the earliest possible diagnosis, as well as understanding the molecular mechanisms of metastatic progression. A number of recent studies have identified multiple biomarkers for metastatic progression. However, most of the studies consider only direct comparison between metastatic and non-metastatic classes of samples.'),
(93110, 2009, 'bmc', 'Exploratory visual analysis of conserved domains on multiple sequence alignments', 'Jankun-Kelly, T J|Lindeman, Andrew D|Bridges, Susan M', 'Institute for Digital Biology and Department of Computer Science and Engineering, Bagley College of Engineering, Mississippi State University, Mississippi, USA. tjk@acm.org|Institute for Digital Biology and Department of Computer Science and Engineering, Bagley College of Engineering, Mississippi State University, Mississippi, USA. tjk@acm.org|Institute for Digital Biology and Department of Computer Science and Engineering, Bagley College of Engineering, Mississippi State University, Mississippi, USA. tjk@acm.org', 'United States|United States|United States', 7, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S11-S7', '19811691', 'Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequences. By fusing this metadata visually, biologists can analyze sequence conservation and functional motifs simultaneously and efficiently.'),
(93111, 2009, 'bmc', 'Structural and functional-annotation of an equine whole genome oligoarray', 'Bright, Lauren A|Burgess, Shane C|Chowdhary, Bhanu|Swiderski, Cyprianna E|McCarthy, Fiona M', 'Department of Clinical Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762, USA. lbright@cvm.msstate.edu.|Department of Clinical Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762, USA. lbright@cvm.msstate.edu.|Department of Clinical Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762, USA. lbright@cvm.msstate.edu.|Department of Clinical Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762, USA. lbright@cvm.msstate.edu.|Department of Clinical Sciences, College of Veterinary Medicine, Mississippi State University, PO Box 6100, Mississippi State, MS, 39762, USA. lbright@cvm.msstate.edu.', 'United States|United States|United States|United States|United States', 17, 'Animals|Databases, Genetic|Genome|Genomics|Horses|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S11-S8', '19811692', 'The horse genome is sequenced, allowing equine researchers to use high-throughput functional genomics platforms such as microarrays, next-generation sequencing for gene expression and proteomics. However, for researchers to derive value from these functional genomics datasets, they must be able to model this data in biologically relevant ways, to do so requires that the equine genome be more fully annotated. There are two interrelated types of genomic annotation: structural and functional. Structural annotation is delineating and demarcating the genomic elements (such as genes, promoters, and regulatory elements). Functional annotation is assigning function to structural elements. The Gene Ontology (GO) is the de facto standard for functional annotation, and is routinely used as a basis for modelling and hypothesis testing, large functional genomics datasets.'),
(93112, 2009, 'bmc', 'Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data', 'van den Berg, Bart H J|Thanthiriwatte, Chamali|Manda, Prashanti|Bridges, Susan M', 'Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA. bvandenberg@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA. bvandenberg@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA. bvandenberg@cvm.msstate.edu|Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS 39762, USA. bvandenberg@cvm.msstate.edu', 'United States|United States|United States|United States', 0, 'Agriculture|Animals|Chickens|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-10-S11-S9', '19811693', 'The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set--both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVIDs annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools.'),
(93113, 2009, 'bmc', 'Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions', 'Fröhlich, Holger|Sahin, Ozgür|Arlt, Dorit|Bender, Christian|Beissbarth, Tim', 'German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de|German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de|German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de|German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de|German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-322', '19814779', 'Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.'),
(93114, 2009, 'bmc', 'A stochastic context free grammar based framework for analysis of protein sequences', 'Dyrka, Witold|Nebel, Jean-Christophe', 'Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Poland. Witold.Dyrka@pwr.wroc.pl|Institute of Biomedical Engineering and Instrumentation, Wroclaw University of Technology, Poland. Witold.Dyrka@pwr.wroc.pl', 'Poland|Poland', 21, 'Algorithms|Artificial Intelligence|Binding Sites|Computational Biology|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-323', '19814800', 'In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm.'),
(93115, 2009, 'bmc', 'KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database', 'Moutselos, Konstantinos|Kanaris, Ioannis|Chatziioannou, Aristotelis|Maglogiannis, Ilias|Kolisis, Fragiskos N', 'Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vassileos Konstantinou 48, 11635 Athens, Greece. kmoutselos@sch.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vassileos Konstantinou 48, 11635 Athens, Greece. kmoutselos@sch.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vassileos Konstantinou 48, 11635 Athens, Greece. kmoutselos@sch.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vassileos Konstantinou 48, 11635 Athens, Greece. kmoutselos@sch.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, Vassileos Konstantinou 48, 11635 Athens, Greece. kmoutselos@sch.gr', 'Greece|Greece|Greece|Greece|Greece', 0, 'Computational Biology|Databases, Factual|Internet|Metabolic Networks and Pathways|Software', 'Journal Article', '10.1186/1471-2105-10-324', '19814801', 'The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.'),
(93116, 2009, 'bmc', 'Riboswitch detection using profile hidden Markov models', 'Singh, Payal|Bandyopadhyay, Pradipta|Bhattacharya, Sudha|Krishnamachari, A|Sengupta, Supratim', 'Centre for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi-110067, India.|Centre for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi-110067, India.|Centre for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi-110067, India.|Centre for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi-110067, India.|Centre for Computational Biology and Bioinformatics, School of Information Technology, Jawaharlal Nehru University, New Delhi-110067, India.', 'India|India|India|India|India', 26, 'Binding Sites|Computational Biology|Ligands|Markov Chains|RNA, Untranslated|Software', 'Journal Article', '10.1186/1471-2105-10-325', '19814811', 'Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM). Our method exploits the high degree of sequence conservation that characterizes the aptamer domain.'),
(93117, 2009, 'bmc', 'Text mining and manual curation of chemical-gene-disease networks for the comparative toxicogenomics database (CTD)', 'Wiegers, Thomas C|Davis, Allan Peter|Cohen, K Bretonnel|Hirschman, Lynette|Mattingly, Carolyn J', 'Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME, USA. twiegers@mdibl.org|Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME, USA. twiegers@mdibl.org|Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME, USA. twiegers@mdibl.org|Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME, USA. twiegers@mdibl.org|Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME, USA. twiegers@mdibl.org', 'United States|United States|United States|United States|United States', 82, 'Computational Biology|Databases, Factual|Gene Regulatory Networks|Information Storage and Retrieval|Toxicogenetics', 'Comparative Study', '10.1186/1471-2105-10-326', '19814812', 'The Comparative Toxicogenomics Database (CTD) is a publicly available resource that promotes understanding about the etiology of environmental diseases. It provides manually curated chemical-gene/protein interactions and chemical- and gene-disease relationships from the peer-reviewed, published literature. The goals of the research reported here were to establish a baseline analysis of current CTD curation, develop a text-mining prototype from readily available open source components, and evaluate its potential value in augmenting curation efficiency and increasing data coverage.'),
(93118, 2009, 'bmc', 'VANLO--interactive visual exploration of aligned biological networks', 'Brasch, Steffen|Linsen, Lars|Fuellen, Georg', '1Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-University, Greifswald, Germany. sbrasch@uni-greifswald.de|1Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-University, Greifswald, Germany. sbrasch@uni-greifswald.de|1Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-University, Greifswald, Germany. sbrasch@uni-greifswald.de', 'Germany|Germany|Germany', 9, 'Databases, Protein|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-327', '19821976', 'Protein-protein interaction (PPI) is fundamental to many biological processes. In the course of evolution, biological networks such as protein-protein interaction networks have developed. Biological networks of different species can be aligned by finding instances (e.g. proteins) with the same common ancestor in the evolutionary process, so-called orthologs. For a better understanding of the evolution of biological networks, such aligned networks have to be explored. Visualization can play a key role in making the various relationships transparent.'),
(93119, 2009, 'bmc', 'miRExpress: analyzing high-throughput sequencing data for profiling microRNA expression', 'Wang, Wei-Chi|Lin, Feng-Mao|Chang, Wen-Chi|Lin, Kuan-Yu|Huang, Hsien-Da|Lin, Na-Sheng', 'Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw|Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. canser.bi95g@nctu.edu.tw', 'China|China|China|China|China|China', 128, 'Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Genome, Plant|Humans|MicroRNAs|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-10-328', '19821977', 'MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93120, 2009, 'bmc', 'PLAST: parallel local alignment search tool for database comparison', 'Nguyen, Van Hoa|Lavenier, Dominique', 'INRIA/IRISA, Campus de Beaulieu, 35042 Rennes Cedex, France. vhnguyen@irisa.fr|INRIA/IRISA, Campus de Beaulieu, 35042 Rennes Cedex, France. vhnguyen@irisa.fr', 'France|France', 36, 'Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Microcomputers|Software', 'Comparative Study', '10.1186/1471-2105-10-329', '19821978', 'Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors.'),
(93121, 2009, 'bmc', 'BASE--2nd generation software for microarray data management and analysis', 'Vallon-Christersson, Johan|Nordborg, Nicklas|Svensson, Martin|Häkkinen, Jari', 'Department of Oncology, Clinical Sciences, Lund University, SE-221 84 Lund, Sweden. johan.vallon-christersson@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-221 84 Lund, Sweden. johan.vallon-christersson@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-221 84 Lund, Sweden. johan.vallon-christersson@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-221 84 Lund, Sweden. johan.vallon-christersson@med.lu.se', 'Sweden|Sweden|Sweden|Sweden', 40, 'Database Management Systems|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-330', '19822003', 'Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.'),
(93122, 2009, 'bmc', 'ConservedPrimers 20: a high-throughput pipeline for comparative genome referenced intron-flanking PCR primer design and its application in wheat SNP discovery', 'You, Frank M|Huo, Naxin|Gu, Yong Q|Lazo, Gerard R|Dvorak, Jan|Anderson, Olin D', 'Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov|Department of Plant Sciences, University of California, Davis, CA 95616, USA. frank.you@ars.usda.gov', 'United States|United States|United States|United States|United States|United States', 28, 'Computational Biology|DNA Primers|Genome, Plant|Introns|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Sequence Alignment|Triticum', 'Comparative Study', '10.1186/1471-2105-10-331', '19825183', 'In some genomic applications it is necessary to design large numbers of PCR primers in exons flanking one or several introns on the basis of orthologous gene sequences in related species. The primer pairs designed by this target gene approach are called intron-flanking primers or because they are located in exonic sequences which are usually conserved between related species, conserved primers. They are useful for large-scale single nucleotide polymorphism (SNP) discovery and marker development, especially in species, such as wheat, for which a large number of ESTs are available but for which genome sequences and intron/exon boundaries are not available. To date, no suitable high-throughput tool is available for this purpose.'),
(93123, 2009, 'bmc', 'STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data', 'Jupiter, Daniel|Chen, Hailin|VanBuren, Vincent', 'Department of Systems Biology and Translational Medicine, College of Medicine, Texas A & M Health Science Center, Temple, TX 76504, USA. djupiter@tamu.edu|Department of Systems Biology and Translational Medicine, College of Medicine, Texas A & M Health Science Center, Temple, TX 76504, USA. djupiter@tamu.edu|Department of Systems Biology and Translational Medicine, College of Medicine, Texas A & M Health Science Center, Temple, TX 76504, USA. djupiter@tamu.edu', 'United States|United States|United States', 46, 'Animals|Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Internet|Mice|Oligonucleotide Array Sequence Analysis|Rats|Software', 'Journal Article', '10.1186/1471-2105-10-332', '19828039', 'Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult.'),
(93124, 2009, 'bmc', 'Phylogenetic analysis of modularity in protein interaction networks', 'Erten, Sinan|Li, Xin|Bebek, Gurkan|Li, Jing|Koyutürk, Mehmet', 'Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.|Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, USA.', 'United States|United States|United States|United States|United States', 23, 'Computational Biology|Phylogeny|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-333', '19828041', 'In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.'),
(93125, 2009, 'bmc', 'Trends in modeling Biomedical Complex Systems', 'Milanesi, Luciano|Romano, Paolo|Castellani, Gastone|Remondini, Daniel|Liò, Petro', 'Institute of Biomedical Technology, National Research Council, Milan, Italy. luciano.milanesi@itb.cnr.it|Institute of Biomedical Technology, National Research Council, Milan, Italy. luciano.milanesi@itb.cnr.it|Institute of Biomedical Technology, National Research Council, Milan, Italy. luciano.milanesi@itb.cnr.it|Institute of Biomedical Technology, National Research Council, Milan, Italy. luciano.milanesi@itb.cnr.it|Institute of Biomedical Technology, National Research Council, Milan, Italy. luciano.milanesi@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy', 23, 'Computational Biology|Disease|Humans|Models, Biological|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S12-I1', '19828068', 'In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented.'),
(93126, 2009, 'bmc', 'Data recovery and integration from public databases uncovers transformation-specific transcriptional downregulation of cAMP-PKA pathway-encoding genes', 'Balestrieri, Chiara|Alberghina, Lilia|Vanoni, Marco|Chiaradonna, Ferdinando', 'Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, Milan, Italy. c.balestrieri@campus.unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, Milan, Italy. c.balestrieri@campus.unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, Milan, Italy. c.balestrieri@campus.unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, Milan, Italy. c.balestrieri@campus.unimib.it', 'Italy|Italy|Italy|Italy', 0, 'Computational Biology|Cyclic AMP-Dependent Protein Kinases|Databases, Genetic|Down-Regulation|Gene Expression Regulation|Information Storage and Retrieval|Signal Transduction|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-S12-S1', '19828069', 'The integration of data from multiple genome-wide assays is essential for understanding dynamic spatio-temporal interactions within cells. Such integration, which leads to a more complete view of cellular processes, offers the opportunity to rationalize better the high amount of omics data freely available in several public databases.In particular, integration of microarray-derived transcriptome data with other high-throughput analyses (genomic and mutational analysis, promoter analysis) may allow us to unravel transcriptional regulatory networks under a variety of physio-pathological situations, such as the alteration in the cross-talk between signal transduction pathways in transformed cells.'),
(93127, 2009, 'bmc', 'Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data', 'Corradi, Luca|Mirisola, Valentina|Porro, Ivan|Torterolo, Livia|Fato, Marco|Romano, Paolo|Pfeffer, Ulrich', 'University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it|University of Genoa, Department of Communication, Computer and System Sciences, Viale Causa 13, Genoa, Italy. luca.corradi@unige.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Breast Neoplasms|Computational Biology|Female|Follow-Up Studies|Gene Expression|Gene Expression Profiling|Humans|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S12-S10', '19828070', 'Complex microarray gene expression datasets can be used for many independent analyses and are particularly interesting for the validation of potential biomarkers and multi-gene classifiers. This article presents a novel method to perform correlations between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools.'),
(93128, 2009, 'bmc', 'A Perl procedure for protein identification by Peptide Mass Fingerprinting', 'Tiengo, Alessandra|Barbarini, Nicola|Troiani, Sonia|Rusconi, Luisa|Magni, Paolo', 'Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. alessandra.tiengo@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. alessandra.tiengo@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. alessandra.tiengo@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. alessandra.tiengo@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. alessandra.tiengo@unipv.it', 'Italy|Italy|Italy|Italy|Italy', 6, 'Algorithms|Computational Biology|Databases, Protein|Peptide Library|Peptide Mapping|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-10-S12-S11', '19828071', 'One of the topics of major interest in proteomics is protein identification. Protein identification can be achieved by analyzing the mass spectrum of a protein sample through different approaches. One of them, called Peptide Mass Fingerprinting (PMF), combines mass spectrometry (MS) data with searching strategies in a suitable database of known protein to provide a list of candidate proteins ranked by a score. To this aim, several algorithms and software tools have been proposed. However, the scoring methods and mainly the statistical evaluation of the results can be significantly improved.'),
(93129, 2009, 'bmc', 'Analysis and modelling of motility of cell populations with MotoCell', 'Cantarella, Concita|Sepe, Leandra|Fioretti, Francesca|Ferrari, Maria Carla|Paolella, Giovanni', 'DBBM Dipartimento di Biochimica e Biotecnologie Mediche, Universita di Napoli FEDERICO II, Via S. Pansini 5, Naples, Italy. cantarella@ceinge.unina.it.|DBBM Dipartimento di Biochimica e Biotecnologie Mediche, Universita di Napoli FEDERICO II, Via S. Pansini 5, Naples, Italy. cantarella@ceinge.unina.it.|DBBM Dipartimento di Biochimica e Biotecnologie Mediche, Universita di Napoli FEDERICO II, Via S. Pansini 5, Naples, Italy. cantarella@ceinge.unina.it.|DBBM Dipartimento di Biochimica e Biotecnologie Mediche, Universita di Napoli FEDERICO II, Via S. Pansini 5, Naples, Italy. cantarella@ceinge.unina.it.|DBBM Dipartimento di Biochimica e Biotecnologie Mediche, Universita di Napoli FEDERICO II, Via S. Pansini 5, Naples, Italy. cantarella@ceinge.unina.it.', 'Italy|Italy|Italy|Italy|Italy', 0, 'Animals|Cell Count|Cell Movement|Cells|Fibroblasts|Mice|Models, Theoretical|NIH 3T3 Cells', 'Journal Article', '10.1186/1471-2105-10-S12-S12', '19828072', 'Cell motility plays a central role in development, wound-healing and tumour invasion. Cultures of eukaryotic cells are a complex system where most cells move according to random patterns, but may also be induced to a more coordinate migration by means of specific stimuli, such as the presence of chemical attractants or the introduction of a mechanical stimulus. Various tools have been developed that work by keeping track of the paths followed by specific objects and by performing statistical analysis on the recorded path data. The available tools include desktop applications or macros running within a commercial package, which address specific aspects of the process.'),
(93130, 2009, 'bmc', 'Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers', 'Stefanini, Federico M|Coradini, Danila|Biganzoli, Elia', 'Dipartimento di Statistica G. Parenti, Università degli Studi di Firenze, viale Morgagni 59, Florence, Italy. stefanini@ds.unifi.it|Dipartimento di Statistica G. Parenti, Università degli Studi di Firenze, viale Morgagni 59, Florence, Italy. stefanini@ds.unifi.it|Dipartimento di Statistica G. Parenti, Università degli Studi di Firenze, viale Morgagni 59, Florence, Italy. stefanini@ds.unifi.it', 'Italy|Italy|Italy', 0, 'Bayes Theorem|Biomarkers, Tumor|Breast Neoplasms|Computational Biology|Female|Humans|Receptors, Estrogen|Receptors, Progesterone', 'Journal Article', '10.1186/1471-2105-10-S12-S13', '19828073', 'The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in zero and high states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.'),
(93131, 2009, 'bmc', 'A computational framework for qualitative simulation of nonlinear dynamical models of gene-regulatory networks', 'Ironi, Liliana|Panzeri, Luigi', 'IMATI-Consiglio Nazionale delle Ricerche, via Ferrata 1, Pavia, Italy. ironi@imati.cnr.it|IMATI-Consiglio Nazionale delle Ricerche, via Ferrata 1, Pavia, Italy. ironi@imati.cnr.it', 'Italy|Italy', 14, 'Algorithms|Computational Biology|Gene Regulatory Networks|Models, Genetic|Nonlinear Dynamics', 'Journal Article', '10.1186/1471-2105-10-S12-S14', '19828074', 'Due to the huge amount of information at genomic level made recently available by high-throughput experimental technologies, networks of regulatory interactions between genes and gene products, the so-called gene-regulatory networks, can be uncovered. Most networks of interest are quite intricate because of both the high dimension of interacting elements and the complexity of the kinds of interactions between them. Then, mathematical and computational modeling frameworks are a must to predict the network behavior in response to environmental stimuli. A specific class of Ordinary Differential Equations (ODE) has shown to be adequate to describe the essential features of the dynamics of gene-regulatory networks. But, deriving quantitative predictions of the network dynamics through the numerical simulation of such models is mostly impracticable as they are currently characterized by incomplete knowledge of biochemical reactions underlying regulatory interactions, and of numeric values of kinetic parameters.'),
(93132, 2009, 'bmc', 'Extraction, integration and analysis of alternative splicing and protein structure distributed information', 'DAntonio, Matteo|Masseroli, Marco', 'IFOM-IEO Campus, Via Adamello 16, Milan, Italy. matteo.dantonio@ifom-ieo-campus.it|IFOM-IEO Campus, Via Adamello 16, Milan, Italy. matteo.dantonio@ifom-ieo-campus.it', 'Italy|Italy', 0, 'Alternative Splicing|Computational Biology|Databases, Protein|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-10-S12-S15', '19828075', 'Alternative splicing has been demonstrated to affect most of human genes, different isoforms from the same gene encode for proteins which differ for a limited number of residues, thus yielding similar structures. This suggests possible correlations between alternative splicing and protein structure. In order to support the investigation of such relationships, we have developed the Alternative Splicing and Protein Structure Scrutinizer (PASS), a Web application to automatically extract, integrate and analyze human alternative splicing and protein structure data sparsely available in the Alternative Splicing Database, Ensembl databank and Protein Data Bank. Primary data from these databases have been integrated and analyzed using the Protein Identifier Cross-Reference, BLAST, CLUSTALW and FeatureMap3D software tools.'),
(93133, 2009, 'bmc', 'Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation', 'Alfieri, Roberta|Barberis, Matteo|Chiaradonna, Ferdinando|Gaglio, Daniela|Milanesi, Luciano|Vanoni, Marco|Klipp, Edda|Alberghina, Lilia', 'Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it|Institute for Biomedical Technology--Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate, Milan, Italy. roberta.alfieri@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Animals|Cell Cycle|Cell Cycle Proteins|Fibroblasts|Humans|Mammals|Mice|NIH 3T3 Cells|S Phase|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S12-S16', '19828076', 'The cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G0/G1 phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated.Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G1/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness.'),
(93134, 2009, 'bmc', 'Protopia: a protein-protein interaction tool', 'Real-Chicharro, Alejandro|Ruiz-Mostazo, Iván|Navas-Delgado, Ismael|Kerzazi, Amine|Chniber, Othmane|Sánchez-Jiménez, Francisca|Medina, Miguel Angel|Aldana-Montes, José F', 'University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es|University of Malaga and CIBER de Enfermedades Raras, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Instituto de Salud Carlos III, Spain. areal@lcc.uma.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 10, 'Algorithms|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-S12-S17', '19828077', 'Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es.'),
(93135, 2009, 'bmc', 'The Human EST Ontology Explorer: a tissue-oriented visualization system for ontologies distribution in human EST collections', 'Merelli, Ivan|Caprera, Andrea|Stella, Alessandra|Del Corvo, Marcello|Milanesi, Luciano|Lazzari, Barbara', 'Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it|Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it|Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it|Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it|Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it|Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Databases, Genetic|Expressed Sequence Tags|Gene Library|Humans|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S12-S2', '19828078', 'The NCBI dbEST currently contains more than eight million human Expressed Sequenced Tags (ESTs). This wide collection represents an important source of information for gene expression studies, provided it can be inspected according to biologically relevant criteria. EST data can be browsed using different dedicated web resources, which allow to investigate library specific gene expression levels and to make comparisons among libraries, highlighting significant differences in gene expression. Nonetheless, no tool is available to examine distributions of quantitative EST collections in Gene Ontology (GO) categories, nor to retrieve information concerning library-dependent EST involvement in metabolic pathways. In this work we present the Human EST Ontology Explorer (HEOE) http://www.itb.cnr.it/ptp/human_est_explorer, a web facility for comparison of expression levels among libraries from several healthy and diseased tissues.'),
(93136, 2009, 'bmc', 'In silico saturation mutagenesis and docking screening for the analysis of protein-ligand interaction: the Endothelial Protein C Receptor case study', 'Chiappori, Federica|DUrsi, Pasqualina|Merelli, Ivan|Milanesi, Luciano|Rovida, Ermanna', 'Institute for Biomedical Technologies-National Research Council (ITB-CNR), via Fratelli Cervi 93, Segrate (MI), Italy. federica.chiappori@itb.cnr.it|Institute for Biomedical Technologies-National Research Council (ITB-CNR), via Fratelli Cervi 93, Segrate (MI), Italy. federica.chiappori@itb.cnr.it|Institute for Biomedical Technologies-National Research Council (ITB-CNR), via Fratelli Cervi 93, Segrate (MI), Italy. federica.chiappori@itb.cnr.it|Institute for Biomedical Technologies-National Research Council (ITB-CNR), via Fratelli Cervi 93, Segrate (MI), Italy. federica.chiappori@itb.cnr.it|Institute for Biomedical Technologies-National Research Council (ITB-CNR), via Fratelli Cervi 93, Segrate (MI), Italy. federica.chiappori@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy', 21, 'Binding Sites|Computational Biology|Databases, Protein|Ligands|Mutagenesis|Proteins|Receptors, Cell Surface|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-10-S12-S3', '19828079', 'The design of mutants in protein functional regions, such as the ligand binding sites, is a powerful approach to recognize the determinants of specific protein activities in cellular pathways. For an exhaustive analysis of selected positions of protein structure large scale mutagenesis techniques are often employed, with laborious and time consuming experimental set-up. In silico mutagenesis and screening simulation represents a valid alternative to laboratory methods to drive the in vivo testing toward more focused objectives.'),
(93137, 2009, 'bmc', 'Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering', 'Menolascina, Filippo|Bellomo, Domenico|Maiwald, Thomas|Bevilacqua, Vitoantonio|Ciminelli, Caterina|Paradiso, Angelo|Tommasi, Stefania', 'Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org|Department of Electrical Engineering and Electronics, Technical University of Bari, Via E. Orabona 4, Bari, Italy. f.menolascina@ieee.org', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Microfluidic Analytical Techniques|Monte Carlo Method|Neoplasms|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-S12-S4', '19828080', 'Mechanistic models are becoming more and more popular in Systems Biology, identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.'),
(93138, 2009, 'bmc', 'Phenotypic and genotypic data integration and exploration through a web-service architecture', 'Nuzzo, Angelo|Riva, Alberto|Bellazzi, Riccardo', 'Department of Computer Engineering and Systems Science, University of Pavia, Via Ferrata 1, Pavia, Italy. angelo.nuzzo@unipv.it|Department of Computer Engineering and Systems Science, University of Pavia, Via Ferrata 1, Pavia, Italy. angelo.nuzzo@unipv.it|Department of Computer Engineering and Systems Science, University of Pavia, Via Ferrata 1, Pavia, Italy. angelo.nuzzo@unipv.it', 'Italy|Italy|Italy', 8, 'Database Management Systems|Genotype|Information Storage and Retrieval|Internet|Phenotype|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-10-S12-S5', '19828081', 'Linking genotypic and phenotypic information is one of the greatest challenges of current genetics research. The definition of an Information Technology infrastructure to support this kind of studies, and in particular studies aimed at the analysis of complex traits, which require the definition of multifaceted phenotypes and the integration genotypic information to discover the most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the use of Information Technology methods and tools to develop a system for the management, inspection and integration of phenotypic and genotypic data.'),
(93139, 2009, 'bmc', 'Clinical bioinformatics for complex disorders: a schizophrenia case study', 'Schwarz, Emanuel|Leweke, F Markus|Bahn, Sabine|Liò, Pietro', 'Institute of Biotechnology, University of Cambridge, Tennis Court Road, Cambridge, UK. es505@cam.ac.uk|Institute of Biotechnology, University of Cambridge, Tennis Court Road, Cambridge, UK. es505@cam.ac.uk|Institute of Biotechnology, University of Cambridge, Tennis Court Road, Cambridge, UK. es505@cam.ac.uk|Institute of Biotechnology, University of Cambridge, Tennis Court Road, Cambridge, UK. es505@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 12, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Humans|Mental Disorders|Schizophrenia', 'Journal Article', '10.1186/1471-2105-10-S12-S6', '19828082', 'In the diagnosis of complex diseases such as neurological pathologies, a wealth of clinical and molecular information is often available to help the interpretation. Yet, the pieces of information are usually considered in isolation and rarely integrated due to the lack of a sound statistical framework. This lack of integration results in the loss of valuable information about how disease associated factors act synergistically to cause the complex phenotype.'),
(93140, 2009, 'bmc', 'XML-based approaches for the integration of heterogeneous bio-molecular data', 'Mesiti, Marco|Jiménez-Ruiz, Ernesto|Sanz, Ismael|Berlanga-Llavori, Rafael|Perlasca, Paolo|Valentini, Giorgio|Manset, David', 'Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it|Università degli Studi di Milano, Via Comelico 39, Milan, Italy. mesiti@dico.unimi.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Databases, Factual|Databases, Genetic|Genomics|Mutation|Polymorphism, Genetic|Programming Languages', 'Journal Article', '10.1186/1471-2105-10-S12-S7', '19828083', 'The todays public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing.'),
(93141, 2009, 'bmc', 'Identification of functionally related genes using data mining and data integration: a breast cancer case study', 'Mosca, Ettore|Bertoli, Gloria|Piscitelli, Eleonora|Vilardo, Laura|Reinbold, Rolland A|Zucchi, Ileana|Milanesi, Luciano', 'Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it|Istituto Tecnologie Biomediche, Consiglio Nazionale Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ettore.mosca@itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 15, 'Breast Neoplasms|Computational Biology|Data Mining|Databases, Genetic|Female|Genes|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S12-S8', '19828084', 'The identification of the organisation and dynamics of molecular pathways is crucial for the understanding of cell function. In order to reconstruct the molecular pathways in which a gene of interest is involved in regulating a cell, it is important to identify the set of genes to which it interacts with to determine cell function. In this context, the mining and the integration of a large amount of publicly available data, regarding the transcriptome and the proteome states of a cell, are a useful resource to complement biological research.'),
(93142, 2009, 'bmc', 'A scale space approach for unsupervised feature selection in mass spectra classification for ovarian cancer detection', 'Ceccarelli, Michele|dAcierno, Antonio|Facchiano, Angelo', 'Department of Biological and Environmental Sciences, University of Sannio, Via PortArsa 11, Benevento, Italy. ceccarelli@unisannio.it|Department of Biological and Environmental Sciences, University of Sannio, Via PortArsa 11, Benevento, Italy. ceccarelli@unisannio.it|Department of Biological and Environmental Sciences, University of Sannio, Via PortArsa 11, Benevento, Italy. ceccarelli@unisannio.it', 'Italy|Italy|Italy', 6, 'Computational Biology|Female|Humans|Mass Spectrometry|Ovarian Neoplasms|Pattern Recognition, Automated|Proteomics', 'Journal Article', '10.1186/1471-2105-10-S12-S9', '19828085', 'Mass spectrometry spectra, widely used in proteomics studies as a screening tool for protein profiling and to detect discriminatory signals, are high dimensional data. A large number of local maxima (a.k.a. peaks) have to be analyzed as part of computational pipelines aimed at the realization of efficient predictive and screening protocols. With this kind of data dimensions and samples size the risk of over-fitting and selection bias is pervasive. Therefore the development of bio-informatics methods based on unsupervised feature extraction can lead to general tools which can be applied to several fields of predictive proteomics.'),
(93143, 2009, 'bmc', 'Magallanes: a web services discovery and automatic workflow composition tool', 'Ríos, Javier|Karlsson, Johan|Trelles, Oswaldo', 'Computer Architecture Department, University of Malaga, 29080, Malaga, Spain. jriosp@uma.es|Computer Architecture Department, University of Malaga, 29080, Malaga, Spain. jriosp@uma.es|Computer Architecture Department, University of Malaga, 29080, Malaga, Spain. jriosp@uma.es', 'Spain|Spain|Spain', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Software|Workflow', 'Journal Article', '10.1186/1471-2105-10-334', '19832968', 'To aid in bioinformatics data processing and analysis, an increasing number of web-based applications are being deployed. Although this is a positive circumstance in general, the proliferation of tools makes it difficult to find the right tool, or more importantly, the right set of tools that can work together to solve real complex problems.'),
(93144, 2009, 'bmc', 'Clustering of protein domains for functional and evolutionary studies', 'Goldstein, Pavle|Zucko, Jurica|Vujaklija, Dusica|Krisko, Anita|Hranueli, Daslav|Long, Paul F|Etchebest, Catherine|Basrak, Bojan|Cullum, John', 'Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.|Department of Genetics, University of Kaiserslautern, Postfach 3049, 67653 Kaiserslautern, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Cluster Analysis|Computational Biology|Databases, Protein|Evolution, Molecular|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-335', '19832975', 'The number of protein family members defined by DNA sequencing is usually much larger than those characterised experimentally. This paper describes a method to divide protein families into subtypes purely on sequence criteria. Comparison with experimental data allows an independent test of the quality of the clustering.'),
(93145, 2009, 'bmc', 'A permutation-based multiple testing method for time-course microarray experiments', 'Sohn, Insuk|Owzar, Kouros|George, Stephen L|Kim, Sujong|Jung, Sin-Ho', 'Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA. insuk.sohn@duke.edu', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Computational Biology|Databases, Factual|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-336', '19832992', 'Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression trajectories change over time within a single biological group, or those that follow different time trajectories among multiple groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course) and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational ease and their intuitive interpretation.'),
(93146, 2009, 'bmc', 'Classification and biomarker identification using gene network modules and support vector machines', 'Yousef, Malik|Ketany, Mohamed|Manevitz, Larry|Showe, Louise C|Showe, Michael K', 'The Institute of Applied Research-The Galilee Society, Shefa-Amr, Israel. yousef@gal-soc.org|The Institute of Applied Research-The Galilee Society, Shefa-Amr, Israel. yousef@gal-soc.org|The Institute of Applied Research-The Galilee Society, Shefa-Amr, Israel. yousef@gal-soc.org|The Institute of Applied Research-The Galilee Society, Shefa-Amr, Israel. yousef@gal-soc.org|The Institute of Applied Research-The Galilee Society, Shefa-Amr, Israel. yousef@gal-soc.org', 'Israel|Israel|Israel|Israel|Israel', 0, 'Artificial Intelligence|Biomarkers|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-337', '19832995', 'Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE) RESULTS: Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form n clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.'),
(93147, 2009, 'bmc', 'Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks', 'Helles, Glennie|Fonseca, Rasmus', 'University of Copenhagen, Department of Computer Science, Universitetsparken 1, 2100 Copenhagen, Denmark. glennie@diku.dk|University of Copenhagen, Department of Computer Science, Universitetsparken 1, 2100 Copenhagen, Denmark. glennie@diku.dk', 'Denmark|Denmark', 14, 'Amino Acid Sequence|Amino Acids|Computational Biology|Databases, Protein|Neural Networks (Computer)|Probability|Protein Structure, Secondary|Proteins', 'Journal Article', '10.1186/1471-2105-10-338', '19835576', 'Predicting the three-dimensional structure of a protein from its amino acid sequence is currently one of the most challenging problems in bioinformatics. The internal structure of helices and sheets is highly recurrent and help reduce the search space significantly. However, random coil segments make up nearly 40% of proteins and they do not have any apparent recurrent patterns, which complicates overall prediction accuracy of protein structure prediction methods. Luckily, previous work has indicated that coil segments are in fact not completely random in structure and flanking residues do seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93148, 2009, 'bmc', 'Transcription factor site dependencies in human, mouse and rat genomes', 'Tomovic, Andrija|Stadler, Michael|Oakeley, Edward J', 'Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland. andrija.tomovic@novartis.com|Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland. andrija.tomovic@novartis.com|Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Basel, Switzerland. andrija.tomovic@novartis.com', 'Switzerland|Switzerland|Switzerland', 0, 'Animals|Binding Sites|Computational Biology|DNA|Genome|Humans|Mice|Rats|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-339', '19835596', 'It is known that transcription factors frequently act together to regulate gene expression in eukaryotes. In this paper we describe a computational analysis of transcription factor site dependencies in human, mouse and rat genomes.'),
(93149, 2009, 'bmc', 'Integrating functional genomics data using maximum likelihood based simultaneous component analysis', 'van den Berg, Robert A|Van Mechelen, Iven|Wilderjans, Tom F|Van Deun, Katrijn|Kiers, Henk A L|Smilde, Age K', 'SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be|SymBioSys, Katholieke Universiteit Leuven, Leuven, Belgium. robert.vandenberg@psy.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 12, 'Algorithms|Computational Biology|Genomics|Likelihood Functions|Metabolomics', 'Journal Article', '10.1186/1471-2105-10-340', '19835617', 'In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set.'),
(93150, 2009, 'bmc', 'Exploiting structural and topological information to improve prediction of RNA-protein binding sites', 'Maetschke, Stefan R|Yuan, Zheng', 'Institute for Molecular Bioscience, University of Queensland, QLD 4072, Australia. s.maetschke@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, QLD 4072, Australia. s.maetschke@imb.uq.edu.au', 'Australia|Australia', 0, 'Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|RNA|RNA-Binding Proteins', 'Journal Article', '10.1186/1471-2105-10-341', '19835626', 'RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction problem. Particularly, different geometrical and network topological properties of protein structures are evaluated to improve interface residue prediction accuracy.'),
(93151, 2009, 'bmc', 'iQuantitator: a tool for protein expression inference using iTRAQ', 'Schwacke, John H|Hill, Elizabeth G|Krug, Edward L|Comte-Walters, Susana|Schey, Kevin L', 'Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu|Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu|Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu|Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu|Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu', 'United States|United States|United States|United States|United States', 43, 'Computational Biology|Databases, Protein|Markov Chains|Monte Carlo Method|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-10-342', '19835628', 'Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.'),
(93152, 2009, 'bmc', 'Bayesian inference of biochemical kinetic parameters using the linear noise approximation', 'Komorowski, Michaxc5x82|Finkenstxc3xa4dt, Bxc3xa4rbel|Harper, Claire V|Rand, David A', 'Department of Statistics, University of Warwick, Coventry, UK. M.Komorowski@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry, UK. M.Komorowski@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry, UK. M.Komorowski@warwick.ac.uk|Department of Statistics, University of Warwick, Coventry, UK. M.Komorowski@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Bayes Theorem|Genes, Reporter|Kinetics|Markov Chains|Monte Carlo Method', 'Journal Article', '10.1186/1471-2105-10-343', '19840370', 'Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the development of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.'),
(93153, 2009, 'bmc', 'TIde: a software for the systematic scanning of drug targets in kinetic network models', 'Schulz, Marvin|Bakker, Barbara M|Klipp, Edda', 'Theoretical Biophysics, Institute for Biology, Humboldt Universität zu Berlin, Invadilenstrasse, 42, 10115 Berlin, Germany. marvin.schulz@biologie.hu-berlin.de|Theoretical Biophysics, Institute for Biology, Humboldt Universität zu Berlin, Invadilenstrasse, 42, 10115 Berlin, Germany. marvin.schulz@biologie.hu-berlin.de|Theoretical Biophysics, Institute for Biology, Humboldt Universität zu Berlin, Invadilenstrasse, 42, 10115 Berlin, Germany. marvin.schulz@biologie.hu-berlin.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Glycolysis|Kinetics|Signal Transduction|Software|Systems Biology|Trypanosoma brucei brucei', 'Journal Article', '10.1186/1471-2105-10-344', '19840374', 'During the stages of the development of a potent drug candidate compounds can fail for several reasons. One of them, the efficacy of a candidate, can be estimated in silico if an appropriate ordinary differential equation model of the affected pathway is available. With such a model at hand it is also possible to detect reactions having a large effect on a certain variable such as a substance concentration.'),
(93154, 2009, 'bmc', 'Highlights from the 5th International Society for Computational Biology Student Council Symposium at the 17th Annual International Conference on Intelligent Systems for Molecular Biology and the 8th European Conference on Computational Biology', 'Abeel, Thomas|de Ridder, Jeroen|Peixoto, Lucia', 'Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium. thomas@abeel.be.|Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium. thomas@abeel.be.|Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium. thomas@abeel.be.', 'Belgium|Belgium|Belgium', 0, 'Computational Biology|Molecular Biology', 'Introductory Journal Article', '10.1186/1471-2105-10-S13-I1', '19840405', '-'),
(93155, 2009, 'bmc', 'Arabidopsis gene co-expression network and its functional modules', 'Mao, Linyong|Van Hemert, John L|Dash, Sudhansu|Dickerson, Julie A', 'Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA. linyongmao@gmail.com|Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA. linyongmao@gmail.com|Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA. linyongmao@gmail.com|Virtual Reality Applications Center, Iowa State University, Ames, IA 50010, USA. linyongmao@gmail.com', 'United States|United States|United States|United States', 124, 'Arabidopsis|Arabidopsis Proteins|Cluster Analysis|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks', 'Journal Article', '10.1186/1471-2105-10-346', '19845953', 'Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated.'),
(93156, 2009, 'bmc', 'Direct calibration of PICKY-designed microarrays', 'Chou, Hui-Hsien|Trisiriroj, Arunee|Park, Sunyoung|Hsing, Yue-Ie C|Ronald, Pamela C|Schnable, Patrick S', 'Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu|Department of Genetics, Development and Cell Biology and Department of Computer Science, Iowa State University, Ames, IA 50011, USA. hhchou@iastate.edu', 'United States|United States|United States|United States|United States|United States', 8, 'Calibration|Computational Biology|DNA, Complementary|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-347', '19849862', 'Few microarrays have been quantitatively calibrated to identify optimal hybridization conditions because it is difficult to precisely determine the hybridization characteristics of a microarray using biologically variable cDNA samples.'),
(93157, 2009, 'bmc', 'GIFtS: annotation landscape analysis with GeneCards', 'Harel, Arye|Inger, Aron|Stelzer, Gil|Strichman-Almashanu, Liora|Dalah, Irina|Safran, Marilyn|Lancet, Doron', 'Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. arik.harel@weizmann.ac.il', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genes|Software', 'Journal Article', '10.1186/1471-2105-10-348', '19852797', 'Gene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more.'),
(93158, 2009, 'bmc', 'Construction of an annotated corpus to support biomedical information extraction', 'Thompson, Paul|Iqbal, Syed A|McNaught, John|Ananiadou, Sophia', 'National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk|National Centre for Text Mining, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. paul.thompson@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 57, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-349', '19852798', 'Information Extraction (IE) is a component of text mining that facilitates knowledge discovery by automatically locating instances of interesting biomedical events from huge document collections. As events are usually centred on verbs and nominalised verbs, understanding the syntactic and semantic behaviour of these words is highly important. Corpora annotated with information concerning this behaviour can constitute a valuable resource in the training of IE components and resources.'),
(93159, 2009, 'bmc', 'Local cell metrics: a novel method for analysis of cell-cell interactions', 'Su, Jing|Zapata, Pedro J|Chen, Chien-Chiang|Meredith, J Carson', 'School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.|School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.', 'Georgia|Georgia|Georgia|Georgia', 6, 'Bayes Theorem|Cell Communication|Cell Proliferation|Image Processing, Computer-Assisted|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-350', '19852804', 'The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs), which addresses this shortcoming.'),
(93160, 2009, 'bmc', 'A structure filter for the Eukaryotic Linear Motif Resource', 'Via, Allegra|Gould, Cathryn M|Gemünd, Christine|Gibson, Toby J|Helmer-Citterich, Manuela', 'Center for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. allegra.via@uniroma1.it|Center for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. allegra.via@uniroma1.it|Center for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. allegra.via@uniroma1.it|Center for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. allegra.via@uniroma1.it|Center for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. allegra.via@uniroma1.it', 'Italy|Italy|Italy|Italy|Italy', 35, 'Amino Acid Motifs|Computational Biology|Databases, Protein|Eukaryota|Peptides|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-351', '19852836', 'Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.'),
(93161, 2009, 'bmc', 'Abstracts from the 5th International Society for Computational Biology Student Council Symposium at the 17th Annual International Conference on Intelligent Systems for Molecular Biology and the 8th European Conference on Computational Biology June 27, 200', '-', '-', '-', 35, 'Computational Biology|Molecular Biology', 'Congress', '10.1186/1471-2105-10-351', '19856525', '-'),
(93162, 2009, 'bmc', 'Bayesian modeling of ChIP-chip data using latent variables', 'Wu, Mingqi|Liang, Faming|Tian, Yanan', 'Department of Statistics, Texas A&M University, College Station, TX 77843, USA. mqwu@stat.tamu.edu|Department of Statistics, Texas A&M University, College Station, TX 77843, USA. mqwu@stat.tamu.edu|Department of Statistics, Texas A&M University, College Station, TX 77843, USA. mqwu@stat.tamu.edu', 'United States|United States|United States', 4, 'Bayes Theorem|Chromatin Immunoprecipitation|Computational Biology|Gene Expression Profiling|Pattern Recognition, Automated|Poisson Distribution', 'Journal Article', '10.1186/1471-2105-10-352', '19857265', 'The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations.'),
(93163, 2009, 'bmc', 'Time warping of evolutionary distant temporal gene expression data based on noise suppression', 'Goltsev, Yury|Papatsenko, Dmitri', 'Department of Molecular and Cell biology, University of California, Berkeley, USA. goltsev@berkeley.edu|Department of Molecular and Cell biology, University of California, Berkeley, USA. goltsev@berkeley.edu', 'United States|United States', 7, 'Biological Evolution|Computational Biology|Fungal Proteins|Gene Expression|Gene Expression Regulation, Fungal|Gene Regulatory Networks|Saccharomyces cerevisiae|Schizosaccharomyces', 'Journal Article', '10.1186/1471-2105-10-353', '19857268', 'Comparative analysis of genome wide temporal gene expression data has a broad potential area of application, including evolutionary biology, developmental biology, and medicine. However, at large evolutionary distances, the construction of global alignments and the consequent comparison of the time-series data are difficult. The main reason is the accumulation of variability in expression profiles of orthologous genes, in the course of evolution.'),
(93164, 2009, 'bmc', 'BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors', '-', '-', '-', 11, 'Binding Sites|Computational Biology|DNA, Fungal|Genome, Fungal|Saccharomyces cerevisiae|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-345', '19857274', 'We have incorporated Bayesian model regularization with biophysical modeling of protein-DNA interactions, and of genome-wide nucleosome positioning to study protein-DNA interactions, using a high-throughput dataset. The newly developed method (BayesPI) includes the estimation of a transcription factor (TF) binding energy matrices, the computation of binding affinity of a TF target site and the corresponding chemical potential.'),
(93165, 2009, 'bmc', 'Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB', 'Chatziioannou, Aristotelis|Moulos, Panagiotis|Kolisis, Fragiskos N', 'Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, 48 Vassileos Constantinou ave., 11635, Athens, Greece. achatzi@eie.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, 48 Vassileos Constantinou ave., 11635, Athens, Greece. achatzi@eie.gr|Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation, 48 Vassileos Constantinou ave., 11635, Athens, Greece. achatzi@eie.gr', 'Greece|Greece|Greece', 37, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-354', '19860866', 'The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.'),
(93166, 2009, 'bmc', 'Stratification of co-evolving genomic groups using ranked phylogenetic profiles', 'Freilich, Shiri|Goldovsky, Leon|Gottlieb, Assaf|Blanc, Eric|Tsoka, Sophia|Ouzounis, Christos A', 'The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com|The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com|The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com|The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com|The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com|The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com', 'Israel|Israel|Israel|Israel|Israel|Israel', 7, 'Computational Biology|Evolution, Molecular|Genome|Genomics|Metagenomics|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-355', '19860884', 'Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database.'),
(93167, 2009, 'bmc', 'phyloXML: XML for evolutionary biology and comparative genomics', 'Han, Mira V|Zmasek, Christian M', 'School of Informatics, Indiana University, Bloomington, IN 47408, USA. mirhan@indiana.edu|School of Informatics, Indiana University, Bloomington, IN 47408, USA. mirhan@indiana.edu', 'India|India', 327, 'Biological Evolution|Computational Biology|Databases, Genetic|Genomics|Phylogeny|Software', 'Comparative Study', '10.1186/1471-2105-10-356', '19860910', 'Evolutionary trees are central to a wide range of biological studies. In many of these studies, tree nodes and branches need to be associated (or annotated) with various attributes. For example, in studies concerned with organismal relationships, tree nodes are associated with taxonomic names, whereas tree branches have lengths and oftentimes support values. Gene trees used in comparative genomics or phylogenomics are usually annotated with taxonomic information, genome-related data, such as gene names and functional annotations, as well as events such as gene duplications, speciations, or exon shufflings, combined with information related to the evolutionary tree itself. The data standards currently used for evolutionary trees have limited capacities to incorporate such annotations of different data types.'),
(93168, 2009, 'bmc', 'AIR: A batch-oriented web program package for construction of supermatrices ready for phylogenomic analyses', 'Kumar, Surendra|Skjaeveland, Asmund|Orr, Russell J S|Enger, Pål|Ruden, Torgeir|Mevik, Bjørn-Helge|Burki, Fabien|Botnen, Andreas|Shalchian-Tabrizi, Kamran', 'Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no|Microbial Evolution Research Group, Department of Biology, University of Oslo, Norway. surendra.kumar@bio.uio.no', 'Norway|Norway|Norway|Norway|Norway|Norway|Norway|Norway|Norway', 197, 'Computational Biology|Databases, Genetic|Internet|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-357', '19863793', 'Large multigene sequence alignments have over recent years been increasingly employed for phylogenomic reconstruction of the eukaryote tree of life. Such supermatrices of sequence data are preferred over single gene alignments as they contain vastly more information about ancient sequence characteristics, and are thus more suitable for resolving deeply diverging relationships. However, as alignments are expanded, increasingly numbers of sites with misleading phylogenetic information are also added. Therefore, a major goal in phylogenomic analyses is to maximize the ratio of information to noise, this can be achieved by the reduction of fast evolving sites.'),
(93169, 2009, 'bmc', 'ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization', 'Glaab, Enrico|Garibaldi, Jonathan M|Krasnogor, Natalio', 'School of Computer Science, Nottingham University, Jubilee Campus, Wollaton Road, Nottingham, UK. enrico.glaab@cs.nott.ac.uk|School of Computer Science, Nottingham University, Jubilee Campus, Wollaton Road, Nottingham, UK. enrico.glaab@cs.nott.ac.uk|School of Computer Science, Nottingham University, Jubilee Campus, Wollaton Road, Nottingham, UK. enrico.glaab@cs.nott.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 79, 'Algorithms|Computational Biology|Databases, Factual|Databases, Genetic|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-358', '19863798', 'Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.'),
(93170, 2009, 'bmc', 'Analysis and comparison of very large metagenomes with fast clustering and functional annotation', 'Li, Weizhong', 'California Institute for Telecommunications and Information Technology, University of California, San Diego, La Jolla, California 92093, USA. liwz@sdsc.edu', 'United States', 82, 'Algorithms|Cluster Analysis|Computational Biology|Metagenomics|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-10-359', '19863816', 'The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand.'),
(93171, 2009, 'bmc', 'Inferring within-patient HIV-1 evolutionary dynamics under anti-HIV therapy using serial virus samples with vSPA', 'Hasegawa, Naoki|Sugiura, Wataru|Shibata, Junko|Matsuda, Masakazu|Ren, Fengrong|Tanaka, Hiroshi', 'School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp|School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp|School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp|School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp|School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp|School of Biomedical Sciences, Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo 113-8510, Japan. hasegawa@bioinfo.tmd.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 6, 'Anti-HIV Agents|Computational Biology|DNA, Viral|Drug Resistance, Viral|Evolution, Molecular|HIV Protease|HIV-1|Humans|RNA-Directed DNA Polymerase', 'Journal Article', '10.1186/1471-2105-10-360', '19863822', 'Analysis of within-patient HIV evolution under anti-HIV therapy is crucial to a better understanding the possible mechanisms of HIV drug-resistance acquisition. The high evolutionary rate of HIV allows us to trace its evolutionary process in real time by analyzing virus samples serially collected from the same patient. However, such studies are still uncommon due to the lack of powerful computational methods designed for serial virus samples. In this study, we develop a computational method, vSPA (viral Sequential Pathway Analysis), which groups viral sequences from the same sampling time into clusters and traces the evolution between clusters over sampling times. The method makes use of information of different sampling times and traces the evolution of important amino acid mutations. Second, a permutation test at the codon level is conducted to determine the threshold of the correlation coefficient for clustering viral quasispecies. We applied vSPA to four large data sets of HIV-1 protease and reverse transcriptase genes serially collected from two AIDS patients undergoing anti-HIV therapy over several years.'),
(93172, 2009, 'bmc', 'Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy', 'Yang, Zheng Rong', 'School of Biosciences, University of Exeter, Exeter EX4 5DE, UK. z.r.yang@ex.ac.uk', 'United Kingdom', 15, 'Algorithms|Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Sequence Analysis, Protein|Tyrosine', 'Journal Article', '10.1186/1471-2105-10-361', '19874585', 'Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.'),
(93173, 2009, 'bmc', 'BARCRAWL and BARTAB: software tools for the design and implementation of barcoded primers for highly multiplexed DNA sequencing', 'Frank, Daniel N', 'Department of Molecular, University of Colorado, Boulder, CO 80309, USA. daniel.frank@colorado.edu', 'United States', 114, 'Algorithms|Base Sequence|Computational Biology|DNA Primers|Genome|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-362', '19874596', 'Advances in automated DNA sequencing technology have greatly increased the scale of genomic and metagenomic studies. An increasingly popular means of increasing project throughput is by multiplexing samples during the sequencing phase. This can be achieved by covalently linking short, unique barcode DNA segments to genomic DNA samples, for instance through incorporation of barcode sequences in PCR primers. Although several strategies have been described to insure that barcode sequences are unique and robust to sequencing errors, these have not been integrated into the overall primer design process, thus potentially introducing bias into PCR amplification and/or sequencing steps.'),
(93174, 2009, 'bmc', 'A novel R-package graphic user interface for the analysis of metabonomic profiles', 'Izquierdo-García, Jose L|Rodríguez, Ignacio|Kyriazis, Angelos|Villa, Palmira|Barreiro, Pilar|Desco, Manuel|Ruiz-Cabello, Jesús', 'Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es|Instituto de Estudios Biofuncionales, UCM, Madrid, Spain. izquierdo@ieb.ucm.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 42, 'Computational Biology|Computer Graphics|Discriminant Analysis|Metabolomics|Principal Component Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-363', '19874601', 'Analysis of the plethora of metabolites found in the NMR spectra of biological fluids or tissues requires data complexity to be simplified. We present a graphical user interface (GUI) for NMR-based metabonomic analysis. The Metabonomic Package has been developed for metabonomics research as open-source software and uses the R statistical libraries.'),
(93175, 2009, 'bmc', 'multiplierz: an extensible API based desktop environment for proteomics data analysis', 'Parikh, Jignesh R|Askenazi, Manor|Ficarro, Scott B|Cashorali, Tanya|Webber, James T|Blank, Nathaniel C|Zhang, Yi|Marto, Jarrod A', 'Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu|Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA, 02115, USA. jparikh@bu.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 56, 'Computational Biology|Database Management Systems|Databases, Factual|Internet|Mass Spectrometry|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-364', '19874609', 'Efficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized, hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge.'),
(93176, 2009, 'bmc', 'Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods', 'Lise, Stefano|Archambeau, Cedric|Pontil, Massimiliano|Jones, David T', 'Department of Computer Science, University College London, UK. s.lise@ucl.ac.uk|Department of Computer Science, University College London, UK. s.lise@ucl.ac.uk|Department of Computer Science, University College London, UK. s.lise@ucl.ac.uk|Department of Computer Science, University College London, UK. s.lise@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 76, 'Alanine|Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Hydrogen Bonding|Protein Interaction Mapping|Proteins|Static Electricity|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-365', '19878545', 'Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues (hot spots) at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.'),
(93177, 2009, 'bmc', '(PS)2-v2: template-based protein structure prediction server', 'Chen, Chih-Chieh|Hwang, Jenn-Kang|Yang, Jinn-Moon', 'Institute of Bioinformatics, National Chiao Tung University, Hsinchu 30050, Taiwan, Republic of China. chieh.bi91g@nctu.edu.tw|Institute of Bioinformatics, National Chiao Tung University, Hsinchu 30050, Taiwan, Republic of China. chieh.bi91g@nctu.edu.tw|Institute of Bioinformatics, National Chiao Tung University, Hsinchu 30050, Taiwan, Republic of China. chieh.bi91g@nctu.edu.tw', 'China|China|China', 69, 'Binding Sites|Computational Biology|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-366', '19878598', 'Template selection and target-template alignment are critical steps for template-based modeling (TBM) methods. To identify the template for the twilight zone of 15~25% sequence similarity between targets and templates is still difficulty for template-based protein structure prediction. This study presents the (PS)2-v2 server, based on our original server with numerous enhancements and modifications, to improve reliability and applicability.'),
(93178, 2009, 'bmc', 'GLIDERS--a web-based search engine for genome-wide linkage disequilibrium between HapMap SNPs', 'Lawrence, Robert|Day-Williams, Aaron G|Mott, Richard|Broxholme, John|Cardon, Lon R|Zeggini, Eleftheria', 'Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk|Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. z.r.yang@ex.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Databases, Genetic|Genome|Genome-Wide Association Study|Humans|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Search Engine|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-367', '19878600', 'A number of tools for the examination of linkage disequilibrium (LD) patterns between nearby alleles exist, but none are available for quickly and easily investigating LD at longer ranges (>500 kb). We have developed a web-based query tool (GLIDERS: Genome-wide LInkage DisEquilibrium Repository and Search engine) that enables the retrieval of pairwise associations with r2 >or= 0.3 across the human genome for any SNP genotyped within HapMap phase 2 and 3, regardless of distance between the markers.'),
(93179, 2009, 'bmc', 'A biosegmentation benchmark for evaluation of bioimage analysis methods', 'Drelie Gelasca, Elisa|Obara, Boguslaw|Fedorov, Dmitry|Kvilekval, Kristian|Manjunath, Bs', 'Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org|Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org|Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org|Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org|Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org', 'United States|United States|United States|United States|United States', 56, 'Algorithms|Benchmarking|Computational Biology|Computer Graphics|Databases, Factual|Image Enhancement|Image Interpretation, Computer-Assisted|Image Processing, Computer-Assisted|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-368', '19878606', 'We present a biosegmentation benchmark that includes infrastructure, datasets with associated ground truth, and validation methods for biological image analysis. The primary motivation for creating this resource comes from the fact that it is very difficult, if not impossible, for an end-user to choose from a wide range of segmentation methods available in the literature for a particular bioimaging problem. No single algorithm is likely to be equally effective on diverse set of images and each method has its own strengths and limitations. We hope that our benchmark resource would be of considerable help to both the bioimaging researchers looking for novel image processing methods and image processing researchers exploring application of their methods to biology.'),
(93180, 2009, 'bmc', 'Leading applications and technologies in bioinformatics Proceedings of the European Molecular Biology Network (EMBnet) Conference 2008: 20th Anniversary Celebration Martina Franca, Italy September 18-20, 2008', '-', '-', '-', 56, 'Animals|Computational Biology|Genomics|Humans|Molecular Biology|Proteomics', 'Congress', '10.1186/1471-2105-10-368', '19882766', '-'),
(93181, 2009, 'bmc', 'RNA folding on the 3D triangular lattice', 'Gillespie, Joel|Mayne, Martin|Jiang, Minghui', 'Department of Computer Science, Utah State University, Logan, Utah 84322-4205, USA. jgillespie@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84322-4205, USA. jgillespie@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84322-4205, USA. jgillespie@cc.usu.edu', 'United States|United States|United States', 15, 'Base Sequence|Computational Biology|Databases, Genetic|Molecular Sequence Data|Nucleic Acid Conformation|RNA', 'Journal Article', '10.1186/1471-2105-10-369', '19891777', 'Difficult problems in structural bioinformatics are often studied in simple exact models to gain insights and to derive general principles. Protein folding, for example, has long been studied in the lattice model. Recently, researchers have also begun to apply the lattice model to the study of RNA folding.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(93182, 2009, 'bmc', 'Elucidation of functional consequences of signalling pathway interactions', 'Ihekwaba, Adaoha E C|Nguyen, Phuong T|Priami, Corrado', 'The Microsoft Research-University of Trento, Centre for Computational Systems Biology, Povo (Trento), Italy. ihekwaba@cosbi.eu|The Microsoft Research-University of Trento, Centre for Computational Systems Biology, Povo (Trento), Italy. ihekwaba@cosbi.eu|The Microsoft Research-University of Trento, Centre for Computational Systems Biology, Povo (Trento), Italy. ihekwaba@cosbi.eu', 'Italy|Italy|Italy', 3, 'Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-370', '19895694', 'A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions.'),
(93183, 2009, 'bmc', 'An empirical Bayesian approach for model-based inference of cellular signaling networks', 'Klinke, David J', 'Department of Chemical Engineering, West Virginia University, Morgantown, WV 26506-6102, USA. david.klinke@mail.wvu.edu', 'United States', 47, 'Animals|Bayes Theorem|Computational Biology|Epidermal Growth Factor|Hepatocytes|Neural Networks (Computer)|Rats|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-371', '19900289', 'A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways.'),
(93184, 2009, 'bmc', 'Biodiversity informatics: the emergence of a field', 'Sarkar, Indra Neil', 'Center for Clinical and Translational Science, University of Vermont, Burlington, VT, USA. neil.sarkar@uvm.edu', 'United States', 10, 'Biodiversity|Computational Biology|Internet|Periodicals as Topic', 'Introductory Journal Article', '10.1186/1471-2105-10-S14-S1', '19900296', '-'),
(93185, 2009, 'bmc', 'DNA barcode analysis: a comparison of phylogenetic and statistical classification methods', 'Austerlitz, Frederic|David, Olivier|Schaeffer, Brigitte|Bleakley, Kevin|Olteanu, Madalina|Leblois, Raphael|Veuille, Michel|Laredo, Catherine', 'CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr|CNRS, Laboratoire Ecologie Systématique et Evolution, UMR 8079, Orsay, F-91405, France. frederic.austerlitz@u-psud.fr', 'France|France|France|France|France|France|France|France', 105, 'Computational Biology|Computer Simulation|Databases, Nucleic Acid|Electronic Data Processing|Mutation|Phylogeny|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-10-S14-S10', '19900297', 'DNA barcoding aims to assign individuals to given species according to their sequence at a small locus, generally part of the CO1 mitochondrial gene. Amongst other issues, this raises the question of how to deal with within-species genetic variability and potential transpecific polymorphism. In this context, we examine several assignation methods belonging to two main categories: (i) phylogenetic methods (neighbour-joining and PhyML) that attempt to account for the genealogical framework of DNA evolution and (ii) supervised classification methods (k-nearest neighbour, CART, random forest and kernel methods). These methods range from basic to elaborate. We investigated the ability of each method to correctly classify query sequences drawn from samples of related species using both simulated and real data. Simulated data sets were generated using coalescent simulations in which we varied the genealogical history, mutation parameter, sample size and number of species.'),
(93186, 2009, 'bmc', 'Towards a data publishing framework for primary biodiversity data: challenges and potentials for the biodiversity informatics community', 'Chavan, Vishwas S|Ingwersen, Peter', 'Global Biodiversity Information Facility Secretariat, Universitetsparken 15, DK 2100, Copenhagen, Denmark. vchavan@gbif.org|Global Biodiversity Information Facility Secretariat, Universitetsparken 15, DK 2100, Copenhagen, Denmark. vchavan@gbif.org', 'Denmark|Denmark', 31, 'Biodiversity|Computational Biology|Databases, Factual|Humans|Policy Making|Publishing', 'Journal Article', '10.1186/1471-2105-10-S14-S2', '19900298', 'Currently primary scientific data, especially that dealing with biodiversity, is neither easily discoverable nor accessible. Amongst several impediments, one is a lack of professional recognition of scientific data publishing efforts. A possible solution is establishment of a Data Publishing Framework which would encourage and recognise investments and efforts by institutions and individuals towards management, and publishing of primary scientific data potentially on a par with recognitions received for scholarly publications.'),
(93187, 2009, 'bmc', 'Location, location, location: utilizing pipelines and services to more effectively georeference the worlds biodiversity data', 'Hill, Andrew W|Guralnick, Robert|Flemons, Paul|Beaman, Reed|Wieczorek, John|Ranipeta, Ajay|Chavan, Vishwas|Remsen, David', 'University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu|University of Colorado Museum of Natural History and Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309-0265, USA. Andrew.Hill@colorado.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 17, 'Biodiversity|Computational Biology|Databases, Factual|Humans', 'Journal Article', '10.1186/1471-2105-10-S14-S3', '19900299', 'Increasing the quantity and quality of data is a key goal of biodiversity informatics, leading to increased fitness for use in scientific research and beyond. This goal is impeded by a legacy of geographic locality descriptions associated with biodiversity records that are often heterogeneous and not in a map-ready format. The biodiversity informatics community has developed best practices and tools that provide the means to do retrospective georeferencing (e.g., the BioGeomancer toolkit), a process that converts heterogeneous descriptions into geographic coordinates and a measurement of spatial uncertainty. Even with these methods and tools, data publishers are faced with the immensely time-consuming task of vetting georeferenced localities. Furthermore, it is likely that overlap in georeferencing effort is occurring across data publishers. Solutions are needed that help publishers more effectively georeference their records, verify their quality, and eliminate the duplication of effort across publishers.'),
(93188, 2009, 'bmc', 'Googling DNA sequences on the World Wide Web', 'Hajibabaei, Mehrdad|Singer, Gregory A C', 'Biodiversity Institute of Ontario, Department of Integrative Biology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada. mhajibab@uoguelph.ca|Biodiversity Institute of Ontario, Department of Integrative Biology, University of Guelph, Guelph, Ontario, N1G 2W1, Canada. mhajibab@uoguelph.ca', 'Canada|Canada', 7, 'Base Sequence|Computational Biology|Databases, Genetic|Information Storage and Retrieval|Internet|Molecular Sequence Data|Sequence Analysis, DNA|Software Design', 'Journal Article', '10.1186/1471-2105-10-S14-S4', '19900300', 'New web-based technologies provide an excellent opportunity for sharing and accessing information and using web as a platform for interaction and collaboration. Although several specialized tools are available for analyzing DNA sequence information, conventional web-based tools have not been utilized for bioinformatics applications. We have developed a novel algorithm and implemented it for searching species-specific genomic sequences, DNA barcodes, by using popular web-based methods such as Google.'),
(93189, 2009, 'bmc', 'bioGUID: resolving, discovering, and minting identifiers for biodiversity informatics', 'Page, Roderic D M', 'Division of Environmental and Evolutionary Biology, Faculty of Biomedical and Life Sciences, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK. r.page@bio.gla.ac.uk', 'United Kingdom', 13, 'Biodiversity|Computational Biology|Databases, Factual|Humans|Internet', 'Journal Article', '10.1186/1471-2105-10-S14-S5', '19900301', 'Linking together the data of interest to biodiversity researchers (including specimen records, images, taxonomic names, and DNA sequences) requires services that can mint, resolve, and discover globally unique identifiers (including, but not limited to, DOIs, HTTP URIs, and LSIDs).'),
(93190, 2009, 'bmc', 'Scratchpads: a data-publishing framework to build, share and manage information on the diversity of life', 'Smith, Vincent S|Rycroft, Simon D|Harman, Kehan T|Scott, Ben|Roberts, David', 'Natural History Museum, Cromwell Road, London, SW7 5BD, UK. vince@vsmith.info|Natural History Museum, Cromwell Road, London, SW7 5BD, UK. vince@vsmith.info|Natural History Museum, Cromwell Road, London, SW7 5BD, UK. vince@vsmith.info|Natural History Museum, Cromwell Road, London, SW7 5BD, UK. vince@vsmith.info|Natural History Museum, Cromwell Road, London, SW7 5BD, UK. vince@vsmith.info', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Biodiversity|Computational Biology|Computer Simulation|Database Management Systems|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-10-S14-S6', '19900302', 'Natural History science is characterised by a single immense goal (to document, describe and synthesise all facets pertaining to the diversity of life) that can only be addressed through a seemingly infinite series of smaller studies. The disciplines failure to meaningfully connect these small studies with natural historys goal has made it hard to demonstrate the value of natural history to a wider scientific community. Digital technologies provide the means to bridge this gap.'),
(93191, 2009, 'bmc', 'Learning to classify species with barcodes', 'Bertolazzi, Paola|Felici, Giovanni|Weitschek, Emanuel', 'Istituto di Analisi dei Sistemi e Informatica Antonio Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni 30, 00185, Rome, Italy. paola.bertolazzi@iasi.cnr.it|Istituto di Analisi dei Sistemi e Informatica Antonio Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni 30, 00185, Rome, Italy. paola.bertolazzi@iasi.cnr.it|Istituto di Analisi dei Sistemi e Informatica Antonio Ruberti, Consiglio Nazionale delle Ricerche, Viale Manzoni 30, 00185, Rome, Italy. paola.bertolazzi@iasi.cnr.it', 'Italy|Italy|Italy', 63, 'Animals|Classification|Computational Biology|Electronic Data Processing|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S14-S7', '19900303', 'According to many field experts, specimens classification based on morphological keys needs to be supported with automated techniques based on the analysis of DNA fragments. The most successful results in this area are those obtained from a particular fragment of mitochondrial DNA, the gene cytochrome c oxidase I (COI) (the barcode). Since 2004 the Consortium for the Barcode of Life (CBOL) promotes the collection of barcode specimens and the development of methods to analyze the barcode for several tasks, among which the identification of rules to correctly classify an individual into its species by reading its barcode.'),
(93192, 2009, 'bmc', 'Rapid DNA barcoding analysis of large datasets using the composition vector method', 'Chu, Ka Hou|Xu, Minli|Li, Chi Pang', 'Department of Biology, The Chinese University of Hong Kong, Hong Kong, PR China. kahouchu@cuhk.edu.hk|Department of Biology, The Chinese University of Hong Kong, Hong Kong, PR China. kahouchu@cuhk.edu.hk|Department of Biology, The Chinese University of Hong Kong, Hong Kong, PR China. kahouchu@cuhk.edu.hk', 'China|China|China', 0, 'Animals|Birds|Computational Biology|Databases, Nucleic Acid|Electronic Data Processing|Fishes|Nematoda|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/1471-2105-10-S14-S8', '19900304', 'Sequence alignment is the rate-limiting step in constructing profile trees for DNA barcoding purposes. We recently demonstrated the feasibility of using unaligned rRNA sequences as barcodes based on a composition vector (CV) approach without sequence alignment (Bioinformatics 22:1690). Here, we further explored the grouping effectiveness of the CV method in large DNA barcode datasets (COI, 18S and 16S rRNA) from a variety of organisms, including birds, fishes, nematodes and crustaceans.'),
(93193, 2009, 'bmc', 'Efficient alignment-free DNA barcode analytics', 'Kuksa, Pavel|Pavlovic, Vladimir', 'Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu|Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu', 'United States|United States', 0, 'Animals|Base Sequence|Computational Biology|Electronic Data Processing|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-S14-S9', '19900305', 'In this work we consider barcode DNA analysis problems and address them using alternative, alignment-free methods and representations which model sequences as collections of short sequence fragments (features). The methods use fixed-length representations (spectrum) for barcode sequences to measure similarities or dissimilarities between sequences coming from the same or different species. The spectrum-based representation not only allows for accurate and computationally efficient species classification, but also opens possibility for accurate clustering analysis of putative species barcodes and identification of critical within-barcode loci distinguishing barcodes of different sample groups.'),
(93194, 2009, 'bmc', 'Illumina WG-6 BeadChip strips should be normalized separately', 'Shi, Wei|Banerjee, Ashish|Ritchie, Matthew E|Gerondakis, Steve|Smyth, Gordon K', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia. shi@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia. shi@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia. shi@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia. shi@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia. shi@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia', 19, 'Animals|Computational Biology|Gene Expression Profiling|Mice|Mice, Inbred C57BL|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-372', '19903361', 'Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values.'),
(93195, 2009, 'bmc', 'A theoretical approach to spot active regions in antimicrobial proteins', 'Torrent, Marc|Nogués, Victòria M|Boix, Ester', 'Dpt, Bioquímica i Biologia Molecular, Fac, Biociències, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. marc.torrent@uab.cat|Dpt, Bioquímica i Biologia Molecular, Fac, Biociències, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. marc.torrent@uab.cat|Dpt, Bioquímica i Biologia Molecular, Fac, Biociències, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. marc.torrent@uab.cat', 'Spain|Spain|Spain', 37, 'Amino Acid Sequence|Anti-Infective Agents|Computational Biology|Drug Design|Models, Theoretical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-373', '19906288', 'Much effort goes into identifying new antimicrobial compounds able to evade the increasing resistance of microorganisms to antibiotics. One strategy relies on antimicrobial peptides, either derived from fragments released by proteolytic cleavage of proteins or designed from known antimicrobial protein regions.'),
(93196, 2009, 'bmc', 'Linguistic feature analysis for protein interaction extraction', 'Fayruzov, Timur|De Cock, Martine|Cornelis, Chris|Hoste, Veronique', 'Ghent University, Department of Applied Mathematics and Computer Science, 9000 Gent, Belgium. timur.fayruzov@ugent.be|Ghent University, Department of Applied Mathematics and Computer Science, 9000 Gent, Belgium. timur.fayruzov@ugent.be|Ghent University, Department of Applied Mathematics and Computer Science, 9000 Gent, Belgium. timur.fayruzov@ugent.be|Ghent University, Department of Applied Mathematics and Computer Science, 9000 Gent, Belgium. timur.fayruzov@ugent.be', 'Belgium|Belgium|Belgium|Belgium', 13, 'Computational Biology|Data Mining|Databases, Factual|Information Storage and Retrieval|Linguistics|Pattern Recognition, Automated|Proteins|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-374', '19909518', 'The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels.'),
(93197, 2009, 'bmc', 'A generic algorithm for layout of biological networks', 'Schreiber, Falk|Dwyer, Tim|Marriott, Kim|Wybrow, Michael', 'Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Gatersleben, Germany. schreibe@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Gatersleben, Germany. schreibe@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Gatersleben, Germany. schreibe@ipk-gatersleben.de|Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, D-06466 Gatersleben, Germany. schreibe@ipk-gatersleben.de', 'Germany|Germany|Germany|Germany', 24, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1186/1471-2105-10-375', '19909528', 'Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration.'),
(93198, 2009, 'bmc', 'Algorithms for effective querying of compound graph-based pathway databases', 'Dogrusoz, Ugur|Cetintas, Ahmet|Demir, Emek|Babur, Ozgun', 'Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey. ugur@cs.bilkent.edu.tr|Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey. ugur@cs.bilkent.edu.tr|Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey. ugur@cs.bilkent.edu.tr|Computer Engineering Dept, Bilkent University, Center for Bioinformatics, Ankara, Turkey. ugur@cs.bilkent.edu.tr', 'Turkey|Turkey|Turkey|Turkey', 18, 'Algorithms|Computational Biology|Computer Graphics|Databases, Factual|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-10-376', '19917102', 'Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.'),
(93199, 2009, 'bmc', 'The ontology of biological sequences', 'Hoehndorf, Robert|Kelso, Janet|Herre, Heinrich', 'Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany. leechuck@leechuck.de|Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany. leechuck@leechuck.de|Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany. leechuck@leechuck.de', 'Germany|Germany|Germany', 16, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-377', '19919720', 'Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike.'),
(93200, 2009, 'bmc', 'Nonlinear preprocessing method for detecting peaks from gas chromatograms', 'Shim, Byonghyo|Min, Hyeyoung|Yoon, Sungroh', 'School of Electrical Engineering, Korea University, Seoul, Korea. bshim@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul, Korea. bshim@korea.ac.kr|School of Electrical Engineering, Korea University, Seoul, Korea. bshim@korea.ac.kr', '-|-|-', 4, 'Algorithms|Chromatography, Gas|Computational Biology|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-10-378', '19922615', 'The problem of locating valid peaks from data corrupted by noise frequently arises while analyzing experimental data. In various biological and chemical data analysis tasks, peak detection thus constitutes a critical preprocessing step that greatly affects downstream analysis and eventual quality of experiments. Many existing techniques require the users to adjust parameters by trial and error, which is error-prone, time-consuming and often leads to incorrect analysis results. Worse, conventional approaches tend to report an excessive number of false alarms by finding fictitious peaks generated by mere noise.'),
(93201, 2009, 'bmc', 'SitesIdentify: a protein functional site prediction tool', 'Bray, Tracey|Chan, Pedro|Bougouffa, Salim|Greaves, Richard|Doig, Andrew J|Warwicker, Jim', 'Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk|Faculty of Life Sciences, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK. t.bray@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Binding Sites|Catalytic Domain|Computational Biology|Databases, Protein|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-379', '19922660', 'The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application.'),
(93202, 2009, 'bmc', 'An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH', 'Allemeersch, Joke|Van Vooren, Steven|Hannes, Femke|De Moor, Bart|Vermeesch, Joris Robert|Moreau, Yves', 'MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be|MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be|MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be|MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be|MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be|MicroArray Facility, VIB, Leuven, Belgium. joke.allemeersch@vib.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 12, 'Chromosome Aberrations|Chromosome Disorders|Chromosome Mapping|Comparative Genomic Hybridization|Gene Dosage|Gene Expression Profiling|Genetic Association Studies|Genome, Human|Genomics|Humans|Research Design', 'Comparative Study', '10.1186/1471-2105-10-380', '19925645', 'Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the normal reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations.'),
(93203, 2009, 'bmc', 'Prediction of protein binding sites in protein structures using hidden Markov support vector machine', 'Liu, Bin|Wang, Xiaolong|Lin, Lei|Tang, Buzhou|Dong, Qiwen|Wang, Xuan', 'Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn|Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, PR China. bliu@insun.hit.edu.cn', 'China|China|China|China|China|China', 45, 'Algorithms|Artificial Intelligence|Binding Sites|Computational Biology|Databases, Protein|Markov Chains|Protein Binding|Proteins', 'Journal Article', '10.1186/1471-2105-10-381', '19925685', 'Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.'),
(93204, 2009, 'bmc', 'Iterative pruning PCA improves resolution of highly structured populations', 'Intarapanich, Apichart|Shaw, Philip J|Assawamakin, Anunchai|Wangkumhang, Pongsakorn|Ngamphiw, Chumpol|Chaichoompu, Kridsadakorn|Piriyapongsa, Jittima|Tongsima, Sissades', 'BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th|BIOTEC 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumtani 12120, Thailand. apichart.int@nectec.or.th', 'Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand|Thailand', 27, 'Algorithms|Animals|Computational Biology|Genetic Variation|Genetics, Population|Humans|Models, Genetic|Population|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-10-382', '19930644', 'Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure, however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming.'),
(93205, 2009, 'bmc', 'Comparison of eukaryotic phylogenetic profiling approaches using species tree aware methods', 'Ruano-Rubio, Valentín|Poch, Olivier|Thompson, Julie D', 'Laboratoire de Biologie et Génomique Intégrative, Département de Biologie et Génomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS/INSERM/UDS, Illkirch, France. ruano@igbmc.fr|Laboratoire de Biologie et Génomique Intégrative, Département de Biologie et Génomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS/INSERM/UDS, Illkirch, France. ruano@igbmc.fr|Laboratoire de Biologie et Génomique Intégrative, Département de Biologie et Génomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS/INSERM/UDS, Illkirch, France. ruano@igbmc.fr', 'France|France|France', 14, 'Computational Biology|Eukaryota|Evolution, Molecular|Models, Genetic|Phylogeny|Species Specificity', 'Comparative Study', '10.1186/1471-2105-10-383', '19930674', 'Phylogenetic profiling encompasses an important set of methodologies for in silico high throughput inference of functional relationships between genes. The simplest profiles represent the distribution of gene presence-absence in a set of species as a sequence of 0s and 1s, and it is assumed that functionally related genes will have more similar profiles. The methodology has been successfully used in numerous studies of prokaryotic genomes, although its application in eukaryotes appears problematic, with reported low accuracy due to the complex genomic organization within this domain of life. Recently some groups have proposed an alternative approach based on the correlation of homologous gene group sizes, taking into account all potentially informative genetic events leading to a change in group size, regardless of whether they result in a de novo group gain or total gene group loss.'),
(93206, 2009, 'bmc', 'Regularized estimation of large-scale gene association networks using graphical Gaussian models', 'Krämer, Nicole|Schäfer, Juliane|Boulesteix, Anne-Laure', 'Machine Learning/Intelligent Data Analysis Group, Berlin Institute of Technology, Franklinstr 28/29, D-10587 Berlin, Germany. nkraemer@cs.tu-berlin.de|Machine Learning/Intelligent Data Analysis Group, Berlin Institute of Technology, Franklinstr 28/29, D-10587 Berlin, Germany. nkraemer@cs.tu-berlin.de|Machine Learning/Intelligent Data Analysis Group, Berlin Institute of Technology, Franklinstr 28/29, D-10587 Berlin, Germany. nkraemer@cs.tu-berlin.de', 'Germany|Germany|Germany', 0, 'Computational Biology|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Regression Analysis', 'Journal Article', '10.1186/1471-2105-10-384', '19930695', 'Graphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares.'),
(93207, 2009, 'bmc', 'Automated vocabulary discovery for geo-parsing online epidemic intelligence', 'Keller, Mikaela|Freifeld, Clark C|Brownstein, John S', 'Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 300 Longwood Ave, Boston, MA 02115, USA. mikaela.keller@childrens.harvard.edu|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 300 Longwood Ave, Boston, MA 02115, USA. mikaela.keller@childrens.harvard.edu|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 300 Longwood Ave, Boston, MA 02115, USA. mikaela.keller@childrens.harvard.edu', 'United States|United States|United States', 0, 'Animals|Disease Outbreaks|Humans|Internet|Population Surveillance|Software|Vocabulary', 'Journal Article', '10.1186/1471-2105-10-385', '19930702', 'Automated surveillance of the Internet provides a timely and sensitive method for alerting on global emerging infectious disease threats. HealthMap is part of a new generation of online systems designed to monitor and visualize, on a real-time basis, disease outbreak alerts as reported by online news media and public health sources. HealthMap is of specific interest for national and international public health organizations and international travelers. A particular task that makes such a surveillance useful is the automated discovery of the geographic references contained in the retrieved outbreak alerts. This task is sometimes referred to as geo-parsing. A typical approach to geo-parsing would demand an expensive training corpus of alerts manually tagged by a human.'),
(93208, 2009, 'bmc', 'Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses', 'Guillén-Gosálbez, Gonzalo|Sorribas, Albert', 'Departament de Ciències Mèdiques Bàsiques, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida, Spain. gonzalo.guillen@urv.es|Departament de Ciències Mèdiques Bàsiques, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida, Spain. gonzalo.guillen@urv.es', 'Spain|Spain', 0, 'Algorithms|Computational Biology|Heat-Shock Response|Kinetics|Metabolic Networks and Pathways|Nonlinear Dynamics|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-386', '19930714', 'Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements.'),
(93209, 2009, 'bmc', 'Modeling lymphocyte homing and encounters in lymph nodes', 'Baldazzi, Valentina|Paci, Paola|Bernaschi, Massimo|Castiglione, Filippo', 'Istituto per le Applicazioni del Calcolo M, Picone, Consiglio Nazionale delle Ricerche (CNR), c/o IASI-CNR, V,le Manzoni 30, 00185 - Rome, Italy. valentina.baldazzi@inria.fr|Istituto per le Applicazioni del Calcolo M, Picone, Consiglio Nazionale delle Ricerche (CNR), c/o IASI-CNR, V,le Manzoni 30, 00185 - Rome, Italy. valentina.baldazzi@inria.fr|Istituto per le Applicazioni del Calcolo M, Picone, Consiglio Nazionale delle Ricerche (CNR), c/o IASI-CNR, V,le Manzoni 30, 00185 - Rome, Italy. valentina.baldazzi@inria.fr|Istituto per le Applicazioni del Calcolo M, Picone, Consiglio Nazionale delle Ricerche (CNR), c/o IASI-CNR, V,le Manzoni 30, 00185 - Rome, Italy. valentina.baldazzi@inria.fr', 'Italy|Italy|Italy|Italy', 34, 'Animals|B-Lymphocytes|Cell Movement|Computational Biology|Dendritic Cells|Humans|Inflammation|Lymph Nodes|Lymphocytes|Models, Biological', 'Journal Article', '10.1186/1471-2105-10-387', '19939270', 'The efficiency of lymph nodes depends on tissue structure and organization, which allow the coordination of lymphocyte traffic. Despite their essential role, our understanding of lymph node specific mechanisms is still incomplete and currently a topic of intense research.'),
(93210, 2009, 'bmc', 'DISPARE: DIScriminative PAttern REfinement for Position Weight Matrices', 'da Piedade, Isabelle|Tang, Man-Hung Eric|Elemento, Olivier', 'Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark. idapiedade@bio.ku.dk|Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark. idapiedade@bio.ku.dk|Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark. idapiedade@bio.ku.dk', 'Denmark|Denmark|Denmark', 0, 'Binding Sites|Computational Biology|DNA|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Position-Specific Scoring Matrices|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-388', '19941641', 'The accurate determination of transcription factor binding affinities is an important problem in biology and key to understanding the gene regulation process. Position weight matrices are commonly used to represent the binding properties of transcription factor binding sites but suffer from low information content and a large number of false matches in the genome. We describe a novel algorithm for the refinement of position weight matrices representing transcription factor binding sites based on experimental data, including ChIP-chip analyses. We present an iterative weight matrix optimization method that is more accurate in distinguishing true transcription factor binding sites from a negative control set. The initial position weight matrix comes from JASPAR, TRANSFAC or other sources. The main new features are the discriminative nature of the method and matrix width and length optimization.'),
(93211, 2009, 'bmc', 'A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability', 'Sontrop, Herman M J|Moerland, Perry D|van den Ham, René|Reinders, Marcel J T|Verhaegh, Wim F J', 'Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com|Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com|Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com|Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com|Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Breast Neoplasms|Computational Biology|Databases, Genetic|Female|Gene Expression Profiling|Genetic Variation|Humans|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-10-389', '19941644', 'Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear.'),
(93212, 2009, 'bmc', 'iLAP: a workflow-driven software for experimental protocol development, data acquisition and analysis', 'Stocker, Gernot|Fischer, Maria|Rieder, Dietmar|Bindea, Gabriela|Kainz, Simon|Oberstolz, Michael|McNally, James G|Trajanoski, Zlatko', 'Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at|Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. gernot.stocker@tugraz.at', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 11, 'Computational Biology|Database Management Systems|Databases, Factual|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-390', '19941647', 'In recent years, the genome biology community has expended considerable effort to confront the challenges of managing heterogeneous data in a structured and organized way and developed laboratory information management systems (LIMS) for both raw and processed data. On the other hand, electronic notebooks were developed to record and manage scientific data, and facilitate data-sharing. Software which enables both, management of large datasets and digital recording of laboratory procedures would serve a real need in laboratories using medium and high-throughput techniques.');
