INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91507, 2007, 'bmc', 'A hybrid multiscale Monte Carlo algorithm (HyMSMC) to cope with disparity in time scales and species populations in intracellular networks', 'Samant, Asawari|Ogunnaike, Babatunde A|Vlachos, Dionisios G', 'Department of Chemical Engineering, University of Delaware, Newark, Delaware 19716, USA. samant@udel.edu <samant@udel.edu>|Department of Chemical Engineering, University of Delaware, Newark, Delaware 19716, USA. samant@udel.edu <samant@udel.edu>|Department of Chemical Engineering, University of Delaware, Newark, Delaware 19716, USA. samant@udel.edu <samant@udel.edu>', 'United States|United States|United States', 16, 'Algorithms|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Biological|Models, Statistical|Monte Carlo Method|Proteome|Sensitivity and Specificity|Signal Transduction|Stochastic Processes|Time Factors', 'Journal Article', '10.1186/1471-2105-8-175', '17524148', 'The fundamental role that intrinsic stochasticity plays in cellular functions has been shown via numerous computational and experimental studies. In the face of such evidence, it is important that intracellular networks are simulated with stochastic algorithms that can capture molecular fluctuations. However, separation of time scales and disparity in species population, two common features of intracellular networks, make stochastic simulation of such networks computationally prohibitive. While recent work has addressed each of these challenges separately, a generic algorithm that can simultaneously tackle disparity in time scales and population scales in stochastic systems is currently lacking. In this paper, we propose the hybrid, multiscale Monte Carlo (HyMSMC) method that fills in this void.'),
(91508, 2007, 'bmc', 'DNA-based watermarks using the DNA-Crypt algorithm', 'Heider, Dominik|Barnekow, Angelika', 'Department of Experimental Tumorbiology, University of Muenster, Muenster, Germany. dominik.heider@uni-muenster.de <dominik.heider@uni-muenster.de>|Department of Experimental Tumorbiology, University of Muenster, Muenster, Germany. dominik.heider@uni-muenster.de <dominik.heider@uni-muenster.de>', 'Germany|Germany', 92, 'Algorithms|Computer Security|Computers, Molecular|DNA|Information Storage and Retrieval|Organisms, Genetically Modified|Product Labeling|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-176', '17535434', 'The aim of this paper is to demonstrate the application of watermarks based on DNA sequences to identify the unauthorized use of genetically modified organisms (GMOs) protected by patents. Predicted mutations in the genome can be corrected by the DNA-Crypt program leaving the encrypted information intact. Existing DNA cryptographic and steganographic algorithms use synthetic DNA sequences to store binary information however, although these sequences can be used for authentication, they may change the target DNA sequence when introduced into living organisms.'),
(91509, 2007, 'bmc', 'Requirements and ontology for a G protein-coupled receptor oligomerization knowledge base', 'Skrabanek, Lucy|Murcia, Marta|Bouvier, Michel|Devi, Lakshmi|George, Susan R|Lohse, Martin J|Milligan, Graeme|Neubig, Richard|Palczewski, Krzysztof|Parmentier, Marc|Pin, Jean-Philippe|Vriend, Gerrit|Javitch, Jonathan A|Campagne, Fabien|Filizola, Marta', 'Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>|Department of Physiology & Biophysics, Weill Medical College of Cornell University, New York, NY, USA. las2017@med.cornell.edu <las2017@med.cornell.edu>', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 39, 'Abstracting and Indexing as Topic|Database Management Systems|Databases, Protein|Dimerization|Information Storage and Retrieval|Knowledge Bases|Receptors, G-Protein-Coupled|User-Computer Interface', 'Editorial', '10.1186/1471-2105-8-177', '17537266', 'G Protein-Coupled Receptors (GPCRs) are a large and diverse family of membrane proteins whose members participate in the regulation of most cellular and physiological processes and therefore represent key pharmacological targets. Although several bioinformatics resources support research on GPCRs, most of these have been designed based on the traditional assumption that monomeric GPCRs constitute the functional receptor unit. The increase in the frequency and number of reports about GPCR dimerization/oligomerization and the implication of oligomerization in receptor function makes necessary the ability to store and access information about GPCR dimers/oligomers electronically.'),
(91510, 2007, 'bmc', 'sIR: siRNA Information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database', 'Shah, Jyoti K|Garner, Harold R|White, Michael A|Shames, David S|Minna, John D', 'Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. jyotikshah@hotmail.com <jyotikshah@hotmail.com>|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. jyotikshah@hotmail.com <jyotikshah@hotmail.com>|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. jyotikshah@hotmail.com <jyotikshah@hotmail.com>|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. jyotikshah@hotmail.com <jyotikshah@hotmail.com>|Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. jyotikshah@hotmail.com <jyotikshah@hotmail.com>', 'United States|United States|United States|United States|United States', 31, 'Base Sequence|Database Management Systems|Databases, Genetic|Gene Silencing|Gene Targeting|Information Storage and Retrieval|Internet|Molecular Sequence Data|RNA|RNA, Small Interfering|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-178', '17540034', 'RNA interference has revolutionized our ability to study the effects of altering the expression of single genes in mammalian (and other) cells through targeted knockdown of gene expression. In this report we describe a web-based computational tool, siRNA Information Resource (sIR), which consists of a new open source database that contains validation information about published siRNA sequences and also provides a user-friendly interface to design and analyze siRNA sequences against a chosen target sequence.'),
(91511, 2007, 'bmc', 'Genome Expression Pathway Analysis Tool--analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context', 'Weniger, Markus|Engelmann, Julia C|Schultz, Jörg', 'Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany. markus.weniger@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany. markus.weniger@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany. markus.weniger@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany', 19, 'Algorithms|Chromosome Mapping|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-179', '17543125', 'Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation.'),
(91512, 2007, 'bmc', 'A new advance in alternative splicing databases: from catalogue to detailed analysis of regulation of expression and function of human alternative splicing variants', 'de la Grange, Pierre|Dutertre, Martin|Correa, Margot|Auboeuf, Didier', 'INSERM U685/AVENIR, Centre G. Hayem, Hôpital Saint-Louis, Paris, France. delagrange@stlouis.inserm.fr <delagrange@stlouis.inserm.fr>|INSERM U685/AVENIR, Centre G. Hayem, Hôpital Saint-Louis, Paris, France. delagrange@stlouis.inserm.fr <delagrange@stlouis.inserm.fr>|INSERM U685/AVENIR, Centre G. Hayem, Hôpital Saint-Louis, Paris, France. delagrange@stlouis.inserm.fr <delagrange@stlouis.inserm.fr>|INSERM U685/AVENIR, Centre G. Hayem, Hôpital Saint-Louis, Paris, France. delagrange@stlouis.inserm.fr <delagrange@stlouis.inserm.fr>', 'France|France|France|France', 0, 'Animals|Chromosome Mapping|Database Management Systems|Databases, Genetic|Gene Expression Regulation|Genetic Variation|Humans|Information Storage and Retrieval|Mice|Proteome|RNA Splice Sites|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-180', '17547750', 'Most human genes produce several transcripts with different exon contents by using alternative promoters, alternative polyadenylation sites and alternative splice sites. Much effort has been devoted to describing known gene transcripts through the development of numerous databases. Nevertheless, owing to the diversity of the transcriptome, there is a need for interactive databases that provide information about the potential function of each splicing variant, as well as its expression pattern.'),
(91513, 2007, 'bmc', 'Joint mapping of genes and conditions via multidimensional unfolding analysis', 'Van Deun, Katrijn|Marchal, Kathleen|Heiser, Willem J|Engelen, Kristof|Van Mechelen, Iven', 'SymBioSys, Catholic University of Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Catholic University of Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Catholic University of Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Catholic University of Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be|SymBioSys, Catholic University of Leuven, Leuven, Belgium. katrijn.vandeun@psy.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Biomarkers, Tumor|Colonic Neoplasms|Databases, Protein|Gene Expression Profiling|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-181', '17550582', 'Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding.'),
(91514, 2007, 'bmc', 'Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features', 'Peek, Andrew S', 'Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, IA 52241, USA. apeek@idtdna.com', 'United States', 26, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|RNA Interference|RNA, Small Interfering|Regression Analysis|Sequence Alignment|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-8-182', '17553157', 'RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.'),
(91515, 2007, 'bmc', 'MPHASYS: a mouse phenotype analysis system', 'Calder, R Brent|Beems, Rudolf B|van Steeg, Harry|Mian, I Saira|Lohman, Paul H M|Vijg, Jan', 'Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org|Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org|Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org|Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org|Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org|Buck Institute for Age Research, Novato, California, USA. bcalder@buckinstitute.org', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Computer Simulation|DNA Repair|Disease Models, Animal|Growth Disorders|Humans|Mice|Models, Genetic|Phenotype|Software|Species Specificity|Xeroderma Pigmentosum Group D Protein', 'Journal Article', '10.1186/1471-2105-8-183', '17553167', 'Systematic, high-throughput studies of mouse phenotypes have been hampered by the inability to analyze individual animal data from a multitude of sources in an integrated manner. Studies generally make comparisons at the level of genotype or treatment thereby excluding associations that may be subtle or involve compound phenotypes. Additionally, the lack of integrated, standardized ontologies and methodologies for data exchange has inhibited scientific collaboration and discovery.'),
(91516, 2007, 'bmc', 'Identification of sequence motifs significantly associated with antisense activity', 'McQuisten, Kyle A|Peek, Andrew S', 'Department of Bioinformatics, Integrated DNA Technologies, Coralville, IA 52241, USA. kmcquisten@idtdna.com|Department of Bioinformatics, Integrated DNA Technologies, Coralville, IA 52241, USA. kmcquisten@idtdna.com', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Computer Simulation|DNA, Antisense|Gene Targeting|Models, Genetic|Molecular Sequence Data|Pattern Recognition, Automated|RNA, Antisense|Sequence Analysis, DNA|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-8-184', '17555590', 'Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features.'),
(91517, 2007, 'bmc', '160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)', 'Li, Isaac T S|Shum, Warren|Truong, Kevin', 'Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. isaac.li@utoronto.ca <isaac.li@utoronto.ca>|Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. isaac.li@utoronto.ca <isaac.li@utoronto.ca>|Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. isaac.li@utoronto.ca <isaac.li@utoronto.ca>', 'Canada|Canada|Canada', 88, 'Algorithms|Base Sequence|Equipment Design|Equipment Failure Analysis|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Signal Processing, Computer-Assisted', 'Journal Article', '10.1186/1471-2105-8-185', '17555593', 'To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive.'),
(91518, 2007, 'bmc', 'An efficient pseudomedian filter for tiling microrrays', 'Royce, Thomas E|Carriero, Nicholas J|Gerstein, Mark B', 'Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. thomas.royce@yale.edu <thomas.royce@yale.edu>|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. thomas.royce@yale.edu <thomas.royce@yale.edu>|Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. thomas.royce@yale.edu <thomas.royce@yale.edu>', 'United States|United States|United States', 15, 'Algorithms|Chromosome Mapping|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Signal Processing, Computer-Assisted', 'Evaluation Studies', '10.1186/1471-2105-8-186', '17555595', 'Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution.'),
(91519, 2007, 'bmc', 'ZPS: visualization of recent adaptive evolution of proteins', 'Chattopadhyay, Sujay|Dykhuizen, Daniel E|Sokurenko, Evgeni V', 'Department of Microbiology, University of Washington, Seattle, WA 98195, USA. sujayc@u.washington.edu|Department of Microbiology, University of Washington, Seattle, WA 98195, USA. sujayc@u.washington.edu|Department of Microbiology, University of Washington, Seattle, WA 98195, USA. sujayc@u.washington.edu', 'United States|United States|United States', 16, 'Algorithms|Computer Graphics|Conserved Sequence|DNA Mutational Analysis|Evolution, Molecular|Polymorphism, Single Nucleotide|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-187', '17555597', 'Detection of adaptive amino acid changes in proteins under recent short-term selection is of great interest for researchers studying microevolutionary processes in microbial pathogens or any other biological species. However, independent occurrence of such point mutations within genetically diverse haplotypes makes it difficult to detect the selection footprint by using traditional molecular evolutionary analyses. The recently developed Zonal Phylogeny (ZP) has been shown to be a useful analytic tool for identifying the footprints of short-term positive selection. ZP separates protein-encoding genes into evolutionarily long-term (with silent diversity) and short-term (without silent diversity) categories, or zones, followed by statistical analysis to detect signs of positive selection in the short-term zone. However, successful broad application of ZP for analysis of large haplotype datasets requires automation of the relatively labor-intensive computational process.'),
(91520, 2007, 'bmc', 'Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data', 'Wu, Wei-Sheng|Li, Wen-Hsiung|Chen, Bor-Sen', 'Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan. wessonwu@gmail.com|Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan. wessonwu@gmail.com|Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan. wessonwu@gmail.com', '-|-|-', 0, 'Algorithms|Cell Cycle|Cell Cycle Proteins|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Regulatory Sequences, Nucleic Acid|Transcription Factors|Transcription, Genetic', 'Evaluation Studies', '10.1186/1471-2105-8-188', '17559637', 'ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop methods to identify regulatory targets of TFs from ChIP-chip data.'),
(91521, 2007, 'bmc', 'MATLIGN: a motif clustering, comparison and matching tool', 'Kankainen, Matti|Löytynoja, Ari', 'Institute of Biotechnology, University of Helsinki, Helsinki, Finland. matti.kankainen@helsinki.fi|Institute of Biotechnology, University of Helsinki, Helsinki, Finland. matti.kankainen@helsinki.fi', 'Finland|Finland', 20, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Cluster Analysis|Consensus Sequence|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-8-189', '17559640', 'Sequence motifs representing transcription factor binding sites (TFBS) are commonly encoded as position frequency matrices (PFM) or degenerate consensus sequences (CS). These formats are used to represent the characterised TFBS profiles stored in transcription factor databases, as well as to represent the potential motifs predicted using computational methods. To fill the gap between the known and predicted motifs, methods are needed for the post-processing of prediction results, i.e. for matching, comparison and clustering of pre-selected motifs. The computational identification of over-represented motifs in sets of DNA sequences is, in particular, a task where post-processing can dramatically simplify the analysis. Efficient post-processing, for example, reduces the redundancy of the motifs predicted and enables them to be annotated.'),
(91522, 2007, 'bmc', 'Can Clustal-style progressive pairwise alignment of multiple sequences be used in RNA secondary structure prediction?', 'Bellamy-Royds, Amelia B|Turcotte, Marcel', 'School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada. turcotte@site.uottawa.ca <turcotte@site.uottawa.ca>|School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada. turcotte@site.uottawa.ca <turcotte@site.uottawa.ca>', 'Canada|Canada', 5, 'Algorithms|Base Pair Mismatch|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-8-190', '17559658', 'In ribonucleic acid (RNA) molecules whose function depends on their final, folded three-dimensional shape (such as those in ribosomes or spliceosome complexes), the secondary structure, defined by the set of internal basepair interactions, is more consistently conserved than the primary structure, defined by the sequence of nucleotides.'),
(91523, 2007, 'bmc', 'Bio::NEXUS: a Perl API for the NEXUS format for comparative biological data', 'Hladish, Thomas|Gopalan, Vivek|Liang, Chengzhi|Qiu, Weigang|Yang, Peter|Stoltzfus, Arlin', 'Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>|Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>|Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>|Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>|Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>|Center for Advanced Research in Biotechnology, Rockville, MD 20850, USA. tjhladish@mail.utexas.edu <tjhladish@mail.utexas.edu>', 'United States|United States|United States|United States|United States|United States', 9, 'Chromosome Mapping|Computational Biology|Databases, Genetic|Evolution, Molecular|Information Storage and Retrieval|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-191', '17559666', 'Evolutionary analysis provides a formal framework for comparative analysis of genomic and other data. In evolutionary analysis, observed data are treated as the terminal states of characters that have evolved (via transitions between states) along the branches of a tree. The NEXUS standard of Maddison, et al. (1997, Syst. Biol. 46: 590-621) provides a portable, expressive and flexible text format for representing character-state data and trees. However, due to its complexity, NEXUS is not well supported by software and is not easily accessible to bioinformatics users and developers.'),
(91524, 2007, 'bmc', 'Additive risk survival model with microarray data', 'Ma, Shuangge|Huang, Jian', 'Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu|Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu', 'United States|United States', 28, 'Biomarkers, Tumor|Gene Expression Profiling|Humans|Incidence|Lymphoma, Mantle-Cell|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Prognosis|Proportional Hazards Models|Risk Assessment|Risk Factors|Survival Analysis|Survival Rate', 'Evaluation Studies', '10.1186/1471-2105-8-192', '17559667', 'Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements.'),
(91525, 2007, 'bmc', 'Improved benchmarks for computational motif discovery', 'Sandve, Geir Kjetil|Abul, Osman|Walseng, Vegard|Drabløs, Finn', 'Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. sandve@ntnu.no|Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. sandve@ntnu.no|Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. sandve@ntnu.no|Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. sandve@ntnu.no', 'Norway|Norway|Norway|Norway', 50, 'Algorithms|Amino Acid Motifs|Base Sequence|Benchmarking|Binding Sites|Chromosome Mapping|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Software Validation|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-193', '17559676', 'An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research.'),
(91526, 2007, 'bmc', 'Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data', 'Yu, Hui|Wang, Feng|Tu, Kang|Xie, Lu|Li, Yuan-Yuan|Li, Yi-Xue', 'Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>|Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>|Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>|Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>|Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>|Shanghai Center for Bioinformation Technology, Shanghai, PR China. yuhui@scbit.org <yuhui@scbit.org>', 'China|China|China|China|China|China', 33, 'Algorithms|Base Sequence|DNA Probes|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-194', '17559689', 'The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level.'),
(91527, 2007, 'bmc', 'Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips', 'Harrison, Andrew P|Johnston, Caroline E|Orengo, Christine A', 'Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, UK. harry@essex.ac.uk|Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, UK. harry@essex.ac.uk|Department of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, UK. harry@essex.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 16, 'Algorithms|Artifacts|Calibration|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity|United Kingdom', 'Journal Article', '10.1186/1471-2105-8-195', '17562008', 'Affymetrix GeneChips are a popular platform for performing whole-genome experiments on the transcriptome. There are a range of different calibration steps, and users are presented with choices of different background subtractions, normalisations and expression measures. We wished to establish which of the calibration steps resulted in the biggest uncertainty in the sets of genes reported to be differentially expressed.'),
(91528, 2007, 'bmc', 'Reconstruction of cell population dynamics using CFSE', 'Yates, Andrew|Chan, Cliburn|Strid, Jessica|Moon, Simon|Callard, Robin|George, Andrew J T|Stark, Jaroslav', 'Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.|Department of Biology, Emory University, Atlanta, GA 30322, USA. ayates2@emory.edu.', 'United States|United States|United States|United States|United States|United States|United States', 35, 'Algorithms|Apoptosis|Cell Proliferation|Cells, Cultured|Computer Simulation|Fluoresceins|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Models, Biological|Succinimides', 'Journal Article', '10.1186/1471-2105-8-196', '17565685', 'Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division in vitro and in vivo and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.'),
(91529, 2007, 'bmc', 'MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data', 'Hartler, Jürgen|Thallinger, Gerhard G|Stocker, Gernot|Sturn, Alexander|Burkard, Thomas R|Körner, Erik|Rader, Robert|Schmidt, Andreas|Mechtler, Karl|Trajanoski, Zlatko', 'Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>|Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, Graz, Austria. juergen.hartler@tugraz.at <juergen.hartler@tugraz.at>', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 37, 'Amino Acid Sequence|Chromatography, Liquid|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Proteome|Sequence Analysis, Protein|Software|Software Design|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-8-197', '17567892', 'The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.'),
(91530, 2007, 'bmc', 'A novel approach to sequence validating protein expression clones with automated decision making', 'Taycher, Elena|Rolfs, Andreas|Hu, Yanhui|Zuo, Dongmei|Mohr, Stephanie E|Williamson, Janice|Labaer, Joshua', 'Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>|Harvard Institute of Proteomics, Harvard Medical School, Cambridge, MA 02141, USA. elena_taycher@hms.harvard.edu <elena_taycher@hms.harvard.edu>', 'United States|United States|United States|United States|United States|United States|United States', 7, 'Artificial Intelligence|Base Sequence|Cloning, Molecular|Decision Support Techniques|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-8-198', '17567908', 'Whereas the molecular assembly of protein expression clones is readily automated and routinely accomplished in high throughput, sequence verification of these clones is still largely performed manually, an arduous and time consuming process. The ultimate goal of validation is to determine if a given plasmid clone matches its reference sequence sufficiently to be acceptable for use in protein expression experiments. Given the accelerating increase in availability of tens of thousands of unverified clones, there is a strong demand for rapid, efficient and accurate software that automates clone validation.'),
(91531, 2007, 'bmc', 'A domain-based approach to predict protein-protein interactions', 'Singhal, Mudita|Resat, Haluk', 'Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA. mudita.singhal@pnl.gov <mudita.singhal@pnl.gov>|Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352, USA. mudita.singhal@pnl.gov <mudita.singhal@pnl.gov>', 'United States|United States', 51, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-199', '17567909', 'Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.'),
(91532, 2007, 'bmc', 'Evaluation of sample size effect on the identification of haplotype blocks', 'Osabe, Dai|Tanahashi, Toshihito|Nomura, Kyoko|Shinohara, Shuichi|Nakamura, Naoto|Yoshikawa, Toshikazu|Shiota, Hiroshi|Keshavarz, Parvaneh|Yamaguchi, Yuka|Kunika, Kiyoshi|Moritani, Maki|Inoue, Hiroshi|Itakura, Mitsuo', 'Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>|Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 3, 'Chromosome Mapping|Chromosomes, Human, Pair 20|Computer Simulation|Genetic Variation|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-200', '17567919', 'Genome-wide maps of linkage disequilibrium (LD) and haplotypes have been created for different populations. Substantial sharing of the boundaries and haplotypes among populations was observed, but haplotype variations have also been reported across populations. Conflicting observations on the extent and distribution of haplotypes require careful examination. The mechanisms that shape haplotypes have not been fully explored, although the effect of sample size has been implicated. We present a close examination of the effect of sample size on haplotype blocks using an original computational simulation.'),
(91533, 2007, 'bmc', 'The 2006 automated function prediction meeting', 'Rodrigues, Ana P C|Grant, Barry J|Godzik, Adam|Friedberg, Iddo', 'Burnham Institute for Medical Research, 10901 N, Torrey Pines Rd, La Jolla, CA 92037 USA. ana@burnham.org|Burnham Institute for Medical Research, 10901 N, Torrey Pines Rd, La Jolla, CA 92037 USA. ana@burnham.org|Burnham Institute for Medical Research, 10901 N, Torrey Pines Rd, La Jolla, CA 92037 USA. ana@burnham.org|Burnham Institute for Medical Research, 10901 N, Torrey Pines Rd, La Jolla, CA 92037 USA. ana@burnham.org', 'United States|United States|United States|United States', 6, 'Computational Biology|Proteins', 'Congress', '10.1186/1471-2105-8-S4-S1', '17570143', '-'),
(91534, 2007, 'bmc', 'Clustering protein environments for function prediction: finding PROSITE motifs in 3D', 'Yoon, Sungroh|Ebert, Jessica C|Chung, Eui-Young|De Micheli, Giovanni|Altman, Russ B', 'Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com|Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com|Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com|Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com|Computer Systems Laboratory, Stanford University, Stanford, CA 94305, USA. sungroh.yoon@intel.com', 'United States|United States|United States|United States|United States', 24, 'Algorithms|Amino Acid Motifs|Binding Sites|Computer Simulation|Imaging, Three-Dimensional|Ligands|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-S4-S10', '17570144', 'Structural genomics initiatives are producing increasing numbers of three-dimensional (3D) structures for which there is little functional information. Structure-based annotation of molecular function is therefore becoming critical. We previously presented FEATURE, a method for describing microenvironments around functional sites in proteins. However, FEATURE uses supervised machine learning and so is limited to building models for sites of known importance and location. We hypothesized that there are a large number of sites in proteins that are associated with function that have not yet been recognized. Toward that end, we have developed a method for clustering protein microenvironments in order to evaluate the potential for discovering novel sites that have not been previously identified.'),
(91535, 2007, 'bmc', 'SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition', 'Melvin, Iain|Ie, Eugene|Kuang, Rui|Weston, Jason|Stafford, William Noble|Leslie, Christina', 'NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. imelvin@gmail.com', 'United States|United States|United States|United States|United States|United States', 35, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Discriminant Analysis|Internet|Molecular Sequence Data|Pattern Recognition, Automated|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-8-S4-S2', '17570145', 'Predicting a proteins structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91536, 2007, 'bmc', 'CORRIE: enzyme sequence annotation with confidence estimates', 'Audit, Benjamin|Levy, Emmanuel D|Gilks, Wally R|Goldovsky, Leon|Ouzounis, Christos A', 'Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr|Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr|Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr|Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr|Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr', 'France|France|France|France|France', 9, 'Algorithms|Amino Acid Sequence|Confidence Intervals|Data Interpretation, Statistical|Enzymes|Molecular Sequence Data|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-8-S4-S3', '17570146', 'Using a previously developed automated method for enzyme annotation, we report the re-annotation of the ENZYME database and the analysis of local error rates per class. In control experiments, we demonstrate that the method is able to correctly re-annotate 91% of all Enzyme Classification (EC) classes with high coverage (755 out of 827). Only 44 enzyme classes are found to contain false positives, while the remaining 28 enzyme classes are not represented. We also show cases where the re-annotation procedure results in partial overlaps for those few enzyme classes where a certain inconsistency might appear between homologous proteins, mostly due to function specificity. Our results allow the interactive exploration of the EC hierarchy for known enzyme families as well as putative enzyme sequences that may need to be classified within the EC hierarchy. These aspects of our framework have been incorporated into a web-server, called CORRIE, which stands for Correspondence Indicator Estimation and allows the interactive prediction of a functional class for putative enzymes from sequence alone, supported by probabilistic measures in the context of the pre-calculated Correspondence Indicators of known enzymes with the functional classes of the EC hierarchy. The CORRIE server is available at: http://www.genomes.org/services/corrie/.'),
(91537, 2007, 'bmc', 'The AnnoLite and AnnoLyze programs for comparative annotation of protein structures', 'Marti-Renom, Marc A|Rossi, Andrea|Al-Shahrour, Fátima|Davis, Fred P|Pieper, Ursula|Dopazo, Joaquín|Sali, Andrej', 'Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es|Structural Genomics Unit, Bioinformatics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain. mmarti@cipf.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain', 33, 'Algorithms|Amino Acid Sequence|Confidence Intervals|Data Interpretation, Statistical|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-8-S4-S4', '17570147', 'Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database.'),
(91538, 2007, 'bmc', 'Using structural motif descriptors for sequence-based binding site prediction', 'Henschel, Andreas|Winter, Christof|Kim, Wan Kyu|Schroeder, Michael', 'Biotechnological Center, TU Dresden, Tatzberg 47-51, Dresden, Germany. ah@biotec.tu-dresden.de|Biotechnological Center, TU Dresden, Tatzberg 47-51, Dresden, Germany. ah@biotec.tu-dresden.de|Biotechnological Center, TU Dresden, Tatzberg 47-51, Dresden, Germany. ah@biotec.tu-dresden.de|Biotechnological Center, TU Dresden, Tatzberg 47-51, Dresden, Germany. ah@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany', 20, 'Amino Acid Motifs|Amino Acid Sequence|Binding Sites|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-S4-S5', '17570148', 'Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites.'),
(91539, 2007, 'bmc', 'Gene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages', 'Li, Jason|Halgamuge, Saman K|Kells, Christopher I|Tang, Sen-Lin', 'Dynamic Systems & Control Group, DoMME, University of Melbourne, Melbourne, Australia. lij@mame.mu.oz.au <lij@mame.mu.oz.au>|Dynamic Systems & Control Group, DoMME, University of Melbourne, Melbourne, Australia. lij@mame.mu.oz.au <lij@mame.mu.oz.au>|Dynamic Systems & Control Group, DoMME, University of Melbourne, Melbourne, Australia. lij@mame.mu.oz.au <lij@mame.mu.oz.au>|Dynamic Systems & Control Group, DoMME, University of Melbourne, Melbourne, Australia. lij@mame.mu.oz.au <lij@mame.mu.oz.au>', 'Australia|Australia|Australia|Australia', 11, 'Algorithms|Artificial Intelligence|Bacteriophages|Base Sequence|Chromosome Mapping|Cluster Analysis|Discriminant Analysis|Genome, Viral|Molecular Sequence Data|Multigene Family|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-8-S4-S6', '17570149', 'Existing methods for whole-genome comparisons require prior knowledge of related species and provide little automation in the function prediction process. Bacteriophage genomes are an example that cannot be easily analyzed by these methods. This work addresses these shortcomings and aims to provide an automated prediction system of gene function.'),
(91540, 2007, 'bmc', 'An improved method for identifying functionally linked proteins using phylogenetic profiles', 'Cokus, Shawn|Mizutani, Sayaka|Pellegrini, Matteo', 'Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA 90095-1606, USA. cokus@mcdb.ucla.edu|Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA 90095-1606, USA. cokus@mcdb.ucla.edu|Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA 90095-1606, USA. cokus@mcdb.ucla.edu', 'United States|United States|United States', 40, 'Algorithms|Chromosome Mapping|Evolution, Molecular|Gene Expression Profiling|Multigene Family|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-8-S4-S7', '17570150', 'Phylogenetic profiles record the occurrence of homologs of genes across fully sequenced organisms. Proteins with similar profiles are typically components of protein complexes or metabolic pathways. Various existing methods measure similarity between two profiles and, hence, the likelihood that the two proteins co-evolve. Some methods ignore phylogenetic relationships between organisms while others account for such with metrics that explicitly model the likelihood of two proteins co-evolving on a tree. The latter methods more sensitively detect co-evolving proteins, but at a significant computational cost. Here we propose a novel heuristic to improve phylogenetic profile analysis that accounts for phylogenetic relationships between genomes in a computationally efficient fashion. We first order the genomes within profiles and then enumerate runs of consecutive matches and accurately compute the probability of observing these. We hypothesize that profiles with many runs are more likely to involve functionally related proteins than profiles in which all the matches are concentrated in one interval of the tree.'),
(91541, 2007, 'bmc', 'Using indirect protein interactions for the prediction of Gene Ontology functions', 'Chua, Hon Nian|Sung, Wing-Kin|Wong, Limsoon', 'School of Computing, National University of Singapore, Singapore, Singapore. g0306417@nus.edu.sg|School of Computing, National University of Singapore, Singapore, Singapore. g0306417@nus.edu.sg|School of Computing, National University of Singapore, Singapore, Singapore. g0306417@nus.edu.sg', 'Singapore|Singapore|Singapore', 47, 'Algorithms|Amino Acid Sequence|Chromosome Mapping|Databases, Protein|Molecular Sequence Data|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-8-S4-S8', '17570151', 'Protein-protein interaction has been used to complement traditional sequence homology to elucidate protein function. Most existing approaches only make use of direct interactions to infer function, and some have studied the application of indirect interactions for functional inference but are unable to improve prediction performance. We have previously proposed an approach, FS-Weighted Averaging, which uses topological weighting and level-2 indirect interactions (protein pairs connected via two interactions) for predicting protein function from protein interactions and have found that it yields predictions with superior precision on yeast proteins over existing approaches. Here we study the use of this technique to predict functional annotations from the Gene Ontology for seven genomes: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Rattus norvegicus, Mus musculus, and Homo sapiens.'),
(91542, 2007, 'bmc', 'A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites', 'Xie, Lei|Bourne, Philip E', 'San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu|San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA. lxie@sdsc.edu', 'United States|United States', 106, 'Algorithms|Binding Sites|Computer Simulation|Ligands|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-S4-S9', '17570152', 'An accurate description of protein shape derived from protein structure is necessary to establish an understanding of protein-ligand interactions, which in turn will lead to improved methods for protein-ligand docking and binding site analysis. Most current shape descriptors characterize only the local properties of protein structure using an all-atom representation and are slow to compute. We need new shape descriptors that have the ability to capture both local and global structural information, are robust for application to models and low quality structures and are computationally efficient to permit high throughput analysis of protein structures.'),
(91543, 2007, 'bmc', 'Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information', 'Pollastri, Gianluca|Martin, Alberto J M|Mooney, Catherine|Vullo, Alessandro', 'Complex and Adaptive Systems Laboratory, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. gianluca.pollastri@ucd.ie|Complex and Adaptive Systems Laboratory, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. gianluca.pollastri@ucd.ie|Complex and Adaptive Systems Laboratory, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. gianluca.pollastri@ucd.ie|Complex and Adaptive Systems Laboratory, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. gianluca.pollastri@ucd.ie', 'Ireland|Ireland|Ireland|Ireland', 91, 'Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Consensus Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Solvents', 'Evaluation Studies', '10.1186/1471-2105-8-201', '17570843', 'Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio.'),
(91544, 2007, 'bmc', 'A Three Stage Integrative Pathway Search (TIPS) framework to identify toxicity relevant genes and pathways', 'Li, Zheng|Srivastava, Shireesh|Mittal, Sheenu|Yang, Xuerui|Sheng, Lufang|Chan, Christina', 'Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>|Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com <lizheng1@gmail.com>', 'United States|United States|United States|United States|United States|United States', 19, 'Apoptosis Regulatory Proteins|Cell Line|Cell Survival|Computer Simulation|Cytotoxins|Hepatocytes|Humans|Models, Biological|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1186/1471-2105-8-202', '17570844', 'The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (TIPS(c)) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.'),
(91545, 2007, 'bmc', 'Using expression arrays for copy number detection: an example from E coli', 'Skvortsov, Dmitriy|Abdueva, Diana|Stitzer, Michael E|Finkel, Steven E|Tavaré, Simon', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. dskvortsov@mednet.ucla.edu <dskvortsov@mednet.ucla.edu>|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. dskvortsov@mednet.ucla.edu <dskvortsov@mednet.ucla.edu>|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. dskvortsov@mednet.ucla.edu <dskvortsov@mednet.ucla.edu>|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. dskvortsov@mednet.ucla.edu <dskvortsov@mednet.ucla.edu>|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. dskvortsov@mednet.ucla.edu <dskvortsov@mednet.ucla.edu>', 'United States|United States|United States|United States|United States', 0, 'Base Sequence|Chromosome Mapping|DNA, Bacterial|Escherichia coli|Gene Dosage|Gene Expression Profiling|Genome, Bacterial|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-203', '17570850', 'The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.'),
(91546, 2007, 'bmc', 'Genomorama: genome visualization and analysis', 'Gans, Jason D|Wolinsky, Murray', 'Biosciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA. jgans@lanl.gov|Biosciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA. jgans@lanl.gov', 'United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Gene Targeting|Genome|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-204', '17570856', 'The ability to visualize genomic features and design experimental assays that can target specific regions of a genome is essential for modern biology. To assist in these tasks, we present Genomorama, a software program for interactively displaying multiple genomes and identifying potential DNA hybridization sites for assay design.'),
(91547, 2007, 'bmc', 'Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks', 'Ferrazzi, Fulvia|Sebastiani, Paola|Ramoni, Marco F|Bellazzi, Riccardo', 'Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. fulvia.ferrazzi@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. fulvia.ferrazzi@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. fulvia.ferrazzi@unipv.it|Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy. fulvia.ferrazzi@unipv.it', 'Italy|Italy|Italy|Italy', 0, 'Bayes Theorem|Cell Cycle|Computer Simulation|Models, Biological|Nonlinear Dynamics|Normal Distribution|Saccharomycetales', 'Journal Article', '10.1186/1471-2105-8-S5-S2', '17570861', 'Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models.'),
(91548, 2007, 'bmc', 'Learning biophysically-motivated parameters for alpha helix prediction', 'Gassend, Blaise|ODonnell, Charles W|Thies, William|Lee, Andrew|van Dijk, Marten|Devadas, Srinivas', 'Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu|Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. gassend@mit.edu', 'United States|United States|United States|United States|United States|United States', 4, 'Artificial Intelligence|Biophysical Phenomena|Biophysics|Models, Biological|Protein Structure, Secondary', 'Journal Article', '10.1186/1471-2105-8-S5-S3', '17570862', 'Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological pseudo-energies. Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.'),
(91549, 2007, 'bmc', 'A stochastic differential equation model for transcriptional regulatory networks', 'Climescu-Haulica, Adriana|Quirk, Michelle D', 'Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, Grenoble, France. adriana.climescu@cea.fr|Laboratoire Biologie, Informatique, Mathématiques, Institute de Recherche en Technologies et Sciences pour le Vivant CEA, Grenoble, France. adriana.climescu@cea.fr', 'France|France', 0, 'Gene Expression|Gene Regulatory Networks|Models, Biological|RNA, Messenger|Saccharomyces cerevisiae|Stochastic Processes|Time Factors', 'Journal Article', '10.1186/1471-2105-8-S5-S4', '17570863', 'This work explores the quantitative characteristics of the local transcriptional regulatory network based on the availability of time dependent gene expression data sets. The dynamics of the gene expression level are fitted via a stochastic differential equation model, yielding a set of specific regulators and their contribution.'),
(91550, 2007, 'bmc', 'On consensus biomarker selection', 'Dutkowski, Janusz|Gambin, Anna', 'Institute of Informatics, Warsaw University, Banacha 2 02-097 Warsaw, Poland. januszd@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2 02-097 Warsaw, Poland. januszd@mimuw.edu.pl', 'Poland|Poland', 27, 'Biomarkers|Consensus|Female|Humans|Male|Markov Chains|Models, Biological|Ovarian Neoplasms|Principal Component Analysis|Prostatic Neoplasms|Proteomics', 'Journal Article', '10.1186/1471-2105-8-S5-S5', '17570864', 'Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biomarkers in human body fluids like serum or plasma, based on which new diagnostic tests for different diseases could be constructed. Various biomarker selection procedures have been exploited in recent studies. It has been noted that they often lead to different biomarker lists and as a consequence, the patient classification may also vary.'),
(91551, 2007, 'bmc', 'Refining intra-protein contact prediction by graph analysis', 'Frenkel-Morgenstern, Milana|Magid, Rachel|Eyal, Eran|Pietrokovski, Shmuel', 'Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. milana.frenkel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. milana.frenkel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. milana.frenkel@weizmann.ac.il|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. milana.frenkel@weizmann.ac.il', 'Israel|Israel|Israel|Israel', 0, 'Models, Biological|Protein Conformation', 'Journal Article', '10.1186/1471-2105-8-S5-S6', '17570865', 'Accurate prediction of intra-protein residue contacts from sequence information will allow the prediction of protein structures. Basic predictions of such specific contacts can be further refined by jointly analyzing predicted contacts, and by adding information on the relative positions of contacts in the protein primary sequence.'),
(91552, 2007, 'bmc', 'Simultaneous identification of long similar substrings in large sets of sequences', 'Kleffe, Jürgen|Möller, Friedrich|Wittig, Burghardt', 'Institut für Molekularbiologie und Bioinformatik, Charite-Universitätsmedizin Berlin, Berlin, Germany. juergen.kleffe@charite.de|Institut für Molekularbiologie und Bioinformatik, Charite-Universitätsmedizin Berlin, Berlin, Germany. juergen.kleffe@charite.de|Institut für Molekularbiologie und Bioinformatik, Charite-Universitätsmedizin Berlin, Berlin, Germany. juergen.kleffe@charite.de', 'Germany|Germany|Germany', 4, 'Algorithms|Chromosomes, Artificial, Bacterial|Genomics|Medicago truncatula|Sample Size|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-8-S5-S7', '17570866', 'Sequence comparison faces new challenges today, with many complete genomes and large libraries of transcripts known. Gene annotation pipelines match these sequences in order to identify genes and their alternative splice forms. However, the software currently available cannot simultaneously compare sets of sequences as large as necessary especially if errors must be considered.'),
(91553, 2007, 'bmc', 'Identification of hot regions in protein-protein interactions by sequential pattern mining', 'Hsu, Chen-Ming|Chen, Chien-Yu|Liu, Baw-Jhiune|Huang, Chih-Chang|Laio, Min-Hung|Lin, Chien-Chieh|Wu, Tzung-Lin', 'Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw|Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan, ROC. cmhsu@saturn.yzu.edu.tw', '-|-|-|-|-|-|-', 0, 'Bacterial Proteins|Carboxypeptidases A|GTP-Binding Protein alpha Subunits, Gi-Go|Heat-Shock Proteins|Models, Biological|Protein Interaction Mapping|rac GTP-Binding Proteins|ras GTPase-Activating Proteins', 'Journal Article', '10.1186/1471-2105-8-S5-S8', '17570867', 'Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.'),
(91554, 2007, 'bmc', 'Automatic extraction of reliable regions from multiple sequence alignments', 'Lassmann, Timo|Sonnhammer, Erik Ll', 'Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden. timolassmann@gmail.com|Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden. timolassmann@gmail.com', 'Sweden|Sweden', 9, 'Algorithms|Consensus Sequence|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-8-S5-S9', '17570868', 'High quality multiple alignments are crucial in the transfer of annotation from one genome to another. Multiple alignment methods strive to achieve ever increasing levels of average accuracy on benchmark sets while the accuracy of individual alignments is often overlooked.'),
(91555, 2007, 'bmc', 'ISHAPE: new rapid and accurate software for haplotyping', 'Delaneau, Olivier|Coulonges, Cédric|Boelle, Pierre-Yves|Nelson, George|Spadoni, Jean-Louis|Zagury, Jean-François', 'Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>|Chaire de Bioinformatique, Conservatoire National des Arts et Métiers, Paris, France. olivier.delaneau@gmail.com <olivier.delaneau@gmail.com>', 'France|France|France|France|France|France', 14, 'Algorithms|Base Sequence|DNA Mutational Analysis|Haplotypes|Molecular Sequence Data|Polymorphism, Single Nucleotide|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-8-205', '17573965', 'We have developed a new haplotyping program based on the combination of an iterative multiallelic EM algorithm (IEM), bootstrap resampling and a pseudo Gibbs sampler. The use of the IEM-bootstrap procedure considerably reduces the space of possible haplotype configurations to be explored, greatly reducing computation time, while the adaptation of the Gibbs sampler with a recombination model on this restricted space maintains high accuracy. On large SNP datasets (>30 SNPs), we used a segmented approach based on a specific partition-ligation strategy. We compared this software, Ishape (Iterative Segmented HAPlotyping by Em), with reference programs such as Phase, Fastphase, and PL-EM. Analogously with Phase, there are 2 versions of Ishape: Ishape1 which uses a simple coalescence model for the pseudo Gibbs sampler step, and Ishape2 which uses a recombination model instead.'),
(91556, 2007, 'bmc', 'Selecting dissimilar genes for multi-class classification, an application in cancer subtyping', 'Cai, Zhipeng|Goebel, Randy|Salavatipour, Mohammad R|Lin, Guohui', 'Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca <zhipeng@cs.ualberta.ca>|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca <zhipeng@cs.ualberta.ca>|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca <zhipeng@cs.ualberta.ca>|Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada. zhipeng@cs.ualberta.ca <zhipeng@cs.ualberta.ca>', 'Canada|Canada|Canada|Canada', 37, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Genetic Predisposition to Disease|Humans|Multigene Family|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-206', '17573973', 'Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging.'),
(91557, 2007, 'bmc', 'Orthogonal projections to latent structures as a strategy for microarray data normalization', 'Bylesjö, Max|Eriksson, Daniel|Sjödin, Andreas|Jansson, Stefan|Moritz, Thomas|Trygg, Johan', 'Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University, Umeå, Sweden. max.bylesjo@chem.umu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 52, 'Algorithms|Artifacts|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Regression Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-207', '17577396', 'During generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.'),
(91558, 2007, 'bmc', 'Seahawk: moving beyond HTML in Web-based bioinformatics analysis', 'Gordon, Paul M K|Sensen, Christoph W', 'University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, Calgary, AB, Canada. gordonp@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, Calgary, AB, Canada. gordonp@ucalgary.ca', 'Canada|Canada', 0, 'Algorithms|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/1471-2105-8-208', '17577405', 'Traditional HTML interfaces for input to and output from Bioinformatics analysis on the Web are highly variable in style, content and data formats. Combining multiple analyses can therefore be an onerous task for biologists. Semantic Web Services allow automated discovery of conceptual links between remote data analysis servers. A shared data ontology and service discovery/execution framework is particularly attractive in Bioinformatics, where data and services are often both disparate and distributed. Instead of biologists copying, pasting and reformatting data between various Web sites, Semantic Web Service protocols such as MOBY-S hold out the promise of seamlessly integrating multi-step analysis.'),
(91559, 2007, 'bmc', 'CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats', 'Bland, Charles|Ramsey, Teresa L|Sabree, Fareedah|Lowe, Micheal|Brown, Kyndall|Kyrpides, Nikos C|Hugenholtz, Philip', 'Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu|Department of Computer Science, Jackson State University, Jackson, MS 39217, USA. charles.bland@jsums.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Base Pairing|Chromosome Mapping|Interspersed Repetitive Sequences|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-8-209', '17577412', 'Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism, that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes.'),
(91560, 2007, 'bmc', 'A multiresolution approach to automated classification of protein subcellular location images', 'Chebira, Amina|Barbotin, Yann|Jackson, Charles|Merryman, Thomas|Srinivasa, Gowri|Murphy, Robert F|Kovacevixc4x87, Jelena', 'Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu|Center for Bioimage Informatics and Dept. of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. amina@cmu.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Image Enhancement|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Proteins|Reproducibility of Results|Sensitivity and Specificity|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-8-210', '17578580', 'Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.'),
(91561, 2007, 'bmc', 'Composition Profiler: a tool for discovery and visualization of amino acid composition differences', 'Vacic, Vladimir|Uversky, Vladimir N|Dunker, A Keith|Lonardi, Stefano', 'Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. vladimir@cs.ucr.edu <vladimir@cs.ucr.edu>|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. vladimir@cs.ucr.edu <vladimir@cs.ucr.edu>|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. vladimir@cs.ucr.edu <vladimir@cs.ucr.edu>|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. vladimir@cs.ucr.edu <vladimir@cs.ucr.edu>', 'United States|United States|United States|United States', 218, 'Algorithms|Amino Acid Sequence|Computer Graphics|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-8-211', '17578581', 'Composition Profiler is a web-based tool for semi-automatic discovery of enrichment or depletion of amino acids, either individually or grouped by their physico-chemical or structural properties.'),
(91562, 2007, 'bmc', 'Optimization based automated curation of metabolic reconstructions', 'Satish Kumar, Vinay|Dasika, Madhukar S|Maranas, Costas D', 'Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. vsk111@psu.edu <vsk111@psu.edu>|Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. vsk111@psu.edu <vsk111@psu.edu>|Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA. vsk111@psu.edu <vsk111@psu.edu>', 'United States|United States|United States', 200, 'Algorithms|Bacteria|Bacterial Proteins|Computer Simulation|Gene Expression|Models, Biological|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-212', '17584497', 'Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions (e.g., Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them.'),
(91563, 2007, 'bmc', 'Three-Dimensional Phylogeny Explorer: distinguishing paralogs, lateral transfer, and violation of molecular clock assumption with 3D visualization', 'Kim, Namshin|Lee, Christopher', 'Center for Computational Biology, Molecular Biology Institute, Institute for Genomics and Proteomics, Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1570, USA. deepreds@gmail.com <deepreds@gmail.com>|Center for Computational Biology, Molecular Biology Institute, Institute for Genomics and Proteomics, Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095-1570, USA. deepreds@gmail.com <deepreds@gmail.com>', 'United States|United States', 0, 'Biological Clocks|Chromosome Mapping|Computer Graphics|Computer Simulation|Evolution, Molecular|Imaging, Three-Dimensional|Models, Genetic|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-8-213', '17584922', 'Construction and interpretation of phylogenetic trees has been a major research topic for understanding the evolution of genes. Increases in sequence data and complexity are creating a need for more powerful and insightful tree visualization tools.'),
(91564, 2007, 'bmc', 'Precise protein quantification based on peptide quantification using iTRAQ', 'Boehm, Andreas M|Pütz, Stephanie|Altenhöfer, Daniela|Sickmann, Albert|Falk, Michael', 'Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine, University of Wurzburg, (Protein Mass Spectrometry and Functional Proteomics), Wurzburg, Germany. ab@andiboehm.de|Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine, University of Wurzburg, (Protein Mass Spectrometry and Functional Proteomics), Wurzburg, Germany. ab@andiboehm.de|Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine, University of Wurzburg, (Protein Mass Spectrometry and Functional Proteomics), Wurzburg, Germany. ab@andiboehm.de|Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine, University of Wurzburg, (Protein Mass Spectrometry and Functional Proteomics), Wurzburg, Germany. ab@andiboehm.de|Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine, University of Wurzburg, (Protein Mass Spectrometry and Functional Proteomics), Wurzburg, Germany. ab@andiboehm.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Chromatography, Ion Exchange|Chromatography, Liquid|Databases, Protein|Genes, Reporter|Ions|Models, Statistical|Peptides|Proteins|Proteome|Proteomics|Reproducibility of Results|Software|Tandem Mass Spectrometry', 'Comparative Study', '10.1186/1471-2105-8-214', '17584939', 'Mass spectrometry based quantification of peptides can be performed using the iTRAQ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.'),
(91565, 2007, 'bmc', 'FlexOracle: predicting flexible hinges by identification of stable domains', 'Flores, Samuel C|Gerstein, Mark B', 'Department of Physics, Yale University, Bass 432, New Haven, CT 06520, USA. samuel.flores@yale.edu <samuel.flores@yale.edu>|Department of Physics, Yale University, Bass 432, New Haven, CT 06520, USA. samuel.flores@yale.edu <samuel.flores@yale.edu>', 'United States|United States', 33, 'Algorithms|Computer Simulation|Elasticity|Models, Chemical|Models, Molecular|Motion|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software|Stress, Mechanical', 'Journal Article', '10.1186/1471-2105-8-215', '17587456', 'Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91566, 2007, 'bmc', 'ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites', 'Hummel, Jan|Niemann, Michaela|Wienkoop, Stefanie|Schulze, Waltraud|Steinhauser, Dirk|Selbig, Joachim|Walther, Dirk|Weckwerth, Wolfram', 'Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, Germany. hummel@mpimp-golm.mpg.de <hummel@mpimp-golm.mpg.de>', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Binding Sites|Database Management Systems|Databases, Protein|Germany|Information Storage and Retrieval|Mass Spectrometry|Peptide Mapping|Phosphorylation|Protein Binding|Proteome|Reference Values|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-216', '17587460', 'In the last decade, techniques were established for the large scale genome-wide analysis of proteins, RNA, and metabolites, and database solutions have been developed to manage the generated data sets. The Golm Metabolome Database for metabolite data (GMD) represents one such effort to make these data broadly available and to interconnect the different molecular levels of a biological system 1. As data interpretation in the light of already existing data becomes increasingly important, these initiatives are an essential part of current and future systems biology.'),
(91567, 2007, 'bmc', 'GenMAPP 2: new features and resources for pathway analysis', 'Salomonis, Nathan|Hanspers, Kristina|Zambon, Alexander C|Vranizan, Karen|Lawlor, Steven C|Dahlquist, Kam D|Doniger, Scott W|Stuart, Josh|Conklin, Bruce R|Pico, Alexander R', 'Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>|Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA. nsalomonis@gladstone.ucsf.edu <nsalomonis@gladstone.ucsf.edu>', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 208, 'Algorithms|Computer Graphics|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-217', '17588266', 'Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses.'),
(91568, 2007, 'bmc', 'Novel and simple transformation algorithm for combining microarray data sets', 'Kim, Ki-Yeol|Ki, Dong Hyuk|Jeong, Ha Jin|Jeung, Hei-Cheul|Chung, Hyun Cheol|Rha, Sun Young', 'Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, Korea. kky1004@yumc.yonsei.ac.kr <kky1004@yumc.yonsei.ac.kr>', '-|-|-|-|-|-', 8, 'Algorithms|Analysis of Variance|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-8-218', '17588268', 'With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis.'),
(91569, 2007, 'bmc', 'Normalization and experimental design for ChIP-chip data', 'Peng, Shouyong|Alekseyenko, Artyom A|Larschan, Erica|Kuroda, Mitzi I|Park, Peter J', 'Howard Hughes Medical Institute, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. speng2@rics.bwh.harvard.edu <speng2@rics.bwh.harvard.edu>|Howard Hughes Medical Institute, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. speng2@rics.bwh.harvard.edu <speng2@rics.bwh.harvard.edu>|Howard Hughes Medical Institute, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. speng2@rics.bwh.harvard.edu <speng2@rics.bwh.harvard.edu>|Howard Hughes Medical Institute, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. speng2@rics.bwh.harvard.edu <speng2@rics.bwh.harvard.edu>|Howard Hughes Medical Institute, Brigham and Womens Hospital, Boston, Massachusetts 02115, USA. speng2@rics.bwh.harvard.edu <speng2@rics.bwh.harvard.edu>', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Chromatin Immunoprecipitation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Research Design', 'Evaluation Studies', '10.1186/1471-2105-8-219', '17592629', 'Chromatin immunoprecipitation on tiling arrays (ChIP-chip) has been widely used to investigate the DNA binding sites for a variety of proteins on a genome-wide scale. However, several issues in the processing and analysis of ChIP-chip data have not been resolved fully, including the effect of background (mock control) subtraction and normalization within and across arrays.'),
(91570, 2007, 'bmc', 'A robust measure of correlation between two genes on a microarray', 'Hardin, Johanna|Mitani, Aya|Hicks, Leanne|VanKoten, Brian', 'Department of Mathematics, Pomona College, Claremont, CA 91711, USA. jo.hardin@pomona.edu|Department of Mathematics, Pomona College, Claremont, CA 91711, USA. jo.hardin@pomona.edu|Department of Mathematics, Pomona College, Claremont, CA 91711, USA. jo.hardin@pomona.edu|Department of Mathematics, Pomona College, Claremont, CA 91711, USA. jo.hardin@pomona.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Gene Expression Profiling|Multigene Family|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-8-220', '17592643', 'The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.)'),
(91571, 2007, 'bmc', 'Ringo--an R/Bioconductor package for analyzing ChIP-chip readouts', 'Toedling, Joern|Skylar, Oleg|Sklyar, Oleg|Krueger, Tammo|Fischer, Jenny J|Sperling, Silke|Huber, Wolfgang', 'EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk|EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. toedling@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 125, 'Algorithms|Chromatin Immunoprecipitation|Chromosome Mapping|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Evaluation Studies', '10.1186/1471-2105-8-221', '17594472', 'Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a high-throughput assay for DNA-protein-binding or post-translational chromatin/histone modifications. However, the raw microarray intensity readings themselves are not immediately useful to researchers, but require a number of bioinformatic analysis steps. Identified enriched regions need to be bioinformatically annotated and compared to related datasets by statistical methods.'),
(91572, 2007, 'bmc', 'Constructing a meaningful evolutionary average at the phylogenetic center of mass', 'Stone, Eric A|Sidow, Arend', 'Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA. eric_stone@ncsu.edu|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA. eric_stone@ncsu.edu', 'United States|United States', 0, 'Biological Evolution|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genetic Variation|Genetics, Population|Models, Genetic|Models, Statistical|Phylogeny', 'Journal Article', '10.1186/1471-2105-8-222', '17594490', 'As a consequence of the evolutionary process, data collected from related species tend to be similar. This similarity by descent can obscure subtler signals in the data such as the evidence of constraint on variation due to shared selective pressures. In comparative sequence analysis, for example, sequence similarity is often used to illuminate important regions of the genome, but if the comparison is between closely related species, then similarity is the rule rather than the interesting exception. Furthermore, and perhaps worse yet, the contribution of a divergent third species may be masked by the strong similarity between the other two. Here we propose a remedy that weighs the contribution of each species according to its phylogenetic placement.'),
(91573, 2007, 'bmc', 'High-throughput identification of interacting protein-protein binding sites', 'Chung, Jo-Lan|Wang, Wei|Bourne, Philip E', 'Department of Pharmacology, University of California, San Diego, Gilman Drive, La Jolla, CA 92093-0743, USA.|Department of Pharmacology, University of California, San Diego, Gilman Drive, La Jolla, CA 92093-0743, USA.|Department of Pharmacology, University of California, San Diego, Gilman Drive, La Jolla, CA 92093-0743, USA.', 'United States|United States|United States', 10, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-223', '17594507', 'With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed.'),
(91574, 2007, 'bmc', 'Characterization of protein-interaction networks in tumors', 'Platzer, Alexander|Perco, Paul|Lukas, Arno|Mayer, Bernd', 'Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. alexanderp@gmx.at <alexanderp@gmx.at>|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. alexanderp@gmx.at <alexanderp@gmx.at>|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. alexanderp@gmx.at <alexanderp@gmx.at>|Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. alexanderp@gmx.at <alexanderp@gmx.at>', 'Austria|Austria|Austria|Austria', 66, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Databases, Protein|Gene Expression Profiling|Humans|Models, Biological|Neoplasm Proteins|Neoplasms|Protein Interaction Mapping|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-224', '17597514', 'Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures. This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database.'),
(91575, 2007, 'bmc', 'Mining prokaryotic genomes for unknown amino acids: a stop-codon-based approach', 'Fujita, Masashi|Mihara, Hisaaki|Goto, Susumu|Esaki, Nobuyoshi|Kanehisa, Minoru', 'Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. fujita@kuicr.kyoto-u.ac.jp <fujita@kuicr.kyoto-u.ac.jp>|Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. fujita@kuicr.kyoto-u.ac.jp <fujita@kuicr.kyoto-u.ac.jp>|Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. fujita@kuicr.kyoto-u.ac.jp <fujita@kuicr.kyoto-u.ac.jp>|Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. fujita@kuicr.kyoto-u.ac.jp <fujita@kuicr.kyoto-u.ac.jp>|Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan. fujita@kuicr.kyoto-u.ac.jp <fujita@kuicr.kyoto-u.ac.jp>', 'Japan|Japan|Japan|Japan|Japan', 0, 'Amino Acids|Base Sequence|Chromosome Mapping|Codon, Terminator|Genome, Bacterial|Information Storage and Retrieval|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-225', '17597547', 'Selenocysteine and pyrrolysine are the 21st and 22nd amino acids, which are genetically encoded by stop codons. Since a number of microbial genomes have been completely sequenced to date, it is tempting to ask whether the 23rd amino acid is left undiscovered in these genomes. Recently, a computational study addressed this question and reported that no tRNA gene for unknown amino acid was found in genome sequences available. However, performance of the tRNA prediction program on an unknown tRNA family, which may have atypical sequence and structure, is unclear, thereby rendering their result inconclusive. A protein-level study will provide independent insight into the novel amino acid.'),
(91576, 2007, 'bmc', 'Recruitment of rare 3-grams at functional sites: is this a mechanism for increasing enzyme specificity?', 'Tobi, Dror|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA. drt6@pitt.edu|Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA. drt6@pitt.edu', 'United States|United States', 0, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Databases, Protein|Enzymes|Models, Chemical|Molecular Sequence Data|Protein Binding|Protein Engineering|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship|Substrate Specificity', 'Journal Article', '10.1186/1471-2105-8-226', '17598909', 'A wealth of unannotated and functionally unknown protein sequences has accumulated in recent years with rapid progresses in sequence genomics, giving rise to ever increasing demands for developing methods to efficiently assess functional sites. Sequence and structure conservations have traditionally been the major criteria adopted in various algorithms to identify functional sites. Here, we focus on the distributions of the 203 different types of 3-grams (or triplets of sequentially contiguous amino acid) in the entire space of sequences accumulated to date in the UniProt database, and focus in particular on the rare 3-grams distinguished by their high entropy-based information content.'),
(91577, 2007, 'bmc', 'Relationship between insertion/deletion (indel) frequency of proteins and essentiality', 'Chan, Simon K|Hsing, Michael|Hormozdiari, Fereydoun|Cherkasov, Artem', 'CIHR/MSFHR Strategic Training Program in Bioinformatics, Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada. sichan@bcgsc.ca <sichan@bcgsc.ca>|CIHR/MSFHR Strategic Training Program in Bioinformatics, Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada. sichan@bcgsc.ca <sichan@bcgsc.ca>|CIHR/MSFHR Strategic Training Program in Bioinformatics, Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada. sichan@bcgsc.ca <sichan@bcgsc.ca>|CIHR/MSFHR Strategic Training Program in Bioinformatics, Canadas Michael Smith Genome Sciences Centre, Vancouver, BC, Canada. sichan@bcgsc.ca <sichan@bcgsc.ca>', 'Canada|Canada|Canada|Canada', 0, 'Bacterial Proteins|Base Sequence|DNA Transposable Elements|Fungal Proteins|Gene Deletion|Gene Frequency|Molecular Sequence Data|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-8-227', '17598914', 'In a previous study, we demonstrated that some essential proteins from pathogenic organisms contained sizable insertions/deletions (indels) when aligned to human proteins of high sequence similarity. Such indels may provide sufficient spatial differences between the pathogenic protein and human proteins to allow for selective targeting. In one example, an indel difference was targeted via large scale in-silico screening. This resulted in selective antibodies and small compounds which were capable of binding to the deletion-bearing essential pathogen protein without any cross-reactivity to the highly similar human protein. The objective of the current study was to investigate whether indels were found more frequently in essential than non-essential proteins.'),
(91578, 2007, 'bmc', 'TMM@: a web application for the analysis of transmembrane helix mobility', 'Skjaerven, Lars|Jonassen, Inge|Reuter, Nathalie', 'Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Bergen, Norway. slars@ii.uib.no <slars@ii.uib.no>|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Bergen, Norway. slars@ii.uib.no <slars@ii.uib.no>|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Bergen, Norway. slars@ii.uib.no <slars@ii.uib.no>', 'Norway|Norway|Norway', 2, 'Algorithms|Cell Membrane|Computer Graphics|Computer Simulation|Internet|Membrane Proteins|Models, Chemical|Models, Molecular|Protein Structure, Secondary|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-232', '17601351', 'To understand the mechanism by which a protein transmits a signal through the cell membrane, an understanding of the flexibility of its transmembrane (TM) region is essential. Normal Mode Analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. It has been widely used to study transmembrane channels and pumps. It relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. In particular NMA can be used to study dynamics of TM regions, but no tool making this approach available for non-experts, has been available so far.'),
(91579, 2007, 'bmc', 'A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data', 'Thomas, Reuben|Paredes, Carlos J|Mehrotra, Sanjay|Hatzimanikatis, Vassily|Papoutsakis, Eleftherios T', 'Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. ThomasR3@niehs.nih.gov <ThomasR3@niehs.nih.gov>|Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. ThomasR3@niehs.nih.gov <ThomasR3@niehs.nih.gov>|Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. ThomasR3@niehs.nih.gov <ThomasR3@niehs.nih.gov>|Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. ThomasR3@niehs.nih.gov <ThomasR3@niehs.nih.gov>|Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. ThomasR3@niehs.nih.gov <ThomasR3@niehs.nih.gov>', 'United States|United States|United States|United States|United States', 24, 'Algorithms|Bacillus anthracis|Bacterial Proteins|Gene Expression Profiling|Gene Expression Regulation|Genes, Bacterial|Kinetics|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Protein Biosynthesis|RNA, Messenger|Spores, Bacterial|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-228', '17603872', 'Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks.'),
(91580, 2007, 'bmc', 'A constrained polynomial regression procedure for estimating the local False Discovery Rate', 'Dalmasso, Cyril|Bar-Hen, Avner|Broët, Philippe', 'JE 2492-Univ. Paris-Sud, Villejuif, France. dalmasso@vjf.inserm.fr|JE 2492-Univ. Paris-Sud, Villejuif, France. dalmasso@vjf.inserm.fr|JE 2492-Univ. Paris-Sud, Villejuif, France. dalmasso@vjf.inserm.fr', 'France|France|France', 5, 'Algorithms|Apoptosis Regulatory Proteins|BRCA1 Protein|BRCA2 Protein|Breast Neoplasms|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Models, Statistical|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Probability|Regression Analysis', 'Comparative Study', '10.1186/1471-2105-8-229', '17603882', 'In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochbergs False Discovery Rate (FDR) for subsets of genes.'),
(91581, 2007, 'bmc', 'A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance', 'Zhang, Shunpu', 'Department of Statistics, University of Nebraska Lincoln, Lincoln, NE 68583-0963, USA. szhang3@unl.edu', 'United States', 0, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Journal Article', '10.1186/1471-2105-8-230', '17603887', 'The Significance Analysis of Microarrays (SAM) is a popular method for detecting significantly expressed genes and controlling the false discovery rate (FDR). Recently, it has been reported in the literature that the FDR is not well controlled by SAM. Due to the vast application of SAM in microarray data analysis, it is of great importance to have an extensive evaluation of SAM and its associated R-package (sam2.20).'),
(91582, 2007, 'bmc', 'Integrated olfactory receptor and microarray gene expression databases', 'Liu, Nian|Crasto, Chiquito J|Ma, Minghong', 'Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06520, USA. nian.liu@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06520, USA. nian.liu@yale.edu|Center for Medical Informatics, Yale University School of Medicine, New Haven, CT 06520, USA. nian.liu@yale.edu', 'United States|United States|United States', 3, 'Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Receptors, Odorant|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-231', '17603910', 'Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction.'),
(91583, 2007, 'bmc', 'Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data', 'Ahdesmäki, Miika|Lähdesmäki, Harri|Gracey, Andrew|Shmulevich, Llya|Yli-Harja, Olli', 'Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi', 'Finland|Finland|Finland|Finland|Finland', 32, 'Algorithms|Data Interpretation, Statistical|Databases, Protein|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Periodicity|Regression Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-8-233', '17605777', 'In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologists opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.'),
(91584, 2007, 'bmc', 'Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation', 'Parsons, Helen M|Ludwig, Christian|Günther, Ulrich L|Viant, Mark R', 'Centre for Systems Biology, The University of Birmingham, Edgbaston, Birmingham, UK. hmp166@bham.ac.uk <hmp166@bham.ac.uk>|Centre for Systems Biology, The University of Birmingham, Edgbaston, Birmingham, UK. hmp166@bham.ac.uk <hmp166@bham.ac.uk>|Centre for Systems Biology, The University of Birmingham, Edgbaston, Birmingham, UK. hmp166@bham.ac.uk <hmp166@bham.ac.uk>|Centre for Systems Biology, The University of Birmingham, Edgbaston, Birmingham, UK. hmp166@bham.ac.uk <hmp166@bham.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 165, 'Algorithms|Analysis of Variance|Data Interpretation, Statistical|Gene Expression Profiling|Magnetic Resonance Spectroscopy|Proteome|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-8-234', '17605789', 'Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1H, projections of 2D 1H, 1H J-resolved (pJRES), and intact 2D J-resolved (JRES).'),
(91585, 2007, 'bmc', 'A transversal approach to predict gene product networks from ontology-based similarity', 'Chabalier, Julie|Mosser, Jean|Burgun, Anita', 'E.A 3888, Modélisation Conceptuelle des Connaissances Biomédicales, Faculté de Médecine, Université de Rennes 1, IFR 140, Rennes Cedex, France. julie.chabalier@univ-rennes1.fr|E.A 3888, Modélisation Conceptuelle des Connaissances Biomédicales, Faculté de Médecine, Université de Rennes 1, IFR 140, Rennes Cedex, France. julie.chabalier@univ-rennes1.fr|E.A 3888, Modélisation Conceptuelle des Connaissances Biomédicales, Faculté de Médecine, Université de Rennes 1, IFR 140, Rennes Cedex, France. julie.chabalier@univ-rennes1.fr', 'France|France|France', 41, 'Caco-2 Cells|Cell Differentiation|Cell Line, Tumor|Cluster Analysis|Data Interpretation, Statistical|Databases, Protein|Enterocytes|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Gene Regulatory Networks|Humans|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteins|User-Computer Interface|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-8-235', '17605807', 'Interpretation of transcriptomic data is usually made through a standard approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.'),
(91586, 2007, 'bmc', 'A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality', 'Hart, G Traver|Lee, Insuk|Marcotte, Edward R', 'Center for Systems and Synthetic Biology Institute for Cellular and Molecular Biology University of Texas at Austin 2500 Speedway, Austin, Texas 78712, USA. traver_hart@yahoo.com <traver_hart@yahoo.com>|Center for Systems and Synthetic Biology Institute for Cellular and Molecular Biology University of Texas at Austin 2500 Speedway, Austin, Texas 78712, USA. traver_hart@yahoo.com <traver_hart@yahoo.com>|Center for Systems and Synthetic Biology Institute for Cellular and Molecular Biology University of Texas at Austin 2500 Speedway, Austin, Texas 78712, USA. traver_hart@yahoo.com <traver_hart@yahoo.com>', 'United States|United States|United States', 188, 'Algorithms|Bayes Theorem|Cluster Analysis|Databases, Protein|Gene Expression Regulation, Fungal|Genes, Fungal|Mass Spectrometry|Multiprotein Complexes|Protein Interaction Mapping|Proteome|Proteomics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Comparative Study', '10.1186/1471-2105-8-236', '17605818', 'Identifying all protein complexes in an organism is a major goal of systems biology. In the past 18 months, the results of two genome-scale tandem affinity purification-mass spectrometry (TAP-MS) assays in yeast have been published, along with corresponding complex maps. For most complexes, the published data sets were surprisingly uncorrelated. It is therefore useful to consider the raw data from each study and generate an accurate complex map from a high-confidence data set that integrates the results of these and earlier assays.'),
(91587, 2007, 'bmc', 'A topological algorithm for identification of structural domains of proteins', 'Emmert-Streib, Frank|Mushegian, Arcady', 'Stowers Institute for Medical Research, Kansas City, MO 64110, USA. v@bio-complexity.com|Stowers Institute for Medical Research, Kansas City, MO 64110, USA. v@bio-complexity.com', 'United States|United States', 21, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-237', '17608939', 'Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.'),
(91588, 2007, 'bmc', 'Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method', 'Nielsen, Morten|Lundegaard, Claus|Lund, Ole', 'Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark. mniel@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark. mniel@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark. mniel@cbs.dtu.dk', 'Denmark|Denmark|Denmark', 272, 'Algorithms|Alleles|Amino Acid Motifs|Amino Acid Sequence|Animals|Databases, Genetic|Epitopes|HLA-DR Antigens|Histocompatibility Antigens Class II|Humans|Inhibitory Concentration 50|Mice|Monte Carlo Method|Peptides|Predictive Value of Tests|Protein Binding|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-8-238', '17608956', 'Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles.'),
(91589, 2007, 'bmc', 'Improving gene set analysis of microarray data by SAM-GS', 'Dinu, Irina|Potter, John D|Mueller, Thomas|Liu, Qi|Adewale, Adeniyi J|Jhangri, Gian S|Einecke, Gunilla|Famulski, Konrad S|Halloran, Philip|Yasui, Yutaka', 'Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>|Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada. idinu@ualberta.ca <idinu@ualberta.ca>', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 187, 'Algorithms|Animals|Computer Simulation|Databases, Genetic|Gene Expression|Gene Expression Profiling|Mice|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-242', '17612399', 'Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).'),
(91590, 2007, 'bmc', 'Probabilistic prediction and ranking of human protein-protein interactions', 'Scott, Michelle S|Barton, Geoffrey J', 'School of Life Sciences Research, College of Life Sciences, University of Dundee, Scotland, UK. michelle@compbio.dundee.ac.uk <michelle@compbio.dundee.ac.uk>|School of Life Sciences Research, College of Life Sciences, University of Dundee, Scotland, UK. michelle@compbio.dundee.ac.uk <michelle@compbio.dundee.ac.uk>', 'United Kingdom|United Kingdom', 97, 'Bayes Theorem|Binding Sites|Chi-Square Distribution|Computational Biology|Databases, Protein|False Positive Reactions|Forecasting|Humans|Likelihood Functions|Odds Ratio|Probability|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteome|ROC Curve|Reproducibility of Results', 'Comparative Study', '10.1186/1471-2105-8-239', '17615067', 'Although the prediction of protein-protein interactions has been extensively investigated for yeast, few such datasets exist for the far larger proteome in human. Furthermore, it has recently been estimated that the overall average false positive rate of available computational and high-throughput experimental interaction datasets is as high as 90%.'),
(91591, 2007, 'bmc', 'Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data', 'Hulshizer, Randall|Blalock, Eric M', 'Department of Molecular and Biomedical Pharmacology, University of Kentucky College of Medicine, Lexington, Kentucky, USA. rhulshizer@uky.edu <rhulshizer@uky.edu>|Department of Molecular and Biomedical Pharmacology, University of Kentucky College of Medicine, Lexington, Kentucky, USA. rhulshizer@uky.edu <rhulshizer@uky.edu>', 'United States|United States', 17, 'Algorithms|Analysis of Variance|Cluster Analysis|Computer Simulation|Decision Trees|Gene Expression|Gene Expression Profiling|Models, Biological|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Probability|Software', 'Comparative Study', '10.1186/1471-2105-8-240', '17615071', 'Researchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, post hoc tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has a priori information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researchers assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers conclusions about a pattern and its constituent genes.'),
(91592, 2007, 'bmc', 'Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks', 'Daraselia, Nikolai|Yuryev, Anton|Egorov, Sergei|Mazo, Ilya|Ispolatov, Iaroslav', 'Ariadne Genomics, Inc, Rockville, MD 20850, USA. nikolai@ariadnegenomics.com <nikolai@ariadnegenomics.com>|Ariadne Genomics, Inc, Rockville, MD 20850, USA. nikolai@ariadnegenomics.com <nikolai@ariadnegenomics.com>|Ariadne Genomics, Inc, Rockville, MD 20850, USA. nikolai@ariadnegenomics.com <nikolai@ariadnegenomics.com>|Ariadne Genomics, Inc, Rockville, MD 20850, USA. nikolai@ariadnegenomics.com <nikolai@ariadnegenomics.com>|Ariadne Genomics, Inc, Rockville, MD 20850, USA. nikolai@ariadnegenomics.com <nikolai@ariadnegenomics.com>', 'United States|United States|United States|United States|United States', 36, 'Cluster Analysis|Computational Biology|Databases, Genetic|Databases, Protein|Genes|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Protein Interaction Mapping|Proteins|PubMed|Reproducibility of Results|Terminology as Topic', 'Comparative Study', '10.1186/1471-2105-8-243', '17620146', 'Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets.'),
(91593, 2007, 'bmc', 'A survey of orphan enzyme activities', 'Pouliot, Yannick|Karp, Peter D', 'Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, California 94025-3493, USA. ypouliot@stanford.edu <ypouliot@stanford.edu>|Bioinformatics Research Group, Artificial Intelligence Center, SRI International, Menlo Park, California 94025-3493, USA. ypouliot@stanford.edu <ypouliot@stanford.edu>', 'United States|United States', 31, 'Computational Biology|Data Collection|Databases, Factual|Databases, Genetic|Databases, Protein|Enzymes|Genomics|Proteomics|Reproducibility of Results|Species Specificity', 'Journal Article', '10.1186/1471-2105-8-244', '17623104', 'Using computational database searches, we have demonstrated previously that no gene sequences could be found for at least 36% of enzyme activities that have been assigned an Enzyme Commission number. Here we present a follow-up literature-based survey involving a statistically significant sample of such orphan activities. The survey was intended to determine whether sequences for these enzyme activities are truly unknown, or whether these sequences are absent from the public sequence databases but can be found in the literature.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91594, 2007, 'bmc', 'Artificial neural network models for prediction of intestinal permeability of oligopeptides', 'Jung, Eunkyoung|Kim, Junhyoung|Kim, Minkyoung|Jung, Dong Hyun|Rhee, Hokyoung|Shin, Jae-Min|Choi, Kihang|Kang, Sang-Kee|Kim, Min-Kook|Yun, Cheol-Heui|Choi, Yun-Jaie|Choi, Seung-Hoon', 'Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>|Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>', '-|-|-|-|-|-|-|-|-|-|-|-', 28, 'Amino Acid Sequence|Computer Simulation|Intestinal Absorption|Models, Biological|Models, Chemical|Molecular Sequence Data|Neural Networks (Computer)|Oligopeptides|Pattern Recognition, Automated|Permeability|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-245', '17623108', 'Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences.'),
(91595, 2007, 'bmc', 'Simcluster: clustering enumeration gene expression data on the simplex space', 'Vêncio, Ricardo Z N|Varuzza, Leonardo|de B Pereira, Carlos A|Brentani, Helena|Shmulevich, Ilya', 'Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com|Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com|Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com|Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com|Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com', 'United States|United States|United States|United States|United States', 10, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-246', '17625017', 'Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST digital northern, are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.'),
(91596, 2007, 'bmc', 'HoxPred: automated classification of Hox proteins using combinations of generalised profiles', 'Thomas-Chollier, Morgane|Leyns, Luc|Ledent, Valérie', 'Belgian EMBnet Node, Université Libre de Bruxelles-CP 257, Brussels, Belgium. mthomasc@vub.ac.be <mthomasc@vub.ac.be>|Belgian EMBnet Node, Université Libre de Bruxelles-CP 257, Brussels, Belgium. mthomasc@vub.ac.be <mthomasc@vub.ac.be>|Belgian EMBnet Node, Université Libre de Bruxelles-CP 257, Brussels, Belgium. mthomasc@vub.ac.be <mthomasc@vub.ac.be>', 'Belgium|Belgium|Belgium', 10, 'Algorithms|Amino Acid Sequence|Animals|Artificial Intelligence|Fishes|Homeodomain Proteins|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-8-247', '17626621', 'Correct identification of individual Hox proteins is an essential basis for their study in diverse research fields. Common methods to classify Hox proteins focus on the homeodomain that characterise homeobox transcription factors. Classification is hampered by the high conservation of this short domain. Phylogenetic tree reconstruction is a widely used but time-consuming classification method.'),
(91597, 2007, 'bmc', 'A novel ensemble learning method for de novo computational identification of DNA binding sites', 'Chakravarty, Arijit|Carlson, Jonathan M|Khetani, Radhika S|Gross, Robert H', 'Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA. chakravarty.a@gmail.com <chakravarty.a@gmail.com>|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA. chakravarty.a@gmail.com <chakravarty.a@gmail.com>|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA. chakravarty.a@gmail.com <chakravarty.a@gmail.com>|Department of Cancer Pharmacology, Millennium Pharmaceuticals Inc., Cambridge, MA, USA. chakravarty.a@gmail.com <chakravarty.a@gmail.com>', 'United States|United States|United States|United States', 35, 'Algorithms|Artificial Intelligence|Base Sequence|Binding Sites|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-249', '17626633', 'Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.'),
(91598, 2007, 'bmc', 'Nearest Neighbor Networks: clustering expression data based on gene neighborhoods', 'Huttenhower, Curtis|Flamholz, Avi I|Landis, Jessica N|Sahi, Sauhard|Myers, Chad L|Olszewski, Kellen L|Hibbs, Matthew A|Siemers, Nathan O|Troyanskaya, Olga G|Coller, Hilary A', 'Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>|Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. chuttenh@princeton.edu <chuttenh@princeton.edu>', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 55, 'Algorithms|Cluster Analysis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genes, Fungal|ROC Curve|Saccharomyces cerevisiae|Software', 'Comparative Study', '10.1186/1471-2105-8-250', '17626636', 'The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks, these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).'),
(91599, 2007, 'bmc', 'Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks', 'Kim, Chang Sik', 'Bioinformatics Group, Turku Centre for Computer Science, Turku, Finland. cskim@kangwon.ac.kr', 'Finland', 11, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-251', '17626641', 'A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an under-determined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.'),
(91600, 2007, 'bmc', 'Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assessment', 'Ferragina, Paolo|Giancarlo, Raffaele|Greco, Valentina|Manzini, Giovanni|Valiente, Gabriel', 'Dipartimento di Matematica Applicazioni, Università di Palermo, Italy. ferragin@di.unipi.it <ferragin@di.unipi.it>|Dipartimento di Matematica Applicazioni, Università di Palermo, Italy. ferragin@di.unipi.it <ferragin@di.unipi.it>|Dipartimento di Matematica Applicazioni, Università di Palermo, Italy. ferragin@di.unipi.it <ferragin@di.unipi.it>|Dipartimento di Matematica Applicazioni, Università di Palermo, Italy. ferragin@di.unipi.it <ferragin@di.unipi.it>|Dipartimento di Matematica Applicazioni, Università di Palermo, Italy. ferragin@di.unipi.it <ferragin@di.unipi.it>', 'Italy|Italy|Italy|Italy|Italy', 83, 'Algorithms|Amino Acid Sequence|Animals|Area Under Curve|Benchmarking|Databases, Protein|Humans|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|ROC Curve|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1186/1471-2105-8-252', '17629909', 'Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. However, the alignment methods seem inadequate for post-genomic studies since they do not scale well with data set size and they seem to be confined only to genomic and proteomic sequences. Therefore, alignment-free similarity measures are actively pursued. Among those, USM (Universal Similarity Metric) has gained prominence. It is based on the deep theory of Kolmogorov Complexity and universality is its most novel striking feature. Since it can only be approximated via data compression, USM is a methodology rather than a formula quantifying the similarity of two strings. Three approximations of USM are available, namely UCD (Universal Compression Dissimilarity), NCD (Normalized Compression Dissimilarity) and CD (Compression Dissimilarity). Their applicability and robustness is tested on various data sets yielding a first massive quantitative estimate that the USM methodology and its approximations are of value. Despite the rich theory developed around USM, its experimental assessment has limitations: only a few data compressors have been tested in conjunction with USM and mostly at a qualitative level, no comparison among UCD, NCD and CD is available and no comparison of USM with existing methods, both based on alignments and not, seems to be available.'),
(91601, 2007, 'bmc', 'Difference-based clustering of short time-course microarray data with replicates', 'Kim, Jihoon|Kim, Ju Han', 'Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea. jihoon@stat.wisc.edu <jihoon@stat.wisc.edu>|Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea. jihoon@stat.wisc.edu <jihoon@stat.wisc.edu>', '-|-', 12, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Time Factors', 'Journal Article', '10.1186/1471-2105-8-253', '17629922', 'There are some limitations associated with conventional clustering methods for short time-course gene expression data. The current algorithms require prior domain knowledge and do not incorporate information from replicates. Moreover, the results are not always easy to interpret biologically.'),
(91602, 2007, 'bmc', 'Progressive multiple sequence alignments from triplets', 'Kruspe, Matthias|Stadler, Peter F', 'Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany. matthias@bioinf-uni-leipzig.de <matthias@bioinf-uni-leipzig.de>|Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany. matthias@bioinf-uni-leipzig.de <matthias@bioinf-uni-leipzig.de>', 'Germany|Germany', 17, 'Algorithms|Artificial Intelligence|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-8-254', '17631683', 'The quality of progressive sequence alignments strongly depends on the accuracy of the individual pairwise alignment steps since gaps that are introduced at one step cannot be removed at later aggregation steps. Adjacent insertions and deletions necessarily appear in arbitrary order in pairwise alignments and hence form an unavoidable source of errors.'),
(91603, 2007, 'bmc', 'The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments', 'Cannataro, Mario|Cuda, Giovanni|Gaspari, Marco|Greco, Sergio|Tradigo, Giuseppe|Veltri, Pierangelo', 'Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>|Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>|Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>|Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>|Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>|Bioinformatics Laboratory, Experimental and Clinical Medicine Department, Magna Graecia University, Catanzaro, Italy. cannataro@unicz.it <cannataro@unicz.it>', 'Italy|Italy|Italy|Italy|Italy|Italy', 11, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Computational Biology|Computer Graphics|Cysteine|Databases, Protein|False Positive Reactions|Humans|Isotope Labeling|Isotopes|Mass Spectrometry|Peptides|Proteome|Reference Values|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-255', '17631686', 'Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.'),
(91604, 2007, 'bmc', 'HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments', 'Zhao, Hongya|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk|Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. hyzhao@ee.cityu.edu.hk', 'Hong Kong|Hong Kong', 11, 'Algorithms|Computer Graphics|Gene Expression|Gene Expression Profiling|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Pharmaceutical Preparations|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-256', '17634089', 'Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays.'),
(91605, 2007, 'bmc', 'Bayesian refinement of protein functional site matching', 'Mardia, Kanti V|Nyirongo, Vysaul B|Green, Peter J|Gold, Nicola D|Westhead, David R', 'Department of Statistics, University of Leeds, Leeds, UK. k.v.mardia@leeds.ac.uk <k.v.mardia@leeds.ac.uk>|Department of Statistics, University of Leeds, Leeds, UK. k.v.mardia@leeds.ac.uk <k.v.mardia@leeds.ac.uk>|Department of Statistics, University of Leeds, Leeds, UK. k.v.mardia@leeds.ac.uk <k.v.mardia@leeds.ac.uk>|Department of Statistics, University of Leeds, Leeds, UK. k.v.mardia@leeds.ac.uk <k.v.mardia@leeds.ac.uk>|Department of Statistics, University of Leeds, Leeds, UK. k.v.mardia@leeds.ac.uk <k.v.mardia@leeds.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, '17-Hydroxysteroid Dehydrogenases|Alcohol Dehydrogenase|Algorithms|Amino Acid Motifs|Amino Acid Sequence|Bayes Theorem|Binding Sites|Databases, Factual|Flavin-Adenine Dinucleotide|Ligands|Likelihood Functions|Markov Chains|Monte Carlo Method|NADP|Protein Binding|Protein Structure, Tertiary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-8-257', '17640336', 'Matching functional sites is a key problem for the understanding of protein function and evolution. The commonly used graph theoretic approach, and other related approaches, require adjustment of a matching distance threshold a priori according to the noise in atomic positions. This is difficult to pre-determine when matching sites related by varying evolutionary distances and crystallographic precision. Furthermore, sometimes the graph method is unable to identify alternative but important solutions in the neighbourhood of the distance based solution because of strict distance constraints. We consider the Bayesian approach to improve graph based solutions. In principle this approach applies to other methods with strict distance matching constraints. The Bayesian method can flexibly incorporate all types of prior information on specific binding sites (e.g. amino acid types) in contrast to combinatorial formulations.'),
(91606, 2007, 'bmc', 'Accelerated search for biomolecular network models to interpret high-throughput experimental data', 'Datta, Suman|Sokhansanj, Bahrad A', 'School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA. sdatta@merrimackpharma.com <sdatta@merrimackpharma.com>|School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA. sdatta@merrimackpharma.com <sdatta@merrimackpharma.com>', 'United States|United States', 9, 'Algorithms|Cell Cycle|Fuzzy Logic|Gene Expression Profiling|Gene Regulatory Networks|HeLa Cells|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Probability|Software', 'Journal Article', '10.1186/1471-2105-8-258', '17640351', 'The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.'),
(91607, 2007, 'bmc', 'Reuse of structural domain-domain interactions in protein networks', 'Schuster-Böckler, Benjamin|Bateman, Alex', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. bsb@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK. bsb@sanger.ac.uk', 'United Kingdom|United Kingdom', 0, 'Databases, Protein|Escherichia coli Proteins|Evolution, Molecular|Humans|Models, Genetic|Protein Interaction Mapping|Protein Structure, Tertiary|Saccharomyces cerevisiae Proteins|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-8-259', '17640363', 'Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.'),
(91608, 2007, 'bmc', 'A procedure for identifying homologous alternative splicing events', 'Talavera, David|Hospital, Adam|Orozco, Modesto|de la Cruz, Xavier', 'Molecular Modelling and Bioinformatics Unit, Institut de Recerca Biomèdica, Parc Científic de Barcelona, Barcelona, Spain. david@mmb.pcb.ub.es <david@mmb.pcb.ub.es>|Molecular Modelling and Bioinformatics Unit, Institut de Recerca Biomèdica, Parc Científic de Barcelona, Barcelona, Spain. david@mmb.pcb.ub.es <david@mmb.pcb.ub.es>|Molecular Modelling and Bioinformatics Unit, Institut de Recerca Biomèdica, Parc Científic de Barcelona, Barcelona, Spain. david@mmb.pcb.ub.es <david@mmb.pcb.ub.es>|Molecular Modelling and Bioinformatics Unit, Institut de Recerca Biomèdica, Parc Científic de Barcelona, Barcelona, Spain. david@mmb.pcb.ub.es <david@mmb.pcb.ub.es>', 'Spain|Spain|Spain|Spain', 3, 'Alternative Splicing|Base Sequence|Molecular Sequence Data|Proteins|RNA Splice Sites|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-8-260', '17640387', 'The study of the functional role of alternative splice isoforms of a gene is a very active area of research in biology. The difficulty of the experimental approach (in particular, in its high-throughput version) leaves ample room for the development of bioinformatics tools that can provide a useful first picture of the problem. Among the possible approaches, one of the simplest is to follow classical protein function annotation protocols and annotate target alternative splice events with the information available from conserved events in other species. However, the application of this protocol requires a procedure capable of recognising such events. Here we present a simple but accurate method developed for this purpose.'),
(91609, 2007, 'bmc', 'False positive reduction in protein-protein interaction predictions using gene ontology annotations', 'Mahdavi, Mahmoud A|Lin, Yen-Han', 'Department of Chemical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. maa943@mail.usask.ca <maa943@mail.usask.ca>|Department of Chemical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. maa943@mail.usask.ca <maa943@mail.usask.ca>', 'Canada|Canada', 48, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Protein|False Positive Reactions|Genome, Fungal|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-262', '17645798', 'Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.'),
(91610, 2007, 'bmc', 'Analysis and prediction of antibacterial peptides', 'Lata, Sneh|Sharma, B K|Raghava, G P S', 'Institute of Microbial Technology, Sector39A, Chandigarh, India. sneh@imtech.res.in|Institute of Microbial Technology, Sector39A, Chandigarh, India. sneh@imtech.res.in|Institute of Microbial Technology, Sector39A, Chandigarh, India. sneh@imtech.res.in', 'India|India|India', 150, 'Amino Acid Sequence|Antimicrobial Cationic Peptides|Computer Simulation|Models, Chemical|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-8-263', '17645800', 'Antibacterial peptides are important components of the innate immune system, used by the host to protect itself from different types of pathogenic bacteria. Over the last few decades, the search for new drugs and drug targets has prompted an interest in these antibacterial peptides. We analyzed 486 antibacterial peptides, obtained from antimicrobial peptide database APD, in order to understand the preference of amino acid residues at specific positions in these peptides.'),
(91611, 2007, 'bmc', 'Using contextual and lexical features to restructure and validate the classification of biomedical concepts', 'Fan, Jung-Wei|Xu, Hua|Friedman, Carol', 'Department of Biomedical Informatics, Columbia University Vanderbilt Clinic, 5th Floor, 622 West 168th Street, New York, NY 10032, USA. jung-wei.fan@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University Vanderbilt Clinic, 5th Floor, 622 West 168th Street, New York, NY 10032, USA. jung-wei.fan@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University Vanderbilt Clinic, 5th Floor, 622 West 168th Street, New York, NY 10032, USA. jung-wei.fan@dbmi.columbia.edu', 'United States|United States|United States', 9, 'Biomedical Research|Medical Informatics|Semantics|Software|Terminology as Topic|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-8-264', '17650333', 'Biomedical ontologies are critical for integration of data from diverse sources and for use by knowledge-based biomedical applications, especially natural language processing as well as associated mining and reasoning systems. The effectiveness of these systems is heavily dependent on the quality of the ontological terms and their classifications. To assist in developing and maintaining the ontologies objectively, we propose automatic approaches to classify and/or validate their semantic categories. In previous work, we developed an approach using contextual syntactic features obtained from a large domain corpus to reclassify and validate concepts of the Unified Medical Language System (UMLS), a comprehensive resource of biomedical terminology. In this paper, we introduce another classification approach based on words of the concept strings and compare it to the contextual syntactic approach.'),
(91612, 2007, 'bmc', 'Semantic integration to identify overlapping functional modules in protein interaction networks', 'Cho, Young-Rae|Hwang, Woochang|Ramanathan, Murali|Zhang, Aidong', 'Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu', 'United States|United States|United States|United States', 123, 'Algorithms|Computer Simulation|Databases, Protein|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Protein Interaction Mapping|Proteome|Semantics|Signal Transduction|Systems Integration', 'Journal Article', '10.1186/1471-2105-8-265', '17650343', 'The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.'),
(91613, 2007, 'bmc', 'BPhyOG: an interactive server for genome-wide inference of bacterial phylogenies based on overlapping genes', 'Luo, Yingqin|Fu, Cong|Zhang, Da-Yong|Lin, Kui', 'MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing, China. luoyq@mail.bnu.edu.cn <luoyq@mail.bnu.edu.cn>|MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing, China. luoyq@mail.bnu.edu.cn <luoyq@mail.bnu.edu.cn>|MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing, China. luoyq@mail.bnu.edu.cn <luoyq@mail.bnu.edu.cn>|MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing, China. luoyq@mail.bnu.edu.cn <luoyq@mail.bnu.edu.cn>', 'China|China|China|China', 15, 'Evolution, Molecular|Gene Expression Regulation, Bacterial|Genes, Overlapping|Genome, Bacterial|Internet|Open Reading Frames|Phylogeny|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-266', '17650344', 'Overlapping genes (OGs) in bacterial genomes are pairs of adjacent genes of which the coding sequences overlap partly or entirely. With the rapid accumulation of sequence data, many OGs in bacterial genomes have now been identified. Indeed, these might prove a consistent feature across all microbial genomes. Our previous work suggests that OGs can be considered as robust markers at the whole genome level for the construction of phylogenies. An online, interactive web server for inferring phylogenies is needed for biologists to analyze phylogenetic relationships among a set of bacterial genomes of interest.'),
(91614, 2007, 'bmc', 'Classification of heterogeneous microarray data by maximum entropy kernel', 'Fujibuchi, Wataru|Kato, Tsuyoshi', 'National Institute of Advanced Industrial Science and Technology (AIST), Computational Biology Research Center, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. w.fujibuchi@aist.go.jp|National Institute of Advanced Industrial Science and Technology (AIST), Computational Biology Research Center, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. w.fujibuchi@aist.go.jp', 'Japan|Japan', 16, 'Algorithms|Databases, Protein|Entropy|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-267', '17651507', 'There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets.'),
(91615, 2007, 'bmc', 'msBayes: pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation', 'Hickerson, Michael J|Stahl, Eli|Takebayashi, Naoki', 'Biology Department, Queens College, CUNY, Flushing, NY 11367-1597, USA. michael.hickerson@qc.cuny.edu|Biology Department, Queens College, CUNY, Flushing, NY 11367-1597, USA. michael.hickerson@qc.cuny.edu|Biology Department, Queens College, CUNY, Flushing, NY 11367-1597, USA. michael.hickerson@qc.cuny.edu', 'United States|United States|United States', 101, 'Algorithms|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genetic Variation|Models, Genetic|Phylogeny|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-8-268', '17655753', 'Although testing for simultaneous divergence (vicariance) across different population-pairs that span the same barrier to gene flow is of central importance to evolutionary biology, researchers often equate the gene tree and population/species tree thereby ignoring stochastic coalescent variance in their conclusions of temporal incongruence. In contrast to other available phylogeographic software packages, msBayes is the only one that analyses data from multiple species/population pairs under a hierarchical model.'),
(91616, 2007, 'bmc', 'Automating document classification for the Immune Epitope Database', 'Wang, Peng|Morgan, Alexander A|Zhang, Qing|Sette, Alessandro|Peters, Bjoern', 'The La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. pwang@liai.org <pwang@liai.org>|The La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. pwang@liai.org <pwang@liai.org>|The La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. pwang@liai.org <pwang@liai.org>|The La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. pwang@liai.org <pwang@liai.org>|The La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA. pwang@liai.org <pwang@liai.org>', 'United States|United States|United States|United States|United States', 37, 'Abstracting and Indexing as Topic|Artificial Intelligence|Databases, Factual|Documentation|Epitope Mapping|Epitopes|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|PubMed', 'Journal Article', '10.1186/1471-2105-8-269', '17655769', 'The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose.'),
(91617, 2007, 'bmc', 'Applying negative rule mining to improve genome annotation', 'Artamonova, Irena I|Frishman, Goar|Frishman, Dmitrij', 'Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany. irena.artamonova@gsf.de <irena.artamonova@gsf.de>|Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany. irena.artamonova@gsf.de <irena.artamonova@gsf.de>|Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany. irena.artamonova@gsf.de <irena.artamonova@gsf.de>', 'Germany|Germany|Germany', 15, 'Algorithms|Amino Acid Sequence|Computational Biology|Databases, Genetic|Databases, Protein|Genome|Information Storage and Retrieval|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-8-261', '17659089', 'Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homology-based transfer of information from existing database entries to the new target sequences. We have previously demonstrated that data mining in large sequence annotation databanks can help identify annotation items that are strongly associated with each other, and that exceptions from strong positive association rules often point to potential annotation errors. Here we investigate the applicability of negative association rule mining to revealing erroneously assigned annotation items.'),
(91618, 2007, 'bmc', 'Recognizing protein-protein interfaces with empirical potentials and reduced amino acid alphabets', 'Launay, Guillaume|Mendez, Raul|Wodak, Shoshana|Simonson, Thomas', 'Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@polytechnique.edu|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@polytechnique.edu', 'France|France|France|France', 16, 'Amino Acid Sequence|Amino Acids|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-270', '17662112', 'In structural genomics, an important goal is the detection and classification of protein-protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein-protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue-residue interactions and a stepwise distance-dependence. We used increased computational resources, however, constructing 290,000 decoys for 219 protein-protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment.'),
(91619, 2007, 'bmc', 'Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization', 'Bauer, Markus|Klau, Gunnar W|Reinert, Knut', 'Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany. mbauer@inf.fu-berlin.de|Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany. mbauer@inf.fu-berlin.de|Department of Mathematics and Computer Science, Free University Berlin, Berlin, Germany. mbauer@inf.fu-berlin.de', 'Germany|Germany|Germany', 67, 'Algorithms|Computer Simulation|Linear Models|Models, Chemical|Models, Genetic|RNA|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Evaluation Studies', '10.1186/1471-2105-8-271', '17662141', 'The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account.'),
(91620, 2007, 'bmc', 'A cis-regulatory logic simulator', 'Zeigler, Robert D|Gertz, Jason|Cohen, Barak A', 'Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. rdzeigle@wustl.edu <rdzeigle@wustl.edu>|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. rdzeigle@wustl.edu <rdzeigle@wustl.edu>|Department of Genetics, Washington University School of Medicine, St. Louis, MO 63108, USA. rdzeigle@wustl.edu <rdzeigle@wustl.edu>', 'United States|United States|United States', 0, 'Chromosome Mapping|Computer Simulation|Gene Expression|Logistic Models|Models, Genetic|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-272', '17662143', 'A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence.'),
(91621, 2007, 'bmc', 'The utility of MAS5 expression summary and detection call algorithms', 'Pepper, Stuart D|Saunders, Emma K|Edwards, Laura E|Wilson, Claire L|Miller, Crispin J', 'Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Withington, Manchester, UK. spepper@picr.man.ac.uk <spepper@picr.man.ac.uk>|Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Withington, Manchester, UK. spepper@picr.man.ac.uk <spepper@picr.man.ac.uk>|Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Withington, Manchester, UK. spepper@picr.man.ac.uk <spepper@picr.man.ac.uk>|Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Withington, Manchester, UK. spepper@picr.man.ac.uk <spepper@picr.man.ac.uk>|Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Withington, Manchester, UK. spepper@picr.man.ac.uk <spepper@picr.man.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 122, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1186/1471-2105-8-273', '17663764', 'Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.'),
(91622, 2007, 'bmc', '3-UTR SIRF: a database for identifying clusters of whort interspersed repeats in 3 untranslated regions', 'Andken, Benjamin B|Lim, In|Benson, Gary|Vincent, John J|Ferenc, Matthew T|Heinrich, Bianca|Jarzylo, Larissa A|Man, Heng-Ye|Deshler, James O', 'Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>|Bioinformatics Program, Boston University, Boston, MA, USA. bkennedy@mit.edu <bkennedy@mit.edu>', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 17, '3 Untranslated Regions|Base Sequence|Databases, Genetic|Interspersed Repetitive Sequences|Molecular Sequence Data|Multigene Family|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-274', '17663765', 'Short (~5 nucleotides) interspersed repeats regulate several aspects of post-transcriptional gene expression. Previously we developed an algorithm (REPFIND) that assigns P-values to all repeated motifs in a given nucleic acid sequence and reliably identifies clusters of short CAC-containing motifs required for mRNA localization in Xenopus oocytes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91623, 2007, 'bmc', 'Large-scale integration of cancer microarray data identifies a robust common cancer signature', 'Xu, Lei|Geman, Donald|Winslow, Raimond L', 'The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu|The Institute for Computational Medicine and Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu', 'United States|United States|United States', 75, 'Artificial Intelligence|Biomarkers, Tumor|Database Management Systems|Databases, Protein|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Information Storage and Retrieval|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Systems Integration', 'Journal Article', '10.1186/1471-2105-8-275', '17663766', 'There is a continuing need to develop molecular diagnostic tools which complement histopathologic examination to increase the accuracy of cancer diagnosis. DNA microarrays provide a means for measuring gene expression signatures which can then be used as components of genomic-based diagnostic tests to determine the presence of cancer.'),
(91624, 2007, 'bmc', 'AffyMAPSDetector: a software tool to characterize Affymetrix GeneChip expression arrays with respect to SNPs', 'Kumari, Sunita|Verma, Lalit K|Weller, Jennifer W', 'Department of Computer and Information Science, Indiana University Purdue University Indianapolis, Indianapolis, IN, 46202, USA. skumari@iupui.edu <skumari@iupui.edu>|Department of Computer and Information Science, Indiana University Purdue University Indianapolis, Indianapolis, IN, 46202, USA. skumari@iupui.edu <skumari@iupui.edu>|Department of Computer and Information Science, Indiana University Purdue University Indianapolis, Indianapolis, IN, 46202, USA. skumari@iupui.edu <skumari@iupui.edu>', 'India|India|India', 14, 'Algorithms|Chromosome Mapping|Computer Graphics|DNA Mutational Analysis|DNA Probes|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-276', '17663786', 'Affymetrix gene expression arrays incorporate paired perfect match (PM) and mismatch (MM) probes to distinguish true signals from those arising from cross-hybridization events. A MM signal often shows greater intensity than a PM signal, we propose that one underlying cause is the presence of allelic variants arising from single nucleotide polymorphisms (SNPs). To annotate and characterize SNP contributions to anomalous probe binding behavior we have developed a software tool called AffyMAPSDetector.'),
(91625, 2007, 'bmc', 'Haplotype-based score test for linkage in nuclear families', 'Nam, Chung Mo|Kang, Dae Ryong|Kim, Jinheum', 'Department of Applied Statistics, University of Suwon, Suwon, South Korea. cmnam@yumc.yonsei.ac.kr|Department of Applied Statistics, University of Suwon, Suwon, South Korea. cmnam@yumc.yonsei.ac.kr|Department of Applied Statistics, University of Suwon, Suwon, South Korea. cmnam@yumc.yonsei.ac.kr', '-|-|-', 0, 'Adolescent|Cohort Studies|Genetic Linkage|Haplotypes|Humans|Korea|Lod Score|Nuclear Family|Peptidyl-Dipeptidase A|Polymorphism, Single Nucleotide', 'Comparative Study', '10.1186/1471-2105-8-277', '17672913', 'To look for genetic linkage between angiotensin-I converting enzyme(ACE) gene and hypertension in a Korean adolescent cohort, we developed a powerful test using the covariances between marginal differences and their variances in a transmission/non-transmission table.'),
(91626, 2007, 'bmc', 'Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants', 'Price, Erin P|Inman-Bamber, John|Thiruvenkataswamy, Venugopal|Huygens, Flavia|Giffard, Philip M', 'Cooperative Research Centre for Diagnostics, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia. esme_epp@hotmail.com <esme_epp@hotmail.com>|Cooperative Research Centre for Diagnostics, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia. esme_epp@hotmail.com <esme_epp@hotmail.com>|Cooperative Research Centre for Diagnostics, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia. esme_epp@hotmail.com <esme_epp@hotmail.com>|Cooperative Research Centre for Diagnostics, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia. esme_epp@hotmail.com <esme_epp@hotmail.com>|Cooperative Research Centre for Diagnostics, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia. esme_epp@hotmail.com <esme_epp@hotmail.com>', 'Australia|Australia|Australia|Australia|Australia', 18, 'Algorithms|Animals|Bacterial Infections|Base Sequence|DNA, Bacterial|DNA, Viral|Diagnosis, Computer-Assisted|Genetic Variation|Humans|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Virus Diseases', 'Journal Article', '10.1186/1471-2105-8-278', '17672919', 'Single nucleotide polymorphisms (SNPs) and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpsons Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species.'),
(91627, 2007, 'bmc', 'Combination of scoring schemes for protein docking', 'Heuser, Philipp|Schomburg, Dietmar', 'Cologne University Bioinformatics Center (CUBIC), University of Cologne, Zuelpicher Str, 47, 50674 Koeln, Germany.|Cologne University Bioinformatics Center (CUBIC), University of Cologne, Zuelpicher Str, 47, 50674 Koeln, Germany.', 'Germany|Germany', 4, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-279', '17678526', 'Docking algorithms are developed to predict in which orientation two proteins are likely to bind under natural conditions. The currently used methods usually consist of a sampling step followed by a scoring step. We developed a weighted geometric correlation based on optimised atom specific weighting factors and combined them with our previously published amino acid specific scoring and with a comprehensive SVM-based scoring function.'),
(91628, 2007, 'bmc', 'PepBank--a database of peptides based on sequence text mining and public peptide data sources', 'Shtatland, Timur|Guettler, Daniel|Kossodo, Misha|Pivovarov, Misha|Weissleder, Ralph', 'Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Bldg, 149, 13th Street, Room 5406, Charlestown, MA 02129, USA. tshtatland@partners.org|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Bldg, 149, 13th Street, Room 5406, Charlestown, MA 02129, USA. tshtatland@partners.org|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Bldg, 149, 13th Street, Room 5406, Charlestown, MA 02129, USA. tshtatland@partners.org|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Bldg, 149, 13th Street, Room 5406, Charlestown, MA 02129, USA. tshtatland@partners.org|Center for Molecular Imaging Research, Massachusetts General Hospital, Harvard Medical School, Bldg, 149, 13th Street, Room 5406, Charlestown, MA 02129, USA. tshtatland@partners.org', 'United States|United States|United States|United States|United States', 104, 'Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Molecular Sequence Data|Natural Language Processing|Peptides|Periodicals as Topic|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-280', '17678535', 'Peptides are important molecules with diverse biological functions and biomedical uses. To date, there does not exist a single, searchable archive for peptide sequences or associated biological data. Rather, peptide sequences still have to be mined from abstracts and full-length articles, and/or obtained from the fragmented public sources.'),
(91629, 2007, 'bmc', 'Identification of discriminative characteristics for clusters from biologic data with InforBIO software', 'Tanaka, Naoto|Uchino, Masataka|Miyazaki, Satoru|Sugawara, Hideaki', 'Center for Information Biology and DDBJ, National Institute of Genetics, Yata, Mishima, Shizuoka, Japan. natanaka@lab.nig.ac.jp|Center for Information Biology and DDBJ, National Institute of Genetics, Yata, Mishima, Shizuoka, Japan. natanaka@lab.nig.ac.jp|Center for Information Biology and DDBJ, National Institute of Genetics, Yata, Mishima, Shizuoka, Japan. natanaka@lab.nig.ac.jp|Center for Information Biology and DDBJ, National Institute of Genetics, Yata, Mishima, Shizuoka, Japan. natanaka@lab.nig.ac.jp', 'Japan|Japan|Japan|Japan', 1, 'Cluster Analysis|DNA, Bacterial|Databases, Genetic|Discriminant Analysis|Pseudomonas|Quantitative Trait Loci|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-8-281', '17683520', 'There are a number of different methods for generation of trees and algorithms for phylogenetic analysis in the study of bacterial taxonomy. Genotypic information, such as SSU rRNA gene sequences, now plays a more prominent role in microbial systematics than does phenotypic information. However, the integration of genotypic and phenotypic information for polyphasic studies is necessary for the classification and identification of microbes. Thus, we devised an algorithm that objectively identifies discriminative characteristics for focused clusters on generated trees from a dataset composed of coded data, such as phenotypic information. Moreover, this algorithm has been integrated into the polyphasic analysis software, InforBIO.'),
(91630, 2007, 'bmc', 'Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model', 'Zuyderduyn, Scott D', 'Victor Ling Laboratory, Department of Cancer Genetics and Developmental Biology, BC Cancer Research Centre, 675 West 10th Ave,, Vancouver, Canada. scottz@bccrc.ca', 'Canada', 8, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Poisson Distribution|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-282', '17683533', 'Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.'),
(91631, 2007, 'bmc', 'Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data', 'Liu, Xiangdong|Jessen, Walter J|Sivaganesan, Siva|Aronow, Bruce J|Medvedovic, Mario', 'Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org', 'United States|United States|United States|United States|United States', 23, 'Algorithms|Artificial Intelligence|Bayes Theorem|Chromatin Immunoprecipitation|Chromosome Mapping|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-283', '17683565', 'Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental technologies, gene expression microarrays and Chromatin Immuno-Precipitation on Chip (ChIP-chip), are capable of producing data informative about expression regulatory mechanism on a genome scale. The optimal approach to joint modeling of data generated by these two complementary biological assays, with the goal of identifying and characterizing TMs, is an important open problem in computational biomedicine.'),
(91632, 2007, 'bmc', 'Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach', 'Andorf, Carson|Dobbs, Drena|Honavar, Vasant', 'Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org|Department of Environmental Health, University of Cincinnati, 3223 Eden Ave, ML 56, Cincinnati, Ohio 45267, USA. xiangdong.liu@cchmc.org', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Chromosome Mapping|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sensitivity and Specificity|Sequence Analysis, Protein', 'Letter', '10.1186/1471-2105-8-284', '17683567', 'Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a proteins Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.'),
(91633, 2007, 'bmc', 'miRAS: a data processing system for miRNA expression profiling study', 'Tian, Feng|Zhang, Huayue|Zhang, Xinyu|Song, Chi|Xia, Yongjing|Wu, Yiqing|Liu, Xiangjun', 'Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>|Department of Biological Science and Biotechnology, Tsinghua University, Beijing, Peoples Republic of China. tianfeng@mails.tsinghua.edu.cn <tianfeng@mails.tsinghua.edu.cn>', 'China|China|China|China|China|China|China', 1, 'Algorithms|Base Sequence|Cloning, Molecular|Gene Expression Profiling|Internet|Molecular Sequence Data|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-285', '17683571', 'The study of microRNAs (miRNAs) is attracting great considerations. Recent studies revealed that miRNAs play as important regulators of gene expression and some even as cancer players or inhibitors. Many studies try to discover new miRNAs and reveal the miRNA expression profile in cancer using a SAGE-based total RNA clone method. However, the data processing of this method is labor-intensive with several different biological databases and more than ten data processing steps involved.'),
(91634, 2007, 'bmc', 'CLUSS: clustering of protein sequences based on a new similarity measure', 'Kelil, Abdellali|Wang, Shengrui|Brzezinski, Ryszard|Fleury, Alain', 'Département dinformatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada. Abdellali.Kelil@USherbrooke.ca|Département dinformatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada. Abdellali.Kelil@USherbrooke.ca|Département dinformatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada. Abdellali.Kelil@USherbrooke.ca|Département dinformatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada. Abdellali.Kelil@USherbrooke.ca', 'Canada|Canada|Canada|Canada', 53, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Evolution, Molecular|Molecular Sequence Data|Multigene Family|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-8-286', '17683581', 'The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term phylogenetic in the sense of relatedness of biological functions.'),
(91635, 2007, 'bmc', 'OReFiL: an online resource finder for life sciences', 'Yamamoto, Yasunori|Takagi, Toshihisa', 'Department of Computational Biology, University of Tokyo, Kashiwanoha, Kashiwa, Chiba, Japan. yayamamo@cb.k.u-tokyo.ac.jp|Department of Computational Biology, University of Tokyo, Kashiwanoha, Kashiwa, Chiba, Japan. yayamamo@cb.k.u-tokyo.ac.jp', 'Japan|Japan', 0, 'Abstracting and Indexing as Topic|Biological Science Disciplines|Information Storage and Retrieval|Internet|Medical Subject Headings|Online Systems|Periodicals as Topic', 'Journal Article', '10.1186/1471-2105-8-287', '17683589', 'Many online resources for the life sciences have been developed and introduced in peer-reviewed papers recently, ranging from databases and web applications to data-analysis software. Some have been introduced in special journal issues or websites with a search function, but others remain scattered throughout the Internet and in the published literature. The searchable resources on these sites are collected and maintained manually and are therefore of higher quality than automatically updated sites, but also require more time and effort.'),
(91636, 2007, 'bmc', 'TomoJ: tomography software for three-dimensional reconstruction in transmission electron microscopy', 'Messaoudii, Cédric|Boudier, Thomas|Sanchez Sorzano, Carlos Oscar|Marco, Sergio', 'Institut Curie, Section Recherche, Laboratoire dImagerie Intégrative, Centre Universitaire dOrsay, 91405 Orsay CEDEX, France. cedric.messaoudi@curie.u-psud.fr|Institut Curie, Section Recherche, Laboratoire dImagerie Intégrative, Centre Universitaire dOrsay, 91405 Orsay CEDEX, France. cedric.messaoudi@curie.u-psud.fr|Institut Curie, Section Recherche, Laboratoire dImagerie Intégrative, Centre Universitaire dOrsay, 91405 Orsay CEDEX, France. cedric.messaoudi@curie.u-psud.fr|Institut Curie, Section Recherche, Laboratoire dImagerie Intégrative, Centre Universitaire dOrsay, 91405 Orsay CEDEX, France. cedric.messaoudi@curie.u-psud.fr', 'France|France|France|France', 0, 'Algorithms|Image Enhancement|Image Interpretation, Computer-Assisted|Imaging, Three-Dimensional|Microscopy, Electron, Transmission|Reproducibility of Results|Sensitivity and Specificity|Software|Tomography, Optical', 'Evaluation Studies', '10.1186/1471-2105-8-288', '17683598', 'Transmission electron tomography is an increasingly common three-dimensional electron microscopy approach that can provide new insights into the structure of subcellular components. Transmission electron tomography fills the gap between high resolution structural methods (X-ray diffraction or nuclear magnetic resonance) and optical microscopy. We developed new software for transmission electron tomography, TomoJ. TomoJ is a plug-in for the now standard image analysis and processing software for optical microscopy, ImageJ.'),
(91637, 2007, 'bmc', 'An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network', 'Holden, Brian J|Pinney, John W|Lovell, Simon C|Amoutzias, Grigoris D|Robertson, David L', 'Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. holdenbj@hotmail.com <holdenbj@hotmail.com>|Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. holdenbj@hotmail.com <holdenbj@hotmail.com>|Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. holdenbj@hotmail.com <holdenbj@hotmail.com>|Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. holdenbj@hotmail.com <holdenbj@hotmail.com>|Faculty of Life Sciences, University of Manchester, Oxford Road, Manchester, UK. holdenbj@hotmail.com <holdenbj@hotmail.com>', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Computational Biology|Computer Graphics|Computer Simulation|Databases, Protein|Models, Biological|Protein Interaction Mapping|Proteins|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-289', '17683601', 'Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix (bHLH) family of transcription factors as an example.'),
(91638, 2007, 'bmc', 'Computation of significance scores of unweighted Gene Set Enrichment Analyses', 'Keller, Andreas|Backes, Christina|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland University, Building E1 1, 66804 Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E1 1, 66804 Saarbrücken, Germany. ack@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, Building E1 1, 66804 Saarbrücken, Germany. ack@bioinf.uni-sb.de', 'Germany|Germany|Germany', 0, 'Algorithms|Biomarkers, Tumor|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Lung Neoplasms|Neoplasm Proteins|Neoplasms, Squamous Cell|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-8-290', '17683603', 'Gene Set Enrichment Analysis (GSEA) is a computational method for the statistical evaluation of sorted lists of genes or proteins. Originally GSEA was developed for interpreting microarray gene expression data, but it can be applied to any sorted list of genes. Given the gene list and an arbitrary biological category, GSEA evaluates whether the genes of the considered category are randomly distributed or accumulated on top or bottom of the list. Usually, significance scores (p-values) of GSEA are computed by nonparametric permutation tests, a time consuming procedure that yields only estimates of the p-values.'),
(91639, 2007, 'bmc', 'Portraits of breast cancer progression', 'Dalgin, Gul S|Alexe, Gabriela|Scanfeld, Daniel|Tamayo, Pablo|Mesirov, Jill P|Ganesan, Shridar|DeLisi, Charles|Bhanot, Gyan', 'Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu|Boston University, Boston, MA 02215, USA. sdalgin@bu.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 27, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Breast Neoplasms|Carcinoma, Ductal|Diagnosis, Computer-Assisted|Disease Progression|Female|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-8-291', '17683614', 'Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems.'),
(91640, 2007, 'bmc', 'rMotifGen: random motif generator for DNA and protein sequences', 'Rouchka, Eric C|Hardin, C Timothy', 'Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu|Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40292, USA. eric.rouchka@louisville.edu', 'United States|United States', 11, 'Amino Acid Motifs|Amino Acid Sequence|Base Sequence|Computer Simulation|Models, Chemical|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-8-292', '17683637', 'Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.'),
(91641, 2007, 'bmc', 'Automatic reconstruction of a bacterial regulatory network using Natural Language Processing', 'Rodríguez-Penagos, Carlos|Salgado, Heladia|Martínez-Flores, Irma|Collado-Vides, Julio', 'Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Apdo, Postal 565-A, Avenida Universidad, Cuernavaca, Morelos, 62100, Mexico. crodrigp@ccg.unam.mx.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Apdo, Postal 565-A, Avenida Universidad, Cuernavaca, Morelos, 62100, Mexico. crodrigp@ccg.unam.mx.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Apdo, Postal 565-A, Avenida Universidad, Cuernavaca, Morelos, 62100, Mexico. crodrigp@ccg.unam.mx.|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Apdo, Postal 565-A, Avenida Universidad, Cuernavaca, Morelos, 62100, Mexico. crodrigp@ccg.unam.mx.', 'Mexico|Mexico|Mexico|Mexico', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Computer Simulation|Escherichia coli K12|Escherichia coli Proteins|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-293', '17683642', 'Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a state-of-the-art Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in Escherichia coli K-12.'),
(91642, 2007, 'bmc', 'Quantitative sequence-function relationships in proteins based on gene ontology', 'Sangar, Vineet|Blankenberg, Daniel J|Altman, Naomi|Lesk, Arthur M', 'Department of Biochemistry and Molecular Biology, Center of Computational Biology and Genomics, The Huck Institute for Genomics, Proteomics and Bioinformatics, The Pennsylvania State University, University Park, PA 16802, USA. vus102@psu.edu|Department of Biochemistry and Molecular Biology, Center of Computational Biology and Genomics, The Huck Institute for Genomics, Proteomics and Bioinformatics, The Pennsylvania State University, University Park, PA 16802, USA. vus102@psu.edu|Department of Biochemistry and Molecular Biology, Center of Computational Biology and Genomics, The Huck Institute for Genomics, Proteomics and Bioinformatics, The Pennsylvania State University, University Park, PA 16802, USA. vus102@psu.edu|Department of Biochemistry and Molecular Biology, Center of Computational Biology and Genomics, The Huck Institute for Genomics, Proteomics and Bioinformatics, The Pennsylvania State University, University Park, PA 16802, USA. vus102@psu.edu', 'United States|United States|United States|United States', 0, 'Base Sequence|Databases, Protein|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-8-294', '17686158', 'The relationship between divergence of amino-acid sequence and divergence of function among homologous proteins is complex. The assumption that homologs share function--the basis of transfer of annotations in databases--must therefore be regarded with caution. Here, we present a quantitative study of sequence and function divergence, based on the Gene Ontology classification of function. We determined the relationship between sequence divergence and function divergence in 6828 protein families from the PFAM database. Within families there is a broad range of sequence similarity from very closely related proteins--for instance, orthologs in different mammals--to very distantly-related proteins at the limit of reliable recognition of homology.'),
(91643, 2007, 'bmc', 'Identification of putative regulatory upstream ORFs in the yeast genome using heuristics and evolutionary conservation', 'Cvijovixc4x87, Marija|Dalevi, Daniel|Bilsland, Elizabeth|Kemp, Graham J L|Sunnerhagen, Per', 'Department of Cell and Molecular Biology, Lundberg Laboratory, Gxc3xb6teborg University, PO Box 462 SE-405 30 Gxc3xb6teborg, Sweden. cvijovic@molgen.mpg.de|Department of Cell and Molecular Biology, Lundberg Laboratory, Gxc3xb6teborg University, PO Box 462 SE-405 30 Gxc3xb6teborg, Sweden. cvijovic@molgen.mpg.de|Department of Cell and Molecular Biology, Lundberg Laboratory, Gxc3xb6teborg University, PO Box 462 SE-405 30 Gxc3xb6teborg, Sweden. cvijovic@molgen.mpg.de|Department of Cell and Molecular Biology, Lundberg Laboratory, Gxc3xb6teborg University, PO Box 462 SE-405 30 Gxc3xb6teborg, Sweden. cvijovic@molgen.mpg.de|Department of Cell and Molecular Biology, Lundberg Laboratory, Gxc3xb6teborg University, PO Box 462 SE-405 30 Gxc3xb6teborg, Sweden. cvijovic@molgen.mpg.de', 'Sweden|Sweden|Sweden|Sweden|Sweden', 34, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Genome, Fungal|Molecular Sequence Data|Open Reading Frames|Protein Biosynthesis|Regulatory Sequences, Nucleic Acid|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-295', '17686169', 'The translational efficiency of an mRNA can be modulated by upstream open reading frames (uORFs) present in certain genes. A uORF can attenuate translation of the main ORF by interfering with translational reinitiation at the main start codon. uORFs also occur by chance in the genome, in which case they do not have a regulatory role. Since the sequence determinants for functional uORFs are not understood, it is difficult to discriminate functional from spurious uORFs by sequence analysis.'),
(91644, 2007, 'bmc', 'Annotation and query of tissue microarray data using the NCI Thesaurus', 'Shah, Nigam H|Rubin, Daniel L|Espinosa, Inigo|Montgomery, Kelli|Musen, Mark A', 'Stanford Medical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Stanford Medical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Stanford Medical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Stanford Medical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu|Stanford Medical Informatics, School of Medicine, Stanford University, Stanford, CA 94305, USA. nigam@stanford.edu', 'United States|United States|United States|United States|United States', 0, 'Database Management Systems|Databases, Factual|Documentation|Gene Expression Profiling|Information Storage and Retrieval|National Library of Medicine (U.S.)|Neoplasm Proteins|Neoplasms|Tissue Array Analysis|United States|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-8-296', '17686183', 'The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.'),
(91645, 2007, 'bmc', 'Influence of degree correlations on network structure and stability in protein-protein interaction networks', 'Friedel, Caroline C|Zimmer, Ralf', 'LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. Caroline.Friedel@bio.ifi.lmu.de|LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany. Caroline.Friedel@bio.ifi.lmu.de', 'Germany|Germany', 24, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-8-297', '17688687', 'The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.'),
(91646, 2007, 'bmc', 'Predicting active site residue annotations in the Pfam database', 'Mistry, Jaina|Bateman, Alex|Finn, Robert D', 'Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. jm14@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. jm14@sanger.ac.uk|Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. jm14@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Amino Acid Sequence|Binding Sites|Databases, Protein|Molecular Sequence Data|Predictive Value of Tests|Sequence Alignment|Sequence Homology, Amino Acid|Software Design', 'Comparative Study', '10.1186/1471-2105-8-298', '17688688', 'Approximately 5% of Pfam families are enzymatic, but only a small fraction of the sequences within these families (<0.5%) have had the residues responsible for catalysis determined. To increase the active site annotations in the Pfam database, we have developed a strict set of rules, chosen to reduce the rate of false positives, which enable the transfer of experimentally determined active site residue data to other sequences within the same Pfam family.'),
(91647, 2007, 'bmc', 'Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory', 'Luo, Feng|Yang, Yunfeng|Zhong, Jianxin|Gao, Haichun|Khan, Latifur|Thompson, Dorothea K|Zhou, Jizhong', 'Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu|Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. luofeng@cs.clemson.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-299', '17697349', 'Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.'),
(91648, 2007, 'bmc', 'Efficacy of different protein descriptors in predicting protein functional families', 'Ong, Serene A K|Lin, Hong Huang|Chen, Yu Zong|Li, Ze Rong|Cao, Zhiwei', 'Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 08-14, 3 Science Drive 2, Singapore 117543, Singapore. renese7@gmail.com|Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 08-14, 3 Science Drive 2, Singapore 117543, Singapore. renese7@gmail.com|Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 08-14, 3 Science Drive 2, Singapore 117543, Singapore. renese7@gmail.com|Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 08-14, 3 Science Drive 2, Singapore 117543, Singapore. renese7@gmail.com|Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 08-14, 3 Science Drive 2, Singapore 117543, Singapore. renese7@gmail.com', 'Singapore|Singapore|Singapore|Singapore|Singapore', 33, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-8-300', '17705863', 'Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families.'),
(91649, 2007, 'bmc', 'Genome bioinformatic analysis of nonsynonymous SNPs', 'Burke, David F|Worth, Catherine L|Priego, Eva-Maria|Cheng, Tammy|Smink, Luc J|Todd, John A|Blundell, Tom L', 'Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK. dave@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Chromosome Mapping|Computational Biology|DNA Mutational Analysis|Genome, Human|Humans|Polymorphism, Single Nucleotide|Proteins|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-301', '17708757', 'Genome-wide association studies of common diseases for common, low penetrance causal variants are underway. A proportion of these will alter protein sequences, the most common of which is the non-synonymous single nucleotide polymorphism (nsSNP). It would be an advantage if the functional effects of an nsSNP on protein structure and function could be predicted, both for the final identification process of a causal variant in a disease-associated chromosome region, and in further functional analyses of the nsSNP and its disease-associated protein.'),
(91650, 2007, 'bmc', 'Prediction of the burial status of transmembrane residues of helical membrane proteins', 'Park, Yungki|Hayat, Sikander|Helms, Volkhard', 'Center for Bioinformatics, Saarland University, 66041 Saarbruecken, Germany. yungki@bioinformatik.uni-saarland.de|Center for Bioinformatics, Saarland University, 66041 Saarbruecken, Germany. yungki@bioinformatik.uni-saarland.de|Center for Bioinformatics, Saarland University, 66041 Saarbruecken, Germany. yungki@bioinformatik.uni-saarland.de', 'Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Cell Membrane|Computer Simulation|Membrane Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-302', '17708758', 'Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.'),
(91651, 2007, 'bmc', 'Correlation between structure and temperature in prokaryotic metabolic networks', 'Takemoto, Kazuhiro|Nacher, Jose C|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. takemoto@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. takemoto@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. takemoto@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan', 27, 'Adaptation, Biological|Algorithms|Animals|Archaea|Bacteria|Cluster Analysis|Databases, Factual|Environment|Models, Biological|Models, Statistical|Proteome|Signal Transduction|Temperature', 'Journal Article', '10.1186/1471-2105-8-303', '17711568', 'In recent years, an extensive characterization of network structures has been made in an effort to elucidate design principles of metabolic networks, providing valuable insights into the functional organization and the evolutionary history of organisms. However, previous analyses have not discussed the effects of environmental factors (i.e., exogenous forces) in shaping network structures. In this work, we investigate the effect of temperature, which is one of the environmental factors that may have contributed to shaping structures of metabolic networks.'),
(91652, 2007, 'bmc', 'Evaluation of 3D-Jury on CASP7 models', 'Kajxc3xa1n, Lxc3xa1szlxc3xb3|Rychlewski, Leszek', 'BioInfoBank Institute, ul, Limanowskiego 24 A, 60-744 Poznaxc5x84, Poland. kajla@bioinfo.pl|BioInfoBank Institute, ul, Limanowskiego 24 A, 60-744 Poznaxc5x84, Poland. kajla@bioinfo.pl', 'Poland|Poland', 0, 'Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Software|Software Validation', 'Evaluation Studies', '10.1186/1471-2105-8-304', '17711571', '3D-Jury, the structure prediction consensus method publicly available in the Meta Server http://meta.bioinfo.pl/, was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91653, 2007, 'bmc', 'Automated smoother for the numerical decoupling of dynamics models', 'Vilela, Marco|Borges, Carlos C H|Vinga, Susana|Vasconcelos, Ana Tereza R|Santos, Helena|Voit, Eberhard O|Almeida, Jonas S', 'Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org|Department of Computatinal and Applied Mathematics, Laboratório Nacional de Computação Científica, Petrópolis, Rio de Janeiro, Brazil. mvilela@mdanderson.org', 'Brazil|Brazil|Brazil|Brazil|Brazil|Brazil|Brazil', 59, 'Algorithms|Computer Simulation|Feedback|Models, Biological|Numerical Analysis, Computer-Assisted|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-8-305', '17711581', 'Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittakers smoother and demonstrate its role within a robust, fully automated structure identification procedure.'),
(91654, 2007, 'bmc', 'Convergent algorithms for protein structural alignment', 'Martínez, Leandro|Andreani, Roberto|Martínez, José Mario', 'Institute of Chemistry, State University of Campinas, Campinas, SP, Brazil. leandromartinez98@gmail.com|Institute of Chemistry, State University of Campinas, Campinas, SP, Brazil. leandromartinez98@gmail.com|Institute of Chemistry, State University of Campinas, Campinas, SP, Brazil. leandromartinez98@gmail.com', 'Brazil|Brazil|Brazil', 49, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-306', '17714583', 'Many algorithms exist for protein structural alignment, based on internal protein coordinates or on explicit superposition of the structures. These methods are usually successful for detecting structural similarities. However, current practical methods are seldom supported by convergence theories. In particular, although the goal of each algorithm is to maximize some scoring function, there is no practical method that theoretically guarantees score maximization. A practical algorithm with solid convergence properties would be useful for the refinement of protein folding maps, and for the development of new scores designed to be correlated with functional similarity.'),
(91655, 2007, 'bmc', 'Protein structural similarity search by Ramachandran codes', 'Lo, Wei-Cheng|Huang, Po-Jung|Chang, Chih-Hung|Lyu, Ping-Chiang', 'Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw|Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan. b861636@life.nthu.edu.tw', '-|-|-|-', 0, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-8-307', '17716377', 'Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods, however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.'),
(91656, 2007, 'bmc', 'Analysis of the role of retrotransposition in gene evolution in vertebrates', 'Yu, Zhan|Morais, David|Ivanga, Mahine|Harrison, Paul M', 'Department of Biology, McGill University, Stewart Biology Building, 1205 Docteur Penfield Ave,, Montreal, QC, H3A 1B1 Canada. zyu9@po-box.mcgill.ca|Department of Biology, McGill University, Stewart Biology Building, 1205 Docteur Penfield Ave,, Montreal, QC, H3A 1B1 Canada. zyu9@po-box.mcgill.ca|Department of Biology, McGill University, Stewart Biology Building, 1205 Docteur Penfield Ave,, Montreal, QC, H3A 1B1 Canada. zyu9@po-box.mcgill.ca|Department of Biology, McGill University, Stewart Biology Building, 1205 Docteur Penfield Ave,, Montreal, QC, H3A 1B1 Canada. zyu9@po-box.mcgill.ca', 'Canada|Canada|Canada|Canada', 31, 'Animals|DNA, Complementary|Databases, Genetic|Dogs|Evolution, Molecular|Genetic Code|Genome, Human|Humans|Mice|Phylogeny|Pseudogenes|RNA, Messenger|Rats|Retroelements|Reverse Transcription|Sequence Analysis, DNA|Species Specificity|Vertebrates', 'Journal Article', '10.1186/1471-2105-8-308', '17718914', 'The dynamics of gene evolution are influenced by several genomic processes. One such process is retrotransposition, where an mRNA transcript is reverse-transcribed and reintegrated into the genomic DNA.'),
(91657, 2007, 'bmc', 'PARE: a tool for comparing protein abundance and mRNA expression data', 'Yu, Eric Z|Burba, Anne E Counterman|Gerstein, Mark', 'Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. acmg@bioinfo.mbb.yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. acmg@bioinfo.mbb.yale.edu|Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. acmg@bioinfo.mbb.yale.edu', 'United States|United States|United States', 17, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Protein Biosynthesis|Proteins|RNA, Messenger|Software', 'Journal Article', '10.1186/1471-2105-8-309', '17718915', 'Techniques for measuring protein abundance are rapidly advancing and we are now in a situation where we anticipate many protein abundance data sets will be available in the near future. Since proteins are translated from mRNAs, their expression is expected to be related to their abundance, to some degree.'),
(91658, 2007, 'bmc', 'Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms', 'Diago, Luis A|Morell, Persy|Aguilera, Longendri|Moreno, Ernesto', 'Department of Bioengineering, Faculty of Electrical Engineering, Havana Institute of Technology, Havana 19390, Cuba. luis_a_diago@yahoo.com|Department of Bioengineering, Faculty of Electrical Engineering, Havana Institute of Technology, Havana 19390, Cuba. luis_a_diago@yahoo.com|Department of Bioengineering, Faculty of Electrical Engineering, Havana Institute of Technology, Havana 19390, Cuba. luis_a_diago@yahoo.com|Department of Bioengineering, Faculty of Electrical Engineering, Havana Institute of Technology, Havana 19390, Cuba. luis_a_diago@yahoo.com', 'Cuba|Cuba|Cuba|Cuba', 11, 'Algorithms|Computational Biology|Crystallography, X-Ray|Databases, Protein|Knowledge Bases|Ligands|Molecular Conformation|Protein Binding|Proteins|Proteomics|Reproducibility of Results|Water', 'Journal Article', '10.1186/1471-2105-8-310', '17718923', 'The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.'),
(91659, 2007, 'bmc', 'Modular co-evolution of metabolic networks', 'Zhao, Jing|Ding, Guo-Hui|Tao, Lin|Yu, Hong|Yu, Zhong-Hao|Luo, Jian-Hua|Cao, Zhi-Wei|Li, Yi-Xue', 'School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn|School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China. zjane_cn@sjtu.edu.cn', 'China|China|China|China|China|China|China|China', 54, 'Computer Simulation|Evolution, Molecular|Genome, Human|Humans|Models, Genetic|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-311', '17723146', 'The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.'),
(91660, 2007, 'bmc', 'MaxAlign: maximizing usable data in an alignment', 'Gouveia-Oliveira, Rodrigo|Sackett, Peter W|Pedersen, Anders G', 'Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark. rodrigo@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark. rodrigo@cbs.dtu.dk|Center for Biological Sequence Analysis, Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark. rodrigo@cbs.dtu.dk', 'Denmark|Denmark|Denmark', 67, 'Algorithms|Databases, Genetic|Information Storage and Retrieval|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-8-312', '17725821', 'The presence of gaps in an alignment of nucleotide or protein sequences is often an inconvenience for bioinformatical studies. In phylogenetic and other analyses, for instance, gapped columns are often discarded entirely from the alignment.'),
(91661, 2007, 'bmc', 'Integrated network reconstruction, visualization and analysis using YANAsquare', 'Schwarz, Roland|Liang, Chunguang|Kaleta, Christoph|Kühnel, Mark|Hoffmann, Eik|Kuznetsov, Sergei|Hecker, Michael|Griffiths, Gareth|Schuster, Stefan|Dandekar, Thomas', 'Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Department of Bioinformatics, Biocenter Am Hubland, D-97074 University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 52, 'Algorithms|Computational Biology|Computer Simulation|Metabolic Networks and Pathways|Software|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-8-313', '17725829', 'Modeling of metabolic networks includes tasks such as network assembly, network overview, calculation of metabolic fluxes and testing the robustness of the network.'),
(91662, 2007, 'bmc', 'A comprehensive system for evaluation of remote sequence similarity detection', 'Qi, Yuan|Sadreyev, Ruslan I|Wang, Yong|Kim, Bong-Hyun|Grishin, Nick V', 'Department of Biochemistry, University of Texas Southwestern Medical Center, 5323, Harry Hines Blvd, Dallas, TX 75390-9050, USA. yuan_qi@med.unc.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323, Harry Hines Blvd, Dallas, TX 75390-9050, USA. yuan_qi@med.unc.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323, Harry Hines Blvd, Dallas, TX 75390-9050, USA. yuan_qi@med.unc.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323, Harry Hines Blvd, Dallas, TX 75390-9050, USA. yuan_qi@med.unc.edu|Department of Biochemistry, University of Texas Southwestern Medical Center, 5323, Harry Hines Blvd, Dallas, TX 75390-9050, USA. yuan_qi@med.unc.edu', 'United States|United States|United States|United States|United States', 19, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-8-314', '17725841', 'Accurate and sensitive performance evaluation is crucial for both effective development of better structure prediction methods based on sequence similarity, and for the comparative analysis of existing methods. Up to date, there has been no satisfactory comprehensive evaluation method that (i) is based on a large and statistically unbiased set of proteins with clearly defined relationships, and (ii) covers all performance aspects of sequence-based structure predictors, such as sensitivity and specificity, alignment accuracy and coverage, and structure template quality.'),
(91663, 2007, 'bmc', 'The topology of metabolic isotope labeling networks', 'Weitzel, Michael|Wiechert, Wolfgang|Nöh, Katharina', 'Institute of Biotechnology, Research Centre Jülich, 52425 Jülich, Germany. michael.weitzel@uni-siegen.de|Institute of Biotechnology, Research Centre Jülich, 52425 Jülich, Germany. michael.weitzel@uni-siegen.de|Institute of Biotechnology, Research Centre Jülich, 52425 Jülich, Germany. michael.weitzel@uni-siegen.de', 'Germany|Germany|Germany', 26, 'Algorithms|Cluster Analysis|Computational Biology|Efficiency|Escherichia coli|Isotope Labeling|Metabolic Networks and Pathways|Neural Networks (Computer)|Research Design|Work Simplification', 'Journal Article', '10.1186/1471-2105-8-315', '17727715', 'Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.'),
(91664, 2007, 'bmc', 'Versatile annotation and publication quality visualization of protein complexes using POLYVIEW-3D', 'Porollo, Aleksey|Meller, Jaroslaw', 'Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267, USA. alexey.porollo@uc.edu|Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45267, USA. alexey.porollo@uc.edu', 'United States|United States', 74, 'Binding Sites|Computer Graphics|Computer Simulation|Documentation|Models, Chemical|Models, Molecular|Multiprotein Complexes|Periodicals as Topic|Protein Binding|Protein Conformation|Protein Interaction Mapping|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-316', '17727718', 'Macromolecular visualization as well as automated structural and functional annotation tools play an increasingly important role in the post-genomic era, contributing significantly towards the understanding of molecular systems and processes. For example, three dimensional (3D) models help in exploring protein active sites and functional hot spots that can be targeted in drug design. Automated annotation and visualization pipelines can also reveal other functionally important attributes of macromolecules. These goals are dependent on the availability of advanced tools that integrate better the existing databases, annotation servers and other resources with state-of-the-art rendering programs.'),
(91665, 2007, 'bmc', 'A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments', 'Larsen, Peter|Almasri, Eyad|Chen, Guanrao|Dai, Yang', 'Core Genomics Laboratory at University of Illinois at Chicago, 845 West Taylor Street Chicago, IL 60607, USA. plarsen@uic.edu|Core Genomics Laboratory at University of Illinois at Chicago, 845 West Taylor Street Chicago, IL 60607, USA. plarsen@uic.edu|Core Genomics Laboratory at University of Illinois at Chicago, 845 West Taylor Street Chicago, IL 60607, USA. plarsen@uic.edu|Core Genomics Laboratory at University of Illinois at Chicago, 845 West Taylor Street Chicago, IL 60607, USA. plarsen@uic.edu', 'United States|United States|United States|United States', 17, 'Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Oligonucleotide Array Sequence Analysis|PubMed|Saccharomyces cerevisiae', 'Evaluation Studies', '10.1186/1471-2105-8-317', '17727721', 'The incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. Most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. However, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. How to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. We propose a novel method based on the statistical analysis of reported gene interactions in PubMed literature.'),
(91666, 2007, 'bmc', 'AUG_hairpin: prediction of a downstream secondary structure influencing the recognition of a translation start site', 'Kochetov, Alex V|Palyanov, Andrey|Titov, Igor I|Grigorovich, Dmitry|Sarai, Akinori|Kolchanov, Nikolay A', 'Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru|Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru|Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru|Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru|Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru|Institute of Cytology and Genetics, Lavrentieva 10, Novosibirsk 630090, Russia. ak@bionet.nsc.ru', '-|-|-|-|-|-', 35, 'Algorithms|Codon, Initiator|Protein Biosynthesis|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-8-318', '17760957', 'The translation start site plays an important role in the control of translation efficiency of eukaryotic mRNAs. The recognition of the start AUG codon by eukaryotic ribosomes is considered to depend on its nucleotide context. However, the fraction of eukaryotic mRNAs with the start codon in a suboptimal context is relatively large. It may be expected that mRNA should possess some features providing efficient translation, including the proper recognition of a translation start site. It has been experimentally shown that a downstream hairpin located in certain positions with respect to start codon can compensate in part for the suboptimal AUG context and also increases translation from non-AUG initiation codons. Prediction of such a compensatory hairpin may be useful in the evaluation of eukaryotic mRNA translation properties.'),
(91667, 2007, 'bmc', 'antiCODE: a natural sense-antisense transcripts database', 'Yin, Yifei|Zhao, Yi|Wang, Jie|Liu, Changning|Chen, Shuguang|Chen, Runsheng|Zhao, Haitao', 'Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com|Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, CAMS & PUMC, Beijing 100730, China. yinyifei2005@yahoo.com', 'China|China|China|China|China|China|China', 23, 'Codon|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|RNA, Antisense|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-319', '17760969', 'Natural antisense transcripts (NATs) are endogenous RNA molecules that exhibit partial or complete complementarity to other RNAs, and that may contribute to the regulation of molecular functions at various levels. In recent years, large-scale NAT screens in several model organisms have produced much data, but there is no database to assemble all these data. AntiCODE intends to function as an integrated NAT database for this purpose.'),
(91668, 2007, 'bmc', 'Apollo2Go: a web service adapter for the Apollo genome viewer to enable distributed genome annotation', 'Klee, Kathrin|Ernst, Rebecca|Spannagl, Manuel|Mayer, Klaus F X', 'MIPS/Institute for Bioinformatics, GSF Research Center for Environment and Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. Kathrin_Klee@web.de|MIPS/Institute for Bioinformatics, GSF Research Center for Environment and Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. Kathrin_Klee@web.de|MIPS/Institute for Bioinformatics, GSF Research Center for Environment and Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. Kathrin_Klee@web.de|MIPS/Institute for Bioinformatics, GSF Research Center for Environment and Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany. Kathrin_Klee@web.de', 'Germany|Germany|Germany|Germany', 3, 'Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-320', '17760972', 'Apollo, a genome annotation viewer and editor, has become a widely used genome annotation and visualization tool for distributed genome annotation projects. When using Apollo for annotation, database updates are carried out by uploading intermediate annotation files into the respective database. This non-direct database upload is laborious and evokes problems of data synchronicity.'),
(91669, 2007, 'bmc', 'Automatic generation of 3D motifs for classification of protein binding sites', 'Nebel, Jean-Christophe|Herzyk, Pawel|Gilbert, David R', 'Faculty of Computing, Information Systems & Mathematics, Kingston University, Kingston-upon-Thames, KT1 2EE, UK. J.Nebel@kingston.ac.uk|Faculty of Computing, Information Systems & Mathematics, Kingston University, Kingston-upon-Thames, KT1 2EE, UK. J.Nebel@kingston.ac.uk|Faculty of Computing, Information Systems & Mathematics, Kingston University, Kingston-upon-Thames, KT1 2EE, UK. J.Nebel@kingston.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 21, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Binding Sites|Computer Graphics|Computer Simulation|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-321', '17760982', 'Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands.'),
(91670, 2007, 'bmc', 'Statistical validation of megavariate effects in ASCA', 'Vis, Daniel J|Westerhuis, Johan A|Smilde, Age K|van der Greef, Jan', 'BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl|BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl|BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl|BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 86, 'Animals|Bromobenzenes|Data Interpretation, Statistical|Dose-Response Relationship, Drug|Genomics|Liver|Metabolic Networks and Pathways|Models, Statistical|Multivariate Analysis|Rats|Time Factors', 'Journal Article', '10.1186/1471-2105-8-322', '17760983', 'Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method, it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.'),
(91671, 2007, 'bmc', 'Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics', 'Jiang, Xinning|Jiang, Xiaogang|Han, Guanghui|Ye, Mingliang|Zou, Hanfa', 'National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. vext@dicp.ac.cn|National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. vext@dicp.ac.cn|National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. vext@dicp.ac.cn|National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. vext@dicp.ac.cn|National Chromatographic R&A Center, Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian 116023, China. vext@dicp.ac.cn', 'China|China|China|China|China', 24, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Proteome|Proteomics|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/1471-2105-8-323', '17761002', 'In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, Delta Cn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.'),
(91672, 2007, 'bmc', 'Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks', 'Hu, Xiaohua|Wu, Fang-Xiang', 'College of Information Science & Technology, Drexel University, Philadelphia, PA 19104, USA. thu@cis.drexel.edu|College of Information Science & Technology, Drexel University, Philadelphia, PA 19104, USA. thu@cis.drexel.edu', 'United States|United States', 4, 'Algorithms|Cell Physiological Phenomena|Cluster Analysis|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Gene Regulatory Networks|Humans|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Systems Biology', 'Journal Article', '10.1186/1471-2105-8-324', '17764552', 'Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.'),
(91673, 2007, 'bmc', 'BIOSMILE: a semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features', 'Tsai, Richard Tzong-Han|Chou, Wen-Chi|Su, Ying-Shan|Lin, Yu-Chun|Sung, Cheng-Lung|Dai, Hong-Jie|Yeh, Irene Tzu-Hsuan|Ku, Wei|Sung, Ting-Yi|Hsu, Wen-Lian', 'Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Nankang, Taipei 115, Taiwan, PRoC. thtsai@iis.sinica.edu.tw', '-|-|-|-|-|-|-|-|-|-', 40, 'Artificial Intelligence|Computational Biology|Computer Simulation|Information Storage and Retrieval|Models, Theoretical|Natural Language Processing|Periodicals as Topic|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-8-325', '17764570', 'Bioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events.'),
(91674, 2007, 'bmc', 'Stratification bias in low signal microarray studies', 'Parker, Brian J|Günter, Simon|Bedo, Justin', 'Statistical Machine Learning Group, NICTA, Canberra, Australia. brian.bj.parker@gmail.com|Statistical Machine Learning Group, NICTA, Canberra, Australia. brian.bj.parker@gmail.com|Statistical Machine Learning Group, NICTA, Canberra, Australia. brian.bj.parker@gmail.com', 'Australia|Australia|Australia', 46, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-8-326', '17764577', 'When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated.'),
(91675, 2007, 'bmc', 'ASMPKS: an analysis system for modular polyketide synthases', 'Tae, Hongseok|Kong, Eun-Bae|Park, Kiejung', 'Information Technology Institute, SmallSoft Co., Ltd., Jang-Dong 59-5, Yusung-Gu, Daejeon 305-343, South Korea. mbio94@naver.com|Information Technology Institute, SmallSoft Co., Ltd., Jang-Dong 59-5, Yusung-Gu, Daejeon 305-343, South Korea. mbio94@naver.com|Information Technology Institute, SmallSoft Co., Ltd., Jang-Dong 59-5, Yusung-Gu, Daejeon 305-343, South Korea. mbio94@naver.com', '-|-|-', 0, 'Algorithms|Carbon|Catalytic Domain|Computational Biology|Computers|Genetic Engineering|Genome, Bacterial|Genomics|Models, Biological|Models, Theoretical|Multienzyme Complexes|Polyketide Synthases|Software', 'Journal Article', '10.1186/1471-2105-8-327', '17764579', 'Polyketides are secondary metabolites of microorganisms with diverse biological activities, including pharmacological functions such as antibiotic, antitumor and agrochemical properties. Polyketides are synthesized by serialized reactions of a set of enzymes called polyketide synthase(PKS)s, which coordinate the elongation of carbon skeletons by the stepwise condensation of short carbon precursors. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing and creating a need for computational analysis methods for efficient polyketide research. Moreover, the increasing use of genetic engineering to research new kinds of polyketides requires genome wide analysis.'),
(91676, 2007, 'bmc', 'GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest', 'Diaz-Uriarte, Ramón', 'Statistical Computing Team, Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, Madrid, 28029, Spain. rdiaz02@gmail.com', 'Spain', 96, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-328', '17767709', 'Microarray data are often used for patient classification and gene selection. An appropriate tool for end users and biomedical researchers should combine user friendliness with statistical rigor, including carefully avoiding selection biases and allowing analysis of multiple solutions, together with access to additional functional information of selected genes. Methodologically, such a tool would be of greater use if it incorporates state-of-the-art computational approaches and makes source code available.'),
(91677, 2007, 'bmc', 'Gene analogue finder: a GRID solution for finding functionally analogous gene products', 'Tulipano, Angelica|Donvito, Giacinto|Licciulli, Flavio|Maggi, Giorgio|Gisel, Andreas', 'Dipartimento Interateneo di Fisica, Università e Politecnico di Bari, via Amendola 173, 70126 Bari, Italy. angelica.tulipano@ba.infn.it|Dipartimento Interateneo di Fisica, Università e Politecnico di Bari, via Amendola 173, 70126 Bari, Italy. angelica.tulipano@ba.infn.it|Dipartimento Interateneo di Fisica, Università e Politecnico di Bari, via Amendola 173, 70126 Bari, Italy. angelica.tulipano@ba.infn.it|Dipartimento Interateneo di Fisica, Università e Politecnico di Bari, via Amendola 173, 70126 Bari, Italy. angelica.tulipano@ba.infn.it|Dipartimento Interateneo di Fisica, Università e Politecnico di Bari, via Amendola 173, 70126 Bari, Italy. angelica.tulipano@ba.infn.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Animals|Computational Biology|Database Management Systems|Databases, Genetic|Humans|Software', 'Journal Article', '10.1186/1471-2105-8-329', '17767718', 'To date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources.'),
(91678, 2007, 'bmc', 'Protein subcellular localization prediction based on compartment-specific features and structure conservation', 'Su, Emily Chia-Yu|Chiu, Hua-Sheng|Lo, Allan|Hwang, Jenn-Kang|Sung, Ting-Yi|Hsu, Wen-Lian', 'Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw|Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan. cysu@iis.sinica.edu.tw', '-|-|-|-|-|-', 54, 'Amino Acid Sequence|Bacterial Proteins|Gene Expression Profiling|Gram-Negative Bacteria|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship|Subcellular Fractions', 'Evaluation Studies', '10.1186/1471-2105-8-330', '17825110', 'Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming, thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins.'),
(91679, 2007, 'bmc', 'A novel approach to detect hot-spots in large-scale multivariate data', 'Wu, Jianhua|Kendrick, Keith M|Feng, Jianfeng', 'Department of Computer Science, Warwick University, Coventry CV4 7AL, UK. Jianhua.Wu@warwick.ac.uk|Department of Computer Science, Warwick University, Coventry CV4 7AL, UK. Jianhua.Wu@warwick.ac.uk|Department of Computer Science, Warwick University, Coventry CV4 7AL, UK. Jianhua.Wu@warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|Brain Mapping|Computer Simulation|Female|Gene Expression Profiling|Humans|Magnetic Resonance Imaging|Male|Multivariate Analysis|Numerical Analysis, Computer-Assisted|Principal Component Analysis|ROC Curve', 'Journal Article', '10.1186/1471-2105-8-331', '17848185', 'Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult to identify the variables (genes, proteins, neurons etc.) significantly changed activities in response to a stimulus within large data sets using conventional statistical approaches. The set of all changed variables is termed hot-spots. The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution.'),
(91680, 2007, 'bmc', 'How to decide which are the most pertinent overly-represented features during gene set enrichment analysis', 'Barriot, Roland|Sherman, David J|Dutour, Isabelle', 'CBiB, Bordeaux Bioinformatics Center, Université Victor Segalen Bordeaux 2, 146 Rue Léo Saignat, 33076 Bordeaux, France. rbarriot@esat.kuleuven.be|CBiB, Bordeaux Bioinformatics Center, Université Victor Segalen Bordeaux 2, 146 Rue Léo Saignat, 33076 Bordeaux, France. rbarriot@esat.kuleuven.be|CBiB, Bordeaux Bioinformatics Center, Université Victor Segalen Bordeaux 2, 146 Rue Léo Saignat, 33076 Bordeaux, France. rbarriot@esat.kuleuven.be', 'France|France|France', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Data Compression|Database Management Systems|Databases, Genetic|Databases, Protein|Efficiency|Gene Expression Profiling|Information Theory|Pattern Recognition, Automated|Proteins|Structure-Activity Relationship|Terminology as Topic|Work Simplification', 'Journal Article', '10.1186/1471-2105-8-332', '17848190', 'The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.'),
(91681, 2007, 'bmc', 'Integrating sequence and structural biology with DAS', 'Prlixc4x87, Andreas|Down, Thomas A|Kulesha, Eugene|Finn, Robert D|Kxc3xa4hxc3xa4ri, Andreas|Hubbard, Tim J P', 'The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk|The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. ap3@sanger.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 54, 'Algorithms|Chromosome Mapping|Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Sequence Analysis|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-333', '17850653', 'The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence.'),
(91682, 2007, 'bmc', 'EDISA: extracting biclusters from multiple time-series of gene expression profiles', 'Supper, Jochen|Strauch, Martin|Wanke, Dierk|Harter, Klaus|Zell, Andreas', 'Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany. Jochen.Supper@uni-tuebingen.de|Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany. Jochen.Supper@uni-tuebingen.de|Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany. Jochen.Supper@uni-tuebingen.de|Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany. Jochen.Supper@uni-tuebingen.de|Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Sand 1, 72076 Tübingen, Germany. Jochen.Supper@uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Pattern Recognition, Automated|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-8-334', '17850657', 'Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91683, 2007, 'bmc', 'PathFinder: mining signal transduction pathway segments from protein-protein interaction networks', 'Bebek, Gurkan|Yang, Jiong', 'EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu|EECS Department, Case Western Reserve University, Cleveland, OH 44106, USA. gurkan.bebek@case.edu', 'United States|United States', 89, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-8-335', '17854489', 'A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.'),
(91684, 2007, 'bmc', 'Reduced modeling of signal transduction - a modular approach', 'Koschorreck, Markus|Conzelmann, Holger|Ebert, Sybille|Ederer, Michael|Gilles, Ernst Dieter', 'Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de|Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de|Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de|Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de|Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr, 1, 39106 Magdeburg, Germany. koschorreck@mpi-magdeburg.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 31, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-8-336', '17854494', 'Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations.'),
(91685, 2007, 'bmc', 'Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs', 'Rashid, Mamoon|Saha, Sudipto|Raghava, Gajendra Ps', 'Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. mamoon@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. mamoon@imtech.res.in|Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India. mamoon@imtech.res.in', 'India|India|India', 100, 'Amino Acid Motifs|Amino Acid Sequence|Artificial Intelligence|Bacterial Proteins|Databases, Protein|Molecular Sequence Data|Mycobacterium tuberculosis|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-8-337', '17854501', 'In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.'),
(91686, 2007, 'bmc', 'OntologyWidget - a reusable, embeddable widget for easily locating ontology terms', 'Beauheim, Catherine C|Wymore, Farrell|Nitzberg, Michael|Zachariah, Zachariah K|Jin, Heng|Skene, J H Pate|Ball, Catherine A|Sherlock, Gavin', 'Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu|Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305-5120, USA. beauheim@genome.stanford.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 2, 'Computational Biology|Programming Languages|Software|Terminology as Topic|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-8-338', '17854506', 'Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form.'),
(91687, 2007, 'bmc', 'A Bayesian nonparametric method for prediction in EST analysis', 'Lijoi, Antonio|Mena, Ramsés H|Prünster, Igor', 'Department of Economics and Quantitative Methods, University of Pavia, 27100 Pavia and Institute for Applied Mathematics and Information Technology, National Research Council, 20133 Milan, Italy. lijoi@unipv.it|Department of Economics and Quantitative Methods, University of Pavia, 27100 Pavia and Institute for Applied Mathematics and Information Technology, National Research Council, 20133 Milan, Italy. lijoi@unipv.it|Department of Economics and Quantitative Methods, University of Pavia, 27100 Pavia and Institute for Applied Mathematics and Information Technology, National Research Council, 20133 Milan, Italy. lijoi@unipv.it', 'Italy|Italy|Italy', 23, 'Algorithms|Amoeba|Animals|Bayes Theorem|Chromosome Mapping|Decision Theory|Expressed Sequence Tags|Gene Expression Profiling|Gene Library|Models, Genetic|Models, Statistical|Predictive Value of Tests|Sample Size|Sequence Analysis, DNA|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-8-339', '17868445', 'Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library.'),
(91688, 2007, 'bmc', 'Impact of image segmentation on high-content screening data quality for SK-BR-3 cells', 'Hill, Andrew A|LaPan, Peter|Li, Yizheng|Haney, Steve', 'Department of Biological Technologies, Wyeth Research, 35 CambridgePark Drive, Cambridge, MA 02140, USA. ahill@wyeth.com|Department of Biological Technologies, Wyeth Research, 35 CambridgePark Drive, Cambridge, MA 02140, USA. ahill@wyeth.com|Department of Biological Technologies, Wyeth Research, 35 CambridgePark Drive, Cambridge, MA 02140, USA. ahill@wyeth.com|Department of Biological Technologies, Wyeth Research, 35 CambridgePark Drive, Cambridge, MA 02140, USA. ahill@wyeth.com', 'United States|United States|United States|United States', 23, 'Cell Line, Tumor|Cells|Humans|Image Interpretation, Computer-Assisted|Research Design', 'Journal Article', '10.1186/1471-2105-8-340', '17868449', 'High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for HCS-unfriendly cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.'),
(91689, 2007, 'bmc', 'MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans', 'Huang, Ting-Hua|Fan, Bin|Rothschild, Max F|Hu, Zhi-Liang|Li, Kui|Zhao, Shu-Hong', 'Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn|Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn|Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn|Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn|Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn|Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan, P R China. huangtinghua@webmail.hzau.edu.cn', 'China|China|China|China|China|China', 98, 'Animals|Artificial Intelligence|Base Sequence|Computational Biology|Computer Simulation|Gene Expression Profiling|Genome|Humans|MicroRNAs|Models, Genetic|Nucleic Acid Conformation|RNA Precursors|Sequence Alignment|Sequence Analysis, RNA|Software Design|Species Specificity', 'Journal Article', '10.1186/1471-2105-8-341', '17868480', 'MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.'),
(91690, 2007, 'bmc', 'A replica exchange Monte Carlo algorithm for protein folding in the HP model', 'Thachuk, Chris|Shmygelska, Alena|Hoos, Holger H', 'Department of Computer Science, University of British Columbia, BC, V6T 1Z4, Canada. cthachuk@sfu.ca|Department of Computer Science, University of British Columbia, BC, V6T 1Z4, Canada. cthachuk@sfu.ca|Department of Computer Science, University of British Columbia, BC, V6T 1Z4, Canada. cthachuk@sfu.ca', 'Canada|Canada|Canada', 75, 'Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Protein Conformation|Protein Folding|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-342', '17875212', 'The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function, it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein folding problem is computationally challenging and has been shown to be NuRho -hard even when conformations are restricted to a lattice. In this work, we implement and evaluate the replica exchange Monte Carlo (REMC) method, which has already been applied very successfully to more complex protein models and other optimization problems with complex energy landscapes, in combination with the highly effective pull move neighbourhood in two widely studied Hydrophobic Polar (HP) lattice models.'),
(91691, 2007, 'bmc', 'Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model', 'Morioka, Ryoko|Kanaya, Shigehiko|Hirai, Masami Y|Yano, Mitsuru|Ogasawara, Naotake|Saito, Kazuki', 'RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp|RIKEN Plant Science Center, Yokohama, Kanagawa, Japan. ryoko@psc.riken.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 12, 'Arabidopsis|Bacillus subtilis|Gene Expression Profiling|Linear Models|Markov Chains|Time Factors', 'Journal Article', '10.1186/1471-2105-8-343', '17875221', 'Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series data. Objective criteria that can be used to evaluate dynamical changes in data are therefore important to filter experimental noise and to enable extraction of unexpected, biologically important information.'),
(91692, 2007, 'bmc', 'Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context', 'Faith, Jeremiah J|Olson, Andrew J|Gardner, Timothy S|Sachidanandam, Ravi', 'Bioinformatics Program, Boston University, USA. faith@bu.edu|Bioinformatics Program, Boston University, USA. faith@bu.edu|Bioinformatics Program, Boston University, USA. faith@bu.edu|Bioinformatics Program, Boston University, USA. faith@bu.edu', 'United States|United States|United States|United States', 8, 'Chromosome Mapping|Computational Biology|Computer Graphics|Database Management Systems|Gene Order|Genome, Bacterial|Genome, Fungal|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Quantitative Trait Loci|Sequence Analysis, DNA|Synteny|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-344', '17877794', 'Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context. With database-based systems, displaying dynamic results requires temporary tables that need to be tracked for removal.'),
(91693, 2007, 'bmc', 'Benchmarking consensus model quality assessment for protein fold recognition', 'McGuffin, Liam J', 'The School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk', 'United Kingdom', 64, 'Algorithms|Cluster Analysis|Confidence Intervals|Databases, Protein|Meta-Analysis as Topic|Models, Biological|Models, Molecular|Pattern Recognition, Automated|Predictive Value of Tests|Protein Conformation|Protein Folding|Proteins|Research Design', 'Journal Article', '10.1186/1471-2105-8-345', '17877795', 'Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called true MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network.'),
(91694, 2007, 'bmc', 'A framework for significance analysis of gene expression data using dimension reduction methods', 'Gidskehaug, Lars|Anderssen, Endre|Flatberg, Arnar|Alsberg, Bjørn K', 'Chemometrics and Bioinformatics Group, Department of Chemistry, Norwegian University of Science and Technology, N-7491 Trondheim, Norway. gidskeha@phys.chem.ntnu.no|Chemometrics and Bioinformatics Group, Department of Chemistry, Norwegian University of Science and Technology, N-7491 Trondheim, Norway. gidskeha@phys.chem.ntnu.no|Chemometrics and Bioinformatics Group, Department of Chemistry, Norwegian University of Science and Technology, N-7491 Trondheim, Norway. gidskeha@phys.chem.ntnu.no|Chemometrics and Bioinformatics Group, Department of Chemistry, Norwegian University of Science and Technology, N-7491 Trondheim, Norway. gidskeha@phys.chem.ntnu.no', 'Norway|Norway|Norway|Norway', 31, 'Algorithms|Artificial Intelligence|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-8-346', '17877799', 'The most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important for any number of categorical or continuous responses. We introduce a general scheme for analysis of expression data that combines significance testing with the interpretative advantages of the dimension reduction methods. This approach is applicable both for explorative analysis and for classification and regression problems.'),
(91695, 2007, 'bmc', 'A unified framework for finding differentially expressed genes from microarray experiments', 'Shaik, Jahangheer S|Yeasin, Mohammed', 'Department of Electrical and Computer Engineering, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA. jshaik@memphis.edu|Department of Electrical and Computer Engineering, CVPIA Lab, University of Memphis, Memphis, TN-38152, USA. jshaik@memphis.edu', 'United States|United States', 15, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-8-347', '17877806', 'This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fishers omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework.'),
(91696, 2007, 'bmc', 'PADB: published association database', 'Rhee, Hwanseok|Lee, Jin-Sung', 'Department of Clinical Genetics, Yonsei University College of Medicine, Seoul, Korea. hwanseok@yonsei.ac.kr|Department of Clinical Genetics, Yonsei University College of Medicine, Seoul, Korea. hwanseok@yonsei.ac.kr', '-|-', 3, 'Database Management Systems|Databases, Genetic|Genetic Markers|Genetic Predisposition to Disease|Genome, Human|Humans|Internet|Polymorphism, Single Nucleotide', 'Journal Article', '10.1186/1471-2105-8-348', '17877839', 'Although molecular pathway information and the International HapMap Project data can help biomedical researchers to investigate the aetiology of complex diseases more effectively, such information is missing or insufficient in current genetic association databases. In addition, only a few of the environmental risk factors are included as gene-environment interactions, and the risk measures of associations are not indexed in any association databases.'),
(91697, 2007, 'bmc', 'A cross-species alignment tool (CAT)', 'Li, Heng|Guan, Liang|Liu, Tao|Guo, Yiran|Zheng, Wei-Mou|Wong, Gane Ka-Shu|Wang, Jun', 'Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk|Beijing Institute of Genomics of Chinese Academy of Sciences, Beijing Genomics Institute, Beijing 101300, China. lh3@sanger.ac.uk', 'China|China|China|China|China|China|China', 2, 'Algorithms|Animals|Base Sequence|Chromosome Mapping|Humans|Molecular Sequence Data|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-8-349', '17880681', 'The main two sorts of automatic gene annotation frameworks are ab initio and alignment-based, the latter splitting into two sub-groups. The first group is used for intra-species alignments, among which are successful ones with high specificity and speed. The other group contains more sensitive methods which are usually applied in aligning inter-species sequences.'),
(91698, 2007, 'bmc', 'Selection of long oligonucleotides for gene expression microarrays using weighted rank-sum strategy', 'Hu, Guangan|Llinás, Manuel|Li, Jingguang|Preiser, Peter Rainer|Bozdech, Zbynek', 'School of Biological Sciences, Nanyang Technological University, No, 60 Nanyang Drive, 637551, Singapore. hu0002an@ntu.edu.sg|School of Biological Sciences, Nanyang Technological University, No, 60 Nanyang Drive, 637551, Singapore. hu0002an@ntu.edu.sg|School of Biological Sciences, Nanyang Technological University, No, 60 Nanyang Drive, 637551, Singapore. hu0002an@ntu.edu.sg|School of Biological Sciences, Nanyang Technological University, No, 60 Nanyang Drive, 637551, Singapore. hu0002an@ntu.edu.sg|School of Biological Sciences, Nanyang Technological University, No, 60 Nanyang Drive, 637551, Singapore. hu0002an@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 45, 'Algorithms|Base Sequence|DNA Probes|Data Interpretation, Statistical|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-350', '17880708', 'The design of long oligonucleotides for spotted DNA microarrays requires detailed attention to ensure their optimal performance in the hybridization process. The main challenge is to select an optimal oligonucleotide element that represents each genetic locus/gene in the genome and is unique, devoid of internal structures and repetitive sequences and its Tm is uniform with all other elements on the microarray. Currently, all of the publicly available programs for DNA long oligonucleotide microarray selection utilize various combinations of cutoffs in which each parameter (uniqueness, Tm, and secondary structure) is evaluated and filtered individually. The use of the cutoffs can, however, lead to information loss and to selection of suboptimal oligonucleotides, especially for genomes with extreme distribution of the GC content, a large proportion of repetitive sequences or the presence of large gene families with highly homologous members.'),
(91699, 2007, 'bmc', 'A machine learning approach for the identification of odorant binding proteins from sequence-derived properties', 'Pugalenthi, Ganesan|Tang, Ke|Suganthan, P N|Archunan, G|Sowdhamini, R', 'School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ganesan@ntu.edu.sg|School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ganesan@ntu.edu.sg|School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ganesan@ntu.edu.sg|School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ganesan@ntu.edu.sg|School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore. ganesan@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Protein Interaction Mapping|Receptors, Odorant|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-351', '17880712', 'Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins.'),
(91700, 2007, 'bmc', 'Tandem mass spectrometry data quality assessment by self-convolution', 'Choo, Keng Wah|Tham, Wai Mun', 'Bioinformatics Group, Nanyang Polytechnic, 569830 Singapore, Republic Of Singapore. choo_keng_wah@nyp.edu.sg|Bioinformatics Group, Nanyang Polytechnic, 569830 Singapore, Republic Of Singapore. choo_keng_wah@nyp.edu.sg', 'Singapore|Singapore', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Databases, Protein|Fourier Analysis|Models, Chemical|Models, Molecular|Numerical Analysis, Computer-Assisted|Peptide Mapping|Peptides|Predictive Value of Tests|Proteins|Reproducibility of Results|Research Design|Sensitivity and Specificity|Sequence Alignment|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-8-352', '17880728', 'Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.'),
(91701, 2007, 'bmc', 'Prediction of potential drug targets based on simple sequence properties', 'Li, Qingliang|Lai, Luhua', 'Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Structural Chemistry for Stable and Unstable Species, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China. qlli@pku.edu.cn|Beijing National Laboratory for Molecular Sciences, State Key Laboratory of Structural Chemistry for Stable and Unstable Species, College of Chemistry and Molecular Engineering, Peking University, 100871 Beijing, China. qlli@pku.edu.cn', 'China|China', 0, 'Computational Biology|Humans|Pharmaceutical Preparations|Proteins', 'Journal Article', '10.1186/1471-2105-8-353', '17883836', 'During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets.'),
(91702, 2007, 'bmc', 'SEARCHPATTOOL: a new method for mining the most specific frequent patterns for binding sites with application to prokaryotic DNA sequences', 'Elloumi, Fathi|Nason, Martha', 'Research Technology Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Blg50/R5505, Bethesda, MD 20892, USA. elloumi.fathi@epa.gov|Research Technology Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Blg50/R5505, Bethesda, MD 20892, USA. elloumi.fathi@epa.gov', 'United States|United States', 0, 'Algorithms|Bacillus subtilis|Base Sequence|Binding Sites|Computational Biology|Conserved Sequence|DNA, Bacterial|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-354', '17883842', 'Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limited to short ones or impose some constraints on the pattern type. Many patterns for binding sites in prokaryotic species are not well characterized but are known to be large, between 16-30 base pairs (bp) and contain at least 2 conserved bases. The length of prokaryotic species promoters (about 400 bp) and our interest in studying a small set of genes that could be a cluster of co-regulated genes from microarray experiments led to the development of a new exhaustive algorithm targeting these large patterns.'),
(91703, 2007, 'bmc', 'Accuracy of structure-based sequence alignment of automatic methods', 'Kim, Changhoon|Lee, Byungkook', 'Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute National Institutes of Health, Bethesda, Maryland, USA. kimchan@mail.nih.gov|Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute National Institutes of Health, Bethesda, Maryland, USA. kimchan@mail.nih.gov', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Software Validation', 'Comparative Study', '10.1186/1471-2105-8-355', '17883866', 'Accurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved than sequence, structure alignments have been used to guide sequence alignments and are commonly used as the gold standard for sequence alignment evaluation. Nonetheless, as far as we know, there is no report of a systematic evaluation of pairwise structure alignment programs in terms of the sequence alignment accuracy.'),
(91704, 2007, 'bmc', 'Identification of homologs in insignificant blast hits by exploiting extrinsic gene properties', 'Boekhorst, Jos|Snel, Berend', 'Bioinformatics, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH, The Netherlands. J.Boekhorst@uu.nl|Bioinformatics, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH, The Netherlands. J.Boekhorst@uu.nl', 'Netherlands|Netherlands', 0, 'Amino Acid Sequence|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-8-356', '17888146', 'Homology is a key concept in both evolutionary biology and genomics. Detection of homology is crucial in fields like the functional annotation of protein sequences and the identification of taxon specific genes. Basic homology searches are still frequently performed by pairwise search methods such as BLAST. Vast improvements have been made in the identification of homologous proteins by using more advanced methods that use sequence profiles. However additional improvement could be made by exploiting sources of genomic information other than the primary sequence or tertiary structure.'),
(91705, 2007, 'bmc', 'An evolutionary method for learning HMM structure: prediction of protein secondary structure', 'Won, Kyoung-Jae|Hamelryck, Thomas|Prügel-Bennett, Adam|Krogh, Anders', 'Bioinformatics Centre, Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark. won@binf.ku.dk|Bioinformatics Centre, Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark. won@binf.ku.dk|Bioinformatics Centre, Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark. won@binf.ku.dk|Bioinformatics Centre, Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200 Copenhagen, Denmark. won@binf.ku.dk', 'Denmark|Denmark|Denmark|Denmark', 40, 'Algorithms|Amino Acid Sequence|Computer Simulation|Markov Chains|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-8-357', '17888163', 'The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs).'),
(91706, 2007, 'bmc', 'Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements', 'Lan, Hui|Carson, Rachel|Provart, Nicholas J|Bonner, Anthony J', 'Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada. lanhui@cs.toronto.edu|Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada. lanhui@cs.toronto.edu|Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada. lanhui@cs.toronto.edu|Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada. lanhui@cs.toronto.edu', 'Canada|Canada|Canada|Canada', 29, 'Arabidopsis|Artificial Intelligence|Cluster Analysis|Cold Temperature|Gene Expression Profiling|Gene Expression Regulation, Plant|Genes, Plant|Predictive Value of Tests|ROC Curve|Sensitivity and Specificity|Sequence Analysis, DNA|Sodium Chloride', 'Journal Article', '10.1186/1471-2105-8-358', '17888165', 'Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress.'),
(91707, 2007, 'bmc', 'Differential analysis for high density tiling microarray data', 'Ghosh, Srinka|Hirsch, Heather A|Sekinger, Edward A|Kapranov, Philipp|Struhl, Kevin|Gingeras, Thomas R', 'Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com|Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com|Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com|Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com|Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com|Affymetrix Inc,, Santa Clara, CA 95051, USA. srinka_ghosh@affymetrix.com', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Computational Biology|DNA Probes|Decision Theory|Gene Components|Gene Expression Profiling|Genomics|HL-60 Cells|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Regulatory Sequences, Nucleic Acid|Transcription, Genetic|Tretinoin', 'Journal Article', '10.1186/1471-2105-8-359', '17892592', 'High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program.'),
(91708, 2007, 'bmc', 'Linking microarray reporters with protein functions', 'Gaj, Stan|van Erk, Arie|van Haaften, Rachel I M|Evelo, Chris T A', 'Nutrigenomics Consortium, Top Institute Food and Nutrition, Wageningen, The Netherlands. Stan.Gaj@BiGCaT.unimaas.nl|Nutrigenomics Consortium, Top Institute Food and Nutrition, Wageningen, The Netherlands. Stan.Gaj@BiGCaT.unimaas.nl|Nutrigenomics Consortium, Top Institute Food and Nutrition, Wageningen, The Netherlands. Stan.Gaj@BiGCaT.unimaas.nl|Nutrigenomics Consortium, Top Institute Food and Nutrition, Wageningen, The Netherlands. Stan.Gaj@BiGCaT.unimaas.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Base Sequence|Gene Expression Profiling|Genes, Reporter|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Proteome|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-360', '17897448', 'The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways.'),
(91709, 2007, 'bmc', 'Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines', 'Bui, Huynh-Hoa|Sidney, John|Li, Wei|Fusseder, Nicolas|Sette, Alessandro', 'La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA. hbui@isisph.com|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA. hbui@isisph.com|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA. hbui@isisph.com|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA. hbui@isisph.com|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 9420 Athena Circle, La Jolla, CA 92037, USA. hbui@isisph.com', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Sequence|Animals|Computational Biology|Computer-Aided Design|Conserved Sequence|Diagnosis, Computer-Assisted|Drug Design|Epitope Mapping|Epitopes|Humans|Internet|Sequence Alignment|Sequence Analysis, Protein|User-Computer Interface|Vaccines', 'Journal Article', '10.1186/1471-2105-8-361', '17897458', 'In an epitope-based vaccine setting, the use of conserved epitopes would be expected to provide broader protection across multiple strains, or even species, than epitopes derived from highly variable genome regions. Conversely, in a diagnostic and disease monitoring setting, epitopes that are specific to a given pathogen strain, for example, can be used to monitor responses to that particular infectious strain. In both cases, concrete information pertaining to the degree of conservancy of the epitope(s) considered is crucial.'),
(91710, 2007, 'bmc', 'Design and implementation of a generalized laboratory data model', 'Wendl, Michael C|Smith, Scott|Pohl, Craig S|Dooling, David J|Chinwalla, Asif T|Crouse, Kevin|Hepler, Todd|Leong, Shin|Carmichael, Lynn|Nhan, Mike|Oberkfell, Benjamin J|Mardis, Elaine R|Hillier, LaDeana W|Wilson, Richard K', 'Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu|Genome Sequencing Center, Washington University, St, Louis, MO 63108, USA. mwendl@wustl.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Laboratories|Models, Theoretical|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-362', '17897463', 'Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.'),
(91711, 2007, 'bmc', 'Estimation of the number of extreme pathways for metabolic networks', 'Yeung, Matthew|Thiele, Ines|Palsson, Bernard O', 'Dept, of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. palsson@ucsd.edu.|Dept, of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. palsson@ucsd.edu.|Dept, of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA. palsson@ucsd.edu.', 'United States|United States|United States', 0, '-', 'Journal Article', '10.1186/1471-2105-8-363', '17897474', 'ABSTRACT: BACKGROUND: The set of extreme pathways (ExPa), {pi}, defines the convex basis vectors used for the mathematical characterization of the null space of the stoichiometric matrix for biochemical reaction networks. ExPa analysis has been used for a number of studies to determine properties of metabolic networks as well as to obtain insight into their physiological and functional states in silico. However, the number of ExPas, p = |{pi}|, grows with the size and complexity of the network being studied, and this poses a computational challenge. For this study, we investigated the relationship between the number of extreme pathways and simple network properties. RESULTS: We established an estimating function for the number of ExPas using these easily obtainable network measurements. In particular, it was found that log [p] had an exponential relationship with log[ summation operatori=1Rd-id+ici] MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacyGGSbaBcqGGVbWBcqGGNbWzdaWadaqaamaaqadabaGaemizaq2aaSbaaSqaaiabgkHiTmaaBaaameaacqWGPbqAaeqaaaWcbeaakiabdsgaKnaaBaaaleaacqGHRaWkdaWgaaadbaGaemyAaKgabeaaaSqabaGccqWGJbWydaWgaaWcbaGaemyAaKgabeaaaeaacqWGPbqAcqGH9aqpcqaIXaqmaeaacqWGsbGua0GaeyyeIuoaaOGaay5waiaaw2faaaaa@4414@, where R = |Reff| is the number of active reactions in a network, d-i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGKbazdaWgaaWcbaGaeyOeI0YaaSbaaWqaaiabdMgaPbqabaaaleqaaaaa@30A9@ and d+i MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGKbazdaWgaaWcbaGaey4kaSYaaSbaaWqaaiabdMgaPbqabaaaleqaaaaa@309E@ the incoming and outgoing degrees of the reactions ri in Reff, and ci the clustering coefficient for each active reaction. CONCLUSION: This relationship typically gave an estimate of the number of extreme pathways to within a factor of 10 of the true number. Such a function providing an estimate for the total number of ExPas for a given system will enable researchers to decide whether ExPas analysis is an appropriate investigative tool.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(91712, 2007, 'bmc', 'A latent variable approach for meta-analysis of gene expression data from multiple microarray experiments', 'Choi, Hyungwon|Shen, Ronglai|Chinnaiyan, Arul M|Ghosh, Debashis', 'Department of Statistics and Huck Institute for Life Sciences, Penn State University, University Park, PA, USA. hwchoi@umich.edu|Department of Statistics and Huck Institute for Life Sciences, Penn State University, University Park, PA, USA. hwchoi@umich.edu|Department of Statistics and Huck Institute for Life Sciences, Penn State University, University Park, PA, USA. hwchoi@umich.edu|Department of Statistics and Huck Institute for Life Sciences, Penn State University, University Park, PA, USA. hwchoi@umich.edu', 'United States|United States|United States|United States', 46, 'Biomarkers, Tumor|Carcinoma|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Markov Chains|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1186/1471-2105-8-364', '17900369', 'With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies.'),
(91713, 2007, 'bmc', 'Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype', 'Martínez-Pérez, Israel M|Zhang, Gong|Ignatova, Zoya|Zimmermann, Karl-Heinz', 'Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de|Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de|Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de|Institute of Computer Technology, Hamburg University of Technology, Hamburg 21073, Germany. martinez-perez@tu-harburg.de', 'Germany|Germany|Germany|Germany', 9, 'Computer Simulation|Computers, Molecular|DNA|DNA Mutational Analysis|Gene Targeting|Genetic Predisposition to Disease|Genetic Testing|Genetic Therapy|Models, Genetic|Mutation|Phenotype', 'Journal Article', '10.1186/1471-2105-8-365', '17903261', 'A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.'),
(91714, 2007, 'bmc', 'Transcriptional regulation at a glance', 'Sperling, Silke', 'Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. sperling@molgen.mpg.de', 'Germany', 0, 'Gene Expression Regulation|Models, Genetic|Proteome|RNA|Transcription Factors|Transcription, Genetic|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-8-S6-S2', '17903283', 'Considering that 80 genomes have been sequenced, providing us with the static information of the genome, it is still a long way to reveal the relationship between complex genotypes and phenotypes. The transcriptional regulation process is one of the obstacles that need to be understood to bridge our current information gap. It describes the first step from the genomic sequence information to RNA templates used for protein production or as direct functional units, like non-coding RNAs (e.g. micro RNAs). This introduction aims to highlight the key aspects of the transcriptional process from our current understanding.'),
(91715, 2007, 'bmc', 'Computational analyses of eukaryotic promoters', 'Zhang, Michael Q', 'Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.', 'United States', 28, 'Animals|Chromosome Mapping|Computational Biology|Humans|Models, Genetic|Promoter Regions, Genetic|Sequence Analysis, DNA|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-S6-S3', '17903284', 'Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005.'),
(91716, 2007, 'bmc', 'Finding regulatory elements and regulatory motifs: a general probabilistic framework', 'van Nimwegen, Erik', 'Biozentrum, University of Basel, and Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, Basel, Switzerland. erik.vannimwegan@unibas.ch', 'Switzerland', 38, 'Algorithms|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Models, Genetic|Models, Statistical|Phylogeny|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-S6-S4', '17903285', 'Over the last two decades a large number of algorithms has been developed for regulatory motif finding. Here we show how many of these algorithms, especially those that model binding specificities of regulatory factors with position specific weight matrices (WMs), naturally arise within a general Bayesian probabilistic framework. We discuss how WMs are constructed from sets of regulatory sites, how sites for a given WM can be discovered by scanning of large sequences, how to cluster WMs, and more generally how to cluster large sets of sites from different WMs into clusters. We discuss how regulatory modules, clusters of sites for subsets of WMs, can be found in large intergenic sequences, and we discuss different methods for ab initio motif finding, including expectation maximization (EM) algorithms, and motif sampling algorithms. Finally, we extensively discuss how module finding methods and ab initio motif finding methods can be extended to take phylogenetic relations between the input sequences into account, i.e. we show how motif finding and phylogenetic footprinting can be integrated in a rigorous probabilistic framework. The article is intended for readers with a solid background in applied mathematics, and preferably with some knowledge of general Bayesian probabilistic methods. The main purpose of the article is to elucidate that all these methods are not a disconnected set of individual algorithmic recipes, but that they are just different facets of a single integrated probabilistic theory.'),
(91717, 2007, 'bmc', 'Inferring cellular networks--a review', 'Markowetz, Florian|Spang, Rainer', 'Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. florian@genomics.princeton.edu|Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. florian@genomics.princeton.edu', 'Germany|Germany', 269, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-S6-S5', '17903286', 'In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.'),
(91718, 2007, 'bmc', 'Dissecting complex transcriptional responses using pathway-level scores based on prior information', 'Bussemaker, Harmen J|Ward, Lucas D|Boorsma, Andre', 'Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, MC 2441, New York, NY 10027, USA. hjb2004@columbia.edu|Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, MC 2441, New York, NY 10027, USA. hjb2004@columbia.edu|Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, MC 2441, New York, NY 10027, USA. hjb2004@columbia.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-S6-S6', '17903287', 'The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses.'),
(91719, 2007, 'bmc', 'From biophysics to evolutionary genetics: statistical aspects of gene regulation', 'Lässig, Michael', 'Institut für Theoretische Physik, Universität zu Köln, Zülpicher Str, 77, 50937 Köln, Germany. lassig@thp.uni-koeln.de', 'Germany', 59, 'Algorithms|Biophysics|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-S6-S7', '17903288', 'This is an introductory review on how genes interact to produce biological functions. Transcriptional interactions involve the binding of proteins to regulatory DNA. Specific binding sites can be identified by genomic analysis, and these undergo a stochastic evolution process governed by selection, mutations, and genetic drift. We focus on the links between the biophysical function and the evolution of regulatory elements. In particular, we infer fitness landscapes of binding sites from genomic data, leading to a quantitative evolutionary picture of regulation.'),
(91720, 2007, 'bmc', 'Graphs in molecular biology', 'Huber, Wolfgang|Carey, Vincent J|Long, Li|Falcon, Seth|Gentleman, Robert', 'European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK. huber@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 79, 'Algorithms|Computational Biology|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-8-S6-S8', '17903289', 'Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network.'),
(91721, 2007, 'bmc', 'Current approaches to gene regulatory network modelling', 'Schlitt, Thomas|Brazma, Alvis', 'Department of Medical and Molecular Genetics, Kings College London School of Medicine, 8th floor Guys Tower, London SE1 9RT, UK. thomas.schlitt@genetics.kcl.ac.uk|Department of Medical and Molecular Genetics, Kings College London School of Medicine, 8th floor Guys Tower, London SE1 9RT, UK. thomas.schlitt@genetics.kcl.ac.uk', 'United Kingdom|United Kingdom', 0, '-', 'Journal Article', '10.1186/1471-2105-8-S6-S9', '17903290', 'Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.'),
(91722, 2007, 'bmc', 'RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules', 'Horesh, Yair|Doniger, Tirza|Michaeli, Shulamit|Unger, Ron', 'The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il|The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il|The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il|The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel. yair@biomodel.os.biu.ac.il', 'Israel|Israel|Israel|Israel', 0, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-8-366', '17908318', 'In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.'),
(91723, 2007, 'bmc', 'BNDB - the Biochemical Network Database', 'Küntzer, Jan|Backes, Christina|Blum, Torsten|Gerasch, Andreas|Kaufmann, Michael|Kohlbacher, Oliver|Lenhof, Hans-Peter', 'Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de|Center for Bioinformatics, Saarland University, 66041 Saarbrücken, Germany. kuentzer@bioinf.uni-sb.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Protein Interaction Mapping|Proteome|Signal Transduction|Systems Integration|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-367', '17910766', 'Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources.'),
(91724, 2007, 'bmc', 'Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data', 'Baross, Agnes|Delaney, Allen D|Li, H Irene|Nayar, Tarun|Flibotte, Stephane|Qian, Hong|Chan, Susanna Y|Asano, Jennifer|Ally, Adrian|Cao, Manqiu|Birch, Patricia|Brown-John, Mabel|Fernandes, Nicole|Go, Anne|Kennedy, Giulia|Langlois, Sylvie|Eydoux, Patrice|Friedman, J M|Marra, Marco A', 'Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca|Genome Sciences Centre, BC Cancer Agency, British Columbia Cancer Agency, Suite 100, 570 West 7th Avenue, Vancouver, BC, V5Z 4S6, Canada. abaross@genomebc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Ca', 0, 'Adult|Algorithms|Child|Gene Dosage|Genetic Variation|Genome, Human|Genomics|Humans|Oligonucleotide Array Sequence Analysis|Software Validation', 'Comparative Study', '10.1186/1471-2105-8-368', '17910767', 'Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays.'),
(91725, 2007, 'bmc', 'Primique: automatic design of specific PCR primers for each sequence in a family', 'Fredslund, Jakob|Lange, Mette', 'BiRC - Bioinformatics Research Center, University of Aarhus, Høegh-Guldbergs Gade 10, Building 1090, DK-8000 Arhus C, Denmark. jakobf@birc.au.dk|BiRC - Bioinformatics Research Center, University of Aarhus, Høegh-Guldbergs Gade 10, Building 1090, DK-8000 Arhus C, Denmark. jakobf@birc.au.dk', 'Denmark|Denmark', 34, 'Algorithms|Artificial Intelligence|DNA Primers|Genomics|Internet|Polymerase Chain Reaction|Sequence Alignment|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-369', '17910777', 'In many contexts, researchers need specific primers for all sequences in a family such that each primer set amplifies only its target sequence and none of the others, e.g. to detect which transcription factor out of a family of very similar proteins that is present in a sample, or to design diagnostic assays for the identification of pathogen strains.'),
(91726, 2007, 'bmc', 'Gene selection for classification of microarray data based on the Bayes error', 'Zhang, Ji-Gang|Deng, Hong-Wen', 'Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, P, R, China. dengh@umkc.edu.|Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, P, R, China. dengh@umkc.edu.', 'China|China', 77, '-', 'Journal Article', '10.1186/1471-2105-8-370', '17915022', 'ABSTRACT: BACKGROUND: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy. RESULTS: In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes. CONCLUSION: The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.'),
(91727, 2007, 'bmc', 'Extended analysis of benchmark datasets for Agilent two-color microarrays', 'Kerr, Kathleen F', 'Department of Biostatistics, University of Washington, Seattle, Washington, USA. katiek@u.washington.edu', 'United States', 0, 'Algorithms|Benchmarking|Databases, Genetic|Microscopy, Fluorescence, Multiphoton|Oligonucleotide Array Sequence Analysis|Reference Standards|Reproducibility of Results|Sensitivity and Specificity|United States', 'Comparative Study', '10.1186/1471-2105-8-371', '17915030', 'As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC) project reported the results of experiments using External RNA Controls (ERCs) on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray.'),
(91728, 2007, 'bmc', 'Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases', 'Berger, Seth I|Posner, Jeremy M|Maayan, Avi', 'Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, 10029, New York, USA. seth.berger@mssm.edu|Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, 10029, New York, USA. seth.berger@mssm.edu|Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, 1425 Madison Avenue, New York, 10029, New York, USA. seth.berger@mssm.edu', 'United States|United States|United States', 110, 'Algorithms|Animals|Computational Biology|Databases, Protein|Humans|Internet|Mammals|Protein Binding|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-372', '17916244', 'In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes.'),
(91729, 2007, 'bmc', 'qPIPSA: relating enzymatic kinetic parameters and interaction fields', 'Gabdoulline, Razif R|Stein, Matthias|Wade, Rebecca C', 'Molecular and Cellular Modeling Group, EML Research gGmbH, Schloss Wolfsbrunnenweg 33, Heidelberg, 69118, Germany. razif.gabdoulline@eml-r.villa-bosch.de|Molecular and Cellular Modeling Group, EML Research gGmbH, Schloss Wolfsbrunnenweg 33, Heidelberg, 69118, Germany. razif.gabdoulline@eml-r.villa-bosch.de|Molecular and Cellular Modeling Group, EML Research gGmbH, Schloss Wolfsbrunnenweg 33, Heidelberg, 69118, Germany. razif.gabdoulline@eml-r.villa-bosch.de', 'Germany|Germany|Germany', 0, 'Algorithms|Binding Sites|Computer Simulation|Enzyme Activation|Enzymes|Kinetics|Models, Chemical|Protein Binding|Protein Conformation|Software', 'Journal Article', '10.1186/1471-2105-8-373', '17919319', 'The simulation of metabolic networks in quantitative systems biology requires the assignment of enzymatic kinetic parameters. Experimentally determined values are often not available and therefore computational methods to estimate these parameters are needed. It is possible to use the three-dimensional structure of an enzyme to perform simulations of a reaction and derive kinetic parameters. However, this is computationally demanding and requires detailed knowledge of the enzyme mechanism. We have therefore sought to develop a general, simple and computationally efficient procedure to relate protein structural information to enzymatic kinetic parameters that allows consistency between the kinetic and structural information to be checked and estimation of kinetic constants for structurally and mechanistically similar enzymes.'),
(91730, 2007, 'bmc', 'Statistical power of phylo-HMM for evolutionarily conserved element detection', 'Fan, Xiaodan|Zhu, Jun|Schadt, Eric E|Liu, Jun S', 'Department of Statistics, Harvard University, Boston, MA, USA. xfan@fas.harvard.edu|Department of Statistics, Harvard University, Boston, MA, USA. xfan@fas.harvard.edu|Department of Statistics, Harvard University, Boston, MA, USA. xfan@fas.harvard.edu|Department of Statistics, Harvard University, Boston, MA, USA. xfan@fas.harvard.edu', 'United States|United States|United States|United States', 8, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|Data Interpretation, Statistical|Evolution, Molecular|Markov Chains|Molecular Sequence Data|Phylogeny|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-8-374', '17919331', 'An important goal of comparative genomics is the identification of functional elements through conservation analysis. Phylo-HMM was recently introduced to detect conserved elements based on multiple genome alignments, but the method has not been rigorously evaluated.'),
(91731, 2007, 'bmc', 'Computational analysis of the relationship between allergenicity and digestibility of allergenic proteins in simulated gastric fluid', 'Jiang, Bingjun|Qu, Hong|Hu, Yuanlei|Ni, Ting|Lin, Zhongping', 'College of Life Sciences, National Lab of Protein Engineering and Genetic Engineering of Plants, Peking University, Beijing 100871, PR China. jiangbingjun@gmail.com|College of Life Sciences, National Lab of Protein Engineering and Genetic Engineering of Plants, Peking University, Beijing 100871, PR China. jiangbingjun@gmail.com|College of Life Sciences, National Lab of Protein Engineering and Genetic Engineering of Plants, Peking University, Beijing 100871, PR China. jiangbingjun@gmail.com|College of Life Sciences, National Lab of Protein Engineering and Genetic Engineering of Plants, Peking University, Beijing 100871, PR China. jiangbingjun@gmail.com|College of Life Sciences, National Lab of Protein Engineering and Genetic Engineering of Plants, Peking University, Beijing 100871, PR China. jiangbingjun@gmail.com', 'China|China|China|China|China', 0, 'Allergens|Body Fluids|Digestion|Food Hypersensitivity|Gastrointestinal Contents|Humans|Proteins|Risk Assessment|Risk Factors|Sequence Analysis, Protein|Statistics as Topic|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-8-375', '17922925', 'Safety assessment of genetically modified (GM) food, with regard to allergenic potential of transgene-encoded xenoproteins, typically involves several different methods, evaluation by digestibility being one thereof. However, there are still debates about whether the allergenicity of food allergens is related to their resistance to digestion by the gastric fluid. The disagreements may in part stem from classification of allergens only by their sources, which we believe is inadequate, and the difficulties in achieving identical experimental conditions for studying digestion by simulated gastric fluid (SGF) so that results can be compared. Here, we reclassify allergenic food allergens into alimentary canal-sensitized (ACS) and non-alimentary canal-sensitized (NACS) allergens and use a computational model that simulates gastric fluid digestion to analyze the digestibilities of these two types.'),
(91732, 2007, 'bmc', 'Single TNFalpha trimers mediating NF-kappaB activation: stochastic robustness of NF-kappaB signaling', 'Lipniacki, Tomasz|Puszynski, Krzysztof|Paszek, Pawel|Brasier, Allan R|Kimmel, Marek', 'Institute of Fundamental Technological Research, Swietokrzyska 21, 00-049 Warsaw, Poland. tlipnia@ippt.gov.pl|Institute of Fundamental Technological Research, Swietokrzyska 21, 00-049 Warsaw, Poland. tlipnia@ippt.gov.pl|Institute of Fundamental Technological Research, Swietokrzyska 21, 00-049 Warsaw, Poland. tlipnia@ippt.gov.pl|Institute of Fundamental Technological Research, Swietokrzyska 21, 00-049 Warsaw, Poland. tlipnia@ippt.gov.pl|Institute of Fundamental Technological Research, Swietokrzyska 21, 00-049 Warsaw, Poland. tlipnia@ippt.gov.pl', 'Poland|Poland|Poland|Poland|Poland', 0, 'Animals|Biological Transport|Dimerization|Dose-Response Relationship, Immunologic|Enzyme Activation|Gene Expression Regulation|Humans|I-kappa B Kinase|I-kappa B Proteins|Ligands|NF-KappaB Inhibitor alpha|NF-kappa B|Nonlinear Dynamics|Nuclear Envelope|Signal Transduction|Stochastic Processes|Transcriptional Activation|Tumor Necrosis Factor-alpha', 'Journal Article', '10.1186/1471-2105-8-376', '17925009', 'The NF-kappaB regulatory network controls innate immune response by transducing variety of pathogen-derived and cytokine stimuli into well defined single-cell gene regulatory events.'),
(91733, 2007, 'bmc', 'Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies', 'Hoehndorf, Robert|Loebe, Frank|Kelso, Janet|Herre, Heinrich', 'Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de|Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. hoehndorf@eva.mpg.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Anatomy|Animals|Artificial Intelligence|Computational Biology|Humans|Information Science|Knowledge|Natural Language Processing|Phenotype|Reproducibility of Results|Semantics|Software|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-8-377', '17925014', 'Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties - mostly phenotype ontologies - are combined with ontologies taking a canonical view of a domain - such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.'),
(91734, 2007, 'bmc', 'Toxicogenomic analysis incorporating operon-transcriptional coupling and toxicant concentration-expression response: analysis of MX-treated Salmonella', 'Ward, William O|Swartz, Carol D|Porwollik, Steffen|Warren, Sarah H|Hanley, Nancy M|Knapp, Geremy W|McClelland, Michael|DeMarini, David M', 'Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov|Environmental Carcinogenesis Division, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA. ward.william@epa.gov', 'United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Bacterial Proteins|Dose-Response Relationship, Drug|Furans|Gene Expression Profiling|Mutagens|Operon|Salmonella|Toxicogenetics|Transcription Factors', 'Journal Article', '10.1186/1471-2105-8-378', '17925033', 'Deficiencies in microarray technology cause unwanted variation in the hybridization signal, obscuring the true measurements of intracellular transcript levels. Here we describe a general method that can improve microarray analysis of toxicant-exposed cells that uses the intrinsic power of transcriptional coupling and toxicant concentration-expression response data. To illustrate this approach, we characterized changes in global gene expression induced in Salmonella typhimurium TA100 by 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX), the primary mutagen in chlorinated drinking water. We used the co-expression of genes within an operon and the monotonic increases or decreases in gene expression relative to increasing toxicant concentration to augment our identification of differentially expressed genes beyond Bayesian-t analysis.'),
(91735, 2007, 'bmc', 'RECOVIR: an application package to automatically identify some single stranded RNA viruses using capsid protein residues that uniquely distinguish among these viruses', 'Zhu, Dianhui|Fox, George E|Chakravarty, Sugoto', 'Dept of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. dianhuiz@bcm.tmc.edu|Dept of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. dianhuiz@bcm.tmc.edu|Dept of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. dianhuiz@bcm.tmc.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Capsid Proteins|Chromosome Mapping|Molecular Sequence Data|Pattern Recognition, Automated|RNA Viruses|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-8-379', '17927830', 'Most single stranded RNA (ssRNA) viruses mutate rapidly to generate large number of strains having highly divergent capsid sequences. Accurate strain recognition in uncharacterized target capsid sequences is essential for epidemiology, diagnostics, and vaccine development. Strain recognition based on similarity scores between target sequences and sequences of homology matched reference strains is often time consuming and ambiguous. This is especially true if only partial target sequences are available or if different ssRNA virus families are jointly analyzed. In such cases, knowledge of residues that uniquely distinguish among known reference strains is critical for rapid and unambiguous strain identification. Conventional sequence comparisons are unable to identify such capsid residues due to high sequence divergence among the ssRNA virus reference strains. Consequently, automated general methods to reliably identify strains using strain distinguishing residues are not currently available.'),
(91736, 2007, 'bmc', 'Detailed estimation of bioinformatics prediction reliability through the Fragmented Prediction Performance Plots', 'Carugo, Oliviero', 'Department of General Chemistry, Pavia University, Viale Taramelli 12, I-27100 Pavia, Italy. oliviero.carugo@univie.ac.at', 'Italy', 31, 'Computational Biology|Predictive Value of Tests|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-8-380', '17931407', 'An important and yet rather neglected question related to bioinformatics predictions is the estimation of the amount of data that is needed to allow reliable predictions. Bioinformatics predictions are usually validated through a series of figures of merit, like for example sensitivity and precision, and little attention is paid to the fact that their performance may depend on the amount of data used to make the predictions themselves.'),
(91737, 2007, 'bmc', 'PFAAT version 20: a tool for editing, annotating, and analyzing multiple sequence alignments', 'Caffrey, Daniel R|Dana, Paul H|Mathur, Vidhya|Ocano, Marco|Hong, Eun-Jong|Wang, Yaoyu E|Somaroo, Shyamal|Caffrey, Brian E|Potluri, Shobha|Huang, Enoch S', 'Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com|Pfizer Global Research and Development, 620 Memorial Drive, Cambridge, MA 02139, USA. daniel.caffrey@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 42, 'Amino Acid Sequence|Computer Simulation|Documentation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-8-381', '17931421', 'By virtue of their shared ancestry, homologous sequences are similar in their structure and function. Consequently, multiple sequence alignments are routinely used to identify trends that relate to function. This type of analysis is particularly productive when it is combined with structural and phylogenetic analysis.'),
(91738, 2007, 'bmc', 'XSTREAM: a practical algorithm for identification and architecture modeling of tandem repeats in protein sequences', 'Newman, Aaron M|Cooper, James B', 'Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA. a_newman@lifesci.ucsb.edu|Biomolecular Science and Engineering Program, University of California, Santa Barbara, CA 93106, USA. a_newman@lifesci.ucsb.edu', 'United States|United States', 0, 'Amino Acid Sequence|Artificial Intelligence|Databases, Protein|Genome, Protozoan|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid|Tandem Repeat Sequences', 'Journal Article', '10.1186/1471-2105-8-382', '17931424', 'Biological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed.'),
(91739, 2007, 'bmc', 'ProbCD: enrichment analysis accounting for categorization uncertainty', 'Vêncio, Ricardo Z N|Shmulevich, Ilya', 'Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com|Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com', 'United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Biological|Models, Statistical|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-8-383', '17935624', 'As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test.'),
(91740, 2007, 'bmc', 'Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling', 'Kwon, Yung-Keun|Choi, Sun Shim|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr', '-|-|-', 19, 'Algorithms|Computer Simulation|Feedback|Gene Expression Profiling|Logistic Models|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-8-384', '17935633', 'A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.'),
(91741, 2007, 'bmc', 'Discriminative motif discovery in DNA and protein sequences using the DEME algorithm', 'Redhead, Emma|Bailey, Timothy L', 'Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, 4072 Australia. e.redhead@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld, 4072 Australia. e.redhead@imb.uq.edu.au', 'Australia|Australia', 0, 'Algorithms|DNA|Discriminant Analysis|Proteins|Sequence Alignment|Sequence Analysis|Sequence Homology, Amino Acid|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-8-385', '17937785', 'Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.'),
(91742, 2007, 'bmc', 'Large scale statistical inference of signaling pathways from RNAi and microarray data', 'Froehlich, Holger|Fellmann, Mark|Sueltmann, Holger|Poustka, Annemarie|Beissbarth, Tim', 'German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de|German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. h.froehlich@dkfz-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany', 40, 'Cell Line, Tumor|Gene Regulatory Networks|Humans|Oligonucleotide Array Sequence Analysis|RNA Interference|Signal Transduction', 'Journal Article', '10.1186/1471-2105-8-386', '17937790', 'The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.');
