INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92720, 2009, 'bmc', 'Towards bioinformatics assisted infectious disease control', 'Sintchenko, Vitali|Gallego, Blanca|Chung, Grace|Coiera, Enrico', 'Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia. vsintchenko@usyd.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia. vsintchenko@usyd.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia. vsintchenko@usyd.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia. vsintchenko@usyd.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Communicable Disease Control|Computational Biology|Disease Outbreaks|Gene Expression Profiling|Genome, Bacterial|Genotype|Population Surveillance|Public Health Informatics|Salmonella typhimurium', 'Journal Article', '10.1186/1471-2105-10-S2-S10', '19208185', 'This paper proposes a novel framework for bioinformatics assisted biosurveillance and early warning to address the inefficiencies in traditional surveillance as well as the need for more timely and comprehensive infection monitoring and control. It leverages on breakthroughs in rapid, high-throughput molecular profiling of microorganisms and text mining.'),
(92721, 2009, 'bmc', 'Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays', 'Liu, Yang|Sam, Lee|Li, Jianrong|Lussier, Yves A', 'Center for Biomedical Informatics and Section of Genetic Medicine, Dept, of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA. yuwang_la@yahoo.com|Center for Biomedical Informatics and Section of Genetic Medicine, Dept, of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA. yuwang_la@yahoo.com|Center for Biomedical Informatics and Section of Genetic Medicine, Dept, of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA. yuwang_la@yahoo.com|Center for Biomedical Informatics and Section of Genetic Medicine, Dept, of Medicine, The University of Chicago, Chicago, 5841 South Maryland Ave, Ill, USA. yuwang_la@yahoo.com', 'United States|United States|United States|United States', 0, 'Bacterial Infections|Computational Biology|Infection|Oligonucleotide Array Sequence Analysis|Parasitic Diseases|Sensitivity and Specificity|Virus Diseases', 'Journal Article', '10.1186/1471-2105-10-S2-S11', '19208186', 'To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging.'),
(92722, 2009, 'bmc', 'Leveraging existing biological knowledge in the identification of candidate genes for facial dysmorphology', 'Tipney, Hannah J|Leach, Sonia M|Feng, Weiguo|Spritz, Richard|Williams, Trevor|Hunter, Lawrence', 'Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu|Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu|Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu|Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu|Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu|Computational Pharmacology Department, University of Colorado at Denver and Health Sciences Center, Aurora, CO, USA. hannah.tipney@uchsc.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Craniofacial Abnormalities|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-S2-S12', '19208187', 'In response to the frequently overwhelming output of high-throughput microarray experiments, we propose a methodology to facilitate interpretation of biological data in the context of existing knowledge. Through the probabilistic integration of explicit and implicit data sources a functional interaction network can be constructed. Each edge connecting two proteins is weighted by a confidence value capturing the strength and reliability of support for that interaction given the combined data sources. The resulting network is examined in conjunction with expression data to identify groups of genes with significant temporal or tissue specific patterns. In contrast to unstructured gene lists, these networks often represent coherent functional groupings.'),
(92723, 2009, 'bmc', 'Identifying hypothetical genetic influences on complex disease phenotypes', 'Keller, Benjamin J|McEachin, Richard C', 'Eastern Michigan University, Computer Science Department, Ypsilanti, MI 48197, USA. bkeller@emich.edu|Eastern Michigan University, Computer Science Department, Ypsilanti, MI 48197, USA. bkeller@emich.edu', 'United States|United States', 0, 'Algorithms|Diabetes Mellitus, Type 2|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genome, Human|Genotype|Humans|Phenotype', 'Journal Article', '10.1186/1471-2105-10-S2-S13', '19208188', 'Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we address is to discover relationships among genes from these interacting regions that may explain the observed statistical interaction and the role of these genes in the disease phenotype.'),
(92724, 2009, 'bmc', 'The etiome: identification and clustering of human disease etiological factors', 'Liu, Yueyi I|Wise, Paul H|Butte, Atul J', 'Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. yliu@stanford.edu|Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. yliu@stanford.edu|Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. yliu@stanford.edu', 'United States|United States|United States', 0, 'Cluster Analysis|Disease|Environment|Genetic Predisposition to Disease|Genome, Human|Genotype|Humans|MEDLINE|Risk Factors', 'Journal Article', '10.1186/1471-2105-10-S2-S14', '19208189', 'Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors.'),
(92725, 2009, 'bmc', 'Ontology driven integration platform for clinical and translational research', 'Mirhaji, Parsa|Zhu, Min|Vagnoni, Mattew|Bernstam, Elmer V|Zhang, Jiajie|Smith, Jack W', 'The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu|The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu|The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu|The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu|The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu|The University of Texas Health Science Center at Houston, Houston, Texas, USA. Parsa.Mirhaji@uth.tmc.edu', 'United States|United States|United States|United States|United States|United States', 14, 'Biomedical Research|Computational Biology|Database Management Systems|Information Storage and Retrieval|Internet|Medical Informatics|Software|Systems Integration|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S2-S2', '19208190', 'Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.'),
(92726, 2009, 'bmc', 'Computational neuroanatomy: ontology-based representation of neural components and connectivity', 'Rubin, Daniel L|Talos, Ion-Florin|Halle, Michael|Musen, Mark A|Kikinis, Ron', 'Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. dlrubin@stanford.edu|Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. dlrubin@stanford.edu|Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. dlrubin@stanford.edu|Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. dlrubin@stanford.edu|Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. dlrubin@stanford.edu', 'United States|United States|United States|United States|United States', 15, 'Computational Biology|Computer Graphics|Information Storage and Retrieval|Medical Informatics|Nervous System|Neural Networks (Computer)|Neuroanatomy', 'Journal Article', '10.1186/1471-2105-10-S2-S3', '19208191', 'A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning.'),
(92727, 2009, 'bmc', 'Metadata mapping and reuse in caBIG', 'Kunz, Isaac|Lin, Ming-Chin|Frey, Lewis', 'Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, USA. Isaac.Kunz@hci.utah.edu|Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, USA. Isaac.Kunz@hci.utah.edu|Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, UT, USA. Isaac.Kunz@hci.utah.edu', 'United States|United States|United States', 17, 'Algorithms|Computational Biology|Database Management Systems|Databases, Factual|Medical Informatics|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S2-S4', '19208192', 'This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI)s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.'),
(92728, 2009, 'bmc', 'Evaluation of a gene information summarization system by users during the analysis process of microarray datasets', 'Yang, Jianji|Cohen, Aaron|Hersh, William', 'Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon 97239, USA. jianji.yang2@va.gov|Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon 97239, USA. jianji.yang2@va.gov|Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon 97239, USA. jianji.yang2@va.gov', 'United States|United States|United States', 3, 'Algorithms|Cluster Analysis|Databases, Factual|Gene Expression Profiling|Genes|Genomics|Information Systems|Oligonucleotide Array Sequence Analysis|PubMed|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-10-S2-S5', '19208193', 'Summarization of gene information in the literature has the potential to help genomics researchers translate basic research into clinical benefits. Gene expression microarrays have been used to study biomarkers for disease and discover novel types of therapeutics and the task of finding information in journal articles on sets of genes is common for translational researchers working with microarray data. However, manually searching and scanning the literature references returned from PubMed is a time-consuming task for scientists. We built and evaluated an automatic summarizer of information on genes studied in microarray experiments. The Gene Information Clustering and Summarization System (GICSS) is a system that integrates two related steps of the microarray data analysis process: functional gene clustering and gene information gathering. The system evaluation was conducted during the process of genomic researchers analyzing their own experimental microarray datasets.'),
(92729, 2009, 'bmc', 'Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text', 'Garten, Yael|Altman, Russ B', 'Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA. ygarten@stanford.edu|Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA. ygarten@stanford.edu', 'United States|United States', 0, 'Computational Biology|Databases, Genetic|Information Storage and Retrieval|Internet|Pharmacogenetics|Software', 'Journal Article', '10.1186/1471-2105-10-S2-S6', '19208194', 'Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities--particularly genes and gene variants, and thus these critical connections are often lost. Text mining techniques can allow us to convert the free-style text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full text articles as input into text mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations.'),
(92730, 2009, 'bmc', 'Phenotype forecasting with SNPs data through gene-based Bayesian networks', 'Malovini, Alberto|Nuzzo, Angelo|Ferrazzi, Fulvia|Puca, Annibale A|Bellazzi, Riccardo', 'IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it|IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it|IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it|IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it|IRCCS Multimedica, Via Fantoli 16/15, I-20138 Milano, Italy. alberto.malovini@unipv.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Bayes Theorem|Gene Regulatory Networks|Genome, Human|Haplotypes|Humans|Models, Genetic|Phenotype|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-S2-S7', '19208195', 'Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.'),
(92731, 2009, 'bmc', 'PhenoGO: an integrated resource for the multiscale mining of clinical and biological data', 'Sam, Lee T|Mendonça, Eneida A|Li, Jianrong|Blake, Judith|Friedman, Carol|Lussier, Yves A', 'Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu|Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu|Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu|Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu|Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu|Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, IL, USA. lsam@umich.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Genetic|Humans|Information Storage and Retrieval|Mice|Natural Language Processing|Phenotype|Software|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-10-S2-S8', '19208196', 'The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org.'),
(92732, 2009, 'bmc', 'BioProspecting: novel marker discovery obtained by mining the bibleome', 'Elkin, Peter L|Tuttle, Mark S|Trusko, Brett E|Brown, Steven H', 'Center for Biomedical Informatics, Mount Sinai School of Medicine, New York, NY, USA. peter.elkin@mssm.edu|Center for Biomedical Informatics, Mount Sinai School of Medicine, New York, NY, USA. peter.elkin@mssm.edu|Center for Biomedical Informatics, Mount Sinai School of Medicine, New York, NY, USA. peter.elkin@mssm.edu|Center for Biomedical Informatics, Mount Sinai School of Medicine, New York, NY, USA. peter.elkin@mssm.edu', 'United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Genetic Markers|Genome, Human|Humans|Internet|Proteins|Software|Systematized Nomenclature of Medicine|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S2-S9', '19208197', 'BioProspecting is a novel approach that enabled our team to mine data related to genetic markers from the New England Journal of Medicine (NEJM) utilizing SNOMED CT and the Human Gene Onotology (HUGO). The Biomedical Informatics Research Collaborative was able to link genes and disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) and natural language processing engine, whose output creates an ontology-network using the semantic encodings of the literature that is organized by these two terminologies. We identified relationships between (genes or proteins) and (diseases or drugs) as linked by metabolic functions and identified potentially novel functional relationships between, for example, genes and diseases (e.g. Article #1 ([Gene - IL27] = > {Enzyme - Dipeptidyl Carboxypeptidase 1}) and Article #2 ({Enzyme - Dipeptidyl Carboxypeptidase 1} < = [Disorder - Type II DM]) showing a metabolic link between IL27 and Type II DM). In this manuscript we describe our method for developing the database and its content as well as its potential to assist in the discovery of novel markers and drugs.'),
(92733, 2009, 'bmc', 'TransCent: computational enzyme design by transferring active sites and considering constraints relevant for catalysis', 'Fischer, André|Enkler, Nils|Neudert, Gerd|Bocola, Marco|Sterner, Reinhard|Merkl, Rainer', 'Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de|Institut für Biophysik und Physikalische Biochemie, Universität Regensburg, Regensburg, Germany. andre.fischer@biologie.uni-regensburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Amino Acid Sequence|Binding Sites|Catalysis|Catalytic Domain|Computational Biology|Databases, Protein|Enzymes|Ligands|Models, Molecular|Molecular Sequence Data|Protein Conformation|Sequence Alignment|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-10-54', '19208235', 'Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used.'),
(92734, 2009, 'bmc', 'Assessing and selecting gene expression signals based upon the quality of the measured dynamics', 'Yang, Eric|Androulakis, Ioannis P', 'Biomedical Engineering Department, Rutgers University, Piscataway, NJ, USA. eyang@eden.rutgers.edu|Biomedical Engineering Department, Rutgers University, Piscataway, NJ, USA. eyang@eden.rutgers.edu', 'United States|United States', 10, 'Algorithms|Cluster Analysis|Gene Expression|Gene Expression Profiling|Signal Transduction', 'Journal Article', '10.1186/1471-2105-10-55', '19208252', 'One of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features.'),
(92735, 2009, 'bmc', 'TACOA: taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach', 'Diaz, Naryttza N|Krause, Lutz|Goesmann, Alexander|Niehaus, Karsten|Nattkemper, Tim W', 'Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. ndiaz@CeBiTec.Uni-Bielefeld.DE|Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. ndiaz@CeBiTec.Uni-Bielefeld.DE|Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. ndiaz@CeBiTec.Uni-Bielefeld.DE|Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. ndiaz@CeBiTec.Uni-Bielefeld.DE|Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. ndiaz@CeBiTec.Uni-Bielefeld.DE', 'Germany|Germany|Germany|Germany|Germany', 134, 'Algorithms|Archaea|Bacteria|Classification|Cluster Analysis|Environmental Microbiology|Genome|Genomics|Software|Software Validation', 'Journal Article', '10.1186/1471-2105-10-56', '19210774', 'Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the unculturable majority. This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning.'),
(92736, 2009, 'bmc', 'A reexamination of information theory-based methods for DNA-binding site identification', 'Erill, Ivan|ONeill, Michael C', 'Department of Biological Sciences, University of Maryland-Baltimore County, Baltimore, MD, USA. erill@umbc.edu|Department of Biological Sciences, University of Maryland-Baltimore County, Baltimore, MD, USA. erill@umbc.edu', 'United States|United States', 0, 'Binding Sites|Computational Biology|DNA|DNA, Bacterial|Genome, Bacterial|Information Theory|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-57', '19210776', 'Searching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.'),
(92737, 2009, 'bmc', 'An effective docking strategy for virtual screening based on multi-objective optimization algorithm', 'Li, Honglin|Zhang, Hailei|Zheng, Mingyue|Luo, Jie|Kang, Ling|Liu, Xiaofeng|Wang, Xicheng|Jiang, Hualiang', 'School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn|School of Pharmacy, East China University of Science and Technology, Shanghai, PR China. hlli@mail.shcnc.ac.cn', 'China|China|China|China|China|China|China|China', 29, 'Algorithms|Binding Sites|Computational Biology|Drug Evaluation, Preclinical|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Protein Conformation|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-58', '19210777', 'Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design.'),
(92738, 2009, 'bmc', 'The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation', 'Loevenich, Sandra N|Brunner, Erich|King, Nichole L|Deutsch, Eric W|Stein, Stephen E|FlyBase Consortium|Aebersold, Ruedi|Hafen, Ernst', 'Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch|Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland. loevenich@imsb.biol.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Animals|Databases, Protein|Drosophila Proteins|Drosophila melanogaster|Genome, Insect|Peptide Fragments|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-10-59', '19210778', 'Crucial foundations of any quantitative systems biology experiment are correct genome and proteome annotations. Protein databases compiled from high quality empirical protein identifications that are in turn based on correct gene models increase the correctness, sensitivity, and quantitative accuracy of systems biology genome-scale experiments.'),
(92739, 2009, 'bmc', 'Motivated proteins: a web application for studying small three-dimensional protein motifs', 'Leader, David P|Milner-White, E James', 'Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK. d.leader@bio.gla.ac.uk|Institute of Biomedical and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK. d.leader@bio.gla.ac.uk', 'United Kingdom|United Kingdom', 0, 'Amino Acid Motifs|Animals|Databases, Protein|Humans|Internet|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-60', '19210785', 'Small loop-shaped motifs are common constituents of the three-dimensional structure of proteins. Typically they comprise between three and seven amino acid residues, and are defined by a combination of dihedral angles and hydrogen bonding partners. The most abundant of these are alphabeta-motifs, asx-motifs, asx-turns, beta-bulges, beta-bulge loops, beta-turns, nests, niches, Schellmann loops, ST-motifs, ST-staples and ST-turns. We have constructed a database of such motifs from a range of high-quality protein structures and built a web application as a visual interface to this.'),
(92740, 2009, 'bmc', 'A comparison on effects of normalisations in the detection of differentially expressed genes', 'Chiogna, Monica|Massa, Maria Sofia|Risso, Davide|Romualdi, Chiara', 'Department of Statistical Sciences, University of Padova, Padova, Italy. monica@stat.unipd.it|Department of Statistical Sciences, University of Padova, Padova, Italy. monica@stat.unipd.it|Department of Statistical Sciences, University of Padova, Padova, Italy. monica@stat.unipd.it|Department of Statistical Sciences, University of Padova, Padova, Italy. monica@stat.unipd.it', 'Italy|Italy|Italy|Italy', 0, 'Computer Simulation|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-10-61', '19216778', 'Various normalisation techniques have been developed in the context of microarray analysis to try to correct expression measurements for experimental bias and random fluctuations. Major techniques include: total intensity normalisation, intensity dependent normalisation, and variance stabilising normalisation. The aim of this paper is to discuss the impact of normalisation techniques for two-channel array technology on the process of identification of differentially expressed genes.'),
(92741, 2009, 'bmc', 'RankAggreg, an R package for weighted rank aggregation', 'Pihur, Vasyl|Datta, Susmita|Datta, Somnath', 'Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA. v0pihu01@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA. v0pihu01@louisville.edu|Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA. v0pihu01@louisville.edu', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-62', '19228411', 'Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise.'),
(92742, 2009, 'bmc', 'Genotype determination for polymorphisms in linkage disequilibrium', 'Yu, Zhaoxia|Garner, Chad|Ziogas, Argyrios|Anton-Culver, Hoda|Schaid, Daniel J', 'Department of Statistics, University of California, Irvine, CA, USA. yu.zhaoxia@uci.edu|Department of Statistics, University of California, Irvine, CA, USA. yu.zhaoxia@uci.edu|Department of Statistics, University of California, Irvine, CA, USA. yu.zhaoxia@uci.edu|Department of Statistics, University of California, Irvine, CA, USA. yu.zhaoxia@uci.edu|Department of Statistics, University of California, Irvine, CA, USA. yu.zhaoxia@uci.edu', 'United States|United States|United States|United States|United States', 7, 'Computational Biology|Genotype|Linkage Disequilibrium|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-63', '19228433', 'Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information.'),
(92743, 2009, 'bmc', 'Inverse Langmuir method for oligonucleotide microarray analysis', 'Mulders, Geert C W M|Barkema, Gerard T|Carlon, Enrico', '1Institute for Theoretical Physics, Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands. G.C.W.M.Mulders@phys.uu.nl|1Institute for Theoretical Physics, Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands. G.C.W.M.Mulders@phys.uu.nl|1Institute for Theoretical Physics, Utrecht University, Leuvenlaan 4, 3584 CE Utrecht, The Netherlands. G.C.W.M.Mulders@phys.uu.nl', 'Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Microarray Analysis|Oligonucleotide Array Sequence Analysis|RNA|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-64', '19232092', 'An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations.'),
(92744, 2009, 'bmc', 'CpG islands or CpG clusters: how to identify functional GC-rich regions in a genome?', 'Han, Leng|Zhao, Zhongming', 'Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA. hanl2@vcu.edu|Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA. hanl2@vcu.edu', 'United States|United States', 32, 'Algorithms|Animals|CpG Islands|DNA Methylation|Genetic Markers|Genome|Humans|Mice|Promoter Regions, Genetic', 'Journal Article', '10.1186/1471-2105-10-65', '19232104', 'CpG islands (CGIs), clusters of CpG dinucleotides in GC-rich regions, are often located in the 5 end of genes and considered gene markers. Hackenberg et al. (2006) recently developed a new algorithm, CpGcluster, which uses a completely different mathematical approach from previous traditional algorithms. Their evaluation suggests that CpGcluster provides a much more efficient approach to detecting functional clusters or islands of CpGs.'),
(92745, 2009, 'bmc', 'Sample entropy analysis of cervical neoplasia gene-expression signatures', 'Botting, Shaleen K|Trzeciakowski, Jerome P|Benoit, Michelle F|Salama, Salama A|Diaz-Arrastia, Concepcion R', 'Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu|Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu|Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu|Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu|Department of Obstetrics & Gynecology, University of Texas Medical Branch, Galveston, Texas, USA. shbarton@utmb.edu', 'United States|United States|United States|United States|United States', 7, 'Cervical Intraepithelial Neoplasia|Cluster Analysis|Entropy|Female|Gene Expression Profiling|Humans|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Uterine Cervical Neoplasms', 'Journal Article', '10.1186/1471-2105-10-66', '19232110', 'We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set.'),
(92746, 2009, 'bmc', 'Quantitative measures for the management and comparison of annotated genomes', 'Eilbeck, Karen|Moore, Barry|Holt, Carson|Yandell, Mark', 'Department of Human genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA. keilbeck@genetics.utah.edu|Department of Human genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA. keilbeck@genetics.utah.edu|Department of Human genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA. keilbeck@genetics.utah.edu|Department of Human genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, UT, USA. keilbeck@genetics.utah.edu', 'United States|United States|United States|United States', 0, 'Alternative Splicing|Animals|Databases, Genetic|Genome|Genomics', 'Comparative Study', '10.1186/1471-2105-10-67', '19236712', 'The ever-increasing number of sequenced and annotated genomes has made management of their annotations a significant undertaking, especially for large eukaryotic genomes containing many thousands of genes. Typically, changes in gene and transcript numbers are used to summarize changes from release to release, but these measures say nothing about changes to individual annotations, nor do they provide any means to identify annotations in need of manual review.'),
(92747, 2009, 'bmc', 'Genotyping and inflated type I error rate in genome-wide association case/control studies', 'Sampson, Joshua N|Zhao, Hongyu', 'Department of Epidemiology and Public Health, Yale University School of Medicine, New haven, CT, USA. joshua.sampson@yale.edu|Department of Epidemiology and Public Health, Yale University School of Medicine, New haven, CT, USA. joshua.sampson@yale.edu', 'United States|United States', 0, 'Algorithms|Case-Control Studies|Computational Biology|False Positive Reactions|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-68', '19236714', 'One common goal of a case/control genome wide association study (GWAS) is to find SNPs associated with a disease. Traditionally, the first step in such studies is to assign a genotype to each SNP in each subject, based on a statistic summarizing fluorescence measurements. When the distributions of the summary statistics are not well separated by genotype, the act of genotype assignment can lead to more potential problems than acknowledged by the literature.'),
(92748, 2009, 'bmc', 'QSRA: a quality-value guided de novo short read assembler', 'Bryant, Douglas W|Wong, Weng-Keen|Mockler, Todd C', 'Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA. bryantjr@eecs.oregonstate.edu|Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA. bryantjr@eecs.oregonstate.edu|Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA. bryantjr@eecs.oregonstate.edu', 'United States|United States|United States', 41, 'Algorithms|Computational Biology|Programming Languages|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-69', '19239711', 'New rapid high-throughput sequencing technologies have sparked the creation of a new class of assembler. Since all high-throughput sequencing platforms incorporate errors in their output, short-read assemblers must be designed to account for this error while utilizing all available data.'),
(92749, 2009, 'bmc', 'An improved ontological representation of dendritic cells as a paradigm for all cell types', 'Masci, Anna Maria|Arighi, Cecilia N|Diehl, Alexander D|Lieberman, Anne E|Mungall, Chris|Scheuermann, Richard H|Smith, Barry|Cowell, Lindsay G', 'Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA. annamaria.masci@duke.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 30, 'Computational Biology|Databases, Factual|Dendritic Cells|Information Storage and Retrieval|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-70', '19243617', 'Recent increases in the volume and diversity of life science data and information and an increasing emphasis on data sharing and interoperability have resulted in the creation of a large number of biological ontologies, including the Cell Ontology (CL), designed to provide a standardized representation of cell types for data annotation. Ontologies have been shown to have significant benefits for computational analyses of large data sets and for automated reasoning applications, leading to organized attempts to improve the structure and formal rigor of ontologies to better support computation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CLs utility for computation and for cross-species data integration.'),
(92750, 2009, 'bmc', 'Gene set-based module discovery in the breast cancer transcriptome', 'Niida, Atsushi|Smith, Andrew D|Imoto, Seiya|Aburatani, Hiroyuki|Zhang, Michael Q|Akiyama, Tetsu', 'Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, Japan. aniida@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Breast Neoplasms|Computational Biology|E2F Transcription Factors|Female|Gene Expression Profiling|Humans|Mice|Oligonucleotide Array Sequence Analysis|Polycomb-Group Proteins|Repressor Proteins', 'Journal Article', '10.1186/1471-2105-10-71', '19243633', 'Although microarray-based studies have revealed global view of gene expression in cancer cells, we still have little knowledge about regulatory mechanisms underlying the transcriptome. Several computational methods applied to yeast data have recently succeeded in identifying expression modules, which is defined as co-expressed gene sets under common regulatory mechanisms. However, such module discovery methods are not applied cancer transcriptome data.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92751, 2009, 'bmc', 'Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data', 'Annest, Amalia|Bumgarner, Roger E|Raftery, Adrian E|Yeung, Ka Yee', 'Institute of Technology/Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA. amanu@u.washington.edu|Institute of Technology/Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA. amanu@u.washington.edu|Institute of Technology/Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA. amanu@u.washington.edu|Institute of Technology/Computing and Software Systems, University of Washington, Tacoma, WA 98402, USA. amanu@u.washington.edu', 'United States|United States|United States|United States', 29, 'Algorithms|Bayes Theorem|Breast Neoplasms|Female|Humans|Lymphoma, B-Cell|Neoplasms|Oligonucleotide Array Sequence Analysis|Survival Analysis', 'Journal Article', '10.1186/1471-2105-10-72', '19245714', 'Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes.'),
(92752, 2009, 'bmc', 'Disease candidate gene identification and prioritization using protein interaction networks', 'Chen, Jing|Aronow, Bruce J|Jegga, Anil G', 'Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA. Jing.Chen@cchmc.org|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA. Jing.Chen@cchmc.org|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA. Jing.Chen@cchmc.org', 'United States|United States|United States', 236, 'Algorithms|Animals|Computational Biology|Databases, Protein|Disease|Humans|Models, Biological|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-73', '19245720', 'Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.'),
(92753, 2009, 'bmc', 'RGG: a general GUI Framework for R scripts', 'Visne, Ilhami|Dilaveroglu, Erkan|Vierlinger, Klemens|Lauss, Martin|Yildiz, Ahmet|Weinhaeusel, Andreas|Noehammer, Christa|Leisch, Friedrich|Kriegner, Albert', 'Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at|Austrian Research Centers GmbH, ARC, Molecular Diagnostics, Seibersdorf, Austria. ilhami.visne@arcs.ac.at', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Computational Biology|Mathematical Computing|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-74', '19254356', 'R is the leading open source statistics software with a vast number of biostatistical and bioinformatical analysis packages. To exploit the advantages of R, extensive scripting/programming skills are required.'),
(92754, 2009, 'bmc', 'Edge detection in microscopy images using curvelets', 'Gebäck, Tobias|Koumoutsakos, Petros', 'Computational Science, ETH Zürich, Universitätstrasse 6, CAB H69,2, ETH Zürich, CH-8092 Zürich, Switzerland. tobias.gebaeck@inf.ethz.ch|Computational Science, ETH Zürich, Universitätstrasse 6, CAB H69,2, ETH Zürich, CH-8092 Zürich, Switzerland. tobias.gebaeck@inf.ethz.ch', 'Switzerland|Switzerland', 0, 'Algorithms|Image Enhancement|Microscopy, Electron|Signal Processing, Computer-Assisted|Software', 'Comparative Study', '10.1186/1471-2105-10-75', '19257905', 'Despite significant progress in imaging technologies, the efficient detection of edges and elongated features in images of intracellular and multicellular structures acquired using light or electron microscopy is a challenging and time consuming task in many laboratories.'),
(92755, 2009, 'bmc', 'RNAslider: a faster engine for consecutive windows folding and its application to the analysis of genomic folding asymmetry', 'Horesh, Yair|Wexler, Ydo|Lebenthal, Ilana|Ziv-Ukelson, Michal|Unger, Ron', 'Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel. yair.horesh@weizmann.ac.il|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel. yair.horesh@weizmann.ac.il|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel. yair.horesh@weizmann.ac.il|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel. yair.horesh@weizmann.ac.il|Department of Physics of Complex Systems, The Weizmann Institute of Science, Rehovot, Israel. yair.horesh@weizmann.ac.il', 'Israel|Israel|Israel|Israel|Israel', 6, 'Algorithms|Genome|Nucleic Acid Conformation|RNA|RNA, Untranslated|Software', 'Journal Article', '10.1186/1471-2105-10-76', '19257906', 'Scanning large genomes with a sliding window in search of locally stable RNA structures is a well motivated problem in bioinformatics. Given a predefined window size L and an RNA sequence S of size N (L < N), the consecutive windows folding problem is to compute the minimal free energy (MFE) for the folding of each of the L-sized substrings of S. The consecutive windows folding problem can be naively solved in O(NL3) by applying any of the classical cubic-time RNA folding algorithms to each of the N-L windows of size L. Recently an O(NL2) solution for this problem has been described.'),
(92756, 2009, 'bmc', 'Development and evaluation of new mask protocols for gene expression profiling in humans and chimpanzees', 'Toleno, Donna M|Renaud, Gabriel|Wolfsberg, Tyra G|Islam, Munirul|Wildman, Derek E|Siegmund, Kimberly D|Hacia, Joseph G', 'Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com|Department of Biochemistry and Molecular Biology, University of Southern California, Los Angeles, CA 90089, USA. dtoleno@gmail.com', 'United States|United States|United States|United States|United States|United States|United States', 8, 'Animals|Computational Biology|DNA Probes|Databases, Genetic|Gene Expression Profiling|Genome|Humans|Oligonucleotide Array Sequence Analysis|Pan troglodytes', 'Evaluation Studies', '10.1186/1471-2105-10-77', '19265541', 'Cross-species gene expression analyses using oligonucleotide microarrays designed to evaluate a single species can provide spurious results due to mismatches between the interrogated transcriptome and arrayed probes. Based on the most recent human and chimpanzee genome assemblies, we developed updated and accessible probe masking methods that allow human Affymetrix oligonucleotide microarrays to be used for robust genome-wide expression analyses in both species. In this process, only data from oligonucleotide probes predicted to have robust hybridization sensitivity and specificity for both transcriptomes are retained for analysis.'),
(92757, 2009, 'bmc', 'Performance of random forest when SNPs are in linkage disequilibrium', 'Meng, Yan A|Yu, Yi|Cupples, L Adrienne|Farrer, Lindsay A|Lunetta, Kathryn L', 'Department of Medicine, Boston University, MA, USA. ymeng@broad.mit.edu|Department of Medicine, Boston University, MA, USA. ymeng@broad.mit.edu|Department of Medicine, Boston University, MA, USA. ymeng@broad.mit.edu|Department of Medicine, Boston University, MA, USA. ymeng@broad.mit.edu|Department of Medicine, Boston University, MA, USA. ymeng@broad.mit.edu', 'United States|United States|United States|United States|United States', 63, 'Algorithms|Alzheimer Disease|Computer Simulation|Genome-Wide Association Study|Haplotypes|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-78', '19265542', 'Single nucleotide polymorphisms (SNPs) may be correlated due to linkage disequilibrium (LD). Association studies look for both direct and indirect associations with disease loci. In a Random Forest (RF) analysis, correlation between a true risk SNP and SNPs in LD may lead to diminished variable importance for the true risk SNP. One approach to address this problem is to select SNPs in linkage equilibrium (LE) for analysis. Here, we explore alternative methods for dealing with SNPs in LD: change the tree-building algorithm by building each tree in an RF only with SNPs in LE, modify the importance measure (IM), and use haplotypes instead of SNPs to build a RF.'),
(92758, 2009, 'bmc', 'Adaptable data management for systems biology investigations', 'Boyle, John|Rovira, Hector|Cavnor, Chris|Burdick, David|Killcoyne, Sarah|Shmulevich, Ilya', 'Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-79', '19265554', 'Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage.'),
(92759, 2009, 'bmc', 'CNV-seq, a new method to detect copy number variation using high-throughput sequencing', 'Xie, Chao|Tammi, Martti T', 'Department of Biological Sciences, National University of Singapore, Singapore. xie@nus.edu.sg|Department of Biological Sciences, National University of Singapore, Singapore. xie@nus.edu.sg', 'Singapore|Singapore', 0, 'Algorithms|DNA|Gene Dosage|Genetic Variation|Genome, Human|Genomics|Humans|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-80', '19267900', 'DNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.'),
(92760, 2009, 'bmc', 'GMFilter and SXTestPlate: software tools for improving the SNPlex genotyping system', 'Teuber, Markus|Wenz, Michael H|Schreiber, Stefan|Franke, Andre', 'Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Arnold-Heller-Str, 3, D-24105 Kiel, Germany. m.teuber@mucosa.de|Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Arnold-Heller-Str, 3, D-24105 Kiel, Germany. m.teuber@mucosa.de|Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Arnold-Heller-Str, 3, D-24105 Kiel, Germany. m.teuber@mucosa.de|Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Arnold-Heller-Str, 3, D-24105 Kiel, Germany. m.teuber@mucosa.de', 'Germany|Germany|Germany|Germany', 13, 'Databases, Genetic|Genome, Human|Genotype|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-81', '19267942', 'Genotyping of single-nucleotide polymorphisms (SNPs) is a fundamental technology in modern genetics. The SNPlex mid-throughput genotyping system (Applied Biosystems, Foster City, CA, USA) enables the multiplexed genotyping of up to 48 SNPs simultaneously in a single DNA sample. The high level of automation and the large amount of data produced in a high-throughput laboratory require advanced software tools for quality control and workflow management.'),
(92761, 2009, 'bmc', 'Finding evolutionarily conserved cis-regulatory modules with a universal set of motifs', 'Wilczynski, Bartek|Dojer, Norbert|Patelak, Mateusz|Tiuryn, Jerzy', 'Institute of Informatics, University of Warsaw, Warsaw, Poland. bartek@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. bartek@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. bartek@mimuw.edu.pl|Institute of Informatics, University of Warsaw, Warsaw, Poland. bartek@mimuw.edu.pl', 'Poland|Poland|Poland|Poland', 18, 'Algorithms|Base Sequence|Computational Biology|DNA|Evolution, Molecular|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-82', '19284541', 'Finding functional regulatory elements in DNA sequences is a very important problem in computational biology and providing a reliable algorithm for this task would be a major step towards understanding regulatory mechanisms on genome-wide scale. Major obstacles in this respect are that the fact that the amount of non-coding DNA is vast, and that the methods for predicting functional transcription factor binding sites tend to produce results with a high percentage of false positives. This makes the problem of finding regions significantly enriched in binding sites difficult.'),
(92762, 2009, 'bmc', 'Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis', 'Wang, Tao|Shao, Kang|Chu, Qinying|Ren, Yanfei|Mu, Yiming|Qu, Lijia|He, Jie|Jin, Changwen|Xia, Bin', 'Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com|Beijing NMR Center, Peking University, PR China. super_wt@sina.com', 'China|China|China|China|China|China|China|China|China', 58, 'Algorithms|Computational Biology|Magnetic Resonance Spectroscopy|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-10-83', '19291281', 'Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.'),
(92763, 2009, 'bmc', 'Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation', 'Guedj, Mickael|Robin, Stephane|Celisse, Alain|Nuel, Gregory', 'Statistics and Genome laboratory, CNRS UMR8071, INRA U1152, University of Evry, Evry, France. mickael.guedj@gmail.com|Statistics and Genome laboratory, CNRS UMR8071, INRA U1152, University of Evry, Evry, France. mickael.guedj@gmail.com|Statistics and Genome laboratory, CNRS UMR8071, INRA U1152, University of Evry, Evry, France. mickael.guedj@gmail.com|Statistics and Genome laboratory, CNRS UMR8071, INRA U1152, University of Evry, Evry, France. mickael.guedj@gmail.com', 'France|France|France|France', 18, 'Computational Biology|False Positive Reactions|Gene Expression Profiling|Genome-Wide Association Study|Internet|Models, Statistical|Software', 'Journal Article', '10.1186/1471-2105-10-84', '19291295', 'The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given rejection region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true.'),
(92764, 2009, 'bmc', 'Statistical identification of gene association by CID in application of constructing ER regulatory network', 'Liu, Li-Yu D|Chen, Chien-Yu|Chen, Mei-Ju M|Tsai, Ming-Shian|Lee, Cho-Han S|Phang, Tzu L|Chang, Li-Yun|Kuo, Wen-Hung|Hwa, Hsiao-Lin|Lien, Huang-Chun|Jung, Shih-Ming|Lin, Yi-Shing|Chang, King-Jen|Hsieh, Fon-Jou', 'Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw|Department of Agronomy, Biometry Division, National Taiwan University, Taipei, Taiwan. lyliu@ntu.edu.tw', '-|-|-|-|-|-|-|-|-|-|-|-|-|-', 0, 'Breast Neoplasms|Computational Biology|Data Interpretation, Statistical|Estrogen Receptor alpha|Female|Gene Expression Profiling|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-85', '19292896', 'A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A).'),
(92765, 2009, 'bmc', 'In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles', 'Lin, Frank P Y|Coiera, Enrico|Lan, Ruiting|Sintchenko, Vitali', 'Centre for Health Informatics, University of New South Wales, Sydney, Australia. frank.lin@student.unsw.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, Australia. frank.lin@student.unsw.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, Australia. frank.lin@student.unsw.edu.au|Centre for Health Informatics, University of New South Wales, Sydney, Australia. frank.lin@student.unsw.edu.au', 'Australia|Australia|Australia|Australia', 4, 'Algorithms|Computational Biology|Gene Expression Profiling|Genes, Bacterial|Genome, Bacterial|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-86', '19292914', 'In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task.'),
(92766, 2009, 'bmc', 'Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data', 'Jaitly, Navdeep|Mayampurath, Anoop|Littlefield, Kyle|Adkins, Joshua N|Anderson, Gordon A|Smith, Richard D', 'Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca|Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca|Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca|Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca|Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca|Pacific Northwest National Laboratory, Richland, WA 99352, USA. ndjaitly@cs.utoronto.ca', 'United States|United States|United States|United States|United States|United States', 164, 'Algorithms|Chromatography, Liquid|Mass Spectrometry|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-10-87', '19292916', 'Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls.'),
(92767, 2009, 'bmc', 'Analyses of domains and domain fusions in human proto-oncogenes', 'Liu, Qi|Huang, Jinling|Liu, Huiqing|Wan, Ping|Ye, Xiuzi|Xu, Ying', 'Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu|Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. qiliu@csbl.bmb.uga.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 5, 'Computational Biology|Databases, Protein|Humans|Oncogene Proteins, Fusion|Protein Structure, Tertiary|Proto-Oncogenes', 'Journal Article', '10.1186/1471-2105-10-88', '19292927', 'Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers.'),
(92768, 2009, 'bmc', 'The PHA Depolymerase Engineering Database: A systematic analysis tool for the diverse family of polyhydroxyalkanoate (PHA) depolymerases', 'Knoll, Michael|Hamm, Thomas M|Wagner, Florian|Martinez, Virginia|Pleiss, Jürgen', 'Institute of Technical Biochemistry, University of Stuttgart, Allmandring, Germany. michael.knoll@itb.uni-stuttgart.de|Institute of Technical Biochemistry, University of Stuttgart, Allmandring, Germany. michael.knoll@itb.uni-stuttgart.de|Institute of Technical Biochemistry, University of Stuttgart, Allmandring, Germany. michael.knoll@itb.uni-stuttgart.de|Institute of Technical Biochemistry, University of Stuttgart, Allmandring, Germany. michael.knoll@itb.uni-stuttgart.de|Institute of Technical Biochemistry, University of Stuttgart, Allmandring, Germany. michael.knoll@itb.uni-stuttgart.de', 'Germany|Germany|Germany|Germany|Germany', 68, 'Binding Sites|Carboxylic Ester Hydrolases|Databases, Protein|Markov Chains|Protein Engineering|Software', 'Journal Article', '10.1186/1471-2105-10-89', '19296857', 'Polyhydroxyalkanoates (PHAs) can be degraded by many microorganisms using intra- or extracellular PHA depolymerases. PHA depolymerases are very diverse in sequence and substrate specificity, but share a common alpha/beta-hydrolase fold and a catalytic triad, which is also found in other alpha/beta-hydrolases.'),
(92769, 2009, 'bmc', 'Bayesian clustering and feature selection for cancer tissue samples', 'Marttinen, Pekka|Myllykangas, Samuel|Corander, Jukka', 'Department of Mathematics and Statistics, University of Helsinki, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and Statistics, University of Helsinki, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and Statistics, University of Helsinki, Finland. pekka.marttinen@helsinki.fi', 'Finland|Finland|Finland', 14, 'Algorithms|Bayes Theorem|Cluster Analysis|Computational Biology|DNA, Neoplasm|Databases, Genetic|Gene Dosage|Gene Expression Profiling|Humans|Neoplasms|Software', 'Journal Article', '10.1186/1471-2105-10-90', '19296858', 'The versatility of DNA copy number amplifications for profiling and categorization of various tissue samples has been widely acknowledged in the biomedical literature. For instance, this type of measurement techniques provides possibilities for exploring sets of cancerous tissues to identify novel subtypes. The previously utilized statistical approaches to various kinds of analyses include traditional algorithmic techniques for clustering and dimension reduction, such as independent and principal component analyses, hierarchical clustering, as well as model-based clustering using maximum likelihood estimation for latent class models.'),
(92770, 2009, 'bmc', 'Improved analysis of bacterial CGH data beyond the log-ratio paradigm', 'Snipen, Lars|Nyquist, Otto L|Solheim, Margrete|Aakra, Agot|Nes, Ingolf F', 'Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no', 'Norway|Norway|Norway|Norway|Norway', 8, 'Comparative Genomic Hybridization|Computational Biology|Gene Expression Profiling|Genome, Bacterial|Oligonucleotide Array Sequence Analysis|ROC Curve', 'Journal Article', '10.1186/1471-2105-10-91', '19298668', 'Existing methods for analyzing bacterial CGH data from two-color arrays are based on log-ratios only, a paradigm inherited from expression studies. We propose an alternative approach, where microarray signals are used in a different way and sequence identity is predicted using a supervised learning approach.'),
(92771, 2009, 'bmc', 'MarVis: a tool for clustering and visualization of metabolic biomarkers', 'Kaever, Alexander|Lingner, Thomas|Feussner, Kirstin|Göbel, Cornelia|Feussner, Ivo|Meinicke, Peter', 'Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de|Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Germany. alex@gobics.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 29, 'Biomarkers|Cluster Analysis|Computational Biology|Pattern Recognition, Automated|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-92', '19302701', 'A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.'),
(92772, 2009, 'bmc', 'New scoring schema for finding motifs in DNA Sequences', 'Zare-Mirakabad, Fatemeh|Ahrabian, Hayedeh|Sadeghi, Mehdei|Nowzari-Dalini, Abbas|Goliaei, Bahram', 'Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. zare@ibb.ut.ac.ir|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. zare@ibb.ut.ac.ir|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. zare@ibb.ut.ac.ir|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. zare@ibb.ut.ac.ir|Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. zare@ibb.ut.ac.ir', '-|-|-|-|-', 10, 'Base Sequence|Binding Sites|Computational Biology|DNA|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-93', '19302709', 'Pattern discovery in DNA sequences is one of the most fundamental problems in molecular biology with important applications in finding regulatory signals and transcription factor binding sites. An important task in this problem is to search (or predict) known binding sites in a new DNA sequence. For this reason, all subsequences of the given DNA sequence are scored based on an scoring function and the prediction is done by selecting the best score. By assuming no dependency between binding site base positions, most of the available tools for known binding site prediction are designed. Recently Tomovic and Oakeley investigated the statistical basis for either a claim of dependence or independence, to determine whether such a claim is generally true, and they presented a scoring function for binding site prediction based on the dependency between binding site base positions. Our primary objective is to investigate the scoring functions which can be used in known binding site prediction based on the assumption of dependency or independency in binding site base positions.'),
(92773, 2009, 'bmc', 'Statistical and visual differentiation of subcellular imaging', 'Hamilton, Nicholas A|Wang, Jack T H|Kerr, Markus C|Teasdale, Rohan D', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. n.hamilton@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. n.hamilton@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. n.hamilton@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia. n.hamilton@imb.uq.edu.au', 'Australia|Australia|Australia|Australia', 18, 'Algorithms|Computational Biology|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Organelles|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-94', '19302715', 'Automated microscopy technologies have led to a rapid growth in imaging data on a scale comparable to that of the genomic revolution. High throughput screens are now being performed to determine the localisation of all of proteins in a proteome. Closer to the bench, large image sets of proteins in treated and untreated cells are being captured on a daily basis to determine function and interactions. Hence there is a need for new methodologies and protocols to test for difference in subcellular imaging both to remove bias and enable throughput. Here we introduce a novel method of statistical testing, and supporting software, to give a rigorous test for difference in imaging. We also outline the key questions and steps in establishing an analysis pipeline.'),
(92774, 2009, 'bmc', 'JNets: exploring networks by integrating annotation', 'Macpherson, Jamie I|Pinney, John W|Robertson, David L', 'Faculty of Life Sciences, Michael Smith Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. jamie.macpherson@postgrad.manchester.ac.uk|Faculty of Life Sciences, Michael Smith Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. jamie.macpherson@postgrad.manchester.ac.uk|Faculty of Life Sciences, Michael Smith Building, University of Manchester, Oxford Road, Manchester, M13 9PT, UK. jamie.macpherson@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 8, 'Computational Biology|Computer Graphics|Databases, Genetic|HIV-1|Humans|Protein Interaction Mapping|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-95', '19323810', 'A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.'),
(92775, 2009, 'bmc', 'Identifying significant temporal variation in time course microarray data without replicates', 'Billups, Stephen C|Neville, Margaret C|Rudolph, Michael|Porter, Weston|Schedin, Pepper', 'Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu|Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu|Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu|Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu|Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, USA. Stephen.Billups@ucdenver.edu', 'United States|United States|United States|United States|United States', 11, 'Algorithms|Animals|Circadian Rhythm|Computational Biology|Gene Expression Profiling|Mammary Glands, Animal|Oligonucleotide Array Sequence Analysis|Rats', 'Journal Article', '10.1186/1471-2105-10-96', '19323838', 'An important component of time course microarray studies is the identification of genes that demonstrate significant time-dependent variation in their expression levels. Until recently, available methods for performing such significance tests required replicates of individual time points. This paper describes a replicate-free method that was developed as part of a study of the estrous cycle in the rat mammary gland in which no replicate data was collected.'),
(92776, 2009, 'bmc', 'Selected Proceedings of the First Summit on Translational Bioinformatics 2008 March 10-12, 2008 San Francisco, California, USA', '-', '-', '-', 11, 'Animals|Computational Biology|Humans', 'Congress', '10.1186/1471-2105-10-96', '19326484', '-'),
(92777, 2009, 'bmc', 'Modeling genomic data with type attributes, balancing stability and maintainability', 'Busch, Norbert|Wedemann, Gero', 'System Engineering und Informationsmanagement, Fachhochschule Stralsund, Stralsund, Germany. norbert.busch@fh-stralsund.de|System Engineering und Informationsmanagement, Fachhochschule Stralsund, Stralsund, Germany. norbert.busch@fh-stralsund.de', 'Germany|Germany', 0, 'Databases, Genetic|Genome|Genomics|Software', 'Journal Article', '10.1186/1471-2105-10-97', '19327130', 'Molecular biology (MB) is a dynamic research domain that benefits greatly from the use of modern software technology in preparing experiments, analyzing acquired data, and even performing in-silico analyses. As ever new findings change the face of this domain, software for MB has to be sufficiently flexible to accommodate these changes. At the same time, however, the efficient development of high-quality and interoperable software requires a stable model of concepts for the subject domain and their relations. The result of these two contradictory requirements is increased complexity in the development of MB software.A common means to reduce complexity is to consider only a small part of the domain, instead of the domain as a whole. As a result, small, specialized programs develop their own domain understanding. They often use one of the numerous data formats or implement proprietary data models. This makes it difficult to incorporate the results of different programs, which is needed by many users in order to work with the software efficiently. The data conversions required to achieve interoperability involve more than just type conversion. Usually they also require complex data mappings and lead to a loss of information.'),
(92778, 2009, 'bmc', 'Characterisation and correction of signal fluctuations in successive acquisitions of microarray images', 'Glatigny, Annie|Delacroix, Hervé|Tang, Thomas|François, Nicolas|Aggerbeck, Lawrence|Mucchielli-Giorgi, Marie-Hélène', 'Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr|Centre de Génétique Moléculaire, CNRS FRE3144, F-91198 Gif-sur-Yvette, France. glatigny@cgm.cnrs-gif.fr', 'France|France|France|France|France|France', 0, 'Gene Expression Profiling|Image Enhancement|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-98', '19331668', 'There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations.'),
(92779, 2009, 'bmc', 'Markov clustering versus affinity propagation for the partitioning of protein interaction graphs', 'Vlasblom, James|Wodak, Shoshana J', 'Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario, Canada. jim.vlasblom@utoronto.ca|Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Ontario, Canada. jim.vlasblom@utoronto.ca', 'Canada|Canada', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Markov Chains|Protein Interaction Mapping|Proteins|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-99', '19331680', 'Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.'),
(92780, 2009, 'bmc', 'Biological sequences as pictures: a generic two dimensional solution for iterated maps', 'Almeida, Jonas S|Vinga, Susana', 'Dept Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. jalmeida@mathbiol.org|Dept Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. jalmeida@mathbiol.org', 'United States|United States', 0, 'Algorithms|Computational Biology|Pattern Recognition, Automated|Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-100', '19335894', 'Representing symbolic sequences graphically using iterated maps has enjoyed an enduring popularity since it was first proposed in Jeffrey 1990 as chaos game representation (CGR). The usefulness of this representation goes beyond the convenience of a scale independent representation. It provides a variable memory length representation of transition. This includes the representation of succession with non-integer order, which comes with the promise of generalizing Markovian formalisms. The original proposal targeted genomic sequences only but since then several generalizations have been proposed, many specifically designed to handle protein data.'),
(92781, 2009, 'bmc', 'Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation', 'Liu, Xiaofeng|Bai, Fang|Ouyang, Sisheng|Wang, Xicheng|Li, Honglin|Jiang, Hualiang', 'Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn|Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, PR China. xfliu@mail.shcnc.ac.cn', 'China|China|China|China|China|China', 0, 'Algorithms|Computer Simulation|Databases, Factual|Drug Design|Models, Molecular|Molecular Conformation|Software', 'Journal Article', '10.1186/1471-2105-10-101', '19335906', 'Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm.'),
(92782, 2009, 'bmc', 'Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty', 'Agrawal, Ankit|Huang, Xiaoqiu', 'Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011-1041, USA. ankitag@iastate.edu|Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, IA 50011-1041, USA. ankitag@iastate.edu', 'United States|United States', 15, 'Algorithms|Computational Biology|Models, Statistical|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S3-S1', '19344477', 'Accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92783, 2009, 'bmc', 'Mining metastasis related genes by primary-secondary tumor comparisons from large-scale databases', 'Kim, Sangwoo|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-Dong, Yu-seong Gu, Daejeon, 305-701, Republic of Korea. swkim@biosoft.kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-Dong, Yu-seong Gu, Daejeon, 305-701, Republic of Korea. swkim@biosoft.kaist.ac.kr', '-|-', 0, 'Computational Biology|Databases, Factual|Gene Expression Profiling|Genes, Neoplasm|Humans|Neoplasm Invasiveness|Neoplasm Metastasis', 'Journal Article', '10.1186/1471-2105-10-S3-S2', '19344478', 'Metastasis is the most dangerous step in cancer progression and causes more than 90% of cancer death. Although many researchers have been working on biological features and characteristics of metastasis, most of its genetic level processes remain uncertain. Some studies succeeded in elucidating metastasis related genes and pathways, followed by predicting prognosis of cancer patients, but there still is a question whether the result genes or pathways contain enough information and noise features have been controlled appropriately.'),
(92784, 2009, 'bmc', 'Passage relevance models for genomics search', 'Urbain, Jay|Frieder, Ophir|Goharian, Nazli', 'Electrical Engineering and Computer Science Department, Milwaukee School of Engineering, Milwaukee, WI, USA. urbain@msoe.edu|Electrical Engineering and Computer Science Department, Milwaukee School of Engineering, Milwaukee, WI, USA. urbain@msoe.edu|Electrical Engineering and Computer Science Department, Milwaukee School of Engineering, Milwaukee, WI, USA. urbain@msoe.edu', 'United States|United States|United States', 5, 'Algorithms|Genomics|Information Storage and Retrieval|Markov Chains|Models, Statistical|Natural Language Processing', 'Journal Article', '10.1186/1471-2105-10-S3-S3', '19344479', 'We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologists information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.'),
(92785, 2009, 'bmc', 'Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts', 'Duan, Weisi|Song, Min|Yates, Alexander', 'Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. weisi.duan@temple.edu|Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. weisi.duan@temple.edu|Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. weisi.duan@temple.edu', 'United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Humans|Information Storage and Retrieval|Pattern Recognition, Automated|PubMed|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S3-S4', '19344480', 'We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort.'),
(92786, 2009, 'bmc', 'Viability of in-house datamarting approaches for population genetics analysis of SNP genotypes', 'Amigo, Jorge|Phillips, Christopher|Salas, Antonio|Carracedo, Angel', 'Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es', 'Spain|Spain|Spain|Spain', 12, 'Computational Biology|Databases, Genetic|Genetics, Population|Genome, Human|Genotype|Humans|Polymorphism, Single Nucleotide|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S3-S5', '19344481', 'Databases containing very large amounts of SNP (Single Nucleotide Polymorphism) data are now freely available for researchers interested in medical and/or population genetics applications. While many of these SNP repositories have implemented data retrieval tools for general-purpose mining, these alone cannot cover the broad spectrum of needs of most medical and population genetics studies.'),
(92787, 2009, 'bmc', 'Identification of temporal association rules from time-series microarray data sets', 'Nam, Hojung|Lee, KiYoung|Lee, Doheon', 'Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea. hjnam@kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea. hjnam@kaist.ac.kr|Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea. hjnam@kaist.ac.kr', '-|-|-', 0, 'Bayes Theorem|Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Comparative Study', '10.1186/1471-2105-10-S3-S6', '19344482', 'One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A upward arrow, gene B downward arrow] --> (7 min) [gene C upward arrow], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.'),
(92788, 2009, 'bmc', 'GLOSSI: a method to assess the association of genetic loci-sets with complex diseases', 'Chai, High-Seng|Sicotte, Hugues|Bailey, Kent R|Turner, Stephen T|Asmann, Yan W|Kocher, Jean-Pierre A', 'Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu|Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu|Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu|Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu|Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu|Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, NY, USA. chai.high@mayo.edu', 'United States|United States|United States|United States|United States|United States', 28, 'Algorithms|Animals|Computer Simulation|Databases, Genetic|Disease|Humans|Phenotype|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-10-102', '19344520', 'The developments of high-throughput genotyping technologies, which enable the simultaneous genotyping of hundreds of thousands of single nucleotide polymorphisms (SNP) have the potential to increase the benefits of genetic epidemiology studies. Although the enhanced resolution of these platforms increases the chance of interrogating functional SNPs that are themselves causative or in linkage disequilibrium with causal SNPs, commonly used single SNP-association approaches suffer from serious multiple hypothesis testing problems and provide limited insights into combinations of loci that may contribute to complex diseases. Drawing inspiration from Gene Set Enrichment Analysis developed for gene expression data, we have developed a method, named GLOSSI (Gene-loci Set Analysis), that integrates prior biological knowledge into the statistical analysis of genotyping data to test the association of a group of SNPs (loci-set) with complex disease phenotypes. The most significant loci-sets can be used to formulate hypotheses from a functional viewpoint that can be validated experimentally.'),
(92789, 2009, 'bmc', 'Proceedings of the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009) January 13-16, 2009 Beijing, China', '-', '-', '-', 0, 'Animals|Computational Biology|Genomics|Humans', 'Congress', '10.1186/1471-2105-10-102', '19348002', '-'),
(92790, 2009, 'bmc', 'Accessibility and partner number of protein residues, their relationship and a webserver, ContPlot for their display', 'Pal, Arumay|Bahadur, Ranjit Prasad|Ray, Partha Sarathi|Chakrabarti, Pinak', 'Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India. arumay@boseinst.ernet.in|Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India. arumay@boseinst.ernet.in|Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India. arumay@boseinst.ernet.in|Department of Biochemistry, Bose Institute, P-1/12 CIT Scheme VIIM, Kolkata, India. arumay@boseinst.ernet.in', 'India|India|India|India', 0, 'Algorithms|Computational Biology|Databases, Protein|Internet|Models, Molecular|Protein Conformation|Protein Folding|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-103', '19356223', 'Depending on chemical features residues have preferred locations -- interior or exterior -- in protein structures, which also determine how many other residues are found around them. The close packing of residues is the hallmark of protein interior and protein-protein interaction sites.'),
(92791, 2009, 'bmc', 'ProtSA: a web application for calculating sequence specific protein solvent accessibilities in the unfolded ensemble', 'Estrada, Jorge|Bernadó, Pau|Blackledge, Martin|Sancho, Javier', 'Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain.|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain.|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain.|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, 50009 Zaragoza, Spain.', 'Spain|Spain|Spain|Spain', 50, 'Binding Sites|Hydrophobic and Hydrophilic Interactions|Internet|Protein Folding|Proteins|Software|Solvents|Thermodynamics', 'Journal Article', '10.1186/1471-2105-10-104', '19356231', 'The stability of proteins is governed by the heat capacity, enthalpy and entropy changes of folding, which are strongly correlated to the change in solvent accessible surface area experienced by the polypeptide. While the surface exposed in the folded state can be easily determined, accessibilities for the unfolded state at the atomic level cannot be obtained experimentally and are typically estimated using simplistic models of the unfolded ensemble. A web application providing realistic accessibilities of the unfolded ensemble of a given protein at the atomic level will prove useful.'),
(92792, 2009, 'bmc', 'Prediction of guide strand of microRNAs from its sequence and secondary structure', 'Ahmed, Firoz|Ansari, Hifzur Rahman|Raghava, Gajendra P S', 'Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India. firoz@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India. firoz@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India. firoz@imtech.res.in', 'India|India|India', 38, 'Algorithms|Base Sequence|Computational Biology|MicroRNAs|Nucleic Acid Conformation|RNA, Small Interfering|Software', 'Journal Article', '10.1186/1471-2105-10-105', '19358699', 'MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA, while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways.'),
(92793, 2009, 'bmc', 'flowCore: a Bioconductor package for high throughput flow cytometry', 'Hahne, Florian|LeMeur, Nolwenn|Brinkman, Ryan R|Ellis, Byron|Haaland, Perry|Sarkar, Deepayan|Spidlen, Josef|Strain, Errol|Gentleman, Robert', 'Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org|Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 194, 'Computational Biology|Database Management Systems|Drug Discovery|Flow Cytometry|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-106', '19358741', 'Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.'),
(92794, 2009, 'bmc', 'EFICAz2: enzyme function inference by a combined approach enhanced by machine learning', 'Arakaki, Adrian K|Huang, Ying|Skolnick, Jeffrey', 'Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA. adrian.arakaki@gatech.edu|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA. adrian.arakaki@gatech.edu|Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia 30318, USA. adrian.arakaki@gatech.edu', 'Georgia|Georgia|Georgia', 0, 'Artificial Intelligence|Computational Biology|Databases, Factual|Enzymes|Humans|Software', 'Journal Article', '10.1186/1471-2105-10-107', '19361344', 'We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAzs performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment.'),
(92795, 2009, 'bmc', 'dbSMR: a novel resource of genome-wide SNPs affecting microRNA mediated regulation', 'Hariharan, Manoj|Scaria, Vinod|Brahmachari, Samir K', 'GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology (CSIR), Delhi, India. mh.manoj@gmail.com|GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology (CSIR), Delhi, India. mh.manoj@gmail.com|GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology (CSIR), Delhi, India. mh.manoj@gmail.com', 'India|India|India', 59, 'Base Sequence|Binding Sites|Computational Biology|Databases, Nucleic Acid|Genome, Human|Humans|MicroRNAs|Molecular Sequence Data|Nucleic Acid Conformation|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-108', '19371411', 'MicroRNAs (miRNAs) regulate several biological processes through post-transcriptional gene silencing. The efficiency of binding of miRNAs to target transcripts depends on the sequence as well as intramolecular structure of the transcript. Single Nucleotide Polymorphisms (SNPs) can contribute to alterations in the structure of regions flanking them, thereby influencing the accessibility for miRNA binding.'),
(92796, 2009, 'bmc', 'Identifying set-wise differential co-expression in gene expression microarray data', 'Cho, Sung Bum|Kim, Jihun|Kim, Ju Han', 'Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea. csb1749@snu.ac.kr|Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea. csb1749@snu.ac.kr|Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea. csb1749@snu.ac.kr', '-|-|-', 50, 'Algorithms|Computer Simulation|Gene Expression Profiling|Lung Neoplasms|Muscular Dystrophy, Duchenne|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-109', '19371436', 'Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.'),
(92797, 2009, 'bmc', 'Data-driven normalization strategies for high-throughput quantitative RT-PCR', 'Mar, Jessica C|Kimura, Yasumasa|Schroder, Kate|Irvine, Katharine M|Hayashizaki, Yoshihide|Suzuki, Harukazu|Hume, David|Quackenbush, John', 'Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Genetic|Gene Expression Profiling|Reverse Transcriptase Polymerase Chain Reaction', 'Journal Article', '10.1186/1471-2105-10-110', '19374774', 'High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.'),
(92798, 2009, 'bmc', 'The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes', 'Riley, Todd|Yu, Xin|Sontag, Eduardo|Levine, Arnold', 'The Institute for Advanced Study, Princeton, NJ, USA. tr2261@columbia.edu|The Institute for Advanced Study, Princeton, NJ, USA. tr2261@columbia.edu|The Institute for Advanced Study, Princeton, NJ, USA. tr2261@columbia.edu|The Institute for Advanced Study, Princeton, NJ, USA. tr2261@columbia.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Binding Sites|Markov Chains|Models, Biological|Molecular Sequence Data|Response Elements|Software|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-10-111', '19379484', 'A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel Corresponded Baum-Welch training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding sites distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate.'),
(92799, 2009, 'bmc', 'Island method for estimating the statistical significance of profile-profile alignment scores', 'Poleksic, Aleksandar', 'Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614, USA. poleksic@cs.uni.edu', 'United States', 4, 'Computational Biology|Models, Statistical|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-112', '19379500', 'In the last decade, a significant improvement in detecting remote similarity between protein sequences has been made by utilizing alignment profiles in place of amino-acid strings. Unfortunately, no analytical theory is available for estimating the significance of a gapped alignment of two profiles. Many experiments suggest that the distribution of local profile-profile alignment scores is of the Gumbel form. However, estimating distribution parameters by random simulations turns out to be computationally very expensive.'),
(92800, 2009, 'bmc', 'Detection of discriminative sequence patterns in the neighborhood of proline cis peptide bonds and their functional annotation', 'Exarchos, Konstantinos P|Exarchos, Themis P|Papaloukas, Costas|Troganis, Anastassios N|Fotiadis, Dimitrios I', 'Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece. kexarcho@cc.uoi.gr|Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece. kexarcho@cc.uoi.gr|Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece. kexarcho@cc.uoi.gr|Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece. kexarcho@cc.uoi.gr|Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Ioannina, Greece. kexarcho@cc.uoi.gr', 'Greece|Greece|Greece|Greece|Greece', 0, 'Databases, Protein|Models, Molecular|Peptides|Proline|Protein Conformation|Proteins', 'Journal Article', '10.1186/1471-2105-10-113', '19379512', 'Polypeptides are composed of amino acids covalently bonded via a peptide bond. The majority of peptide bonds in proteins is found to occur in the trans conformation. In spite of their infrequent occurrence, cis peptide bonds play a key role in the protein structure and function, as well as in many significant biological processes.'),
(92801, 2009, 'bmc', 'POINeT: protein interactome with sub-network analysis and hub prioritization', 'Lee, Sheng-An|Chan, Chen-Hsiung|Chen, Tzu-Chi|Yang, Chia-Ying|Huang, Kuo-Chuan|Tsai, Chi-Hung|Lai, Jin-Mei|Wang, Feng-Sheng|Kao, Cheng-Yan|Huang, Chi-Ying F', 'Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw|Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan, ROC. d93922005@ntu.edu.tw', '-|-|-|-|-|-|-|-|-|-', 39, 'Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins|Proteome|Software', 'Journal Article', '10.1186/1471-2105-10-114', '19379523', 'Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.'),
(92802, 2009, 'bmc', 'Most parsimonious haplotype allele sharing determination', 'Cai, Zhipeng|Sabaa, Hadi|Wang, Yining|Goebel, Randy|Wang, Zhiquan|Xu, Jiaofen|Stothard, Paul|Lin, Guohui', 'Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 17, 'Alleles|Chromosome Mapping|Computer Simulation|Genome, Human|Genome-Wide Association Study|Genotype|Haplotypes|Humans|Pedigree|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-115', '19379528', 'The common disease--common variant hypothesis and genome-wide association studies have achieved numerous successes in the last three years, particularly in genetic mapping in human diseases. Nevertheless, the power of the association study methods are still low, in particular on quantitative traits, and the description of the full allelic spectrum is deemed still far from reach. Given increasing density of single nucleotide polymorphisms available and suggested by the block-like structure of the human genome, a popular and prosperous strategy is to use haplotypes to try to capture the correlation structure of SNPs in regions of little recombination. The key to the success of this strategy is thus the ability to unambiguously determine the haplotype allele sharing status among the members. The association studies based on haplotype sharing status would have significantly reduced degrees of freedom and be able to capture the combined effects of tightly linked causal variants.'),
(92803, 2009, 'bmc', 'Profile analysis and prediction of tissue-specific CpG island methylation classes', 'Previti, Christopher|Harari, Oscar|Zwir, Igor|del Val, Coral', 'Department of Molecular Biophysics, DKFZ, German Cancer Research Center, Heidelberg, Germany. christopher.previti@bccs.uib.no|Department of Molecular Biophysics, DKFZ, German Cancer Research Center, Heidelberg, Germany. christopher.previti@bccs.uib.no|Department of Molecular Biophysics, DKFZ, German Cancer Research Center, Heidelberg, Germany. christopher.previti@bccs.uib.no|Department of Molecular Biophysics, DKFZ, German Cancer Research Center, Heidelberg, Germany. christopher.previti@bccs.uib.no', 'Germany|Germany|Germany|Germany', 0, 'Computational Biology|CpG Islands|DNA Methylation|Databases, Genetic|Epigenesis, Genetic|Genome, Human|Humans|Organ Specificity|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-10-116', '19383127', 'The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern.'),
(92804, 2009, 'bmc', 'Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins', 'Durek, Pawel|Schudoma, Christian|Weckwerth, Wolfram|Selbig, Joachim|Walther, Dirk', 'Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany. durek@mpimp-golm.mpg.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany. durek@mpimp-golm.mpg.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany. durek@mpimp-golm.mpg.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany. durek@mpimp-golm.mpg.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany. durek@mpimp-golm.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Amino Acid Motifs|Binding Sites|Computational Biology|Databases, Protein|Phosphoproteins|Phosphorylation|Protein Kinases|Sequence Analysis, Protein|Serine|Tyrosine', 'Journal Article', '10.1186/1471-2105-10-117', '19383128', 'Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.'),
(92805, 2009, 'bmc', 'ACID: annotation of cassette and integron data', 'Joss, Michael J|Koenig, Jeremy E|Labbate, Maurizio|Polz, Martin F|Gillings, Michael R|Stokes, Harold W|Doolittle, W Ford|Boucher, Yan', 'Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com|Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, 02139, USA. Michael.joss@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Databases, Genetic|Evolution, Molecular|Genes, Bacterial|Genome, Bacterial|Integrons', 'Journal Article', '10.1186/1471-2105-10-118', '19383137', 'Although integrons and their associated gene cassettes are present in ~10% of bacteria and can represent up to 3% of the genome in which they are found, very few have been properly identified and annotated in public databases. These genetic elements have been overlooked in comparison to other vectors that facilitate lateral gene transfer between microorganisms.'),
(92806, 2009, 'bmc', 'A bag-of-words approach for Drosophila gene expression pattern annotation', 'Ji, Shuiwang|Li, Ying-Xin|Zhou, Zhi-Hua|Kumar, Sudhir|Ye, Jieping', 'Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.', 'United States|United States|United States|United States|United States', 34, 'Animals|Computational Biology|Databases, Genetic|Drosophila|Gene Expression|Gene Expression Profiling|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-119', '19383139', 'Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.'),
(92807, 2009, 'bmc', 'Multichromosomal median and halving problems under different genomic distances', 'Tannier, Eric|Zheng, Chunfang|Sankoff, David', 'INRIA Rhône-Alpes, Inovallée, Montbonnot, Saint Ismier Cedex, France. Eric.Tannier@inria.fr|INRIA Rhône-Alpes, Inovallée, Montbonnot, Saint Ismier Cedex, France. Eric.Tannier@inria.fr|INRIA Rhône-Alpes, Inovallée, Montbonnot, Saint Ismier Cedex, France. Eric.Tannier@inria.fr', 'France|France|France', 0, 'Algorithms|Chromosomes|Computational Biology|Databases, Genetic|Gene Rearrangement|Genome|Genomics', 'Journal Article', '10.1186/1471-2105-10-120', '19386099', 'Genome median and genome halving are combinatorial optimization problems that aim at reconstructing ancestral genomes as well as the evolutionary events leading from the ancestor to extant species. Exploring complexity issues is a first step towards devising efficient algorithms. The complexity of the median problem for unichromosomal genomes (permutations) has been settled for both the breakpoint distance and the reversal distance. Although the multichromosomal case has often been assumed to be a simple generalization of the unichromosomal case, it is also a relaxation so that complexity in this context does not follow from existing results, and is open for all distances.'),
(92808, 2009, 'bmc', 'SNP HiTLink: a high-throughput linkage analysis system employing dense SNP data', 'Fukuda, Yoko|Nakahara, Yasuo|Date, Hidetoshi|Takahashi, Yuji|Goto, Jun|Miyashita, Akinori|Kuwano, Ryozo|Adachi, Hiroki|Nakamura, Eiji|Tsuji, Shoji', 'Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp|Department of Neurology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. yokofukuda-tky@umin.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Computational Biology|Genetic Linkage|Genome-Wide Association Study|Genotype|Humans|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-10-121', '19393044', 'During this recent decade, microarray-based single nucleotide polymorphism (SNP) data are becoming more widely used as markers for linkage analysis in the identification of loci for disease-associated genes. Although microarray-based SNP analyses have markedly reduced genotyping time and cost compared with microsatellite-based analyses, applying these enormous data to linkage analysis programs is a time-consuming step, thus, necessitating a high-throughput platform.'),
(92809, 2009, 'bmc', 'Granger causality vs dynamic Bayesian network inference: a comparative study', 'Zou, Cunlu|Denby, Katherine J|Feng, Jianfeng', 'Department of Computer Science, University of Warwick, Coventry, UK. csrcbh@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. csrcbh@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. csrcbh@dcs.warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 60, 'Bayes Theorem|Computational Biology|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Comparative Study', '10.1186/1471-2105-10-122', '19393071', 'In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.'),
(92810, 2009, 'bmc', 'FlyPhy: a phylogenomic analysis platform for Drosophila genes and gene families', 'Wu, Jinyu|Xu, Xiang|Xiao, Jian|Xu, Long|Yi, Huiguang|Gao, Shengjie|Liu, Jing|Bao, Qiyu|Zhao, Fangqing|Li, Xiaokun', 'Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn|Institute of Biomedical Informatics/Zhejiang Provincial Key Laboratory of Medical Genetics, Wenzhou Medical College, Wenzhou, PR China. iamwujy@yahoo.com.cn', 'China|China|China|China|China|China|China|China|China|China', 0, 'Animals|Computational Biology|Databases, Genetic|Drosophila|Genes, Insect|Genome, Insect|Genomics|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-123', '19393099', 'The availability of 12 fully sequenced Drosophila species genomes provides an excellent opportunity to explore the evolutionary mechanism, structure and function of gene families in Drosophila. Currently, several important resources, such as FlyBase, FlyMine and DroSpeGe, have been devoted to integrating genetic, genomic, and functional data of Drosophila into a well-organized form. However, all of these resources are gene-centric and lack the information of the gene families in Drosophila.'),
(92811, 2009, 'bmc', 'Marker-free image registration of electron tomography tilt-series', 'Sorzano, Carlos Oscar Sanchez|Messaoudi, Cédric|Eibauer, Matthias|Bilbao-Castro, J R|Hegerl, R|Nickell, S|Marco, S|Carazo, J M', 'Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es|Universidad San Pablo-CEU, Madrid, Spain. coss@cnb.csic.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Computer Simulation|Electron Microscope Tomography|Image Processing, Computer-Assisted|Models, Biological|Phantoms, Imaging', 'Journal Article', '10.1186/1471-2105-10-124', '19397789', 'Tilt series are commonly used in electron tomography as a means of collecting three-dimensional information from two-dimensional projections. A common problem encountered is the projection alignment prior to 3D reconstruction. Current alignment techniques usually employ gold particles or image derived markers to correctly align the images. When these markers are not present, correlation between adjacent views is used to align them. However, sequential pairwise correlation is prone to bias and the resulting alignment is not always optimal.'),
(92812, 2009, 'bmc', 'Survey-based naming conventions for use in OBO Foundry ontology development', 'Schober, Daniel|Smith, Barry|Lewis, Suzanna E|Kusnierczyk, Waclaw|Lomax, Jane|Mungall, Chris|Taylor, Chris F|Rocca-Serra, Philippe|Sansone, Susanna-Assunta', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. schober@imbi.uni-freiburg.de', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 50, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-125', '19397794', 'A wide variety of ontologies relevant to the biological and medical domains are available through the OBO Foundry portal, and their number is growing rapidly. Integration of these ontologies, while requiring considerable effort, is extremely desirable. However, heterogeneities in format and style pose serious obstacles to such integration. In particular, inconsistencies in naming conventions can impair the readability and navigability of ontology class hierarchies, and hinder their alignment and integration. While other sources of diversity are tremendously complex and challenging, agreeing a set of common naming conventions is an achievable goal, particularly if those conventions are based on lessons drawn from pooled practical experience and surveys of community opinion.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92813, 2009, 'bmc', 'Identifying recombinants in human and primate immunodeficiency virus sequence alignments using quartet scanning', 'Lemey, Philippe|Lott, Martin|Martin, Darren P|Moulton, Vincent', 'Rega Institute, Katholieke Universiteit Leuven, Minderbroedersstraat 10, 3000 Leuven, Belgium. philippe.lemey@gmail.com|Rega Institute, Katholieke Universiteit Leuven, Minderbroedersstraat 10, 3000 Leuven, Belgium. philippe.lemey@gmail.com|Rega Institute, Katholieke Universiteit Leuven, Minderbroedersstraat 10, 3000 Leuven, Belgium. philippe.lemey@gmail.com|Rega Institute, Katholieke Universiteit Leuven, Minderbroedersstraat 10, 3000 Leuven, Belgium. philippe.lemey@gmail.com', 'Belgium|Belgium|Belgium|Belgium', 0, 'Animals|Computational Biology|Evolution, Molecular|HIV Infections|HIV-1|Humans|Lentiviruses, Primate|Phylogeny|Recombination, Genetic|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-10-126', '19397803', 'Recombination has a profound impact on the evolution of viruses, but characterizing recombination patterns in molecular sequences remains a challenging endeavor. Despite its importance in molecular evolutionary studies, identifying the sequences that exhibit such patterns has received comparatively less attention in the recombination detection framework. Here, we extend a quartet-mapping based recombination detection method to enable identification of recombinant sequences without prior specifications of either query and reference sequences. Through simulations we evaluate different recombinant identification statistics and significance tests. We compare the quartet approach with triplet-based methods that employ additional heuristic tests to identify parental and recombinant sequences.'),
(92814, 2009, 'bmc', 'Automated FingerPrint Background removal: FPB', 'Scalabrin, Simone|Morgante, Michele|Policriti, Alberto', 'Istituto di Genomica Applicata (IGA), via J, Linussio 51, I-33100 Udine, Italy. scalabrin@appliedgenomics.org|Istituto di Genomica Applicata (IGA), via J, Linussio 51, I-33100 Udine, Italy. scalabrin@appliedgenomics.org|Istituto di Genomica Applicata (IGA), via J, Linussio 51, I-33100 Udine, Italy. scalabrin@appliedgenomics.org', 'Italy|Italy|Italy', 10, 'Chromosome Mapping|Chromosomes, Artificial, Bacterial|Computational Biology|Databases, Genetic|Genome, Human|Genomics|Human Genome Project|Humans|Physical Chromosome Mapping', 'Journal Article', '10.1186/1471-2105-10-127', '19405935', 'The construction of a whole-genome physical map has been an essential component of numerous genome projects initiated since the inception of the Human Genome Project. Its usefulness has been proved for whole-genome shotgun projects as a post-assembly validation and recently it has also been used in the assembly step to constrain on BACs positions. Fingerprinting is usually the method of choice for construction of physical maps. A clone fingerprint is composed of true peaks representing real fragments and background peaks, mainly composed of E. coli genomic DNA, partial digestions, star activity by-products, and machine background. High-throughput fingerprinting leads to the production of thousands of BAC clone fingerprints per day. That is why background peaks removal has become an important issue and needs to be automatized, especially in capillary electrophoresis based fingerprints.'),
(92815, 2009, 'bmc', 'Identification of differentially expressed subnetworks based on multivariate ANOVA', 'Hwang, Taeyoung|Park, Taesung', 'Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea. hwangty@snu.ac.kr|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Republic of Korea. hwangty@snu.ac.kr', '-|-', 0, 'Algorithms|Analysis of Variance|Computer Simulation|Fibroblasts|Gene Expression Profiling|Humans|Male|Oligonucleotide Array Sequence Analysis|Prostatic Neoplasms|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteomics|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-10-128', '19405941', 'Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genome-wide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks.'),
(92816, 2009, 'bmc', 'Improving classification in protein structure databases using text mining', 'Koussounadis, Antonis|Redfern, Oliver C|Jones, David T', 'Bioinformatics Group, Department of Computer Science, University College of London, London, WC1E 6BT, UK. a.koussounadis@cs.ucl.ac.uk|Bioinformatics Group, Department of Computer Science, University College of London, London, WC1E 6BT, UK. a.koussounadis@cs.ucl.ac.uk|Bioinformatics Group, Department of Computer Science, University College of London, London, WC1E 6BT, UK. a.koussounadis@cs.ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 12, 'Algorithms|Databases, Protein|Information Storage and Retrieval|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-129', '19416501', 'The classification of protein domains in the CATH resource is primarily based on structural comparisons, sequence similarity and manual analysis. One of the main bottlenecks in the processing of new entries is the evaluation of borderline cases by human curators with reference to the literature, and better tools for helping both expert and non-expert users quickly identify relevant functional information from text are urgently needed. A text based method for protein classification is presented, which complements the existing sequence and structure-based approaches, especially in cases exhibiting low similarity to existing members and requiring manual intervention. The method is based on the assumption that textual similarity between sets of documents relating to proteins reflects biological function similarities and can be exploited to make classification decisions.'),
(92817, 2009, 'bmc', 'Willows: a memory efficient tree and forest construction package', 'Zhang, Heping|Wang, Minghui|Chen, Xiang', 'Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu|Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu', 'United States|United States|United States', 22, 'Computational Biology|Genome-Wide Association Study|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-10-130', '19416535', 'Existing tree and forest methods are powerful bioinformatics tools to explore high dimensional data including high throughput genomic data. However, they cannot deal with the data generated by recent genotyping platforms for single nucleotide polymorphisms due to the massive size of the data and its excessive memory demand.'),
(92818, 2009, 'bmc', 'SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip', 'Fujisawa, Hironori|Horiuchi, Youko|Harushima, Yoshiaki|Takada, Toyoyuki|Eguchi, Shinto|Mochizuki, Takako|Sakaguchi, Takayuki|Shiroishi, Toshihiko|Kurata, Nori', 'The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp|The Institute of Statistical Mathematics, Tokyo, Japan. fujisawa@ism.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Gene Expression Profiling|Genomics|Hordeum|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis|Oryza|Polymorphism, Single Nucleotide|RNA, Messenger|ROC Curve|Research Design|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-10-131', '19419536', 'High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately.'),
(92819, 2009, 'bmc', 'On validation and invalidation of biological models', 'Anderson, James|Papachristodoulou, Antonis', 'Doctoral Training Centre, University of Oxford, Oxford, UK. james.anderson@dtc.ox.ac.uk|Doctoral Training Centre, University of Oxford, Oxford, UK. james.anderson@dtc.ox.ac.uk', 'United Kingdom|United Kingdom', 48, 'Algorithms|Computer Simulation|Models, Biological|Nonlinear Dynamics|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-132', '19422679', 'Very frequently the same biological system is described by several, sometimes competing mathematical models. This usually creates confusion around their validity, ie, which one is correct. However, this is unnecessary since validity of a model cannot be established, model validation is actually a misnomer. In principle the only statement that one can make about a system model is that it is incorrect, ie, invalid, a fact which can be established given appropriate experimental data. Nonlinear models of high dimension and with many parameters are impossible to invalidate through simulation and as such the invalidation process is often overlooked or ignored.'),
(92820, 2009, 'bmc', 'SDRF2GRAPH: a visualization tool of a spreadsheet-based description of experimental processes', 'Kawaji, Hideya|Hayashizaki, Yoshihide|Daub, Carsten O', 'RIKEN Omics Science Center, RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan. kawaji@gsc.riken.jp|RIKEN Omics Science Center, RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan. kawaji@gsc.riken.jp|RIKEN Omics Science Center, RIKEN Yokohama Institute, Yokohama, Kanagawa, Japan. kawaji@gsc.riken.jp', 'Japan|Japan|Japan', 0, 'Computational Biology|Computer Graphics|Databases, Genetic|Information Dissemination|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-133', '19422683', 'As larger datasets are produced with the development of genome-scale experimental techniques, it has become essential to explicitly describe the meta-data (information describing the data) generated by an experiment. The experimental process is a part of the meta-data required to interpret the produced data, and SDRF (Sample and Data Relationship Format) supports its description in a spreadsheet or tab-delimited file. This format was primarily developed to describe microarray studies in MAGE-tab, and it is being applied in a broader context in ISA-tab. While the format provides an explicit framework to describe experiments, increase of experimental steps makes it less obvious to understand the content of the SDRF files.'),
(92821, 2009, 'bmc', 'Validating subcellular localization prediction tools with mycobacterial proteins', 'Restrepo-Montoya, Daniel|Vizcaíno, Carolina|Niño, Luis F|Ocampo, Marisol|Patarroyo, Manuel E|Patarroyo, Manuel A', 'Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com|Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com|Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com|Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com|Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com|Fundación Instituto de Inmunología de Colombia (FIDIC), Carrera 50 No, 26-20 Bogotá DC, Colombia. drestmont@gmail.com', 'Colombia|Colombia|Colombia|Colombia|Colombia|Colombia', 22, 'Algorithms|Bacterial Proteins|Computational Biology|Databases, Protein|Mycobacterium|Software', 'Journal Article', '10.1186/1471-2105-10-134', '19422713', 'The computational prediction of mycobacterial proteins subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthews correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.'),
(92822, 2009, 'bmc', 'CURE-Chloroplast: a chloroplast C-to-U RNA editing predictor for seed plants', 'Du, Pufeng|Jia, Liyan|Li, Yanda', 'MOE Key Laboratory of Bioinformatics and Bioinformatics Div. TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. dpf05@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Div. TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. dpf05@mails.tsinghua.edu.cn|MOE Key Laboratory of Bioinformatics and Bioinformatics Div. TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China. dpf05@mails.tsinghua.edu.cn', 'China|China|China', 19, 'Algorithms|Databases, Genetic|Mitochondria|Plants|RNA Editing|RNA, Chloroplast|RNA, Plant|Seeds', 'Journal Article', '10.1186/1471-2105-10-135', '19422723', 'RNA editing is a type of post-transcriptional modification of RNA and belongs to the class of mechanisms that contribute to the complexity of transcriptomes. C-to-U RNA editing is commonly observed in plant mitochondria and chloroplasts. The in vivo mechanism of recognizing C-to-U RNA editing sites is still unknown. In recent years, many efforts have been made to computationally predict C-to-U RNA editing sites in the mitochondria of seed plants, but there is still no algorithm available for C-to-U RNA editing site prediction in the chloroplasts of seed plants.'),
(92823, 2009, 'bmc', 'Identifying promoter features of co-regulated genes with similar network motifs', 'Harari, Oscar|del Val, Coral|Romero-Zaliz, Rocío|Shin, Dongwoo|Huang, Henry|Groisman, Eduardo A|Zwir, Igor', 'Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, c/ Daniel Saucedo Aranda, s/n 18071, Granada, Spain. oharari@decsai.ugr.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Amino Acid Sequence|Binding Sites|Computational Biology|DNA-Directed RNA Polymerases|Escherichia coli|Gene Expression Regulation, Bacterial|Gene Regulatory Networks|Genome, Bacterial|Molecular Sequence Data|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Salmonella typhi|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-S4-S1', '19426448', 'A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.'),
(92824, 2009, 'bmc', 'Analysis of multiplex gene expression maps obtained by voxelation', 'An, Li|Xie, Hongbo|Chin, Mark H|Obradovic, Zoran|Smith, Desmond J|Megalooikonomou, Vasileios', 'Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu|Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu|Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu|Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu|Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu|Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA. anli@temple.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Brain|Cluster Analysis|Computational Biology|Gene Expression|Mice', 'Journal Article', '10.1186/1471-2105-10-S4-S10', '19426449', 'Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a genes expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.'),
(92825, 2009, 'bmc', 'Efficient use of unlabeled data for protein sequence classification: a comparative study', 'Kuksa, Pavel|Huang, Pai-Hsi|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|Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu', 'United States|United States|United States', 0, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Pattern Recognition, Automated|Proteins|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/1471-2105-10-S4-S2', '19426450', 'Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers.'),
(92826, 2009, 'bmc', 'Using tree diversity to compare phylogenetic heuristics', 'Sul, Seung-Jin|Matthews, Suzanne|Williams, Tiffani L', 'Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sulsj@cs.tamu.edu|Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sulsj@cs.tamu.edu|Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sulsj@cs.tamu.edu', 'United States|United States|United States', 9, 'Algorithms|Biological Evolution|Computational Biology|Phylogeny', 'Journal Article', '10.1186/1471-2105-10-S4-S3', '19426451', 'Evolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms.'),
(92827, 2009, 'bmc', 'Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling', 'Caragea, Cornelia|Sinapov, Jivko|Dobbs, Drena|Honavar, Vasant', 'Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu|Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA 50010, USA. cornelia@cs.iastate.edu', 'United States|United States|United States|United States', 4, 'Cluster Analysis|Computational Biology|DNA|Databases, Genetic|Logistic Models|Proteins|RNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-S4-S4', '19426452', 'Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences.'),
(92828, 2009, 'bmc', 'Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms', 'Lin, Xiaotong|Liu, Mei|Chen, Xue-wen', 'Bioinformatics and Computational Life-Science Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 west 15th Street, Lawrence, KS 66045, USA. cindylin@ku.edu|Bioinformatics and Computational Life-Science Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 west 15th Street, Lawrence, KS 66045, USA. cindylin@ku.edu|Bioinformatics and Computational Life-Science Laboratory, ITTC, Department of Electrical Engineering and Computer Science, The University of Kansas, 1520 west 15th Street, Lawrence, KS 66045, USA. cindylin@ku.edu', 'United States|United States|United States', 13, 'Bayes Theorem|Binding Sites|Computational Biology|Databases, Protein|Genome, Human|Humans|Protein Interaction Mapping|Proteins|Yeasts', 'Journal Article', '10.1186/1471-2105-10-S4-S5', '19426453', 'Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data.'),
(92829, 2009, 'bmc', 'Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli', 'Wang, Rui-Sheng|Jin, Guangxu|Zhang, Xiang-Sun|Chen, Luonan', 'School of Information, Renmin University of China, Beijing 100872, PR China. wangrsh@amss.ac.cn|School of Information, Renmin University of China, Beijing 100872, PR China. wangrsh@amss.ac.cn|School of Information, Renmin University of China, Beijing 100872, PR China. wangrsh@amss.ac.cn|School of Information, Renmin University of China, Beijing 100872, PR China. wangrsh@amss.ac.cn', 'China|China|China|China', 9, 'Algorithms|Computational Biology|Escherichia coli|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Genetic|RNA Processing, Post-Transcriptional|RNA, Untranslated', 'Journal Article', '10.1186/1471-2105-10-S4-S6', '19426454', 'Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner.'),
(92830, 2009, 'bmc', 'A kernel-based approach for detecting outliers of high-dimensional biological data', 'Oh, Jung Hun|Gao, Jean', 'Department of Computer Science and Engineering, The University of Texas, Arlington, Texas, USA. jung.oh@uta.edu|Department of Computer Science and Engineering, The University of Texas, Arlington, Texas, USA. jung.oh@uta.edu', 'United States|United States', 0, 'Algorithms|Computer Simulation|Mass Spectrometry|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-10-S4-S7', '19426455', 'In many cases biomedical data sets contain outliers that make it difficult to achieve reliable knowledge discovery. Data analysis without removing outliers could lead to wrong results and provide misleading information.'),
(92831, 2009, 'bmc', 'Core and periphery structures in protein interaction networks', 'Luo, Feng|Li, Bo|Wan, Xiu-Feng|Scheuermann, Richard H', 'School of Computing, Clemson University, Clemson, SC, USA. luofeng@clemson.edu|School of Computing, Clemson University, Clemson, SC, USA. luofeng@clemson.edu|School of Computing, Clemson University, Clemson, SC, USA. luofeng@clemson.edu|School of Computing, Clemson University, Clemson, SC, USA. luofeng@clemson.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-S4-S8', '19426456', 'Characterizing the structural properties of protein interaction networks will help illuminate the organizational and functional relationships among elements in biological systems.'),
(92832, 2009, 'bmc', 'Biclustering of microarray data with MOSPO based on crowding distance', 'Liu, Junwan|Li, Zhoujun|Hu, Xiaohua|Chen, Yiming', 'School of Computer, National University of Deference Technology, Changsha, PR China. ljwnudt@163.com|School of Computer, National University of Deference Technology, Changsha, PR China. ljwnudt@163.com|School of Computer, National University of Deference Technology, Changsha, PR China. ljwnudt@163.com|School of Computer, National University of Deference Technology, Changsha, PR China. ljwnudt@163.com', 'China|China|China|China', 0, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S4-S9', '19426457', 'High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery.'),
(92833, 2009, 'bmc', 'Issues in learning an ontology from text', 'Brewster, Christopher|Jupp, Simon|Luciano, Joanne|Shotton, David|Stevens, Robert D|Zhang, Ziqi', 'Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk|Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk|Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk|Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk|Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk|Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. C.A.Brewster@aston.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 21, 'Algorithms|Animals|Computational Biology|Database Management Systems|Information Storage and Retrieval|Pattern Recognition, Automated|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S5-S1', '19426458', 'Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus.'),
(92834, 2009, 'bmc', 'Practical application of ontologies to annotate and analyse large scale raw mouse phenotype data', 'Beck, Tim|Morgan, Hugh|Blake, Andrew|Wells, Sara|Hancock, John M|Mallon, Ann-Marie', 'MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk|MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk|MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk|MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk|MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk|MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK. t.beck@har.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 35, 'Animals|Computational Biology|Databases, Genetic|Mice|Mice, Knockout|Phenotype', 'Journal Article', '10.1186/1471-2105-10-S5-S2', '19426459', 'Large-scale international projects are underway to generate collections of knockout mouse mutants and subsequently to perform high throughput phenotype assessments, raising new challenges for computational researchers due to the complexity and scale of the phenotype data. Phenotypes can be described using ontologies in two differing methodologies. Traditionally an individual phenotypic character has either been defined using a single compound term, originating from a species-specific dedicated phenotype ontology, or alternatively by a combinatorial annotation, using concepts from a range of disparate ontologies, to define a phenotypic character as an entity with an associated quality (EQ). Both methods have their merits, which include the dedicated approach allowing use of community standard terminology, and the combinatorial approach facilitating cross-species phenotypic statement comparisons. Previously databases have favoured one approach over another. The EUMODIC project will generate large amounts of mouse phenotype data, generated as a result of the execution of a set of Standard Operating Procedures (SOPs) and will implement both ontological approaches to capture the phenotype data generated.'),
(92835, 2009, 'bmc', 'TGF-beta signaling proteins and the Protein Ontology', 'Arighi, Cecilia N|Liu, Hongfang|Natale, Darren A|Barker, Winona C|Drabkin, Harold|Blake, Judith A|Smith, Barry|Wu, Cathy H', 'Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu|Delaware Biotechnology Institute, University of Delaware, Newark, DE, USA. arighi@dbi.udel.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Computational Biology|Databases, Genetic|Databases, Protein|Humans|Information Storage and Retrieval|Intracellular Signaling Peptides and Proteins|Transforming Growth Factor beta|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S5-S3', '19426460', 'The Protein Ontology (PRO) is designed as a formal and principled Open Biomedical Ontologies (OBO) Foundry ontology for proteins. The components of PRO extend from a classification of proteins on the basis of evolutionary relationships at the homeomorphic level to the representation of the multiple protein forms of a gene, including those resulting from alternative splicing, cleavage and/or post-translational modifications. Focusing specifically on the TGF-beta signaling proteins, we describe the building, curation, usage and dissemination of PRO.'),
(92836, 2009, 'bmc', 'Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships', 'Qu, Xiaoyan A|Gudivada, Ranga C|Jegga, Anil G|Neumann, Eric K|Aronow, Bruce J', 'Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. qux@email.uc.edu|Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. qux@email.uc.edu|Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. qux@email.uc.edu|Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. qux@email.uc.edu|Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. qux@email.uc.edu', 'United States|United States|United States|United States|United States', 56, 'Computational Biology|Databases, Factual|Drug Discovery|Humans|Information Storage and Retrieval|Lupus Erythematosus, Systemic|Tamoxifen|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-S5-S4', '19426461', 'Discovering that drug entities already approved for one disease are effective treatments for other distinct diseases can be highly beneficial and cost effective. To do this predictively, our conjecture is that a semantic infrastructure linking mechanistic relationships between pharmacologic entities and multidimensional knowledge of biological systems and disease processes will be highly enabling.'),
(92837, 2009, 'bmc', 'BOWiki: an ontology-based wiki for annotation of data and integration of knowledge in biology', 'Hoehndorf, Robert|Bacher, Joshua|Backhaus, Michael|Gregorio, Sergio E|Loebe, Frank|Prüfer, Kay|Uciteli, Alexandr|Visagie, Johann|Herre, Heinrich|Kelso, Janet', 'Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 18, 'Computational Biology|Database Management Systems|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S5-S5', '19426462', 'Ontology development and the annotation of biological data using ontologies are time-consuming exercises that currently require input from expert curators. Open, collaborative platforms for biological data annotation enable the wider scientific community to become involved in developing and maintaining such resources. However, this openness raises concerns regarding the quality and correctness of the information added to these knowledge bases. The combination of a collaborative web-based platform with logic-based approaches and Semantic Web technology can be used to address some of these challenges and concerns.'),
(92838, 2009, 'bmc', 'Open Biomedical Ontology-based Medline exploration', 'Xuan, Weijian|Dai, Manhong|Mirel, Barbara|Song, Jean|Athey, Brian|Watson, Stanley J|Meng, Fan', 'Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu|Psychiatry Department and Molecular and Behavioral Neuroscience Institute, University of Michigan, USA. wxuan@umich.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|MEDLINE|United States|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S5-S6', '19426463', 'Effective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation.'),
(92839, 2009, 'bmc', 'Infrastructure for the life sciences: design and implementation of the UniProt website', 'Jain, Eric|Bairoch, Amos|Duvaud, Severine|Phan, Isabelle|Redaschi, Nicole|Suzek, Baris E|Martin, Maria J|McGarvey, Peter|Gasteiger, Elisabeth', 'Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com|Swiss-Prot Group, Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland. eric.jain@gmail.com', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 391, 'Databases, Protein|Information Storage and Retrieval|Internet|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-136', '19426475', 'The UniProt consortium was formed in 2002 by groups from the Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) at Georgetown University, and soon afterwards the website http://www.uniprot.org was set up as a central entry point to UniProt resources. Requests to this address were redirected to one of the three organisations websites. While these sites shared a set of static pages with general information about UniProt, their pages for searching and viewing data were different. To provide users with a consistent view and to cut the cost of maintaining three separate sites, the consortium decided to develop a common website for UniProt. Following several years of intense development and a year of public beta testing, the http://www.uniprot.org domain was switched to the newly developed site described in this paper in July 2008.'),
(92840, 2009, 'bmc', 'On finding minimal absent words', 'Pinho, Armando J|Ferreira, Paulo J S G|Garcia, Sara P|Rodrigues, João M O S', 'Signal Processing Lab, DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal. ap@ua.pt|Signal Processing Lab, DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal. ap@ua.pt|Signal Processing Lab, DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal. ap@ua.pt|Signal Processing Lab, DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal. ap@ua.pt', 'Portugal|Portugal|Portugal|Portugal', 29, 'Algorithms|Base Sequence|DNA|Databases, Nucleic Acid|Genomics|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-137', '19426495', 'The problem of finding the shortest absent words in DNA data has been recently addressed, and algorithms for its solution have been described. It has been noted that longer absent words might also be of interest, but the existing algorithms only provide generic absent words by trivially extending the shortest ones.'),
(92841, 2009, 'bmc', 'PCOPGene-Net: holistic characterisation of cellular states from microarray data based on continuous and non-continuous analysis of gene-expression relationships', 'Huerta, Mario|Cedano, Juan|Peña, Dario|Rodriguez, Antonio|Querol, Enrique', 'Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain. mario@bioinf.aub.es|Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain. mario@bioinf.aub.es|Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain. mario@bioinf.aub.es|Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain. mario@bioinf.aub.es|Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain. mario@bioinf.aub.es', 'Spain|Spain|Spain|Spain|Spain', 0, 'Algorithms|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-138', '19426548', 'Microarray technology is so expensive and powerful that it is essential to extract maximum value from microarray data, specially from large-sample-series microarrays. Our web tools attempt to respond to these researchers needs by facilitating the possibility to test and formulate from a hypothesis to entire models under a holistic point of view.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92842, 2009, 'bmc', 'eCOMPAGT -- efficient combination and management of phenotypes and genotypes for genetic epidemiology', 'Schönherr, Sebastian|Weissensteiner, Hansi|Coassin, Stefan|Specht, Günther|Kronenberg, Florian|Brandstätter, Anita', 'Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at|Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at|Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at|Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at|Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at|Department of Database and Information Systems, Institute of Computer Science, University of Innsbruck, Innsbruck, Austria. sebastian.schoenherr@uibk.ac.at', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Computational Biology|Database Management Systems|Genotype|Molecular Epidemiology|Phenotype|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-139', '19432954', 'High-throughput genotyping and phenotyping projects of large epidemiological study populations require sophisticated laboratory information management systems. Most epidemiological studies include subject-related personal information, which needs to be handled with care by following data privacy protection guidelines. In addition, genotyping core facilities handling cooperative projects require a straightforward solution to monitor the status and financial resources of the different projects.'),
(92843, 2009, 'bmc', 'Estimating parameters for generalized mass action models with connectivity information', 'Ko, Chih-Lung|Voit, Eberhard O|Wang, Feng-Sheng', 'Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan. ko0980@yahoo.com.tw|Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan. ko0980@yahoo.com.tw|Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan. ko0980@yahoo.com.tw', '-|-|-', 13, 'Algorithms|Aspartic Acid|Biochemical Phenomena|Computer Simulation|Databases, Factual|Escherichia coli|Escherichia coli Proteins|Feedback, Physiological|Models, Biological|Models, Statistical|Reproducibility of Results|Threonine', 'Journal Article', '10.1186/1471-2105-10-140', '19432964', 'Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems.'),
(92844, 2009, 'bmc', 'Robust MS quantification method for phospho-peptides using 18O/16O labeling', 'Andersen, Claus A|Gotta, Stefano|Magnoni, Letizia|Raggiaschi, Roberto|Kremer, Andreas|Terstappen, Georg C', 'Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it|Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it|Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it|Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it|Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it|Siena Biotech SpA, Discovery Research, Via Fiorentina 1, 53100 Siena, Italy. candersen@sienabiotech.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 4, 'Computational Biology|Mass Spectrometry|Oxygen Isotopes|Phosphopeptides|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-10-141', '19432989', 'Quantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using 18O/16O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-beta treatment of neurons used as a cellular model system with relevance to Alzheimers disease, but is generally applicable.'),
(92845, 2009, 'bmc', 'Incorporating functional inter-relationships into protein function prediction algorithms', 'Pandey, Gaurav|Myers, Chad L|Kumar, Vipin', 'Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA. gaurav@cs.umn.edu|Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA. gaurav@cs.umn.edu|Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA. gaurav@cs.umn.edu', 'United States|United States|United States', 63, 'Algorithms|Computational Biology|Databases, Protein|Proteins', 'Journal Article', '10.1186/1471-2105-10-142', '19435516', 'Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.'),
(92846, 2009, 'bmc', 'PanGEA: identification of allele specific gene expression using the 454 technology', 'Kofler, Robert|Teixeira Torres, Tatiana|Lelley, Tamas|Schlötterer, Christian', 'University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology, Institute for Plant Production Biotechnology Konrad Lorenz Str 20, A-3430 Tulln, Austria. robert@kofler.or.at|University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology, Institute for Plant Production Biotechnology Konrad Lorenz Str 20, A-3430 Tulln, Austria. robert@kofler.or.at|University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology, Institute for Plant Production Biotechnology Konrad Lorenz Str 20, A-3430 Tulln, Austria. robert@kofler.or.at|University of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology, Institute for Plant Production Biotechnology Konrad Lorenz Str 20, A-3430 Tulln, Austria. robert@kofler.or.at', 'Austria|Austria|Austria|Austria', 18, 'Alleles|Base Sequence|Databases, Genetic|Expressed Sequence Tags|Gene Expression|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-143', '19442283', 'Next generation sequencing technologies hold great potential for many biological questions. While mainly used for genomic sequencing, they are also very promising for gene expression profiling. Sequencing of cDNA does not only provide an estimate of the absolute expression level, it can also be used for the identification of allele specific gene expression.'),
(92847, 2009, 'bmc', 'Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery', 'Tan, Niclas C|Fisher, Wayne G|Rosenblatt, Kevin P|Garner, Harold R', 'Department of Internal Medicine, Division of Translational Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. chiang.tan@utsouthwestern.edu|Department of Internal Medicine, Division of Translational Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. chiang.tan@utsouthwestern.edu|Department of Internal Medicine, Division of Translational Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. chiang.tan@utsouthwestern.edu|Department of Internal Medicine, Division of Translational Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA. chiang.tan@utsouthwestern.edu', 'United States|United States|United States|United States', 0, 'Biomarkers|Data Interpretation, Statistical|Mass Spectrometry|Regression Analysis|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-10-144', '19442303', 'Mass spectrometry-based biomarker discovery has long been hampered by the difficulty in reconciling lists of discriminatory peaks identified by different laboratories for the same diseases studied. We describe a multi-statistical analysis procedure that combines several independent computational methods. This approach capitalizes on the strengths of each to analyze the same high-resolution mass spectral data set to discover consensus differential mass peaks that should be robust biomarkers for distinguishing between disease states.'),
(92848, 2009, 'bmc', 'flowClust: a Bioconductor package for automated gating of flow cytometry data', 'Lo, Kenneth|Hahne, Florian|Brinkman, Ryan R|Gottardo, Raphael', 'Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, V6T1Z2, Canada. c.lo@stat.ubc.ca|Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, V6T1Z2, Canada. c.lo@stat.ubc.ca|Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, V6T1Z2, Canada. c.lo@stat.ubc.ca|Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, V6T1Z2, Canada. c.lo@stat.ubc.ca', 'Canada|Canada|Canada|Canada', 102, 'Antigens, CD|Cluster Analysis|Databases, Factual|Flow Cytometry|Graft vs Host Disease|Models, Statistical|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-10-145', '19442304', 'As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.'),
(92849, 2009, 'bmc', 'Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments', 'Liu, Tianqing|Lin, Nan|Shi, Ningzhong|Zhang, Baoxue', 'Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com|Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com|Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com|Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, PR China. tianqingliu@gmail.com', 'China|China|China|China', 0, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computer Simulation|Databases, Factual|Female|Gene Expression Profiling|Genes|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-10-146', '19445669', 'Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. 1 proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.'),
(92850, 2009, 'bmc', 'Effects of sample size on robustness and prediction accuracy of a prognostic gene signature', 'Kim, Seon-Young', 'Medical Genomics Research Center, KRIBB, Yuseong-Gu, Daejeon, Republic of Korea. kimsy@kribb.re.kr', '-', 0, 'Breast Neoplasms|Computational Biology|Databases, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Oligonucleotide Array Sequence Analysis|Sample Size', 'Journal Article', '10.1186/1471-2105-10-147', '19445687', 'Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.'),
(92851, 2009, 'bmc', 'The High Throughput Sequence Annotation Service (HT-SAS) - the shortcut from sequence to true Medline words', 'Kaczanowski, Szymon|Siedlecki, Pawel|Zielenkiewicz, Piotr', 'Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul Pawinskiego 5a, Warszawa 02-106, Poland. szymon@ibb.waw.pl|Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul Pawinskiego 5a, Warszawa 02-106, Poland. szymon@ibb.waw.pl|Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul Pawinskiego 5a, Warszawa 02-106, Poland. szymon@ibb.waw.pl', 'Poland|Poland|Poland', 4, 'Abstracting and Indexing as Topic|Algorithms|Amino Acid Sequence|Animals|Data Interpretation, Statistical|Database Management Systems|Databases, Protein|Internet|MEDLINE|Plasmodium berghei|Proteins|Proteomics|Protozoan Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-148', '19445703', 'Advances in high-throughput technologies available to modern biology have created an increasing flood of experimentally determined facts. Ordering, managing and describing these raw results is the first step which allows facts to become knowledge. Currently there are limited ways to automatically annotate such data, especially utilizing information deposited in published literature.'),
(92852, 2009, 'bmc', 'How to find simple and accurate rules for viral protease cleavage specificities', 'Rögnvaldsson, Thorsteinn|Etchells, Terence A|You, Liwen|Garwicz, Daniel|Jarman, Ian|Lisboa, Paulo J G', 'Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se|Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se|Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se|Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se|Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se|Embedded and Intelligent Systems, Halmstad University, Halmstad, Sweden. denni@ide.hh.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Amino Acid Sequence|Catalytic Domain|Cluster Analysis|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|HIV Protease|Humans|Peptide Hydrolases|Protease Inhibitors|Proteomics|ROC Curve|Reproducibility of Results|Serine Endopeptidases|Viral Nonstructural Proteins|Viral Proteins', 'Journal Article', '10.1186/1471-2105-10-149', '19445713', 'Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way.'),
(92853, 2009, 'bmc', 'Protein-protein interaction based on pairwise similarity', 'Zaki, Nazar|Lazarova-Molnar, Sanja|El-Hajj, Wassim|Campbell, Piers', 'Bioinformatics Laboratory, Department of Computer Science, College of Information Technology, UAE University, Al Ain 17551, UAE. nzaki@uaeu.ac.ae|Bioinformatics Laboratory, Department of Computer Science, College of Information Technology, UAE University, Al Ain 17551, UAE. nzaki@uaeu.ac.ae|Bioinformatics Laboratory, Department of Computer Science, College of Information Technology, UAE University, Al Ain 17551, UAE. nzaki@uaeu.ac.ae|Bioinformatics Laboratory, Department of Computer Science, College of Information Technology, UAE University, Al Ain 17551, UAE. nzaki@uaeu.ac.ae', '-|-|-|-', 0, 'Amino Acid Sequence|Databases, Protein|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteomics|ROC Curve|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-10-150', '19445721', 'Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.'),
(92854, 2009, 'bmc', 'MIMAS 30 is a Multiomics Information Management and Annotation System', 'Gattiker, Alexandre|Hermida, Leandro|Liechti, Robin|Xenarios, Ioannis|Collin, Olivier|Rougemont, Jacques|Primig, Michael', 'Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch|Inserm, U625, GERHM, IFR-140, Université de Rennes 1, Rennes F-35042, France. alexandre.gattiker@epfl.ch', 'France|France|France|France|France|France|France', 13, 'Abstracting and Indexing as Topic|Database Management Systems|Databases, Genetic|Genomics|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-151', '19450266', 'DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types.'),
(92855, 2009, 'bmc', 'Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach', 'Francisco, Alexandre P|Bugalho, Miguel|Ramirez, Mário|Carriço, João A', 'Instituto de Engenharia de Sistemas e Computadores - ID em Lisboa, Lisboa, Portugal. aplf@kdbio.inesc-id.pt|Instituto de Engenharia de Sistemas e Computadores - ID em Lisboa, Lisboa, Portugal. aplf@kdbio.inesc-id.pt|Instituto de Engenharia de Sistemas e Computadores - ID em Lisboa, Lisboa, Portugal. aplf@kdbio.inesc-id.pt|Instituto de Engenharia de Sistemas e Computadores - ID em Lisboa, Lisboa, Portugal. aplf@kdbio.inesc-id.pt', 'Portugal|Portugal|Portugal|Portugal', 276, 'Algorithms|Bacteria|Cluster Analysis|Databases, Genetic|Evolution, Molecular|Genes, Bacterial|Genomics|Models, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-152', '19450271', 'Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed.'),
(92856, 2009, 'bmc', 'Tableau-based protein substructure search using quadratic programming', 'Stivala, Alex|Wirth, Anthony|Stuckey, Peter J', 'Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia. a.stivala@pgrad.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia. a.stivala@pgrad.unimelb.edu.au|Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia. a.stivala@pgrad.unimelb.edu.au', 'Australia|Australia|Australia', 12, 'Area Under Curve|Databases, Protein|Models, Molecular|Models, Statistical|Protein Structure, Secondary|Proteins|Proteomics|ROC Curve|Software', 'Journal Article', '10.1186/1471-2105-10-153', '19450287', 'Searching for proteins that contain similar substructures is an important task in structural biology. The exact solution of most formulations of this problem, including a recently published method based on tableaux, is too slow for practical use in scanning a large database.'),
(92857, 2009, 'bmc', 'EDGAR: a software framework for the comparative analysis of prokaryotic genomes', 'Blom, Jochen|Albaum, Stefan P|Doppmeier, Daniel|Pühler, Alfred|Vorhölter, Frank-Jörg|Zakrzewski, Martha|Goesmann, Alexander', 'Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de|Computational Genomics, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany. jblom@cebitec.uni-bielefeld.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 276, 'Cluster Analysis|Databases, Genetic|Genome, Bacterial|Genomics|Models, Genetic|Phylogeny|Sequence Analysis, DNA|Software|User-Computer Interface|Xanthomonas', 'Journal Article', '10.1186/1471-2105-10-154', '19457249', 'The introduction of next generation sequencing approaches has caused a rapid increase in the number of completely sequenced genomes. As one result of this development, it is now feasible to analyze large groups of related genomes in a comparative approach. A main task in comparative genomics is the identification of orthologous genes in different genomes and the classification of genes as core genes or singletons.'),
(92858, 2009, 'bmc', 'BRNI: Modular analysis of transcriptional regulatory programs', 'Nachman, Iftach|Regev, Aviv', 'FAS Center for System Biology, Harvard University, Cambridge, MA 02138, USA. inachman@cgr.harvard.edu|FAS Center for System Biology, Harvard University, Cambridge, MA 02138, USA. inachman@cgr.harvard.edu', 'United States|United States', 5, 'Algorithms|Artificial Intelligence|Cell Cycle|Cluster Analysis|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Regulation|Gene Regulatory Networks|Genomics|Models, Genetic|Schizosaccharomyces|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-155', '19457258', 'Transcriptional responses often consist of regulatory modules - sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.'),
(92859, 2009, 'bmc', 'Differential splicing using whole-transcript microarrays', 'Robinson, Mark D|Speed, Terence P', 'Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia. mrobinson@wehi.edu.au|Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia. mrobinson@wehi.edu.au', 'Australia|Australia', 16, 'Alternative Splicing|Animals|Data Interpretation, Statistical|Genomics|Humans|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis|Rats|Reproducibility of Results|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-156', '19463149', 'The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events.'),
(92860, 2009, 'bmc', 'IDSS: deformation invariant signatures for molecular shape comparison', 'Liu, Yu-Shen|Fang, Yi|Ramani, Karthik', 'School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com|School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com|School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA. liuyushen00@gmail.com', 'United States|United States|United States', 34, 'Algorithms|Cluster Analysis|Computer Simulation|Databases, Factual|Mechanical Phenomena|Models, Molecular|Protein Conformation|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-157', '19463181', 'Many molecules of interest are flexible and undergo significant shape deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules.'),
(92861, 2009, 'bmc', 'Orymold: ontology based gene expression data integration and analysis tool applied to rice', 'Mercadé, Jaume|Espinosa, Antonio|Adsuara, José-Enrique|Adrados, Rosa|Segura, Jordi|Maes, Tamara', 'Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com|Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com|Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com|Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com|Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com|Oryzon Genomics, Parc Científic de Barcelona, 08028 Barcelona, Spain. jmercade@oryzon.com', 'Spain|Spain|Spain|Spain|Spain|Spain', 2, 'Databases, Genetic|Gene Expression|Genomics|Image Processing, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Oryza|Reproducibility of Results|Semantics|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-158', '19463186', 'Integration and exploration of data obtained from genome wide monitoring technologies has become a major challenge for many bioinformaticists and biologists due to its heterogeneity and high dimensionality. A widely accepted approach to solve these issues has been the creation and use of controlled vocabularies (ontologies). Ontologies allow for the formalization of domain knowledge, which in turn enables generalization in the creation of querying interfaces as well as in the integration of heterogeneous data, providing both human and machine readable interfaces.'),
(92862, 2009, 'bmc', 'Transmembrane protein topology prediction using support vector machines', 'Nugent, Timothy|Jones, David T', 'Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK. t.nugent@cs.ucl.ac.uk|Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK. t.nugent@cs.ucl.ac.uk', 'United Kingdom|United Kingdom', 262, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Membrane Proteins|Models, Statistical|Pattern Recognition, Automated|Protein Folding|Protein Sorting Signals|Protein Structure, Secondary|Proteomics|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-159', '19470175', 'Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.'),
(92863, 2009, 'bmc', 'IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico', 'Wu, Tzong-Yuan|Hsieh, Chi-Chun|Hong, Jun-Jie|Chen, Chung-Yung|Tsai, Yuh-Show', 'Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li, Taiwan. tywu@cycu.edu.tw|Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li, Taiwan. tywu@cycu.edu.tw|Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li, Taiwan. tywu@cycu.edu.tw|Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li, Taiwan. tywu@cycu.edu.tw|Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li, Taiwan. tywu@cycu.edu.tw', '-|-|-|-|-', 21, 'Base Sequence|Computer Simulation|Discriminant Analysis|Genome, Viral|Internet|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Messenger|RNA, Viral|Sequence Alignment|Sequence Analysis, RNA|Software|Thermodynamics|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-160', '19473520', 'Internal ribosomal entry sites (IRESs) provide alternative, cap-independent translation initiation sites in eukaryotic cells. IRES elements are important factors in viral genomes and are also useful tools for bi-cistronic expression vectors. Most existing RNA structure prediction programs are unable to deal with IRES elements.'),
(92864, 2009, 'bmc', 'GAGE: generally applicable gene set enrichment for pathway analysis', 'Luo, Weijun|Friedman, Michael S|Shedden, Kerby|Hankenson, Kurt D|Woolf, Peter J', 'Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu', 'United States|United States|United States|United States|United States', 505, 'Algorithms|Bone Morphogenetic Protein 6|Computer Simulation|Gene Expression Profiling|Gene Regulatory Networks|Humans|Lung Neoplasms|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-10-161', '19473525', 'Gene set analysis (GSA) is a widely used strategy for gene expression data analysis based on pathway knowledge. GSA focuses on sets of related genes and has established major advantages over individual gene analyses, including greater robustness, sensitivity and biological relevance. However, previous GSA methods have limited usage as they cannot handle datasets of different sample sizes or experimental designs.'),
(92865, 2009, 'bmc', 'Hydra: software for tailored processing of H/D exchange data from MS or tandem MS analyses', 'Slysz, Gordon W|Baker, Charles A H|Bozsa, Benjamin M|Dang, Anthony|Percy, Andrew J|Bennett, Melissa|Schriemer, David C', 'Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca|Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada. gslysz@ucalgary.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 60, 'Algorithms|Computer Graphics|Databases, Protein|Deuterium Exchange Measurement|Mass Spectrometry|Models, Statistical|Peptide Fragments|Peptides|Protein Folding|Protein Interaction Domains and Motifs|Proteins|Reproducibility of Results|Software|Tandem Mass Spectrometry|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-162', '19473537', 'Hydrogen/deuterium exchange mass spectrometry (H/DX-MS) experiments implemented to characterize protein interaction and protein folding generate large quantities of data. Organizing, processing and visualizing data requires an automated solution, particularly when accommodating new tandem mass spectrometry modes for H/DX measurement. We sought to develop software that offers flexibility in defining workflows so as to support exploratory treatments of H/DX-MS data, with a particular focus on the analysis of very large protein systems and the mining of tandem mass spectrometry data.'),
(92866, 2009, 'bmc', 'Analyzing miRNA co-expression networks to explore TF-miRNA regulation', 'Bandyopadhyay, Sanghamitra|Bhattacharyya, Malay', 'Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in|Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in', 'India|India', 36, 'Algorithms|Cluster Analysis|Computer Graphics|Databases, Nucleic Acid|Fuzzy Logic|Gene Expression|Gene Expression Profiling|Gene Regulatory Networks|Humans|MicroRNAs|Models, Statistical|Reproducibility of Results|Schizophrenia|Stem Cells|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-163', '19476620', 'Current microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs.'),
(92867, 2009, 'bmc', 'Sample size calculation for microarray experiments with blocked one-way design', 'Jung, Sin-Ho|Sohn, Insuk|George, Stephen L|Feng, Liping|Leppert, Phyllis C', 'Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu|Department of Biostatistics and Bioinformatics, Duke University Medical Center, North Carolina 27710, USA. sinho.jung@duke.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Analysis of Variance|Cluster Analysis|Computer Simulation|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-10-164', '19476634', 'One of the main objectives of microarray analysis is to identify differentially expressed genes for different types of cells or treatments. Many statistical methods have been proposed to assess the treatment effects in microarray experiments.'),
(92868, 2009, 'bmc', 'A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data', 'Dai, Xiaofeng|Erkkilä, Timo|Yli-Harja, Olli|Lähdesmäki, Harri', 'Department of Signal Processing, Tampere University of Technology, Tampere, Finland. xiaofeng.dai@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. xiaofeng.dai@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. xiaofeng.dai@tut.fi|Department of Signal Processing, Tampere University of Technology, Tampere, Finland. xiaofeng.dai@tut.fi', 'Finland|Finland|Finland|Finland', 7, 'Algorithms|Animals|Cluster Analysis|Gene Expression Profiling|Mice|Normal Distribution', 'Journal Article', '10.1186/1471-2105-10-165', '19480678', 'Cluster analysis has become a standard computational method for gene function discovery as well as for more general explanatory data analysis. A number of different approaches have been proposed for that purpose, out of which different mixture models provide a principled probabilistic framework. Cluster analysis is increasingly often supplemented with multiple data sources nowadays, and these heterogeneous information sources should be made as efficient use of as possible.'),
(92869, 2009, 'bmc', 'mbs: modifying Hudsons ms software to generate samples of DNA sequences with a biallelic site under selection', 'Teshima, Kosuke M|Innan, Hideki', 'Hayama Center for Advanced Studies, Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan. kteshima@soken.ac.jp|Hayama Center for Advanced Studies, Graduate University for Advanced Studies, Hayama, Kanagawa 240-0193, Japan. kteshima@soken.ac.jp', 'Japan|Japan', 32, 'Alleles|Base Sequence|Computational Biology|DNA|Demography|Genetics, Population|Polymorphism, Single Nucleotide|Selection, Genetic|Software', 'Journal Article', '10.1186/1471-2105-10-166', '19480708', 'The pattern of single nucleotide polymorphisms, or SNPs, contains a tremendous amount of information with respect to the mechanisms of the micro-evolutionary process of a species. The inference of the roles of these mechanisms, including natural selection, relies heavily on computer simulations. A coalescent simulation is extremely powerful in generating a large number of samples of DNA sequences from a population (species) when all mutations are neutral, and Hudsons ms software is frequently used for this purpose.However, it has been difficult to incorporate natural selection into the coalescent framework.'),
(92870, 2009, 'bmc', 'Measure of synonymous codon usage diversity among genes in bacteria', 'Suzuki, Haruo|Saito, Rintaro|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan. haruo@g-language.org|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan. haruo@g-language.org|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan. haruo@g-language.org', 'Japan|Japan|Japan', 11, 'Base Composition|Codon|Data Interpretation, Statistical|Genes, Bacterial|Genome, Bacterial|Helicobacter pylori|Models, Genetic', 'Journal Article', '10.1186/1471-2105-10-167', '19480720', 'In many bacteria, intragenomic diversity in synonymous codon usage among genes has been reported. However, no quantitative attempt has been made to compare the diversity levels among different genomes. Here, we introduce a mean dissimilarity-based index (Dmean) for quantifying the level of diversity in synonymous codon usage among all genes within a genome.'),
(92871, 2009, 'bmc', 'Fpocket: an open source platform for ligand pocket detection', 'Le Guilloux, Vincent|Schmidtke, Peter|Tuffery, Pierre', 'ICOA - Institut de chimie organique et analytique - UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University of Orléans, Orléans, France. vincent.le-guilloux@univ-orleans.fr|ICOA - Institut de chimie organique et analytique - UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University of Orléans, Orléans, France. vincent.le-guilloux@univ-orleans.fr|ICOA - Institut de chimie organique et analytique - UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University of Orléans, Orléans, France. vincent.le-guilloux@univ-orleans.fr', 'France|France|France', 508, 'Algorithms|Binding Sites|Ligands|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Proteins|Software', 'Journal Article', '10.1186/1471-2105-10-168', '19486540', 'Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.'),
(92872, 2009, 'bmc', 'A core-attachment based method to detect protein complexes in PPI networks', 'Wu, Min|Li, Xiaoli|Kwoh, Chee-Keong|Ng, See-Kiong', 'School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 277, 'Algorithms|Data Interpretation, Statistical|Databases, Protein|Multiprotein Complexes|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Proteins|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-10-169', '19486541', 'How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI networks may correspond to protein complexes, but fail to consider the inherent organization within protein complexes.'),
(92873, 2009, 'bmc', 'A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling', 'Pinheiro, Daniel G|Galante, Pedro A F|de Souza, Sandro J|Zago, Marco A|Silva, Wilson A', 'Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil. dgpinheiro@usp.br|Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil. dgpinheiro@usp.br|Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil. dgpinheiro@usp.br|Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil. dgpinheiro@usp.br|Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil. dgpinheiro@usp.br', 'Brazil|Brazil|Brazil|Brazil|Brazil', 2, 'Animals|Cluster Analysis|Colonic Neoplasms|Data Interpretation, Statistical|Databases, Genetic|Expressed Sequence Tags|Gene Expression|Gene Expression Profiling|Humans|Mice|RNA', 'Journal Article', '10.1186/1471-2105-10-170', '19500384', 'High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92874, 2009, 'bmc', 'T-REX: software for the processing and analysis of T-RFLP data', 'Culman, Steven W|Bukowski, Robert|Gauch, Hugh G|Cadillo-Quiroz, Hinsby|Buckley, Daniel H', 'Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu|Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu|Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu|Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu|Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA. swculman@ucdavis.edu', 'United States|United States|United States|United States|United States', 325, 'Analysis of Variance|DNA Fingerprinting|Database Management Systems|Databases, Genetic|Genes, Bacterial|Internet|Models, Statistical|Polymorphism, Restriction Fragment Length|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-171', '19500385', 'Despite increasing popularity and improvements in terminal restriction fragment length polymorphism (T-RFLP) and other microbial community fingerprinting techniques, there are still numerous obstacles that hamper the analysis of these datasets. Many steps are required to process raw data into a format ready for analysis and interpretation. These steps can be time-intensive, error-prone, and can introduce unwanted variability into the analysis. Accordingly, we developed T-REX, free, online software for the processing and analysis of T-RFLP data.'),
(92875, 2009, 'bmc', 'Accounting for uncertainty when assessing association between copy number and disease: a latent class model', 'González, Juan R|Subirana, Isaac|Escaramís, Geòrgia|Peraza, Solymar|Cáceres, Alejandro|Estivill, Xavier|Armengol, Lluís', 'Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat|Center for research in environmental epidemiology (CREAL), Barcelona, Spain. jrgonzalez@creal.cat', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 17, 'Algorithms|Comparative Genomic Hybridization|Computer Simulation|Disease Progression|Gene Dosage|Genetic Predisposition to Disease|Humans|Models, Statistical', 'Journal Article', '10.1186/1471-2105-10-172', '19500389', 'Copy number variations (CNVs) may play an important role in disease risk by altering dosage of genes and other regulatory elements, which may have functional and, ultimately, phenotypic consequences. Therefore, determining whether a CNV is associated or not with a given disease might be relevant in understanding the genesis and progression of human diseases. Current stage technology give CNV probe signal from which copy number status is inferred. Incorporating uncertainty of CNV calling in the statistical analysis is therefore a highly important aspect. In this paper, we present a framework for assessing association between CNVs and disease in case-control studies where uncertainty is taken into account. We also indicate how to use the model to analyze continuous traits and adjust for confounding covariates.'),
(92876, 2009, 'bmc', 'Improved ChIP-chip analysis by a mixture model approach', 'Sun, Wei|Buck, Michael J|Patel, Mukund|Davis, Ian J', 'Department of Biostatistics, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. wsun@bios.unc.edu|Department of Biostatistics, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. wsun@bios.unc.edu|Department of Biostatistics, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. wsun@bios.unc.edu|Department of Biostatistics, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. wsun@bios.unc.edu', 'United States|United States|United States|United States', 4, 'Algorithms|CCCTC-Binding Factor|Chromatin Immunoprecipitation|Computer Simulation|DNA-Binding Proteins|Databases, Genetic|Humans|Microarray Analysis|Models, Statistical|Poisson Distribution|Protein Binding|Protein Structure, Tertiary|ROC Curve|Repressor Proteins', 'Journal Article', '10.1186/1471-2105-10-173', '19500407', 'Microarray analysis of immunoprecipitated chromatin (ChIP-chip) has evolved from a novel technique to a standard approach for the systematic study of protein-DNA interactions. In ChIP-chip, sites of protein-DNA interactions are identified by signals from the hybridization of selected DNA to tiled oligomers and are graphically represented as peaks. Most existing methods were designed for the identification of relatively sparse peaks, in the presence of replicates.'),
(92877, 2009, 'bmc', 'Combining specificity determining and conserved residues improves functional site prediction', 'Kalinina, Olga V|Gelfand, Mikhail S|Russell, Robert B', 'EMBL Heidelberg, Heidelberg, Germany. kalinina@embl.de|EMBL Heidelberg, Heidelberg, Germany. kalinina@embl.de|EMBL Heidelberg, Heidelberg, Germany. kalinina@embl.de', 'Germany|Germany|Germany', 26, 'Amino Acid Sequence|Computational Biology|Conserved Sequence|Databases, Protein|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-174', '19508719', 'Predicting the location of functionally important sites from protein sequence and/or structure is a long-standing problem in computational biology. Most current approaches make use of sequence conservation, assuming that amino acid residues conserved within a protein family are most likely to be functionally important. Most often these approaches do not consider many residues that act to define specific sub-functions within a family, or they make no distinction between residues important for function and those more relevant for maintaining structure (e.g. in the hydrophobic core). Many protein families bind and/or act on a variety of ligands, meaning that conserved residues often only bind a common ligand sub-structure or perform general catalytic activities.'),
(92878, 2009, 'bmc', 'Local alignment of two-base encoded DNA sequence', 'Homer, Nils|Merriman, Barry|Nelson, Stanley F', 'Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu|Department of Computer Science, University of California Los Angeles, Los Angeles, California 90095, USA. nhomer@cs.ucla.edu', 'United States|United States|United States', 31, 'Algorithms|Base Sequence|DNA|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-10-175', '19508732', 'DNA sequence comparison is based on optimal local alignment of two sequences using a similarity score. However, some new DNA sequencing technologies do not directly measure the base sequence, but rather an encoded form, such as the two-base encoding considered here. In order to compare such data to a reference sequence, the data must be decoded into sequence. The decoding is deterministic, but the possibility of measurement errors requires searching among all possible error modes and resulting alignments to achieve an optimal balance of fewer errors versus greater sequence similarity.'),
(92879, 2009, 'bmc', 'PolyMin: software for identification of the minimum number of polymorphisms required for haplotype and genotype differentiation', 'Frei, Ursula K|Wollenweber, Bernd|Lübberstedt, Thomas', 'Iowa State University, Department of Agronomy, Iowa 50011-1010, USA. ufrei@iastate.edu|Iowa State University, Department of Agronomy, Iowa 50011-1010, USA. ufrei@iastate.edu|Iowa State University, Department of Agronomy, Iowa 50011-1010, USA. ufrei@iastate.edu', 'United States|United States|United States', 4, 'Algorithms|Genes, Plant|Genotype|Haplotypes|Humans|Mutagenesis, Insertional|Polymorphism, Genetic|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Deletion|Software', 'Journal Article', '10.1186/1471-2105-10-176', '19515225', 'Analysis of allelic variation for relevant genes and monitoring chromosome segment transmission during selection are important approaches in plant breeding and ecology. To minimize the number of required molecular markers for this purpose is crucial due to cost and time constraints. To date, software for identification of the minimum number of required markers has been optimized for human genetics and is only partly matching the needs of plant scientists and breeders. In addition, different software packages with insufficient interoperability need to be combined to extract this information from available allele sequence data, resulting in an error-prone multi-step process of data handling.'),
(92880, 2009, 'bmc', 'Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens', 'Zaremba, Sam|Ramos-Santacruz, Mila|Hampton, Thomas|Shetty, Panna|Fedorko, Joel|Whitmore, Jon|Greene, John M|Perna, Nicole T|Glasner, Jeremy D|Plunkett, Guy|Shaker, Matthew|Pot, David', 'ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com|ERIC-BRC, SRA International Inc, Global Health Sector, Rockville, MD 20852, USA. Sam_Zaremba@sra.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 16, 'Abstracting and Indexing as Topic|Bacterial Physiological Phenomena|Computational Biology|Database Management Systems|Databases, Factual|Enterobacteriaceae|Escherichia coli|Information Storage and Retrieval|Internet|Natural Language Processing|PubMed|Salmonella|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-177', '19515247', 'The Enteropathogen Resource Integration Center (ERIC, http://www.ericbrc.org) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.'),
(92881, 2009, 'bmc', 'TOMOBFLOW: feature-preserving noise filtering for electron tomography', 'Fernandez, Jose-Jesus', 'Dept Computer Architecture and Electronics, University of Almería, Almería, Spain. jjfdez@ual.es', 'Spain', 24, 'Algorithms|Electron Microscope Tomography|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-10-178', '19523199', 'Noise filtering techniques are needed in electron tomography to allow proper interpretation of datasets. The standard linear filtering techniques are characterized by a tradeoff between the amount of reduced noise and the blurring of the features of interest. On the other hand, sophisticated anisotropic nonlinear filtering techniques allow noise reduction with good preservation of structures. However, these techniques are computationally intensive and are difficult to be tuned to the problem at hand.'),
(92882, 2009, 'bmc', 'Improved results in proteomics by use of local and peptide-class specific false discovery rates', 'Sennels, Lau|Bukowski-Wills, Jimi-Carlo|Rappsilber, Juri', 'Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, UK. l.sennels@sms.ed.ac.uk|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, UK. l.sennels@sms.ed.ac.uk|Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3JR, UK. l.sennels@sms.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 34, 'Databases, Protein|False Positive Reactions|Peptides|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-10-179', '19523214', 'Proteomic protein identification results need to be compared across laboratories and platforms, and thus a reliable method is needed to estimate false discovery rates. The target-decoy strategy is a platform-independent and thus a prime candidate for standardized reporting of data. In its current usage based on global population parameters, the method does not utilize individual peptide scores optimally.'),
(92883, 2009, 'bmc', 'Seq4SNPs: new software for retrieval of multiple, accurately annotated DNA sequences, ready formatted for SNP assay design', 'Field, Helen I|Scollen, Serena A|Luccarini, Craig|Baynes, Caroline|Morrison, Jonathan|Dunning, Alison M|Easton, Douglas F|Pharoah, Paul D P', 'Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk|Department of Oncology, University of Cambridge, Cambridge, UK. helen.field@srl.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Base Sequence|Computational Biology|Databases, Genetic|Genotype|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-180', '19523221', 'In moderate-throughput SNP genotyping there was a gap in the workflow, between choosing a set of SNPs and submitting their sequences to proprietary assay design software, which was not met by existing software. Retrieval and formatting of sequences flanking each SNP, prior to assay design, becomes rate-limiting for more than about ten SNPs, especially if annotated for repetitive regions and adjacent variations. We routinely process up to 50 SNPs at once.'),
(92884, 2009, 'bmc', 'Computing paths and cycles in biological interaction graphs', 'Klamt, Steffen|von Kamp, Axel', 'Max Planck Institute for Dynamics of Complex Technical Systems, D-39106 Magdeburg, Germany. klamt@mpi-magdeburg.mpg.de|Max Planck Institute for Dynamics of Complex Technical Systems, D-39106 Magdeburg, Germany. klamt@mpi-magdeburg.mpg.de', 'Germany|Germany', 19, 'Algorithms|Computer Graphics|Feedback, Physiological|Models, Biological|Models, Statistical|Signal Transduction|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-181', '19527491', 'Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.'),
(92885, 2009, 'bmc', 'Structural motifs recurring in different folds recognize the same ligand fragments', 'Ausiello, Gabriele|Gherardini, Pier Federico|Gatti, Elena|Incani, Ottaviano|Helmer-Citterich, Manuela', 'Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. gabriele.ausiello@uniroma2.it|Centre for Molecular Bioinformatics, Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, Rome, Italy. gabriele.ausiello@uniroma2.it', 'Italy|Italy|Italy|Italy|Italy', 12, 'Algorithms|Binding Sites|Databases, Protein|Ligands|Models, Molecular|Peptide Fragments|Protein Binding|Protein Folding|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-182', '19527512', 'The structural analysis of protein ligand binding sites can provide information relevant for assigning functions to unknown proteins, to guide the drug discovery process and to infer relations among distant protein folds. Previous approaches to the comparative analysis of binding pockets have usually been focused either on the ligand or the protein component. Even though several useful observations have been made with these approaches they both have limitations. In the former case the analysis is restricted to binding pockets interacting with similar ligands, while in the latter it is difficult to systematically check whether the observed structural similarities have a functional significance.'),
(92886, 2009, 'bmc', 'The textual characteristics of traditional and Open Access scientific journals are similar', 'Verspoor, Karin|Cohen, K Bretonnel|Hunter, Lawrence', 'Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu|Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu|Center for Computational Pharmacology, University of Colorado Denver School of Medicine, Aurora, CO 80045, USA. karin.verspoor@ucdenver.edu', 'United States|United States|United States', 25, 'Access to Information|Biomedical Research|Databases, Bibliographic|Linguistics|Natural Language Processing|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-10-183', '19527520', 'Recent years have seen an increased amount of natural language processing (NLP) work on full text biomedical journal publications. Much of this work is done with Open Access journal articles. Such work assumes that Open Access articles are representative of biomedical publications in general and that methods developed for analysis of Open Access full text publications will generalize to the biomedical literature as a whole. If this assumption is wrong, the cost to the community will be large, including not just wasted resources, but also flawed science. This paper examines that assumption.'),
(92887, 2009, 'bmc', 'FuGEFlow: data model and markup language for flow cytometry', 'Qian, Yu|Tchuvatkina, Olga|Spidlen, Josef|Wilkinson, Peter|Gasparetto, Maura|Jones, Andrew R|Manion, Frank J|Scheuermann, Richard H|Sekaly, Rafick-Pierre|Brinkman, Ryan R', 'Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu|Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. yu.qian@utsouthwestern.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 7, 'Computational Biology|Databases, Factual|Flow Cytometry|Information Storage and Retrieval|Programming Languages', 'Journal Article', '10.1186/1471-2105-10-184', '19531228', 'Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.'),
(92888, 2009, 'bmc', 'Automated genome mining for natural products', 'Li, Michael H T|Ung, Peter M U|Zajkowski, James|Garneau-Tsodikova, Sylvie|Sherman, David H', 'Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu', 'United States|United States|United States|United States|United States', 150, 'Biological Products|Computational Biology|Genome|Internet|Macrolides', 'Journal Article', '10.1186/1471-2105-10-185', '19531248', 'Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit.'),
(92889, 2009, 'bmc', 'The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community', 'DElia, Domenica|Gisel, Andreas|Eriksson, Nils-Einar|Kossida, Sophia|Mattila, Kimmo|Klucar, Lubos|Bongcam-Rudloff, Erik', 'Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu|Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA. mikeleez@umich.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Congresses as Topic|Genomics|History, 20th Century|History, 21st Century|Molecular Biology|Proteomics', 'Historical Article', '10.1186/1471-2105-10-S6-S1', '19534734', 'The EMBnet Conference 2008, focusing on Leading Applications and Technologies in Bioinformatics, was organized by the European Molecular Biology network (EMBnet) to celebrate its 20th anniversary. Since its foundation in 1988, EMBnet has been working to promote collaborative development of bioinformatics services and tools to serve the European community of molecular biology laboratories. This conference was the first meeting organized by the network that was open to the international scientific community outside EMBnet. The conference covered a broad range of research topics in bioinformatics with a main focus on new achievements and trends in emerging technologies supporting genomics, transcriptomics and proteomics analyses such as high-throughput sequencing and data managing, text and data-mining, ontologies and Grid technologies. Papers selected for publication, in this supplement to BMC Bioinformatics, cover a broad range of the topics treated, providing also an overview of the main bioinformatics research fields that the EMBnet community is involved in.'),
(92890, 2009, 'bmc', 'EasyCluster: a fast and efficient gene-oriented clustering tool for large-scale transcriptome data', 'Picardi, Ernesto|Mignone, Flavio|Pesole, Graziano', 'Dipartimento di Biochimica e Biologia Molecolare E, Quagliariello, Università degli Studi di Bari, 70126 Bari, Italy. e.picardi@biologia.uniba.it|Dipartimento di Biochimica e Biologia Molecolare E, Quagliariello, Università degli Studi di Bari, 70126 Bari, Italy. e.picardi@biologia.uniba.it|Dipartimento di Biochimica e Biologia Molecolare E, Quagliariello, Università degli Studi di Bari, 70126 Bari, Italy. e.picardi@biologia.uniba.it', 'Italy|Italy|Italy', 0, 'Chromosome Mapping|Cluster Analysis|Expressed Sequence Tags|Gene Expression Profiling|Humans|RNA Splice Sites|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S10', '19534735', 'ESTs and full-length cDNAs represent an invaluable source of evidence for inferring reliable gene structures and discovering potential alternative splicing events. In newly sequenced genomes, these tasks may not be practicable owing to the lack of appropriate training sets. However, when expression data are available, they can be used to build EST clusters related to specific genomic transcribed loci. Common strategies recently employed to this end are based on sequence similarity between transcripts and can lead, in specific conditions, to inconsistent and erroneous clustering. In order to improve the cluster building and facilitate all downstream annotation analyses, we developed a simple genome-based methodology to generate gene-oriented clusters of ESTs when a genomic sequence and a pool of related expressed sequences are provided. Our procedure has been implemented in the software EasyCluster and takes into account the spliced nature of ESTs after an ad hoc genomic mapping.'),
(92891, 2009, 'bmc', 'GIBA: a clustering tool for detecting protein complexes', 'Moschopoulos, Charalampos N|Pavlopoulos, Georgios A|Schneider, Reinhard|Likothanassis, Spiridon D|Kossida, Sophia', 'Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece. mosxopul@ceid.upatras.gr|Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece. mosxopul@ceid.upatras.gr|Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece. mosxopul@ceid.upatras.gr|Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece. mosxopul@ceid.upatras.gr|Pattern Recognition Lab, Department of Computer Engineering & Informatics, University of Patras, Patra, Rio, GR-26500, Greece. mosxopul@ceid.upatras.gr', 'Greece|Greece|Greece|Greece|Greece', 20, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Multiprotein Complexes|Protein Interaction Mapping|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S11', '19534736', 'During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings.'),
(92892, 2009, 'bmc', 'Brukin2D: a 2D visualization and comparison tool for LC-MS data', 'Tsagkrasoulis, Dimosthenis|Zerefos, Panagiotis|Loudos, George|Vlahou, Antonia|Baumann, Marc|Kossida, Sophia', 'Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr|Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr|Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr|Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr|Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr|Protein Chemistry/Proteomics Laboratory, Neuroscience Research Program, Biomedicum Helsinki, P.O. Box 63 (Haartmaninkatu 8) 00014 University of Helsinki, Finland. piv07010@di.uoa.gr', 'Finland|Finland|Finland|Finland|Finland|Finland', 2, 'Chromatography, Liquid|Databases, Protein|Mass Spectrometry|Proteins|Proteomics|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S6-S12', '19534737', 'Liquid Chromatography-Mass Spectrometry (LC-MS) is a commonly used technique to resolve complex protein mixtures. Visualization of large data sets produced from LC-MS, namely the chromatogram and the mass spectra that correspond to its compounds is the focus of this work.'),
(92893, 2009, 'bmc', 'A chemogenomics view on protein-ligand spaces', 'Strömbergsson, Helena|Kleywegt, Gerard J', 'Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. helena.strombergsson@lcb.uu.se|Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden. helena.strombergsson@lcb.uu.se', 'Sweden|Sweden', 31, 'Binding Sites|Computational Biology|Databases, Protein|Drug Discovery|Genomics|Ligands|Proteins', 'Journal Article', '10.1186/1471-2105-10-S6-S13', '19534738', 'Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces.'),
(92894, 2009, 'bmc', 'iBarcodeorg: web-based molecular biodiversity analysis', 'Singer, Gregory A C|Hajibabaei, Mehrdad', 'Biodiversity Institute of Ontario, Department of Integrative Biology, University of Guelph, Guelph, N1G 2W1, Canada. gacsinger@gmail.com|Biodiversity Institute of Ontario, Department of Integrative Biology, University of Guelph, Guelph, N1G 2W1, Canada. gacsinger@gmail.com', 'Canada|Canada', 0, 'Base Sequence|Biodiversity|Computational Biology|DNA|Databases, Genetic|Evolution, Molecular|Haplotypes|Internet|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S14', '19534739', 'DNA sequences have become a primary source of information in biodiversity analysis. For example, short standardized species-specific genomic regions, DNA barcodes, are being used as a global standard for species identification and biodiversity studies. Most DNA barcodes are being generated by laboratories that have an expertise in DNA sequencing but not in bioinformatics data analysis. Therefore, we have developed a web-based suite of tools to help the DNA barcode researchers analyze their vast datasets.'),
(92895, 2009, 'bmc', 'Towards barcode markers in Fungi: an intron map of Ascomycota mitochondria', 'Santamaria, Monica|Vicario, Saverio|Pappadà, Graziano|Scioscia, Gaetano|Scazzocchio, Claudio|Saccone, Cecilia', 'CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it|CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it|CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it|CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it|CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it|CNR - Istituto di Tecnologie Biomediche, Sede di Bari, Via Amendola 122/D, Bari, 70126, Italy. monica.santamaria@ba.itb.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 34, 'Ascomycota|DNA, Mitochondrial|Genes, Fungal|Genetic Markers|Genome, Fungal|Genome, Mitochondrial|Introns', 'Journal Article', '10.1186/1471-2105-10-S6-S15', '19534740', 'A standardized and cost-effective molecular identification system is now an urgent need for Fungi owing to their wide involvement in human life quality. In particular the potential use of mitochondrial DNA species markers has been taken in account. Unfortunately, a serious difficulty in the PCR and bioinformatic surveys is due to the presence of mobile introns in almost all the fungal mitochondrial genes. The aim of this work is to verify the incidence of this phenomenon in Ascomycota, testing, at the same time, a new bioinformatic tool for extracting and managing sequence databases annotations, in order to identify the mitochondrial gene regions where introns are missing so as to propose them as species markers.'),
(92896, 2009, 'bmc', 'Modeling heterocyst pattern formation in cyanobacteria', 'Gerdtzen, Ziomara P|Salgado, J Cristian|Osses, Axel|Asenjo, Juan A|Rapaport, Ivan|Andrews, Barbara A', 'Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl|Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl|Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl|Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl|Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl|Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Av, Beauchef 850, Santiago 837-0448, Chile. zgerdtze@ing.uchile.cl', 'Chile|Chile|Chile|Chile|Chile|Chile', 0, 'Bacterial Proteins|Cyanobacteria|Gene Regulatory Networks|Genes, Bacterial|Models, Genetic|Nitrogen Fixation', 'Journal Article', '10.1186/1471-2105-10-S6-S16', '19534741', 'To allow the survival of the population in the absence of nitrogen, some cyanobacteria strains have developed the capability of differentiating into nitrogen fixing cells, forming a characteristic pattern. In this paper, the process by which cyanobacteria differentiates from vegetative cells into heterocysts in the absence of nitrogen and the elements of the gene network involved that allow the formation of such a pattern are investigated.'),
(92897, 2009, 'bmc', 'Deciphering the connectivity structure of biological networks using MixNet', 'Picard, Franck|Miele, Vincent|Daudin, Jean-Jacques|Cottret, Ludovic|Robin, Stéphane', 'CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr|CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr|CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr|CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr|CNRS UMR 5558, Université Lyon-1, Laboratoire de Biométrie et Biologie Evolutive, 43 bd du 11 novembre 1918, F-69622, Villeurbanne, France. picard@biomserv.univ-lyon1.fr', 'France|France|France|France|France', 21, 'Algorithms|Computational Biology|Metabolic Networks and Pathways|Models, Biological|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S17', '19534742', 'As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the networks topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.'),
(92898, 2009, 'bmc', 'Annotation and visualization of endogenous retroviral sequences using the Distributed Annotation System (DAS) and eBioX', 'Barrio, Alvaro Martínez|Lagercrantz, Erik|Sperber, Göran O|Blomberg, Jonas|Bongcam-Rudloff, Erik', 'The Linnaeus Centre for Bioinformatics, Uppsala University, Biomedical centre, P,O, Box 598, SE-75124 Uppsala, Sweden. Alvaro.Martinez.Barrio@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Biomedical centre, P,O, Box 598, SE-75124 Uppsala, Sweden. Alvaro.Martinez.Barrio@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Biomedical centre, P,O, Box 598, SE-75124 Uppsala, Sweden. Alvaro.Martinez.Barrio@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Biomedical centre, P,O, Box 598, SE-75124 Uppsala, Sweden. Alvaro.Martinez.Barrio@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Biomedical centre, P,O, Box 598, SE-75124 Uppsala, Sweden. Alvaro.Martinez.Barrio@lcb.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Base Sequence|Computational Biology|Computer Communication Networks|Endogenous Retroviruses|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S18', '19534743', 'The Distributed Annotation System (DAS) is a widely used network protocol for sharing biological information. The distributed aspects of the protocol enable the use of various reference and annotation servers for connecting biological sequence data to pertinent annotations in order to depict an integrated view of the data for the final user.'),
(92899, 2009, 'bmc', 'Visualising biological data: a semantic approach to tool and database integration', 'Pettifer, Steve|Thorne, David|McDermott, Philip|Marsh, James|Villéger, Alice|Kell, Douglas B|Attwood, Teresa K', 'School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk|School of Computer Science, University of Manchester, Manchester, M13 9PL, UK. steve.pettifer@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|Semantics|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-S6-S19', '19534744', 'In the biological sciences, the need to analyse vast amounts of information has become commonplace. Such large-scale analyses often involve drawing together data from a variety of different databases, held remotely on the internet or locally on in-house servers. Supporting these tasks are ad hoc collections of data-manipulation tools, scripting languages and visualisation software, which are often combined in arcane ways to create cumbersome systems that have been customized for a particular purpose, and are consequently not readily adaptable to other uses. For many day-to-day bioinformatics tasks, the sizes of current databases, and the scale of the analyses necessary, now demand increasing levels of automation, nevertheless, the unique experience and intuition of human researchers is still required to interpret the end results in any meaningful biological way. Putting humans in the loop requires tools to support real-time interaction with these vast and complex data-sets. Numerous tools do exist for this purpose, but many do not have optimal interfaces, most are effectively isolated from other tools and databases owing to incompatible data formats, and many have limited real-time performance when applied to realistically large data-sets: much of the users cognitive capacity is therefore focused on controlling the software and manipulating esoteric file formats rather than on performing the research.'),
(92900, 2009, 'bmc', 'Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements', 'Creanza, Teresa M|Horner, David S|DAddabbo, Annarita|Maglietta, Rosalia|Mignone, Flavio|Ancona, Nicola|Pesole, Graziano', 'Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it|Istituto di Studi sui Sistemi Intelligenti per lAutomazione, CNR, Via Amendola 122/D-I, Bari, Italy. creanza@ba.issia.cnr.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 2, 'Animals|Base Sequence|Conserved Sequence|Genomics|Humans|Mice|Open Reading Frames|Proteins|Rats|Sequence Analysis|Species Specificity', 'Journal Article', '10.1186/1471-2105-10-S6-S2', '19534745', 'The identification of protein coding elements in sets of mammalian conserved elements is one of the major challenges in the current molecular biology research. Many features have been proposed for automatically distinguishing coding and non coding conserved sequences, making so necessary a systematic statistical assessment of their differences. A comprehensive study should be composed of an association study, i.e. a comparison of the distributions of the features in the two classes, and a prediction study in which the prediction accuracies of classifiers trained on single and groups of features are analyzed, conditionally to the compared species and to the sequence lengths.'),
(92901, 2009, 'bmc', 'Human microRNA target analysis and gene ontology clustering by GOmir, a novel stand-alone application', 'Roubelakis, Maria G|Zotos, Pantelis|Papachristoudis, Georgios|Michalopoulos, Ioannis|Pappa, Kalliopi I|Anagnou, Nicholas P|Kossida, Sophia', 'Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr|Cell & Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, 11527, Athens, Greece. mroubelaki@bioacademy.gr', 'Greece|Greece|Greece|Greece|Greece|Greece|Greece', 35, 'Cluster Analysis|Computational Biology|Gene Expression Profiling|Humans|MicroRNAs|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S20', '19534746', 'microRNAs (miRNAs) are single-stranded RNA molecules of about 20-23 nucleotides length found in a wide variety of organisms. miRNAs regulate gene expression, by interacting with target mRNAs at specific sites in order to induce cleavage of the message or inhibit translation. Predicting or verifying mRNA targets of specific miRNAs is a difficult process of great importance.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92902, 2009, 'bmc', 'The GENIUS Grid Portal and robot certificates: a new tool for e-Science', 'Barbera, Roberto|Donvito, Giacinto|Falzone, Alberto|La Rocca, Giuseppe|Milanesi, Luciano|Maggi, Giorgio Pietro|Vicario, Saverio', 'Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it|Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Via S, Sofia 64, I-95123 Catania, Italy. roberto.barbera@ct.infn.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computer Communication Networks|Computer Security|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S21', '19534747', 'Grid technology is the computing model which allows users to share a wide pletora of distributed computational resources regardless of their geographical location. Up to now, the high security policy requested in order to access distributed computing resources has been a rather big limiting factor when trying to broaden the usage of Grids into a wide community of users. Grid security is indeed based on the Public Key Infrastructure (PKI) of X.509 certificates and the procedure to get and manage those certificates is unfortunately not straightforward. A first step to make Grids more appealing for new users has recently been achieved with the adoption of robot certificates.'),
(92903, 2009, 'bmc', 'Massive non-natural proteins structure prediction using grid technologies', 'Minervini, Giovanni|Evangelista, Giuseppe|Villanova, Laura|Slanzi, Debora|De Lucrezia, Davide|Poli, Irene|Luisi, Pier Luigi|Polticelli, Fabio', 'Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it|Department of Biology, University Roma Tre, Viale G, Marconi 446, Rome, I-00146, Italy. gminervini@uniroma3.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 11, 'Amino Acid Sequence|Computational Biology|Databases, Protein|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-10-S6-S22', '19534748', 'The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of proteins never sampled by nature, the so called never born proteins (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is their ability to form a well defined three-dimensional structure. Thus, the structural study of NBPs can help to understand if natural protein sequences were selected for their peculiar properties or if they are just one of the possible stable and functional ensembles.'),
(92904, 2009, 'bmc', 'A bioinformatics knowledge discovery in text application for grid computing', 'Castellano, Marcello|Mastronardi, Giuseppe|Bellotti, Roberto|Tarricone, Gianfranco', 'DEE Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via Orabona, 4, 70125, Bari, Italy. castellano@poliba.it|DEE Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via Orabona, 4, 70125, Bari, Italy. castellano@poliba.it|DEE Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via Orabona, 4, 70125, Bari, Italy. castellano@poliba.it|DEE Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, via Orabona, 4, 70125, Bari, Italy. castellano@poliba.it', 'Italy|Italy|Italy|Italy', 2, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S23', '19534749', 'A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources.'),
(92905, 2009, 'bmc', 'A novel similarity-measure for the analysis of genetic data in complex phenotypes', 'Lagani, Vincenzo|Montesanto, Alberto|Di Cianni, Fausta|Moreno, Victor|Landi, Stefano|Conforti, Domenico|Rose, Giuseppina|Passarino, Giuseppe', 'Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it|Department of Electronis, Informatics and Systems, University of Calabria, Via Ponte Pietro Bucci 41C, 87036, Rende, Italy. vlagani@deis.unical.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 3, 'Artificial Intelligence|Computer Simulation|Gene Expression Profiling|Gene Frequency|Genetics, Population|Genotype|Models, Genetic|Phenotype', 'Journal Article', '10.1186/1471-2105-10-S6-S24', '19534750', 'Recent technological advances in DNA sequencing and genotyping have led to the accumulation of a remarkable quantity of data on genetic polymorphisms. However, the development of new statistical and computational tools for effective processing of these data has not been equally as fast. In particular, Machine Learning literature is limited to relatively few papers which are focused on the development and application of data mining methods for the analysis of genetic variability. On the other hand, these papers apply to genetic data procedures which had been developed for a different kind of analysis and do not take into account the peculiarities of population genetics. The aim of our study was to define a new similarity measure, specifically conceived for measuring the similarity between the genetic profiles of two groups of subjects (i.e., cases and controls) taking into account that genetic profiles are usually distributed in a population group according to the Hardy Weinberg equilibrium.'),
(92906, 2009, 'bmc', 'Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining', 'Turi, Antonio|Loglisci, Corrado|Salvemini, Eliana|Grillo, Giorgio|Malerba, Donato|DElia, Domenica', 'Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it|Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it|Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it|Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it|Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it|Department of Computer Science, University of Bari, Via Orabona 4, 70125 Bari, Italy. turi@di.uniba.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Computational Biology|Humans|Sequence Analysis, RNA|Untranslated Regions', 'Journal Article', '10.1186/1471-2105-10-S6-S25', '19534751', 'Many studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.'),
(92907, 2009, 'bmc', 'Databases of homologous gene families for comparative genomics', 'Penel, Simon|Arigon, Anne-Muriel|Dufayard, Jean-François|Sertier, Anne-Sophie|Daubin, Vincent|Duret, Laurent|Gouy, Manolo|Perrière, Guy', 'Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Claude Bernard - Lyon 1, 43 bd, du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. penel@biomserv.univ-lyon1.fr', 'France|France|France|France|France|France|France|France', 0, 'Algorithms|Cluster Analysis|Databases, Genetic|Genomics|Internet|Phylogeny|Sequence Alignment|Software', 'Comparative Study', '10.1186/1471-2105-10-S6-S3', '19534752', 'Comparative genomics is a central step in many sequence analysis studies, from gene annotation and the identification of new functional regions in genomes, to the study of evolutionary processes at the molecular level (speciation, single gene or whole genome duplications, etc.) and phylogenetics. In that context, databases providing users high quality homologous families and sequence alignments as well as phylogenetic trees based on state of the art algorithms are becoming indispensable.'),
(92908, 2009, 'bmc', 'RetroTector online, a rational tool for analysis of retroviral elements in small and medium size vertebrate genomic sequences', 'Sperber, Göran|Lövgren, Anders|Eriksson, Nils-Einar|Benachenhou, Farid|Blomberg, Jonas', 'Physiology unit, Department of Neuroscience, Box 593, Uppsala, Sweden. goran.sperber@neuro.uu.se|Physiology unit, Department of Neuroscience, Box 593, Uppsala, Sweden. goran.sperber@neuro.uu.se|Physiology unit, Department of Neuroscience, Box 593, Uppsala, Sweden. goran.sperber@neuro.uu.se|Physiology unit, Department of Neuroscience, Box 593, Uppsala, Sweden. goran.sperber@neuro.uu.se|Physiology unit, Department of Neuroscience, Box 593, Uppsala, Sweden. goran.sperber@neuro.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 29, 'Algorithms|Animals|Endogenous Retroviruses|Genome|Genomics|Humans|Internet|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S4', '19534753', 'The rapid accumulation of genomic information in databases necessitates rapid and specific algorithms for extracting biologically meaningful information. More or less complete retroviral sequences, also called proviral or endogenous retroviral sequences, ERVs, constitutes at least 5% of vertebrate genomes. After infecting the host, these retroviruses have integrated in germ line cells, and have then been carried in genomes for at least several 100 million years. A better understanding of structure and function of these sequences can have profound biological and medical consequences.'),
(92909, 2009, 'bmc', 'RegExpBlasting (REB), a Regular Expression Blasting algorithm based on multiply aligned sequences', 'Rubino, Francesco|Attimonelli, Marcella', 'Department of Biochemistry and Molecular Biology E, Quagliariello - Bari, 70126, Italy. rubino.francesco@gmail.com|Department of Biochemistry and Molecular Biology E, Quagliariello - Bari, 70126, Italy. rubino.francesco@gmail.com', 'Italy|Italy', 2, 'Algorithms|Amino Acid Sequence|Base Sequence|Computational Biology|Databases, Factual|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S5', '19534754', 'One of the most frequent uses of bioinformatics tools concerns functional characterization of a newly produced nucleotide sequence (a query sequence) by applying Blast or FASTA against a set of sequences (the subject sequences). However, in some specific contexts, it is useful to compare the query sequence against a cluster such as a MultiAlignment (MA). We present here the RegExpBlasting (REB) algorithm, which compares an unclassified sequence with a dataset of patterns defined by application of Regular Expression rules to a given-as-input MA datasets. The REB algorithm workflow consists in i. the definition of a dataset of multialignments ii. the association of each MA to a pattern, defined by application of regular expression rules, iii. automatic characterization of a submitted biosequence according to the function of the sequences described by the pattern best matching the query sequence.'),
(92910, 2009, 'bmc', 'DoOPSearch: a web-based tool for finding and analysing common conserved motifs in the promoter regions of different chordate and plant genes', 'Sebestyén, Endre|Nagy, Tibor|Suhai, Sándor|Barta, Endre', 'Agricultural Research Institute of the Hungarian Academy of Sciences, Martonvásár, Brunszvik u, 2, H-2462, Hungary. sebestyene@mail.mgki.hu|Agricultural Research Institute of the Hungarian Academy of Sciences, Martonvásár, Brunszvik u, 2, H-2462, Hungary. sebestyene@mail.mgki.hu|Agricultural Research Institute of the Hungarian Academy of Sciences, Martonvásár, Brunszvik u, 2, H-2462, Hungary. sebestyene@mail.mgki.hu|Agricultural Research Institute of the Hungarian Academy of Sciences, Martonvásár, Brunszvik u, 2, H-2462, Hungary. sebestyene@mail.mgki.hu', 'Hungary|Hungary|Hungary|Hungary', 6, 'Animals|Chordata|Computational Biology|Conserved Sequence|Databases, Genetic|Genes, Plant|Genomics|Internet|Promoter Regions, Genetic|Software', 'Journal Article', '10.1186/1471-2105-10-S6-S6', '19534755', 'The comparative genomic analysis of a large number of orthologous promoter regions of the chordate and plant genes from the DoOP databases shows thousands of conserved motifs. Most of these motifs differ from any known transcription factor binding site (TFBS). To identify common conserved motifs, we need a specific tool to be able to search amongst them. Since conserved motifs from the DoOP databases are linked to genes, the result of such a search can give a list of genes that are potentially regulated by the same transcription factor(s).'),
(92911, 2009, 'bmc', 'The Mycoplasma conjunctivae genome sequencing, annotation and analysis', 'Calderon-Copete, Sandra P|Wigger, George|Wunderlin, Christof|Schmidheini, Tobias|Frey, Joachim|Quail, Michael A|Falquet, Laurent', 'Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch|Swiss Institute of Bioinformatics, Génopode-UNIL, 1015 Lausanne, Switzerland. Sandra.Calderon@unil.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 30, 'Computational Biology|Databases, Genetic|Genome, Bacterial|Genomics|Mycoplasma conjunctivae|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-S6-S7', '19534756', 'The mollicute Mycoplasma conjunctivae is the etiological agent leading to infectious keratoconjunctivitis (IKC) in domestic sheep and wild caprinae. Although this pathogen is relatively benign for domestic animals treated by antibiotics, it can lead wild animals to blindness and death. This is a major cause of death in the protected species in the Alps (e.g., Capra ibex, Rupicapra rupicapra).'),
(92912, 2009, 'bmc', 'Expression pattern divergence of duplicated genes in rice', 'Li, Zhe|Zhang, He|Ge, Song|Gu, Xiaocheng|Gao, Ge|Luo, Jingchu', 'College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn|College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn|College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn|College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn|College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn|College of Life Sciences, National Laboratory of Plant Genetic Engineering and Protein Engineering, Center for Bioinformatics, Peking University, Beijing 100871, PR China. liz@mail.cbi.pku.edu.cn', 'China|China|China|China|China|China', 33, 'Computational Biology|Gene Expression Regulation, Plant|Genes, Duplicate|Genetic Variation|Genome, Plant|Oryza', 'Journal Article', '10.1186/1471-2105-10-S6-S8', '19534757', 'Genome-wide duplication is ubiquitous during diversification of the angiosperms, and gene duplication is one of the most important mechanisms for evolutionary novelties. As an indicator of functional evolution, the divergence of expression patterns following duplication events has drawn great attention in recent years. Using large-scale whole-genome microarray data, we systematically analyzed expression divergence patterns of rice genes from block, tandem and dispersed duplications.'),
(92913, 2009, 'bmc', 'Functional assessment of time course microarray data', 'Nueda, María José|Sebastián, Patricia|Tarazona, Sonia|García-García, Francisco|Dopazo, Joaquín|Ferrer, Alberto|Conesa, Ana', 'Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es|Department of Statistics and Operation Research, University of Alicante, Ctra, San Vicente del Raspeig, S/N 03690 Alicante, Spain. mj.nueda@ua.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 17, 'Computer Simulation|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Time Factors', 'Journal Article', '10.1186/1471-2105-10-S6-S9', '19534758', 'Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.'),
(92914, 2009, 'bmc', 'The development of a comparison approach for Illumina bead chips unravels unexpected challenges applying newest generation microarrays', 'Eggle, Daniela|Debey-Pascher, Svenja|Beyer, Marc|Schultze, Joachim L', 'Molecular Tumor Biology and Tumor Immunology, Department of Internal Medicine I, University of Cologne, Cologne, Germany. daniela.eggle@freenet.de|Molecular Tumor Biology and Tumor Immunology, Department of Internal Medicine I, University of Cologne, Cologne, Germany. daniela.eggle@freenet.de|Molecular Tumor Biology and Tumor Immunology, Department of Internal Medicine I, University of Cologne, Cologne, Germany. daniela.eggle@freenet.de|Molecular Tumor Biology and Tumor Immunology, Department of Internal Medicine I, University of Cologne, Cologne, Germany. daniela.eggle@freenet.de', 'Germany|Germany|Germany|Germany', 4, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-10-186', '19538710', 'The MAQC project demonstrated that microarrays with comparable content show inter- and intra-platform reproducibility. However, since the content of gene databases still increases, the development of new generations of microarrays covering new content is mandatory. To better understand the potential challenges updated microarray content might pose on clinical and biological projects we developed a methodology consisting of in silico analyses combined with performance analysis using real biological samples.'),
(92915, 2009, 'bmc', 'geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research', 'Glez-Peña, Daniel|Díaz, Fernando|Hernández, Jesús M|Corchado, Juan M|Fdez-Riverola, Florentino', 'ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain. dgpena@uvigo.es|ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain. dgpena@uvigo.es|ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain. dgpena@uvigo.es|ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain. dgpena@uvigo.es|ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain. dgpena@uvigo.es', 'Spain|Spain|Spain|Spain|Spain', 32, 'Biomedical Research|Computational Biology|Database Management Systems|Diagnosis, Computer-Assisted|Humans|Information Storage and Retrieval|Interdisciplinary Communication|Medical Informatics Applications|Neoplasms|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-187', '19538727', 'Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine.'),
(92916, 2009, 'bmc', 'designGG: an R-package and web tool for the optimal design of genetical genomics experiments', 'Li, Yang|Swertz, Morris A|Vera, Gonzalo|Fu, Jingyuan|Breitling, Rainer|Jansen, Ritsert C', 'Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl|Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl|Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl|Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl|Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl|Groningen Bioinformatics Center, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Haren, The Netherlands. yang.li@rug.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 8, 'Animals|Data Interpretation, Statistical|Databases, Genetic|Genomics|Internet|Research Design|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-188', '19538731', 'High-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial.'),
(92917, 2009, 'bmc', 'Platform dependence of inference on gene-wise and gene-set involvement in human lung development', 'Du, Rose|Tantisira, Kelan|Carey, Vincent|Bhattacharya, Soumyaroop|Metje, Stephanie|Kho, Alvin T|Klanderman, Barbara J|Gaedigk, Roger|Lazarus, Ross|Mariani, Thomas J|Leeder, J Steven|Weiss, Scott T', 'Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org|Channing Laboratory, Brigham and Womens Hospital, Boston, MA 02115, USA. rdu@partners.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Computational Biology|Gene Expression Profiling|Humans|Lung', 'Journal Article', '10.1186/1471-2105-10-189', '19545372', 'With the recent development of microarray technologies, the comparability of gene expression data obtained from different platforms poses an important problem. We evaluated two widely used platforms, Affymetrix U133 Plus 2.0 and the Illumina HumanRef-8 v2 Expression Bead Chips, for comparability in a biological system in which changes may be subtle, namely fetal lung tissue as a function of gestational age.'),
(92918, 2009, 'bmc', 'Modeling the role of p53 pulses in DNA damage- induced cell death decision', 'Sun, Tingzhe|Chen, Chun|Wu, Yuanyuan|Zhang, Shuai|Cui, Jun|Shen, Pingping', 'State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com|State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com|State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com|State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com|State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com|State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210093, PR China. confucian007@126.com', 'China|China|China|China|China|China', 28, 'Animals|Ataxia Telangiectasia Mutated Proteins|Cell Cycle Proteins|Cell Death|DNA Damage|DNA-Binding Proteins|Humans|Models, Theoretical|Protein-Serine-Threonine Kinases|Proto-Oncogene Proteins c-mdm2|Tumor Suppressor Protein p53|Tumor Suppressor Proteins|bcl-2-Associated X Protein', 'Journal Article', '10.1186/1471-2105-10-190', '19545411', 'The tumor suppressor p53 plays pivotal roles in tumorigenesis suppression. Although oscillations of p53 have been extensively studied, the mechanism of p53 pulses and their physiological roles in DNA damage response remain unclear.'),
(92919, 2009, 'bmc', 'Unsupervised assessment of microarray data quality using a Gaussian mixture model', 'Howard, Brian E|Sick, Beate|Heber, Steffen', 'Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. itsbehoward@hotmail.com|Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. itsbehoward@hotmail.com|Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. itsbehoward@hotmail.com', 'United States|United States|United States', 2, 'Algorithms|Computational Biology|Gene Expression Profiling|Normal Distribution|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-191', '19545436', 'Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.'),
(92920, 2009, 'bmc', 'OrthoClusterDB: an online platform for synteny blocks', 'Ng, Man-Ping|Vergara, Ismael A|Frech, Christian|Chen, Qingkang|Zeng, Xinghuo|Pei, Jian|Chen, Nansheng', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada. pennyng119@gmail.com', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 23, 'Databases, Genetic|Genome|Genomics|Internet|Software|Synteny|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-10-192', '19549318', 'The recent availability of an expanding collection of genome sequences driven by technological advances has facilitated comparative genomics and in particular the identification of synteny among multiple genomes. However, the development of effective and easy-to-use methods for identifying such conserved gene clusters among multiple genomes-synteny blocks-as well as databases, which host synteny blocks from various groups of species (especially eukaryotes) and also allow users to run synteny-identification programs, lags behind.'),
(92921, 2009, 'bmc', 'Filtering genes for cluster and network analysis', 'Tritchler, David|Parkhomenko, Elena|Beyene, Joseph', 'Department of Biostatistics, University of Toronto, Toronto, Ontario, Canada. dtritch@rogers.com|Department of Biostatistics, University of Toronto, Toronto, Ontario, Canada. dtritch@rogers.com|Department of Biostatistics, University of Toronto, Toronto, Ontario, Canada. dtritch@rogers.com', 'Canada|Canada|Canada', 31, 'Algorithms|Cluster Analysis|Computer Simulation|Escherichia coli|Gene Regulatory Networks|Genes|Genomics|Models, Genetic|Models, Statistical|Principal Component Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-10-193', '19549335', 'Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias.'),
(92922, 2009, 'bmc', 'Computational analysis of the interaction between transcription factors and the predicted secreted proteome of the yeast Kluyveromyces lactis', 'Brustolini, Otávio J B|Fietto, Luciano G|Cruz, Cosme D|Passos, Flávia M L', 'Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, MG, Brazil. tavinbio@yahoo.com.br|Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, MG, Brazil. tavinbio@yahoo.com.br|Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, MG, Brazil. tavinbio@yahoo.com.br|Departamento de Microbiologia, Instituto de Biotecnologia Aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, MG, Brazil. tavinbio@yahoo.com.br', 'Brazil|Brazil|Brazil|Brazil', 6, 'Algorithms|Computational Biology|Fungal Proteins|Genome, Fungal|Kluyveromyces|Proteome|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-194', '19555482', 'Protein secretion is a cell translocation process of major biological and technological significance. The secretion and downstream processing of proteins by recombinant cells is of great commercial interest. The yeast Kluyveromyces lactis is considered a promising host for heterologous protein production. Because yeasts naturally do not secrete as many proteins as filamentous fungi, they can produce secreted recombinant proteins with few contaminants in the medium. An ideal system to address the secretion of a desired protein could be exploited among the native proteins in certain physiological conditions. By applying algorithms to the completed K. lactis genome sequence, such a system could be selected. To this end, we predicted protein subcellular locations and correlated the resulting extracellular secretome with the transcription factors that modulate the cellular response to a particular environmental stimulus.'),
(92923, 2009, 'bmc', 'Ab initio and homology based prediction of protein domains by recursive neural networks', 'Walsh, Ian|Martin, Alberto J M|Mooney, Catherine|Rubagotti, Enrico|Vullo, Alessandro|Pollastri, Gianluca', 'School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. ian.walsh@ucd.ie', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 13, 'Algorithms|Databases, Protein|Neural Networks (Computer)|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1186/1471-2105-10-195', '19558651', 'Proteins, especially larger ones, are often composed of individual evolutionary units, domains, which have their own function and structural fold. Predicting domains is an important intermediate step in protein analyses, including the prediction of protein structures.'),
(92924, 2009, 'bmc', 'Triangle network motifs predict complexes by complementing high-error interactomes with structural information', 'Andreopoulos, Bill|Winter, Christof|Labudde, Dirk|Schroeder, Michael', 'Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany. williama@biotec.tu-dresden.de|Biotechnology Center (BIOTEC), Technische Universität Dresden, 01307 Dresden, Germany. williama@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany', 5, 'Binding Sites|Computational Biology|Databases, Protein|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-196', '19558694', 'A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.'),
(92925, 2009, 'bmc', 'ResBoost: characterizing and predicting catalytic residues in enzymes', 'Alterovitz, Ron|Arvey, Aaron|Sankararaman, Sriram|Dallett, Carolina|Freund, Yoav|Sjölander, Kimmen', 'Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu|Department of Computer Science, University of North Carolina at Chapel Hill, USA. ron@cs.unc.edu', 'United States|United States|United States|United States|United States|United States', 14, 'Binding Sites|Catalysis|Computational Biology|Databases, Protein|Enzymes|Software', 'Journal Article', '10.1186/1471-2105-10-197', '19558703', 'Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzymes function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed.'),
(92926, 2009, 'bmc', 'A Bayesian approach to efficient differential allocation for resampling-based significance testing', 'Jensen, Shane T|Soi, Sameer|Wang, Li-San', 'Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. stjensen@wharton.upenn.edu|Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. stjensen@wharton.upenn.edu|Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. stjensen@wharton.upenn.edu', 'United States|United States|United States', 5, 'Algorithms|Bayes Theorem|Computational Biology|Gene Expression Profiling|Genome-Wide Association Study|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-10-198', '19558706', 'Large-scale statistical analyses have become hallmarks of post-genomic era biological research due to advances in high-throughput assays and the integration of large biological databases. One accompanying issue is the simultaneous estimation of p-values for a large number of hypothesis tests. In many applications, a parametric assumption in the null distribution such as normality may be unreasonable, and resampling-based p-values are the preferred procedure for establishing statistical significance. Using resampling-based procedures for multiple testing is computationally intensive and typically requires large numbers of resamples.'),
(92927, 2009, 'bmc', 'DOTcvpSB, a software toolbox for dynamic optimization in systems biology', 'Hirmajer, Tomás|Balsa-Canto, Eva|Banga, Julio R', 'Instituto de Investigaciones Marinas, IIM-CSIC, Spanish Council for Scientific Research, C/Eduardo Cabello 6, 36208 Vigo, Spain. thirmajer@gmail.com|Instituto de Investigaciones Marinas, IIM-CSIC, Spanish Council for Scientific Research, C/Eduardo Cabello 6, 36208 Vigo, Spain. thirmajer@gmail.com|Instituto de Investigaciones Marinas, IIM-CSIC, Spanish Council for Scientific Research, C/Eduardo Cabello 6, 36208 Vigo, Spain. thirmajer@gmail.com', 'Spain|Spain|Spain', 36, 'Databases, Factual|Internet|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-10-199', '19558728', 'Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units.'),
(92928, 2009, 'bmc', 'Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis', 'Yi, Ming|Mudunuri, Uma|Che, Anney|Stephens, Robert M', 'Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA. myi@ncifcrf.gov|Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA. myi@ncifcrf.gov|Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA. myi@ncifcrf.gov|Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA. myi@ncifcrf.gov', 'United States|United States|United States|United States', 14, 'Computational Biology|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Comparative Study', '10.1186/1471-2105-10-200', '19563622', 'One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.'),
(92929, 2009, 'bmc', 'A-MADMAN: annotation-based microarray data meta-analysis tool', 'Bisognin, Andrea|Coppe, Alessandro|Ferrari, Francesco|Risso, Davide|Romualdi, Chiara|Bicciato, Silvio|Bortoluzzi, Stefania', 'University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com|University of Padova, Department of Biology, Padova, Italy. andrea.bisognin@gmail.com', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Gene Expression Profiling|Internet|Meta-Analysis as Topic|Microarray Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-201', '19563634', 'Publicly available datasets of microarray gene expression signals represent an unprecedented opportunity for extracting genomic relevant information and validating biological hypotheses. However, the exploitation of this exceptionally rich mine of information is still hampered by the lack of appropriate computational tools, able to overcome the critical issues raised by meta-analysis.'),
(92930, 2009, 'bmc', 'NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction', 'Nguyen Ba, Alex N|Pogoutse, Anastassia|Provart, Nicholas|Moses, Alan M', 'Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alex.nguyenba@utoronto.ca|Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alex.nguyenba@utoronto.ca|Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alex.nguyenba@utoronto.ca|Department of Cell & Systems Biology, University of Toronto, Toronto, Canada. alex.nguyenba@utoronto.ca', 'Canada|Canada|Canada|Canada', 0, 'Active Transport, Cell Nucleus|Markov Chains|Models, Theoretical|Nuclear Localization Signals|Software', 'Journal Article', '10.1186/1471-2105-10-202', '19563654', 'Nuclear localization signals (NLSs) are stretches of residues within a protein that are important for the regulated nuclear import of the protein. Of the many import pathways that exist in yeast, the best characterized is termed the classical NLS pathway. The classical NLS contains specific patterns of basic residues and computational methods have been designed to predict the location of these motifs on proteins. The consensus sequences, or patterns, for the other import pathways are less well-understood.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92931, 2009, 'bmc', 'Integrated analysis of DNA copy number and gene expression microarray data using gene sets', 'Menezes, Renée X|Boetzer, Marten|Sieswerda, Melle|van Ommen, Gert-Jan B|Boer, Judith M', 'Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. r.x.menezes@lumc.nl|Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. r.x.menezes@lumc.nl|Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. r.x.menezes@lumc.nl|Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. r.x.menezes@lumc.nl|Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands. r.x.menezes@lumc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Breast Neoplasms|Computational Biology|DNA|Databases, Genetic|Female|Gene Dosage|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-10-203', '19563656', 'Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes.'),
(92932, 2009, 'bmc', 'Comparison of sequence-dependent tiling array normalization approaches', 'Chung, Ho-Ryun|Vingron, Martin', 'Max-Planck-Institut für Molekulare Genetik, Department of Computational Molecular Biology, Berlin, Germany. chung@molgen.mpg.de|Max-Planck-Institut für Molekulare Genetik, Department of Computational Molecular Biology, Berlin, Germany. chung@molgen.mpg.de', 'Germany|Germany', 5, 'Computational Biology|DNA Probes|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-10-204', '19566918', 'The detection of enriched DNA or RNA fragments by tiling microarrays has become more and more popular. These microarrays contain a high number of small probes covering genomic loci. However, to achieve high coverage the probe sequences cannot be selected for their hybridization properties. The affinity of the probes towards their targets varies in a sequence-dependent manner. In order to remove this bias a number of approaches have been developed and shown to increase the detection of enriched DNA or RNA fragments. However, these approaches also employ a peak detection algorithm that is different from the one used previously. Thus, it seems possible that the enhancement of detection is due to the peak detection algorithm rather than the sequence-dependent normalization.'),
(92933, 2009, 'bmc', 'Clique-based data mining for related genes in a biomedical database', 'Matsunaga, Tsutomu|Yonemori, Chikara|Tomita, Etsuji|Muramatsu, Masaaki', 'Research and Development Headquarters, NTT DATA Corporation, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp|Research and Development Headquarters, NTT DATA Corporation, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp|Research and Development Headquarters, NTT DATA Corporation, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp|Research and Development Headquarters, NTT DATA Corporation, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp', 'Japan|Japan|Japan|Japan', 22, 'Computational Biology|Databases, Genetic|Information Storage and Retrieval', 'Journal Article', '10.1186/1471-2105-10-205', '19566964', 'Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph.'),
(92934, 2009, 'bmc', 'Representative transcript sets for evaluating a translational initiation sites predictor', 'Zeng, Jia|Alhajj, Reda|Demetrick, Douglas J', 'Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada. jzeng@ucalgary.ca|Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada. jzeng@ucalgary.ca|Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada. jzeng@ucalgary.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Gene Expression Profiling|Peptide Chain Initiation, Translational|Proteins|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-10-206', '19573244', 'Translational initiation site (TIS) prediction is a very important and actively studied topic in bioinformatics. In order to complete a comparative analysis, it is desirable to have several benchmark data sets which can be used to test the effectiveness of different algorithms. An ideal benchmark data set should be reliable, representative and readily available. Preferably, proteins encoded by members of the data set should also be representative of the protein population actually expressed in cellular specimens.'),
(92935, 2009, 'bmc', 'Ensemble approach to predict specificity determinants: benchmarking and validation', 'Chakrabarti, Saikat|Panchenko, Anna R', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. chakraba@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. chakraba@ncbi.nlm.nih.gov', 'United States|United States', 21, 'Benchmarking|Binding Sites|Computational Biology|Databases, Protein|Models, Molecular|Protein Conformation|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-10-207', '19573245', 'It is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.'),
(92936, 2009, 'bmc', 'OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif', 'Drawid, Amar|Gupta, Nupur|Nagaraj, Vijayalakshmi H|Gélinas, Céline|Sengupta, Anirvan M', 'BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA. adrawid@gmail.com|BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA. adrawid@gmail.com|BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA. adrawid@gmail.com|BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA. adrawid@gmail.com|BioMAPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA. adrawid@gmail.com', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Binding Sites|Computational Biology|Markov Chains|Molecular Sequence Data|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-208', '19583839', 'DNA sequence binding motifs for several important transcription factors happen to be self-overlapping. Many of the current regulatory site identification methods do not explicitly take into account the overlapping sites. Moreover, most methods use arbitrary thresholds and fail to provide a biophysical interpretation of statistical quantities. In addition, commonly used approaches do not include the location of a site with respect to the transcription start site (TSS) in an integrated probabilistic framework while identifying sites. Ignoring these features can lead to inaccurate predictions as well as incorrect design and interpretation of experimental results.'),
(92937, 2009, 'bmc', 'A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests', 'Carvajal-Rodríguez, Antonio|de Uña-Alvarez, Jacobo|Rolán-Alvarez, Emilio', 'Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain. acraaj@uvigo.es|Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain. acraaj@uvigo.es|Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain. acraaj@uvigo.es', 'Spain|Spain|Spain', 0, 'Computer Simulation|Models, Statistical|Sample Size', 'Journal Article', '10.1186/1471-2105-10-209', '19586526', 'The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.'),
(92938, 2009, 'bmc', 'Iterative refinement of structure-based sequence alignments by Seed Extension', 'Kim, Changhoon|Tai, Chin-Hsien|Lee, Byungkook', 'Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA. kimchan@mail.nih.gov|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA. kimchan@mail.nih.gov|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA. kimchan@mail.nih.gov', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Protein|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-210', '19589133', 'Accurate sequence alignment is required in many bioinformatics applications but, when sequence similarity is low, it is difficult to obtain accurate alignments based on sequence similarity alone. The accuracy improves when the structures are available, but current structure-based sequence alignment procedures still mis-align substantial numbers of residues. In order to correct such errors, we previously explored the possibility of replacing the residue-based dynamic programming algorithm in structure alignment procedures with the Seed Extension algorithm, which does not use a gap penalty. Here, we describe a new procedure called RSE (Refinement with Seed Extension) that iteratively refines a structure-based sequence alignment.'),
(92939, 2009, 'bmc', 'puma: a Bioconductor package for propagating uncertainty in microarray analysis', 'Pearson, Richard D|Liu, Xuejun|Sanguinetti, Guido|Milo, Marta|Lawrence, Neil D|Rattray, Magnus', 'School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk|School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 64, 'Computational Biology|Gene Expression Profiling|Genomics|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-10-211', '19589155', 'Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied.'),
(92940, 2009, 'bmc', 'Error statistics of hidden Markov model and hidden Boltzmann model results', 'Newberg, Lee A', 'The Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA. lee.newberg@wadsworth.org', 'United States', 8, 'Computational Biology|False Positive Reactions|Markov Chains|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-10-212', '19589158', 'Hidden Markov models and hidden Boltzmann models are employed in computational biology and a variety of other scientific fields for a variety of analyses of sequential data. Whether the associated algorithms are used to compute an actual probability or, more generally, an odds ratio or some other score, a frequent requirement is that the error statistics of a given score be known. What is the chance that random data would achieve that score or better? What is the chance that a real signal would achieve a given score threshold?'),
(92941, 2009, 'bmc', 'A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data', 'Menze, Bjoern H|Kelm, B Michael|Masuch, Ralf|Himmelreich, Uwe|Bachert, Peter|Petrich, Wolfgang|Hamprecht, Fred A', 'Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany. bjoern.menze@iwr.uni-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 275, 'Classification|Computational Biology|Magnetic Resonance Spectroscopy|Models, Statistical|Pattern Recognition, Automated|Regression Analysis|Spectrophotometry, Infrared', 'Comparative Study', '10.1186/1471-2105-10-213', '19591666', 'Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space.'),
(92942, 2009, 'bmc', 'Construction and use of gene expression covariation matrix', 'Hennetin, Jérôme|Pehkonen, Petri|Bellis, Michel', 'Centre de Recherches en Biochimie Macromoléculaire, CNRS, Montpellier, France. jerome.hennetin@tiscali.fr|Centre de Recherches en Biochimie Macromoléculaire, CNRS, Montpellier, France. jerome.hennetin@tiscali.fr|Centre de Recherches en Biochimie Macromoléculaire, CNRS, Montpellier, France. jerome.hennetin@tiscali.fr', 'France|France|France', 0, 'Algorithms|Animals|Computational Biology|Databases, Protein|Gene Expression|Gene Expression Profiling|Humans|Mice', 'Journal Article', '10.1186/1471-2105-10-214', '19594909', 'One essential step in the massive analysis of transcriptomic profiles is the calculation of the correlation coefficient, a value used to select pairs of genes with similar or inverse transcriptional profiles across a large fraction of the biological conditions examined. Until now, the choice between the two available methods for calculating the coefficient has been dictated mainly by technological considerations. Specifically, in analyses based on double-channel techniques, researchers have been required to use covariation correlation, i.e. the correlation between gene expression changes measured between several pairs of biological conditions, expressed for example as fold-change. In contrast, in analyses of single-channel techniques scientists have been restricted to the use of coexpression correlation, i.e. correlation between gene expression levels. To our knowledge, nobody has ever examined the possible benefits of using covariation instead of coexpression in massive analyses of single channel microarray results.'),
(92943, 2009, 'bmc', 'Regression based predictor for p53 transactivation', 'Gowrisankar, Sivakumar|Jegga, Anil G', 'Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA. sgowrisankar@partners.org|Division of Biomedical Informatics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, USA. sgowrisankar@partners.org', 'United States|United States', 5, 'Algorithms|Computational Biology|Polymorphism, Single Nucleotide|Regression Analysis|Response Elements|Transcriptional Activation|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-10-215', '19602281', 'The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers.'),
(92944, 2009, 'bmc', 'Linked region detection using high-density SNP genotype data via the minimum recombinant model of pedigree haplotype inference', 'Wang, Lusheng|Wang, Zhanyong|Yang, Wanling', 'Department of Computer Science, City University of Hong Kong, Hong Kong, PR China. lwang@cs.cityu.edu.hk|Department of Computer Science, City University of Hong Kong, Hong Kong, PR China. lwang@cs.cityu.edu.hk|Department of Computer Science, City University of Hong Kong, Hong Kong, PR China. lwang@cs.cityu.edu.hk', 'China|China|China', 5, 'Algorithms|Genetic Linkage|Genotype|Haplotypes|Models, Genetic|Pedigree|Polymorphism, Single Nucleotide|Recombination, Genetic|Software', 'Journal Article', '10.1186/1471-2105-10-216', '19604391', 'With the rapid development of high-throughput genotyping technologies, efficient methods for identifying linked regions using high-density SNP genotype data have become more and more important. Recently, a deterministic method that works very well on SNP genotyping data has been developed (Lin et al. Bioinformatics 2008, 24(1): 86-93). However, that program can only work on a limited number of family structures. In particular, the results (if any) will be poor when the genotype data for the whole chromosome of one of the parents in a nuclear family is missing.'),
(92945, 2009, 'bmc', 'A compartmentalized approach to the assembly of physical maps', 'Bozdag, Serdar|Close, Timothy J|Lonardi, Stefano', 'National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. bozdags@mail.nih.gov|National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. bozdags@mail.nih.gov|National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. bozdags@mail.nih.gov', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Contig Mapping|Databases, Factual|Genome|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-217', '19604400', 'Physical maps have been historically one of the cornerstones of genome sequencing and map-based cloning strategies. They also support marker assisted breeding and EST mapping. The problem of building a high quality physical map is computationally challenging due to unavoidable noise in the input fingerprint data.'),
(92946, 2009, 'bmc', 'Transcriptional programs: modelling higher order structure in transcriptional control', 'Reid, John E|Ott, Sascha|Wernisch, Lorenz', 'MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge CB2 0SR, UK. john.reid@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge CB2 0SR, UK. john.reid@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge CB2 0SR, UK. john.reid@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 3, 'Animals|Binding Sites|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Humans|Mice|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-10-218', '19607663', 'Transcriptional regulation is an important part of regulatory control in eukaryotes. Even if binding motifs for transcription factors are known, the task of finding binding sites by scanning sequences is plagued by false positives. One way to improve the detection of binding sites from motifs is by taking cooperativity of transcription factor binding into account. We propose a non-parametric probabilistic model, similar to a document topic model, for detecting transcriptional programs, groups of cooperative transcription factors and co-regulated genes. The analysis results in transcriptional programs which generalise both transcriptional modules and TF-target gene incidence matrices and provide a higher-level summary of these structures. The method is independent of prior specification of training sets of genes, for example, via gene expression data. The analysis is based on known binding motifs.'),
(92947, 2009, 'bmc', 'OrthoSelect: a protocol for selecting orthologous groups in phylogenomics', 'Schreiber, Fabian|Pick, Kerstin|Erpenbeck, Dirk|Wörheide, Gert|Morgenstern, Burkhard', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com', 'Germany|Germany|Germany|Germany|Germany', 22, 'Computational Biology|Expressed Sequence Tags|Genome|Genomics|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-10-219', '19607672', 'Phylogenetic studies using expressed sequence tags (EST) are becoming a standard approach to answer evolutionary questions. Such studies are usually based on large sets of newly generated, unannotated, and error-prone EST sequences from different species. A first crucial step in EST-based phylogeny reconstruction is to identify groups of orthologous sequences. From these data sets, appropriate target genes are selected, and redundant sequences are eliminated to obtain suitable sequence sets as input data for tree-reconstruction software. Generating such data sets manually can be very time consuming. Thus, software tools are needed that carry out these steps automatically.'),
(92948, 2009, 'bmc', 'An introduction to scripting in Ruby for biologists', 'Aerts, Jan|Law, Andy', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Göttingen, Germany. fab.schreiber@gmail.com', 'Germany|Germany', 0, 'Computational Biology|Databases, Factual|Programming Languages', 'Letter', '10.1186/1471-2105-10-221', '19607723', 'The Ruby programming language has a lot to offer to any scientist with electronic data to process. Not only is the initial learning curve very shallow, but its reflection and meta-programming capabilities allow for the rapid creation of relatively complex applications while still keeping the code short and readable. This paper provides a gentle introduction to this scripting language for researchers without formal informatics training such as many wet-lab scientists. We hope this will provide such researchers an idea of how powerful a tool Ruby can be for their data management tasks and encourage them to learn more about it.'),
(92949, 2009, 'bmc', 'PCI-SS: MISO dynamic nonlinear protein secondary structure prediction', 'Green, James R|Korenberg, Michael J|Aboul-Magd, Mohammed O', 'Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. jrgreen@sce.carleton.ca|Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. jrgreen@sce.carleton.ca|Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. jrgreen@sce.carleton.ca', 'Canada|Canada|Canada', 21, 'Algorithms|Amino Acid Sequence|Databases, Protein|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-10-222', '19615046', 'Since the function of a protein is largely dictated by its three dimensional configuration, determining a proteins structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing alpha-helices, beta-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification.'),
(92950, 2009, 'bmc', 'Incorporating rich background knowledge for gene named entity classification and recognition', 'Li, Yanpeng|Lin, Hongfei|Yang, Zhihao', 'Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com|Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com|Department of Computer Science and Engineering, Dalian University of Technology, Dalian, PR China. liyanpeng.lyp@gmail.com', 'China|China|China', 36, 'Artificial Intelligence|Computational Biology|Genes|Information Storage and Retrieval|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-10-223', '19615051', 'Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information.'),
(92951, 2009, 'bmc', 'FISim: a new similarity measure between transcription factor binding sites based on the fuzzy integral', 'Garcia, Fernando|Lopez, Francisco J|Cano, Carlos|Blanco, Armando', 'Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain. fernan@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain. fernan@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain. fernan@decsai.ugr.es|Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain. fernan@decsai.ugr.es', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Base Sequence|Binding Sites|Cluster Analysis|Computational Biology|DNA|Fuzzy Logic|Molecular Sequence Data|Sequence Alignment|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-10-224', '19615102', 'Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources.'),
(92952, 2009, 'bmc', 'A statistical framework to evaluate virtual screening', 'Zhao, Wei|Hevener, Kirk E|White, Stephen W|Lee, Richard E|Boyett, James M', 'Department of Biostatistics, St Jude Childrens Research Hospital, Memphis, TN, USA. wei.zhao@stjude.org|Department of Biostatistics, St Jude Childrens Research Hospital, Memphis, TN, USA. wei.zhao@stjude.org|Department of Biostatistics, St Jude Childrens Research Hospital, Memphis, TN, USA. wei.zhao@stjude.org|Department of Biostatistics, St Jude Childrens Research Hospital, Memphis, TN, USA. wei.zhao@stjude.org|Department of Biostatistics, St Jude Childrens Research Hospital, Memphis, TN, USA. wei.zhao@stjude.org', 'United States|United States|United States|United States|United States', 60, 'Computational Biology|Gene Expression Profiling|Models, Statistical|ROC Curve', 'Journal Article', '10.1186/1471-2105-10-225', '19619306', 'Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the early recognition problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize early recognition have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.'),
(92953, 2009, 'bmc', 'Information management for high content live cell imaging', 'Jameson, Daniel|Turner, David A|Ankers, John|Kennedy, Stephnie|Ryan, Sheila|Swainston, Neil|Griffiths, Tony|Spiller, David G|Oliver, Stephen G|White, Michael R H|Kell, Douglas B|Paton, Norman W', 'Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk|Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Computational Biology|Databases, Factual|Image Processing, Computer-Assisted|Information Management|Information Storage and Retrieval|Microscopy|Software', 'Journal Article', '10.1186/1471-2105-10-226', '19622144', 'High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments.'),
(92954, 2009, 'bmc', 'Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour rules', 'Draper, John|Enot, David P|Parker, David|Beckmann, Manfred|Snowdon, Stuart|Lin, Wanchang|Zubair, Hassan', 'Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk|Institute of Biological Environmental and Rural Sciences, Aberystwyth University, Aberystwyth SY23 3DA, UK. jhd@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Factual|Metabolomics|Software|Spectrometry, Mass, Electrospray Ionization', 'Journal Article', '10.1186/1471-2105-10-227', '19622150', 'Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite fingerprint or metabolite profile data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).'),
(92955, 2009, 'bmc', 'Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation', 'Van Auken, Kimberly|Jaffery, Joshua|Chan, Juancarlos|Müller, Hans-Michael|Sternberg, Paul W', 'Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vanauken@caltech.edu|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vanauken@caltech.edu|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vanauken@caltech.edu|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vanauken@caltech.edu|Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vanauken@caltech.edu', 'United States|United States|United States|United States|United States', 40, 'Algorithms|Computational Biology|Information Storage and Retrieval|Proteins', 'Journal Article', '10.1186/1471-2105-10-228', '19622167', 'Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts.'),
(92956, 2009, 'bmc', 'mSpecs: a software tool for the administration and editing of mass spectral libraries in the field of metabolomics', 'Thielen, Bernhard|Heinen, Stephanie|Schomburg, Dietmar', 'Institute of Biochemistry, University of Cologne, Cologne, Germany. bernhard.thielen@googlemail.com|Institute of Biochemistry, University of Cologne, Cologne, Germany. bernhard.thielen@googlemail.com|Institute of Biochemistry, University of Cologne, Cologne, Germany. bernhard.thielen@googlemail.com', 'Germany|Germany|Germany', 3, 'Databases, Protein|Mass Spectrometry|Metabolomics|Software', 'Journal Article', '10.1186/1471-2105-10-229', '19624850', 'Metabolome analysis with GC/MS has meanwhile been established as one of the omics techniques. Compound identification is done by comparison of the MS data with compound libraries. Mass spectral libraries in the field of metabolomics ought to connect the relevant mass traces of the metabolites to other relevant data, e.g. formulas, chemical structures, identification numbers to other databases etc. Since existing solutions are either commercial and therefore only available for certain instruments or not capable of storing such information, there is need to provide a software tool for the management of such data.'),
(92957, 2009, 'bmc', 'CSA: an efficient algorithm to improve circular DNA multiple alignment', 'Fernandes, Francisco|Pereira, Luísa|Freitas, Ana T', 'Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), Lisboa, Portugal. fjdf@kdbio.inesc-id.pt|Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), Lisboa, Portugal. fjdf@kdbio.inesc-id.pt|Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), Lisboa, Portugal. fjdf@kdbio.inesc-id.pt', 'Portugal|Portugal|Portugal', 0, 'Algorithms|Base Sequence|Computational Biology|DNA, Circular|Databases, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-10-230', '19627599', 'The comparison of homologous sequences from different species is an essential approach to reconstruct the evolutionary history of species and of the genes they harbour in their genomes. Several complete mitochondrial and nuclear genomes are now available, increasing the importance of using multiple sequence alignment algorithms in comparative genomics. MtDNA has long been used in phylogenetic analysis and errors in the alignments can lead to errors in the interpretation of evolutionary information. Although a large number of multiple sequence alignment algorithms have been proposed to date, they all deal with linear DNA and cannot handle directly circular DNA. Researchers interested in aligning circular DNA sequences must first rotate them to the right place using an essentially manual process, before they can use multiple sequence alignment tools.'),
(92958, 2009, 'bmc', 'Enzyme classification with peptide programs: a comparative study', 'Faria, Daniel|Ferreira, António E N|Falcão, André O', 'Department of Informatics, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal. dfaria@xldb.di.fc.ul.pt|Department of Informatics, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal. dfaria@xldb.di.fc.ul.pt|Department of Informatics, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal. dfaria@xldb.di.fc.ul.pt', 'Portugal|Portugal|Portugal', 4, 'Artificial Intelligence|Computational Biology|Databases, Protein|Enzymes|Peptides|Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-10-231', '19630945', 'Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length.'),
(92959, 2009, 'bmc', 'BSMAP: whole genome bisulfite sequence MAPping program', 'Xi, Yuanxin|Li, Wei', 'Division of Biostatistics, Dan L Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA. yxi@bcm.edu|Division of Biostatistics, Dan L Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA. yxi@bcm.edu', 'United States|United States', 465, 'Base Sequence|DNA|DNA Methylation|Genomics|Sequence Alignment|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/1471-2105-10-232', '19635165', 'Bisulfite sequencing is a powerful technique to study DNA cytosine methylation. Bisulfite treatment followed by PCR amplification specifically converts unmethylated cytosines to thymine. Coupled with next generation sequencing technology, it is able to detect the methylation status of every cytosine in the genome. However, mapping high-throughput bisulfite reads to the reference genome remains a great challenge due to the increased searching space, reduced complexity of bisulfite sequence, asymmetric cytosine to thymine alignments, and multiple CpG heterogeneous methylation.'),
(92960, 2009, 'bmc', 'A realistic assessment of methods for extracting gene/protein interactions from free text', 'Kabiljo, Renata|Clegg, Andrew B|Shepherd, Adrian J', 'School of Crystallography and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX UK. r.kabiljo@mail.cryst.bbk.ac.uk|School of Crystallography and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX UK. r.kabiljo@mail.cryst.bbk.ac.uk|School of Crystallography and Institute of Structural and Molecular Biology, Birkbeck College, University of London, Malet Street, London WC1E 7HX UK. r.kabiljo@mail.cryst.bbk.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Databases, Genetic|Databases, Protein|Genes|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-10-233', '19635172', 'The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger.'),
(92961, 2009, 'bmc', 'CLEAN: CLustering Enrichment ANalysis', 'Freudenberg, Johannes M|Joshi, Vineet K|Hu, Zhen|Medvedovic, Mario', 'Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, 3223 Eden Av, ML 56, Cincinnati OH 45267-0056, USA. freudejm@uc.edu|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, 3223 Eden Av, ML 56, Cincinnati OH 45267-0056, USA. freudejm@uc.edu|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, 3223 Eden Av, ML 56, Cincinnati OH 45267-0056, USA. freudejm@uc.edu|Laboratory for Statistical Genomics and Systems Biology, Department of Environmental Health, University of Cincinnati College of Medicine, 3223 Eden Av, ML 56, Cincinnati OH 45267-0056, USA. freudejm@uc.edu', 'United States|United States|United States|United States', 51, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Gene Expression Profiling|Genomics|Humans|Mice|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-10-234', '19640299', 'Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation.');
