INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90753, 2006, 'bmc', 'Multiscale Hy3S: hybrid stochastic simulation for supercomputers', 'Salis, Howard|Sotiropoulos, Vassilios|Kaznessis, Yiannis N', 'Dept of Chemical Engineering & Materials Science and the Digital Technology Center, University of Minnesota, Minneapolis, Minnesota, 55455, USA. salis@cems.umn.edu|Dept of Chemical Engineering & Materials Science and the Digital Technology Center, University of Minnesota, Minneapolis, Minnesota, 55455, USA. salis@cems.umn.edu|Dept of Chemical Engineering & Materials Science and the Digital Technology Center, University of Minnesota, Minneapolis, Minnesota, 55455, USA. salis@cems.umn.edu', 'United States|United States|United States', 75, 'Cell Physiological Phenomena|Computer Simulation|Computing Methodologies|Models, Biological|Models, Statistical|Signal Transduction|Software|Stochastic Processes|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-7-93', '16504125', 'Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of small systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs.'),
(90754, 2006, 'bmc', 'GONOME: measuring correlations between GO terms and genomic positions', 'Stanley, Stefan M|Bailey, Timothy L|Mattick, John S', 'Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia. stanley@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia. stanley@imb.uq.edu.au|Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia. stanley@imb.uq.edu.au', 'Australia|Australia|Australia', 0, 'Algorithms|Chromosome Mapping|Database Management Systems|Databases, Genetic|Genome, Fungal|Proteasome Endopeptidase Complex|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA|Statistics as Topic', 'Evaluation Studies', '10.1186/1471-2105-7-94', '16504139', 'Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable balls in a bag, as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions.'),
(90755, 2006, 'bmc', 'Gene selection algorithms for microarray data based on least squares support vector machine', 'Tang, E Ke|Suganthan, P N|Yao, Xin', 'School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore. tangke@pmail.ntu.edu.sg|School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore. tangke@pmail.ntu.edu.sg|School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore. tangke@pmail.ntu.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Least-Squares Analysis|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-7-95', '16504159', 'In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes.'),
(90756, 2006, 'bmc', 'Microarray image analysis: background estimation using quantile and morphological filters', 'Bengtsson, Anders|Bengtsson, Henrik', 'Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. ab@maths.lth.se|Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. ab@maths.lth.se', 'Sweden|Sweden', 20, 'Algorithms|Animals|Gene Expression Profiling|Humans|Image Processing, Computer-Assisted|Microscopy, Confocal|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results', 'Comparative Study', '10.1186/1471-2105-7-96', '16504173', 'In a microarray experiment the difference in expression between genes on the same slide is up to 103 fold or more. At low expression, even a small error in the estimate will have great influence on the final test and reference ratios. In addition to the true spot intensity the scanned signal consists of different kinds of noise referred to as background. In order to assess the true spot intensity background must be subtracted. The standard approach to estimate background intensities is to assume they are equal to the intensity levels between spots. In the literature, morphological opening is suggested to be one of the best methods for estimating background this way.'),
(90757, 2006, 'bmc', 'The Ontology Lookup Service, a lightweight cross-platform tool for controlled vocabulary queries', 'Côté, Richard G|Jones, Philip|Apweiler, Rolf|Hermjakob, Henning', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rcote@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rcote@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rcote@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. rcote@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Database Management Systems|Databases, Factual|Information Storage and Retrieval|Natural Language Processing|Programming Languages|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-97', '16507094', 'With the vast amounts of biomedical data being generated by high-throughput analysis methods, controlled vocabularies and ontologies are becoming increasingly important to annotate units of information for ease of search and retrieval. Each scientific community tends to create its own locally available ontology. The interfaces to query these ontologies tend to vary from group to group. We saw the need for a centralized location to perform controlled vocabulary queries that would offer both a lightweight web-accessible user interface as well as a consistent, unified SOAP interface for automated queries.'),
(90758, 2006, 'bmc', 'REtools: a laboratory program for restriction enzyme work: enzyme selection and reaction condition assistance', 'Martin, Patrick|Boulukos, Kim E|Pognonec, Philippe', 'CNRS UMR 6548, Parc Valrose, Université de Nice Sophia Antipolis, Nice, France. pmartin@unice.fr|CNRS UMR 6548, Parc Valrose, Université de Nice Sophia Antipolis, Nice, France. pmartin@unice.fr|CNRS UMR 6548, Parc Valrose, Université de Nice Sophia Antipolis, Nice, France. pmartin@unice.fr', 'France|France|France', 0, 'DNA Restriction Enzymes|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-98', '16507099', 'Restriction enzymes are one of the everyday tools used in molecular biology. The continuously expanding panel of known restriction enzymes (several thousands) renders their optimal use virtually impossible without computerized assistance. Several manufacturers propose on-line sites that assist scientists in their restriction enzyme work, however, none of these sites meet all the actual needs of laboratory workers, and they do not take into account the enzymes actually present in ones own laboratory.'),
(90759, 2006, 'bmc', 'Determination of strongly overlapping signaling activity from microarray data', 'Bidaut, Ghislain|Suhre, Karsten|Claverie, Jean-Michel|Ochs, Michael F', 'Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA. ghbidaut@pcbi.upenn.edu|Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA. ghbidaut@pcbi.upenn.edu|Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA. ghbidaut@pcbi.upenn.edu|Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA. ghbidaut@pcbi.upenn.edu', 'United States|United States|United States|United States', 19, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-99', '16507110', 'As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups.'),
(90760, 2006, 'bmc', 'A multivariate prediction model for microarray cross-hybridization', 'Chen, Yian A|Chou, Cheng-Chung|Lu, Xinghua|Slate, Elizabeth H|Peck, Konan|Xu, Wenying|Voit, Eberhard O|Almeida, Jonas S', 'Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu|Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 28, 'Algorithms|Artifacts|Computer Simulation|In Situ Hybridization|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-101', '16509965', 'Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays, it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization.'),
(90761, 2006, 'bmc', 'SimArray: a user-friendly and user-configurable microarray design tool', 'Auburn, Richard P|Russell, Roslin R|Fischer, Bettina|Meadows, Lisa A|Sevillano Matilla, Santiago|Russell, Steven', 'Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk|Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk|Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk|Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk|Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk|Department of Genetics, University of Cambridge, Cambridge, UK. r.auburn@gen.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Documentation|Equipment Design|Equipment Failure Analysis|In Situ Hybridization, Fluorescence|Oligonucleotide Array Sequence Analysis|Robotics|Software|Software Design|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-102', '16509966', 'Microarrays were first developed to assess gene expression but are now also used to map protein-binding sites and to assess allelic variation between individuals. Regardless of the intended application, efficient production and appropriate array design are key determinants of experimental success. Inefficient production can make larger-scale studies prohibitively expensive, whereas poor array design makes normalisation and data analysis problematic.'),
(90762, 2006, 'bmc', 'Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method', 'Bengtsson, Henrik|Hössjer, Ola', 'Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. hb@maths.lth.se|Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. hb@maths.lth.se', 'Sweden|Sweden', 22, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Image Processing, Computer-Assisted|Likelihood Functions|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-7-100', '16509971', 'Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general.'),
(90763, 2006, 'bmc', 'SinicView: a visualization environment for comparisons of multiple nucleotide sequence alignment tools', 'Shih, Arthur Chun-Chieh|Lee, D T|Lin, Laurent|Peng, Chin-Lin|Chen, Shiang-Heng|Wu, Yu-Wei|Wong, Chun-Yi|Chou, Meng-Yuan|Shiao, Tze-Chang|Hsieh, Mu-Fen', 'Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan. arthur@iis.sinica.edu.tw', '-|-|-|-|-|-|-|-|-|-', 15, 'Algorithms|Base Sequence|Computer Graphics|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|Software Validation|User-Computer Interface', 'Comparative Study', '10.1186/1471-2105-7-103', '16509994', 'Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy.'),
(90764, 2006, 'bmc', 'SCOWLP: a web-based database for detailed characterization and visualization of protein interfaces', 'Teyra, Joan|Doms, Andreas|Schroeder, Michael|Pisabarro, M Teresa', 'Department of Bioinformatics, BIOTEC TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany. joan.teyra@biotec.tu-dresden.de|Department of Bioinformatics, BIOTEC TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany. joan.teyra@biotec.tu-dresden.de|Department of Bioinformatics, BIOTEC TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany. joan.teyra@biotec.tu-dresden.de|Department of Bioinformatics, BIOTEC TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany. joan.teyra@biotec.tu-dresden.de', 'Germany|Germany|Germany|Germany', 38, 'Algorithms|Binding Sites|Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-104', '16512892', 'Currently there is a strong need for methods that help to obtain an accurate description of protein interfaces in order to be able to understand the principles that govern molecular recognition and protein function. Many of the recent efforts to computationally identify and characterize protein networks extract protein interaction information at atomic resolution from the PDB. However, they pay none or little attention to small protein ligands and solvent. They are key components and mediators of protein interactions and fundamental for a complete description of protein interfaces. Interactome profiling requires the development of computational tools to extract and analyze protein-protein, protein-ligand and detailed solvent interaction information from the PDB in an automatic and comparative fashion. Adding this information to the existing one on protein-protein interactions will allow us to better understand protein interaction networks and protein function.'),
(90765, 2006, 'bmc', 'A simple method for assessing sample sizes in microarray experiments', 'Tibshirani, Robert', 'Health Research & Policy, Stanford University, Stanford, CA 94305, USA. tibs@stanford.edu', 'United States', 72, 'Computer Simulation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sample Size', 'Comparative Study', '10.1186/1471-2105-7-106', '16512900', 'In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments.'),
(90766, 2006, 'bmc', 'The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies', 'Verhaak, Roel G W|Staal, Frank J T|Valk, Peter J M|Lowenberg, Bob|Reinders, Marcel J T|de Ridder, Dick', 'Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 20, 'Algorithms|Biomarkers, Tumor|Clinical Trials as Topic|Cohort Studies|Databases, Genetic|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1186/1471-2105-7-105', '16512908', 'Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.'),
(90767, 2006, 'bmc', 'A quantitative analysis of secondary RNA structure using domination based parameters on trees', 'Haynes, Teresa|Knisley, Debra|Seier, Edith|Zou, Yue', 'Mathematics and Statistics Department, East Tennessee State University, Box 70663, Johnson City, TN, USA. haynes@etsu.edu|Mathematics and Statistics Department, East Tennessee State University, Box 70663, Johnson City, TN, USA. haynes@etsu.edu|Mathematics and Statistics Department, East Tennessee State University, Box 70663, Johnson City, TN, USA. haynes@etsu.edu|Mathematics and Statistics Department, East Tennessee State University, Box 70663, Johnson City, TN, USA. haynes@etsu.edu', 'United States|United States|United States|United States', 42, 'Algorithms|Computer Simulation|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Evaluation Studies', '10.1186/1471-2105-7-108', '16515683', 'It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure.'),
(90768, 2006, 'bmc', 'Evaluation of absolute quantitation by nonlinear regression in probe-based real-time PCR', 'Goll, Rasmus|Olsen, Trine|Cui, Guanglin|Florholmen, Jon', 'Institute of Clinical Medicine, University of Tromso, Tromso, Norway. rasmus.goll@unn.no|Institute of Clinical Medicine, University of Tromso, Tromso, Norway. rasmus.goll@unn.no|Institute of Clinical Medicine, University of Tromso, Tromso, Norway. rasmus.goll@unn.no|Institute of Clinical Medicine, University of Tromso, Tromso, Norway. rasmus.goll@unn.no', 'Norway|Norway|Norway|Norway', 42, 'Computer Systems|Humans|Models, Biological|Nonlinear Dynamics|Polymerase Chain Reaction|RNA', 'Comparative Study', '10.1186/1471-2105-7-107', '16515700', 'In real-time PCR data analysis, the cycle threshold (CT) method is currently the gold standard. This method is based on an assumption of equal PCR efficiency in all reactions, and precision may suffer if this condition is not met. Nonlinear regression analysis (NLR) or curve fitting has therefore been suggested as an alternative to the cycle threshold method for absolute quantitation. The advantages of NLR are that the individual sample efficiency is simulated by the model and that absolute quantitation is possible without a standard curve, releasing reaction wells for unknown samples. However, the calculation method has not been evaluated systematically and has not previously been applied to a TaqMan platform.'),
(90769, 2006, 'bmc', 'Metabolite coupling in genome-scale metabolic networks', 'Becker, Scott A|Price, Nathan D|Palsson, Bernhard xc3x98', 'Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA. sabecker@ucsd.edu|Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA. sabecker@ucsd.edu|Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA. sabecker@ucsd.edu', 'United States|United States|United States', 30, 'Chromosome Mapping|Computer Simulation|Models, Biological|Models, Chemical|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-111', '16519800', 'Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein metabolite coupling. These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix xc5x9dxc5x9dT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where xc5x9d is the binary form of S.'),
(90770, 2006, 'bmc', 'Lower expression of genes near microRNA in C elegans germline', 'Inaoka, Hidenori|Fukuoka, Yutaka|Kohane, Isaac S', 'Department of Biosystem Modeling, School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan. inaoka.bmi@tmd.ac.jp|Department of Biosystem Modeling, School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan. inaoka.bmi@tmd.ac.jp|Department of Biosystem Modeling, School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan. inaoka.bmi@tmd.ac.jp', 'Japan|Japan|Japan', 8, 'Animals|Caenorhabditis elegans Proteins|Chromosome Mapping|Gene Expression Profiling|Gene Expression Regulation|MicroRNAs|Micronucleus, Germline|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-112', '16519803', 'MicroRNAs (miRNAs) are recently discovered short non-protein-coding RNA molecules. miRNAs are increasingly implicated in tissue-specific transcriptional control and particularly in development. Because there is mounting evidence for the localized component of transcriptional control, we investigated if there is a distance-dependent effect of miRNA.'),
(90771, 2006, 'bmc', 'IsoSVM--distinguishing isoforms and paralogs on the protein level', 'Spitzer, Michael|Lorkowski, Stefan|Cullen, Paul|Sczyrba, Alexander|Fuellen, Georg', 'Division of Bioinformatics, Biology Department, Schlossplatz 4, 48149 Münster, Germany. michael.spitzer@uni-muenster.de|Division of Bioinformatics, Biology Department, Schlossplatz 4, 48149 Münster, Germany. michael.spitzer@uni-muenster.de|Division of Bioinformatics, Biology Department, Schlossplatz 4, 48149 Münster, Germany. michael.spitzer@uni-muenster.de|Division of Bioinformatics, Biology Department, Schlossplatz 4, 48149 Münster, Germany. michael.spitzer@uni-muenster.de|Division of Bioinformatics, Biology Department, Schlossplatz 4, 48149 Münster, Germany. michael.spitzer@uni-muenster.de', 'Germany|Germany|Germany|Germany|Germany', 13, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Molecular Sequence Data|Pattern Recognition, Automated|Protein Isoforms|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1186/1471-2105-7-110', '16519805', 'Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred protein sequences without ready access to the underlying genomic data. Analysis of this information (e.g. for EST clustering or phylogenetic reconstruction from proteome data) is hampered because it is not known if two protein sequences are isoforms (splice variants) or not (i.e. paralogs/orthologs). However, even without knowing the intron/exon structure, visual analysis of the pattern of similarity across the alignment of the two protein sequences is usually helpful since paralogs and orthologs feature substitutions with respect to each other, as opposed to isoforms, which do not.'),
(90772, 2006, 'bmc', 'VANTED: a system for advanced data analysis and visualization in the context of biological networks', 'Junker, Björn H|Klukas, Christian|Schreiber, Falk', 'Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr, 3, 06466 Gatersleben, Germany. junker@ipk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr, 3, 06466 Gatersleben, Germany. junker@ipk-gatersleben.de|Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr, 3, 06466 Gatersleben, Germany. junker@ipk-gatersleben.de', 'Germany|Germany|Germany', 339, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Signal Transduction|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-109', '16519817', 'Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes.'),
(90773, 2006, 'bmc', 'Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs', 'Jin, Victor X|Singer, Gregory A C|Agosto-Pérez, Francisco J|Liyanarachchi, Sandya|Davuluri, Ramana V', 'Human Cancer Genetics Program, Comprehensive Cancer Center, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Human Cancer Genetics Program, Comprehensive Cancer Center, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Human Cancer Genetics Program, Comprehensive Cancer Center, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Human Cancer Genetics Program, Comprehensive Cancer Center, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu|Human Cancer Genetics Program, Comprehensive Cancer Center, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. victor.jin@osumc.edu', 'United States|United States|United States|United States|United States', 52, 'Animals|Base Sequence|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Genome, Human|Humans|Mice|Molecular Sequence Data|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-7-114', '16522199', 'The canonical core promoter elements consist of the TATA box, initiator (Inr), downstream core promoter element (DPE), TFIIB recognition element (BRE) and the newly-discovered motif 10 element (MTE). The motifs for these core promoter elements are highly degenerate, which tends to lead to a high false discovery rate when attempting to detect them in promoter sequences.'),
(90774, 2006, 'bmc', 'An improved map of conserved regulatory sites for Saccharomyces cerevisiae', 'MacIsaac, Kenzie D|Wang, Ting|Gordon, D Benjamin|Gifford, David K|Stormo, Gary D|Fraenkel, Ernest', 'Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu|Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu|Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu|Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu|Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu|Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA. macisaac@mit.edu', 'United States|United States|United States|United States|United States|United States', 495, 'Algorithms|Chromosome Mapping|Conserved Sequence|Gene Expression Regulation, Fungal|Phylogeny|Regulatory Elements, Transcriptional|Saccharomyces cerevisiae|Sequence Analysis, DNA|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-7-113', '16522208', 'The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data.'),
(90775, 2006, 'bmc', 'MAGIC-SPP: a database-driven DNA sequence processing package with associated management tools', 'Liang, Chun|Sun, Feng|Wang, Haiming|Qu, Junfeng|Freeman, Robert M|Pratt, Lee H|Cordonnier-Pratt, Marie-Michèle', 'Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu|Laboratory for Genomics and Bioinformatics, Department of Plant Biology, University of Georgia, Athens, GA 30602, USA. liangc@muohio.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia|Georgia|Georgia', 15, 'Algorithms|Computer Graphics|Database Management Systems|Databases, Nucleic Acid|Information Storage and Retrieval|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1186/1471-2105-7-115', '16522212', 'Processing raw DNA sequence data is an especially challenging task for relatively small laboratories and core facilities that produce as many as 5000 or more DNA sequences per week from multiple projects in widely differing species. To meet this challenge, we have developed the flexible, scalable, and automated sequence processing package described here.'),
(90776, 2006, 'bmc', 'TF Target Mapper: a BLAST search tool for the identification of Transcription Factor target genes', 'Horsman, Sebastiaan|Moorhouse, Michael J|de Jager, Victor C L|van der Spek, Peter|Grosveld, Frank|Strouboulis, John|Katsantoni, Eleni Z', 'Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl|Department of Cell Biology, Erasmus Medical Center, Dr Molewaterplein 50, 3015GE Rotterdam, The Netherlands. s.horsman@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 7, 'Amino Acid Sequence|Base Sequence|Binding Sites|Chromatin Immunoprecipitation|Chromosome Mapping|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Protein Binding|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-120', '16524462', 'In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it.'),
(90777, 2006, 'bmc', 'Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms', 'König, Rainer|Schramm, Gunnar|Oswald, Marcus|Seitz, Hanna|Sager, Sebastian|Zapatka, Marc|Reinelt, Gerhard|Eils, Roland', 'Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de|Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology, University of Heidelberg, 69120 Heidelberg, Germany. r.koenig@dkfz.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 18, 'Algorithms|Anaerobiosis|Computer Simulation|Energy Metabolism|Escherichia coli|Escherichia coli Proteins|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Oxygen|Protein Interaction Mapping|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-119', '16524469', 'Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli.'),
(90778, 2006, 'bmc', 'Low degree metabolites explain essential reactions and enhance modularity in biological networks', 'Samal, Areejit|Singh, Shalini|Giri, Varun|Krishna, Sandeep|Raghuram, Nandula|Jain, Sanjay', 'Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in|Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in|Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in|Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in|Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in|Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India. areejit@physics.du.ac.in', 'India|India|India|India|India|India', 51, 'Bacteria|Bacterial Proteins|Cluster Analysis|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Models, Biological|Protein Interaction Mapping|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-118', '16524470', 'Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints.'),
(90779, 2006, 'bmc', 'Microbial identification by mass cataloging', 'Zhang, Zhengdong|Jackson, George W|Fox, George E|Willson, Richard C', 'Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. zhengdong.zhang@yale.edu|Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. zhengdong.zhang@yale.edu|Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. zhengdong.zhang@yale.edu|Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Avenue, Houston, TX 77204-5001, USA. zhengdong.zhang@yale.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Bacteria|Base Sequence|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|RNA, Ribosomal, 16S|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-117', '16524471', 'The public availability of over 180,000 bacterial 16S ribosomal RNA (rRNA) sequences has facilitated microbial identification and classification using hybridization and other molecular approaches. In their usual format, such assays are based on the presence of unique subsequences in the target RNA and require a prior knowledge of what organisms are likely to be in a sample. They are thus limited in generality when analyzing an unknown sample.Herein, we demonstrate the utility of catalogs of masses to characterize the bacterial 16S rRNA(s) in any sample. Sample nucleic acids are digested with a nuclease of known specificity and the products characterized using mass spectrometry. The resulting catalogs of masses can subsequently be compared to the masses known to occur in previously-sequenced 16S rRNAs allowing organism identification. Alternatively, if the organism is not in the existing database, it will still be possible to determine its genetic affinity relative to the known organisms.'),
(90780, 2006, 'bmc', 'Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data', 'Wang, Haiying|Zheng, Huiru|Simpson, David|Azuaje, Francisco', 'School of Computing and Mathematics, University of Ulster, UK. wang@ulster.ac.uk|School of Computing and Mathematics, University of Ulster, UK. wang@ulster.ac.uk|School of Computing and Mathematics, University of Ulster, UK. wang@ulster.ac.uk|School of Computing and Mathematics, University of Ulster, UK. wang@ulster.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Artificial Intelligence|Computer Simulation|Eye Proteins|Feasibility Studies|Gene Expression Profiling|Humans|Models, Biological|Pattern Recognition, Automated|Photoreceptor Cells|Signal Transduction|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-7-116', '16524483', 'Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data.'),
(90781, 2006, 'bmc', 'Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false discovery rate', 'Motulsky, Harvey J|Brown, Ronald E', 'GraphPad Software, Inc,, San Diego, CA, USA. hmotulsky@graphpad.com|GraphPad Software, Inc,, San Diego, CA, USA. hmotulsky@graphpad.com', 'United States|United States', 0, 'Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Models, Biological|Models, Statistical|Nonlinear Dynamics|Numerical Analysis, Computer-Assisted|Regression Analysis', 'Journal Article', '10.1186/1471-2105-7-123', '16526949', 'Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90782, 2006, 'bmc', 'An unsupervised classification scheme for improving predictions of prokaryotic TIS', 'Tech, Maike|Meinicke, Peter', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. maike@gobics.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. maike@gobics.de', 'Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Cluster Analysis|Codon, Initiator|Molecular Sequence Data|Pattern Recognition, Automated|Prokaryotic Initiation Factors|Protein Biosynthesis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-121', '16526950', 'Although it is not difficult for state-of-the-art gene finders to identify coding regions in prokaryotic genomes, exact prediction of the corresponding translation initiation sites (TIS) is still a challenging problem. Recently a number of post-processing tools have been proposed for improving the annotation of prokaryotic TIS. However, inherent difficulties of these approaches arise from the considerable variation of TIS characteristics across different species. Therefore prior assumptions about the properties of prokaryotic gene starts may cause suboptimal predictions for newly sequenced genomes with TIS signals differing from those of well-investigated genomes.'),
(90783, 2006, 'bmc', 'Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information', 'Song, Jiangning|Burrage, Kevin|Yuan, Zheng|Huber, Thomas', 'Advanced Computational Modelling Centre, The University of Queensland, Brisbane Qld 4072, Australia. sjn@maths.uq.edu.au|Advanced Computational Modelling Centre, The University of Queensland, Brisbane Qld 4072, Australia. sjn@maths.uq.edu.au|Advanced Computational Modelling Centre, The University of Queensland, Brisbane Qld 4072, Australia. sjn@maths.uq.edu.au|Advanced Computational Modelling Centre, The University of Queensland, Brisbane Qld 4072, Australia. sjn@maths.uq.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Databases, Protein|Isomerism|Models, Chemical|Predictive Value of Tests|Protein Structure, Secondary|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-7-124', '16526956', 'The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function.'),
(90784, 2006, 'bmc', 'Selection of antisense oligonucleotides based on multiple predicted target mRNA structures', 'Bo, Xiaochen|Lou, Shaoke|Sun, Daochun|Shu, Wenjie|Yang, Jing|Wang, Shengqi', 'Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn|Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, P R China. boxc@bmi.ac.cn', 'China|China|China|China|China|China', 0, 'Algorithms|Antisense Elements (Genetics)|Base Sequence|Computer Simulation|Computer-Aided Design|Gene Targeting|Models, Genetic|Molecular Sequence Data|Oligoribonucleotides, Antisense|RNA, Messenger|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-122', '16526963', 'Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed.'),
(90785, 2006, 'bmc', 'A two-sample Bayesian t-test for microarray data', 'Fox, Richard J|Dimmic, Matthew W', 'Codexis, Inc., Redwood City, CA 94063, USA. richard.fox@codexis.com|Codexis, Inc., Redwood City, CA 94063, USA. richard.fox@codexis.com', 'United States|United States', 69, 'Algorithms|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Logistic Models|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1186/1471-2105-7-126', '16529652', 'Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use of t-tests with pooled estimates of the sample variance based on similarly expressed genes. These methods do not display consistent behavior across the entire range of pooling and can be biased when the prior hyperparameters are specified heuristically.'),
(90786, 2006, 'bmc', 'Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training', 'Meissner, Michael|Schmuker, Michael|Schneider, Gisbert', 'Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstrasse 70, D-60323 Frankfurt, Germany. meissner@chemie.uni-frankfurt.de|Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstrasse 70, D-60323 Frankfurt, Germany. meissner@chemie.uni-frankfurt.de|Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstrasse 70, D-60323 Frankfurt, Germany. meissner@chemie.uni-frankfurt.de', 'Germany|Germany|Germany', 0, 'Algorithms|Blood Proteins|Blood-Brain Barrier|Cell Membrane Permeability|Computer Simulation|Humans|Models, Biological|Neural Networks (Computer)|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-7-125', '16529661', 'Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several strategy parameters. Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.'),
(90787, 2006, 'bmc', 'Protein protein interactions, evolutionary rate, abundance and age', 'Saeed, Ramazan|Deane, Charlotte M', 'Department of Statistics, Oxford OX1 3TG, UK. saeed@stats.ox.ac.uk|Department of Statistics, Oxford OX1 3TG, UK. saeed@stats.ox.ac.uk', 'United Kingdom|United Kingdom', 0, 'Aging|Amino Acid Sequence|Animals|Conserved Sequence|Evolution, Molecular|Genetic Variation|Molecular Sequence Data|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-7-128', '16533385', 'Does a relationship exist between a proteins evolutionary rate and its number of interactions? This relationship has been put forward many times, based on a biological premise that a highly interacting protein will be more restricted in its sequence changes. However, to date several studies have voiced conflicting views on the presence or absence of such a relationship.'),
(90788, 2006, 'bmc', 'Extraction of phylogenetic network modules from the metabolic network', 'Yamada, Takuji|Kanehisa, Minoru|Goto, Susumu', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp.|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. goto@kuicr.kyoto-u.ac.jp.', 'Japan|Japan|Japan', 42, 'Animals|Computational Biology|Evolution, Molecular|Humans|Metabolism|Models, Biological|Models, Genetic|Phylogeny|Protein Interaction Mapping|Systems Biology', 'Journal Article', '10.1186/1471-2105-7-130', '16533389', 'In bio-systems, genes, proteins and compounds are related to each other, thus forming complex networks. Although each organism has its individual network, some organisms contain common sub-networks based on function. Given a certain sub-network, the distribution of organisms common to it represents the diversity of its function.'),
(90789, 2006, 'bmc', 'Improved variance estimation of classification performance via reduction of bias caused by small sample size', 'Wickenberg-Bolin, Ulrika|Göransson, Hanna|Fryknäs, Mårten|Gustafsson, Mats G|Isaksson, Anders', 'Department of Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 85 Uppsala, Sweden. bolin@genpat.uu.se|Department of Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 85 Uppsala, Sweden. bolin@genpat.uu.se|Department of Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 85 Uppsala, Sweden. bolin@genpat.uu.se|Department of Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 85 Uppsala, Sweden. bolin@genpat.uu.se|Department of Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 85 Uppsala, Sweden. bolin@genpat.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Analysis of Variance|Artificial Intelligence|Bias|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Models, Biological|Models, Statistical|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-7-127', '16533392', 'Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT).'),
(90790, 2006, 'bmc', 'Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition', 'Barrett, Christian L|Price, Nathan D|Palsson, Bernhard O', 'Bioengineering Department, University of California--San Diego, 9500 Gilman Drive, La Jolla, California, 92093-0412, USA. cbarrett@bioeng.ucsd.edu|Bioengineering Department, University of California--San Diego, 9500 Gilman Drive, La Jolla, California, 92093-0412, USA. cbarrett@bioeng.ucsd.edu|Bioengineering Department, University of California--San Diego, 9500 Gilman Drive, La Jolla, California, 92093-0412, USA. cbarrett@bioeng.ucsd.edu', 'United States|United States|United States', 27, 'Algorithms|Artificial Intelligence|Blood Proteins|Computer Simulation|Erythrocytes|Gene Expression Regulation|Homeostasis|Models, Cardiovascular|Sample Size|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-132', '16533395', 'Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices.'),
(90791, 2006, 'bmc', 'EpiJen: a server for multistep T cell epitope prediction', 'Doytchinova, Irini A|Guan, Pingping|Flower, Darren R', 'Edward Jenner Institute for Vaccine Research, Compton, RG20 7NN, UK. irini.doytchinova@jenner.ac.uk|Edward Jenner Institute for Vaccine Research, Compton, RG20 7NN, UK. irini.doytchinova@jenner.ac.uk|Edward Jenner Institute for Vaccine Research, Compton, RG20 7NN, UK. irini.doytchinova@jenner.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computer Simulation|Epitopes, T-Lymphocyte|Histocompatibility Antigens Class I|Internet|Models, Immunological|Online Systems|Sequence Analysis, Protein|Software|Systems Integration', 'Journal Article', '10.1186/1471-2105-7-131', '16533401', 'The main processing pathway for MHC class I ligands involves degradation of proteins by the proteasome, followed by transport of products by the transporter associated with antigen processing (TAP) to the endoplasmic reticulum (ER), where peptides are bound by MHC class I molecules, and then presented on the cell surface by MHCs. The whole process is modeled here using an integrated approach, which we call EpiJen. EpiJen is based on quantitative matrices, derived by the additive method, and applied successively to select epitopes. EpiJen is available free online.'),
(90792, 2006, 'bmc', 'SNA--a toolbox for the stoichiometric analysis of metabolic networks', 'Urbanczik, Robert', 'Institute of Pharmacology, University of Bern, Friedbühlstr. 49, CH-3010 Bern, Switzerland. urbanczik@physik.uni-wuerzburg.de', 'Switzerland', 26, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software|Transcription Factors|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-129', '16533403', 'Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem.'),
(90793, 2006, 'bmc', 'Choosing the best heuristic for seeded alignment of DNA sequences', 'Sun, Yanni|Buhler, Jeremy', 'Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA. yanni@cse.wustl.edu|Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA. yanni@cse.wustl.edu', 'United States|United States', 15, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Comparative Study', '10.1186/1471-2105-7-133', '16533404', 'Seeded alignment is an important component of algorithms for fast, large-scale DNA similarity search. A good seed matching heuristic can reduce the execution time of genomic-scale sequence comparison without degrading sensitivity. Recently, many types of seed have been proposed to improve on the performance of traditional contiguous seeds as used in, e.g., NCBI BLASTN. Choosing among these seed types, particularly those that use information besides the presence or absence of matching residue pairs, requires practical guidance based on a rigorous comparison, including assessment of sensitivity, specificity, and computational efficiency. This work performs such a comparison, focusing on alignments in DNA outside widely studied coding regions.'),
(90794, 2006, 'bmc', 'Effect of data normalization on fuzzy clustering of DNA microarray data', 'Kim, Seo Young|Lee, Jae Won|Bae, Jong Sung', 'Research Institute for Basic Science, Chonnam National University, Gwangju, 500-757, Korea. gong@chonnam.ac.kr|Research Institute for Basic Science, Chonnam National University, Gwangju, 500-757, Korea. gong@chonnam.ac.kr|Research Institute for Basic Science, Chonnam National University, Gwangju, 500-757, Korea. gong@chonnam.ac.kr', '-|-|-', 58, 'Algorithms|Cluster Analysis|Fuzzy Logic|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1186/1471-2105-7-134', '16533412', 'Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich, however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap.'),
(90795, 2006, 'bmc', 'Gene functional similarity search tool (GFSST)', 'Zhang, Peisen|Zhang, Jinghui|Sheng, Huitao|Russo, James J|Osborne, Brian|Buetow, Kenneth', 'Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov|Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov|Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov|Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov|Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov|Laboratory of Population Genetics, National Cancer Institute, NIH, Bethesda, USA. zhangpeis@mail.nih.gov', 'United States|United States|United States|United States|United States|United States', 41, 'Chromosome Mapping|Database Management Systems|Databases, Protein|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1186/1471-2105-7-135', '16536867', 'With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity.'),
(90796, 2006, 'bmc', 'Identification and evolutionary analysis of novel exons and alternative splicing events using cross-species EST-to-genome comparisons in human, mouse and rat', 'Chen, Feng-Chi|Chen, Chuang-Jong|Ho, Jar-Yi|Chuang, Trees-Juen', 'Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw|Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw|Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw|Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw', '-|-|-|-', 27, 'Algorithms|Alternative Splicing|Animals|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Exons|Expressed Sequence Tags|Genetic Variation|Humans|Introns|Linkage Disequilibrium|Mice|Rats|Sequence Alignment|Sequence Analysis, DNA|Software|Species Specificity|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-136', '16536879', 'Alternative splicing (AS) is important for evolution and major biological functions in complex organisms. However, the extent of AS in mammals other than human and mouse is largely unknown, making it difficult to study AS evolution in mammals and its biomedical implications.'),
(90797, 2006, 'bmc', 'mGrid: a load-balanced distributed computing environment for the remote execution of the user-defined Matlab code', 'Karpievitch, Yuliya V|Almeida, Jonas S', 'Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. karpik@musc.edu|Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. karpik@musc.edu', 'United States|United States', 6, 'Computational Biology|Computer Communication Networks|Models, Biological|Programming Languages|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-139', '16539707', 'Matlab, a powerful and productive language that allows for rapid prototyping, modeling and simulation, is widely used in computational biology. Modeling and simulation of large biological systems often require more computational resources then are available on a single computer. Existing distributed computing environments like the Distributed Computing Toolbox, MatlabMPI, Matlab*G and others allow for the remote (and possibly parallel) execution of Matlab commands with varying support for features like an easy-to-use application programming interface, load-balanced utilization of resources, extensibility over the wide area network, and minimal system administration skill requirements. However, all of these environments require some level of access to participating machines to manually distribute the user-defined libraries that the remote call may invoke.'),
(90798, 2006, 'bmc', 'A joint model for nonparametric functional mapping of longitudinal trajectory and time-to-event', 'Lin, Min|Wu, Rongling', 'Department of Statistics, University of Florida, Gainesville, FL 32611, USA. annie.lin@duke.edu|Department of Statistics, University of Florida, Gainesville, FL 32611, USA. annie.lin@duke.edu', 'United States|United States', 29, 'Algorithms|Biometry|Data Interpretation, Statistical|Longitudinal Studies|Models, Genetic|Models, Statistical|Quantitative Trait Loci|Time Factors', 'Journal Article', '10.1186/1471-2105-7-138', '16539724', 'The characterization of the relationship between a longitudinal response process and a time-to-event has been a pressing challenge in biostatistical research. This has emerged as an important issue in genetic studies when one attempts to detect the common genes or quantitative trait loci (QTL) that govern both a longitudinal trajectory and developmental event.'),
(90799, 2006, 'bmc', 'How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results', 'Millenaar, Frank F|Okyere, John|May, Sean T|van Zanten, Martijn|Voesenek, Laurentius A C J|Peeters, Anton J M', 'Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl|Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl|Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl|Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl|Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl|Plant Ecophysiology, Institute of Environmental Biology, Faculty of Science, Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. f.f.millenaar@bio.uu.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 120, 'Algorithms|Arabidopsis Proteins|Decision Making|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1186/1471-2105-7-137', '16539732', 'Short oligonucleotide arrays for transcript profiling have been available for several years. Generally, raw data from these arrays are analysed with the aid of the Microarray Analysis Suite or GeneChip Operating Software (MAS or GCOS) from Affymetrix. Recently, more methods to analyse the raw data have become available. Ideally all these methods should come up with more or less the same results. We set out to evaluate the different methods and include work on our own data set, in order to test which method gives the most reliable results.'),
(90800, 2006, 'bmc', 'A computational approach to discovering the functions of bacterial phytochromes by analysis of homolog distributions', 'Lamparter, Tilman', 'Freie Universität Berlin, Pflanzenphysiologie, Königin-Luise Str, 12-16, D-14195 Berlin, Germany. lamparte@zedat.fu-berlin.de', 'Germany', 7, 'Amino Acid Sequence|Bacterial Physiological Phenomena|Bacterial Proteins|Computer Simulation|Models, Biological|Models, Chemical|Models, Statistical|Molecular Sequence Data|Phytochrome|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-7-141', '16539742', 'Phytochromes are photoreceptors, discovered in plants, that control a wide variety of developmental processes. They have also been found in bacteria and fungi, but for many species their biological role remains obscure. This work concentrates on the phytochrome system of Agrobacterium tumefaciens, a non-photosynthetic soil bacterium with two phytochromes. To identify proteins that might share common functions with phytochromes, a co-distribution analysis was performed on the basis of protein sequences from 138 bacteria.'),
(90801, 2006, 'bmc', 'Exploring supervised and unsupervised methods to detect topics in biomedical text', 'Lee, Minsuk|Wang, Weiqing|Yu, Hong', 'Department of Biomedical Informatics, Columbia University, 622West, 168th Street, VC-5, NY 10032, USA. ml1065@columbia.edu|Department of Biomedical Informatics, Columbia University, 622West, 168th Street, VC-5, NY 10032, USA. ml1065@columbia.edu|Department of Biomedical Informatics, Columbia University, 622West, 168th Street, VC-5, NY 10032, USA. ml1065@columbia.edu', 'United States|United States|United States', 23, 'Abstracting and Indexing as Topic|Artificial Intelligence|Humans|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic|Vocabulary, Controlled', 'Evaluation Studies', '10.1186/1471-2105-7-140', '16539745', 'Topic detection is a task that automatically identifies topics (e.g., biochemistry and protein structure) in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering, and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.'),
(90802, 2006, 'bmc', 'Genetic algorithm learning as a robust approach to RNA editing site prediction', 'Thompson, James|Gopal, Shuba', 'Department of Biological Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. tex@u.washington.edu|Department of Biological Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA. tex@u.washington.edu', 'United States|United States', 18, 'Algorithms|Arabidopsis|Binding Sites|Brassica napus|Computational Biology|DNA, Mitochondrial|DNA, Plant|Genes, Plant|Genome, Plant|Models, Genetic|Oryza|Predictive Value of Tests|RNA|RNA Editing|RNA Processing, Post-Transcriptional|RNA, Mitochondrial|ROC Curve|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-7-145', '16542417', 'RNA editing is one of several post-transcriptional modifications that may contribute to organismal complexity in the face of limited gene complement in a genome. One form, known as C --> U editing, appears to exist in a wide range of organisms, but most instances of this form of RNA editing have been discovered serendipitously. With the large amount of genomic and transcriptomic data now available, a computational analysis could provide a more rapid means of identifying novel sites of C --> U RNA editing. Previous efforts have had some success but also some limitations. We present a computational method for identifying C --> U RNA editing sites in genomic sequences that is both robust and generalizable. We evaluate its potential use on the best data set available for these purposes: C --> U editing sites in plant mitochondrial genomes.'),
(90803, 2006, 'bmc', 'INTEGRATOR: interactive graphical search of large protein interactomes over the Web', 'Chang, Aaron N|McDermott, Jason|Frazier, Zachary|Guerquin, Michal|Samudrala, Ram', 'Dept. of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98195, USA. anchang@u.washington.edu|Dept. of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98195, USA. anchang@u.washington.edu|Dept. of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98195, USA. anchang@u.washington.edu|Dept. of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98195, USA. anchang@u.washington.edu|Dept. of Biomedical and Health Informatics, University of Washington, Seattle, Washington 98195, USA. anchang@u.washington.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Binding Sites|Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Protein Binding|Protein Interaction Mapping|Proteome|Sequence Analysis, Protein|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-146', '16542421', 'The rapid growth of protein interactome data has elevated the necessity and importance of network analysis tools. However, unlike pure text data, network search spaces are of exponential complexity. This poses special challenges for storing, searching, and navigating this data efficiently. Moreover, development of effective web interfaces has been difficult.'),
(90804, 2006, 'bmc', 'Sigma: multiple alignment of weakly-conserved non-coding DNA sequence', 'Siddharthan, Rahul', 'Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600113, India. rsidd@imsc.res.in', 'India', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Conserved Sequence|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1186/1471-2105-7-143', '16542424', 'Existing tools for multiple-sequence alignment focus on aligning protein sequence or protein-coding DNA sequence, and are often based on extensions to Needleman-Wunsch-like pairwise alignment methods. We introduce a new tool, Sigma, with a new algorithm and scoring scheme designed specifically for non-coding DNA sequence. This problem acquires importance with the increasing number of published sequences of closely-related species. In particular, studies of gene regulation seek to take advantage of comparative genomics, and recent algorithms for finding regulatory sites in phylogenetically-related intergenic sequence require alignment as a preprocessing step. Much can also be learned about evolution from intergenic DNA, which tends to evolve faster than coding DNA. Sigma uses a strategy of seeking the best possible gapless local alignments (a strategy earlier used by DiAlign), at each step making the best possible alignment consistent with existing alignments, and scores the significance of the alignment based on the lengths of the aligned fragments and a background model which may be supplied or estimated from an auxiliary file of intergenic DNA.'),
(90805, 2006, 'bmc', 'Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models', 'Waack, Stephan|Keller, Oliver|Asper, Roman|Brodag, Thomas|Damm, Carsten|Fricke, Wolfgang Florian|Surovcik, Katharina|Meinicke, Peter|Merkl, Rainer', 'Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.|Institut für Informatik, Universität Göttingen, Lotzestr, 16-18, 37083 Göttingen, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Bacillus subtilis|Base Composition|Codon|Computational Biology|DNA, Bacterial|Escherichia coli K12|Gene Transfer, Horizontal|Genome, Bacterial|Genomic Islands|Markov Chains|Models, Genetic|Software|Vibrio cholerae', 'Journal Article', '10.1186/1471-2105-7-142', '16542435', 'Horizontal gene transfer (HGT) is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs) or more specifically pathogenicity or symbiotic islands.'),
(90806, 2006, 'bmc', 'Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological features', 'Semeiks, J R|Rizki, A|Bissell, M J|Mian, I S', 'Life Sciences Division (MS 977-225A), Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. jrs@farviolet.com|Life Sciences Division (MS 977-225A), Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. jrs@farviolet.com|Life Sciences Division (MS 977-225A), Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. jrs@farviolet.com|Life Sciences Division (MS 977-225A), Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. jrs@farviolet.com', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-147', '16542449', 'Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a user-defined list of genes and/or proteins. The strategy exploits annotation data present in gene-centered corpora and utilizes ideas from statistical information retrieval to discover and characterize properties shared by subsets of the list. The practical utility of this method is demonstrated by employing it in a retrospective study of two non-overlapping sets of genes defined by a published investigation as markers for normal human breast luminal epithelial cells and myoepithelial cells.'),
(90807, 2006, 'bmc', 'The 3of5 web application for complex and comprehensive pattern matching in protein sequences', 'Seiler, Markus|Mehrle, Alexander|Poustka, Annemarie|Wiemann, Stefan', 'Division of Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. m.seiler@dkfz.de|Division of Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. m.seiler@dkfz.de|Division of Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. m.seiler@dkfz.de|Division of Molecular Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany. m.seiler@dkfz.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Internet|Molecular Sequence Data|Online Systems|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-144', '16542452', 'The identification of patterns in biological sequences is a key challenge in genome analysis and in proteomics. Frequently such patterns are complex and highly variable, especially in protein sequences. They are frequently described using terms of regular expressions (RegEx) because of the user-friendly terminology. Limitations arise for queries with the increasing complexity of patterns and are accompanied by requirements for enhanced capabilities. This is especially true for patterns containing ambiguous characters and positions and/or length ambiguities.'),
(90808, 2006, 'bmc', 'Identification of physicochemical selective pressure on protein encoding nucleotide sequences', 'Wong, Wendy S W|Sainudiin, Raazesh|Nielsen, Rasmus', 'Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. ww3@sanger.ac.uk|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. ww3@sanger.ac.uk|Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. ww3@sanger.ac.uk', 'United States|United States|United States', 24, 'Algorithms|Evolution, Molecular|Open Reading Frames|Proteins|Selection, Genetic|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-7-148', '16542458', 'Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account.'),
(90809, 2006, 'bmc', 'CARMA: A platform for analyzing microarray datasets that incorporate replicate measures', 'Greer, Kevin A|McReynolds, Matthew R|Brooks, Heddwen L|Hoying, James B', 'Biomedical Engineering Program, Genomics Research Laboratory, University of Arizona, Tucson, Arizona 85724, USA. kgreer@u.arizona.edu|Biomedical Engineering Program, Genomics Research Laboratory, University of Arizona, Tucson, Arizona 85724, USA. kgreer@u.arizona.edu|Biomedical Engineering Program, Genomics Research Laboratory, University of Arizona, Tucson, Arizona 85724, USA. kgreer@u.arizona.edu|Biomedical Engineering Program, Genomics Research Laboratory, University of Arizona, Tucson, Arizona 85724, USA. kgreer@u.arizona.edu', 'United States|United States|United States|United States', 0, 'Analysis of Variance|Animals|Computational Biology|Computer Simulation|Databases, Genetic|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Comparative Study', '10.1186/1471-2105-7-149', '16542461', 'The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance.'),
(90810, 2006, 'bmc', 'Domain-based small molecule binding site annotation', 'Snyder, Kevin A|Feldman, Howard J|Dumontier, Michel|Salama, John J|Hogue, Christopher W V', 'The Blueprint Initiative, Toronto ON, M5T 1K4, Canada. ksnyder@blueprint.org|The Blueprint Initiative, Toronto ON, M5T 1K4, Canada. ksnyder@blueprint.org|The Blueprint Initiative, Toronto ON, M5T 1K4, Canada. ksnyder@blueprint.org|The Blueprint Initiative, Toronto ON, M5T 1K4, Canada. ksnyder@blueprint.org|The Blueprint Initiative, Toronto ON, M5T 1K4, Canada. ksnyder@blueprint.org', 'Canada|Canada|Canada|Canada|Canada', 0, 'Binding Sites|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Ligands|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-152', '16545112', 'Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites.'),
(90811, 2006, 'bmc', 'Development of an unbiased statistical method for the analysis of unigenic evolution', 'Behrsin, Colleen D|Brandl, Chris J|Litchfield, David W|Shilton, Brian H|Wahl, Lindi M', 'Department of Biochemistry, University of Western Ontario, London, Ontario, Canada. chehrsin@skynet.ca|Department of Biochemistry, University of Western Ontario, London, Ontario, Canada. chehrsin@skynet.ca|Department of Biochemistry, University of Western Ontario, London, Ontario, Canada. chehrsin@skynet.ca|Department of Biochemistry, University of Western Ontario, London, Ontario, Canada. chehrsin@skynet.ca|Department of Biochemistry, University of Western Ontario, London, Ontario, Canada. chehrsin@skynet.ca', 'Canada|Canada|Canada|Canada|Canada', 0, 'Computer Simulation|DNA Mutational Analysis|Data Interpretation, Statistical|Evolution, Molecular|Genetic Variation|Models, Genetic|Models, Statistical|Mutation, Missense|Proteins|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-150', '16545116', 'Unigenic evolution is a powerful genetic strategy involving random mutagenesis of a single gene product to delineate functionally important domains of a protein. This method involves selection of variants of the protein which retain function, followed by statistical analysis comparing expected and observed mutation frequencies of each residue. Resultant mutability indices for each residue are averaged across a specified window of codons to identify hypomutable regions of the protein. As originally described, the effect of changes to the length of this averaging window was not fully eludicated. In addition, it was unclear when sufficient functional variants had been examined to conclude that residues conserved in all variants have important functional roles.'),
(90812, 2006, 'bmc', 'GOurmet: a tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions', 'Doherty, Jason M|Carmichael, Lynn K|Mills, Jason C', 'Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA. jdoherty@pathology.wustl.edu|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA. jdoherty@pathology.wustl.edu|Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA. jdoherty@pathology.wustl.edu', 'United States|United States|United States', 24, 'Artificial Intelligence|Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Natural Language Processing|Proteins|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-151', '16545118', 'The ever-expanding population of gene expression profiles (EPs) from specified cells and tissues under a variety of experimental conditions is an important but difficult resource for investigators to utilize effectively. Software tools have been recently developed to use the distribution of gene ontology (GO) terms associated with the genes in an EP to identify specific biological functions or processes that are over- or under-represented in that EP relative to other EPs. Additionally, it is possible to use the distribution of GO terms inherent to each EP to relate that EP as a whole to other EPs. Because GO term annotation is organized in a tree-like cascade of variable granularity, this approach allows the user to relate (e.g., by hierarchical clustering) EPs of varying length and from different platforms (e.g., GeneChip, SAGE, EST library).'),
(90813, 2006, 'bmc', 'Predicting population coverage of T-cell epitope-based diagnostics and vaccines', 'Bui, Huynh-Hoa|Sidney, John|Dinh, Kenny|Southwood, Scott|Newman, Mark J|Sette, Alessandro', 'La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org|La Jolla Institute for Allergy and Immunology, Division of Vaccine Discovery, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. hbui@liai.org', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Epitope Mapping|Epitopes, T-Lymphocyte|Gene Frequency|Genetics, Population|Genotype|HLA Antigens|Humans|Immunoassay|Software|T-Lymphocytes|Vaccines', 'Journal Article', '10.1186/1471-2105-7-153', '16545123', 'T cells recognize a complex between a specific major histocompatibility complex (MHC) molecule and a particular pathogen-derived epitope. A given epitope will elicit a response only in individuals that express an MHC molecule capable of binding that particular epitope. MHC molecules are extremely polymorphic and over a thousand different human MHC (HLA) alleles are known. A disproportionate amount of MHC polymorphism occurs in positions constituting the peptide-binding region, and as a result, MHC molecules exhibit a widely varying binding specificity. In the design of peptide-based vaccines and diagnostics, the issue of population coverage in relation to MHC polymorphism is further complicated by the fact that different HLA types are expressed at dramatically different frequencies in different ethnicities. Thus, without careful consideration, a vaccine or diagnostic with ethnically biased population coverage could result.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90814, 2006, 'bmc', 'Empirical validation of the S-Score algorithm in the analysis of gene expression data', 'Kennedy, Richard E|Archer, Kellie J|Miles, Michael F', 'Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA. rkennedy@vcu.edu', 'United States|United States|United States', 0, 'Algorithms|Cell Line, Tumor|Cluster Analysis|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Models, Genetic|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software Validation', 'Journal Article', '10.1186/1471-2105-7-154', '16545131', 'Current methods of analyzing Affymetrix GeneChip microarray data require the estimation of probe set expression summaries, followed by application of statistical tests to determine which genes are differentially expressed. The S-Score algorithm described by Zhang and colleagues is an alternative method that allows tests of hypotheses directly from probe level data. It is based on an error model in which the detected signal is proportional to the probe pair signal for highly expressed genes, but approaches a background level (rather than 0) for genes with low levels of expression. This model is used to calculate relative change in probe pair intensities that converts probe signals into multiple measurements with equalized errors, which are summed over a probe set to form the S-Score. Assuming no expression differences between chips, the S-Score follows a standard normal distribution, allowing direct tests of hypotheses to be made. Using spike-in and dilution datasets, we validated the S-Score method against comparisons of gene expression utilizing the more recently developed methods RMA, dChip, and MAS5.'),
(90815, 2006, 'bmc', 'DNPTrapper: an assembly editing tool for finishing and analysis of complex repeat regions', 'Arner, Erik|Tammi, Martti T|Tran, Anh-Nhi|Kindlund, Ellen|Andersson, Bjorn', 'Karolinska Institute, Center for Genomics and Bioinformatics, Stockholm, Sweden. erik.arner@cgb.ki.se|Karolinska Institute, Center for Genomics and Bioinformatics, Stockholm, Sweden. erik.arner@cgb.ki.se|Karolinska Institute, Center for Genomics and Bioinformatics, Stockholm, Sweden. erik.arner@cgb.ki.se|Karolinska Institute, Center for Genomics and Bioinformatics, Stockholm, Sweden. erik.arner@cgb.ki.se|Karolinska Institute, Center for Genomics and Bioinformatics, Stockholm, Sweden. erik.arner@cgb.ki.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 12, 'Algorithms|Base Sequence|DNA|Documentation|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-155', '16549006', 'Many genome projects are left unfinished due to complex, repeated regions. Finishing is the most time consuming step in sequencing and current finishing tools are not designed with particular attention to the repeat problem.'),
(90816, 2006, 'bmc', 'Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays', 'Matsui, Shigeyuki', 'Department of Pharmacoepidemiology, School of Public Health, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan. matsui@pbh.med.kyoto-u.ac.jp', 'Japan', 15, 'Biomarkers|Computational Biology|Disease Progression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Genetic Predisposition to Disease|Humans|Likelihood Functions|Lymphoma|Models, Statistical|Oligonucleotide Array Sequence Analysis|Prognosis|Proportional Hazards Models|Treatment Outcome', 'Journal Article', '10.1186/1471-2105-7-156', '16549007', 'Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies.'),
(90817, 2006, 'bmc', 'Modeling Sage data with a truncated gamma-Poisson model', 'Thygesen, Helene H|Zwinderman, Aeilko H', 'Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands. h.h.thygesen@amc.uva.nl|Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, The Netherlands. h.h.thygesen@amc.uva.nl', 'Netherlands|Netherlands', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Expressed Sequence Tags|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Poisson Distribution|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-157', '16549008', 'Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means that part of the variance in SAGE data should be understood as the sampling error in a binomial or Poisson distribution, whereas other variance sources, in particular biological variance, should be modeled using a continuous distribution function, i.e. a prior on the intensity of the Poisson distribution. One challenge is that such a model predicts a large number of genes with zero counts, which cannot be observed.'),
(90818, 2006, 'bmc', '2DDB - a bioinformatics solution for analysis of quantitative proteomics data', 'Malmström, Lars|Marko-Varga, György|Westergren-Thorsson, Gunilla|Laurell, Thomas|Malmström, Johan', 'Department of Electrical Measurements, LTH, P,O Box 118, SE-221 00, Lund, Sweden. lars.malmstrom@elmat.lth.se|Department of Electrical Measurements, LTH, P,O Box 118, SE-221 00, Lund, Sweden. lars.malmstrom@elmat.lth.se|Department of Electrical Measurements, LTH, P,O Box 118, SE-221 00, Lund, Sweden. lars.malmstrom@elmat.lth.se|Department of Electrical Measurements, LTH, P,O Box 118, SE-221 00, Lund, Sweden. lars.malmstrom@elmat.lth.se|Department of Electrical Measurements, LTH, P,O Box 118, SE-221 00, Lund, Sweden. lars.malmstrom@elmat.lth.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Computational Biology|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Proteome|Proteomics|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-158', '16549013', 'We present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.'),
(90819, 2006, 'bmc', 'Amplification of the Gene Ontology annotation of Affymetrix probe sets', 'Muro, Enrique M|Perez-Iratxeta, Carolina|Andrade-Navarro, Miguel A', 'Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth Rd, Ottawa ON, K1H 8L6, Canada . emuro@ohri.ca|Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth Rd, Ottawa ON, K1H 8L6, Canada . emuro@ohri.ca|Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth Rd, Ottawa ON, K1H 8L6, Canada . emuro@ohri.ca', 'Canada|Canada|Canada', 5, 'Algorithms|DNA Probes|Database Management Systems|Databases, Protein|Documentation|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Proteins', 'Evaluation Studies', '10.1186/1471-2105-7-159', '16549014', 'The annotations of Affymetrix DNA microarray probe sets with Gene Ontology terms are carefully selected for correctness. This results in very accurate but incomplete annotations which is not always desirable for microarray experiment evaluation.'),
(90820, 2006, 'bmc', 'More robust detection of motifs in coexpressed genes by using phylogenetic information', 'Monsieurs, Pieter|Thijs, Gert|Fadda, Abeer A|De Keersmaecker, Sigrid C J|Vanderleyden, Jozef|De Moor, Bart|Marchal, Kathleen', 'ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be|ESAT-SCD/SISTA, K.U. Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. Pieter.Monsieurs@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Base Sequence|Cluster Analysis|Consensus Sequence|DNA Footprinting|DNA, Bacterial|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Phylogeny|Regulatory Sequences, Nucleic Acid|Reproducibility of Results|Sequence Analysis, DNA|Yersinia pestis', 'Comparative Study', '10.1186/1471-2105-7-160', '16549017', 'Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates.'),
(90821, 2006, 'bmc', 'GOPET: a tool for automated predictions of Gene Ontology terms', 'Vinayagam, Arunachalam|del Val, Coral|Schubert, Falk|Eils, Roland|Glatting, Karl-Heinz|Suhai, Sándor|König, Rainer', 'Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69121 Heidelberg, Germany. a.vinayagam@dkfz.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 37, 'Artificial Intelligence|Database Management Systems|Databases, Genetic|Documentation|Online Systems|Proteins|Sequence Analysis|Software', 'Comparative Study', '10.1186/1471-2105-7-161', '16549020', 'Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions.'),
(90822, 2006, 'bmc', 'An application of statistics to comparative metagenomics', 'Rodriguez-Brito, Beltran|Rohwer, Forest|Edwards, Robert A', 'Computational Science Research Center, San Diego State University, San Diego, USA. beltran.rodriguezbrito@gmail.com|Computational Science Research Center, San Diego State University, San Diego, USA. beltran.rodriguezbrito@gmail.com|Computational Science Research Center, San Diego State University, San Diego, USA. beltran.rodriguezbrito@gmail.com', 'United States|United States|United States', 124, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Genomics|Models, Biological|Models, Statistical|Proteome|Sequence Analysis, DNA|Signal Transduction', 'Comparative Study', '10.1186/1471-2105-7-162', '16549025', 'Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments.'),
(90823, 2006, 'bmc', 'PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory', 'Xue, Yu|Li, Ao|Wang, Lirong|Feng, Huanqing|Yao, Xuebiao', 'School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn|School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn|School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn|School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn|School of Life Science, University of Science and Technology of China, Hefei, Anhui, 230027, China. yxue@mail.ustc.edu.cn', 'China|China|China|China|China', 0, 'Amino Acid Sequence|Bayes Theorem|Binding Sites|Molecular Sequence Data|Phosphorylation|Protein Binding|Protein Kinases|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-163', '16549034', 'As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated.'),
(90824, 2006, 'bmc', 'Application of a sensitive collection heuristic for very large protein families: evolutionary relationship between adipose triglyceride lipase (ATGL) and classic mammalian lipases', 'Schneider, Georg|Neuberger, Georg|Wildpaner, Michael|Tian, Sun|Berezovsky, Igor|Eisenhaber, Frank', 'IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at|IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at|IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at|IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at|IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at|IMP - Research Institute of Molecular Pathology, Dr, Bohr-Gasse 7, A-1030 Vienna, Republic of Austria. schneider@imp.univie.ac.at', 'Austria|Austria|Austria|Austria|Austria|Austria', 0, 'Adipose Tissue|Algorithms|Animals|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Humans|Linkage Disequilibrium|Lipase|Mammals|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-7-164', '16551354', 'Manually finding subtle yet statistically significant links to distantly related homologues becomes practically impossible for very populated protein families due to the sheer number of similarity searches to be invoked and analyzed. The unclear evolutionary relationship between classical mammalian lipases and the recently discovered human adipose triglyceride lipase (ATGL, a patatin family member) is an exemplary case for such a problem.'),
(90825, 2006, 'bmc', 'Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae', 'Kim, Hyunsoo|Hu, William|Kluger, Yuval', 'Department of Cell Biology, NYU School of Medicine, Skirball Institute of Biomolecular Medicine, 540 First Avenue, New York, NY 10016, USA. hskim@cc.gatech.edu|Department of Cell Biology, NYU School of Medicine, Skirball Institute of Biomolecular Medicine, 540 First Avenue, New York, NY 10016, USA. hskim@cc.gatech.edu|Department of Cell Biology, NYU School of Medicine, Skirball Institute of Biomolecular Medicine, 540 First Avenue, New York, NY 10016, USA. hskim@cc.gatech.edu', 'United States|United States|United States', 21, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-7-165', '16551355', 'Gene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.'),
(90826, 2006, 'bmc', 'SNPs3D: candidate gene and SNP selection for association studies', 'Yue, Peng|Melamud, Eugene|Moult, John', 'Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, MD 20850, USA. yue@umbi.umd.edu|Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, MD 20850, USA. yue@umbi.umd.edu|Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville, MD 20850, USA. yue@umbi.umd.edu', 'United States|United States|United States', 0, 'Databases, Genetic|Genetic Markers|Genetic Predisposition to Disease|Genetic Testing|Humans|Internet|Online Systems|Polymorphism, Single Nucleotide|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-166', '16551372', 'The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level.'),
(90827, 2006, 'bmc', 'Prediction of indirect interactions in proteins', 'Prusis, Peteris|Uhlén, Staffan|Petrovska, Ramona|Lapinsh, Maris|Wikberg, Jarl E S', 'Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. peteris.prusis@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. peteris.prusis@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. peteris.prusis@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. peteris.prusis@farmbio.uu.se|Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden. peteris.prusis@farmbio.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 34, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Molecular Sequence Data|Peptides|Protein Binding|Protein Interaction Mapping|Receptors, Melanocortin|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-167', '16553946', 'Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the proteins amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling.'),
(90828, 2006, 'bmc', 'GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes', 'Arakawa, Kazuharu|Yamada, Yohei|Shinoda, Kosaku|Nakayama, Yoichi|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan. gaou@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan. gaou@sfc.keio.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Chromosome Mapping|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation|Genome, Bacterial|Models, Biological|Pilot Projects|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-7-168', '16553966', 'Successful realization of a systems biology approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology.'),
(90829, 2006, 'bmc', 'AltTrans: transcript pattern variants annotated for both alternative splicing and alternative polyadenylation', 'Le Texier, Vincent|Riethoven, Jean-Jack|Kumanduri, Vasudev|Gopalakrishnan, Chellappa|Lopez, Fabrice|Gautheret, Daniel|Thanaraj, Thangavel Alphonse', 'European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk|European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. texier@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Alternative Splicing|Chromosome Mapping|DNA Mutational Analysis|Genetic Variation|Polyadenylation|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-169', '16556303', 'The three major mechanisms that regulate transcript formation involve the selection of alternative sites for transcription start (TS), splicing, and polyadenylation. Currently there are efforts that collect data & annotation individually for each of these variants. It is important to take an integrated view of these data sets and to derive a data set of alternate transcripts along with consolidated annotation. We have been developing in the past computational pipelines that generate value-added data at genome-scale on individual variant types, these include AltSplice on splicing and AltPAS on polyadenylation. We now extend these pipelines and integrate the resultant data sets to facilitate an integrated view of the contributions from splicing and polyadenylation in the formation of transcript variants.'),
(90830, 2006, 'bmc', 'BioWarehouse: a bioinformatics database warehouse toolkit', 'Lee, Thomas J|Pouliot, Yannick|Wagner, Valerie|Gupta, Priyanka|Stringer-Calvert, David W J|Tenenbaum, Jessica D|Karp, Peter D', 'Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com|Bioinformatics Research Group, SRI International, Menlo Park, USA. tomlee@sri.com', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Protein|Protein Engineering|Proteins|Semantics|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-7-170', '16556315', 'This article addresses the problem of interoperation of heterogeneous bioinformatics databases.'),
(90831, 2006, 'bmc', 'Automatic pathway building in biological association networks', 'Yuryev, Anton|Mulyukov, Zufar|Kotelnikova, Ekaterina|Maslov, Sergei|Egorov, Sergei|Nikitin, Alexander|Daraselia, Nikolai|Mazo, Ilya', 'Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com|Ariadne Genomics Inc, 9700 Great Seneca Hwy, Suite 113, Rockville, MD 20850, USA. ayuryev@ariadnegenomics.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 76, 'Databases, Bibliographic|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteins|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-7-171', '16563163', 'Scientific literature is a source of the most reliable and comprehensive knowledge about molecular interaction networks. Formalization of this knowledge is necessary for computational analysis and is achieved by automatic fact extraction using various text-mining algorithms. Most of these techniques suffer from high false positive rates and redundancy of the extracted information. The extracted facts form a large network with no pathways defined.'),
(90832, 2006, 'bmc', 'GENOMEMASKER package for designing unique genomic PCR primers', 'Andreson, Reidar|Reppo, Eric|Kaplinski, Lauris|Remm, Maido', 'Department of Bioinformatics, University of Tartu, Tartu, Estonia. reidar.andreson@ut.ee|Department of Bioinformatics, University of Tartu, Tartu, Estonia. reidar.andreson@ut.ee|Department of Bioinformatics, University of Tartu, Tartu, Estonia. reidar.andreson@ut.ee|Department of Bioinformatics, University of Tartu, Tartu, Estonia. reidar.andreson@ut.ee', 'Estonia|Estonia|Estonia|Estonia', 0, 'Base Sequence|Binding Sites|DNA Primers|Genome, Human|Humans|Molecular Sequence Data|Polymerase Chain Reaction', 'Comparative Study', '10.1186/1471-2105-7-172', '16566824', 'The design of oligonucleotides and PCR primers for studying large genomes is complicated by the redundancy of sequences. The eukaryotic genomes are particularly difficult to study due to abundant repeats. The speed of most existing primer evaluation programs is not sufficient for large-scale experiments.'),
(90833, 2006, 'bmc', 'Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change', 'Uzilov, Andrew V|Keegan, Joshua M|Mathews, David H', 'Department of Biochemistry & Biophysics, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642, USA. andrew.uzilov@gmail.com|Department of Biochemistry & Biophysics, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642, USA. andrew.uzilov@gmail.com|Department of Biochemistry & Biophysics, University of Rochester Medical Center, 601 Elmwood Avenue, Box 712, Rochester, New York 14642, USA. andrew.uzilov@gmail.com', 'United States|United States|United States', 138, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Computer Simulation|Energy Transfer|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Pattern Recognition, Automated|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-173', '16566836', 'Non-coding RNAs (ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs.'),
(90834, 2006, 'bmc', 'UVPAR: fast detection of functional shifts in duplicate genes', 'Arnau, Vicente|Gallach, Miguel|Lucas, J Ignasi|Marín, Ignacio', 'Departamento de Genética, Universidad de Valencia, Burjassot, Spain. vicente.arnau@uv.es|Departamento de Genética, Universidad de Valencia, Burjassot, Spain. vicente.arnau@uv.es|Departamento de Genética, Universidad de Valencia, Burjassot, Spain. vicente.arnau@uv.es|Departamento de Genética, Universidad de Valencia, Burjassot, Spain. vicente.arnau@uv.es', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Evolution, Molecular|Genes, Duplicate|Proteins|Selection, Genetic|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-174', '16569227', 'The imprint of natural selection on gene sequences is often difficult to detect. A plethora of methods have been devised to detect genetic changes due to selective processes. However, many of those methods depend heavily on underlying assumptions regarding the mode of change of DNA sequences and often require sophisticated mathematical treatments that made them computationally slow. The development of fast and effective methods to detect modifications in the selective constraints of genes is therefore of great interest.'),
(90835, 2006, 'bmc', 'LS-NMF: a modified non-negative matrix factorization algorithm utilizing uncertainty estimates', 'Wang, Guoli|Kossenkov, Andrew V|Ochs, Michael F', 'Division of Population Science, Fox Chase Cancer Center, Philadelphia, PA, USA. guoli.wang@fccc.edu|Division of Population Science, Fox Chase Cancer Center, Philadelphia, PA, USA. guoli.wang@fccc.edu|Division of Population Science, Fox Chase Cancer Center, Philadelphia, PA, USA. guoli.wang@fccc.edu', 'United States|United States|United States', 62, 'Algorithms|Databases, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|RNA, Messenger|Uncertainty', 'Comparative Study', '10.1186/1471-2105-7-175', '16569230', 'Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to identify patterns that together explain the data as a linear combination of expression signatures. Microarray data generally includes individual estimates of uncertainty for each gene in each condition, however NMF does not exploit this information. Previous work has shown that such uncertainties can be extremely valuable for pattern recognition.'),
(90836, 2006, 'bmc', 'The Gaggle: an open-source software system for integrating bioinformatics software and data sources', 'Shannon, Paul T|Reiss, David J|Bonneau, Richard|Baliga, Nitin S', 'Institute for Systems Biology, Seattle, WA 98103, USA. pshannon@systemsbiology.org|Institute for Systems Biology, Seattle, WA 98103, USA. pshannon@systemsbiology.org|Institute for Systems Biology, Seattle, WA 98103, USA. pshannon@systemsbiology.org|Institute for Systems Biology, Seattle, WA 98103, USA. pshannon@systemsbiology.org', 'United States|United States|United States|United States', 129, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Programming Languages|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-176', '16569235', 'Systems biologists work with many kinds of data, from many different sources, using a variety of software tools. Each of these tools typically excels at one type of analysis, such as of microarrays, of metabolic networks and of predicted protein structure. A crucial challenge is to combine the capabilities of these (and other forthcoming) data resources and tools to create a data exploration and analysis environment that does justice to the variety and complexity of systems biology data sets. A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing.'),
(90837, 2006, 'bmc', 'Identifying metabolic enzymes with multiple types of association evidence', 'Kharchenko, Peter|Chen, Lifeng|Freund, Yoav|Vitkup, Dennis|Church, George M', 'Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu|Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu|Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu|Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu|Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu', 'United States|United States|United States|United States|United States', 77, 'Energy Metabolism|Enzymes|Escherichia coli Proteins|Genome, Bacterial|Phylogeny|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-7-177', '16571130', 'Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes.'),
(90838, 2006, 'bmc', 'Protein secondary structure prediction for a single-sequence using hidden semi-Markov models', 'Aydin, Zafer|Altunbasak, Yucel|Borodovsky, Mark', 'School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. aydinz@ece.gatech.edu|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. aydinz@ece.gatech.edu|School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. aydinz@ece.gatech.edu', 'Georgia|Georgia|Georgia', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Markov Chains|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-178', '16571137', 'The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present.'),
(90839, 2006, 'bmc', 'AtRTPrimer: database for Arabidopsis genome-wide homogeneous and specific RT-PCR primer-pairs', 'Han, Sangjo|Kim, Dongsup', 'Department of BioSystems, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea. Sangjo_Han@kaist.ac.kr|Department of BioSystems, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea. Sangjo_Han@kaist.ac.kr', '-|-', 48, 'Arabidopsis|Chromosome Mapping|DNA Primers|DNA, Plant|Databases, Genetic|Genome, Plant|Reproducibility of Results|Reverse Transcriptase Polymerase Chain Reaction|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-179', '16571141', 'Primer design is a critical step in all types of RT-PCR methods to ensure specificity and efficiency of a target amplicon. However, most traditional primer design programs suggest primers on a single template of limited genetic complexity. To provide researchers with a sufficient number of pre-designed specific RT-PCR primer pairs for whole genes in Arabidopsis, we aimed to construct a genome-wide primer-pair database.'),
(90840, 2006, 'bmc', 'A two-stage approach for improved prediction of residue contact maps', 'Vullo, Alessandro|Walsh, Ian|Pollastri, Gianluca', 'School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. alessandro.vullo@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. alessandro.vullo@ucd.ie|School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. alessandro.vullo@ucd.ie', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computational Biology|Hydrophobic and Hydrophilic Interactions|Models, Molecular|Neural Networks (Computer)|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-7-180', '16573808', 'Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts), the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a maps principal eigenvector (PE) from the primary sequence, the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned.'),
(90841, 2006, 'bmc', 'Detection of divergent genes in microbial aCGH experiments', 'Snipen, Lars|Repsilber, Dirk|Nyquist, Ludvig|Ziegler, Andreas|Aakra, Agot|Aastveit, Are', 'Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no|Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, N-1432 As, Norway. lars.snipen@umb.no', 'Norway|Norway|Norway|Norway|Norway|Norway', 8, 'Computational Biology|Data Interpretation, Statistical|Genetic Variation|Genome, Bacterial|Models, Statistical|Nucleic Acid Hybridization|Reproducibility of Results|Staphylococcus aureus|Streptococcus pneumoniae', 'Comparative Study', '10.1186/1471-2105-7-181', '16573812', 'Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.'),
(90842, 2006, 'bmc', 'Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models', 'Liu, Wen|Meng, Xiangshan|Xu, Qiqi|Flower, Darren R|Li, Tongbin', 'Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. liuwen@biocompute.umn.edu|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. liuwen@biocompute.umn.edu|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. liuwen@biocompute.umn.edu|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. liuwen@biocompute.umn.edu|Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA. liuwen@biocompute.umn.edu', 'United States|United States|United States|United States|United States', 88, 'Algorithms|Amino Acid Sequence|Animals|Artificial Intelligence|Binding Sites|Computer Simulation|Histocompatibility Antigens Class I|Mice|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Peptides|Protein Binding|Protein Interaction Mapping|Quantitative Structure-Activity Relationship|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-182', '16579851', 'The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as binders or non-binders or as strong binders and weak binders, recent methods seek to make predictions about precise binding affinities.'),
(90843, 2006, 'bmc', 'Exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data', 'Vinciotti, Veronica|Liu, Xiaohui|Turk, Rolf|de Meijer, Emile J|t Hoen, Peter A C', 'Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk|Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk|Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk|Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk|Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Mice|Mice, Inbred C57BL|Mice, Inbred mdx|Muscular Dystrophies', 'Comparative Study', '10.1186/1471-2105-7-183', '16584545', 'The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling T2-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.'),
(90844, 2006, 'bmc', 'Use of physiological constraints to identify quantitative design principles for gene expression in yeast adaptation to heat shock', 'Vilaprinyo, Ester|Alves, Rui|Sorribas, Albert', 'Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida, Spain. evilaprinyo@cmb.udl.es|Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida, Spain. evilaprinyo@cmb.udl.es|Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, Montserrat Roig 2, 25008-Lleida, Spain. evilaprinyo@cmb.udl.es', 'Spain|Spain|Spain', 28, 'Adaptation, Physiological|Computer Simulation|Feedback|Gene Expression Regulation, Fungal|Heat-Shock Proteins|Heat-Shock Response|Models, Biological|Oxidative Stress|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-184', '16584550', 'Understanding the relationship between gene expression changes, enzyme activity shifts, and the corresponding physiological adaptive response of organisms to environmental cues is crucial in explaining how cells cope with stress. For example, adaptation of yeast to heat shock involves a characteristic profile of changes to the expression levels of genes coding for enzymes of the glycolytic pathway and some of its branches. The experimental determination of changes in gene expression profiles provides a descriptive picture of the adaptive response to stress. However, it does not explain why a particular profile is selected for any given response.'),
(90845, 2006, 'bmc', 'Ebbie: automated analysis and storage of small RNA cloning data using a dynamic web server', 'Ebhardt, H Alexander|Wiese, Kay C|Unrau, Peter J', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Drive, V5A 1S6 Burnaby, B.C., Canada. aebhardt@sfu.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Drive, V5A 1S6 Burnaby, B.C., Canada. aebhardt@sfu.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, 8888 University Drive, V5A 1S6 Burnaby, B.C., Canada. aebhardt@sfu.ca', 'Canada|Canada|Canada', 3, 'Algorithms|Base Sequence|Cloning, Molecular|Databases, Nucleic Acid|Electronic Data Processing|Information Storage and Retrieval|Internet|Molecular Sequence Data|RNA, Small Interfering', 'Comparative Study', '10.1186/1471-2105-7-185', '16584563', 'DNA sequencing is used ubiquitously: from deciphering genomes to determining the primary sequence of small RNAs (smRNAs). The cloning of smRNAs is currently the most conventional method to determine the actual sequence of these important regulators of gene expression. Typical smRNA cloning projects involve the sequencing of hundreds to thousands of smRNA clones that are delimited at their 5 and 3 ends by fixed sequence regions. These primers result from the biochemical protocol used to isolate and convert the smRNA into clonable PCR products. Recently we completed a smRNA cloning project involving tobacco plants, where analysis was required for approximately 700 smRNA sequences. Finding no easily accessible research tool to enter and analyze smRNA sequences we developed Ebbie to assist us with our study.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90846, 2006, 'bmc', 'Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis', 'Schwartz, Jean-Marc|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. jean@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. jean@kuicr.kyoto-u.ac.jp', 'Japan|Japan', 62, 'Algorithms|Ethanol|Glucokinase|Glucose|Glucose-6-Phosphate Isomerase|Glycolysis|Kinetics|Models, Biological|Phosphofructokinases|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-7-186', '16584566', 'Elementary mode analysis of metabolic pathways has proven to be a valuable tool for assessing the properties and functions of biochemical systems. However, little comprehension of how individual elementary modes are used in real cellular states has been achieved so far. A quantitative measure of fluxes carried by individual elementary modes is of great help to identify dominant metabolic processes, and to understand how these processes are redistributed in biological cells in response to changes in environmental conditions, enzyme kinetics, or chemical concentrations.'),
(90847, 2006, 'bmc', 'Classification of protein quaternary structure by functional domain composition', 'Yu, Xiaojing|Wang, Chuan|Li, Yixue', 'Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. xjyu@sibs.ac.cn|Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. xjyu@sibs.ac.cn|Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. xjyu@sibs.ac.cn', 'China|China|China', 25, 'Algorithms|Computational Biology|Databases, Protein|Protein Structure, Quaternary|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-7-187', '16584572', 'The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences.'),
(90848, 2006, 'bmc', 'Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics', 'Dutheil, Julien|Gaillard, Sylvain|Bazin, Eric|Glémin, Sylvain|Ranwez, Vincent|Galtier, Nicolas|Belkhir, Khalid', 'CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr|CNRS UMR 5171--Génome, Populations, Interactions, Adaptation (GPIA), Université Montpellier 2, France. Julien.Dutheil@univ-montp2.fr', 'France|France|France|France|France|France|France', 85, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Evolution, Molecular|Genetics, Population|Humans|Models, Statistical|Molecular Sequence Data|Phylogeny|Programming Languages|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-7-188', '16594991', 'A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications.'),
(90849, 2006, 'bmc', 'Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins', 'Bagos, Pantelis G|Liakopoulos, Theodore D|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. pbagos@biol.uoa.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. pbagos@biol.uoa.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. pbagos@biol.uoa.gr', 'Greece|Greece|Greece', 55, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Information Storage and Retrieval|Markov Chains|Membrane Proteins|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-189', '16597327', 'Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature.'),
(90850, 2006, 'bmc', 'MIMAS: an innovative tool for network-based high density oligonucleotide microarray data management and annotation', 'Hermida, Leandro|Schaad, Olivier|Demougin, Philippe|Descombes, Patrick|Primig, Michael', 'Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70 CH-4056 Basel Switzerland. leandro.hermida@unibas.ch|Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70 CH-4056 Basel Switzerland. leandro.hermida@unibas.ch|Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70 CH-4056 Basel Switzerland. leandro.hermida@unibas.ch|Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70 CH-4056 Basel Switzerland. leandro.hermida@unibas.ch|Biozentrum and Swiss Institute of Bioinformatics Klingelbergstrasse 50-70 CH-4056 Basel Switzerland. leandro.hermida@unibas.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 11, 'Database Management Systems|Databases, Genetic|Documentation|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-190', '16597336', 'The high-density oligonucleotide microarray (GeneChip) is an important tool for molecular biological research aiming at large-scale detection of small nucleotide polymorphisms in DNA and genome-wide analysis of mRNA concentrations. Local array data management solutions are instrumental for efficient processing of the results and for subsequent uploading of data and annotations to a global certified data repository at the EBI (ArrayExpress) or the NCBI (GeneOmnibus).'),
(90851, 2006, 'bmc', 'STEM: a tool for the analysis of short time series gene expression data', 'Ernst, Jason|Bar-Joseph, Ziv', 'Center for Automated and Learning and Discovery, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jernst@cs.cmu.edu|Center for Automated and Learning and Discovery, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jernst@cs.cmu.edu', 'United States|United States', 610, 'Cluster Analysis|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Signal Transduction|Software|Time Factors|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-7-191', '16597342', 'Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3-8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.'),
(90852, 2006, 'bmc', 'AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies', 'Major, Sylvia M|Nishizuka, Satoshi|Morita, Daisaku|Rowland, Rick|Sunshine, Margot|Shankavaram, Uma|Washburn, Frank|Asin, Daniel|Kouros-Mehr, Hosein|Kane, David|Weinstein, John N', 'Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, USA. pnym8@yahoo.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 25, 'Antibodies, Monoclonal|Computational Biology|Database Management Systems|Databases, Protein|Genomics|Immunologic Techniques|Information Storage and Retrieval|Internet|Protein Array Analysis|Proteomics|Research|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-192', '16600027', 'Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on reverse-phase protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java.'),
(90853, 2006, 'bmc', 'An interactive tool for visualization of relationships between gene expression profiles', 'Ruzanov, Peter|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Research Centre, Vancouver, BC V5Z 4S6, Canada. pruzanov@bcgsc.ca|Genome Sciences Centre, BC Cancer Research Centre, Vancouver, BC V5Z 4S6, Canada. pruzanov@bcgsc.ca', 'Canada|Canada', 1, 'Algorithms|Animals|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Software|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-7-193', '16600045', 'Application of phenetic methods to gene expression analysis proved to be a successful approach. Visualizing the results in a 3-dimentional space may further enhance these techniques.'),
(90854, 2006, 'bmc', 'Mining gene expression data by interpreting principal components', 'Roden, Joseph C|King, Brandon W|Trout, Diane|Mortazavi, Ali|Wold, Barbara J|Hart, Christopher E', 'Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov|Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov|Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov|Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov|Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov|Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA. joe.roden@jpl.nasa.gov', 'United States|United States|United States|United States|United States|United States', 44, 'Algorithms|Animals|Automation|Cluster Analysis|Computational Biology|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-7-194', '16600052', 'There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering, e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.'),
(90855, 2006, 'bmc', 'A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila', 'Pacifico, Svetlana|Liu, Guozhen|Guest, Stephen|Parrish, Jodi R|Fotouhi, Farshad|Finley, Russell L', 'Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu|Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu|Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu|Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu|Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu|Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA. ak5950@wayne.edu', 'United States|United States|United States|United States|United States|United States', 24, 'Database Management Systems|Databases, Genetic|Drosophila Proteins|Gene Expression Profiling|Information Storage and Retrieval|Internet|Protein Interaction Mapping|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-195', '16603075', 'Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.'),
(90856, 2006, 'bmc', 'A case study in pathway knowledgebase verification', 'Racunas, Stephen A|Shah, Nigam H|Fedoroff, Nina V', 'Computational Learning Laboratory, Stanford University, CA, USA. sracunas@csli.stanford.edu|Computational Learning Laboratory, Stanford University, CA, USA. sracunas@csli.stanford.edu|Computational Learning Laboratory, Stanford University, CA, USA. sracunas@csli.stanford.edu', 'United States|United States|United States', 8, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Databases, Factual|Information Storage and Retrieval|Knowledge Bases|Models, Biological|Programming Languages|Proteome|Signal Transduction', 'Comparative Study', '10.1186/1471-2105-7-196', '16603083', 'Biological databases and pathway knowledge-bases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledge-base as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.'),
(90857, 2006, 'bmc', 'Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data', 'Zhang, Xuegong|Lu, Xin|Shi, Qian|Xu, Xiu-Qin|Leung, Hon-Chiu E|Harris, Lyndsay N|Iglehart, James D|Miron, Alexander|Liu, Jun S|Wong, Wing H', 'Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn|Bioinformatics Div, TNLIST, Dept of Automation, Tsinghua University, Beijing, 100084, China. xgzhang@tsinghua.edu.cn', 'China|China|China|China|China|China|China|China|China|China', 208, 'Algorithms|Animals|Artificial Intelligence|Biomarkers|Cluster Analysis|Gene Expression Profiling|Humans|Mass Spectrometry|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Rats|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-7-197', '16606446', 'Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data.'),
(90858, 2006, 'bmc', 'Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables', 'Van Belle, Werner|Anensen, Nina|Haaland, Ingvild|Bruserud, Øystein|Høgda, Kjell-Arild|Gjertsen, Bjørn Tore', 'Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no|Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no|Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no|Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no|Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no|Bioinformatics Group, Norut IT, Research Park Tromsø, Postboks 6434, N9294 Tromsø, NO, Norway. werner.van.belle@itek.norut.no', 'Norway|Norway|Norway|Norway|Norway|Norway', 25, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Electrophoresis, Gel, Two-Dimensional|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Leukemia|Models, Biological|Neoplasm Proteins|Protein Processing, Post-Translational|Statistics as Topic|Tumor Suppressor Protein p53', 'Journal Article', '10.1186/1471-2105-7-198', '16606449', 'Two-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detection is a necessary step to assess relations between spots and biological variables. This often proves time consuming and difficult when working with non-perfect gels. We developed an analysis technique to measure correlation between 2DE images and biological variables on a pixel by pixel basis. After image alignment and normalization, the biological parameters and pixel values are replaced by their specific rank. These rank adjusted images and parameters are then put into a standard linear Pearson correlation and further tested for significance and variance.'),
(90859, 2006, 'bmc', 'QPath: a method for querying pathways in a protein-protein interaction network', 'Shlomi, Tomer|Segal, Daniel|Ruppin, Eytan|Sharan, Roded', 'School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il', 'Israel|Israel|Israel|Israel', 139, 'Algorithms|Computer Simulation|Gene Expression Regulation|Information Storage and Retrieval|Models, Biological|Protein Interaction Mapping|Sequence Analysis|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-199', '16606460', 'Sequence comparison is one of the most prominent tools in biological research, and is instrumental in studying gene function and evolution. The rapid development of high-throughput technologies for measuring protein interactions calls for extending this fundamental operation to the level of pathways in protein networks.'),
(90860, 2006, 'bmc', 'Biologically relevant effects of mRNA amplification on gene expression profiles', 'van Haaften, Rachel I M|Schroen, Blanche|Janssen, Ben J A|van Erk, Arie|Debets, Jacques J M|Smeets, Hubert J M|Smits, Jos F M|van den Wijngaard, Arthur|Pinto, Yigal M|Evelo, Chris T A', 'BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl|BiGCaT Bioinformatics, University of Maastricht and Technical University Eindhoven, Maastricht, The Netherlands. rachel.vanhaaften@bigcat.unimaas.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 24, 'Animals|Artifacts|Computer Simulation|Gene Expression Profiling|Genes, Reporter|Heart Ventricles|Models, Genetic|Models, Statistical|Nucleic Acid Amplification Techniques|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Rats|Reproducibility of Results|Sensitivity and Specificity|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-200', '16608515', 'Gene expression microarray technology permits the analysis of global gene expression profiles. The amount of sample needed limits the use of small excision biopsies and/or needle biopsies from human or animal tissues. Linear amplification techniques have been developed to increase the amount of sample derived cDNA. These amplified samples can be hybridised on microarrays. However, little information is available whether microarrays based on amplified and unamplified material yield comparable results. In the present study we compared microarray data obtained from amplified mRNA derived from biopsies of rat cardiac left ventricle and non-amplified mRNA derived from the same organ. Biopsies were linearly amplified to acquire enough material for a microarray experiment. Both amplified and unamplified samples were hybridized to the Rat Expression Set 230 Array of Affymetrix.'),
(90861, 2006, 'bmc', 'A phylogenomic gene cluster resource: the Phylogenetically Inferred Groups (PhIGs) database', 'Dehal, Paramvir S|Boore, Jeffrey L', 'Evolutionary Genomics Department, DOE Joint Genome Institute and Lawrence, Berkeley National Laboratory, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. psdehal@lbl.gov|Evolutionary Genomics Department, DOE Joint Genome Institute and Lawrence, Berkeley National Laboratory, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. psdehal@lbl.gov', 'United States|United States', 43, 'Base Sequence|Chromosome Mapping|Databases, Genetic|Information Storage and Retrieval|Internet|Molecular Sequence Data|Multigene Family|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-201', '16608522', 'We present here the PhIGs database, a phylogenomic resource for sequenced genomes. Although many methods exist for clustering gene families, very few attempt to create truly orthologous clusters sharing descent from a single ancestral gene across a range of evolutionary depths. Although these non-phylogenetic gene family clusters have been used broadly for gene annotation, errors are known to be introduced by the artifactual association of slowly evolving paralogs and lack of annotation for those more rapidly evolving. A full phylogenetic framework is necessary for accurate inference of function and for many studies that address pattern and mechanism of the evolution of the genome. The automated generation of evolutionary gene clusters, creation of gene trees, determination of orthology and paralogy relationships, and the correlation of this information with gene annotations, expression information, and genomic context is an important resource to the scientific community.'),
(90862, 2006, 'bmc', 'Profiling alternatively spliced mRNA isoforms for prostate cancer classification', 'Zhang, Chaolin|Li, Hai-Ri|Fan, Jian-Bing|Wang-Rodriguez, Jessica|Downs, Tracy|Fu, Xiang-Dong|Zhang, Michael Q', 'Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu|Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu', 'United States|United States|United States|United States|United States|United States|United States', 78, 'Algorithms|Alternative Splicing|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Male|Neoplasm Proteins|Prostatic Neoplasms|Protein Isoforms|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-202', '16608523', 'Prostate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.'),
(90863, 2006, 'bmc', 'Integrative investigation of metabolic and transcriptomic data', 'Pir, Pinar|Kirdar, Betxc3xbcl|Hayes, Andrew|Onsan, Z Ylsen|Ulgen, Kutlu O|Oliver, Stephen G', 'Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr|Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr|Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr|Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr|Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr|Department of Chemical Engineering, Boxc4x9fazixc3xa7i University, Bebek 34342, Istanbul, Turkey. pinarpir@boun.edu.tr', 'Turkey|Turkey|Turkey|Turkey|Turkey|Turkey', 30, 'Algorithms|Computer Simulation|Databases, Protein|Information Storage and Retrieval|Models, Biological|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Systems Integration|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-203', '16611354', 'New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods.'),
(90864, 2006, 'bmc', 'Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data', 'Thornton-Wells, Tricia A|Moore, Jason H|Haines, Jonathan L', 'Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA. t.thornton-wells@vanderbilt.edu|Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA. t.thornton-wells@vanderbilt.edu|Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA. t.thornton-wells@vanderbilt.edu', 'United States|United States|United States', 21, 'Algorithms|Bayes Theorem|Cluster Analysis|Computer Simulation|Fuzzy Logic|Genetic Heterogeneity|Genetic Predisposition to Disease|Genotype|Models, Genetic|Phenotype|Quantitative Trait Loci', 'Comparative Study', '10.1186/1471-2105-7-204', '16611359', 'Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods--Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering--appropriate for categorical data were compared. Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic models of complex human disease. To determine the efficacy of applying the Bayesian Classification method to real data, the reliability of its internal clustering metrics at finding good clusterings was evaluated using permutation testing.'),
(90865, 2006, 'bmc', 'SIMAGE: simulation of DNA-microarray gene expression data', 'Albers, Casper J|Jansen, Ritsert C|Kok, Jan|Kuipers, Oscar P|van Hijum, Sacha Aft', 'Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, The Netherlands. c.j.albers@open.ac.uk|Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, The Netherlands. c.j.albers@open.ac.uk|Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, The Netherlands. c.j.albers@open.ac.uk|Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, The Netherlands. c.j.albers@open.ac.uk|Groningen Bioinformatics Centre, University of Groningen, Groningen Biomolecular Sciences and Biotechnology Institute, PO Box 14, 9750 AA Haren, The Netherlands. c.j.albers@open.ac.uk', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 14, 'Algorithms|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-205', '16613602', 'Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments.'),
(90866, 2006, 'bmc', 'ROC and confusion analysis of structure comparison methods identify the main causes of divergence from manual protein classification', 'Sam, Vichetra|Tai, Chin-Hsien|Garnier, Jean|Gibrat, Jean-Francois|Lee, Byungkook|Munson, Peter J', 'Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov|Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov|Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov|Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov|Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov|Mathematical and Statistical Computing Laboratory, DCB, CIT, NIH, DHHS, Bethesda, MD, USA. vsam@mail.nih.gov', 'United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Amino Acid Sequence|Artifacts|Data Interpretation, Statistical|Molecular Sequence Data|Observer Variation|Protein Folding|Proteins|ROC Curve|Sequence Alignment|Sequence Analysis, Protein', 'Comparative Study', '10.1186/1471-2105-7-206', '16613604', 'Current classification of protein folds are based, ultimately, on visual inspection of similarities. Previous attempts to use computerized structure comparison methods show only partial agreement with curated databases, but have failed to provide detailed statistical and structural analysis of the causes of these divergences.'),
(90867, 2006, 'bmc', 'Development and implementation of an algorithm for detection of protein complexes in large interaction networks', 'Altaf-Ul-Amin, Md|Shinbo, Yoko|Mihara, Kenji|Kurokawa, Ken|Kanaya, Shigehiko', 'Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan. amin-m@is.naist.jp|Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan. amin-m@is.naist.jp|Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan. amin-m@is.naist.jp|Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan. amin-m@is.naist.jp|Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0101, Japan. amin-m@is.naist.jp', 'Japan|Japan|Japan|Japan|Japan', 355, 'Algorithms|Cell Physiological Phenomena|Cluster Analysis|Computer Simulation|Escherichia coli|Escherichia coli Proteins|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-207', '16613608', 'After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes.'),
(90868, 2006, 'bmc', 'Length-dependent prediction of protein intrinsic disorder', 'Peng, Kang|Radivojac, Predrag|Vucetic, Slobodan|Dunker, A Keith|Obradovic, Zoran', 'Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA. kangpeng@ist.temple.edu', 'United States|United States|United States|United States|United States', 549, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-208', '16618368', 'Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (< or =30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions.'),
(90869, 2006, 'bmc', 'PedGenie: an analysis approach for genetic association testing in extended pedigrees and genealogies of arbitrary size', 'Allen-Brady, Kristina|Wong, Jathine|Camp, Nicola J', 'Genetic Epidemiology Division, Department of Medical Informatics, University of Utah Salt Lake City, Utah, USA. kristina.allen@utah.edu|Genetic Epidemiology Division, Department of Medical Informatics, University of Utah Salt Lake City, Utah, USA. kristina.allen@utah.edu|Genetic Epidemiology Division, Department of Medical Informatics, University of Utah Salt Lake City, Utah, USA. kristina.allen@utah.edu', 'United States|United States|United States', 0, 'Biomarkers, Tumor|Breast Neoplasms|Cell Cycle Proteins|Chromosome Mapping|Genetic Predisposition to Disease|Humans|Linkage Disequilibrium|Nuclear Proteins|Pedigree|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-7-209', '16620382', 'We present a general approach to perform association analyses in pedigrees of arbitrary size and structure, which also allows for a mixture of pedigree members and independent individuals to be analyzed together, to test genetic markers and qualitative or quantitative traits. Our software, PedGenie, uses Monte Carlo significance testing to provide a valid test for related individuals that can be applied to any test statistic, including transmission disequilibrium statistics. Single locus at a time, composite genotype tests, and haplotype analyses may all be performed. We illustrate the validity and functionality of PedGenie using simulated and real data sets. For the real data set, we evaluated the role of two tagging-single nucleotide polymorphisms (tSNPs) in the DNA repair gene, NBS1, and their association with female breast cancer in 462 cases and 572 controls selected to be BRCA1/2 mutation negative from 139 high-risk Utah breast cancer families.'),
(90870, 2006, 'bmc', 'An integrated system for genetic analysis', 'Fiddy, Simon|Cattermole, David|Xie, Dong|Duan, Xiao Yuan|Mott, Richard', 'Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. simon.fiddy@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. simon.fiddy@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. simon.fiddy@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. simon.fiddy@well.ox.ac.uk|Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK. simon.fiddy@well.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Chromosome Mapping|Computer Graphics|DNA|Database Management Systems|Internet|Linkage Disequilibrium|Pedigree|Sequence Alignment|Sequence Analysis, DNA|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-210', '16623936', 'Large-scale genetic mapping projects require data management systems that can handle complex phenotypes and detect and correct high-throughput genotyping errors, yet are easy to use.'),
(90871, 2006, 'bmc', 'Assessment of the relationship between pre-chip and post-chip quality measures for Affymetrix GeneChip expression data', 'Jones, Lesley|Goldstein, Darlene R|Hughes, Gareth|Strand, Andrew D|Collin, Francois|Dunnett, Stephen B|Kooperberg, Charles|Aragaki, Aaron|Olson, James M|Augood, Sarah J|Faull, Richard L M|Luthi-Carter, Ruth|Moskvina, Valentina|Hodges, Angela K', 'Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk|Depts. of Psychological Medicine and Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK. jonesl1@cf.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 26, 'Artifacts|Computer Simulation|Data Interpretation, Statistical|Equipment Design|Equipment Failure Analysis|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Quality Assurance, Health Care|Quality Control|Reproducibility of Results|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-7-211', '16623940', 'Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures.'),
(90872, 2006, 'bmc', 'Quality control for terms and definitions in ontologies and taxonomies', 'Köhler, Jacob|Munn, Katherine|Rüegg, Alexander|Skusa, Andre|Smith, Barry', 'Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, UK. jacob.koehler@bbsrc.ac.uk|Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, UK. jacob.koehler@bbsrc.ac.uk|Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, UK. jacob.koehler@bbsrc.ac.uk|Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, UK. jacob.koehler@bbsrc.ac.uk|Biomathematics and Bioinformatics Division, Rothamsted Research, Harpenden, UK. jacob.koehler@bbsrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Classification|Databases, Genetic|Documentation|Information Storage and Retrieval|Natural Language Processing|Phylogeny|Proteins|Quality Control|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-212', '16623942', 'Ontologies and taxonomies are among the most important computational resources for molecular biology and bioinformatics. A series of recent papers has shown that the Gene Ontology (GO), the most prominent taxonomic resource in these fields, is marked by flaws of certain characteristic types, which flow from a failure to address basic ontological principles. As yet, no methods have been proposed which would allow ontology curators to pinpoint flawed terms or definitions in ontologies in a systematic way.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90873, 2006, 'bmc', 'An analysis of the Sargasso Sea resource and the consequences for database composition', 'Tress, Michael L|Cozzetto, Domenico|Tramontano, Anna|Valencia, Alfonso', 'Protein Design Group, CNB-CSIC, Calle Darwin, Cantoblanco 28049 Madrid, Spain. mtress@cnio.es|Protein Design Group, CNB-CSIC, Calle Darwin, Cantoblanco 28049 Madrid, Spain. mtress@cnio.es|Protein Design Group, CNB-CSIC, Calle Darwin, Cantoblanco 28049 Madrid, Spain. mtress@cnio.es|Protein Design Group, CNB-CSIC, Calle Darwin, Cantoblanco 28049 Madrid, Spain. mtress@cnio.es', 'Spain|Spain|Spain|Spain', 16, 'Amino Acid Sequence|Bacterial Proteins|Base Sequence|Databases, Protein|Genes, Bacterial|Genetic Variation|Information Storage and Retrieval|Marine Biology|Molecular Sequence Data|Oceans and Seas|Water Microbiology', 'Journal Article', '10.1186/1471-2105-7-213', '16623953', 'The environmental sequencing of the Sargasso Sea has introduced a huge new resource of genomic information. Unlike the protein sequences held in the current searchable databases, the Sargasso Sea sequences originate from a single marine environment and have been sequenced from species that are not easily obtainable by laboratory cultivation. The resource also contains very many fragments of whole protein sequences, a side effect of the shotgun sequencing method.These sequences form a significant addendum to the current searchable databases but also present us with some intrinsic difficulties. While it is important to know whether it is possible to assign function to these sequences with the current methods and whether they will increase our capacity to explore sequence space, it is also interesting to know how current bioinformatics techniques will deal with the new sequences in the resource.'),
(90874, 2006, 'bmc', 'The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison', 'Sioson, Allan A|Mane, Shrinivasrao P|Li, Pinghua|Sha, Wei|Heath, Lenwood S|Bohnert, Hans J|Grene, Ruth', 'Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu|Department of Computer Science, Virginia Tech, Blacksburg, USA. asioson@vt.edu', 'United States|United States|United States|United States|United States|United States|United States', 13, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1186/1471-2105-7-215', '16626497', 'Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data.'),
(90875, 2006, 'bmc', 'A factor analysis model for functional genomics', 'Kustra, Rafal|Shioda, Romy|Zhu, Mu', 'Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca|Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca|Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Factor Analysis, Statistical|Gene Expression Profiling|Genomics|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Evaluation Studies', '10.1186/1471-2105-7-216', '16630343', 'Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories.'),
(90876, 2006, 'bmc', 'Predicting deleterious nsSNPs: an analysis of sequence and structural attributes', 'Dobson, Richard J|Munroe, Patricia B|Caulfield, Mark J|Saqi, Mansoor As', 'Clinical Pharmacology, The William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. r.j.dobson@qmul.ac.uk|Clinical Pharmacology, The William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. r.j.dobson@qmul.ac.uk|Clinical Pharmacology, The William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. r.j.dobson@qmul.ac.uk|Clinical Pharmacology, The William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. r.j.dobson@qmul.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 68, 'Algorithms|Computational Biology|Databases, Genetic|Databases, Protein|Genetic Predisposition to Disease|Humans|Models, Genetic|Polymorphism, Single Nucleotide|Protein Conformation|Proteins|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-217', '16630345', 'There has been an explosion in the number of single nucleotide polymorphisms (SNPs) within public databases. In this study we focused on non-synonymous protein coding single nucleotide polymorphisms (nsSNPs), some associated with disease and others which are thought to be neutral. We describe the distribution of both types of nsSNPs using structural and sequence based features and assess the relative value of these attributes as predictors of function using machine learning methods. We also address the common problem of balance within machine learning methods and show the effect of imbalance on nsSNP function prediction. We show that nsSNP function prediction can be significantly improved by 100% undersampling of the majority class. The learnt rules were then applied to make predictions of function on all nsSNPs within Ensembl.'),
(90877, 2006, 'bmc', 'XcisClique: analysis of regulatory bicliques', 'Pati, Amrita|Vasquez-Robinet, Cecilia|Heath, Lenwood S|Grene, Ruth|Murali, T M', 'Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. apati@vt.edu|Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. apati@vt.edu|Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. apati@vt.edu|Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. apati@vt.edu|Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. apati@vt.edu', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Base Sequence|Chromosome Mapping|Gene Expression Regulation|Regulatory Elements, Transcriptional|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-218', '16630346', 'Modeling of cis-elements or regulatory motifs in promoter (upstream) regions of genes is a challenging computational problem. In this work, set of regulatory motifs simultaneously present in the promoters of a set of genes is modeled as a biclique in a suitably defined bipartite graph. A biologically meaningful co-occurrence of multiple cis-elements in a gene promoter is assessed by the combined analysis of genomic and gene expression data. Greater statistical significance is associated with a set of genes that shares a common set of regulatory motifs, while simultaneously exhibiting highly correlated gene expression under given experimental conditions.'),
(90878, 2006, 'bmc', 'Exploration of biological network centralities with CentiBiN', 'Junker, Björn H|Koschützki, Dirk|Schreiber, Falk', 'Bioinformatics Research Group, SRI International, 333 Ravenswood Ave EK207, Menlo Park, CA 94025, USA. heladia@ccg.unam.mx|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave EK207, Menlo Park, CA 94025, USA. heladia@ccg.unam.mx|Bioinformatics Research Group, SRI International, 333 Ravenswood Ave EK207, Menlo Park, CA 94025, USA. heladia@ccg.unam.mx', 'United States|United States|United States', 0, 'Computer Graphics|Computer Simulation|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-219', '16630347', 'The elucidation of whole-cell regulatory, metabolic, interaction and other biological networks generates the need for a meaningful ranking of network elements. Centrality analysis ranks network elements according to their importance within the network structure and different centrality measures focus on different importance concepts. Central elements of biological networks have been found to be, for example, essential for viability.'),
(90879, 2006, 'bmc', 'Retrieval with gene queries', 'Sehgal, Aditya K|Srinivasan, Padmini', 'Department of Computer Science, The University of Iowa, Iowa City, IA 52246, USA. aditya-sehgal@uiowa.edu|Department of Computer Science, The University of Iowa, Iowa City, IA 52246, USA. aditya-sehgal@uiowa.edu', 'United States|United States', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Genes|Genome, Human|Humans|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-220', '16630348', 'Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings.'),
(90880, 2006, 'bmc', 'RNAmute: RNA secondary structure mutation analysis tool', 'Churkin, Alexander|Barash, Danny', 'Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel.|Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel.', 'Israel|Israel', 27, 'Animals|Computational Biology|Nucleic Acid Conformation|Point Mutation|RNA|RNA, Protozoan|Sequence Analysis, RNA|Software|Tetrahymena thermophila|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-221', '16638137', 'RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Viennas RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiros coarse grain tree graph representation.'),
(90881, 2006, 'bmc', 'A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data', 'Zhang, Zhuo|Sun, Shiwei|Zhu, Xiaopeng|Chang, Suhua|Liu, Xiaofei|Yu, Chungong|Bu, Dongbo|Chen, Runsheng', 'Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn|Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China. zhangzhuo77@moon.ibp.ac.cn', 'China|China|China|China|China|China|China|China', 0, 'Algorithms|Amino Acid Sequence|Databases, Protein|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-7-222', '16638152', 'Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra.'),
(90882, 2006, 'bmc', 'Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition', 'Zhou, Tong|Weng, Jianhong|Sun, Xiao|Lu, Zuhong', 'State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China. tzhou@seu.edu.cn|State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China. tzhou@seu.edu.cn|State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China. tzhou@seu.edu.cn|State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China. tzhou@seu.edu.cn', 'China|China|China|China', 21, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Codon|Genome, Fungal|Meiosis|Pattern Recognition, Automated|Recombination, Genetic|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-223', '16640774', 'Meiotic double-strand breaks occur at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Hotspots and coldspots are receiving increasing attention in research into the mechanism of meiotic recombination. However, predicting hotspots and coldspots from DNA sequence information is still a challenging task.'),
(90883, 2006, 'bmc', 'IMGT/GeneInfo: T cell receptor gamma TRG and delta TRD genes in database give access to all TR potential V(D)J recombinations', 'Baum, Thierry-Pascal|Hierle, Vivien|Pasqual, Nicolas|Bellahcene, Fatena|Chaume, Denys|Lefranc, Marie-Paule|Jouvin-Marche, Evelyne|Marche, Patrice Noël|Demongeot, Jacques', 'Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr|Laboratoire TIMC-IMAG-CNRS UMR 5525, Techniques de lImagerie, de la Modélisation et de la Cognition, Université Joseph Fourier, Grenoble 1, Faculté de Médecine, Domaine de la Merci, 38706 La Tronche, France. tpbaum@imag.fr', 'France|France|France|France|France|France|France|France|France', 0, 'Animals|Computational Biology|Databases, Nucleic Acid|Gene Rearrangement|Genes, Immunoglobulin|Genes, T-Cell Receptor delta|Genes, T-Cell Receptor gamma|Humans|Information Storage and Retrieval|Internet|Mice|Receptors, Antigen, T-Cell|Recombination, Genetic', 'Journal Article', '10.1186/1471-2105-7-224', '16640788', 'Adaptative immune repertoire diversity in vertebrate species is generated by recombination of variable (V), diversity (D) and joining (J) genes in the immunoglobulin (IG) loci of B lymphocytes and in the T cell receptor (TR) loci of T lymphocytes. These V-J and V-D-J gene rearrangements at the DNA level involve recombination signal sequences (RSS). Whereas many data exist, they are scattered in non specialized resources with different nomenclatures (eg. flat files) and are difficult to extract.'),
(90884, 2006, 'bmc', 'JColorGrid: software for the visualization of biological measurements', 'Joachimiak, Marcin P|Weisman, Jennifer L|May, Barnaby Ch', 'Department of Plant and Microbial Biology, 461 Koshland Hall, University of California Berkeley, CA 94720-3102, USA. marcin@compbio.berkeley.edu|Department of Plant and Microbial Biology, 461 Koshland Hall, University of California Berkeley, CA 94720-3102, USA. marcin@compbio.berkeley.edu|Department of Plant and Microbial Biology, 461 Koshland Hall, University of California Berkeley, CA 94720-3102, USA. marcin@compbio.berkeley.edu', 'United States|United States|United States', 83, 'Biology|Color|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-225', '16640789', 'Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such color-grid representations have found increasing use in the biological sciences (e.g. microarray heat maps and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge.'),
(90885, 2006, 'bmc', 'GNU polyxmass: a software framework for mass spectrometric simulations of linear (bio-)polymeric analytes', 'Rusconi, Filippo', 'CNRS, UMR5153, Paris, F-75231 France. rusconi@mnhn.fr', 'France', 0, 'Biopolymers|Database Management Systems|Mass Spectrometry|Molecular Structure|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-226', '16643644', 'Nowadays, a variety of (bio-)polymers can be analyzed by mass spectrometry. The detailed interpretation of the spectra requires a huge number of hypothesis cycles, comprising the following three actions 1) put forth a structural hypothesis, 2) test it, 3) (in)validate it. This time-consuming and painstaking data scrutiny is alleviated by using specialized software tools. However, all the software tools available to date are polymer chemistry-specific. This imposes a heavy overhead to researchers who do mass spectrometry on a variety of (bio-)polymers, as each polymer type will require a different software tool to perform data simulations and analyses. We developed a software to address the lack of an integrated software framework able to deal with different polymer chemistries.'),
(90886, 2006, 'bmc', 'MIDAS: software for analysis and visualisation of interallelic disequilibrium between multiallelic markers', 'Gaunt, Tom R|Rodriguez, Santiago|Zapata, Carlos|Day, Ian N M', 'Human Genetics Division, University of Southampton, School of Medicine, Duthie Building (MP 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK. Tom.Gaunt@soton.ac.uk|Human Genetics Division, University of Southampton, School of Medicine, Duthie Building (MP 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK. Tom.Gaunt@soton.ac.uk|Human Genetics Division, University of Southampton, School of Medicine, Duthie Building (MP 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK. Tom.Gaunt@soton.ac.uk|Human Genetics Division, University of Southampton, School of Medicine, Duthie Building (MP 808), Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK. Tom.Gaunt@soton.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 89, 'Algorithms|Alleles|Chromosome Mapping|Computer Graphics|DNA Mutational Analysis|Genetic Markers|Linkage Disequilibrium|Polymorphism, Genetic|Polymorphism, Single Nucleotide|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-227', '16643648', 'Various software tools are available for the display of pairwise linkage disequilibrium across multiple single nucleotide polymorphisms. The HapMap project also presents these graphics within their website. However, these approaches are limited in their use of data from multiallelic markers and provide limited information in a graphical form.'),
(90887, 2006, 'bmc', 'A stable gene selection in microarray data analysis', 'Yang, Kun|Cai, Zhipeng|Li, Jianzhong|Lin, Guohui', 'Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. kunyang@hit.edu.cn|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. kunyang@hit.edu.cn|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. kunyang@hit.edu.cn|Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. kunyang@hit.edu.cn', 'China|China|China|China', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Genomic Instability|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1186/1471-2105-7-228', '16643657', 'Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot.'),
(90888, 2006, 'bmc', 'Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data', 'Huber, Bertrand R|Bulyk, Martha L', 'Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA. bertrand_huber@student.hms.harvard.edu|Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA 02115, USA. bertrand_huber@student.hms.harvard.edu', 'United States|United States', 0, 'Algorithms|Animals|Base Sequence|Binding Sites|Gene Expression Profiling|Humans|Mammals|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Systems Integration|Transcription Factors', 'Evaluation Studies', '10.1186/1471-2105-7-229', '16643658', 'A key step in the regulation of gene expression is the sequence-specific binding of transcription factors (TFs) to their DNA recognition sites. However, elucidating TF binding site (TFBS) motifs in higher eukaryotes has been challenging, even when employing cross-species sequence conservation. We hypothesized that for human and mouse, many orthologous genes expressed in a similarly tissue-specific manner in both human and mouse gene expression data, are likely to be co-regulated by orthologous TFs that bind to DNA sequence motifs present within noncoding sequence conserved between these genomes.'),
(90889, 2006, 'bmc', 'Parameter estimation for stiff equations of biosystems using radial basis function networks', 'Matsubara, Yoshiya|Kikuchi, Shinichi|Sugimoto, Masahiro|Tomita, Masaru', 'Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp|Institute for Advanced Biosciences, Keio University, Fujisawa, Kanagawa, 252-8520, Japan. yoshiya@sfc.keio.ac.jp', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Computational Biology|Kinetics|Models, Biological', 'Journal Article', '10.1186/1471-2105-7-230', '16643665', 'The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for parameter estimation, e.g. genetic algorithms (GA), consume enormous computational time because they require iterative numerical integrations for differential equations. When the target model is stiff, the computational time for reaching a solution increases further.'),
(90890, 2006, 'bmc', 'Simplifying gene trees for easier comprehension', 'Lott, Paul-Ludwig|Mundry, Marvin|Sassenberg, Christoph|Lorkowski, Stefan|Fuellen, Georg', 'Division of Bioinformatics, Biology Department, University Münster, Schlossplatz 4, 48149 Münster, Germany. mail@pluti.de|Division of Bioinformatics, Biology Department, University Münster, Schlossplatz 4, 48149 Münster, Germany. mail@pluti.de|Division of Bioinformatics, Biology Department, University Münster, Schlossplatz 4, 48149 Münster, Germany. mail@pluti.de|Division of Bioinformatics, Biology Department, University Münster, Schlossplatz 4, 48149 Münster, Germany. mail@pluti.de|Division of Bioinformatics, Biology Department, University Münster, Schlossplatz 4, 48149 Münster, Germany. mail@pluti.de', 'Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Databases, Protein|Evolution, Molecular|Phylogeny|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/1471-2105-7-231', '16643669', 'In the genomic age, gene trees may contain large amounts of data making them hard to read and understand. Therefore, an automated simplification is important.'),
(90891, 2006, 'bmc', 'Design of microarray probes for virus identification and detection of emerging viruses at the genus level', 'Chou, Cheng-Chung|Lee, Te-Tsui|Chen, Chun-Houh|Hsiao, Hsiang-Yun|Lin, Yi-Ling|Ho, Mei-Shang|Yang, Pan-Chyr|Peck, Konan', 'Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net|Center for Genomic Medicine, National Taiwan University, Taipei, 100, ROC. ccchou_2005@xuite.net', '-|-|-|-|-|-|-|-', 39, 'Animals|Base Sequence|Communicable Diseases, Emerging|Computer-Aided Design|DNA Probes|DNA, Viral|Humans|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA|Virus Diseases|Viruses', 'Evaluation Studies', '10.1186/1471-2105-7-232', '16643672', 'Most virus detection methods are geared towards the detection of specific single viruses or just a few known targets, and lack the capability to uncover the novel viruses that cause emerging viral infections. To address this issue, we developed a computational method that identifies the conserved viral sequences at the genus level for all viral genomes available in GenBank, and established a virus probe library. The virus probes are used not only to identify known viruses but also for discerning the genera of emerging or uncharacterized ones.'),
(90892, 2006, 'bmc', 'PDA: Pooled DNA analyzer', 'Yang, Hsin-Chou|Pan, Chia-Ching|Lin, Chin-Yu|Fann, Cathy S J', 'Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, 115, Taiwan. hsinchou@ibms.sinica.edu.tw|Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, 115, Taiwan. hsinchou@ibms.sinica.edu.tw|Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, 115, Taiwan. hsinchou@ibms.sinica.edu.tw|Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, 115, Taiwan. hsinchou@ibms.sinica.edu.tw', '-|-|-|-', 0, 'Algorithms|Alleles|Computational Biology|DNA|Gene Frequency|Genetic Techniques|Genotype|Haplotypes|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-7-233', '16643673', 'Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, PDA (Pooled DNA Analyzer), to analyze pooled DNA data.'),
(90893, 2006, 'bmc', 'Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm', 'Kind, Tobias|Fiehn, Oliver', 'University of California Davis, Genome Center, Davis, CA 95616, USA. tkind@ucdavis.edu|University of California Davis, Genome Center, Davis, CA 95616, USA. tkind@ucdavis.edu', 'United States|United States', 0, 'Algorithms|Database Management Systems|Databases, Factual|Gene Expression Profiling|Information Storage and Retrieval|Mass Spectrometry|Microchemistry|Peptide Mapping|Proteome|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-7-234', '16646969', 'Metabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept.'),
(90894, 2006, 'bmc', 'A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets', 'Lai, Carmen|Reinders, Marcel J T|vant Veer, Laura J|Wessels, Lodewyk F A', 'Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands. c.lai@ewi.tudelft.nl|Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands. c.lai@ewi.tudelft.nl|Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands. c.lai@ewi.tudelft.nl|Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands. c.lai@ewi.tudelft.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Factual|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Multivariate Analysis|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Comparative Study', '10.1186/1471-2105-7-235', '16670007', 'Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifies a relevant subset of genes. Many univariate and multivariate gene selection approaches have been proposed. Frequently the claim is made that genes are co-regulated (due to pathway dependencies) and that multivariate approaches are therefore per definition more desirable than univariate selection approaches. Based on the published performances of all these approaches a fair comparison of the available results can not be made. This mainly stems from two factors. First, the results are often biased, since the validation set is in one way or another involved in training the predictor, resulting in optimistically biased performance estimates. Second, the published results are often based on a small number of relatively simple datasets. Consequently no generally applicable conclusions can be drawn.'),
(90895, 2006, 'bmc', 'Characterizing disease states from topological properties of transcriptional regulatory networks', 'Tuck, David P|Kluger, Harriet M|Kluger, Yuval', 'Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA. david.tuck@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA. david.tuck@yale.edu|Department of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510, USA. david.tuck@yale.edu', 'United States|United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Diagnosis, Computer-Assisted|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Neoplasms|Oligonucleotide Array Sequence Analysis|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-236', '16670008', 'High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome.'),
(90896, 2006, 'bmc', 'Components of the antigen processing and presentation pathway revealed by gene expression microarray analysis following B cell antigen receptor (BCR) stimulation', 'Lee, Jamie A|Sinkovits, Robert S|Mock, Dennis|Rab, Eva L|Cai, Jennifer|Yang, Peng|Saunders, Brian|Hsueh, Robert C|Choi, Sangdun|Subramaniam, Shankar|Scheuermann, Richard H|Alliance for Cellular Signaling', 'Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.edu|Department of Pathology, Laboratory of Molecular Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Jamie.lee@utsouthwestern.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', 31, 'Algorithms|Antigen Presentation|Antigens|B-Lymphocytes|Cells, Cultured|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Lymphocyte Activation|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Receptors, Antigen, B-Cell|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-7-237', '16670020', 'Activation of naïve B lymphocytes by extracellular ligands, e.g. antigen, lipopolysaccharide (LPS) and CD40 ligand, induces a combination of common and ligand-specific phenotypic changes through complex signal transduction pathways. For example, although all three of these ligands induce proliferation, only stimulation through the B cell antigen receptor (BCR) induces apoptosis in resting splenic B cells. In order to define the common and unique biological responses to ligand stimulation, we compared the gene expression changes induced in normal primary B cells by a panel of ligands using cDNA microarrays and a statistical approach, CLASSIFI (Cluster Assignment for Biological Inference), which identifies significant co-clustering of genes with similar Gene Ontology annotation.'),
(90897, 2006, 'bmc', 'K-SPMM: a database of murine spermatogenic promoters modules & motifs', 'Lu, Yi|Platts, Adrian E|Ostermeier, G Charles|Krawetz, Stephen A', 'Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. luyi@wayne.edu|Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. luyi@wayne.edu|Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. luyi@wayne.edu|Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. luyi@wayne.edu', 'United States|United States|United States|United States', 0, 'Amino Acid Motifs|Animals|Database Management Systems|Databases, Protein|Gene Expression Regulation|Mice|Promoter Regions, Genetic|Spermatogenesis|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-238', '16670029', 'Understanding the regulatory processes that coordinate the cascade of gene expression leading to male gamete development has proven challenging. Research has been hindered in part by an incomplete picture of the regulatory elements that are both characteristic of and distinctive to the broad population of spermatogenically expressed genes.'),
(90898, 2006, 'bmc', 'A hidden Markov model approach for determining expression from genomic tiling micro arrays', 'Munch, Kasper|Gardner, Paul P|Arctander, Peter|Krogh, Anders', 'Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark. Kasper@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark. Kasper@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark. Kasper@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark. Kasper@binf.ku.dk', 'Denmark|Denmark|Denmark|Denmark', 27, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Computer Simulation|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Markov Chains|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Regulatory Elements, Transcriptional', 'Journal Article', '10.1186/1471-2105-7-239', '16672042', 'Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion.'),
(90899, 2006, 'bmc', 'Evaluation of sequence alignments and oligonucleotide probes with respect to three-dimensional structure of ribosomal RNA using ARB software package', 'Kumar, Yadhu|Westram, Ralf|Kipfer, Peter|Meier, Harald|Ludwig, Wolfgang', 'Lehrstuhl für Mikrobiologie, Technische Universität München, D-85350 Freising, Germany. yadhu@mikro.biologie.tu-muenchen.de|Lehrstuhl für Mikrobiologie, Technische Universität München, D-85350 Freising, Germany. yadhu@mikro.biologie.tu-muenchen.de|Lehrstuhl für Mikrobiologie, Technische Universität München, D-85350 Freising, Germany. yadhu@mikro.biologie.tu-muenchen.de|Lehrstuhl für Mikrobiologie, Technische Universität München, D-85350 Freising, Germany. yadhu@mikro.biologie.tu-muenchen.de|Lehrstuhl für Mikrobiologie, Technische Universität München, D-85350 Freising, Germany. yadhu@mikro.biologie.tu-muenchen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Computer Simulation|Crystallography|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Oligonucleotide Probes|RNA, Ribosomal|Sequence Alignment|Sequence Analysis, RNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-240', '16672074', 'Availability of high-resolution RNA crystal structures for the 30S and 50S ribosomal subunits and the subsequent validation of comparative secondary structure models have prompted the biologists to use three-dimensional structure of ribosomal RNA (rRNA) for evaluating sequence alignments of rRNA genes. Furthermore, the secondary and tertiary structural features of rRNA are highly useful and successfully employed in designing rRNA targeted oligonucleotide probes intended for in situ hybridization experiments. RNA3D, a program to combine sequence alignment information with three-dimensional structure of rRNA was developed. Integration into ARB software package, which is used extensively by the scientific community for phylogenetic analysis and molecular probe designing, has substantially extended the functionality of ARB software suite with 3D environment.'),
(90900, 2006, 'bmc', 'Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets', 'Aubry, Marc|Monnier, Annabelle|Chicault, Celine|de Tayrac, Marie|Galibert, Marie-Dominique|Burgun, Anita|Mosser, Jean', 'CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr|CNRS UMR 6061 Génétique et Développement, Université de Rennes 1, Groupe Oncogénomique, IFR140 GFAS, Faculté de Médecine, 2 avenue du Pr, Léon Bernard, CS 34317, 35043 Rennes Cedex, France. marc.aubry@univ-rennes1.fr', 'France|France|France|France|France|France|France', 6, 'Algorithms|Artificial Intelligence|Documentation|Evidence-Based Medicine|Humans|MEDLINE|Models, Genetic|Models, Statistical|Multigene Family|Natural Language Processing|Proteins|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-241', '16674810', 'Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling).');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90901, 2006, 'bmc', 'Multipolar representation of protein structure', 'Gramada, Apostol|Bourne, Philip E', 'Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA. agramada@sdsc.edu|Department of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA. agramada@sdsc.edu', 'United States|United States', 14, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Static Electricity', 'Journal Article', '10.1186/1471-2105-7-242', '16674828', 'That the structure determines the function of proteins is a central paradigm in biology. However, protein functions are more directly related to cooperative effects at the residue and multi-residue scales. As such, current representations based on atomic coordinates can be considered inadequate. Bridging the gap between atomic-level structure and overall protein-level functionality requires parameterizations of the protein structure (and other physicochemical properties) in a quasi-continuous range, from a simple collection of unrelated amino acids coordinates to the highly synergistic organization of the whole protein entity, from a microscopic view in which each atom is completely resolved to a macroscopic description such as the one encoded in the three-dimensional protein shape.'),
(90902, 2006, 'bmc', 'Chaos game representation for comparison of whole genomes', 'Joseph, Jijoy|Sasikumar, Roschen', 'Computational Modelling and Simulation, Regional Research Laboratory (CSIR), Thiruvananthapuram, 695019, India. jijoyjos@yahoo.com|Computational Modelling and Simulation, Regional Research Laboratory (CSIR), Thiruvananthapuram, 695019, India. jijoyjos@yahoo.com', 'India|India', 12, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|DNA Mutational Analysis|Game Theory|Molecular Sequence Data|Nonlinear Dynamics|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Comparative Study', '10.1186/1471-2105-7-243', '16677374', 'Chaos game representation of genome sequences has been used for visual representation of genome sequence patterns as well as alignment-free comparisons of sequences based on oligonucleotide frequencies. However the potential of this representation for making alignment-based comparisons of whole genome sequences has not been exploited.'),
(90903, 2006, 'bmc', 'Identification of consensus RNA secondary structures using suffix arrays', 'Anwar, Mohammad|Nguyen, Truong|Turcotte, Marcel', 'School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada. manwar@site.uottawa.ca|School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada. manwar@site.uottawa.ca|School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada. manwar@site.uottawa.ca', 'Canada|Canada|Canada', 14, 'Algorithms|Base Sequence|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-244', '16677380', 'The identification of a consensus RNA motif often consists in finding a conserved secondary structure with minimum free energy in an ensemble of aligned sequences. However, an alignment is often difficult to obtain without prior structural information. Thus the need for tools to automate this process.'),
(90904, 2006, 'bmc', 'Identification of gene expression patterns using planned linear contrasts', 'Li, Hao|Wood, Constance L|Liu, Yushu|Getchell, Thomas V|Getchell, Marilyn L|Stromberg, Arnold J', 'Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Linear Models|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-245', '16677382', 'In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With the same timing of the initial change, genes which share similar patterns of expression for any number of sampling intervals from the beginning should be considered co-expressed at certain level(s) in the gene networks. In addition, multiple testing problems are complicated in experiments with multi-level treatments when thousands of genes are involved.'),
(90905, 2006, 'bmc', 'Optimizing amino acid substitution matrices with a local alignment kernel', 'Saigo, Hiroto|Vert, Jean-Philippe|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. hiroto@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. hiroto@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. hiroto@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan', 0, 'Algorithms|Amino Acid Sequence|Amino Acid Substitution|Artificial Intelligence|Conserved Sequence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-7-246', '16677385', 'Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, called a local alignment kernel, that exhibits good performance for this task in combination with a support vector machine. The local alignment kernel depends on an amino acid substitution matrix. Since commonly used BLOSUM or PAM matrices for scoring amino acid matches have been optimized to be used in combination with the Smith-Waterman algorithm, the matrices optimal for the local alignment kernel can be different.'),
(90906, 2006, 'bmc', 'Bayesian models for pooling microarray studies with multiple sources of replications', 'Conlon, Erin M|Song, Joon J|Liu, Jun S', 'Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, USA. econlon@mathstat.umass.edu|Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, USA. econlon@mathstat.umass.edu|Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, USA. econlon@mathstat.umass.edu', 'United States|United States|United States', 0, 'Bacillus subtilis|Bacterial Proteins|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genes, Bacterial|Models, Statistical|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-7-247', '16677390', 'Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently.'),
(90907, 2006, 'bmc', 'Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress', 'Wang, Huiquan|Benham, Craig J', 'Genome Center, University of California, Davis, CA 95616, USA. hqwang@ucdavis.edu|Genome Center, University of California, Davis, CA 95616, USA. hqwang@ucdavis.edu', 'United States|United States', 41, 'Bacillus subtilis|Chromosome Mapping|DNA, Bacterial|Genome, Bacterial|Oxidative Stress|Promoter Regions, Genetic|Sequence Analysis, DNA', 'Evaluation Studies', '10.1186/1471-2105-7-248', '16677393', 'In our previous studies, we found that the sites in prokaryotic genomes which are most susceptible to duplex destabilization under the negative superhelical stresses that occur in vivo are statistically highly significantly associated with intergenic regions that are known or inferred to contain promoters. In this report we investigate how this structural property, either alone or together with other structural and sequence attributes, may be used to search prokaryotic genomes for promoters.'),
(90908, 2006, 'bmc', 'Applying dynamic Bayesian networks to perturbed gene expression data', 'Dojer, Norbert|Gambin, Anna|Mizera, Andrzej|Wilczyxc5x84ski, Bartek|Tiuryn, Jerzy', 'Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl|Institute of Informatics, Warsaw University, Banacha 2, 02-097 Warszawa, Poland. dojer@mimuw.edu.pl', 'Poland|Poland|Poland|Poland|Poland', 122, 'Algorithms|Artifacts|Artificial Intelligence|Bayes Theorem|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-249', '16681847', 'A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.'),
(90909, 2006, 'bmc', 'Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span', 'Blei, D M|Franks, K|Jordan, M I|Mian, I S', 'Computer Science Department, Princeton University, Princeton, New Jersey 08540 USA. blei@cs.princeton.edu|Computer Science Department, Princeton University, Princeton, New Jersey 08540 USA. blei@cs.princeton.edu|Computer Science Department, Princeton University, Princeton, New Jersey 08540 USA. blei@cs.princeton.edu|Computer Science Department, Princeton University, Princeton, New Jersey 08540 USA. blei@cs.princeton.edu', 'Jersey|Jersey|Jersey|Jersey', 0, 'Animals|Bayes Theorem|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Databases, Bibliographic|Information Storage and Retrieval|Longevity|Models, Statistical|Natural Language Processing|Pattern Recognition, Automated|Telomere-Binding Proteins|Terminology as Topic|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-7-250', '16681860', 'The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made from analysis of molecular sequence and profiling data. Here, the potential of such modeling is demonstrated by examining the 5,225 free-text items in the Caenorhabditis Genetic Center (CGC) Bibliography using techniques from statistical information retrieval. Items in the CGC biomedical text corpus were modeled using the Latent Dirichlet Allocation (LDA) model. LDA is a hierarchical Bayesian model which represents a document as a random mixture over latent topics, each topic is characterized by a distribution over words.'),
(90910, 2006, 'bmc', 'Correction of scaling mismatches in oligonucleotide microarray data', 'Barenco, Martino|Stark, Jaroslav|Brewer, Daniel|Tomescu, Daniela|Callard, Robin|Hubank, Michael', 'Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk|Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk|Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk|Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk|Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk|Institute of Child Health, University College London, UK. m.barenco@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 6, 'Algorithms|Cell Line|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Research Design|T-Lymphocytes', 'Journal Article', '10.1186/1471-2105-7-251', '16684345', 'Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive.'),
(90911, 2006, 'bmc', 'Phylophenetic properties of metabolic pathway topologies as revealed by global analysis', 'Zhang, Yong|Li, Shaojuan|Skogerbø, Geir|Zhang, Zhihua|Zhu, Xiaopeng|Zhang, Zefeng|Sun, Shiwei|Lu, Hongchao|Shi, Baochen|Chen, Runsheng', 'Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn|Bioinformatics Laboratory and National Laboratory of Bromacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China. zhangyong@moon.ibp.ac.cn', 'China|China|China|China|China|China|China|China|China|China', 0, 'Chromosome Mapping|Computer Simulation|Gene Expression|Models, Genetic|Phylogeny|Proteins|RNA, Ribosomal, 16S|Sequence Analysis, RNA|Signal Transduction', 'Journal Article', '10.1186/1471-2105-7-252', '16684350', 'As phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the phylophenetic contents of different pathways topologies from a global perspective.'),
(90912, 2006, 'bmc', 'Regularized binormal ROC method in disease classification using microarray data', 'Ma, Shuangge|Song, Xiao|Huang, Jian', 'Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. shuangge@u.washington.edu|Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. shuangge@u.washington.edu|Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. shuangge@u.washington.edu', 'United States|United States|United States', 16, 'Biomarkers, Tumor|Computer Simulation|Data Interpretation, Statistical|Diagnosis, Computer-Assisted|Gene Expression Profiling|Genetic Predisposition to Disease|Humans|Models, Biological|Models, Statistical|Neoplasm Proteins|Neoplasms|Normal Distribution|Oligonucleotide Array Sequence Analysis|ROC Curve|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-7-253', '16684357', 'An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers.'),
(90913, 2006, 'bmc', 'Bounded search for de novo identification of degenerate cis-regulatory elements', 'Carlson, Jonathan M|Chakravarty, Arijit|Khetani, Radhika S|Gross, Robert H', 'Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA. jcarlson@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA. jcarlson@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA. jcarlson@cs.washington.edu|Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105, USA. jcarlson@cs.washington.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Binding Sites|Computer Simulation|Consensus Sequence|Models, Genetic|Models, Statistical|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Proteins|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-254', '16700920', 'The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.'),
(90914, 2006, 'bmc', 'Prediction of amphipathic in-plane membrane anchors in monotopic proteins using a SVM classifier', 'Sapay, Nicolas|Guermeur, Yann|Deléage, Gilbert', 'Institut de Biologie et Chimie des Protéines, UMR 5086 CNRS-Univ, Lyon 1 - IFR128 BioSciences Lyon-Gerland, F-69367 Lyon Cedex 07, France. n.sapay@ibcp.fr|Institut de Biologie et Chimie des Protéines, UMR 5086 CNRS-Univ, Lyon 1 - IFR128 BioSciences Lyon-Gerland, F-69367 Lyon Cedex 07, France. n.sapay@ibcp.fr|Institut de Biologie et Chimie des Protéines, UMR 5086 CNRS-Univ, Lyon 1 - IFR128 BioSciences Lyon-Gerland, F-69367 Lyon Cedex 07, France. n.sapay@ibcp.fr', 'France|France|France', 93, 'Algorithms|Computational Biology|Databases, Protein|Membrane Proteins|Pattern Recognition, Automated|Protein Structure, Secondary|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-255', '16704727', 'Membrane proteins are estimated to represent about 25% of open reading frames in fully sequenced genomes. However, the experimental study of proteins remains difficult. Considerable efforts have thus been made to develop prediction methods. Most of these were conceived to detect transmembrane helices in polytopic proteins. Alternatively, a membrane protein can be monotopic and anchored via an amphipathic helix inserted in a parallel way to the membrane interface, so-called in-plane membrane (IPM) anchors. This type of membrane anchor is still poorly understood and no suitable prediction method is currently available.'),
(90915, 2006, 'bmc', 'The development and validation of the Virtual Tissue Matrix, a software application that facilitates the review of tissue microarrays on line', 'Conway, Catherine M|OShea, Deirdre|OBrien, Sallyann|Lawler, Darragh K|Dodrill, Graham D|OGrady, Anthony|Barrett, Helen|Gulmann, Christian|ODriscoll, Lorraine|Gallagher, William M|Kay, Elaine W|OShea, Daniel G', 'Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie|Medical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland. catherine.conway3@mail.dcu.ie', 'Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland|Ireland', 12, 'Algorithms|Equipment Failure Analysis|Image Interpretation, Computer-Assisted|Online Systems|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Software|Software Design|Software Validation|Tissue Array Analysis|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-256', '16707006', 'The Tissue Microarray (TMA) facilitates high-throughput analysis of hundreds of tissue specimens simultaneously. However, bottlenecks in the storage and manipulation of the data generated from TMA reviews have become apparent. A number of software applications have been developed to assist in image and data management, however no solution currently facilitates the easy online review, scoring and subsequent storage of images and data associated with TMA experimentation.'),
(90916, 2006, 'bmc', 'Use of a multi-way method to analyze the amino acid composition of a conserved group of orthologous proteins in prokaryotes', 'Pasamontes, Alberto|Garcia-Vallve, Santiago', 'Chemometrics, Qualimetrics and Nanosensors Group, Analytical and Organic Chemistry Department, Rovira i Virgili University (URV), Campus Sescelades, c/Marcelli Domingo s/n., 43007 Tarragona, Spain.|Chemometrics, Qualimetrics and Nanosensors Group, Analytical and Organic Chemistry Department, Rovira i Virgili University (URV), Campus Sescelades, c/Marcelli Domingo s/n., 43007 Tarragona, Spain.', 'Spain|Spain', 11, 'Amino Acids|Amino Acyl-tRNA Synthetases|Archaea|Bacterial Proteins|Base Composition|Codon|Computational Biology|Conserved Sequence|Helicobacter|Methanococcus|Peptide Elongation Factors|Ribosomal Proteins|Sequence Analysis, Protein|Species Specificity|Temperature', 'Comparative Study', '10.1186/1471-2105-7-257', '16709240', 'Amino acids in proteins are not used equally. Some of the differences in the amino acid composition of proteins are between species (mainly due to nucleotide composition and lifestyle) and some are between proteins from the same species (related to protein function, expression or subcellular localization, for example). As several factors contribute to the different amino acid usage in proteins, it is difficult both to analyze these differences and to separate the contributions made by each factor.'),
(90917, 2006, 'bmc', 'Haplotype-based quantitative trait mapping using a clustering algorithm', 'Li, Jing|Zhou, Yingyao|Elston, Robert C', 'Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA. jingli@case.edu', 'United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Simulation|Crohn Disease|Genetic Predisposition to Disease|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Recombination, Genetic|Reproducibility of Results|Sample Size|Software', 'Comparative Study', '10.1186/1471-2105-7-258', '16709248', 'With the availability of large-scale, high-density single-nucleotide polymorphism (SNP) markers, substantial effort has been made in identifying disease-causing genes using linkage disequilibrium (LD) mapping by haplotype analysis of unrelated individuals. In addition to complex diseases, many continuously distributed quantitative traits are of primary clinical and health significance. However the development of association mapping methods using unrelated individuals for quantitative traits has received relatively less attention.'),
(90918, 2006, 'bmc', 'The effects of multiple features of alternatively spliced exons on the K(A)/K(S) ratio test', 'Chen, Feng-Chi|Chuang, Trees-Juen', 'Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw|Genomics Research Center, Academia Sinica, Academia Road, Nankang, Taipei 11529, Taiwan. fcchen@iis.sinica.edu.tw', '-|-', 8, 'Alternative Splicing|Animals|Base Sequence|Conserved Sequence|Databases, Genetic|Enhancer Elements, Genetic|Evolution, Molecular|Exons|Humans|Mice|Models, Genetic|Protein Structure, Tertiary|Proteins|RNA Splice Sites|RNA, Messenger|Sequence Alignment', 'Comparative Study', '10.1186/1471-2105-7-259', '16709259', 'The evolution of alternatively spliced exons (ASEs) is of primary interest because these exons are suggested to be a major source of functional diversity of proteins. Many exon features have been suggested to affect the evolution of ASEs. However, previous studies have relied on the KA/KS ratio test without taking into consideration information sufficiency (i.e., exon length > 75 bp, cross-species divergence > 5%) of the studied exons, leading to potentially biased interpretations. Furthermore, which exon feature dominates the results of the KA/KS ratio test and whether multiple exon features have additive effects have remained unexplored.'),
(90919, 2006, 'bmc', 'On the persistence of supplementary resources in biomedical publications', 'Anderson, Nicholas R|Tarczy-Hornoch, Peter|Bumgarner, Roger E', 'Division of Biomedical Health Informatics, University of Washington, USA. nicka@u.washington.edu|Division of Biomedical Health Informatics, University of Washington, USA. nicka@u.washington.edu|Division of Biomedical Health Informatics, University of Washington, USA. nicka@u.washington.edu', 'United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Editorial Policies|Information Dissemination|Information Storage and Retrieval|Periodicals as Topic|PubMed|Time Factors', 'Journal Article', '10.1186/1471-2105-7-260', '16712726', 'Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers.'),
(90920, 2006, 'bmc', 'Empirical array quality weights in the analysis of microarray data', 'Ritchie, Matthew E|Diyagama, Dileepa|Neilson, Jody|van Laar, Ryan|Dobrovic, Alexander|Holloway, Andrew|Smyth, Gordon K', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 183, 'Algorithms|Analysis of Variance|Data Interpretation, Statistical|Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software', 'Journal Article', '10.1186/1471-2105-7-261', '16712727', 'Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or filtered from further analysis to avoid misleading results.'),
(90921, 2006, 'bmc', 'Predicting DNA-binding sites of proteins from amino acid sequence', 'Yan, Changhui|Terribilini, Michael|Wu, Feihong|Jernigan, Robert L|Dobbs, Drena|Honavar, Vasant', 'Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu|Department of Computer Science, Utah State University, Logan, Utah 84341, USA. cyan@cc.usu.edu', 'United States|United States|United States|United States|United States|United States', 118, 'Algorithms|Bayes Theorem|Binding Sites|Computational Biology|Databases, Protein|Entropy|Genetic Techniques|Humans|Models, Molecular|ROC Curve|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-262', '16712732', 'Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.'),
(90922, 2006, 'bmc', 'Automatic generation of gene finders for eukaryotic species', 'Munch, Kasper|Krogh, Anders', 'Bioinformatics Centre, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen Ø, Denmark. kasper@binf.ku.dk|Bioinformatics Centre, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen Ø, Denmark. kasper@binf.ku.dk', 'Denmark|Denmark', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Computer Simulation|Conserved Sequence|Eukaryotic Cells|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|RNA, Messenger|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-7-263', '16712739', 'The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity and quality of reliable gene annotation grows.'),
(90923, 2006, 'bmc', 'Spatial normalization of array-CGH data', 'Neuvial, Pierre|Hupé, Philippe|Brito, Isabel|Liva, Stéphane|Manié, Elodie|Brennetot, Caroline|Radvanyi, François|Aurias, Alain|Barillot, Emmanuel', 'Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr|Institut Curie, Service de Bioinformatique, 26, rue dUlm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr', 'France|France|France|France|France|France|France|France|France', 67, 'Algorithms|Artifacts|Base Sequence|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Gene Dosage|In Situ Hybridization|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-264', '16716215', 'Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.'),
(90924, 2006, 'bmc', 'A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes', 'Schultz, Anne-Kathrin|Zhang, Ming|Leitner, Thomas|Kuiken, Carla|Korber, Bette|Morgenstern, Burkhard|Stanke, Mario', 'Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de|Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr, 1, 37077 Göttingen, Germany. aschult2@gwdg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Databases, Nucleic Acid|Genome, Viral|HIV-1|Hepacivirus|Markov Chains|Models, Genetic|Recombination, Genetic|Sequence Alignment', 'Comparative Study', '10.1186/1471-2105-7-265', '16716226', 'Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events.'),
(90925, 2006, 'bmc', 'Phylogeny based discovery of regulatory elements', 'Gertz, Jason|Fay, Justin C|Cohen, Barak A', 'Department of Genetics, Washington University School of Medicine, 4444 Forest Park Parkway, St. Louis, MO 63108, USA. jgertz@genetics.wustl.edu|Department of Genetics, Washington University School of Medicine, 4444 Forest Park Parkway, St. Louis, MO 63108, USA. jgertz@genetics.wustl.edu|Department of Genetics, Washington University School of Medicine, 4444 Forest Park Parkway, St. Louis, MO 63108, USA. jgertz@genetics.wustl.edu', 'United States|United States|United States', 10, 'Algorithms|Base Sequence|Conserved Sequence|Molecular Sequence Data|Phylogeny|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-266', '16716228', 'Algorithms that locate evolutionarily conserved sequences have become powerful tools for finding functional DNA elements, including transcription factor binding sites, however, most methods do not take advantage of an explicit model for the constrained evolution of functional DNA sequences.'),
(90926, 2006, 'bmc', 'The cluster variation method for efficient linkage analysis on extended pedigrees', 'Albers, Cornelis A|Leisink, Martijn A R|Kappen, Hilbert J', 'Department of Medical Physics and Biophysics, Radboud University, Nijmegen, The Netherlands. k.albers@science.ru.nl|Department of Medical Physics and Biophysics, Radboud University, Nijmegen, The Netherlands. k.albers@science.ru.nl|Department of Medical Physics and Biophysics, Radboud University, Nijmegen, The Netherlands. k.albers@science.ru.nl', 'Netherlands|Netherlands|Netherlands', 0, 'Alleles|Bayes Theorem|Cluster Analysis|Computational Biology|Genetic Linkage|Humans|Lod Score|Models, Genetic|Pedigree|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-7-S1-S1', '16723002', 'Computing exact multipoint LOD scores for extended pedigrees rapidly becomes infeasible as the number of markers and untyped individuals increase. When markers are excluded from the computation, significant power may be lost. Therefore accurate approximate methods which take into account all markers are desirable.'),
(90927, 2006, 'bmc', 'Protein ranking by semi-supervised network propagation', 'Weston, Jason|Kuang, Rui|Leslie, Christina|Noble, William Stafford', 'NEC LABS AMERICA, 4 Independence Way, Princeton, NJ, USA. jasonw@nec-labs.com|NEC LABS AMERICA, 4 Independence Way, Princeton, NJ, USA. jasonw@nec-labs.com|NEC LABS AMERICA, 4 Independence Way, Princeton, NJ, USA. jasonw@nec-labs.com|NEC LABS AMERICA, 4 Independence Way, Princeton, NJ, USA. jasonw@nec-labs.com', 'United States|United States|United States|United States', 30, 'Algorithms|Computational Biology|DNA|Humans|Neural Networks (Computer)|Protein Conformation|Proteins|Proteomics|Reproducibility of Results|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-S1-S10', '16723003', 'Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, focus on detecting statistically significant pairwise sequence alignments and often miss more subtle sequence similarity. Recent work in the machine learning community has shown that exploiting the global structure of the network defined by these pairwise similarities can help detect more remote relationships than a purely local measure.'),
(90928, 2006, 'bmc', 'A regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data', 'Yao, Zizhen|Ruzzo, Walter L', 'Department of Computer Science and Engineering, AC101 Paul G. Allen Center, University of Washington, Seattle WA 98195, USA. yzizhen@cs.washington.edu|Department of Computer Science and Engineering, AC101 Paul G. Allen Center, University of Washington, Seattle WA 98195, USA. yzizhen@cs.washington.edu', 'United States|United States', 40, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Escherichia coli Proteins|Gene Expression Regulation|Genes, Bacterial|Genome, Bacterial|Models, Genetic|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Probability|Regression Analysis|Reproducibility of Results|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-S1-S11', '16723004', 'As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources.'),
(90929, 2006, 'bmc', 'Choosing negative examples for the prediction of protein-protein interactions', 'Ben-Hur, Asa|Noble, William Stafford', 'Department of Computer Science, Colorado State University, Fort Collins CO, USA. asa@cs.colostate.edu|Department of Computer Science, Colorado State University, Fort Collins CO, USA. asa@cs.colostate.edu', 'United States|United States', 0, 'Algorithms|Binding Sites|Computational Biology|Databases, Protein|Molecular Conformation|Oligonucleotide Array Sequence Analysis|Phosphorylation|Protein Folding|Protein Interaction Mapping|Proteins|Proteomics|ROC Curve|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-7-S1-S2', '16723005', 'The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions.'),
(90930, 2006, 'bmc', 'PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions', 'Hertz, Tomer|Yanover, Chen', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel. tomboy@cs.huji.ac.il|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel. tomboy@cs.huji.ac.il', 'Israel|Israel', 28, 'Algorithms|Alleles|Binding Sites|Computational Biology|Databases, Protein|Drug Design|Genes, MHC Class I|HLA Antigens|Humans|Models, Statistical|Peptides|Protein Binding|Software', 'Journal Article', '10.1186/1471-2105-7-S1-S3', '16723006', 'Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design.'),
(90931, 2006, 'bmc', 'Network-based de-noising improves prediction from microarray data', 'Kato, Tsuyoshi|Murata, Yukio|Miura, Koh|Asai, Kiyoshi|Horton, Paul B|Koji, Tsuda|Fujibuchi, Wataru', 'Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp|Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277 - 8562, Japan. kato-tsuyoshi@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 2, 'Algorithms|Data Interpretation, Statistical|Databases, Protein|Gene Expression Regulation, Neoplastic|Humans|Models, Statistical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Protein Interaction Mapping|Regression Analysis|Reproducibility of Results|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-S1-S4', '16723007', 'Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90932, 2006, 'bmc', 'A classification-based framework for predicting and analyzing gene regulatory response', 'Kundaje, Anshul|Middendorf, Manuel|Shah, Mihir|Wiggins, Chris H|Freund, Yoav|Leslie, Christina', 'Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu|Department of Computer Science, Columbia University, New York, NY 10027, USA. abk2001@cs.columbia.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Binding Sites|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation|Heat-Shock Proteins|Molecular Chaperones|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-7-S1-S5', '16723008', 'We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences (motifs) in the genes regulatory region and the expression levels of regulators such as transcription factors in the experiment (parents). GeneClass formulates the learning task as a classification problem--predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree.'),
(90933, 2006, 'bmc', 'The secrets of a functional synapse--from a computational and experimental viewpoint', 'Linial, Michal', 'Dept of Biological Chemistry, The Hebrew University of Jerusalem, 91904, Israel. michall@cc.huji.ac.il', 'Israel', 6, 'Animals|Cell Membrane|Computational Biology|Dendrites|Genomics|Humans|Models, Biological|Neurons|Neurotransmitter Agents|Phenotype|Proteomics|Signal Transduction|Software|Synapses|Synaptic Transmission', 'Journal Article', '10.1186/1471-2105-7-S1-S6', '16723009', 'Neuronal communication is tightly regulated in time and in space. The neuronal transmission takes place in the nerve terminal, at a specialized structure called the synapse. Following neuronal activation, an electrical signal triggers neurotransmitter (NT) release at the active zone. The process starts by the signal reaching the synapse followed by a fusion of the synaptic vesicle and diffusion of the released NT in the synaptic cleft, the NT then binds to the appropriate receptor, and as a result, a potential change at the target cell membrane is induced. The entire process lasts for only a fraction of a millisecond. An essential property of the synapse is its capacity to undergo biochemical and morphological changes, a phenomenon that is referred to as synaptic plasticity.'),
(90934, 2006, 'bmc', 'ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context', 'Margolin, Adam A|Nemenman, Ilya|Basso, Katia|Wiggins, Chris|Stolovitzky, Gustavo|Dalla Favera, Riccardo|Califano, Andrea', 'Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. adam@dbmi.columbia.edu', 'United States|United States|United States|United States|United States|United States|United States', 1535, 'Algorithms|Animals|B-Lymphocytes|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Statistical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Phenotype|Reproducibility of Results|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-7-S1-S7', '16723010', 'Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide reverse engineering of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.'),
(90935, 2006, 'bmc', 'Discrete profile comparison using information bottleneck', 'ORourke, Sean|Chechik, Gal|Friedman, Robin|Eskin, Eleazar', 'Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093, USA. seano@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093, USA. seano@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093, USA. seano@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093, USA. seano@cs.ucsd.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computational Biology|Databases, Protein|Models, Biological|Models, Statistical|Molecular Sequence Data|Probability|Protein Folding|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-7-S1-S8', '16723011', 'Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are an order of magnitude slower than sequence comparisons, making profiles impractical for large datasets. Also, because they are such a rich representation, profiles are difficult to visualize. To address these problems, we describe a method to map probabilistic profiles to a discrete alphabet while preserving most of the information in the profiles. We find an informationally optimal discretization using the Information Bottleneck approach (IB). We observe that an 80-character IB alphabet captures nearly 90% of the amino acid occurrence information found in profiles, compared to the consensus sequences 78%. Distant homolog search with IB sequences is 88% as sensitive as with profiles compared to 61% with consensus sequences (AUC scores 0.73, 0.83, and 0.51, respectively), but like simple sequence comparison, is 30 times faster. Discrete IB encoding can therefore expand the range of sequence problems to which profile information can be applied to include batch queries over large databases like SwissProt, which were previously computationally infeasible.'),
(90936, 2006, 'bmc', 'Learning interpretable SVMs for biological sequence classification', 'Rätsch, Gunnar|Sonnenburg, Sören|Schäfer, Christin', 'Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, Tübingen, Germany. gunnar.raetsch@tuebingen.mpg.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, Tübingen, Germany. gunnar.raetsch@tuebingen.mpg.de|Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, Tübingen, Germany. gunnar.raetsch@tuebingen.mpg.de', 'Germany|Germany|Germany', 46, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Binding Sites|Cluster Analysis|Computational Biology|Models, Statistical|Neural Networks (Computer)|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-S1-S9', '16723012', 'Support Vector Machines (SVMs)--using a variety of string kernels--have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack interpretability. In many applications, it does not suffice that an algorithm just detects a biological signal in the sequence, but it should also provide means to interpret its solution in order to gain biological insight.'),
(90937, 2006, 'bmc', 'The use of concept maps during knowledge elicitation in ontology development processes--the nutrigenomics use case', 'Castro, Alexander Garcia|Rocca-Serra, Philippe|Stevens, Robert|Taylor, Chris|Nashar, Karim|Ragan, Mark A|Sansone, Susanna-Assunta', 'Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au|Microarray Informatics Team, The European Bioinformatics Institute--European Molecular Biology Laboratory Outstation, Wellcome Trust Genome Campus CB10 1SD, Cambridge Hinxton, UK. a.garcia@imb.uq.edu.au', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 47, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Factual|Genomics|Models, Biological|Natural Language Processing|Nutritional Physiological Phenomena|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-7-267', '16725019', 'Incorporation of ontologies into annotations has enabled semantic integration of complex data, making explicit the knowledge within a certain field. One of the major bottlenecks in developing bio-ontologies is the lack of a unified methodology. Different methodologies have been proposed for different scenarios, but there is no agreed-upon standard methodology for building ontologies. The involvement of geographically distributed domain experts, the need for domain experts to lead the design process, the application of the ontologies and the life cycles of bio-ontologies are amongst the features not considered by previously proposed methodologies.'),
(90938, 2006, 'bmc', 'Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration', 'Xiong, Jianghui|Rayner, Simon|Luo, Kunyi|Li, Yinghui|Chen, Shanguang', 'Laboratory of Space Cell and Molecular Biology, China Astronaut Research and Training Center, Beijing, PR, China. laserxiong@gmail.com|Laboratory of Space Cell and Molecular Biology, China Astronaut Research and Training Center, Beijing, PR, China. laserxiong@gmail.com|Laboratory of Space Cell and Molecular Biology, China Astronaut Research and Training Center, Beijing, PR, China. laserxiong@gmail.com|Laboratory of Space Cell and Molecular Biology, China Astronaut Research and Training Center, Beijing, PR, China. laserxiong@gmail.com|Laboratory of Space Cell and Molecular Biology, China Astronaut Research and Training Center, Beijing, PR, China. laserxiong@gmail.com', 'China|China|China|China|China', 22, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Databases, Protein|Evidence-Based Medicine|Information Storage and Retrieval|Natural Language Processing|Proteins|Signal Transduction|Systems Integration', 'Journal Article', '10.1186/1471-2105-7-268', '16725034', 'The automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets.'),
(90939, 2006, 'bmc', 'An integrated approach to the prediction of domain-domain interactions', 'Lee, Hyunju|Deng, Minghua|Sun, Fengzhu|Chen, Ting', 'Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA. hyunjul@usc.edu|Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA. hyunjul@usc.edu|Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA. hyunjul@usc.edu|Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA. hyunjul@usc.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Databases, Protein|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Systems Integration', 'Journal Article', '10.1186/1471-2105-7-269', '16725050', 'The development of high-throughput technologies has produced several large scale protein interaction data sets for multiple species, and significant efforts have been made to analyze the data sets in order to understand protein activities. Considering that the basic units of protein interactions are domain interactions, it is crucial to understand protein interactions at the level of the domains. The availability of many diverse biological data sets provides an opportunity to discover the underlying domain interactions within protein interactions through an integration of these biological data sets.'),
(90940, 2006, 'bmc', 'Improving the specificity of high-throughput ortholog prediction', 'Fulton, Debra L|Li, Yvonne Y|Laird, Matthew R|Horsman, Benjamin G S|Roche, Fiona M|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. debra@cmmt.ubc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 69, 'Algorithms|Base Sequence|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Genetic Variation|Molecular Sequence Data|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-7-270', '16729895', 'Orthologs (genes that have diverged after a speciation event) tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. The gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis, therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-BLAST-hits (RBH) approach. One problem with RBH is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. In addition, there is an increasing interest in identifying orthologs most likely to have retained similar function.'),
(90941, 2006, 'bmc', 'Demonstration of two novel methods for predicting functional siRNA efficiency', 'Jia, Peilin|Shi, Tieliu|Cai, Yudong|Li, Yixue', 'Bioinformatics Center, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn|Bioinformatics Center, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn|Bioinformatics Center, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn|Bioinformatics Center, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn', 'China|China|China|China', 33, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Genetic|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|RNA Interference|RNA, Small Interfering|Sequence Alignment|Sequence Analysis, RNA', 'Comparative Study', '10.1186/1471-2105-7-271', '16729898', 'siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs.'),
(90942, 2006, 'bmc', 'Epigenetic acquisition of inducibility of type III cytotoxicity in P aeruginosa', 'Filopon, Didier|Mérieau, Annabelle|Bernot, Gilles|Comet, Jean-Paul|Leberre, Rozenne|Guery, Benoit|Polack, Benoit|Guespin-Michel, Janine', 'GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr|GREPI EA 2938 CHU de Grenoble, BP 217, 38043 Grenoble cedex 9, France. dfilopon@chu-grenoble.fr', 'France|France|France|France|France|France|France|France', 0, 'Bacterial Proteins|Computer Simulation|Epigenesis, Genetic|Genetic Predisposition to Disease|Humans|Models, Genetic|Phenotype|Pseudomonas Infections|Pseudomonas aeruginosa', 'Journal Article', '10.1186/1471-2105-7-272', '16734902', 'Pseudomonas aeruginosa, an opportunistic pathogen, is often encountered in chronic lung diseases such as cystic fibrosis or chronic obstructive pneumonia, as well as acute settings like mechanical ventilation acquired pneumonia or neutropenic patients. It is a major cause of mortality and morbidity in these diseases. In lungs, P. aeruginosa settles in a biofilm mode of growth with the secretion of exopolysaccharides in which it is encapsulated, enhancing its antibiotic resistance and contributing to the respiratory deficiency of patients. However, bacteria must first multiply to a high density and display a cytotoxic phenotype to avoid the hosts defences. A virulence determinant implicated in this step of infection is the type III secretion system (TTSS), allowing toxin injection directly into host cells. At the beginning of the infection, most strains isolated from patients lungs possess an inducible TTSS allowing toxins injection or secretion upon in vivo or in vitro activation signals. As the infection persists most of the bacteria permanently loose this capacity, although no mutations have been evidenced. We name non inducible this phenotype. As suggested by the presence of a positive feedback circuit in the regulatory network controlling TTSS expression, it may be due to an epigenetic switch allowing heritable phenotypic modifications without genotypes mutations.'),
(90943, 2006, 'bmc', 'Agile methods in biomedical software development: a multi-site experience report', 'Kane, David W|Hohman, Moses M|Cerami, Ethan G|McCormick, Michael W|Kuhlmman, Karl F|Byrd, Jeff A', 'SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com|SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com|SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com|SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com|SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com|SRA International, 4300 Fair Lakes Court, Fairfax, VA 22033, USA. david_kane@sra.com', 'United States|United States|United States|United States|United States|United States', 38, 'Algorithms|Automation|Computational Biology|Computers|Database Management Systems|Databases, Genetic|Diffusion of Innovation|Hospital Information Systems|Hospitals|Humans|Medical Informatics|Multicenter Studies as Topic|Programming Languages|Software|Software Design|Systems Integration', 'Journal Article', '10.1186/1471-2105-7-273', '16734914', 'Agile is an iterative approach to software development that relies on strong collaboration and automation to keep pace with dynamic environments. We have successfully used agile development approaches to create and maintain biomedical software, including software for bioinformatics. This paper reports on a qualitative study of our experiences using these methods.'),
(90944, 2006, 'bmc', 'Noise-injected neural networks show promise for use on small-sample expression data', 'Hua, Jianping|Lowey, James|Xiong, Zixiang|Dougherty, Edward R', 'Computational Biology Division, Translational Genomics Research Institute, Phoenix, USA. jhua@tgen.org|Computational Biology Division, Translational Genomics Research Institute, Phoenix, USA. jhua@tgen.org|Computational Biology Division, Translational Genomics Research Institute, Phoenix, USA. jhua@tgen.org|Computational Biology Division, Translational Genomics Research Institute, Phoenix, USA. jhua@tgen.org', 'United States|United States|United States|United States', 11, 'Algorithms|Breast Neoplasms|Cluster Analysis|Computer Simulation|Data Interpretation, Statistical|Diagnosis, Computer-Assisted|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Linear Models|Neural Networks (Computer)|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Prognosis|Survival Analysis', 'Comparative Study', '10.1186/1471-2105-7-274', '16737545', 'Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting is not merely a consequence of the classifier family, it is highly dependent on the classification rule used to design a classifier from the sample data. Thus, it is possible to consider families that are rather complex but for which there are classification rules that perform well for small samples. Such classification rules can be advantageous because they facilitate satisfactory classification when the class-conditional distributions are not easily separated and the sample is not large. Here we consider neural networks, from the perspectives of classical design based solely on the sample data and from noise-injection-based design.'),
(90945, 2006, 'bmc', 'AceTree: a tool for visual analysis of Caenorhabditis elegans embryogenesis', 'Boyle, Thomas J|Bao, Zhirong|Murray, John I|Araya, Carlos L|Waterston, Robert H', 'Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. biowolp@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. biowolp@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. biowolp@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. biowolp@u.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. biowolp@u.washington.edu', 'United States|United States|United States|United States|United States', 57, 'Animals|Caenorhabditis elegans|Cell Lineage|Computational Biology|Embryo, Nonmammalian|Green Fluorescent Proteins|Histones|Mutation|Phenotype|Phylogeny|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-275', '16740163', 'The invariant lineage of the nematode Caenorhabditis elegans has potential as a powerful tool for the description of mutant phenotypes and gene expression patterns. We previously described procedures for the imaging and automatic extraction of the cell lineage from C. elegans embryos. That method uses time-lapse confocal imaging of a strain expressing histone-GFP fusions and a software package, StarryNite, processes the thousands of images and produces output files that describe the location and lineage relationship of each nucleus at each time point.'),
(90946, 2006, 'bmc', 'Hybridization interactions between probesets in short oligo microarrays lead to spurious correlations', 'Okoniewski, Michaxc5x82 J|Miller, Crispin J', 'Paterson Institute For Cancer Research, Christie Hospital site, University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK. MOkoniewski@PICR.man.ac.uk|Paterson Institute For Cancer Research, Christie Hospital site, University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK. MOkoniewski@PICR.man.ac.uk', 'United Kingdom|United Kingdom', 113, 'Artifacts|Computer Simulation|DNA Probes|Data Interpretation, Statistical|Equipment Design|Equipment Failure|Equipment Failure Analysis|Gene Expression Profiling|In Situ Hybridization|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-7-276', '16749918', 'Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These multiply targeted probes can result in non-independence between measured expression levels.'),
(90947, 2006, 'bmc', 'EVEREST: automatic identification and classification of protein domains in all protein sequences', 'Portugaly, Elon|Harel, Amir|Linial, Nathan|Linial, Michal', 'School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. elonp@cs.huji.ac.il|School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. elonp@cs.huji.ac.il|School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. elonp@cs.huji.ac.il|School of Computer Science & Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. elonp@cs.huji.ac.il', 'Israel|Israel|Israel|Israel', 27, 'Cluster Analysis|Computational Biology|Databases, Protein|Models, Statistical|Pattern Recognition, Automated|Protein Structure, Tertiary|Proteins|Reproducibility of Results|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Comparative Study', '10.1186/1471-2105-7-277', '16749920', 'Proteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again.'),
(90948, 2006, 'bmc', 'Automated functional classification of experimental and predicted protein structures', 'Wang, Kai|Samudrala, Ram', 'Computational Genomics Group, Department of Microbiology, University of Washington, Seattle, WA 98195, USA.|Computational Genomics Group, Department of Microbiology, University of Washington, Seattle, WA 98195, USA.', 'United States|United States', 6, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Comparative Study', '10.1186/1471-2105-7-278', '16749925', 'Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity.'),
(90949, 2006, 'bmc', 'Optimized mixed Markov models for motif identification', 'Huang, Weichun|Umbach, David M|Ohler, Uwe|Li, Leping', 'Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA. huang6@niehs.nih.gov|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA. huang6@niehs.nih.gov|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA. huang6@niehs.nih.gov|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27606, USA. huang6@niehs.nih.gov', 'United States|United States|United States|United States', 11, 'Algorithms|Binding Sites|Computational Biology|Computer Simulation|Humans|Likelihood Functions|Markov Chains|Models, Statistical|Models, Theoretical|Pattern Recognition, Automated|Programming Languages|Protein Binding|Proteins|Proteomics|ROC Curve|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-279', '16749929', 'Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples.'),
(90950, 2006, 'bmc', 'Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks', 'Reiss, David J|Baliga, Nitin S|Bonneau, Richard', 'Institute for Systems Biology, 1441 N, 34th St, Seattle, WA 98103-8904, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th St, Seattle, WA 98103-8904, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th St, Seattle, WA 98103-8904, USA. dreiss@systemsbiology.org', 'United States|United States|United States', 172, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Escherichia coli|Gene Expression Regulation|Genome|Genomics|Halobacterium|Helicobacter pylori|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Promoter Regions, Genetic|Saccharomyces cerevisiae|Sequence Alignment|Species Specificity', 'Journal Article', '10.1186/1471-2105-7-280', '16749936', 'The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively co-regulated groups, as opposed to those that are simply co-expressed. Be cause genes may be co-regulated only across a subset of all observed experimental conditions, biclustering (clustering of genes and conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such a priori known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-regulatory motifs. In organisms where these motifs are not known, their de novo detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solutions.'),
(90951, 2006, 'bmc', 'MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics', 'Spasixc4x87, Irena|Dunn, Warwick B|Velarde, Giles|Tseng, Andy|Jenkins, Helen|Hardy, Nigel|Oliver, Stephen G|Kell, Douglas B', 'School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk|School of Chemistry, Faraday Building, The University of Manchester, Manchester, M60 1QD, UK. i.spasic@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 32, 'Computer Simulation|Database Management Systems|Genomics|Information Storage and Retrieval|Metabolism|Models, Biological|Proteome|Signal Transduction|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-281', '16753052', 'The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions.'),
(90952, 2006, 'bmc', 'Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring', 'Toedling, Joern|Rhein, Peter|Ratei, Richard|Karawajew, Leonid|Spang, Rainer', 'Max Planck Institute for Molecular Genetics & Berlin Center for Genome Based Bioinformatics, Ihnestrasse, 73, D-14195 Berlin, Germany. toedling@ebi.ac.uk|Max Planck Institute for Molecular Genetics & Berlin Center for Genome Based Bioinformatics, Ihnestrasse, 73, D-14195 Berlin, Germany. toedling@ebi.ac.uk|Max Planck Institute for Molecular Genetics & Berlin Center for Genome Based Bioinformatics, Ihnestrasse, 73, D-14195 Berlin, Germany. toedling@ebi.ac.uk|Max Planck Institute for Molecular Genetics & Berlin Center for Genome Based Bioinformatics, Ihnestrasse, 73, D-14195 Berlin, Germany. toedling@ebi.ac.uk|Max Planck Institute for Molecular Genetics & Berlin Center for Genome Based Bioinformatics, Ihnestrasse, 73, D-14195 Berlin, Germany. toedling@ebi.ac.uk', 'Germany|Germany|Germany|Germany|Germany', 36, 'Computational Biology|Computers|Cytogenetic Analysis|Data Interpretation, Statistical|Flow Cytometry|Genetic Markers|Humans|Immunophenotyping|Leukemia|Multivariate Analysis|Prognosis|Programming Languages|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-7-282', '16753055', 'Identification of minor cell populations, e.g. leukemic blasts within blood samples, has become increasingly important in therapeutic disease monitoring. Modern flow cytometers enable researchers to reliably measure six and more variables, describing cellular size, granularity and expression of cell-surface and intracellular proteins, for thousands of cells per second. Currently, analysis of cytometry readouts relies on visual inspection and manual gating of one- or two-dimensional projections of the data. This procedure, however, is labor-intensive and misses potential characteristic patterns in higher dimensions.'),
(90953, 2006, 'bmc', 'The bovine QTL viewer: a web accessible database of bovine Quantitative Trait Loci', 'Polineni, Pavana|Aragonda, Prathyusha|Xavier, Suresh R|Furuta, Richard|Adelson, David L', 'Dept. of Animal Science, Texas A&M University, Mail Stop 2471, College Station, TX 77843-2471, USA. Pavana_Polineni@Dell.com|Dept. of Animal Science, Texas A&M University, Mail Stop 2471, College Station, TX 77843-2471, USA. Pavana_Polineni@Dell.com|Dept. of Animal Science, Texas A&M University, Mail Stop 2471, College Station, TX 77843-2471, USA. Pavana_Polineni@Dell.com|Dept. of Animal Science, Texas A&M University, Mail Stop 2471, College Station, TX 77843-2471, USA. Pavana_Polineni@Dell.com|Dept. of Animal Science, Texas A&M University, Mail Stop 2471, College Station, TX 77843-2471, USA. Pavana_Polineni@Dell.com', 'United States|United States|United States|United States|United States', 34, 'Animals|Cattle|Chromosome Mapping|Databases, Genetic|Genome|Information Dissemination|Internet|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-7-283', '16753058', 'Many important agricultural traits such as weight gain, milk fat content and intramuscular fat (marbling) in cattle are quantitative traits. Most of the information on these traits has not previously been integrated into a genomic context. Without such integration application of these data to agricultural enterprises will remain slow and inefficient. Our goal was to populate a genomic database with data mined from the bovine quantitative trait literature and to make these data available in a genomic context to researchers via a user friendly query interface.'),
(90954, 2006, 'bmc', 'Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks', 'Oh, S June|Joung, Je-Gun|Chang, Jeong-Ho|Zhang, Byoung-Tak', 'Department of Pharmacology, Inje University College of Medicine, Busan, 614-735, Korea. o@biophilos.org|Department of Pharmacology, Inje University College of Medicine, Busan, 614-735, Korea. o@biophilos.org|Department of Pharmacology, Inje University College of Medicine, Busan, 614-735, Korea. o@biophilos.org|Department of Pharmacology, Inje University College of Medicine, Busan, 614-735, Korea. o@biophilos.org', '-|-|-|-', 21, 'Archaeal Proteins|Cluster Analysis|Computational Biology|Genes, Archaeal|Glycolysis|Metabolism|Models, Biological|Models, Genetic|Models, Statistical|Phylogeny|Proteomics', 'Comparative Study', '10.1186/1471-2105-7-284', '16753070', 'To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.'),
(90955, 2006, 'bmc', 'Gene Designer: a synthetic biology tool for constructing artificial DNA segments', 'Villalobos, Alan|Ness, Jon E|Gustafsson, Claes|Minshull, Jeremy|Govindarajan, Sridhar', 'DNA 2.0, Inc., 1430 OBrien Drive Suite E, Menlo Park, CA 94025, USA. avillalobos@dna20.com|DNA 2.0, Inc., 1430 OBrien Drive Suite E, Menlo Park, CA 94025, USA. avillalobos@dna20.com|DNA 2.0, Inc., 1430 OBrien Drive Suite E, Menlo Park, CA 94025, USA. avillalobos@dna20.com|DNA 2.0, Inc., 1430 OBrien Drive Suite E, Menlo Park, CA 94025, USA. avillalobos@dna20.com|DNA 2.0, Inc., 1430 OBrien Drive Suite E, Menlo Park, CA 94025, USA. avillalobos@dna20.com', 'United States|United States|United States|United States|United States', 237, 'Base Sequence|Computer-Aided Design|DNA|Drug Design|Genes, Synthetic|Genetic Engineering|Molecular Sequence Data|Sequence Analysis, DNA|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-285', '16756672', 'Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes.'),
(90956, 2006, 'bmc', 'SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology', 'Marzolf, Bruz|Deutsch, Eric W|Moss, Patrick|Campbell, David|Johnson, Michael H|Galitski, Timothy', 'Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, Washington, USA. bmarzolf@systemsbiology.org', 'United States|United States|United States|United States|United States|United States', 44, 'Chromosome Mapping|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-286', '16756676', 'The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.'),
(90957, 2006, 'bmc', 'BLISS: binding site level identification of shared signal-modules in DNA regulatory sequences', 'Meng, Hailong|Banerjee, Arunava|Zhou, Lei', 'Department of Molecular Genetics and Microbiology, UF Shands Cancer Center, College of Medicine, University of Florida, Gainesville, FL 32610, USA. hmeng@cise.ufl.edu|Department of Molecular Genetics and Microbiology, UF Shands Cancer Center, College of Medicine, University of Florida, Gainesville, FL 32610, USA. hmeng@cise.ufl.edu|Department of Molecular Genetics and Microbiology, UF Shands Cancer Center, College of Medicine, University of Florida, Gainesville, FL 32610, USA. hmeng@cise.ufl.edu', 'United States|United States|United States', 2, 'Animals|Base Sequence|Binding Sites|Computer Simulation|Conserved Sequence|DNA|Databases, Genetic|Drosophila melanogaster|Evolution, Molecular|Internet|Models, Genetic|Normal Distribution|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors|User-Computer Interface', 'Comparative Study', '10.1186/1471-2105-7-287', '16756683', 'Regulatory modules are segments of the DNA that control particular aspects of gene expression. Their identification is therefore of great importance to the field of molecular genetics. Each module is composed of a distinct set of binding sites for specific transcription factors. Since experimental identification of regulatory modules is an arduous process, accurate computational techniques that supplement this process can be very beneficial. Functional modules are under selective pressure to be evolutionarily conserved. Most current approaches therefore attempt to detect conserved regulatory modules through similarity comparisons at the DNA sequence level. However, some regulatory modules, despite the conservation of their responsible binding sites, are embedded in sequences that have little overall similarity.'),
(90958, 2006, 'bmc', 'High throughput profile-profile based fold recognition for the entire human proteome', 'McGuffin, Liam J|Smith, Richard T|Bryson, Kevin|Sørensen, Søren-Aksel|Jones, David T', 'The BioCentre, University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk|The BioCentre, University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk|The BioCentre, University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk|The BioCentre, University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk|The BioCentre, University of Reading, Whiteknights, PO Box 221, Reading RG6 6AS, UK. l.j.mcguffin@reading.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Humans|Molecular Sequence Data|Pattern Recognition, Automated|Protein Folding|Proteome|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-288', '16759376', 'In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90959, 2006, 'bmc', 'Cell_motility: a cross-platform, open source application for the study of cell motion paths', 'Martens, Lennart|Monsieur, Geert|Ampe, Christophe|Gevaert, Kris|Vandekerckhove, Joël', 'Department of Medical Protein Research, Flanders Interuniversity Institute for Biotechnology, Department of Biochemistry, Ghent University, A, Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Medical Protein Research, Flanders Interuniversity Institute for Biotechnology, Department of Biochemistry, Ghent University, A, Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Medical Protein Research, Flanders Interuniversity Institute for Biotechnology, Department of Biochemistry, Ghent University, A, Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Medical Protein Research, Flanders Interuniversity Institute for Biotechnology, Department of Biochemistry, Ghent University, A, Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be|Department of Medical Protein Research, Flanders Interuniversity Institute for Biotechnology, Department of Biochemistry, Ghent University, A, Baertsoenkaai 3, B-9000 Ghent, Belgium. lennart.martens@UGent.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 14, 'Animals|Cell Movement|Computer Simulation|Humans|Models, Biological|Models, Statistical|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-289', '16762054', 'Migration is an important aspect of cellular behaviour and is therefore widely studied in cell biology. Numerous components are known to participate in this process in a highly dynamic manner. In order to obtain a better insight in cell migration, mutants or drugs are used and their motive phenotype is then linked with the disturbing factors. One of the typical approaches to study motion paths of individual cells relies on fitting mean square displacements to a persistent random walk function. Since the numerous calculations involved often rely on diverse commercial software packages, the analysis can be expensive, labour-intensive and error-prone work. Additionally, due to the nature of algorithms employed the calculations involved are not readily reproducible without access to the exact software package(s) used.'),
(90960, 2006, 'bmc', 'Independent component analysis reveals new and biologically significant structures in micro array data', 'Frigyesi, Attila|Veerla, Srinivas|Lindgren, David|Höglund, Mattias', 'Department of Cardiology, University Hospital, SE-221-85 Lund, Sweden. attila.frigyesi@kard.lu.se|Department of Cardiology, University Hospital, SE-221-85 Lund, Sweden. attila.frigyesi@kard.lu.se|Department of Cardiology, University Hospital, SE-221-85 Lund, Sweden. attila.frigyesi@kard.lu.se|Department of Cardiology, University Hospital, SE-221-85 Lund, Sweden. attila.frigyesi@kard.lu.se', 'Sweden|Sweden|Sweden|Sweden', 61, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Multigene Family|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Signal Processing, Computer-Assisted|Software|Translocation, Genetic', 'Journal Article', '10.1186/1471-2105-7-290', '16762055', 'An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, sources may correspond to specific cellular responses or to co-regulated genes.'),
(90961, 2006, 'bmc', 'Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease', 'Masseroli, Marco|Kilicoglu, Halil|Lang, François-Michel|Rindflesch, Thomas C', 'Bioengineering Department, Politecnico di Milano, Milan, Italy. masseroli@biomed.polimi.it|Bioengineering Department, Politecnico di Milano, Milan, Italy. masseroli@biomed.polimi.it|Bioengineering Department, Politecnico di Milano, Milan, Italy. masseroli@biomed.polimi.it|Bioengineering Department, Politecnico di Milano, Milan, Italy. masseroli@biomed.polimi.it', 'Italy|Italy|Italy|Italy', 20, 'Genetic Diseases, Inborn|Genetic Predisposition to Disease|Humans|Information Storage and Retrieval|Natural Language Processing|Parkinson Disease|Semantics|Software', 'Journal Article', '10.1186/1471-2105-7-291', '16762065', 'Genomic functional information is valuable for biomedical research. However, such information frequently needs to be extracted from the scientific literature and structured in order to be exploited by automatic systems. Natural language processing is increasingly used for this purpose although it inherently involves errors. A postprocessing strategy that selects relations most likely to be correct is proposed and evaluated on the output of SemGen, a system that extracts semantic predications on the etiology of genetic diseases. Based on the number of intervening phrases between an argument and its predicate, we defined a heuristic strategy to filter the extracted semantic relations according to their likelihood of being correct. We also applied this strategy to relations identified with co-occurrence processing. Finally, we exploited postprocessed SemGen predications to investigate the genetic basis of Parkinsons disease.'),
(90962, 2006, 'bmc', 'MCALIGN2: faster, accurate global pairwise alignment of non-coding DNA sequences based on explicit models of indel evolution', 'Wang, Jun|Keightley, Peter D|Johnson, Toby', 'Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK. j.wang-13@sms.ed.ac.uk|Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK. j.wang-13@sms.ed.ac.uk|Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, UK. j.wang-13@sms.ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Animals|Computational Biology|DNA|Drosophila|Evolution, Molecular|Genome|Humans|Mice|Models, Genetic|Molecular Sequence Data|Probability|Rats|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-7-292', '16762073', 'Non-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants. Unraveling the functional significance of non-coding DNA depends on how well we are able to align non-coding DNA sequences. However, the alignment of non-coding DNA sequences is more difficult than aligning protein-coding sequences.'),
(90963, 2006, 'bmc', 'Representation of target-bound drugs by computed conformers: implications for conformational libraries', 'Günther, Stefan|Senger, Christian|Michalsky, Elke|Goede, Andrean|Preissner, Robert', 'Institute for Biochemistry, Charité, Monbijoustr, 2, 10117 Berlin, Germany. stefan.guenther@charite.de|Institute for Biochemistry, Charité, Monbijoustr, 2, 10117 Berlin, Germany. stefan.guenther@charite.de|Institute for Biochemistry, Charité, Monbijoustr, 2, 10117 Berlin, Germany. stefan.guenther@charite.de|Institute for Biochemistry, Charité, Monbijoustr, 2, 10117 Berlin, Germany. stefan.guenther@charite.de|Institute for Biochemistry, Charité, Monbijoustr, 2, 10117 Berlin, Germany. stefan.guenther@charite.de', 'Germany|Germany|Germany|Germany|Germany', 27, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Databases, Protein|Drug Delivery Systems|Drug Design|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pharmaceutical Preparations|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-7-293', '16764718', 'The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.'),
(90964, 2006, 'bmc', 'ROKU: a novel method for identification of tissue-specific genes', 'Kadota, Koji|Ye, Jiazhen|Nakai, Yuji|Terada, Tohru|Shimizu, Kentaro', 'Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp|Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 57, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Entropy|Gene Expression Profiling|Humans|Models, Statistical|Programming Languages|Protein Array Analysis|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-7-294', '16764735', 'One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.'),
(90965, 2006, 'bmc', 'Analysis of circular genome rearrangement by fusions, fissions and block-interchanges', 'Lu, Chin Lung|Huang, Yen Lin|Wang, Tsui Ching|Chiu, Hsien-Tai', 'Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, ROC, Taiwan. cllu@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, ROC, Taiwan. cllu@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, ROC, Taiwan. cllu@mail.nctu.edu.tw|Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, ROC, Taiwan. cllu@mail.nctu.edu.tw', '-|-|-|-', 20, 'Algorithms|Chromosome Inversion|Computational Biology|Evolution, Molecular|Genome, Bacterial|Genomics|Models, Genetic|Models, Theoretical|RNA, Ribosomal, 16S|Recombination, Genetic|Sequence Analysis, DNA|Software|Vibrio', 'Journal Article', '10.1186/1471-2105-7-295', '16768797', 'Analysis of genomes evolving via block-interchange events leads to a combinatorial problem of sorting by block-interchanges, which has been studied recently to evaluate the evolutionary relationship in distance between two biological species since block-interchange can be considered as a generalization of transposition. However, for genomes consisting of multiple chromosomes, their evolutionary history should also include events of chromosome fusions and fissions, where fusion merges two chromosomes into one and fission splits a chromosome into two.'),
(90966, 2006, 'bmc', 'Accelerating the reconstruction of genome-scale metabolic networks', 'Notebaart, Richard A|van Enckevort, Frank H J|Francke, Christof|Siezen, Roland J|Teusink, Bas', 'Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen, The Netherlands. R.notebaart@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen, The Netherlands. R.notebaart@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen, The Netherlands. R.notebaart@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen, The Netherlands. R.notebaart@cmbi.ru.nl|Center for Molecular and Biomolecular Informatics, Radboud University Nijmegen, The Netherlands. R.notebaart@cmbi.ru.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 121, 'Bacillus subtilis|Bacterial Proteins|Computational Biology|Computer Simulation|Databases, Genetic|Enzymes|Escherichia coli K12|Genome, Bacterial|Lactobacillus plantarum|Lactococcus lactis|Models, Genetic|Protein Interaction Mapping|Software Validation', 'Journal Article', '10.1186/1471-2105-7-296', '16772023', 'The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.'),
(90967, 2006, 'bmc', 'Impact of RNA structure on the prediction of donor and acceptor splice sites', 'Marashi, Sayed-Amir|Eslahchi, Changiz|Pezeshk, Hamid|Sadeghi, Mehdi', 'Department of Biotechnology, University College of Science, University of Tehran, Tehran, Iran. marashie@khayam.ut.ac.ir|Department of Biotechnology, University College of Science, University of Tehran, Tehran, Iran. marashie@khayam.ut.ac.ir|Department of Biotechnology, University College of Science, University of Tehran, Tehran, Iran. marashie@khayam.ut.ac.ir|Department of Biotechnology, University College of Science, University of Tehran, Tehran, Iran. marashie@khayam.ut.ac.ir', '-|-|-|-', 18, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Humans|Likelihood Functions|Markov Chains|Models, Genetic|Nucleic Acid Conformation|RNA Splice Sites|RNA Splicing|RNA, Messenger|RNA, Plant|Sequence Analysis, RNA|Software', 'Journal Article', '10.1186/1471-2105-7-297', '16772025', 'gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.'),
(90968, 2006, 'bmc', 'Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains', 'Bulashevska, Alla|Eils, Roland', 'Theoretical Bioinformatics Department, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. A.Bulashevska@dkfz.de|Theoretical Bioinformatics Department, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. A.Bulashevska@dkfz.de', 'Germany|Germany', 79, 'Amino Acid Sequence|Bayes Theorem|Markov Chains|Models, Biological|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-7-298', '16774677', 'The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction.'),
(90969, 2006, 'bmc', 'Kernel-based distance metric learning for microarray data classification', 'Xiong, Huilin|Chen, Xue-wen', 'Bioinformatics and Computational Life Sciences Laboratory, Department of Electrical Engineering and Computer Science, University of Kansas, 2335 Irving Hill Road, Lawrence, Kansas 66045, USA. hlxiong@ittc.ku.edu|Bioinformatics and Computational Life Sciences Laboratory, Department of Electrical Engineering and Computer Science, University of Kansas, 2335 Irving Hill Road, Lawrence, Kansas 66045, USA. hlxiong@ittc.ku.edu', 'United States|United States', 48, 'Artificial Intelligence|Biomarkers, Tumor|Databases, Protein|Diagnosis, Computer-Assisted|Discriminant Analysis|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Tissue Array Analysis', 'Journal Article', '10.1186/1471-2105-7-299', '16774678', 'The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging.'),
(90970, 2006, 'bmc', 'Evaluation of microarray data normalization procedures using spike-in experiments', 'Rydén, Patrik|Andersson, Henrik|Landfors, Mattias|Näslund, Linda|Hartmanová, Blanka|Noppa, Laila|Sjöstedt, Anders', 'Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se|Department of Clinical Microbiology Division of Clinical Bacteriology, Umeå University, SE-90187 Umeå, Sweden. patrik.ryden@climi.umu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 16, 'Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Image Processing, Computer-Assisted|Models, Statistical|Oligonucleotide Array Sequence Analysis|ROC Curve|Reproducibility of Results', 'Comparative Study', '10.1186/1471-2105-7-300', '16774679', 'Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods.'),
(90971, 2006, 'bmc', 'Improving the accuracy of protein secondary structure prediction using structural alignment', 'Montgomerie, Scott|Sundararaj, Shan|Gallin, Warren J|Wishart, David S', 'Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada. montgomerie@shaw.ca|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada. montgomerie@shaw.ca|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada. montgomerie@shaw.ca|Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada. montgomerie@shaw.ca', 'Canada|Canada|Canada|Canada', 105, 'Algorithms|Computer Simulation|Databases, Protein|Internet|Protein Structure, Secondary|Proteins|Proteomics|Sequence Alignment|Software|Structural Homology, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-301', '16774686', 'The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high.'),
(90972, 2006, 'bmc', 'A new measure for functional similarity of gene products based on Gene Ontology', 'Schlicker, Andreas|Domingues, Francisco S|Rahnenführer, Jörg|Lengauer, Thomas', 'Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. schlandi@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. schlandi@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. schlandi@mpi-inf.mpg.de|Department of Computational Biology and Applied Algorithmics, Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. schlandi@mpi-inf.mpg.de', 'Germany|Germany|Germany|Germany', 434, 'Algorithms|Artificial Intelligence|Databases, Protein|Genes|Information Storage and Retrieval|Multigene Family|Natural Language Processing|Proteins|Structure-Activity Relationship|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-302', '16776819', 'Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.'),
(90973, 2006, 'bmc', 'A model-based approach to selection of tag SNPs', 'Nicolas, Pierre|Sun, Fengzhu|Li, Lei M', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, USA. pierre.nicolas@jouy.inra.fr|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, USA. pierre.nicolas@jouy.inra.fr|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, USA. pierre.nicolas@jouy.inra.fr', 'United States|United States|United States', 21, 'Databases, Genetic|Genetic Markers|Haplotypes|Humans|Linkage Disequilibrium|Markov Chains|Models, Genetic|Polymorphism, Single Nucleotide|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1186/1471-2105-7-303', '16776821', 'Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannons framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD), a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets.'),
(90974, 2006, 'bmc', 'Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences', 'Chiu, Shih-Hau|Chen, Chien-Chi|Yuan, Gwo-Fang|Lin, Thy-Hou', 'Bioresource Collection and Research Center, Food Industry Research and Development Institute, HsinChu, Taiwan. shc@firdi.org.tw|Bioresource Collection and Research Center, Food Industry Research and Development Institute, HsinChu, Taiwan. shc@firdi.org.tw|Bioresource Collection and Research Center, Food Industry Research and Development Institute, HsinChu, Taiwan. shc@firdi.org.tw|Bioresource Collection and Research Center, Food Industry Research and Development Institute, HsinChu, Taiwan. shc@firdi.org.tw', '-|-|-|-', 11, 'Algorithms|Database Management Systems|Databases, Protein|Enzymes|Fungal Proteins|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-304', '16776838', 'The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity.'),
(90975, 2006, 'bmc', 'Taking U out, with two nucleases?', 'Mian, I Saira|Worthey, Elizabeth A|Salavati, Reza', 'Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-8265, USA. SMian@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-8265, USA. SMian@lbl.gov|Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-8265, USA. SMian@lbl.gov', 'United States|United States|United States', 5, 'Binding Sites|Computer Simulation|Models, Biological|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|RNA Editing|Sequence Analysis, Protein|UDPglucose-Hexose-1-Phosphate Uridylyltransferase', 'Journal Article', '10.1186/1471-2105-7-305', '16780580', 'REX1 and REX2 are protein components of the RNA editing complex (the editosome) and function as exouridylylases. The exact roles of REX1 and REX2 in the editosome are unclear and the consequences of the presence of two related proteins are not fully understood. Here, a variety of computational studies were performed to enhance understanding of the structure and function of REX proteins in Trypanosoma and Leishmania species.'),
(90976, 2006, 'bmc', 'Improving missing value imputation of microarray data by using spot quality weights', 'Johansson, Peter|Häkkinen, Jari', 'Computational Biology, Department of Theoretical Physics, Lund University, SE-223 62 Lund, Sweden. peter@thep.lu.se|Computational Biology, Department of Theoretical Physics, Lund University, SE-223 62 Lund, Sweden. peter@thep.lu.se', 'Sweden|Sweden', 20, 'Algorithms|Breast Neoplasms|Cell Line, Tumor|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation, Fungal|Gene Expression Regulation, Neoplastic|Humans|Melanoma|Models, Statistical|Mycorrhizae|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Comparative Study', '10.1186/1471-2105-7-306', '16780582', 'Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools require complete data, but measurement values are often missing A way to overcome the problem of incomplete data is to impute the missing data before analysis. Many imputation methods have been suggested, some naïve and other more sophisticated taking into account correlation in data. However, these methods are binary in the sense that each spot is considered either missing or present. Hence, they are depending on a cutoff separating poor spots from good spots. We suggest a different approach in which a continuous spot quality weight is built into the imputation methods, allowing for smooth imputations of all spots to larger or lesser degree.'),
(90977, 2006, 'bmc', 'Array2BIO: from microarray expression data to functional annotation of co-regulated genes', 'Loots, Gabriela G|Chain, Patrick S G|Mabery, Shalini|Rasley, Amy|Garcia, Emilio|Ovcharenko, Ivan', 'Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov|Lawrence Livermore National Laboratory, Livermore, California 94550, USA. loots1@llnl.gov', 'United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Chromosome Mapping|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-7-307', '16780584', 'There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility.'),
(90978, 2006, 'bmc', 'Identification of QTLs controlling gene expression networks defined a priori', 'Kliebenstein, Daniel J|West, Marilyn A L|van Leeuwen, Hans|Loudet, Olivier|Doerge, R W|St Clair, Dina A', 'University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu|University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu|University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu|University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu|University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu|University of California-Davis, Department of Plant Sciences, Mail Stop 3, One Shields Ave, Davis, CA 95616-8780, USA. kliebenstein@ucdavis.edu', 'United States|United States|United States|United States|United States|United States', 94, 'Arabidopsis|Arabidopsis Proteins|Cell Line|Cluster Analysis|Flavonols|Gene Expression Profiling|Gene Expression Regulation, Plant|Glucosinolates|Models, Genetic|Oligonucleotide Array Sequence Analysis|Phenotype|Quantitative Trait Loci|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-308', '16780591', 'Gene expression microarrays allow the quantification of transcript accumulation for many or all genes in a genome. This technology has been utilized for a range of investigations, from assessments of gene regulation in response to genetic or environmental fluctuation to global expression QTL (eQTL) analyses of natural variation. Current analysis techniques facilitate the statistical querying of individual genes to evaluate the significance of a change in response, also known as differential expression. Since genes are also known to respond as groups due to their membership in networks, effective approaches are needed to investigate transcriptome variation as related to gene network responses.'),
(90979, 2006, 'bmc', 'In silico identification of putative promoter motifs of White Spot Syndrome Virus', 'Marks, Hendrik|Ren, Xin-Ying|Sandbrink, Hans|van Hulten, Mariëlle C W|Vlak, Just M', 'Laboratory of Virology, Wageningen University, Binnenhaven 11, 6709 PD Wageningen, The Netherlands. hendrikmarks@hotmail.com|Laboratory of Virology, Wageningen University, Binnenhaven 11, 6709 PD Wageningen, The Netherlands. hendrikmarks@hotmail.com|Laboratory of Virology, Wageningen University, Binnenhaven 11, 6709 PD Wageningen, The Netherlands. hendrikmarks@hotmail.com|Laboratory of Virology, Wageningen University, Binnenhaven 11, 6709 PD Wageningen, The Netherlands. hendrikmarks@hotmail.com|Laboratory of Virology, Wageningen University, Binnenhaven 11, 6709 PD Wageningen, The Netherlands. hendrikmarks@hotmail.com', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Base Sequence|Conserved Sequence|Gene Expression Regulation, Viral|Herpesvirus 1, Human|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Open Reading Frames|Polyadenylation|Promoter Regions, Genetic|RNA Polymerase II|RNA, Messenger|Sequence Analysis, DNA|TATA Box|Transcription, Genetic|Vaccinia virus|Viral Proteins|White spot syndrome virus 1', 'Journal Article', '10.1186/1471-2105-7-309', '16784526', 'White Spot Syndrome Virus, a member of the virus family Nimaviridae, is a large dsDNA virus infecting shrimp and other crustacean species. Although limited information is available on the mode of transcription, previous data suggest that WSSV gene expression occurs in a coordinated and cascaded fashion. To search in silico for conserved promoter motifs (i) the abundance of all 4 through 8 nucleotide motifs in the upstream sequences of WSSV genes relative to the complete genome was determined, and (ii) a MEME search was performed in the upstream sequences of either early or late WSSV genes, as assigned by microarray analysis. Both methods were validated by alignments of empirically determined 5 ends of various WSSV mRNAs.'),
(90980, 2006, 'bmc', 'Docking protein domains in contact space', 'Lise, Stefano|Walker-Taylor, Alice|Jones, David T', 'Department of Biochemistry and Molecular Biology, University College London, UK. s.lise@ucl.ac.uk|Department of Biochemistry and Molecular Biology, University College London, UK. s.lise@ucl.ac.uk|Department of Biochemistry and Molecular Biology, University College London, UK. s.lise@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 11, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-310', '16790041', 'Many biological processes involve the physical interaction between protein domains. Understanding these functional associations requires knowledge of the molecular structure. Experimental investigations though present considerable difficulties and there is therefore a need for accurate and reliable computational methods. In this paper we present a novel method that seeks to dock protein domains using a contact map representation. Rather than providing a full three dimensional model of the complex, the method predicts contacting residues across the interface. We use a scoring function that combines structural, physicochemical and evolutionary information, where each potential residue contact is assigned a value according to the scoring function and the hypothesis is that the real configuration of contacts is the one that maximizes the score. The search is performed with a simulated annealing algorithm directly in contact space.'),
(90981, 2006, 'bmc', 'Web-based analysis of the mouse transcriptome using Genevestigator', 'Laule, Oliver|Hirsch-Hoffmann, Matthias|Hruz, Tomas|Gruissem, Wilhelm|Zimmermann, Philip', 'Institute of Plant Sciences, ETH Zurich, 8092 Zurich, Switzerland. ola@ethz.ch|Institute of Plant Sciences, ETH Zurich, 8092 Zurich, Switzerland. ola@ethz.ch|Institute of Plant Sciences, ETH Zurich, 8092 Zurich, Switzerland. ola@ethz.ch|Institute of Plant Sciences, ETH Zurich, 8092 Zurich, Switzerland. ola@ethz.ch|Institute of Plant Sciences, ETH Zurich, 8092 Zurich, Switzerland. ola@ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 23, 'Animals|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Internet|Mice|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-311', '16790046', 'Gene function analysis often requires a complex and laborious sequence of laboratory and computer-based experiments. Choosing an effective experimental design generally results from hypotheses derived from prior knowledge or experimentation. Knowledge obtained from meta-analyzing compendia of expression data with annotation libraries can provide significant clues in understanding gene and network function, resulting in better hypotheses that can be tested in the laboratory.'),
(90982, 2006, 'bmc', 'Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties', 'Petrova, Natalia V|Wu, Cathy H', 'Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20007, USA. np6@georgetown.edu|Protein Information Resource, Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20007, USA. np6@georgetown.edu', 'United States|United States', 0, 'Algorithms|Animals|Automation|Catalytic Domain|Computational Biology|Databases, Protein|Evolution, Molecular|Genetic Vectors|Humans|Pattern Recognition, Automated|Protein Conformation|Proteomics|Sequence Analysis, Protein|Software|Solvents', 'Journal Article', '10.1186/1471-2105-7-312', '16790052', 'The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.'),
(90983, 2006, 'bmc', 'Subfamily logos: visualization of sequence deviations at alignment positions with high information content', 'Beitz, Eric', 'Dept. of Pharmaceutical Chemistry, University of Tübingen, Morgenstelle 8, 72076 Tübingen, Germany. eric.beitz@uni-tuebingen.de', 'Germany', 0, 'Algorithms|Amino Acid Sequence|Computer Graphics|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-313', '16790056', 'Recognition of relevant sequence deviations can be valuable for elucidating functional differences between protein subfamilies. Interesting residues at highly conserved positions can then be mutated and experimentally analyzed. However, identification of such sites is tedious because automated approaches are scarce.'),
(90984, 2006, 'bmc', 'AgdbNet - antigen sequence database software for bacterial typing', 'Jolley, Keith A|Maiden, Martin C J', 'The Peter Medawar Building for Pathogen Research and Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3SY, UK. keith.jolley@medawar.ox.ac.uk|The Peter Medawar Building for Pathogen Research and Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3SY, UK. keith.jolley@medawar.ox.ac.uk', 'United Kingdom|United Kingdom', 25, 'Algorithms|Bacterial Typing Techniques|Campylobacter jejuni|Computational Biology|Databases, Genetic|Databases, Protein|Internet|Neisseria meningitidis|Peptides|Programming Languages|Sequence Analysis, DNA|Software|Streptococcus equi|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-314', '16790057', 'Bacterial typing schemes based on the sequences of genes encoding surface antigens require databases that provide a uniform, curated, and widely accepted nomenclature of the variants identified. Due to the differences in typing schemes, imposed by the diversity of genes targeted, creating these databases has typically required the writing of one-off code to link the database to a web interface. Here we describe agdbNet, widely applicable web database software that facilitates simultaneous BLAST querying of multiple loci using either nucleotide or peptide sequences.'),
(90985, 2006, 'bmc', 'PHYFI: fast and easy online creation and manipulation of phylogeny color figures', 'Fredslund, Jakob', 'Bioinformatics Research Center, Aarhus University, Høegh-Guldbergs Gade 10, Bldg, 1090, DK-8000 Arhus C, Denmark. jakobf@birc.au.dk', 'Denmark', 31, 'Algorithms|Color|Computer Graphics|Models, Genetic|Online Systems|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-315', '16792795', 'The need to depict a phylogeny, or some other kind of abstract tree, is very frequently experienced by researchers from a broad range of biological and computational disciplines. Thousands of papers and talks include phylogeny figures, and often during everyday work, one would like to quickly get a graphical display of, e.g., the phylogenetic relationship between a set of sequences as calculated by an alignment program such as ClustalW or the phylogenetic package Phylip. A wealth of software tools capable of tree drawing exists, most are comprehensive packages that also perform various types of analysis, and hence they are available only for download and installing. Some online tools exist, too.'),
(90986, 2006, 'bmc', 'CAVER: a new tool to explore routes from protein clefts, pockets and cavities', 'Petrek, Martin|Otyepka, Michal|Banás, Pavel|Kosinová, Pavlína|Koca, Jaroslav|Damborský, Jirí', 'National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz|National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz|National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz|National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz|National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz|National Center for Biomolecular Research, Masaryk University, Kamenice 5/A4, 625 00 Brno, Czech Republic. petrek@chemi.muni.cz', '-|-|-|-|-|-', 412, 'Algorithms|Computer Simulation|Crystallography, X-Ray|Internet|Magnetic Resonance Spectroscopy|Protein Conformation|Protein Structure, Secondary|Proteins|Proteomics|Rhodococcus|Software|Solvents|Sphingomonas|Xanthobacter', 'Journal Article', '10.1186/1471-2105-7-316', '16792811', 'The main aim of this study was to develop and implement an algorithm for the rapid, accurate and automated identification of paths leading from buried protein clefts, pockets and cavities in dynamic and static protein structures to the outside solvent.'),
(90987, 2006, 'bmc', 'PSMIX: an R package for population structure inference via maximum likelihood method', 'Wu, Baolin|Liu, Nianjun|Zhao, Hongyu', 'Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. baolin@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. baolin@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. baolin@biostat.umn.edu', 'United States|United States|United States', 39, 'Algorithms|American Native Continental Ancestry Group|Bayes Theorem|Chromosome Mapping|Computer Simulation|European Continental Ancestry Group|Genetics, Population|Humans|Likelihood Functions|Models, Genetic|Software', 'Comparative Study', '10.1186/1471-2105-7-317', '16792813', 'Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings.'),
(90988, 2006, 'bmc', 'MACSIMS: multiple alignment of complete sequences information management system', 'Thompson, Julie D|Muller, Arnaud|Waterhouse, Andrew|Procter, Jim|Barton, Geoffrey J|Plewniak, Frédéric|Poch, Olivier', 'Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr|Laboratoire de Biologie et Genomique Structurales, Institut de Génétique et de Biologie Moléculaire et Cellulaire, Illkirch, France. julie@igbmc.u-strasbg.fr', 'France|France|France|France|France|France|France', 34, 'Algorithms|Animals|Computational Biology|Databases, Protein|Evolution, Molecular|Genomics|Humans|Management Information Systems|Mutation|Programming Languages|Proteomics|Reproducibility of Results|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-7-318', '16792820', 'In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family.'),
(90989, 2006, 'bmc', 'Protein disorder prediction by condensed PSSM considering propensity for order or disorder', 'Su, Chung-Tsai|Chen, Chien-Yu|Ou, Yu-Yen', 'Department of Bio-industrial Mechatronics Engineering, National Taiwan University, Taipei, 106, Taiwan, ROC. sbb@mars.csie.ntu.edu.tw|Department of Bio-industrial Mechatronics Engineering, National Taiwan University, Taipei, 106, Taiwan, ROC. sbb@mars.csie.ntu.edu.tw|Department of Bio-industrial Mechatronics Engineering, National Taiwan University, Taipei, 106, Taiwan, ROC. sbb@mars.csie.ntu.edu.tw', '-|-|-', 81, 'Amino Acid Sequence|Cluster Analysis|Computer Simulation|Databases, Protein|Models, Molecular|Molecular Sequence Data|Proteins|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1186/1471-2105-7-319', '16796745', 'More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90990, 2006, 'bmc', 'Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data', 'Ooi, Chia Huey|Chetty, Madhu|Teng, Shyh Wei', 'Gippsland School of Information Technology, Monash University, Churchill, VIC 3842, Australia. Chia.Huey.Ooi@infotech.monash.edu.au|Gippsland School of Information Technology, Monash University, Churchill, VIC 3842, Australia. Chia.Huey.Ooi@infotech.monash.edu.au|Gippsland School of Information Technology, Monash University, Churchill, VIC 3842, Australia. Chia.Huey.Ooi@infotech.monash.edu.au', 'Australia|Australia|Australia', 28, 'Animals|Computational Biology|Computers|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-7-320', '16796748', 'Due to the large number of genes in a typical microarray dataset, feature selection looks set to play an important role in reducing noise and computational cost in gene expression-based tissue classification while improving accuracy at the same time. Surprisingly, this does not appear to be the case for all multiclass microarray datasets. The reason is that many feature selection techniques applied on microarray datasets are either rank-based and hence do not take into account correlations between genes, or are wrapper-based, which require high computational cost, and often yield difficult-to-reproduce results. In studies where correlations between genes are considered, attempts to establish the merit of the proposed techniques are hampered by evaluation procedures which are less than meticulous, resulting in overly optimistic estimates of accuracy.'),
(90991, 2006, 'bmc', 'Bacterial genotyping by 16S rRNA mass cataloging', 'Jackson, George W|McNichols, Roger J|Fox, George E|Willson, Richard C', 'BioTex, Inc. 8058 El Rio St. Houston, TX 77054, USA. bill@biotexmedical.com|BioTex, Inc. 8058 El Rio St. Houston, TX 77054, USA. bill@biotexmedical.com|BioTex, Inc. 8058 El Rio St. Houston, TX 77054, USA. bill@biotexmedical.com|BioTex, Inc. 8058 El Rio St. Houston, TX 77054, USA. bill@biotexmedical.com', 'United States|United States|United States|United States', 11, 'Bacteria|Chromosome Mapping|Databases, Genetic|Genome, Bacterial|Genotype|Information Storage and Retrieval|Mass Spectrometry|RNA, Ribosomal, 16S|Sequence Analysis, RNA', 'Journal Article', '10.1186/1471-2105-7-321', '16796754', 'It has recently been demonstrated that organism identifications can be recovered from mass spectra using various methods including base-specific fragmentation of nucleic acids. Because mass spectrometry is extremely rapid and widely available such techniques offer significant advantages in some applications. A key element in favor of mass spectrometric analysis of RNA fragmentation patterns is that a reference database for analysis of the results can be generated from sequence information. In contrast to hybridization approaches, the genetic affinity of any unknown isolate can in principle be determined within the context of all previously sequenced 16S rRNAs without prior knowledge of what the organism is. In contrast to the original RNase T1 cataloging method, when digestion products are analyzed by mass spectrometry, products with the same base composition cannot be distinguished. Hence, it is possible that organisms that are not closely related (having different underlying sequences) might be falsely identified by mass spectral coincidence. We present a convenient spectral coincidence function for expressing the degree of similarity (or distance) between any two mass-spectra. Trees constructed using this function are consistent with those produced by direct comparison of primary sequences, demonstrating that the inherent degeneracy in mass spectrometric analysis of RNA fragments does not preclude correct organism identification.'),
(90992, 2006, 'bmc', 'SPODOBASE: an EST database for the lepidopteran crop pest Spodoptera', 'Nègre, Vincent|Hôtelier, Thierry|Volkoff, Anne-Nathalie|Gimenez, Sylvie|Cousserans, François|Mita, Kazuei|Sabau, Xavier|Rocher, Janick|López-Ferber, Miguel|dAlençon, Emmanuelle|Audant, Pascaline|Sabourault, Cécile|Bidegainberry, Vincent|Hilliou, Frédérique|Fournier, Philippe', '1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr|1Unité Informatique de Centre, INRA-AgroM, 2 place Viala, 34060 Montpellier Cedex 2, France. Vincent.Negre@igh.cnrs.fr', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 72, 'Animals|Cluster Analysis|Computational Biology|Contig Mapping|DNA, Complementary|Databases, Genetic|Expressed Sequence Tags|Gene Library|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|Spodoptera|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-7-322', '16796757', 'The Lepidoptera Spodoptera frugiperda is a pest which causes widespread economic damage on a variety of crop plants. It is also well known through its famous Sf9 cell line which is used for numerous heterologous protein productions. Species of the Spodoptera genus are used as model for pesticide resistance and to study virus host interactions. A genomic approach is now a critical step for further new developments in biology and pathology of these insects, and the results of ESTs sequencing efforts need to be structured into databases providing an integrated set of tools and informations.'),
(90993, 2006, 'bmc', 'Identification of putative domain linkers by a neural network - application to a large sequence database', 'Miyazaki, Satoshi|Kuroda, Yutaka|Yokoyama, Shigeyuki', 'Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.|Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.|Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.', 'Japan|Japan|Japan', 15, 'Algorithms|Amino Acid Sequence|Binding Sites|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Binding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-7-323', '16800897', 'The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences).'),
(90994, 2006, 'bmc', 'Novel knowledge-based mean force potential at the profile level', 'Dong, Qiwen|Wang, Xiaolong|Lin, Lei', 'School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China. qwdong@insun.hit.edu.cn|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China. qwdong@insun.hit.edu.cn|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, PR China. qwdong@insun.hit.edu.cn', 'China|China|China', 28, 'Algorithms|Animals|Artificial Intelligence|Biophysics|Computational Biology|Evolution, Molecular|Humans|Knowledge Bases|Models, Statistical|Peptides|Probability|Protein Conformation|Protein Folding|Software', 'Journal Article', '10.1186/1471-2105-7-324', '16803615', 'The development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials.'),
(90995, 2006, 'bmc', 'The Autoimmune Disease Database: a dynamically compiled literature-derived database', 'Karopka, Thomas|Fluck, Juliane|Mevissen, Heinz-Theodor|Glass, Anne', 'Institute for Medical Informatics and Biometry, University of Rostock, Rembrandt-Str, 16/17, 18055 Rostock, Germany. thomas.karopka@uni-rostock.de|Institute for Medical Informatics and Biometry, University of Rostock, Rembrandt-Str, 16/17, 18055 Rostock, Germany. thomas.karopka@uni-rostock.de|Institute for Medical Informatics and Biometry, University of Rostock, Rembrandt-Str, 16/17, 18055 Rostock, Germany. thomas.karopka@uni-rostock.de|Institute for Medical Informatics and Biometry, University of Rostock, Rembrandt-Str, 16/17, 18055 Rostock, Germany. thomas.karopka@uni-rostock.de', 'Germany|Germany|Germany|Germany', 20, 'Autoimmune Diseases|Databases, Factual|Databases, Genetic|Evaluation Studies as Topic|Humans|Internet|MEDLINE|Medical Subject Headings|Systems Integration|User-Computer Interface|Vocabulary, Controlled', 'Comparative Study', '10.1186/1471-2105-7-325', '16803617', 'Autoimmune diseases are disorders caused by an immune response directed against the bodys own organs, tissues and cells. In practice more than 80 clinically distinct diseases, among them systemic lupus erythematosus and rheumatoid arthritis, are classified as autoimmune diseases. Although their etiology is unclear these diseases share certain similarities at the molecular level i.e. susceptibility regions on the chromosomes or the involvement of common genes. To gain an overview of these related diseases it is not feasible to do a literary review but it requires methods of automated analyses of the more than 500,000 Medline documents related to autoimmune disorders.'),
(90996, 2006, 'bmc', 'A maximum likelihood framework for protein design', 'Kleinman, Claudia L|Rodrigue, Nicolas|Bonnard, Cécile|Philippe, Hervé|Lartillot, Nicolas', 'Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. cl.kleinman@umontreal.ca|Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. cl.kleinman@umontreal.ca|Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. cl.kleinman@umontreal.ca|Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. cl.kleinman@umontreal.ca|Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. cl.kleinman@umontreal.ca', 'Canada|Canada|Canada|Canada|Canada', 25, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Drug Delivery Systems|Drug Design|Likelihood Functions|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Binding|Protein Engineering|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-326', '16808841', 'The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility.'),
(90997, 2006, 'bmc', 'Using ESTs to improve the accuracy of de novo gene prediction', 'Wei, Chaochun|Brent, Michael R', 'Laboratory for Computational Genomics and Department of Computer Science and Engineering, Washington University, One Brookings Drive, St, Louis, MO 63130, USA. wei@cse.wustl.edu|Laboratory for Computational Genomics and Department of Computer Science and Engineering, Washington University, One Brookings Drive, St, Louis, MO 63130, USA. wei@cse.wustl.edu', 'United States|United States', 32, 'Algorithms|Animals|Caenorhabditis elegans|Computational Biology|DNA, Complementary|Exons|Expressed Sequence Tags|Genome, Human|Genomics|Humans|Models, Genetic|Programming Languages|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-7-327', '16817966', 'ESTs are a tremendous resource for determining the exon-intron structures of genes, but even extensive EST sequencing tends to leave many exons and genes untouched. Gene prediction systems based exclusively on EST alignments miss these exons and genes, leading to poor sensitivity. De novo gene prediction systems, which ignore ESTs in favor of genomic sequence, can predict such untouched exons, but they are less accurate when predicting exons to which ESTs align. TWINSCAN is the most accurate de novo gene finder available for nematodes and N-SCAN is the most accurate for mammals, as measured by exact CDS gene prediction and exact exon prediction.'),
(90998, 2006, 'bmc', 'Computational expression deconvolution in a complex mammalian organ', 'Wang, Min|Master, Stephen R|Chodosh, Lewis A', 'Departments of Cancer Biology, Medicine, and Cell & Developmental Biology, and the Abramson Family Cancer Research Institute, University of Pennsylvania, 612 BRB II/III, 421 Curie Blvd, Philadelphia, PA 19104, USA. minwang@mail.med.upenn.edu|Departments of Cancer Biology, Medicine, and Cell & Developmental Biology, and the Abramson Family Cancer Research Institute, University of Pennsylvania, 612 BRB II/III, 421 Curie Blvd, Philadelphia, PA 19104, USA. minwang@mail.med.upenn.edu|Departments of Cancer Biology, Medicine, and Cell & Developmental Biology, and the Abramson Family Cancer Research Institute, University of Pennsylvania, 612 BRB II/III, 421 Curie Blvd, Philadelphia, PA 19104, USA. minwang@mail.med.upenn.edu', 'United States|United States|United States', 55, 'Adipose Tissue, Brown|Algorithms|Animals|Cell Line|Computational Biology|Doxycycline|Female|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Developmental|Immunohistochemistry|Keratins|Male|Mammary Glands, Animal|Mice|Mice, Transgenic|Oligonucleotide Array Sequence Analysis|Pregnancy|Tubulin|ras Proteins', 'Journal Article', '10.1186/1471-2105-7-328', '16817968', 'Microarray expression profiling has been widely used to identify differentially expressed genes in complex cellular systems. However, while such methods can be used to directly infer intracellular regulation within homogeneous cell populations, interpretation of in vivo gene expression data derived from complex organs composed of multiple cell types is more problematic. Specifically, observed changes in gene expression may be due either to changes in gene regulation within a given cell type or to changes in the relative abundance of expressing cell types. Consequently, bona fide changes in intrinsic gene regulation may be either mimicked or masked by changes in the relative proportion of different cell types. To date, few analytical approaches have addressed this problem.'),
(90999, 2006, 'bmc', 'An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices', 'Bulka, Blazej|desJardins, Marie|Freeland, Stephen J', 'Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. bulka1@umbc.edu|Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. bulka1@umbc.edu|Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. bulka1@umbc.edu', 'United States|United States|United States', 19, 'Amino Acid Sequence|Amino Acid Substitution|Amino Acids|Computational Biology|Databases, Protein|Hydrophobic and Hydrophilic Interactions|Internet|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-329', '16817972', 'Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors.'),
(91000, 2006, 'bmc', 'Genome-wide prediction of transcriptional regulatory elements of human promoters using gene expression and promoter analysis data', 'Kim, Seon-Young|Kim, YongSung', 'Human Genomics Laboratory, Genome Research Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoeun-dong, Yuseong-gu, Daejeon 305-333, Korea. kimsy@kribb.re.kr|Human Genomics Laboratory, Genome Research Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoeun-dong, Yuseong-gu, Daejeon 305-333, Korea. kimsy@kribb.re.kr', '-|-', 24, 'Algorithms|Binding Sites|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Humans|Oligonucleotide Array Sequence Analysis|Promoter Regions, Genetic|RNA, Messenger|Regulatory Elements, Transcriptional|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-7-330', '16817975', 'A complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast.'),
(91001, 2006, 'bmc', 'PROMPT: a protein mapping and comparison tool', 'Schmidt, Thorsten|Frishman, Dmitrij', 'Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. t.schmidt@wzw.tum.de|Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. t.schmidt@wzw.tum.de', 'Germany|Germany', 23, 'Computational Biology|Computer Graphics|Databases, Protein|Information Storage and Retrieval|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-7-331', '16817977', 'Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available.'),
(91002, 2006, 'bmc', 'A joint model of regulatory and metabolic networks', 'Yeang, Chen-Hsiang|Vingron, Martin', 'Center for Biomolecular Science & Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. chyeang@soe.ucsc.edu|Center for Biomolecular Science & Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. chyeang@soe.ucsc.edu', 'United States|United States', 33, 'Cell Physiological Phenomena|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Multienzyme Complexes|Proteome|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-7-332', '16820044', 'Gene regulation and metabolic reactions are two primary activities of life. Although many works have been dedicated to study each system, the coupling between them is less well understood. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions.'),
(91003, 2006, 'bmc', 'A methodology for global validation of microarray experiments', 'Miron, Mathieu|Woody, Owen Z|Marcil, Alexandre|Murie, Carl|Sladek, Robert|Nadon, Robert', 'McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, H3A 1A4, Canada. mathieu.miron@mail.mcgill.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, '3T3-L1 Cells|Animals|Cluster Analysis|Computer Simulation|DNA Primers|Data Interpretation, Statistical|Gene Expression Profiling|Mice|Models, Statistical|Oligonucleotide Array Sequence Analysis|Regression Analysis|Reproducibility of Results|Research Design|Sample Size|Software', 'Journal Article', '10.1186/1471-2105-7-333', '16822306', 'DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies.'),
(91004, 2006, 'bmc', 'Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues', 'Xu, Hua|Markatou, Marianthi|Dimova, Rositsa|Liu, Hongfang|Friedman, Carol', 'Department of Biomedical Informatics, Columbia University, 622 168th St, New York City, New York, USA. hua.xu@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 168th St, New York City, New York, USA. hua.xu@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 168th St, New York City, New York, USA. hua.xu@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 168th St, New York City, New York, USA. hua.xu@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, 622 168th St, New York City, New York, USA. hua.xu@dbmi.columbia.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Data Interpretation, Statistical|Humans|Models, Statistical|Natural Language Processing|Numerical Analysis, Computer-Assisted|Pattern Recognition, Automated|Terminology as Topic|Unified Medical Language System|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-7-334', '16822321', 'Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information retrieval systems because ambiguous words negatively impact accurate access to literature containing biomolecular entities, such as genes, proteins, cells, diseases, and other important entities. Automated techniques have been developed that address the WSD problem for a number of text processing situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the effectiveness and generalizability of the methods because these factors interact with each other and affect the final results. Thus, there is a need to explicitly address the factors and to systematically quantify their effects on performance.'),
(91005, 2006, 'bmc', 'AMDA: an R package for the automated microarray data analysis', 'Pelizzola, Mattia|Pavelka, Norman|Foti, Maria|Ricciardi-Castagnoli, Paola', 'Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. mattia.pelizzola@unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. mattia.pelizzola@unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. mattia.pelizzola@unimib.it|Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. mattia.pelizzola@unimib.it', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Evolution, Molecular|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-7-335', '16824223', 'Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users.'),
(91006, 2006, 'bmc', 'ProtRepeatsDB: a database of amino acid repeats in genomes', 'Kalita, Mridul K|Ramasamy, Gowthaman|Duraisamy, Sekhar|Chauhan, Virander S|Gupta, Dinesh', 'Structural and Computational Biology Group, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India. mridulkk@icgeb.res.in|Structural and Computational Biology Group, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India. mridulkk@icgeb.res.in|Structural and Computational Biology Group, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India. mridulkk@icgeb.res.in|Structural and Computational Biology Group, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India. mridulkk@icgeb.res.in|Structural and Computational Biology Group, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India. mridulkk@icgeb.res.in', 'India|India|India|India|India', 0, 'Amino Acid Sequence|Animals|Chromosome Mapping|Database Management Systems|Databases, Protein|Humans|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Repetitive Sequences, Amino Acid|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-7-336', '16827924', 'Genome wide and cross species comparisons of amino acid repeats is an intriguing problem in biology mainly due to the highly polymorphic nature and diverse functions of amino acid repeats. Innate protein repeats constitute vital functional and structural regions in proteins. Repeats are of great consequence in evolution of proteins, as evident from analysis of repeats in different organisms. In the post genomic era, availability of protein sequences encoded in different genomes provides a unique opportunity to perform large scale comparative studies of amino acid repeats. ProtRepeatsDB http://bioinfo.icgeb.res.in/repeats/ is a relational database of perfect and mismatch repeats, access to which is designed as a resource and collection of tools for detection and cross species comparisons of different types of amino acid repeats.'),
(91007, 2006, 'bmc', 'HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics', 'Verhaak, Roel G W|Sanders, Mathijs A|Bijl, Maarten A|Delwel, Ruud|Horsman, Sebastiaan|Moorhouse, Michael J|van der Spek, Peter J|Löwenberg, Bob|Valk, Peter J M', 'Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl|Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Neth', 21, 'Antigens, CD34|Cluster Analysis|Data Interpretation, Statistical|Gene Expression Profiling|Genotype|Humans|Karyotyping|Leukemia, Myeloid|Nuclear Proteins|Oligonucleotide Array Sequence Analysis|Phenotype|Software', 'Journal Article', '10.1186/1471-2105-7-337', '16836741', 'Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata.'),
(91008, 2006, 'bmc', 'Cluster analysis of protein array results via similarity of Gene Ontology annotation', 'Wolting, Cheryl|McGlade, C Jane|Tritchler, David', 'Department of Medical Biophysics, University of Toronto, Toronto, Canada. cheryl.wolting@utoronto.ca|Department of Medical Biophysics, University of Toronto, Toronto, Canada. cheryl.wolting@utoronto.ca|Department of Medical Biophysics, University of Toronto, Toronto, Canada. cheryl.wolting@utoronto.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Animals|Cell Proliferation|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Fungal Proteins|Oligonucleotide Array Sequence Analysis|Open Reading Frames|Protein Array Analysis|Protein Binding|Proteomics|Sequence Analysis, Protein|Sirolimus', 'Journal Article', '10.1186/1471-2105-7-338', '16836750', 'With the advent of high-throughput proteomic experiments such as arrays of purified proteins comes the need to analyse sets of proteins as an ensemble, as opposed to the traditional one-protein-at-a-time approach. Although there are several publicly available tools that facilitate the analysis of protein sets, they do not display integrated results in an easily-interpreted image or do not allow the user to specify the proteins to be analysed.'),
(91009, 2006, 'bmc', 'Tools for integrated sequence-structure analysis with UCSF Chimera', 'Meng, Elaine C|Pettersen, Eric F|Couch, Gregory S|Huang, Conrad C|Ferrin, Thomas E', 'Computer Graphics Laboratory, University of California San Francisco, 600 16th Street, San Francisco, CA 94143-2440, USA. meng@cgl.ucsf.edu|Computer Graphics Laboratory, University of California San Francisco, 600 16th Street, San Francisco, CA 94143-2440, USA. meng@cgl.ucsf.edu|Computer Graphics Laboratory, University of California San Francisco, 600 16th Street, San Francisco, CA 94143-2440, USA. meng@cgl.ucsf.edu|Computer Graphics Laboratory, University of California San Francisco, 600 16th Street, San Francisco, CA 94143-2440, USA. meng@cgl.ucsf.edu|Computer Graphics Laboratory, University of California San Francisco, 600 16th Street, San Francisco, CA 94143-2440, USA. meng@cgl.ucsf.edu', 'United States|United States|United States|United States|United States', 283, 'Computer Graphics|Computer Simulation|Database Management Systems|Databases, Protein|Models, Chemical|Models, Molecular|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1186/1471-2105-7-339', '16836757', 'Comparing related structures and viewing the structures in the context of sequence alignments are important tasks in protein structure-function research. While many programs exist for individual aspects of such work, there is a need for interactive visualization tools that: (a) provide a deep integration of sequence and structure, far beyond mapping where a sequence region falls in the structure and vice versa, (b) facilitate changing data of one type based on the other (for example, using only sequence-conserved residues to match structures, or adjusting a sequence alignment based on spatial fit), (c) can be used with a researchers own data, including arbitrary sequence alignments and annotations, closely or distantly related sets of proteins, etc., and (d) interoperate with each other and with a full complement of molecular graphics features. We describe enhancements to UCSF Chimera to achieve these goals.'),
(91010, 2006, 'bmc', 'Quaternary structure predictions of transmembrane proteins starting from the monomer: a docking-based approach', 'Casciari, D|Seeber, M|Fanelli, F', 'Department of Chemistry, Dulbecco Telethon Institute, University of Modena e Reggio Emilia, Via Campi 183, 41100 Modena, Italy. casciari.daniele@unimo.it|Department of Chemistry, Dulbecco Telethon Institute, University of Modena e Reggio Emilia, Via Campi 183, 41100 Modena, Italy. casciari.daniele@unimo.it|Department of Chemistry, Dulbecco Telethon Institute, University of Modena e Reggio Emilia, Via Campi 183, 41100 Modena, Italy. casciari.daniele@unimo.it', 'Italy|Italy|Italy', 0, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Dimerization|Membrane Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Multiprotein Complexes|Protein Binding|Protein Structure, Quaternary|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-340', '16836758', 'We introduce a computational protocol for effective predictions of the supramolecular organization of integral transmembrane proteins, starting from the monomer. Despite the demonstrated constitutive and functional importance of supramolecular assemblies of transmembrane subunits or proteins, effective tools for structure predictions of such assemblies are still lacking. Our computational approach consists in rigid-body docking samplings, starting from the docking of two identical copies of a given monomer. Each docking run is followed by membrane topology filtering and cluster analysis. Prediction of the native oligomer is therefore accomplished by a number of progressive growing steps, each made of one docking run, filtering and cluster analysis. With this approach, knowledge about the oligomerization status of the protein is required neither for improving sampling nor for the filtering step. Furthermore, there are no size-limitations in the systems under study, which are not limited to the transmembrane domains but include also the water-soluble portions.'),
(91011, 2006, 'bmc', 'Curation of complex, context-dependent immunological data', 'Vita, Randi|Vaughan, Kerrie|Zarebski, Laura|Salimi, Nima|Fleri, Ward|Grey, Howard|Sathiamurthy, Muthu|Mokili, John|Bui, Huynh-Hoa|Bourne, Philip E|Ponomarenko, Julia|de Castro, Romulo|Chan, Russell K|Sidney, John|Wilson, Stephen S|Stewart, Scott|Way, Scott|Peters, Bjoern|Sette, Alessandro', 'La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, California, USA. rvita@liai.org', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 29, 'Allergy and Immunology|Animals|Artificial Intelligence|Computational Biology|Database Management Systems|Databases, Factual|Databases, Protein|Epitopes|Humans|Immune System|Information Storage and Retrieval|Models, Statistical|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-7-341', '16836764', 'The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org.'),
(91012, 2006, 'bmc', 'EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences', 'Hu, Jianjun|Yang, Yifeng D|Kihara, Daisuke', 'Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA. hu5@purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA. hu5@purdue.edu|Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA. hu5@purdue.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Cluster Analysis|Computational Biology|DNA|Escherichia coli|Gene Expression Profiling|Pattern Recognition, Automated|Phylogeny|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-342', '16839417', 'Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms.'),
(91013, 2006, 'bmc', 'Multivariate curve resolution of time course microarray data', 'Wentzell, Peter D|Karakach, Tobias K|Roy, Sushmita|Martinez, M Juanita|Allen, Christopher P|Werner-Washburne, Margaret', 'Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca|Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca|Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca|Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca|Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca|Department of Chemistry, Dalhousie University, Halifax, NS B3H 4J3, Canada. peter.wentzell@dal.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 78, 'Algorithms|Cell Cycle|Computational Biology|Data Interpretation, Statistical|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Saccharomyces cerevisiae|Time Factors', 'Journal Article', '10.1186/1471-2105-7-343', '16839419', 'Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.'),
(91014, 2006, 'bmc', 'Optimised amino acid specific weighting factors for unbound protein docking', 'Heuser, Philipp|Schomburg, Dietmar', 'Cologne University Bioinformatics Center, University of Cologne, Zuelpicher Str, 47, 50674 Koeln, Germany. philipp.heuser@uni-koeln.de|Cologne University Bioinformatics Center, University of Cologne, Zuelpicher Str, 47, 50674 Koeln, Germany. philipp.heuser@uni-koeln.de', 'Germany|Germany', 0, 'Algorithms|Amino Acids|Animals|Computational Biology|Computer Simulation|Databases, Protein|Humans|Models, Molecular|Models, Statistical|Protein Binding|Protein Folding|Proteins|Reproducibility of Results|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-7-344', '16842615', 'One of the most challenging aspects of protein-protein docking is the inclusion of flexibility into the docking procedure. We developed a postfilter where the grid-representation of proteins for docking is extended by an optimised weighting factor for each amino acid.'),
(91015, 2006, 'bmc', 'Individualized markers optimize class prediction of microarray data', 'Pavlidis, Pavlos|Poirazi, Panayiota', 'Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Vassilika Vouton PO Box 1385, GR-71110, Heraklion, Crete, Greece. pavlidis@egeen.ee|Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas (FORTH), Vassilika Vouton PO Box 1385, GR-71110, Heraklion, Crete, Greece. pavlidis@egeen.ee', 'Greece|Greece', 5, 'Biomarkers|Biomarkers, Tumor|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Gene Expression Regulation|Humans|Models, Biological|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype', 'Journal Article', '10.1186/1471-2105-7-345', '16842618', 'Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker--selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample.'),
(91016, 2006, 'bmc', 'Structure alignment based on coding of local geometric measures', 'Chang, Peter L|Rinne, Andrew W|Dewey, T Gregory', 'Keck Graduate Institute of Applied Life Sciences, Claremont, CA, USA. peter_chang@kgi.edu|Keck Graduate Institute of Applied Life Sciences, Claremont, CA, USA. peter_chang@kgi.edu|Keck Graduate Institute of Applied Life Sciences, Claremont, CA, USA. peter_chang@kgi.edu', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Databases, Protein|Models, Statistical|Sequence Alignment|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-7-346', '16842622', 'A structure alignment method based on a local geometric property is presented and its performance is tested in pairwise and multiple structure alignments. In this approach, the writhing number, a quantity originating from integral formulas of Vassiliev knot invariants, is used as a local geometric measure. This measure is used in a sliding window to calculate the local writhe down the length of the protein chain. By encoding the distribution of writhing numbers across all the structures in the protein databank (PDB), protein geometries are represented in a 20-letter alphabet. This encoding transforms the structure alignment problem into a sequence alignment problem and allows the well-established algorithms of sequence alignment to be employed. Such geometric alignments offer distinct advantages over structural alignments in Cartesian coordinates as it better handles structural subtleties associated with slight twists and bends that distort one structure relative to another.');
