INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90253, 2004, 'bmc', 'SS-Wrapper: a package of wrapper applications for similarity searches on Linux clusters', 'Wang, Chunlin|Lefkowitz, Elliot J', 'Department of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama 35294-2170, USA. wangcl@uab.edu <wangcl@uab.edu>|Department of Microbiology, University of Alabama at Birmingham, Birmingham, Alabama 35294-2170, USA. wangcl@uab.edu <wangcl@uab.edu>', 'United States|United States', 7, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Computer Graphics|Computers|Computing Methodologies|Databases, Factual|Databases, Genetic|Databases, Protein|Information Storage and Retrieval|Programming Languages|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-5-171', '15511296', 'Large-scale sequence comparison is a powerful tool for biological inference in modern molecular biology. Comparing new sequences to those in annotated databases is a useful source of functional and structural information about these sequences. Using software such as the basic local alignment search tool (BLAST) or HMMPFAM to identify statistically significant matches between newly sequenced segments of genetic material and those in databases is an important task for most molecular biologists. Searching algorithms are intrinsically slow and data-intensive, especially in light of the rapid growth of biological sequence databases due to the emergence of high throughput DNA sequencing techniques. Thus, traditional bioinformatics tools are impractical on PCs and even on dedicated UNIX servers. To take advantage of larger databases and more reliable methods, high performance computation becomes necessary.'),
(90254, 2004, 'bmc', 'd-matrix - database exploration, visualization and analysis', 'Seelow, Dominik|Galli, Raffaello|Mebus, Siegrun|Sperling, Hans-Peter|Lehrach, Hans|Sperling, Silke', 'Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>|Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>|Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>|Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>|Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>|Vertebrate Genomics, Max-Planck-Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. dominik.seelow@web.de <dominik.seelow@web.de>', 'Germany|Germany|Germany|Germany|Germany|Germany', 3, 'Cardiovascular Diseases|Computer Graphics|Database Management Systems|Databases, Genetic|Humans|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-5-168', '15511298', 'Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis.'),
(90255, 2004, 'bmc', 'The InDeVal insertion/deletion evaluation tool: a program for finding target regions in DNA sequences and for aiding in sequence comparison', 'Stoneberg Holt, Sierra D|Holt, Jason A', 'Department of Botany, Masaryk University, Brno, Czech Republic. sierra@elanor.sci.muni.cz|Department of Botany, Masaryk University, Brno, Czech Republic. sierra@elanor.sci.muni.cz', '-|-', 0, 'Codon|Conserved Sequence|DNA Mutational Analysis|DNA, Plant|Mutagenesis, Insertional|Poaceae|Sequence Alignment|Sequence Deletion|Software', 'Comparative Study', '10.1186/1471-2105-5-173', '15516260', 'The program InDeVal was originally developed to help researchers find known regions of insertion/deletion activity (with the exception of isolated single-base indels) in newly determined Poaceae trnL-F sequences and compare them with 533 previously determined sequences. It is supplied with input files designed for this purpose. More broadly, the program is applicable for finding specific target regions (referred to as variable regions) in DNA sequence. A variable region is any specific sequence fragment of interest, such as an indel region, a codon or codons, or sequence coding for a particular RNA secondary structure.'),
(90256, 2004, 'bmc', 'The Molecular Pages of the mesotelencephalic dopamine consortium (DopaNet)', 'Le Novère, Nicolas|Donizelli, Marco', 'Computational Neurobiology, EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton Cambridge, CB10 1SD UK. lenov@ebi.ac.uk|Computational Neurobiology, EMBL-EBI, Wellcome-Trust Genome Campus, Hinxton Cambridge, CB10 1SD UK. lenov@ebi.ac.uk', 'United Kingdom|United Kingdom', 4, 'Animals|Cooperative Behavior|Databases, Bibliographic|Databases, Genetic|Dopamine|Humans|Internet|Ligands|Models, Genetic|Neurons|Protein Conformation|Protein Transport|Rats|Receptors, Nicotinic|Software|Systems Biology|Telencephalon', 'Journal Article', '10.1186/1471-2105-5-174', '15518589', 'DopaNet http://www.dopanet.org is a Systems Biology initiative that aims to investigate precisely and quantitatively all the aspects of neurotransmission in a specific neuronal system, the mesotelencephalic dopamine system. The project should lead to large-scale models of molecular and cellular processes involved in neuronal signaling. A prerequisite is the proper storage of knowledge coming from the literature.'),
(90257, 2004, 'bmc', 'Computation of elementary modes: a unifying framework and the new binary approach', 'Gagneur, Julien|Klamt, Steffen', 'Cellzome AG, Meyerhofstr. 1, 69117 Heidelberg, Germany. julien.gagneur@cellzome.com <julien.gagneur@cellzome.com>|Cellzome AG, Meyerhofstr. 1, 69117 Heidelberg, Germany. julien.gagneur@cellzome.com <julien.gagneur@cellzome.com>', 'Germany|Germany', 163, 'Algorithms|Cell Physiological Phenomena|Computational Biology|Computer Simulation|Linear Models|Metabolism|Models, Biological|Models, Theoretical|Signal Transduction|Software|Software Design|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-5-175', '15527509', 'Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods.'),
(90258, 2004, 'bmc', 'ESTIMA, a tool for EST management in a multi-project environment', 'Kumar, Charu G|LeDuc, Richard|Gong, George|Roinishivili, Levan|Lewin, Harris A|Liu, Lei', 'Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>|Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>|Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>|Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>|Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>|Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. cgkumar@uiuc.edu <cgkumar@uiuc.edu>', 'United States|United States|United States|United States|United States|United States', 0, 'Animals|Bees|Catfishes|Cattle|Databases, Genetic|Expressed Sequence Tags|Genes, Plant|Honey|Internet|Malus|Software|Songbirds', 'Journal Article', '10.1186/1471-2105-5-176', '15527510', 'Single-pass, partial sequencing of complementary DNA (cDNA) libraries generates thousands of chromatograms that are processed into high quality expressed sequence tags (ESTs), and then assembled into contigs representative of putative genes. Usually, to be of value, ESTs and contigs must be associated with meaningful annotations, and made available to end-users.'),
(90259, 2004, 'bmc', 'Calibration and assessment of channel-specific biases in microarray data with extended dynamical range', 'Bengtsson, Henrik|Jönsson, Göran|Vallon-Christersson, Johan', '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|Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden. hb@maths.lth.se', 'Sweden|Sweden|Sweden', 37, 'Calibration|Carbocyanines|DNA|Image Interpretation, Computer-Assisted|Models, Statistical|Nucleic Acid Hybridization|Observer Variation|Oligonucleotide Array Sequence Analysis|RNA', 'Comparative Study', '10.1186/1471-2105-5-177', '15541170', 'Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software.'),
(90260, 2004, 'bmc', 'GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes', 'Martin, David M A|Berriman, Matthew|Barton, Geoffrey J', 'Post-Genomics and Molecular Interactions Centre, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. d.m.a.martin@dundee.ac.uk|Post-Genomics and Molecular Interactions Centre, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. d.m.a.martin@dundee.ac.uk|Post-Genomics and Molecular Interactions Centre, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK. d.m.a.martin@dundee.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 184, 'Animals|Arabidopsis|Caenorhabditis elegans|Drosophila melanogaster|Genome|Genome, Fungal|Genome, Human|Genome, Plant|Genome, Protozoan|Humans|Plasmodium falciparum|Predictive Value of Tests|Proteins|Saccharomyces cerevisiae|Software', 'Journal Article', '10.1186/1471-2105-5-178', '15550167', 'The function of a novel gene product is typically predicted by transitive assignment of annotation from similar sequences. We describe a novel method, GOtcha, for predicting gene product function by annotation with Gene Ontology (GO) terms. GOtcha predicts GO term associations with term-specific probability (P-score) measures of confidence. Term-specific probabilities are a novel feature of GOtcha and allow the identification of conflicts or uncertainty in annotation.'),
(90261, 2004, 'bmc', 'CLOE: identification of putative functional relationships among genes by comparison of expression profiles between two species', 'Pellegrino, Maurizio|Provero, Paolo|Silengo, Lorenzo|Di Cunto, Ferdinando', 'Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy. pellegrino@lycos.com <pellegrino@lycos.com>|Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy. pellegrino@lycos.com <pellegrino@lycos.com>|Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy. pellegrino@lycos.com <pellegrino@lycos.com>|Department of Genetics, Biology and Biochemistry, Via Santena 5 bis, 10126, Torino, Italy. pellegrino@lycos.com <pellegrino@lycos.com>', 'Italy|Italy|Italy|Italy', 21, 'Animals|Computational Biology|Conserved Sequence|DNA Probes|DNA, Complementary|Evolution, Molecular|Expressed Sequence Tags|Gene Expression Profiling|Gene Expression Regulation|Humans|Mice|Predictive Value of Tests|Sequence Homology, Nucleic Acid|Software', 'Comparative Study', '10.1186/1471-2105-5-179', '15550177', 'Public repositories of microarray data contain an incredible amount of information that is potentially relevant to explore functional relationships among genes by meta-analysis of expression profiles. However, the widespread use of this resource by the scientific community is at the moment limited by the limited availability of effective tools of analysis. We here describe CLOE, a simple cDNA microarray data mining strategy based on meta-analysis of datasets from pairs of species. The method consists in ranking EST probes in the datasets of the two species according to the similarity of their expression profiles with that of two EST probes from orthologous genes, and extracting orthologous EST pairs from a given top interval of the ranked lists. The Gene Ontology annotation of the obtained candidate partners is then analyzed for keywords overrepresentation.'),
(90262, 2004, 'bmc', 'Performance of a genetic algorithm for mass spectrometry proteomics', 'Jeffries, Neal O', 'National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA. neal.jeffries@nih.gov', 'United States', 36, 'Algorithms|Databases, Protein|Genetic Techniques|Mass Spectrometry|Models, Genetic|Proteomics', 'Evaluation Studies', '10.1186/1471-2105-5-180', '15555060', 'Recently, mass spectrometry data have been mined using a genetic algorithm to produce discriminatory models that distinguish healthy individuals from those with cancer. This algorithm is the basis for claims of 100% sensitivity and specificity in two related publicly available datasets. To date, no detailed attempts have been made to explore the properties of this genetic algorithm within proteomic applications. Here the algorithms performance on these datasets is evaluated relative to other methods.'),
(90263, 2004, 'bmc', 'Discriminative topological features reveal biological network mechanisms', 'Middendorf, Manuel|Ziv, Etay|Adams, Carter|Hom, Jen|Koytcheff, Robin|Levovitz, Chaya|Woods, Gregory|Chen, Linda|Wiggins, Chris', 'Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu|Department of Physics, Columbia University, New York, USA. mjm2007@columbia.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Animals|Caenorhabditis elegans|Computational Biology|Escherichia coli K12|Models, Biological|Models, Genetic|Models, Neurological|Nerve Net|Neural Networks (Computer)|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-5-181', '15555081', 'Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest, moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them.'),
(90264, 2004, 'bmc', 'A new mixture model approach to analyzing allelic-loss data using Bayes factors', 'Desai, Manisha|Emond, Mary J', 'Department of Biostatistics, Columbia University, 722 West 168th Street, R629, New York, NY 10032, USA. manisha.desai@columbia.edu|Department of Biostatistics, Columbia University, 722 West 168th Street, R629, New York, NY 10032, USA. manisha.desai@columbia.edu', 'United States|United States', 2, 'Adenocarcinoma|Bayes Theorem|Chromosomes, Human|Databases, Genetic|Esophageal Neoplasms|Genes, Neoplasm|Genes, Tumor Suppressor|Humans|Loss of Heterozygosity|Models, Genetic', 'Journal Article', '10.1186/1471-2105-5-182', '15563371', 'Allelic-loss studies record data on the loss of genetic material in tumor tissue relative to normal tissue at various loci along the genome. As the deletion of a tumor suppressor gene can lead to tumor development, one objective of these studies is to determine which, if any, chromosome arms harbor tumor suppressor genes.'),
(90265, 2004, 'bmc', 'Protein family comparison using statistical models and predicted structural information', 'Chung, Richard|Yona, Golan', 'Department of Computer Science, Cornell University, Ithaca, NY 14850, USA. rc238@cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14850, USA. rc238@cornell.edu', 'United States|United States', 12, 'Amino Acid Sequence|Models, Statistical|Predictive Value of Tests|Protein Structure, Secondary|Proteins|Sequence Homology, Amino Acid', 'Comparative Study', '10.1186/1471-2105-5-183', '15563734', 'This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.'),
(90266, 2004, 'bmc', 'Integrating partonomic hierarchies in anatomy ontologies', 'Burger, Albert|Davidson, Duncan|Yang, Yiya|Baldock, Richard', 'MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. Albert.Burger@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. Albert.Burger@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. Albert.Burger@hgu.mrc.ac.uk|MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. Albert.Burger@hgu.mrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 5, 'Anatomy|Animals|Computational Biology|Databases, Genetic|Embryo, Mammalian|Embryonic Development|Gene Expression Regulation, Developmental|Mesoderm|Mice|Models, Anatomic|Software|Software Design', 'Evaluation Studies', '10.1186/1471-2105-5-184', '15566564', 'Anatomy ontologies play an increasingly important role in developing integrated bioinformatics applications. One of the primary relationships between anatomical tissues represented in such ontologies is part-of. As there are a number of ways to divide up the anatomical structure of an organism, each may be represented by more than one valid partonomic (part-of) hierarchy. This raises the issue of how to represent and integrate multiple such hierarchies.'),
(90267, 2004, 'bmc', 'Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer', 'Aris, Virginie M|Cody, Michael J|Cheng, Jeff|Dermody, James J|Soteropoulos, Patricia|Recce, Michael|Tolias, Peter P', 'Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>|Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA. arisvm@umdnj.edu <arisvm@umdnj.edu>', 'United States|United States|United States|United States|United States|United States|United States', 24, 'Algorithms|Bias|Biomarkers, Tumor|Breast Neoplasms|False Positive Reactions|Female|Gene Expression Profiling|Humans|Lung Neoplasms|Male|Mouth Neoplasms|Neoplasms|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Prostatic Neoplasms|Software|Statistics, Nonparametric', 'Comparative Study', '10.1186/1471-2105-5-185', '15569388', 'A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups.'),
(90268, 2004, 'bmc', 'prot4EST: translating expressed sequence tags from neglected genomes', 'Wasmuth, James D|Blaxter, Mark L', 'Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3JT, UK. james.wasmuth@ed.ac.uk|Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, EH9 3JT, UK. james.wasmuth@ed.ac.uk', 'United Kingdom|United Kingdom', 107, 'Animals|Arabidopsis|Artificial Intelligence|Base Composition|Benchmarking|Caenorhabditis elegans|Codon|DNA, Helminth|DNA, Plant|Databases, Genetic|Expressed Sequence Tags|Genome|Genome, Bacterial|Genome, Plant|Humans|Protein Biosynthesis|Software|Software Design|Spirurida', 'Journal Article', '10.1186/1471-2105-5-187', '15571632', 'The genomes of an increasing number of species are being investigated through generation of expressed sequence tags (ESTs). However, ESTs are prone to sequencing errors and typically define incomplete transcripts, making downstream annotation difficult. Annotation would be greatly improved with robust polypeptide translations. Many current solutions for EST translation require a large number of full-length gene sequences for training purposes, a resource that is not available for the majority of EST projects.'),
(90269, 2004, 'bmc', 'Constraint Logic Programming approach to protein structure prediction', 'Dal Palù, Alessandro|Dovier, Agostino|Fogolari, Federico', 'Dipartimento di Matematica e Informatica, Università di Udine, Via delle Scienze 206, 33100 Udine, Italy. dalpalu@dimi.uniud.it <dalpalu@dimi.uniud.it>|Dipartimento di Matematica e Informatica, Università di Udine, Via delle Scienze 206, 33100 Udine, Italy. dalpalu@dimi.uniud.it <dalpalu@dimi.uniud.it>|Dipartimento di Matematica e Informatica, Università di Udine, Via delle Scienze 206, 33100 Udine, Italy. dalpalu@dimi.uniud.it <dalpalu@dimi.uniud.it>', 'Italy|Italy|Italy', 50, 'Computational Biology|Computer Simulation|Models, Molecular|Predictive Value of Tests|Protein Structure, Secondary|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-5-186', '15571634', 'The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.'),
(90270, 2004, 'bmc', 'Haplotype frequency estimation error analysis in the presence of missing genotype data', 'Kelly, Enda D|Sievers, Fabian|McManus, Ross', 'Hitachi Dublin Lab,, Hitachi Europe Ltd., OReilly Institute, Trinity College, Dublin 2, Ireland. enda.kelly@hitachi-eu.com|Hitachi Dublin Lab,, Hitachi Europe Ltd., OReilly Institute, Trinity College, Dublin 2, Ireland. enda.kelly@hitachi-eu.com|Hitachi Dublin Lab,, Hitachi Europe Ltd., OReilly Institute, Trinity College, Dublin 2, Ireland. enda.kelly@hitachi-eu.com', 'Ireland|Ireland|Ireland', 9, 'Alleles|Bias|Cystic Fibrosis|Cystic Fibrosis Transmembrane Conductance Regulator|Gene Frequency|Genetic Markers|Genetics, Population|Genotype|Haplotypes|Humans|Polymorphism, Single Nucleotide|Sample Size|Software', 'Journal Article', '10.1186/1471-2105-5-188', '15574202', 'Increasingly researchers are turning to the use of haplotype analysis as a tool in population studies, the investigation of linkage disequilibrium, and candidate gene analysis. When the phase of the data is unknown, computational methods, in particular those employing the Expectation-Maximisation (EM) algorithm, are frequently used for estimating the phase and frequency of the underlying haplotypes. These methods have proved very successful, predicting the phase-known frequencies from data for which the phase is unknown with a high degree of accuracy. Recently there has been much speculation as to the effect of unknown, or missing allelic data - a common phenomenon even with modern automated DNA analysis techniques - on the performance of EM-based methods. To this end an EM-based program, modified to accommodate missing data, has been developed, incorporating non-parametric bootstrapping for the calculation of accurate confidence intervals.'),
(90271, 2004, 'bmc', 'DEDB: a database of Drosophila melanogaster exons in splicing graph form', 'Lee, Bernett T K|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Biochemistry, National University of Singapore, Singapore. bernett@bic.nus.edu.sg <bernett@bic.nus.edu.sg>|Department of Biochemistry, National University of Singapore, Singapore. bernett@bic.nus.edu.sg <bernett@bic.nus.edu.sg>|Department of Biochemistry, National University of Singapore, Singapore. bernett@bic.nus.edu.sg <bernett@bic.nus.edu.sg>', 'Singapore|Singapore|Singapore', 0, 'Alternative Splicing|Animals|Classification|Computer Graphics|Databases, Genetic|Drosophila melanogaster|Exons|Forecasting|Genome', 'Journal Article', '10.1186/1471-2105-5-189', '15581431', 'A wealth of quality genomic and mRNA/EST sequences in recent years has provided the data required for large-scale genome-wide analysis of alternative splicing. We have capitalized on this by constructing a database that contains alternative splicing information organized as splicing graphs, where all transcripts arising from a single gene are collected, organized and classified. The splicing graph then serves as the basis for the classification of the various types of alternative splicing events.'),
(90272, 2004, 'bmc', 'A two-way interface between limited Systems Biology Markup Language and R', 'Radivoyevitch, Tomas', 'Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106, USA. radivot@hal.cwru.edu', 'United States', 8, 'Language|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-5-190', '15585059', 'Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.'),
(90273, 2004, 'bmc', 'Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information', 'Chen, Yian A|McKillen, David J|Wu, Shuyuan|Jenny, Matthew J|Chapman, Robert|Gross, Paul S|Warr, Gregory W|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', 0, 'Animals|Cluster Analysis|Computational Biology|DNA, Complementary|Expressed Sequence Tags|Genetic Variation|Genome|Microarray Analysis|Penaeidae|Research Design', 'Journal Article', '10.1186/1471-2105-5-191', '15585062', 'Expression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes.'),
(90274, 2004, 'bmc', 'ABC: software for interactive browsing of genomic multiple sequence alignment data', 'Cooper, Gregory M|Singaravelu, Senthil A G|Sidow, Arend', 'Department of Genetics, Stanford University, Stanford, CA 94305-9010, USA. coopergm@stanford.edu <coopergm@stanford.edu>|Department of Genetics, Stanford University, Stanford, CA 94305-9010, USA. coopergm@stanford.edu <coopergm@stanford.edu>|Department of Genetics, Stanford University, Stanford, CA 94305-9010, USA. coopergm@stanford.edu <coopergm@stanford.edu>', 'United States|United States|United States', 7, 'Animals|Base Sequence|Cats|Cattle|DNA|Databases, Genetic|Dogs|Genome|Genome, Human|Humans|Mice|Molecular Sequence Data|Pan troglodytes|Papio|Phylogeny|Programming Languages|Rats|Sequence Alignment|Software|Swine', 'Journal Article', '10.1186/1471-2105-5-192', '15588288', 'Alignment and comparison of related genome sequences is a powerful method to identify regions likely to contain functional elements. Such analyses are data intensive, requiring the inclusion of genomic multiple sequence alignments, sequence annotations, and scores describing regional attributes of columns in the alignment. Visualization and browsing of results can be difficult, and there are currently limited software options for performing this task.'),
(90275, 2004, 'bmc', 'Optimized LOWESS normalization parameter selection for DNA microarray data', 'Berger, John A|Hautaniemi, Sampsa|Järvinen, Anna-Kaarina|Edgren, Henrik|Mitra, Sanjit K|Astola, Jaakko', 'Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu|Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu|Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu|Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu|Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu|Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Breast Neoplasms|Calibration|Cell Line, Tumor|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Normal Distribution|Oligonucleotide Array Sequence Analysis|Reverse Transcriptase Polymerase Chain Reaction', 'Comparative Study', '10.1186/1471-2105-5-194', '15588297', 'Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail.'),
(90276, 2004, 'bmc', 'Comparing functional annotation analyses with Catmap', 'Breslin, Thomas|Edén, Patrik|Krogh, Morten', 'Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden. thomas@thep.lu.se <thomas@thep.lu.se>|Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden. thomas@thep.lu.se <thomas@thep.lu.se>|Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden. thomas@thep.lu.se <thomas@thep.lu.se>', 'Sweden|Sweden|Sweden', 59, 'Classification|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Genes|Software', 'Comparative Study', '10.1186/1471-2105-5-193', '15588298', 'Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis.'),
(90277, 2004, 'bmc', 'GECKO: a complete large-scale gene expression analysis platform', 'Theilhaber, Joachim|Ulyanov, Anatoly|Malanthara, Anish|Cole, Jack|Xu, Dapeng|Nahf, Robert|Heuer, Michael|Brockel, Christoph|Bushnell, Steven', 'Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com|Cambridge Genomics Center, Sanofi-Aventis, 26 Landsdowne Street, Cambridge, MA 02139, USA. joachim.theilhaber@aventis.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 20, 'Carcinoma|Cell Line, Tumor|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Humans|Kidney Neoplasms|Oligonucleotide Array Sequence Analysis|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-5-195', '15588317', 'Gecko (Gene Expression: Computation and Knowledge Organization) is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community.'),
(90278, 2004, 'bmc', 'A functional hierarchical organization of the protein sequence space', 'Kaplan, Noam|Friedlich, Moriah|Fromer, Menachem|Linial, Michal', 'Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il|Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il|Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il|Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il', 'Israel|Israel|Israel|Israel', 27, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Automation|Cations|Cluster Analysis|Computational Biology|Databases, Protein|Information Storage and Retrieval|Numerical Analysis, Computer-Assisted|Pattern Recognition, Automated|Proteins|Proteomics|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-5-196', '15596019', 'It is a major challenge of computational biology to provide a comprehensive functional classification of all known proteins. Most existing methods seek recurrent patterns in known proteins based on manually-validated alignments of known protein families. Such methods can achieve high sensitivity, but are limited by the necessary manual labor. This makes our current view of the protein world incomplete and biased. This paper concerns ProtoNet, a automatic unsupervised global clustering system that generates a hierarchical tree of over 1,000,000 proteins, based solely on sequence similarity.'),
(90279, 2004, 'bmc', '4SCOPmap: automated assignment of protein structures to evolutionary superfamilies', 'Cheek, Sara|Qi, Yuan|Krishna, S Sri|Kinch, Lisa N|Grishin, Nick V', 'Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd,, Dallas, Texas 75390, USA. sara.cheek@utsouthwestern.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd,, Dallas, Texas 75390, USA. sara.cheek@utsouthwestern.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd,, Dallas, Texas 75390, USA. sara.cheek@utsouthwestern.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd,, Dallas, Texas 75390, USA. sara.cheek@utsouthwestern.edu|Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd,, Dallas, Texas 75390, USA. sara.cheek@utsouthwestern.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Evolution, Molecular|False Positive Reactions|Peptide Mapping|Peptides|Protein Folding|Protein Structure, Tertiary', 'Comparative Study', '10.1186/1471-2105-5-197', '15598351', 'Inference of remote homology between proteins is very challenging and remains a prerogative of an expert. Thus a significant drawback to the use of evolutionary-based protein structure classifications is the difficulty in assigning new proteins to unique positions in the classification scheme with automatic methods. To address this issue, we have developed an algorithm to map protein domains to an existing structural classification scheme and have applied it to the SCOP database.'),
(90280, 2004, 'bmc', 'GenomeViz: visualizing microbial genomes', 'Ghai, Rohit|Hain, Torsten|Chakraborty, Trinad', 'Institute of Medical Microbiology, Faculty of Medicine, Justus-Liebig-University, Frankfurter Strasse 107, D-35392 Giessen, Germany. Rohit.Ghai@mikrobio.med.uni-giessen.de|Institute of Medical Microbiology, Faculty of Medicine, Justus-Liebig-University, Frankfurter Strasse 107, D-35392 Giessen, Germany. Rohit.Ghai@mikrobio.med.uni-giessen.de|Institute of Medical Microbiology, Faculty of Medicine, Justus-Liebig-University, Frankfurter Strasse 107, D-35392 Giessen, Germany. Rohit.Ghai@mikrobio.med.uni-giessen.de', 'Germany|Germany|Germany', 71, 'Bacterial Proteins|Computer Graphics|CpG Islands|Escherichia coli K12|GC Rich Sequence|Genes, Bacterial|Genome, Bacterial|Listeria|Software|Species Specificity', 'Comparative Study', '10.1186/1471-2105-5-198', '15601465', 'An increasing number of microbial genomes are being sequenced and deposited in public databases. In addition, several closely related strains are also being sequenced in order to understand the genetic basis of diversity and mechanisms that lead to the acquisition of new genetic traits. These exercises have necessitated the requirement for visualizing microbial genomes and performing genome comparisons on a finer scale. We have developed GenomeViz to enable rapid visualization and subsequent comparisons of several microbial genomes in an interactive environment.'),
(90281, 2004, 'bmc', 'A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data', 'Najarian, Kayvan|Zaheri, Maryam|Rad, Ali A|Najarian, Siamak|Dargahi, Javad', 'Computer Science Department, University of North Carolina Charlotte, University City Blvd, Charlotte, NC, USA. knajaria@uncc.edu|Computer Science Department, University of North Carolina Charlotte, University City Blvd, Charlotte, NC, USA. knajaria@uncc.edu|Computer Science Department, University of North Carolina Charlotte, University City Blvd, Charlotte, NC, USA. knajaria@uncc.edu|Computer Science Department, University of North Carolina Charlotte, University City Blvd, Charlotte, NC, USA. knajaria@uncc.edu|Computer Science Department, University of North Carolina Charlotte, University City Blvd, Charlotte, NC, USA. knajaria@uncc.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Cell Line, Tumor|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Hypophosphatemia|Mice|Mice, Transgenic|Models, Biological|Models, Statistical|Models, Theoretical|Multivariate Analysis|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-5-201', '15603585', 'The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters.'),
(90282, 2004, 'bmc', 'Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations', 'Gurunathan, Rajalakshmi|Van Emden, Bernard|Panchanathan, Sethuraman|Kumar, Sudhir', 'Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. Rajalakshmi.Gurunathan@asu.edu <Rajalakshmi.Gurunathan@asu.edu>|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. Rajalakshmi.Gurunathan@asu.edu <Rajalakshmi.Gurunathan@asu.edu>|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. Rajalakshmi.Gurunathan@asu.edu <Rajalakshmi.Gurunathan@asu.edu>|Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA. Rajalakshmi.Gurunathan@asu.edu <Rajalakshmi.Gurunathan@asu.edu>', 'United States|United States|United States|United States', 29, 'Algorithms|Animals|Automation|Cluster Analysis|Computational Biology|Databases, Genetic|Developmental Biology|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Developmental|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-5-202', '15603586', 'Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns.'),
(90283, 2004, 'bmc', 'Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach', 'Ma, Hong-Wu|Buer, Jan|Zeng, An-Ping', 'Department of Genome Analysis, GBF - German Research Center for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany. hwm@gbf.de <hwm@gbf.de>|Department of Genome Analysis, GBF - German Research Center for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany. hwm@gbf.de <hwm@gbf.de>|Department of Genome Analysis, GBF - German Research Center for Biotechnology, Mascheroder Weg 1, 38124 Braunschweig, Germany. hwm@gbf.de <hwm@gbf.de>', 'Germany|Germany|Germany', 0, 'Amino Acid Motifs|Base Composition|Cluster Analysis|Computational Biology|DNA, Bacterial|Escherichia coli|Escherichia coli Proteins|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genes, Regulator|Models, Biological|Software|Systems Biology|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-5-199', '15603590', 'Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN) of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear.'),
(90284, 2004, 'bmc', 'Analysis of superfamily specific profile-profile recognition accuracy', 'Casbon, James A|Saqi, Mansoor A S', 'Bioinformatics Group, Centre for Infectious Disease, Institute of Cell and Molecular Science, Queen Marys School of Medicine and Dentistry, University of London, 32 Newark St, London E1 2AA, UK. j.a.casbon@qmul.ac.uk <j.a.casbon@qmul.ac.uk>|Bioinformatics Group, Centre for Infectious Disease, Institute of Cell and Molecular Science, Queen Marys School of Medicine and Dentistry, University of London, 32 Newark St, London E1 2AA, UK. j.a.casbon@qmul.ac.uk <j.a.casbon@qmul.ac.uk>', 'United Kingdom|United Kingdom', 0, 'Benchmarking|Computational Biology|Conserved Sequence|Databases, Protein|Genetic Variation|Peptides|Protein Array Analysis|Protein Structure, Tertiary|Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-5-200', '15603591', 'Annotation of sequences that share little similarity to sequences of known function remains a major obstacle in genome annotation. Some of the best methods of detecting remote relationships between protein sequences are based on matching sequence profiles. We analyse the superfamily specific performance of sequence profile-profile matching. Our benchmark consists of a set of 16 protein superfamilies that are highly diverse at the sequence level. We relate the performance to the number of sequences in the profiles, the profile diversity and the extent of structural conservation in the superfamily.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90285, 2004, 'bmc', 'A power law global error model for the identification of differentially expressed genes in microarray data', 'Pavelka, Norman|Pelizzola, Mattia|Vizzardelli, Caterina|Capozzoli, Monica|Splendiani, Andrea|Granucci, Francesca|Ricciardi-Castagnoli, Paola', 'Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>|Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. norman.pavelka@unimib.it <norman.pavelka@unimib.it>', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy', 76, 'Algorithms|Automation|Cell Line|Computational Biology|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Fibroblasts|Gene Expression Profiling|Gene Expression Regulation|Humans|Models, Biological|Models, Genetic|Models, Statistical|Muscles|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-5-203', '15606915', 'High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called differentially expressed genes (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG.'),
(90286, 2004, 'bmc', 'Predicting binding sites of hydrolase-inhibitor complexes by combining several methods', 'Sen, Taner Z|Kloczkowski, Andrzej|Jernigan, Robert L|Yan, Changhui|Honavar, Vasant|Ho, Kai-Ming|Wang, Cai-Zhuang|Ihm, Yungok|Cao, Haibo|Gu, Xun|Dobbs, Drena', 'L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu|L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA. taner@iastate.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Bacterial Proteins|Binding Sites|Computational Biology|Computer Simulation|Enzyme Inhibitors|Hydrolases|Models, Molecular|Models, Statistical|Molecular Structure|Pattern Recognition, Automated|Peptides|Phylogeny|Protein Binding|Protein Conformation|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Streptomyces griseus', 'Journal Article', '10.1186/1471-2105-5-205', '15606919', 'Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.'),
(90287, 2004, 'bmc', 'An SVD-based comparison of nine whole eukaryotic genomes supports a coelomate rather than ecdysozoan lineage', 'Stuart, Gary W|Berry, Michael W', 'Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu|Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu', 'India|India', 31, 'Amino Acid Motifs|Animals|Biological Evolution|Computational Biology|Databases, Genetic|Databases, Protein|Evolution, Molecular|Genome|Genomics|Humans|Peptides|Phylogeny|Proteins|Proteome|Proteomics|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-5-204', '15606920', 'Eukaryotic whole genome sequences are accumulating at an impressive rate. Effective methods for comparing multiple whole eukaryotic genomes on a large scale are needed. Most attempted solutions involve the production of large scale alignments, and many of these require a high stringency pre-screen for putative orthologs in order to reduce the effective size of the dataset and provide a reasonably high but unknown fraction of correctly aligned homologous sites for comparison. As an alternative, highly efficient methods that do not require the pre-alignment of operationally defined orthologs are also being explored.'),
(90288, 2004, 'bmc', 'An empirical analysis of training protocols for probabilistic gene finders', 'Majoros, William H|Salzberg, Steven L', 'The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. bmajoros@tigr.org|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. bmajoros@tigr.org', 'United States|United States', 16, 'Algorithms|Arabidopsis|Aspergillus fumigatus|Cell Proliferation|Computational Biology|Databases, Genetic|Gene Expression Profiling|Likelihood Functions|Markov Chains|Models, Biological|Models, Genetic|Models, Statistical|Probability|Programming Languages|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-5-206', '15613242', 'Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters.'),
(90289, 2004, 'bmc', 'Clustering under the line graph transformation: application to reaction network', 'Nacher, Jose C|Ueda, Nobuhisa|Yamada, Takuji|Kanehisa, Minoru|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 12, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Models, Chemical|Models, Statistical|Stochastic Processes|Systems Biology|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-5-207', '15617578', 'Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out.'),
(90290, 2004, 'bmc', 'A database for G proteins and their interaction with GPCRs', 'Elefsinioti, Antigoni L|Bagos, Pantelis G|Spyropoulos, Ioannis C|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. aelef_79@yahoo.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. aelef_79@yahoo.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. aelef_79@yahoo.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. aelef_79@yahoo.gr', 'Greece|Greece|Greece|Greece', 36, 'Databases, Protein|GTP-Binding Proteins|Protein Interaction Mapping|Receptors, G-Protein-Coupled|Software|Software Design', 'Journal Article', '10.1186/1471-2105-5-208', '15619328', 'G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (alpha,beta,gamma). The alpha subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors.'),
(90291, 2004, 'bmc', 'Analysis of oligonucleotide array experiments with repeated measures using mixed models', 'Li, Hao|Wood, Constance L|Getchell, Thomas V|Getchell, Marilyn L|Stromberg, Arnold J', 'Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu', 'United States|United States|United States|United States|United States', 21, 'Algorithms|Alzheimer Disease|Cerebellum|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Down-Regulation|Gene Expression Profiling|Humans|Models, Biological|Models, Genetic|Models, Statistical|Multivariate Analysis|Olfactory Bulb|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Software|Up-Regulation', 'Journal Article', '10.1186/1471-2105-5-209', '15626348', 'Two or more factor mixed factorial experiments are becoming increasingly common in microarray data analysis. In this case study, the two factors are presence (Patients with Alzheimers disease) or absence (Control) of the disease, and brain regions including olfactory bulb (OB) or cerebellum (CER). In the design considered in this manuscript, OB and CER are repeated measurements from the same subject and, hence, are correlated. It is critical to identify sources of variability in the analysis of oligonucleotide array experiments with repeated measures and correlations among data points have to be considered. In addition, multiple testing problems are more complicated in experiments with multi-level treatments or treatment combinations.'),
(90292, 2005, 'bmc', 'A novel method for prokaryotic promoter prediction based on DNA stability', 'Kanhere, Aditi|Bansal, Manju', 'Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India. aditi@mbu.iisc.ernet.in <aditi@mbu.iisc.ernet.in>|Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India. aditi@mbu.iisc.ernet.in <aditi@mbu.iisc.ernet.in>', 'India|India', 62, 'Amino Acid Motifs|Bacillus subtilis|Computational Biology|Corynebacterium glutamicum|DNA|Escherichia coli|Genes, Bacterial|Genome, Bacterial|Models, Statistical|Promoter Regions, Genetic|RNA|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA|Thermodynamics|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-6-1', '15631638', 'In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.'),
(90293, 2005, 'bmc', 'Visualization of comparative genomic analyses by BLAST score ratio', 'Rasko, David A|Myers, Garry S A|Ravel, Jacques', 'The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. drasko@tigr.org <drasko@tigr.org>|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. drasko@tigr.org <drasko@tigr.org>|The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD 20850, USA. drasko@tigr.org <drasko@tigr.org>', 'United States|United States|United States', 160, 'Algorithms|Chromosome Inversion|Computational Biology|Computer Graphics|Databases, Genetic|Gene Deletion|Genome|Genome, Bacterial|Genomics|Internet|Peptides|Programming Languages|Proteomics|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-2', '15634352', 'The first microbial genome sequence, Haemophilus influenzae, was published in 1995. Since then, more than 400 microbial genome sequences have been completed or commenced. This massive influx of data provides the opportunity to obtain biological insights through comparative genomics. However few tools are available for this scale of comparative analysis.'),
(90294, 2005, 'bmc', 'Differences in codon bias cannot explain differences in translational power among microbes', 'Dethlefsen, Les|Schmidt, Thomas M', 'Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA. dethlefs@stanford.edu|Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA. dethlefs@stanford.edu', 'United States|United States', 10, 'Bacterial Physiological Phenomena|Bias|Codon|Computational Biology|Escherichia coli|Gene Expression Regulation, Bacterial|Genes, Archaeal|Genes, Bacterial|Models, Genetic|Models, Statistical|Protein Biosynthesis|Ribosomes|Selection, Genetic|Thermodynamics', 'Journal Article', '10.1186/1471-2105-6-3', '15636642', 'Translational power is the cellular rate of protein synthesis normalized to the biomass invested in translational machinery. Published data suggest a previously unrecognized pattern: translational power is higher among rapidly growing microbes, and lower among slowly growing microbes. One factor known to affect translational power is biased use of synonymous codons. The correlation within an organism between expression level and degree of codon bias among genes of Escherichia coli and other bacteria capable of rapid growth is commonly attributed to selection for high translational power. Conversely, the absence of such a correlation in some slowly growing microbes has been interpreted as the absence of selection for translational power. Because codon bias caused by translational selection varies between rapidly growing and slowly growing microbes, we investigated whether observed differences in translational power among microbes could be explained entirely by differences in the degree of codon bias. Although the data are not available to estimate the effect of codon bias in other species, we developed an empirically-based mathematical model to compare the translation rate of E. coli to the translation rate of a hypothetical strain which differs from E. coli only by lacking codon bias.'),
(90295, 2005, 'bmc', 'EvDTree: structure-dependent substitution profiles based on decision tree classification of 3D environments', 'Gelly, Jean-Christophe|Chiche, Laurent|Gracy, Jérôme', 'Centre de Biochimie Structurale, Faculté de Pharmacie, Université Montpellier I, 15 avenue Charles Flahault, 34093 Montpellier Cedex 5, France. gelly@cbs.cnrs.fr <gelly@cbs.cnrs.fr>|Centre de Biochimie Structurale, Faculté de Pharmacie, Université Montpellier I, 15 avenue Charles Flahault, 34093 Montpellier Cedex 5, France. gelly@cbs.cnrs.fr <gelly@cbs.cnrs.fr>|Centre de Biochimie Structurale, Faculté de Pharmacie, Université Montpellier I, 15 avenue Charles Flahault, 34093 Montpellier Cedex 5, France. gelly@cbs.cnrs.fr <gelly@cbs.cnrs.fr>', 'France|France|France', 9, 'Alanine|Algorithms|Automation|Cluster Analysis|Computational Biology|Computer Simulation|Database Management Systems|Databases, Protein|Decision Trees|Disulfides|Evolution, Molecular|Humans|Information Storage and Retrieval|Models, Statistical|Molecular Sequence Data|Odds Ratio|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-4', '15638949', 'Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold recognition. We propose a new automated method, EvDTree, based on a decision tree algorithm, for automatic derivation of amino acid substitution probabilities from a set of sequence-structure alignments. The main advantage over other approaches is an unbiased automatic selection of the most informative structural descriptors and associated values or thresholds. This feature allows automatic derivation of structure-dependent substitution scores for any specific set of structures, without the need to empirically determine best descriptors and parameters.'),
(90296, 2005, 'bmc', 'Critical evaluation of the JDO API for the persistence and portability requirements of complex biological databases', 'Srdanovic, Marko|Schenk, Ulf|Schwieger, Michael|Campagne, Fabien', 'Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. mas2062@med.cornell.edu <mas2062@med.cornell.edu>|Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. mas2062@med.cornell.edu <mas2062@med.cornell.edu>|Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. mas2062@med.cornell.edu <mas2062@med.cornell.edu>|Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. mas2062@med.cornell.edu <mas2062@med.cornell.edu>', 'United States|United States|United States|United States', 5, 'Computational Biology|Computer Graphics|Database Management Systems|Databases as Topic|Databases, Factual|Databases, Genetic|Evaluation Studies as Topic|Information Storage and Retrieval|Information Systems|Internet|Language|Medical Informatics|Models, Biological|Probability|Programming Languages|Sequence Alignment|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-5', '15642112', 'Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships.'),
(90297, 2005, 'bmc', 'Cyclic nucleotide binding proteins in the Arabidopsis thaliana and Oryza sativa genomes', 'Bridges, Dave|Fraser, Marie E|Moorhead, Greg B G', 'Department of Biological Sciences, University of Calgary, AB, T2N 1N4 Canada. debridge@ucalgary.ca <debridge@ucalgary.ca>|Department of Biological Sciences, University of Calgary, AB, T2N 1N4 Canada. debridge@ucalgary.ca <debridge@ucalgary.ca>|Department of Biological Sciences, University of Calgary, AB, T2N 1N4 Canada. debridge@ucalgary.ca <debridge@ucalgary.ca>', 'Canada|Canada|Canada', 42, 'Amino Acid Sequence|Arabidopsis|Arabidopsis Proteins|Blotting, Western|Carrier Proteins|Computational Biology|Cyclic AMP|Gene Expression Regulation, Plant|Genes, Plant|Genome|Genome, Plant|Ion Channels|Molecular Sequence Data|Nucleotides, Cyclic|Oryza|Phylogeny|Plant Proteins|Potassium Channels|Protein Structure, Tertiary|Sequence Homology, Amino Acid|Signal Transduction|Thiolester Hydrolases|Transcription Factors', 'Journal Article', '10.1186/1471-2105-6-6', '15644130', 'Cyclic nucleotides are ubiquitous intracellular messengers. Until recently, the roles of cyclic nucleotides in plant cells have proven difficult to uncover. With an understanding of the protein domains which can bind cyclic nucleotides (CNB and GAF domains) we scanned the completed genomes of the higher plants Arabidopsis thaliana (mustard weed) and Oryza sativa (rice) for the effectors of these signalling molecules.'),
(90298, 2005, 'bmc', 'Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method', 'Bagos, Pantelis G|Liakopoulos, Theodore D|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 15701, Greece. pbagos@biol.uoa.gr <pbagos@biol.uoa.gr>|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 15701, Greece. pbagos@biol.uoa.gr <pbagos@biol.uoa.gr>|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Panepistimiopolis, Athens 15701, Greece. pbagos@biol.uoa.gr <pbagos@biol.uoa.gr>', 'Greece|Greece|Greece', 99, 'Algorithms|Analysis of Variance|Artificial Intelligence|Bacterial Outer Membrane Proteins|Computational Biology|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|Evaluation Studies as Topic|Internet|Markov Chains|Membrane Proteins|Models, Chemical|Models, Molecular|Neisseria meningitidis|Neural Networks (Computer)|Programming Languages|Protein Conformation|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Software|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-6-7', '15647112', 'Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method.'),
(90299, 2005, 'bmc', 'Structural comparison of metabolic networks in selected single cell organisms', 'Zhu, Dongxiao|Qin, Zhaohui S', 'Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA. zhud@umich.edu', 'United States|United States', 57, 'Algorithms|Amino Acid Motifs|Archaea|Bacteria|Biodiversity|Cell Physiological Phenomena|Cells|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Databases, Factual|Eukaryotic Cells|Models, Biological|Models, Chemical|Models, Statistical|Neural Networks (Computer)|Protein Binding|Protein Interaction Mapping|Proteome|Regression Analysis|Saccharomyces cerevisiae|Signal Transduction|Software|Software Design|Species Specificity|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-8', '15649332', 'There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life.'),
(90300, 2005, 'bmc', 'GATA: a graphic alignment tool for comparative sequence analysis', 'Nix, David A|Eisen, Michael B', 'Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA. danix@lbl.gov|Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA. danix@lbl.gov', 'United States|United States', 58, 'Algorithms|Amino Acid Sequence|Animals|Base Sequence|Binding Sites|Chromosome Mapping|Computational Biology|Computer Graphics|Conserved Sequence|DNA|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Drosophila|Gene Deletion|Gene Duplication|Genome|Genomics|Molecular Sequence Data|Mutation|Programming Languages|Proteins|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-9', '15655071', 'Several problems exist with current methods used to align DNA sequences for comparative sequence analysis. Most dynamic programming algorithms assume that conserved sequence elements are collinear. This assumption appears valid when comparing orthologous protein coding sequences. Functional constraints on proteins provide strong selective pressure against sequence inversions, and minimize sequence duplications and feature shuffling. For non-coding sequences this collinearity assumption is often invalid. For example, enhancers contain clusters of transcription factor binding sites that change in number, orientation, and spacing during evolution yet the enhancer retains its activity. Dot plot analysis is often used to estimate non-coding sequence relatedness. Yet dot plots do not actually align sequences and thus cannot account well for base insertions or deletions. Moreover, they lack an adequate statistical framework for comparing sequence relatedness and are limited to pairwise comparisons. Lastly, dot plots and dynamic programming text outputs fail to provide an intuitive means for visualizing DNA alignments.'),
(90301, 2005, 'bmc', 'Modelling the correlation between the activities of adjacent genes in Drosophila', 'Thygesen, Helene H|Zwinderman, Aeilko H', 'Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, Netherlands. h.h.thygesen@amc.uva.nl|Clinical Epidemiology and Biostatistics, Academisch Medisch Centrum, University of Amsterdam, Meibergdreef 9, 1100 DD Amsterdam, Netherlands. h.h.thygesen@amc.uva.nl', 'Netherlands|Netherlands', 0, 'Algorithms|Animals|Caenorhabditis elegans|Chromatin|Chromosome Mapping|Chromosomes|Cluster Analysis|Computational Biology|Computer Simulation|Drosophila melanogaster|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation|Genetic Variation|Genome|Genomics|Models, Biological|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1186/1471-2105-6-10', '15659243', 'Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure.'),
(90302, 2005, 'bmc', 'Using large-scale perturbations in gene network reconstruction', 'MacCarthy, Thomas|Pomiankowski, Andrew|Seymour, Robert', 'COMPLEX, University College London, 4 Stephenson Way, London NW1 2HE, UK. t.maccarthy@ucl.ac.uk|COMPLEX, University College London, 4 Stephenson Way, London NW1 2HE, UK. t.maccarthy@ucl.ac.uk|COMPLEX, University College London, 4 Stephenson Way, London NW1 2HE, UK. t.maccarthy@ucl.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 13, 'Algorithms|Calibration|Cell Physiological Phenomena|Cluster Analysis|Computational Biology|Computer Simulation|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation|Genes|Genes, Fungal|Genetics|Genomics|Models, Biological|Models, Genetic|Models, Statistical|Models, Theoretical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Systems Biology|Time Factors', 'Journal Article', '10.1186/1471-2105-6-11', '15659246', 'Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computationally feasible. A simple enumerative reconstruction method based on a discrete dynamical system model is used to study how microarray experiments involving modulated global perturbations can be designed to obtain reasonably accurate reconstructions. The method is tested on artificial gene networks with biologically realistic in/out degree characteristics.'),
(90303, 2005, 'bmc', 'Non-linear mapping for exploratory data analysis in functional genomics', 'Azuaje, Francisco|Wang, Haiying|Chesneau, Alban', 'School of Computing and Mathematics, University of Ulster, BT37 0QB, UK. fj.azuaje@ulster.ac.uk|School of Computing and Mathematics, University of Ulster, BT37 0QB, UK. fj.azuaje@ulster.ac.uk|School of Computing and Mathematics, University of Ulster, BT37 0QB, UK. fj.azuaje@ulster.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 5, 'Algorithms|Animals|Caenorhabditis elegans|Cell Line, Tumor|Chromosome Mapping|Cluster Analysis|Computational Biology|Computer Graphics|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Genome|Genomics|Humans|Leukemia|Models, Biological|Models, Genetic|Models, Statistical|Multigene Family|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Parkinson Disease|Pattern Recognition, Automated|Protein Interaction Mapping|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-6-13', '15661072', 'Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated.'),
(90304, 2005, 'bmc', 'MILANO--custom annotation of microarray results using automatic literature searches', 'Rubinstein, Ran|Simon, Itamar', 'Department of Molecular Biology, Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel. ranrub@md.huji.ac.il|Department of Molecular Biology, Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel. ranrub@md.huji.ac.il', 'Israel|Israel', 46, 'Algorithms|Animals|Apoptosis|Automation|Computational Biology|Computer Graphics|Databases, Bibliographic|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Genomics|Humans|Information Storage and Retrieval|Internet|Literature|MEDLINE|Microarray Analysis|Models, Biological|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteomics|PubMed|RNA, Messenger|Software|Software Design|Systems Integration|Tumor Suppressor Protein p53|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-12', '15661078', 'High-throughput genomic research tools are becoming standard in the biologists toolbox. After processing the genomic data with one of the many available statistical algorithms to identify statistically significant genes, these genes need to be further analyzed for biological significance in light of all the existing knowledge. Literature mining--the process of representing literature data in a fashion that is easy to relate to genomic data--is one solution to this problem.'),
(90305, 2005, 'bmc', 'A robust two-way semi-linear model for normalization of cDNA microarray data', 'Wang, Deli|Huang, Jian|Xie, Hehuang|Manzella, Liliana|Soares, Marcelo Bento', 'Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, the University of Alabama at Birmingham, Birmingham, AL 35294, USA. deli.wang@ccc.uab.edu|Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, the University of Alabama at Birmingham, Birmingham, AL 35294, USA. deli.wang@ccc.uab.edu|Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, the University of Alabama at Birmingham, Birmingham, AL 35294, USA. deli.wang@ccc.uab.edu|Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, the University of Alabama at Birmingham, Birmingham, AL 35294, USA. deli.wang@ccc.uab.edu|Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, the University of Alabama at Birmingham, Birmingham, AL 35294, USA. deli.wang@ccc.uab.edu', 'United States|United States|United States|United States|United States', 13, 'Algorithms|Analysis of Variance|Calibration|Computational Biology|Computer Graphics|DNA, Complementary|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Likelihood Functions|Linear Models|Models, Genetic|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Placenta|Reference Standards|Regression Analysis|Software', 'Journal Article', '10.1186/1471-2105-6-14', '15663789', 'Normalization is a basic step in microarray data analysis. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression values.'),
(90306, 2005, 'bmc', 'Large scale hierarchical clustering of protein sequences', 'Krause, Antje|Stoye, Jens|Vingron, Martin', 'Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. akrause@igw.tfh-wildau.de|Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. akrause@igw.tfh-wildau.de|Max Planck Institute for Molecular Genetics, Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. akrause@igw.tfh-wildau.de', 'Germany|Germany|Germany', 58, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Factual|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Fungal Proteins|Genetic Linkage|Genome|Information Storage and Retrieval|Models, Biological|Multigene Family|Phylogeny|Protein Structure, Tertiary|Proteins|Proteomics|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-15', '15663796', 'Searching a biological sequence database with a query sequence looking for homologues has become a routine operation in computational biology. In spite of the high degree of sophistication of currently available search routines it is still virtually impossible to identify quickly and clearly a group of sequences that a given query sequence belongs to.'),
(90307, 2005, 'bmc', 'Efficient decoding algorithms for generalized hidden Markov model gene finders', 'Majoros, William H|Pertea, Mihaela|Delcher, Arthur L|Salzberg, Steven L', 'Bioinformatics Department, The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD, USA. bmajoros@tigr.org|Bioinformatics Department, The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD, USA. bmajoros@tigr.org|Bioinformatics Department, The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD, USA. bmajoros@tigr.org|Bioinformatics Department, The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, MD, USA. bmajoros@tigr.org', 'United States|United States|United States|United States', 25, 'Algorithms|Bayes Theorem|Computational Biology|Computer Simulation|DNA|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Genome|Genome, Human|Humans|Likelihood Functions|Markov Chains|Models, Biological|Models, Genetic|Models, Statistical|Probability|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-16', '15667658', 'The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity.'),
(90308, 2005, 'bmc', 'Evaluating concentration estimation errors in ELISA microarray experiments', 'Daly, Don Simone|White, Amanda M|Varnum, Susan M|Anderson, Kevin K|Zangar, Richard C', 'Statistical and Mathematical Sciences, Pacific Northwest National Laboratory, PO Box 999, Richland, WA, USA. DS.Daly@PNL.gov|Statistical and Mathematical Sciences, Pacific Northwest National Laboratory, PO Box 999, Richland, WA, USA. DS.Daly@PNL.gov|Statistical and Mathematical Sciences, Pacific Northwest National Laboratory, PO Box 999, Richland, WA, USA. DS.Daly@PNL.gov|Statistical and Mathematical Sciences, Pacific Northwest National Laboratory, PO Box 999, Richland, WA, USA. DS.Daly@PNL.gov|Statistical and Mathematical Sciences, Pacific Northwest National Laboratory, PO Box 999, Richland, WA, USA. DS.Daly@PNL.gov', 'United States|United States|United States|United States|United States', 26, 'Algorithms|Biomarkers, Tumor|Breast Neoplasms|Calibration|Computational Biology|Computer Simulation|Confidence Intervals|Data Interpretation, Statistical|Enzyme-Linked Immunosorbent Assay|Evaluation Studies as Topic|Gene Expression Profiling|Humans|Logistic Models|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Research Design|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-6-17', '15673468', 'Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a proteins concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors.'),
(90309, 2005, 'bmc', 'Identification of novel prognostic markers in cervical intraepithelial neoplasia using LDMAS (LOH Data Management and Analysis Software)', 'Hamoudi, Rifat A|El-Hamidi, Amina|Du, Ming-Qing', 'Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Hills Road, Cambridge, CB2 2QQ, UK. rh335@cam.ac.uk|Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Hills Road, Cambridge, CB2 2QQ, UK. rh335@cam.ac.uk|Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Hills Road, Cambridge, CB2 2QQ, UK. rh335@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Algorithms|Alleles|Biomarkers, Tumor|Cervical Intraepithelial Neoplasia|Computational Biology|DNA Probes, HPV|DNA, Neoplasm|DNA, Viral|Data Interpretation, Statistical|Databases, Genetic|Female|Hospital Information Systems|Humans|Loss of Heterozygosity|Microsatellite Repeats|Models, Genetic|Oligonucleotide Array Sequence Analysis|Papillomaviridae|Papillomavirus Infections|Precancerous Conditions|Prognosis|Software|Uterine Cervical Neoplasms', 'Journal Article', '10.1186/1471-2105-6-18', '15673474', 'Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). However, with the increasing number of samples analysed using different software, no tool is currently available to integrate and facilitate the extensive and efficient data retrieval and analyses, such as correlation of LOH data with various clinical data sets.'),
(90310, 2005, 'bmc', 'Graph-representation of oxidative folding pathways', 'Agoston, Vilmos|Cemazar, Masa|Kaján, László|Pongor, Sándor', 'Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt. 62, 6726 Szeged, Hungary. vilagos@brc.hu|Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt. 62, 6726 Szeged, Hungary. vilagos@brc.hu|Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt. 62, 6726 Szeged, Hungary. vilagos@brc.hu|Bioinformatics Group, Biological Research Center, Hungarian Academy of Sciences, Temesvári krt. 62, 6726 Szeged, Hungary. vilagos@brc.hu', 'Hungary|Hungary|Hungary|Hungary', 0, 'Animals|Aprotinin|Cattle|Computational Biology|Computer Graphics|Crystallography|Disulfides|Epidermal Growth Factor|Internet|Models, Molecular|Models, Statistical|Oxidation-Reduction|Oxygen|Peptides|Protein Conformation|Protein Denaturation|Protein Folding|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Software|Somatomedins|Sulfhydryl Compounds|Trypsin Inhibitors', 'Journal Article', '10.1186/1471-2105-6-19', '15676070', 'The process of oxidative folding combines the formation of native disulfide bond with conformational folding resulting in the native three-dimensional fold. Oxidative folding pathways can be described in terms of disulfide intermediate species (DIS) which can also be isolated and characterized. Each DIS corresponds to a family of folding states (conformations) that the given DIS can adopt in three dimensions.'),
(90311, 2005, 'bmc', 'GeneNotes--a novel information management software for biologists', 'Hong, Pengyu|Wong, Wing H', 'BioX program, Department of Statistics and HRP, Stanford University, Stanford, CA 94305-4065, USA. pengyuhong@stanford.edu|BioX program, Department of Statistics and HRP, Stanford University, Stanford, CA 94305-4065, USA. pengyuhong@stanford.edu', 'United States|United States', 0, 'Biology|Computational Biology|Computer Communication Networks|Computer Systems|Computers|Database Management Systems|Databases as Topic|Databases, Factual|Databases, Genetic|Databases, Nucleic Acid|Expressed Sequence Tags|Genome|Humans|Information Storage and Retrieval|Internet|MEDLINE|Models, Genetic|Online Systems|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-20', '15686593', 'Collecting and managing information is a challenging task in a genome-wide profiling research project. Most databases and online computational tools require a direct human involvement. Information and computational results are presented in various multimedia formats (e.g., text, image, PDF, word files, etc.), many of which cannot be automatically processed by computers in biologically meaningful ways. In addition, the quality of computational results is far from perfect and requires nontrivial manual examination. The timely selection, integration and interpretation of heterogeneous biological information still heavily rely on the sensibility of biologists. Biologists often feel overwhelmed by the huge amount of and the great diversity of distributed heterogeneous biological information.'),
(90312, 2005, 'bmc', 'The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications', 'Moreland, John L|Gramada, Apostol|Buzko, Oleksandr V|Zhang, Qing|Bourne, Philip E', 'San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, USA. moreland@sdsc.edu|San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, USA. moreland@sdsc.edu|San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, USA. moreland@sdsc.edu|San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, USA. moreland@sdsc.edu|San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, CA 92093, USA. moreland@sdsc.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Genetic|Databases, Protein|Humans|Information Systems|Internet|Ligands|Models, Molecular|Molecular Biology|Molecular Conformation|Programming Languages|Protein Conformation|Proteins|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-21', '15694009', 'The large amount of data that are currently produced in the biological sciences can no longer be explored and visualized efficiently with traditional, specialized software. Instead, new capabilities are needed that offer flexibility, rapid application development and deployment as standalone applications or available through the Web.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90313, 2005, 'bmc', 'Proteins with two SUMO-like domains in chromatin-associated complexes: the RENi (Rad60-Esc2-NIP45) family', 'Novatchkova, Maria|Bachmair, Andreas|Eisenhaber, Birgit|Eisenhaber, Frank', 'Gregor Mendel-Institut GMI, Austrian Academy of Sciences, Vienna Biocenter, A-1030 Vienna, Austria. maria.novatchkova@gmi.oeaw.ac.at|Gregor Mendel-Institut GMI, Austrian Academy of Sciences, Vienna Biocenter, A-1030 Vienna, Austria. maria.novatchkova@gmi.oeaw.ac.at|Gregor Mendel-Institut GMI, Austrian Academy of Sciences, Vienna Biocenter, A-1030 Vienna, Austria. maria.novatchkova@gmi.oeaw.ac.at|Gregor Mendel-Institut GMI, Austrian Academy of Sciences, Vienna Biocenter, A-1030 Vienna, Austria. maria.novatchkova@gmi.oeaw.ac.at', 'Austria|Austria|Austria|Austria', 42, 'Amino Acid Motifs|Amino Acid Sequence|Animals|Cell Nucleus|Chromatin|Chromosomal Proteins, Non-Histone|Computational Biology|Computer Graphics|Fungal Proteins|Histone Deacetylases|Humans|Intracellular Signaling Peptides and Proteins|Mice|Models, Molecular|Molecular Sequence Data|Multigene Family|Nuclear Proteins|Plant Proteins|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Proteins|Schizosaccharomyces pombe Proteins|Sequence Homology, Amino Acid|Species Specificity|Transcription Factors|Transcription, Genetic|Ubiquitin|Ubiquitin-Conjugating Enzymes|Ubiquitins', 'Journal Article', '10.1186/1471-2105-6-22', '15698469', 'Post-translational modification by Small Ubiquitin-like Modifiers (SUMO) has been implicated in protein targeting, in the maintenance of genomic integrity and in transcriptional control. But the specific molecular effects of SUMO modification on many target proteins remain to be elucidated. Recent findings point at the importance of SUMO-mediated histone NAD-dependent deacetylase (HDAC) recruitment in transcriptional regulation.'),
(90314, 2005, 'bmc', 'Recent Hits Acquired by BLAST (ReHAB): a tool to identify new hits in sequence similarity searches', 'Whitney, Joe|Esteban, David J|Upton, Chris', 'Department of Microbiology and Biochemistry, University of Victoria, Victoria, BC, V8W 3P6, Canada. jwhitney@gmail.com|Department of Microbiology and Biochemistry, University of Victoria, Victoria, BC, V8W 3P6, Canada. jwhitney@gmail.com|Department of Microbiology and Biochemistry, University of Victoria, Victoria, BC, V8W 3P6, Canada. jwhitney@gmail.com', 'Canada|Canada|Canada', 0, 'Algorithms|Amino Acid Sequence|Computational Biology|Computers|DNA|Data Interpretation, Statistical|Databases, Factual|Databases, Genetic|Databases, Protein|Genome|Information Storage and Retrieval|Internet|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-23', '15701178', 'Sequence similarity searching is a powerful tool to help develop hypotheses in the quest to assign functional, structural and evolutionary information to DNA and protein sequences. As sequence databases continue to grow exponentially, it becomes increasingly important to repeat searches at frequent intervals, and similarity searches retrieve larger and larger sets of results. New and potentially significant results may be buried in a long list of previously obtained sequence hits from past searches.'),
(90315, 2005, 'bmc', 'Clustering the annotation space of proteins', 'Kunin, Victor|Ouzounis, Christos A', 'Computational Genomics Group, EMBL-EBI, Cambridge, CB10 1SO, UK. kunin@ebi.ac.uk|Computational Genomics Group, EMBL-EBI, Cambridge, CB10 1SO, UK. kunin@ebi.ac.uk', 'United Kingdom|United Kingdom', 14, 'Adenosine Triphosphatases|Adenosine Triphosphate|Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Databases, Factual|Databases, Genetic|Databases, Protein|False Negative Reactions|Genome|Humans|Information Storage and Retrieval|Internet|Models, Statistical|Programming Languages|Protein Folding|Proteins|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Software|Structural Homology, Protein|User-Computer Interface|Vacuolar Proton-Translocating ATPases', 'Journal Article', '10.1186/1471-2105-6-24', '15703069', 'Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas.'),
(90316, 2005, 'bmc', 'Integrating alternative splicing detection into gene prediction', 'Foissac, Sylvain|Schiex, Thomas', 'Unité de Biométrie et Intelligence Artificielle, INRA, 31326 Castanet Tolosan, France. foissac@toulouse.inra.fr|Unité de Biométrie et Intelligence Artificielle, INRA, 31326 Castanet Tolosan, France. foissac@toulouse.inra.fr', 'France|France', 0, 'Algorithms|Alternative Splicing|Arabidopsis|Codon|Computer Graphics|DNA, Complementary|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Exons|Expressed Sequence Tags|Gene Expression Profiling|Genes, Plant|Genome|Genome, Human|Genomics|Humans|Introns|Models, Genetic|Proteomics|RNA Splice Sites|Sequence Alignment|Sequence Analysis, Protein|Sequence Analysis, RNA|Software|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-25', '15705189', 'Alternative splicing (AS) is now considered as a major actor in transcriptome/proteome diversity and it cannot be neglected in the annotation process of a new genome. Despite considerable progresses in term of accuracy in computational gene prediction, the ability to reliably predict AS variants when there is local experimental evidence of it remains an open challenge for gene finders.'),
(90317, 2005, 'bmc', 'Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data', 'Shedden, Kerby|Chen, Wei|Kuick, Rork|Ghosh, Debashis|Macdonald, James|Cho, Kathleen R|Giordano, Thomas J|Gruber, Stephen B|Fearon, Eric R|Taylor, Jeremy M G|Hanash, Samir', 'Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu|Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA. kshedden@umich.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Analysis of Variance|Calibration|Computational Biology|Computer Simulation|DNA Probes|Data Interpretation, Statistical|False Positive Reactions|Gene Expression|Gene Expression Profiling|Humans|Models, Biological|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|RNA|Random Allocation|Reproducibility of Results|Research Design|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Transcription, Genetic', 'Comparative Study', '10.1186/1471-2105-6-26', '15705192', 'A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for non-differentially expressed genes, and test statistic size for truly differentially expressed genes.'),
(90318, 2005, 'bmc', 'A statistical approach for array CGH data analysis', 'Picard, Franck|Robin, Stephane|Lavielle, Marc|Vaisse, Christian|Daudin, Jean-Jacques', 'Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr|Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr|Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr|Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr|Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr', 'France|France|France|France|France', 0, 'Algorithms|Chromosome Aberrations|Chromosome Mapping|Chromosomes, Artificial, Bacterial|Computational Biology|Computer Graphics|Computer Simulation|DNA|Data Interpretation, Statistical|Database Management Systems|Gene Dosage|Gene Expression Profiling|Genetic Markers|Genome|Genome, Human|Humans|Models, Genetic|Models, Statistical|Normal Distribution|Nucleic Acid Conformation|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-27', '15705208', 'Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise: to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile.'),
(90319, 2005, 'bmc', 'An adaptive method for cDNA microarray normalization', 'Zhao, Yingdong|Li, Ming-Chung|Simon, Richard', 'Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA. zhaoy@helix.nih.gov|Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA. zhaoy@helix.nih.gov|Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA. zhaoy@helix.nih.gov', 'United States|United States|United States', 26, 'Calibration|Computational Biology|DNA, Complementary|Data Interpretation, Statistical|Gene Expression|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Models, Statistical|Multivariate Analysis|Normal Distribution|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Polymerase Chain Reaction|RNA|RNA, Neoplasm|Reference Standards|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-6-28', '15707486', 'Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.'),
(90320, 2005, 'bmc', 'Ontological visualization of protein-protein interactions', 'Drabkin, Harold J|Hollenbeck, Christopher|Hill, David P|Blake, Judith A', 'Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. hjd@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. hjd@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. hjd@informatics.jax.org|Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA. hjd@informatics.jax.org', 'United States|United States|United States|United States', 14, 'Animals|Binding Sites|Cloning, Molecular|Computational Biology|Databases, Genetic|Databases, Protein|Genes|Genome|Genomics|Humans|Information Storage and Retrieval|Mice|Models, Theoretical|Molecular Biology|Molecular Conformation|Phosphorylation|Protein Binding|Protein Folding|Protein Interaction Mapping|Proteins|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-6-29', '15707487', 'Cellular processes require the interaction of many proteins across several cellular compartments. Determining the collective network of such interactions is an important aspect of understanding the role and regulation of individual proteins. The Gene Ontology (GO) is used by model organism databases and other bioinformatics resources to provide functional annotation of proteins. The annotation process provides a mechanism to document the binding of one protein with another. We have constructed protein interaction networks for mouse proteins utilizing the information encoded in the GO annotations. The work reported here presents a methodology for integrating and visualizing information on protein-protein interactions.'),
(90321, 2005, 'bmc', 'An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem', 'Shmygelska, Alena|Hoos, Holger H', 'Department of Computer Science, University of British Columbia, Vancouver, Canada. oshmygel@cs.ubc.ca <oshmygel@cs.ubc.ca>|Department of Computer Science, University of British Columbia, Vancouver, Canada. oshmygel@cs.ubc.ca <oshmygel@cs.ubc.ca>', 'Canada|Canada', 189, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Computational Biology|Databases, Factual|Evolution, Molecular|Models, Biological|Models, Chemical|Models, Molecular|Models, Statistical|Models, Theoretical|Molecular Conformation|Monte Carlo Method|Protein Conformation|Protein Denaturation|Protein Folding|Proteins|Proteomics|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-30', '15710037', 'The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a proteins conformation from its amino acid sequence under a widely studied, conceptually simple model - the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model.'),
(90322, 2005, 'bmc', 'Automated generation of heuristics for biological sequence comparison', 'Slater, Guy St C|Birney, Ewan', 'The Ensembl Group, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. guy@ebi.ac.uk|The Ensembl Group, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. guy@ebi.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Automation|Computational Biology|Computer Simulation|DNA|DNA, Complementary|Databases, Factual|Genome|Humans|Information Storage and Retrieval|Mathematical Computing|Models, Biological|Models, Theoretical|RNA, Messenger|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-31', '15713233', 'Exhaustive methods of sequence alignment are accurate but slow, whereas heuristic approaches run quickly, but their complexity makes them more difficult to implement. We introduce bounded sparse dynamic programming (BSDP) to allow rapid approximation to exhaustive alignment. This is used within a framework whereby the alignment algorithms are described in terms of their underlying model, to allow automated development of efficient heuristic implementations which may be applied to a general set of sequence comparison problems.'),
(90323, 2005, 'bmc', 'A note on generalized Genome Scan Meta-Analysis statistics', 'Koziol, James A|Feng, Anne C', 'Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, MEM216, La Jolla, CA 92037, USA. koziol@scripps.edu|Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, MEM216, La Jolla, CA 92037, USA. koziol@scripps.edu', 'United States|United States', 0, 'Chromosome Mapping|Chromosomes|Computational Biology|Data Interpretation, Statistical|Genetic Linkage|Genetic Predisposition to Disease|Genome|Genome, Human|Humans|Meta-Analysis as Topic|Models, Biological|Models, Statistical|Models, Theoretical|Probability|Research Design|Schizophrenia|Software', 'Journal Article', '10.1186/1471-2105-6-32', '15717930', 'Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis, and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.'),
(90324, 2005, 'bmc', 'PSSM-based prediction of DNA binding sites in proteins', 'Ahmad, Shandar|Sarai, Akinori', 'Department of Bioinformatics and Bioscience, Kyushu Institute of Technology, Iizuka 820 8502, Fukuoka, Japan. shandar@bse.kyutech.ac.jp|Department of Bioinformatics and Bioscience, Kyushu Institute of Technology, Iizuka 820 8502, Fukuoka, Japan. shandar@bse.kyutech.ac.jp', 'Japan|Japan', 0, 'Algorithms|Amino Acid Sequence|Base Sequence|Binding Sites|Computational Biology|Computer Simulation|Computers|DNA|Databases, Protein|Genome|Internet|Models, Molecular|Neural Networks (Computer)|Protein Binding|Protein Conformation|Protein Structure, Secondary|Proteins|ROC Curve|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-33', '15720719', 'Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.'),
(90325, 2005, 'bmc', 'Atlas - a data warehouse for integrative bioinformatics', 'Shah, Sohrab P|Huang, Yong|Xu, Tao|Yuen, Macaire M S|Ling, John|Ouellette, B F Francis', 'UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>|UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>|UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>|UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>|UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>|UBC Bioinformatics Centre, University of British Columbia, Vancouver, BC, Canada. sohrab@bioinformatics.ubc.ca <sohrab@bioinformatics.ubc.ca>', 'Canada|Canada|Canada|Canada|Canada|Canada', 98, 'Computational Biology|Computer Graphics|Database Management Systems|Databases as Topic|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Genome|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Internet|Models, Genetic|Programming Languages|Protein Interaction Mapping|RNA, Messenger|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-34', '15723693', 'We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.'),
(90326, 2005, 'bmc', 'GANN: genetic algorithm neural networks for the detection of conserved combinations of features in DNA', 'Beiko, Robert G|Charlebois, Robert L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia. r.beiko@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia. r.beiko@imb.uq.edu.au', 'Australia|Australia', 21, 'Algorithms|Artificial Intelligence|Binding Sites|Computational Biology|Computer Simulation|Computing Methodologies|DNA|Databases, Genetic|Internet|Markov Chains|Models, Genetic|Models, Molecular|Molecular Sequence Data|Neural Networks (Computer)|Open Reading Frames|Pattern Recognition, Automated|Protein Binding|Proteins|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-36', '15725347', 'The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence.'),
(90327, 2005, 'bmc', 'Two-part permutation tests for DNA methylation and microarray data', 'Neuhäuser, Markus|Boes, Tanja|Jöckel, Karl-Heinz', 'Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, D-45122 Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de|Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, D-45122 Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de|Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, D-45122 Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de', 'Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Simulation|DNA|DNA Methylation|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Likelihood Functions|Models, Genetic|Models, Statistical|Monte Carlo Method|Oligonucleotide Array Sequence Analysis|Sample Size|Sequence Alignment|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-35', '15725357', 'One important application of microarray experiments is to identify differentially expressed genes. Often, small and negative expression levels were clipped-off to be equal to an arbitrarily chosen cutoff value before a statistical test is carried out. Then, there are two types of data: truncated values and original observations. The truncated values are not just another point on the continuum of possible values and, therefore, it is appropriate to combine two statistical tests in a two-part model rather than using standard statistical methods. A similar situation occurs when DNA methylation data are investigated. In that case, there are null values (undetectable methylation) and observed positive values. For these data, we propose a two-part permutation test.'),
(90328, 2005, 'bmc', 'Can Zipfs law be adapted to normalize microarrays?', 'Lu, Tim|Costello, Christine M|Croucher, Peter J P|Häsler, Robert|Deuschl, Günther|Schreiber, Stefan', 'Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>|Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>|Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>|Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>|Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>|Department of Medicine, Christian-Albrechts-University, Kiel, Germany. t.lu@mucosa.de <t.lu@mucosa.de>', 'Germany|Germany|Germany|Germany|Germany|Germany', 17, 'Algorithms|Analysis of Variance|Animals|Calibration|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Expressed Sequence Tags|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Likelihood Functions|Models, Genetic|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|RNA|Rats|Regression Analysis|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-6-37', '15727680', 'Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipfs law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented.'),
(90329, 2005, 'bmc', 'htSNPer10: software for haplotype block partition and htSNPs selection', 'Ding, Keyue|Zhang, Jing|Zhou, Kaixin|Shen, Yan|Zhang, Xuegong', 'National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China. ding.keyue@mayo.edu|National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China. ding.keyue@mayo.edu|National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China. ding.keyue@mayo.edu|National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China. ding.keyue@mayo.edu|National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100005, China. ding.keyue@mayo.edu', 'China|China|China|China|China', 27, 'Algorithms|Chromosome Mapping|Computational Biology|Databases, Genetic|Expressed Sequence Tags|Genetic Markers|Genome|Genome, Human|Genomics|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Programming Languages|Recombination, Genetic|Selection, Genetic|Sequence Analysis, DNA|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-38', '15740612', 'There is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection.'),
(90330, 2005, 'bmc', 'The use of edge-betweenness clustering to investigate biological function in protein interaction networks', 'Dunn, Ruth|Dudbridge, Frank|Sanderson, Christopher M', 'The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. rd3@sanger.ac.uk <rd3@sanger.ac.uk>|The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. rd3@sanger.ac.uk <rd3@sanger.ac.uk>|The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. rd3@sanger.ac.uk <rd3@sanger.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom', 144, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|False Positive Reactions|Genome, Human|Genomics|Humans|Models, Biological|Programming Languages|Protein Interaction Mapping|Proteins|Proteomics|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-6-39', '15740614', 'This paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.'),
(90331, 2005, 'bmc', 'Computational verification of protein-protein interactions by orthologous co-expression', 'Tirosh, Itay|Barkai, Naama', 'Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. itay.tirosh@weizmann.ac.il <itay.tirosh@weizmann.ac.il>|Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. itay.tirosh@weizmann.ac.il <itay.tirosh@weizmann.ac.il>', 'Israel|Israel', 0, 'Animals|Binding Sites|Cloning, Molecular|Computational Biology|Computer Graphics|Databases, Genetic|Databases, Protein|Fungal Proteins|Gene Expression|Gene Expression Regulation, Fungal|Genes, Fungal|Humans|Models, Genetic|Models, Statistical|Molecular Conformation|Protein Binding|Protein Folding|Protein Interaction Mapping|Proteins|Proteomics|Saccharomyces cerevisiae|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-6-40', '15740634', 'High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.'),
(90332, 2005, 'bmc', 'PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information', 'Angly, Florent|Rodriguez-Brito, Beltran|Bangor, David|McNairnie, Pat|Breitbart, Mya|Salamon, Peter|Felts, Ben|Nulton, James|Mahaffy, Joseph|Rohwer, Forest', 'Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>|Ecole Supérieure de Biotechnologie de Strasbourg, Boulevard Sébastien Brandt, 67413 Illkirch, France. fangly@projects.sdsu.edu <fangly@projects.sdsu.edu>', 'France|France|France|France|France|France|France|France|France|France', 138, 'Algorithms|Bacteriophages|Biodiversity|Computational Biology|Contig Mapping|DNA|DNA Viruses|Databases, Genetic|Genes, Viral|Genetic Variation|Genome, Viral|Genotype|Internet|Models, Genetic|Models, Statistical|Protein Interaction Mapping|Sequence Analysis, DNA|Software|Viruses', 'Journal Article', '10.1186/1471-2105-6-41', '15743531', 'Phages, viruses that infect prokaryotes, are the most abundant microbes in the world. A major limitation to studying these viruses is the difficulty of cultivating the appropriate prokaryotic hosts. One way around this limitation is to directly clone and sequence shotgun libraries of uncultured viral communities (i.e., metagenomic analyses). PHACCS http://phage.sdsu.edu/phaccs, Phage Communities from Contig Spectrum, is an online bioinformatic tool to assess the biodiversity of uncultured viral communities. PHACCS uses the contig spectrum from shotgun DNA sequence assemblies to mathematically model the structure of viral communities and make predictions about diversity.'),
(90333, 2005, 'bmc', 'A normalization strategy applied to HiCEP (an AFLP-based expression profiling) analysis: toward the strict alignment of valid fragments across electrophoretic patterns', 'Kadota, Koji|Fukumura, Ryutaro|Rodrigue, Joseph J|Araki, Ryoko|Abe, Masumi', 'Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>|Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>|Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>|Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>|Transcriptome Research Center, National Institute of Radiological Sciences (NIRS), 9-1, Anagawa-4-chome, Chiba-shi 263-8555, Japan. kadota@nirs.go.jp <kadota@nirs.go.jp>', 'Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Animals|Computational Biology|DNA|DNA Fingerprinting|DNA Primers|Databases, Genetic|Electrophoresis|Gene Expression Profiling|Image Processing, Computer-Assisted|Mice|Models, Genetic|Models, Statistical|Molecular Weight|Normal Distribution|Oligonucleotide Array Sequence Analysis|Polymorphism, Restriction Fragment Length|RNA, Messenger|Random Amplified Polymorphic DNA Technique|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Time Factors', 'Journal Article', '10.1186/1471-2105-6-43', '15748295', 'Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed.'),
(90334, 2005, 'bmc', 'Quantitative inference of dynamic regulatory pathways via microarray data', 'Chang, Wen-Chieh|Li, Chang-Wei|Chen, Bor-Sen', 'Lab. of System Biology, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan. cwli@moti.ee.nthu.edu.tw <cwli@moti.ee.nthu.edu.tw>|Lab. of System Biology, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan. cwli@moti.ee.nthu.edu.tw <cwli@moti.ee.nthu.edu.tw>|Lab. of System Biology, National Tsing Hua University, 101, Sec 2, Kuang Fu Road, Hsinchu, 300, Taiwan. cwli@moti.ee.nthu.edu.tw <cwli@moti.ee.nthu.edu.tw>', '-|-|-', 38, 'Algorithms|Arabidopsis|Circadian Rhythm|Computational Biology|Computer Simulation|DNA-Binding Proteins|Fermentation|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Genes, Fungal|Genes, Plant|Genome, Fungal|Likelihood Functions|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Protein Binding|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-6-44', '15748298', 'The cellular signaling pathway (network) is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach.'),
(90335, 2005, 'bmc', 'wFleaBase: the Daphnia genome database', 'Colbourne, John K|Singan, Vasanth R|Gilbert, Don G', 'Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana, USA. jcolbour@cgb.indiana.edu|Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana, USA. jcolbour@cgb.indiana.edu|Center for Genomics and Bioinformatics, Indiana University, Bloomington, Indiana, USA. jcolbour@cgb.indiana.edu', 'India|India|India', 0, 'Animals|Chromosome Mapping|Computational Biology|Computer Graphics|Daphnia|Database Management Systems|Databases, Factual|Databases, Genetic|Ecology|Ecosystem|Evolution, Molecular|Genetics, Population|Genome|Genomics|Information Services|Information Storage and Retrieval|Internet|Software', 'Journal Article', '10.1186/1471-2105-6-45', '15752432', 'wFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnias ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments.'),
(90336, 2005, 'bmc', 'Automatic detection of false annotations via binary property clustering', 'Kaplan, Noam|Linial, Michal', 'Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il|Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel. kaplann@cc.huji.ac.il', 'Israel|Israel', 9, 'Algorithms|Amino Acid Motifs|Automation|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Protein|Models, Statistical|Protein Binding|Protein Interaction Mapping|Proteins|Proteomics|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-46', '15755318', 'Computational protein annotation methods occasionally introduce errors. False-positive (FP) errors are annotations that are mistakenly associated with a protein. Such false annotations introduce errors that may spread into databases through similarity with other proteins. Generally, methods used to minimize the chance for FPs result in decreased sensitivity or low throughput. We present a novel protein-clustering method that enables automatic separation of FP from true hits. The method quantifies the biological similarity between pairs of proteins by examining each proteins annotations, and then proceeds by clustering sets of proteins that received similar annotation into biological groups.'),
(90337, 2005, 'bmc', 'JAtlasView: a Java atlas-viewer for browsing biomedical 3D images and atlases', 'Feng, Guangjie|Burton, Nick|Hill, Bill|Davidson, Duncan|Kerwin, Janet|Scott, Mark|Lindsay, Susan|Baldock, Richard', 'MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>|MRC Human Genetics Unit, Western General Hospital, Crewe Road, EH4 2XU, Edinburgh, UK. Guangjie.Feng@hgu.mrc.ac.uk <Guangjie.Feng@hgu.mrc.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Anatomy|Anatomy, Cross-Sectional|Animals|Brain|Cervical Atlas|Computational Biology|Computer Graphics|Computer Simulation|Computer-Assisted Instruction|Computers|Data Display|Databases, Protein|Humans|Image Interpretation, Computer-Assisted|Image Processing, Computer-Assisted|Imaging, Three-Dimensional|Information Storage and Retrieval|Mice|Models, Anatomic|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-47', '15757508', 'Many three-dimensional (3D) images are routinely collected in biomedical research and a number of digital atlases with associated anatomical and other information have been published. A number of tools are available for viewing this data ranging from commercial visualization packages to freely available, typically system architecture dependent, solutions. Here we discuss an atlas viewer implemented to run on any workstation using the architecture neutral Java programming language.'),
(90338, 2005, 'bmc', 'A Taxonomic Search Engine: federating taxonomic databases using web services', 'Page, Roderic D M', 'Division of Environmental and Evolutionary Biology, Institute of Biomedical and Life Sciences, Graham Kerr Building, University of Glasgow, Glasgow G12 8QQ, UK. r.page@bio.gla.ac.uk', 'United Kingdom', 24, 'Classification|Computational Biology|Computer Communication Networks|Database Management Systems|Databases as Topic|Databases, Factual|Databases, Genetic|Databases, Protein|Information Dissemination|Information Services|Information Storage and Retrieval|Information Systems|Internet|Medical Informatics|National Institutes of Health (U.S.)|National Library of Medicine (U.S.)|Sequence Analysis, Protein|Software|Software Design|Systems Integration|Unified Medical Language System|United States|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-48', '15757517', 'The taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism.'),
(90339, 2005, 'bmc', 'A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities', 'Bastien, Olivier|Ortet, Philippe|Roy, Sylvaine|Maréchal, Eric', 'UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. obastien@cea.fr <obastien@cea.fr>|UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. obastien@cea.fr <obastien@cea.fr>|UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. obastien@cea.fr <obastien@cea.fr>|UMR 5019 CNRS-CEA-INRA-Université Joseph Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. obastien@cea.fr <obastien@cea.fr>', 'France|France|France|France', 0, 'Algorithms|Amino Acid Sequence|Animals|Base Sequence|Computational Biology|Evolution, Molecular|Genome|Glucose-6-Phosphate Isomerase|Humans|Likelihood Functions|Models, Genetic|Models, Statistical|Models, Theoretical|Molecular Sequence Data|Monte Carlo Method|Phosphopyruvate Hydratase|Phylogeny|Probability|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Analysis, RNA|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-49', '15757521', 'Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90340, 2005, 'bmc', 'Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling', 'Freyhult, Eva|Prusis, Peteris|Lapinsh, Maris|Wikberg, Jarl E S|Moulton, Vincent|Gustafsson, Mats G', 'The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se|The Linnaeus Centre for Bioinformatics, Uppsala University, Box 598, S-751 24 Uppsala, Sweden. Eva.Freyhult@lcb.uu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 35, 'Algorithms|Animals|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Humans|Ligands|Models, Biological|Models, Chemical|Models, Molecular|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Programming Languages|Protein Binding|Protein Conformation|Rats|Receptors, Adrenergic, alpha-1|Receptors, G-Protein-Coupled|Regression Analysis|Reproducibility of Results|Selection, Genetic|Software', 'Journal Article', '10.1186/1471-2105-6-50', '15760465', 'Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.'),
(90341, 2005, 'bmc', 'CoPub Mapper: mining MEDLINE based on search term co-publication', 'Alako, Blaise T F|Veldhoven, Antoine|van Baal, Sjozef|Jelier, Rob|Verhoeven, Stefan|Rullmann, Ton|Polman, Jan|Jenster, Guido', 'Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl|Department of Molecular Design & Informatics, Organon NV, P.O. Box 20, 5340 BH Oss, The Netherlands. blaise.alako@wur.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 53, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computational Biology|Computer Graphics|Databases, Bibliographic|Databases, Factual|Databases, Genetic|Expressed Sequence Tags|False Positive Reactions|Gene Expression Profiling|Genes|Humans|Information Storage and Retrieval|MEDLINE|Meta-Analysis as Topic|Models, Molecular|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|PubMed|ROC Curve|Sequence Alignment|Sequence Analysis, DNA|Software|Subject Headings|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-51', '15760478', 'High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned.'),
(90342, 2005, 'bmc', 'Evolutionary sequence analysis of complete eukaryote genomes', 'Blair, Jaime E|Shah, Prachi|Hedges, S Blair', 'NASA Astrobiology Institute, Department of Biology, The Pennsylvania State University, 208 Mueller Laboratory, University Park, Pennsylvania 16802-5301, USA. jeb322@psu.edu|NASA Astrobiology Institute, Department of Biology, The Pennsylvania State University, 208 Mueller Laboratory, University Park, Pennsylvania 16802-5301, USA. jeb322@psu.edu|NASA Astrobiology Institute, Department of Biology, The Pennsylvania State University, 208 Mueller Laboratory, University Park, Pennsylvania 16802-5301, USA. jeb322@psu.edu', 'United States|United States|United States', 43, 'Animals|Biodiversity|Biological Evolution|Computational Biology|Databases, Genetic|Databases, Protein|Eukaryotic Cells|Evolution, Molecular|Fossils|Gene Duplication|Genome|Genome, Archaeal|Humans|Models, Genetic|Phylogeny|Programming Languages|Proteins|Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-53', '15762985', 'Gene duplication and gene loss during the evolution of eukaryotes have hindered attempts to estimate phylogenies and divergence times of species. Although current methods that identify clusters of orthologous genes in complete genomes have helped to investigate gene function and gene content, they have not been optimized for evolutionary sequence analyses requiring strict orthology and complete gene matrices. Here we adopt a relatively simple and fast genome comparison approach designed to assemble orthologs for evolutionary analysis. Our approach identifies single-copy genes representing only species divergences (panorthologs) in order to minimize potential errors caused by gene duplication. We apply this approach to complete sets of proteins from published eukaryote genomes specifically for phylogeny and time estimation.'),
(90343, 2005, 'bmc', 'WEBnm@: a web application for normal mode analyses of proteins', 'Hollup, Siv Midtun|Salensminde, Gisle|Reuter, Nathalie', 'Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgt. 55, N-5008 Bergen, Norway. sivh@ii.uib.no|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgt. 55, N-5008 Bergen, Norway. sivh@ii.uib.no|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, Thormøhlensgt. 55, N-5008 Bergen, Norway. sivh@ii.uib.no', 'Norway|Norway|Norway', 0, 'Algorithms|Calcium-Transporting ATPases|Computational Biology|Computer Graphics|Cytoplasm|Database Management Systems|Databases, Protein|Genetic Vectors|Information Storage and Retrieval|Internet|Models, Statistical|Molecular Structure|Nucleic Acids|Peptides|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Sarcoplasmic Reticulum Calcium-Transporting ATPases|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-52', '15762993', 'Normal mode analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. NMA is especially useful for large biomolecular assemblies, such as transmembrane channels or virus capsids. NMA relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant.'),
(90344, 2005, 'bmc', 'Speeding disease gene discovery by sequence based candidate prioritization', 'Adie, Euan A|Adams, Richard R|Evans, Kathryn L|Porteous, David J|Pickard, Ben S', 'Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk|Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk|Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk|Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk|Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh, Edinburgh, UK. euan.adie@ed.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 200, 'Algorithms|Automation|Computational Biology|Conserved Sequence|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Decision Trees|Gene Expression Profiling|Genetic Diseases, Inborn|Genetic Linkage|Genetic Predisposition to Disease|Genetic Testing|Genome|Genome, Human|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Phenotype|Polymorphism, Genetic|ROC Curve|Reproducibility of Results|Research Design|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-55', '15766383', 'Regions of interest identified through genetic linkage studies regularly exceed 30 centimorgans in size and can contain hundreds of genes. Traditionally this number is reduced by matching functional annotation to knowledge of the disease or phenotype in question. However, here we show that disease genes share patterns of sequence-based features that can provide a good basis for automatic prioritization of candidates by machine learning.'),
(90345, 2005, 'bmc', 'In silico microdissection of microarray data from heterogeneous cell populations', 'Lähdesmäki, Harri|Shmulevich, Llya|Dunmire, Valerie|Yli-Harja, Olli|Zhang, Wei', 'Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland. harri.lahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland. harri.lahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland. harri.lahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland. harri.lahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland. harri.lahdesmaki@tut.fi', 'Finland|Finland|Finland|Finland|Finland', 65, 'Algorithms|Cell Line, Tumor|Computational Biology|Computer Simulation|DNA, Complementary|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Microdissection|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Protein Interaction Mapping|Reproducibility of Results|Research Design|Sample Size|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-54', '15766384', 'Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification.'),
(90346, 2005, 'bmc', 'TMB-Hunt: an amino acid composition based method to screen proteomes for beta-barrel transmembrane proteins', 'Garrow, Andrew G|Agnew, Alison|Westhead, David R', 'School of Biochemistry and Microbiology, University of Leeds, Leeds, LS2 9JT, UK. bmbagg@bmb.leeds.ac.uk|School of Biochemistry and Microbiology, University of Leeds, Leeds, LS2 9JT, UK. bmbagg@bmb.leeds.ac.uk|School of Biochemistry and Microbiology, University of Leeds, Leeds, LS2 9JT, UK. bmbagg@bmb.leeds.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bacterial Outer Membrane Proteins|Calibration|Cell Membrane|Chloroplasts|Computational Biology|Databases, Protein|Escherichia coli|Evolution, Molecular|Genome|Markov Chains|Membrane Proteins|Models, Molecular|Models, Statistical|Protein Structure, Secondary|Protein Structure, Tertiary|Proteins|Proteome|Proteomics|Reproducibility of Results|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-56', '15769290', 'Beta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria (both gram negative and acid fast gram positive), mitochondria and chloroplasts. Despite recent publications describing reasonable levels of accuracy for discriminating between bbtm proteins and other proteins, screening of entire genomes remains troublesome as these molecules only constitute a small fraction of the sequences screened. Therefore, novel methods are still required capable of detecting new families of bbtm protein in diverse genomes.'),
(90347, 2005, 'bmc', 'Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein', 'Raghava, Gajendra P S|Han, Joon H', 'Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31 Hyo-Ja Dong, Pohang 790-784, Republic of Korea. raghava@imtech.res.in|Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31 Hyo-Ja Dong, Pohang 790-784, Republic of Korea. raghava@imtech.res.in', '-|-', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Cell Membrane|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Databases, Genetic|Databases, Protein|Fungal Proteins|Gene Expression Regulation, Fungal|Genes, Fungal|Information Storage and Retrieval|Internet|Models, Statistical|Models, Theoretical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Peptides|Proteins|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-59', '15773999', 'A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition.'),
(90348, 2005, 'bmc', 'Towards precise classification of cancers based on robust gene functional expression profiles', 'Guo, Zheng|Zhang, Tianwen|Li, Xia|Wang, Qi|Xu, Jianzhen|Yu, Hui|Zhu, Jing|Wang, Haiyun|Wang, Chenguang|Topol, Eric J|Wang, Qing|Rao, Shaoqi', 'Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn|Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China. guoz@ems.hrbmu.edu.cn', 'China|China|China|China|China|China|China|China|China|China|China|China', 0, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Decision Trees|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Genetic Predisposition to Disease|Genomics|Humans|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-58', '15774002', 'Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.'),
(90349, 2005, 'bmc', 'Combining Affymetrix microarray results', 'Stevens, John R|Doerge, R W', 'Department of Statistics, Purdue University, 150 N, University Street, West Lafayette, Indiana 47907-2067, USA. jrsteven@stat.purdue.edu|Department of Statistics, Purdue University, 150 N, University Street, West Lafayette, Indiana 47907-2067, USA. jrsteven@stat.purdue.edu', 'India|India', 58, 'Algorithms|Analysis of Variance|Animals|Chromosome Mapping|Computational Biology|Computer Graphics|Computer Simulation|DNA, Complementary|Databases, Genetic|Disease Models, Animal|Encephalomyelitis, Autoimmune, Experimental|Gene Expression Profiling|Genome|Humans|Meta-Analysis as Topic|Mice|Microarray Analysis|Models, Biological|Models, Genetic|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-6-57', '15774008', 'As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.'),
(90350, 2005, 'bmc', 'SNPHunter: a bioinformatic software for single nucleotide polymorphism data acquisition and management', 'Wang, Lin|Liu, Simin|Niu, Tianhua|Xu, Xin', 'Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA. linwang@hsph.harvard.edu|Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA. linwang@hsph.harvard.edu|Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA. linwang@hsph.harvard.edu|Program for Population Genetics, Harvard School of Public Health, Boston, MA 02115, USA. linwang@hsph.harvard.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Automation|Computational Biology|Computer Simulation|Database Management Systems|Databases, Genetic|Genetic Markers|Heterozygote|Humans|Information Storage and Retrieval|Internet|Linkage Disequilibrium|Models, Genetic|Polymorphism, Single Nucleotide|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-60', '15774022', 'Single nucleotide polymorphisms (SNPs) provide an important tool in pinpointing susceptibility genes for complex diseases and in unveiling human molecular evolution. Selection and retrieval of an optimal SNP set from publicly available databases have emerged as the foremost bottlenecks in designing large-scale linkage disequilibrium studies, particularly in case-control settings.'),
(90351, 2005, 'bmc', 'Graphical representation of ribosomal RNA probe accessibility data using ARB software package', 'Kumar, Yadhu|Westram, Ralf|Behrens, Sebastian|Fuchs, Bernhard|Glöckner, Frank Oliver|Amann, Rudolf|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|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|Germany|Germany|Germany', 0, 'Algorithms|Bacterial Proteins|Computer Graphics|Computer Simulation|DNA Primers|Data Interpretation, Statistical|Databases, Genetic|Databases, Nucleic Acid|Genes, Bacterial|In Situ Hybridization|In Situ Hybridization, Fluorescence|Internet|Nucleic Acid Hybridization|Oligonucleotide Probes|Phylogeny|Programming Languages|RNA|RNA Probes|RNA, Bacterial|RNA, Ribosomal|Ribosomes|Sequence Alignment|Sequence Analysis, Protein|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-61', '15777482', 'Taxon specific hybridization probes in combination with a variety of commonly used hybridization formats nowadays are standard tools in microbial identification. A frequently applied technology, fluorescence in situ hybridization (FISH), besides single cell identification, allows the localization and functional studies of the microbial community composition. Careful in silico design and evaluation of potential oligonucleotide probe targets is therefore crucial for performing successful hybridization experiments.'),
(90352, 2005, 'bmc', 'A standard curve based method for relative real time PCR data processing', 'Larionov, Alexey|Krause, Andreas|Miller, William', 'Breast Unit, Western General Hospital, Edinburgh, UK. alexey_larionov@hotmail.com|Breast Unit, Western General Hospital, Edinburgh, UK. alexey_larionov@hotmail.com|Breast Unit, Western General Hospital, Edinburgh, UK. alexey_larionov@hotmail.com', 'United Kingdom|United Kingdom|United Kingdom', 596, 'Algorithms|Artifacts|Breast Neoplasms|Cloning, Molecular|Computational Biology|Computer Graphics|DNA Primers|DNA, Complementary|Data Interpretation, Statistical|Fluorescent Dyes|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Indicators and Reagents|Polymerase Chain Reaction|RNA, Complementary|Regression Analysis|Reproducibility of Results|Reverse Transcriptase Polymerase Chain Reaction|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-6-62', '15780134', 'Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR.'),
(90353, 2005, 'bmc', 'Evolutionary models for insertions and deletions in a probabilistic modeling framework', 'Rivas, Elena', 'Department of Genetics, Washington University School of Medicine, 4444 Forest Park Blvd., Saint Louis, Missouri 63108, USA. elena@genetics.wustl.edu', 'United States', 0, 'Algorithms|Biological Evolution|Computational Biology|Computer Simulation|Evolution, Molecular|Gene Deletion|Likelihood Functions|Markov Chains|Models, Biological|Models, Genetic|Models, Statistical|Models, Theoretical|Molecular Sequence Data|Mutation|Probability|Protein Conformation|Protein Structure, Tertiary|RNA|RNA, Messenger|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Analysis, RNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-63', '15780137', 'Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterparts for structural RNAs) often assume a fixed degree of divergence. Ideally we would like these models to be conditional on evolutionary divergence time. Probabilistic models of substitution events are well established, but there has not been a completely satisfactory theoretical framework for modeling insertion and deletion events.'),
(90354, 2005, 'bmc', 'MBEToolbox: a MATLAB toolbox for sequence data analysis in molecular biology and evolution', 'Cai, James J|Smith, David K|Xia, Xuhua|Yuen, Kwok-Yung', 'Department of Microbiology, University of Hong Kong, Pokfulam, Hong Kong, China. jamescai@hkusua.hku.hk|Department of Microbiology, University of Hong Kong, Pokfulam, Hong Kong, China. jamescai@hkusua.hku.hk|Department of Microbiology, University of Hong Kong, Pokfulam, Hong Kong, China. jamescai@hkusua.hku.hk|Department of Microbiology, University of Hong Kong, Pokfulam, Hong Kong, China. jamescai@hkusua.hku.hk', 'China|China|China|China', 0, 'Algorithms|Biological Evolution|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Evolution, Molecular|Gene Expression Profiling|Information Storage and Retrieval|Internet|Ligands|Models, Genetic|Molecular Biology|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Software|Software Design|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-64', '15780146', 'MATLAB is a high-performance language for technical computing, integrating computation, visualization, and programming in an easy-to-use environment. It has been widely used in many areas, such as mathematics and computation, algorithm development, data acquisition, modeling, simulation, and scientific and engineering graphics. However, few functions are freely available in MATLAB to perform the sequence data analyses specifically required for molecular biology and evolution.'),
(90355, 2005, 'bmc', 'DIALIGN-T: an improved algorithm for segment-based multiple sequence alignment', 'Subramanian, Amarendran R|Weyer-Menkhoff, Jan|Kaufmann, Michael|Morgenstern, Burkhard', 'University of Tübingen, Wilhelm-Schickard-Institut für Informatik, Sand 13, 72076 Tübingen, Germany. subraman@informatik.uni-tuebingen.de|University of Tübingen, Wilhelm-Schickard-Institut für Informatik, Sand 13, 72076 Tübingen, Germany. subraman@informatik.uni-tuebingen.de|University of Tübingen, Wilhelm-Schickard-Institut für Informatik, Sand 13, 72076 Tübingen, Germany. subraman@informatik.uni-tuebingen.de|University of Tübingen, Wilhelm-Schickard-Institut für Informatik, Sand 13, 72076 Tübingen, Germany. subraman@informatik.uni-tuebingen.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Conserved Sequence|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Genome|Models, Statistical|Molecular Probe Techniques|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Software Design|Software Validation', 'Journal Article', '10.1186/1471-2105-6-66', '15784139', 'We present a complete re-implementation of the segment-based approach to multiple protein alignment that contains a number of improvements compared to the previous version 2.2 of DIALIGN. This previous version is superior to Needleman-Wunsch-based multi-alignment programs on locally related sequence sets. However, it is often outperformed by these methods on data sets with global but weak similarity at the primary-sequence level.'),
(90356, 2005, 'bmc', 'Evaluation of gene importance in microarray data based upon probability of selection', 'Fu, Li M|Fu-Liu, Casey S', 'Pacific Tuberculosis and Cancer Research Organization, Pasadena, California, USA. lifu@patcar.org|Pacific Tuberculosis and Cancer Research Organization, Pasadena, California, USA. lifu@patcar.org', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Cell Line, Tumor|Colonic Neoplasms|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Evaluation Studies as Topic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genome, Human|Humans|Leukemia|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Probability|Protein Structure, Tertiary|Reproducibility of Results|Selection, Genetic|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-6-67', '15784140', 'Microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty.'),
(90357, 2005, 'bmc', 'Harshlighting small blemishes on microarrays', 'Suárez-Fariñas, Mayte|Haider, Asifa|Wittkowski, Knut M', 'Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Ave, Box 212, New York, NY 10021, USA. mayte@babel.rockefeller.edu|Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Ave, Box 212, New York, NY 10021, USA. mayte@babel.rockefeller.edu|Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Ave, Box 212, New York, NY 10021, USA. mayte@babel.rockefeller.edu', 'United States|United States|United States', 22, 'Algorithms|Artifacts|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Microarray Analysis|Microscopy, Fluorescence|Models, Genetic|Models, Statistical|Multivariate Analysis|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Psoriasis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-6-65', '15784152', 'Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans show similar artefacts, which affect the analysis, particularly when one tries to detect subtle changes. However, most blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs).'),
(90358, 2005, 'bmc', 'Feature selection and nearest centroid classification for protein mass spectrometry', 'Levner, Ilya', 'Department of Computing Science, University of Alberta, Canada. ilya@cs.ualberta.ca', 'Canada', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Information Storage and Retrieval|Male|Mass Spectrometry|Models, Genetic|Models, Statistical|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Pattern Recognition, Automated|Prostatic Neoplasms|Proteins|Proteomics|Reproducibility of Results|Sensitivity and Specificity|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization|Time Factors', 'Journal Article', '10.1186/1471-2105-6-68', '15788095', 'The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the curse of dimensionality needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry.'),
(90359, 2005, 'bmc', 'Wildfire: distributed, Grid-enabled workflow construction and execution', 'Tang, Francis|Chua, Ching Lian|Ho, Liang-Yoong|Lim, Yun Ping|Issac, Praveen|Krishnan, Arun', 'Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg|Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg|Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg|Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg|Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg|Information Science Research, Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore. francis@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 34, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computers|Computing Methodologies|Database Management Systems|Databases, Factual|Databases, Genetic|Databases, Protein|Exons|Humans|Hypermedia|Information Storage and Retrieval|Models, Theoretical|Online Systems|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software|Software Design|Systems Biology|Systems Integration|Tissue Distribution|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-69', '15788106', 'We observe two trends in bioinformatics: (i) analyses are increasing in complexity, often requiring several applications to be run as a workflow, and (ii) multiple CPU clusters and Grids are available to more scientists. The traditional solution to the problem of running workflows across multiple CPUs required programming, often in a scripting language such as perl. Programming places such solutions beyond the reach of many bioinformatics consumers.'),
(90360, 2005, 'bmc', 'Accelerated probabilistic inference of RNA structure evolution', 'Holmes, Ian', 'Department of Bioengineering, University of California, Berkeley, CA 94720-1762, USA. ihh@berkeley.edu', 'United States', 98, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Databases, Genetic|Databases, Protein|Models, Statistical|Nucleic Acid Conformation|Pattern Recognition, Automated|Probability|Protein Structure, Tertiary|RNA|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-73', '15790387', 'Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoffs 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered.'),
(90361, 2005, 'bmc', 'An entropy-based gene selection method for cancer classification using microarray data', 'Liu, Xiaoxing|Krishnan, Arun|Mondry, Adrian', 'Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore. xiaoxing@bii.a-star.edu.sg|Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore. xiaoxing@bii.a-star.edu.sg|Bioinformatics Institute, 30, Biopolis Street, #07-01, (S) 138671, Singapore. xiaoxing@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Artificial Intelligence|Breast Neoplasms|Cluster Analysis|Colonic Neoplasms|Computational Biology|Computer Simulation|DNA, Complementary|Data Interpretation, Statistical|Diagnosis, Computer-Assisted|Entropy|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Models, Statistical|Neoplasms|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-6-76', '15790388', 'Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach, yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes.'),
(90362, 2005, 'bmc', 'Providing visualisation support for the analysis of anatomy ontology data', 'Dadzie, Aba-Sah|Burger, Albert', 'School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, Scotland. ceead@macs.hw.ac.uk|School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, Scotland. ceead@macs.hw.ac.uk', '-|-', 0, 'Anatomy|Animals|Artificial Intelligence|Cell Lineage|Cervical Atlas|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Developmental Biology|Imaging, Three-Dimensional|Information Storage and Retrieval|Mice|Models, Anatomic|Phylogeny|Programming Languages|Software|Software Design|Systems Integration|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-74', '15790390', 'Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management, ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data.'),
(90363, 2005, 'bmc', 'Paircomp, FamilyRelationsII and Cartwheel: tools for interspecific sequence comparison', 'Brown, C Titus|Xie, Yuan|Davidson, Eric H|Cameron, R Andrew', 'Division of Biological Sciences, California Institute of Technology, Pasadena, CA 91125, USA. titus@caltech.edu|Division of Biological Sciences, California Institute of Technology, Pasadena, CA 91125, USA. titus@caltech.edu|Division of Biological Sciences, California Institute of Technology, Pasadena, CA 91125, USA. titus@caltech.edu|Division of Biological Sciences, California Institute of Technology, Pasadena, CA 91125, USA. titus@caltech.edu', 'United States|United States|United States|United States', 30, 'Algorithms|Animals|Base Sequence|Chromosomes, Artificial, Bacterial|Cluster Analysis|Computational Biology|Computer Graphics|Databases, Genetic|Databases, Nucleic Acid|Exons|Genes, Regulator|Genome|Genome, Human|Genomics|Humans|Mice|Molecular Sequence Data|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-70', '15790396', 'Comparative sequence analysis is an effective and increasingly common way to identify cis-regulatory regions in animal genomes.'),
(90364, 2005, 'bmc', 'GeneKeyDB: a lightweight, gene-centric, relational database to support data mining environments', 'Kirov, S A|Peng, X|Baker, E|Schmoyer, D|Zhang, B|Snoddy, J', 'Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu|Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu|Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu|Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu|Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu|Graduate School for Genome Science and Technology, Oak Ridge National Laboratory-University of Tennessee, Oak Ridge, USA. skirov@utk.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computers|Data Interpretation, Statistical|Database Management Systems|Databases as Topic|Databases, Factual|Databases, Genetic|Databases, Protein|Genomics|Humans|Information Storage and Retrieval|Internet|Programming Languages|Software|Software Design|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-72', '15790402', 'The analysis of biological data is greatly enhanced by existing or emerging databases. Most existing databases, with few exceptions are not designed to easily support large scale computational analysis, but rather offer exclusively a web interface to the resource. We have recognized the growing need for a database which can be used successfully as a backend to computational analysis tools and pipelines. Such database should be sufficiently versatile to allow easy system integration.'),
(90365, 2005, 'bmc', 'Prediction of a common structural scaffold for proteasome lid, COP9-signalosome and eIF3 complexes', 'Scheel, Hartmut|Hofmann, Kay', 'Bioinformatics Group, Memorec Biotec GmbH, Stöckheimer Weg 1, D-50829 Köln, Germany. hartmut.scheel@memorec.com|Bioinformatics Group, Memorec Biotec GmbH, Stöckheimer Weg 1, D-50829 Köln, Germany. hartmut.scheel@memorec.com', 'Germany|Germany', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Animals|COP9 Signalosome Complex|Computational Biology|Cysteine Endopeptidases|Databases, Protein|Eukaryotic Initiation Factor-3|Genome|Humans|Molecular Sequence Data|Multienzyme Complexes|Multiprotein Complexes|Peptide Hydrolases|Peptides|Proteasome Endopeptidase Complex|Protein Structure, Tertiary|Sequence Homology, Amino Acid|Signal Transduction|Transcription Factors|Ubiquitin', 'Journal Article', '10.1186/1471-2105-6-71', '15790418', 'The lid subcomplex of the 26S proteasome and the COP9 signalosome (CSN complex) share a common architecture consisting of six subunits harbouring a so-called PCI domain (proteasome, CSN, eIF3) at their C-terminus, plus two subunits containing MPN domains (Mpr1/Pad1 N-terminal). The translation initiation complex eIF3 also contains PCI- and MPN-domain proteins, but seems to deviate from the 6+2 stoichiometry. Initially, the PCI domain was defined as the region of detectable sequence similarity between the components mentioned above.'),
(90366, 2005, 'bmc', 'Ranking the whole MEDLINE database according to a large training set using text indexing', 'Suomela, Brian P|Andrade, Miguel A', 'Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth Rd, Ottawa, Ontario K1H 8L6, Canada. bsuomela@ohri.ca|Ontario Genomics Innovation Centre, Ottawa Health Research Institute, 501 Smyth Rd, Ottawa, Ontario K1H 8L6, Canada. bsuomela@ohri.ca', 'Canada|Canada', 37, 'Abstracting and Indexing as Topic|Algorithms|Bayes Theorem|Computational Biology|Database Management Systems|Databases as Topic|Databases, Bibliographic|Databases, Protein|False Positive Reactions|Humans|Information Storage and Retrieval|Information Systems|Language|MEDLINE|Natural Language Processing|Stem Cells|Subject Headings|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-75', '15790421', 'The MEDLINE database contains over 12 million references to scientific literature, with about 3/4 of recent articles including an abstract of the publication. Retrieval of entries using queries with keywords is useful for human users that need to obtain small selections. However, particular analyses of the literature or database developments may need the complete ranking of all the references in the MEDLINE database as to their relevance to a topic of interest. This report describes a method that does this ranking using the differences in word content between MEDLINE entries related to a topic and the whole of MEDLINE, in a computational time appropriate for an article search query engine.'),
(90367, 2005, 'bmc', 'Comparative mapping of sequence-based and structure-based protein domains', 'Zhang, Ya|Chandonia, John-Marc|Ding, Chris|Holbrook, Stephen R', 'Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. yzz100@psu.edu|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. yzz100@psu.edu|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. yzz100@psu.edu|Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. yzz100@psu.edu', 'United States|United States|United States|United States', 14, 'Algorithms|Amino Acid Sequence|Animals|Base Sequence|Chromosome Mapping|Cluster Analysis|Computational Biology|Databases, Factual|Databases, Protein|Evolution, Molecular|Genome|Humans|Information Storage and Retrieval|Models, Molecular|Models, Statistical|Molecular Sequence Data|Peptide Mapping|Peptides|Phylogeny|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Proteome|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-6-77', '15790427', 'Protein domains have long been an ill-defined concept in biology. They are generally described as autonomous folding units with evolutionary and functional independence. Both structure-based and sequence-based domain definitions have been widely used. But whether these types of models alone can capture all essential features of domains is still an open question.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90368, 2005, 'bmc', 'CGMIM: automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes', 'Bajdik, Chris D|Kuo, Byron|Rusaw, Shawn|Jones, Steven|Brooks-Wilson, Angela', 'Cancer Control Research Program, BC Cancer Agency, 600 West 10th Avenue, Vancouver BC, V5Z 4E6, Canada. cbajdik@bccancer.bc.ca|Cancer Control Research Program, BC Cancer Agency, 600 West 10th Avenue, Vancouver BC, V5Z 4E6, Canada. cbajdik@bccancer.bc.ca|Cancer Control Research Program, BC Cancer Agency, 600 West 10th Avenue, Vancouver BC, V5Z 4E6, Canada. cbajdik@bccancer.bc.ca|Cancer Control Research Program, BC Cancer Agency, 600 West 10th Avenue, Vancouver BC, V5Z 4E6, Canada. cbajdik@bccancer.bc.ca|Cancer Control Research Program, BC Cancer Agency, 600 West 10th Avenue, Vancouver BC, V5Z 4E6, Canada. cbajdik@bccancer.bc.ca', 'Canada|Canada|Canada|Canada|Canada', 19, 'Algorithms|Chromosome Mapping|Computational Biology|Databases, Factual|Databases, Genetic|Esophageal Neoplasms|Gene Expression Regulation, Neoplastic|Genetic Diseases, Inborn|Genetics, Medical|Humans|Information Storage and Retrieval|Internet|Lymphoma|Models, Statistical|Neoplasms|Online Systems|Software|Stomach Neoplasms|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-78', '15796777', 'Online Mendelian Inheritance in Man (OMIM) is a computerized database of information about genes and heritable traits in human populations, based on information reported in the scientific literature. Our objective was to establish an automated text-mining system for OMIM that will identify genetically-related cancers and cancer-related genes. We developed the computer program CGMIM to search for entries in OMIM that are related to one or more cancer types. We performed manual searches of OMIM to verify the program results.'),
(90369, 2005, 'bmc', 'MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes', 'Marinescu, Voichita D|Kohane, Isaac S|Riva, Alberto', 'Childrens Hospital Boston, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Boston, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Boston, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org', 'United States|United States|United States', 0, 'Algorithms|Animals|Base Sequence|Binding Sites|Cell Cycle Proteins|Computational Biology|Computers|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Evolution, Molecular|Gene Expression Regulation|Gene Library|Genome|Humans|Internet|Markov Chains|Programming Languages|Protein Binding|Sequence Alignment|Software|Transcription Factors|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-79', '15799782', 'Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.'),
(90370, 2005, 'bmc', 'Correlation test to assess low-level processing of high-density oligonucleotide microarray data', 'Ploner, Alexander|Miller, Lance D|Hall, Per|Bergh, Jonas|Pawitan, Yudi', 'Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se|Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se|Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se|Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se|Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 40, 'Algorithms|Cluster Analysis|Computational Biology|DNA Probes|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Oligonucleotides|RNA Probes|Research Design|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-6-80', '15799785', 'There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data.'),
(90371, 2005, 'bmc', 'Columba: an integrated database of proteins, structures, and annotations', 'Trissl, Silke|Rother, Kristian|Müller, Heiko|Steinke, Thomas|Koch, Ina|Preissner, Robert|Frömmel, Cornelius|Leser, Ulf', 'Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de|Institute of Informatics, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany. silke.trissl@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 22, 'Base Sequence|Computational Biology|Database Management Systems|Databases, Factual|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Natural Language Processing|Programming Languages|Proteins|Proteomics|Sequence Analysis, Protein|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-81', '15801979', 'Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures.'),
(90372, 2005, 'bmc', 'Bayesian coestimation of phylogeny and sequence alignment', 'Lunter, Gerton|Miklós, István|Drummond, Alexei|Jensen, Jens Ledet|Hein, Jotun', 'Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. lunter@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. lunter@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. lunter@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. lunter@stats.ox.ac.uk|Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. lunter@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 118, 'Algorithms|Amino Acid Sequence|Animals|Base Sequence|Bayes Theorem|Computational Biology|Computer Simulation|Evolution, Molecular|Gene Deletion|Humans|Likelihood Functions|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Mutation|Myoglobin|Phylogeny|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-6-83', '15804354', 'Two central problems in computational biology are the determination of the alignment and phylogeny of a set of biological sequences. The traditional approach to this problem is to first build a multiple alignment of these sequences, followed by a phylogenetic reconstruction step based on this multiple alignment. However, alignment and phylogenetic inference are fundamentally interdependent, and ignoring this fact leads to biased and overconfident estimations. Whether the main interest be in sequence alignment or phylogeny, a major goal of computational biology is the co-estimation of both.'),
(90373, 2005, 'bmc', 'Super paramagnetic clustering of protein sequences', 'Tetko, Igor V|Facius, Axel|Ruepp, Andreas|Mewes, Hans-Werner', 'GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. i.tetko@gsf.de|GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. i.tetko@gsf.de|GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. i.tetko@gsf.de|GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. i.tetko@gsf.de', 'Germany|Germany|Germany|Germany', 40, 'Algorithms|Bacillus subtilis|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Genetic|Databases, Nucleic Acid|Databases, Protein|Genes, Bacterial|Genome|Genome, Bacterial|Helicobacter pylori|Information Storage and Retrieval|Listeria|Listeria monocytogenes|Magnetics|Markov Chains|Monte Carlo Method|Programming Languages|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-82', '15804359', 'Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annotation methods. The presence of domains in protein families of diverse function, inhomogeneity and different sizes of protein families create considerable difficulties for the application of published clustering methods.'),
(90374, 2005, 'bmc', 'Scoring functions for transcription factor binding site prediction', 'Friberg, Markus|von Rohr, Peter|Gonnet, Gaston', 'Institute of Computational Science, ETH, 8092 Zurich, Switzerland. friberg@inf.ethz.ch|Institute of Computational Science, ETH, 8092 Zurich, Switzerland. friberg@inf.ethz.ch|Institute of Computational Science, ETH, 8092 Zurich, Switzerland. friberg@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Amino Acid Motifs|Binding Sites|Computational Biology|Computer Simulation|DNA|Data Interpretation, Statistical|Databases, Protein|Entropy|Fungal Proteins|Ligands|Models, Statistical|Protein Binding|Protein Conformation|Proteins|Reproducibility of Results|Saccharomyces cerevisiae|Sequence Alignment|Software|Transcription Factors', 'Journal Article', '10.1186/1471-2105-6-84', '15807889', 'Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations.'),
(90375, 2005, 'bmc', 'CGHPRO -- a comprehensive data analysis tool for array CGH', 'Chen, Wei|Erdogan, Fikret|Ropers, H-Hilger|Lenzner, Steffen|Ullmann, Reinhard', 'Max-Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. wei@molgen.mpg.de|Max-Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. wei@molgen.mpg.de|Max-Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. wei@molgen.mpg.de|Max-Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. wei@molgen.mpg.de|Max-Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. wei@molgen.mpg.de', 'Germany|Germany|Germany|Germany|Germany', 90, 'Algorithms|Chromosome Aberrations|Chromosome Disorders|Chromosome Mapping|Chromosomes|Chromosomes, Artificial, Bacterial|Computational Biology|Computer Graphics|Computer Simulation|DNA|Data Interpretation, Statistical|Female|Genome|Genome, Human|Humans|Image Processing, Computer-Assisted|Internet|Male|Markov Chains|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Programming Languages|Sex Factors|Software|Software Design', 'Journal Article', '10.1186/1471-2105-6-85', '15807904', 'Array CGH (Comparative Genomic Hybridisation) is a molecular cytogenetic technique for the genome wide detection of chromosomal imbalances. It is based on the co-hybridisation of differentially labelled test and reference DNA onto arrays of genomic BAC clones, cDNAs or oligonucleotides, and after correction for various intervening variables, loss or gain in the test DNA can be indicated from spots showing aberrant signal intensity ratios. Now that this technique is no longer confined to highly specialized laboratories and is entering the realm of clinical application, there is a need for a user-friendly software package that facilitates estimates of DNA dosage from raw signal intensities obtained by array CGH experiments, and which does not depend on a sophisticated computational environment.'),
(90376, 2005, 'bmc', 'HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data', 'Trivedi, Prinal|Edwards, Jode W|Wang, Jelai|Gadbury, Gary L|Srinivasasainagendra, Vinodh|Zakharkin, Stanislav O|Kim, Kyoungmi|Mehta, Tapan|Brand, Jacob P L|Patki, Amit|Page, Grier P|Allison, David B', 'Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.edu|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. PatelHP@uab.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', 0, 'Algorithms|Biology|Computational Biology|Computer Graphics|Computers|Data Interpretation, Statistical|Database Management Systems|Gene Expression Profiling|Genomics|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteomics|Quality Control|Sequence Alignment|Sequence Analysis, DNA|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-86', '15813968', 'Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing.'),
(90377, 2005, 'bmc', 'Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator', 'Garcia Castro, Alexander|Thoraval, Samuel|Garcia, Leyla J|Ragan, Mark A', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072, Australia. a.garcia@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072, Australia. a.garcia@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072, Australia. a.garcia@imb.uq.edu.au|Institute for Molecular Bioscience, The University of Queensland, Brisbane, Qld 4072, Australia. a.garcia@imb.uq.edu.au', 'Australia|Australia|Australia|Australia', 29, 'Algorithms|Artificial Intelligence|Computational Biology|Computer Communication Networks|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Databases, Genetic|Information Storage and Retrieval|Models, Statistical|Models, Theoretical|Natural Language Processing|Online Systems|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software|Software Design|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-87', '15813976', 'Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities, surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported.'),
(90378, 2005, 'bmc', 'A method for aligning RNA secondary structures and its application to RNA motif detection', 'Liu, Jianghui|Wang, Jason T L|Hu, Jun|Tian, Bin', 'Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA. jl7@oak.njit.edu|Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA. jl7@oak.njit.edu|Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA. jl7@oak.njit.edu|Department of Biochemistry and Molecular Biology, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA. jl7@oak.njit.edu', 'Jersey|Jersey|Jersey|Jersey', 0, 'Algorithms|Base Sequence|Computational Biology|Computer Simulation|Conserved Sequence|Databases, Genetic|Humans|Molecular Sequence Data|Nucleic Acid Conformation|RNA|RNA, Untranslated|Regulatory Sequences, Ribonucleic Acid|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-6-89', '15817128', 'Alignment of RNA secondary structures is important in studying functional RNA motifs. In recent years, much progress has been made in RNA motif finding and structure alignment. However, existing tools either require a large number of prealigned structures or suffer from high time complexities. This makes it difficult for the tools to process RNAs whose prealigned structures are unavailable or process very large RNA structure databases.'),
(90379, 2005, 'bmc', 'Building a protein name dictionary from full text: a machine learning term extraction approach', 'Shi, Lei|Campagne, Fabien', 'Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. les2007@med.cornell.edu|Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medical College, 1300 York Ave, New York, NY 10021, USA. les2007@med.cornell.edu', 'United States|United States', 21, 'Abstracting and Indexing as Topic|Algorithms|Animals|Artificial Intelligence|Bibliometrics|Computational Biology|Databases, Bibliographic|Databases, Genetic|Databases, Protein|Dictionaries as Topic|Humans|Information Dissemination|Information Services|Information Storage and Retrieval|MEDLINE|Names|Natural Language Processing|Proteins|PubMed|Systems Integration|Terminology as Topic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-88', '15817129', 'The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.'),
(90380, 2005, 'bmc', 'Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences', 'Gibbs, Mark J|Armstrong, John S|Gibbs, Adrian J', 'School of Botany and Zoology, Faculty of Science, Australian National University, ACT 0200, Australia. mark.gibbs@anu.edu.au|School of Botany and Zoology, Faculty of Science, Australian National University, ACT 0200, Australia. mark.gibbs@anu.edu.au|School of Botany and Zoology, Faculty of Science, Australian National University, ACT 0200, Australia. mark.gibbs@anu.edu.au', 'Australia|Australia|Australia', 5, 'Algorithms|Animals|Base Sequence|Computational Biology|DNA|DNA Primers|Data Interpretation, Statistical|Databases, Genetic|Electron Transport Complex IV|Gene Expression Profiling|Gene Expression Regulation|Genotype|Molecular Sequence Data|Moths|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-90', '15817134', 'Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridize with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by dichotomous keys that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way.'),
(90381, 2005, 'bmc', 'A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles', 'Möglich, Andreas|Weinfurtner, Daniel|Maurer, Till|Gronwald, Wolfram|Kalbitzer, Hans Robert', 'Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, D-93053 Regensburg, Germany. andreas.moeglich@unibas.ch|Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, D-93053 Regensburg, Germany. andreas.moeglich@unibas.ch|Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, D-93053 Regensburg, Germany. andreas.moeglich@unibas.ch|Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, D-93053 Regensburg, Germany. andreas.moeglich@unibas.ch|Institut für Biophysik und physikalische Biochemie, Universität Regensburg, Universitätsstr. 31, D-93053 Regensburg, Germany. andreas.moeglich@unibas.ch', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Bacterial Proteins|Computational Biology|Computer Graphics|Computer Simulation|Crystallography, X-Ray|Databases, Protein|Enterococcus faecalis|Escherichia coli|Histidine|Humans|Hydrogen Bonding|Magnetic Resonance Spectroscopy|Models, Chemical|Models, Molecular|Models, Statistical|Models, Theoretical|Molecular Conformation|PPAR gamma|Phosphoenolpyruvate Sugar Phosphotransferase System|Protein Conformation|Protein Folding|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-91', '15819976', 'We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Doker et al. (1999, BBRC, 257, 348-350). The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS.'),
(90382, 2005, 'bmc', 'Shortest triplet clustering: reconstructing large phylogenies using representative sets', 'Vinh, Le Sy|von Haeseler, Arndt', 'Heinrich-Heine-Universität Düsseldorf, WE Informatik, Universitätstr. 1, D-040225 Düsseldorf, Germany. vinh@cs.uni-duesseldorf.de|Heinrich-Heine-Universität Düsseldorf, WE Informatik, Universitätstr. 1, D-040225 Düsseldorf, Germany. vinh@cs.uni-duesseldorf.de', 'Germany|Germany', 0, 'Algorithms|Base Sequence|Cluster Analysis|Computational Biology|Computer Simulation|Computers|Data Interpretation, Statistical|Evolution, Molecular|Internet|Likelihood Functions|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-92', '15819989', 'Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics.'),
(90383, 2005, 'bmc', 'Windows NET Network Distributed Basic Local Alignment Search Toolkit (WND-BLAST)', 'Dowd, Scot E|Zaragoza, Joaquin|Rodriguez, Javier R|Oliver, Melvin J|Payton, Paxton R', 'Livestock Issues Research Unit, Agriculture Research Service, USDA, Lubbock, TX, USA. sdowd@lbk.ars.usda.gov|Livestock Issues Research Unit, Agriculture Research Service, USDA, Lubbock, TX, USA. sdowd@lbk.ars.usda.gov|Livestock Issues Research Unit, Agriculture Research Service, USDA, Lubbock, TX, USA. sdowd@lbk.ars.usda.gov|Livestock Issues Research Unit, Agriculture Research Service, USDA, Lubbock, TX, USA. sdowd@lbk.ars.usda.gov|Livestock Issues Research Unit, Agriculture Research Service, USDA, Lubbock, TX, USA. sdowd@lbk.ars.usda.gov', 'United States|United States|United States|United States|United States', 114, 'Algorithms|Amino Acid Sequence|Computational Biology|Computer Graphics|Computers|Computing Methodologies|Data Interpretation, Statistical|Database Management Systems|Databases, Genetic|Databases, Protein|Information Storage and Retrieval|Programming Languages|Reproducibility of Results|Sequence Alignment|Sequence Analysis|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-93', '15819992', 'BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST), which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality.'),
(90384, 2005, 'bmc', 'PACLIMS: a component LIM system for high-throughput functional genomic analysis', 'Donofrio, Nicole|Rajagopalon, Ravi|Brown, Douglas|Diener, Stephen|Windham, Donald|Nolin, Shelly|Floyd, Anna|Mitchell, Thomas|Galadima, Natalia|Tucker, Sara|Orbach, Marc J|Patel, Gayatri|Farman, Mark|Pampanwar, Vishal|Soderlund, Cari|Lee, Yong-Hwan|Dean, Ralph A', 'Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu|Department of Plant Pathology, Fungal Genomics Laboratory, North Carolina State University, Raleigh, NC, USA. ndonofr@unity.ncsu.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Computational Biology|DNA|DNA Mutational Analysis|Data Interpretation, Statistical|Database Management Systems|Databases, Factual|Databases, Genetic|Evolution, Molecular|Gene Library|Genes, Fungal|Genome|Genome, Fungal|Internet|Magnaporthe|Mutation|Open Reading Frames|Proteome|Sequence Analysis, DNA|Sequence Homology|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-94', '15826298', 'Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the approximately 11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required.'),
(90385, 2005, 'bmc', 'Statistical Viewer: a tool to upload and integrate linkage and association data as plots displayed within the Ensembl genome browser', 'Stenger, Judith E|Xu, Hong|Haynes, Carol|Hauser, Elizabeth R|Pericak-Vance, Margaret|Goldschmidt-Clermont, Pascal J|Vance, Jeffery M', 'The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu|The Duke Center for Human Genetics, Duke University Medical Center, Durham, North Carolina 27710, USA. judy.stenger@duke.edu', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Chromosome Mapping|Computational Biology|Computer Graphics|Contig Mapping|Database Management Systems|Databases, Factual|Databases, Genetic|Genetic Linkage|Genome|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Models, Statistical|Programming Languages|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-95', '15826305', 'To facilitate efficient selection and the prioritization of candidate complex disease susceptibility genes for association analysis, increasingly comprehensive annotation tools are essential to integrate, visualize and analyze vast quantities of disparate data generated by genomic screens, public human genome sequence annotation and ancillary biological databases. We have developed a plug-in package for Ensembl called Statistical Viewer that facilitates the analysis of genomic features and annotation in the regions of interest defined by linkage analysis.'),
(90386, 2005, 'bmc', 'PREP-Mt: predictive RNA editor for plant mitochondrial genes', 'Mower, Jeffrey P', 'Department of Biology, Indiana University, Bloomington, IN 47405, USA. jpmower@indiana.edu', 'India', 63, 'Amino Acid Sequence|Base Sequence|Binding Sites|Chloroplasts|Computational Biology|Conserved Sequence|Cytidine|DNA|DNA, Plant|Databases, Protein|Genes, Plant|Genome, Plant|Mitochondrial Proteins|Models, Statistical|Molecular Sequence Data|Phylogeny|Plant Proteins|Protein Binding|RNA|RNA Editing|RNA, Chloroplast|RNA, Mitochondrial|Reproducibility of Results|Sensitivity and Specificity|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-96', '15826309', 'In plants, RNA editing is a process that converts specific cytidines to uridines and uridines to cytidines in transcripts from virtually all mitochondrial protein-coding genes. There are thousands of plant mitochondrial genes in the sequence databases, but sites of RNA editing have not been determined for most. Accurate methods of RNA editing site prediction will be important in filling in this information gap and could reduce or even eliminate the need for experimental determination of editing sites for many sequences. Because RNA editing tends to increase protein conservation across species by correcting codons that specify unconserved amino acids, this principle can be used to predict editing sites by identifying positions where an RNA editing event would increase the conservation of a protein to homologues from other plants. PREP-Mt takes this approach to predict editing sites for any protein-coding gene in plant mitochondria.'),
(90387, 2005, 'bmc', 'Many accurate small-discriminatory feature subsets exist in microarray transcript data: biomarker discovery', 'Grate, Leslie R', 'Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. lesliegrate@comcast.net', 'United States', 0, 'Biomarkers|Biomarkers, Tumor|Carcinoma, Hepatocellular|Cell Line, Tumor|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Liver Neoplasms|Markov Chains|Models, Molecular|Models, Theoretical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Protein Conformation|Sequence Analysis, DNA|Software|Software Design', 'Journal Article', '10.1186/1471-2105-6-97', '15826317', 'Molecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such two-class high-dimensional data, many methods have been proposed for classifying data points into one of the two classes. However, finding very small sets of features able to correctly classify the data is problematic as the fundamental mathematical proposition is hard. Existing methods can find small feature sets, but give no hint how close this is to the true minimum size. Without fundamental mathematical advances, finding true minimum-size sets will remain elusive, and more importantly for the microarray community there will be no methods for finding them.'),
(90388, 2005, 'bmc', 'ProGenExpress: visualization of quantitative data on prokaryotic genomes', 'Watson, Michael', 'Institute for Animal Health, Compton laboratory, High street, Compton, Newbury, RG20 7NN, UK. michael.watson@bbsrc.ac.uk', 'United Kingdom', 7, 'Computational Biology|Computer Graphics|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Genes, Bacterial|Genome, Bacterial|Oligonucleotide Array Sequence Analysis|Plasmids|Programming Languages|Proteomics|RNA, Bacterial|Salmonella typhimurium|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-6-98', '15829007', 'The integration of genomic information with quantitative experimental data is a key component of systems biology. An increasing number of microbial genomes are being sequenced, leading to an increasing amount of data from post-genomics technologies. The genomes of prokaryotes contain many structures of interest, such as operons, pathogenicity islands and prophage sequences, whose behaviour is of interest during infection and disease. There is a need for simple and novel tools to display and analyse data from these integrated datasets, and we have developed ProGenExpress as a tool for visualising arbitrarily complex numerical data in the context of prokaryotic genomes.'),
(90389, 2005, 'bmc', 'Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER', 'Wistrand, Markus|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden. markus.wistrand@cgb.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden. markus.wistrand@cgb.ki.se', 'Sweden|Sweden', 40, 'Algorithms|Base Sequence|Computational Biology|Data Interpretation, Statistical|Evaluation Studies as Topic|Markov Chains|Models, Biological|Models, Chemical|Models, Genetic|Models, Molecular|Models, Statistical|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-99', '15831105', 'Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.'),
(90390, 2005, 'bmc', 'Filtering high-throughput protein-protein interaction data using a combination of genomic features', 'Patil, Ashwini|Nakamura, Haruki', 'Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan. ashwini@protein.osaka-u.ac.jp|Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan. ashwini@protein.osaka-u.ac.jp', 'Japan|Japan', 0, 'Animals|Automation|Binding Sites|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genes, Fungal|Genome|Humans|Likelihood Functions|Models, Statistical|Protein Binding|Protein Folding|Protein Interaction Mapping|Proteomics|ROC Curve|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity|Software|Software Design|Transcription Factors', 'Journal Article', '10.1186/1471-2105-6-100', '15833142', 'Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.'),
(90391, 2005, 'bmc', 'MARS: microarray analysis, retrieval, and storage system', 'Maurer, Michael|Molidor, Robert|Sturn, Alexander|Hartler, Juergen|Hackl, Hubert|Stocker, Gernot|Prokesch, Andreas|Scheideler, Marcel|Trajanoski, Zlatko', 'Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com|Institute for Genomics and Bioinformatics, Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria. michael.maurer@gmail.com', 'Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria|Austria', 46, 'Algorithms|Computational Biology|Computer Graphics|Database Management Systems|Gene Expression Profiling|Genome|Information Storage and Retrieval|Internet|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|Software Design|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-101', '15836795', 'Microarray analysis has become a widely used technique for the study of gene-expression patterns on a genomic scale. As more and more laboratories are adopting microarray technology, there is a need for powerful and easy to use microarray databases facilitating array fabrication, labeling, hybridization, and data analysis. The wealth of data generated by this high throughput approach renders adequate database and analysis tools crucial for the pursuit of insights into the transcriptomic behavior of cells.'),
(90392, 2005, 'bmc', 'Evolutionary distance estimation and fidelity of pair wise sequence alignment', 'Rosenberg, Michael S', 'Center for Evolutionary Functional Genomics, The Biodesign Institute, and The School of Life Sciences, Arizona State University, Tempe, AZ 84287-4501, USA. msr@asu.edu', 'United States', 29, 'Algorithms|Animals|Biological Evolution|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Protein|Evolution, Molecular|Gene Deletion|Genomics|Humans|Models, Genetic|Models, Molecular|Pattern Recognition, Automated|Phylogeny|Proteins|Sensitivity and Specificity|Sequence Analysis, Protein|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-6-102', '15840174', 'Evolutionary distances are a critical measure in comparative genomics and molecular evolutionary biology. A simulation study was used to examine the effect of alignment accuracy of DNA sequences on evolutionary distance estimation.'),
(90393, 2005, 'bmc', 'A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models', 'Sgourakis, Nikolaos G|Bagos, Pantelis G|Papasaikas, Panagiotis K|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. nsgourakis@biol.uoa.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. nsgourakis@biol.uoa.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. nsgourakis@biol.uoa.gr|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece. nsgourakis@biol.uoa.gr', 'Greece|Greece|Greece|Greece', 0, 'Algorithms|Amino Acid Sequence|Animals|Binding Sites|Computational Biology|Databases, Protein|Humans|Ligands|Markov Chains|Models, Biological|Models, Chemical|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Interaction Mapping|Receptors, Cell Surface|Receptors, G-Protein-Coupled|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-104', '15847681', 'G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90394, 2005, 'bmc', 'Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts', 'Cohen, A M|Hersh, W R|Dubay, C|Spackman, K', 'Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu|Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu|Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu|Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 S,W, Sam Jackson Park Road, Portland, Oregon 97239-3098, USA. cohenaa@ohsu.edu', 'United States|United States|United States|United States', 44, 'Algorithms|Artificial Intelligence|Automation|Computational Biology|Computer Graphics|Computers|Database Management Systems|Databases, Bibliographic|Databases, Genetic|Gene Expression Regulation, Neoplastic|Genome|Humans|Information Storage and Retrieval|Information Systems|MEDLINE|Natural Language Processing|Neoplasms|Neural Networks (Computer)|Pattern Recognition, Automated|Programming Languages|Reproducibility of Results|Software|Software Design|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-103', '15847682', 'Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction.'),
(90395, 2005, 'bmc', 'Detailed protein sequence alignment based on Spectral Similarity Score (SSS)', 'Gupta, Kshitiz|Thomas, Dina|Vidya, S V|Venkatesh, K V|Ramakumar, S', 'Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in|Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in|Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in|Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in|Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in', 'India|India|India|India|India', 7, 'Algorithms|Amino Acid Sequence|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Databases, Factual|Databases, Protein|Humans|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Peptide Mapping|Phosphotransferases|Protein Conformation|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-105', '15850477', 'The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.'),
(90396, 2005, 'bmc', 'Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments', 'Liu, Hua|Tarima, Sergey|Borders, Aaron S|Getchell, Thomas V|Getchell, Marilyn L|Stromberg, Arnold J', 'Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu|Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. hualiu@ms.uky.edu', 'United States|United States|United States|United States|United States|United States', 39, 'Algorithms|Analysis of Variance|Animals|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Gene Library|Genomics|Humans|Models, Theoretical|Olfactory Receptor Neurons|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Probability|Regression Analysis|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Software|Time Factors', 'Journal Article', '10.1186/1471-2105-6-106', '15850479', 'Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together.'),
(90397, 2005, 'bmc', 'Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements', 'Carter, Scott L|Eklund, Aron C|Mecham, Brigham H|Kohane, Isaac S|Szallasi, Zoltan', 'Childrens Hospital Informatics Program, Harvard Medical School, Boston, MA 02115 USA. scott.carter@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard Medical School, Boston, MA 02115 USA. scott.carter@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard Medical School, Boston, MA 02115 USA. scott.carter@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard Medical School, Boston, MA 02115 USA. scott.carter@childrens.harvard.edu|Childrens Hospital Informatics Program, Harvard Medical School, Boston, MA 02115 USA. scott.carter@childrens.harvard.edu', 'United States|United States|United States|United States|United States', 100, 'Breast Neoplasms|Cell Line, Tumor|Cluster Analysis|Computational Biology|DNA Probes|DNA, Complementary|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Lung Neoplasms|Neoplasms|Oligonucleotide Array Sequence Analysis|Phylogeny|RNA|RNA, Neoplasm|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-6-107', '15850491', 'Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples.'),
(90398, 2005, 'bmc', 'Some statistical properties of regulatory DNA sequences, and their use in predicting regulatory regions in the Drosophila genome: the fluffy-tail test', 'Abnizova, Irina|te Boekhorst, Rene|Walter, Klaudia|Gilks, Walter R', 'MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK. irina.abnizova@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK. irina.abnizova@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK. irina.abnizova@mrc-bsu.cam.ac.uk|MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, UK. irina.abnizova@mrc-bsu.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Amino Acid Motifs|Animals|Base Sequence|Binding Sites|Cell Nucleus|Chromatin|Cluster Analysis|Computational Biology|DNA|Databases, Genetic|Drosophila melanogaster|Genes, Insect|Genes, Regulator|Genome|Genomics|Models, Statistical|Molecular Sequence Data|Sequence Analysis, DNA|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-6-109', '15857505', 'This paper addresses the problem of recognising DNA cis-regulatory modules which are located far from genes. Experimental procedures for this are slow and costly, and computational methods are hard, because they lack positional information.'),
(90399, 2005, 'bmc', 'Scoredist: a simple and robust protein sequence distance estimator', 'Sonnhammer, Erik L L|Hollich, Volker', 'Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius väg 35, 171 77 Stockholm, Sweden. erik.sonnhammer@cgb.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius väg 35, 171 77 Stockholm, Sweden. erik.sonnhammer@cgb.ki.se', 'Sweden|Sweden', 82, 'Algorithms|Amino Acid Sequence|Biological Evolution|Calibration|Computational Biology|Computer Simulation|Evolution, Molecular|Likelihood Functions|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Pattern Recognition, Automated|Phylogeny|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-108', '15857510', 'Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.'),
(90400, 2005, 'bmc', 'Ab initio identification of putative human transcription factor binding sites by comparative genomics', 'Corà, D|Herrmann, C|Dieterich, C|Di Cunto, F|Provero, P|Caselle, M', 'Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it|Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it|Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it|Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it|Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it|Dipartimento di Fisica Teorica, Università degli Studi di Torino and INFN, Via P. Giuria 1 - I 10125 Turin, Italy. cora@to.infn.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 26, 'Algorithms|Amino Acid Motifs|Binding Sites|Computational Biology|Computer Graphics|DNA|Gene Expression Regulation|Genome|Genome, Human|Genomics|Humans|Nucleotides|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-6-110', '15865625', 'Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors, which typically bind to specific, short DNA sequence motifs usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the ab initio identification of these cis-regulatory motifs. The method we present integrates several elements: human-mouse comparison, statistical analysis of genomic sequences and the concept of coregulation. We apply it to a complete scan of the human genome.'),
(90401, 2005, 'bmc', 'Refined repetitive sequence searches utilizing a fast hash function and cross species information retrievals', 'Reneker, Jeff|Shyu, Chi-Ren', 'Department of Computer Science, University of Missouri, Columbia, USA. jeff@diglib1.cecs.missouri.edu|Department of Computer Science, University of Missouri, Columbia, USA. jeff@diglib1.cecs.missouri.edu', 'United States|United States', 9, 'Algorithms|Animals|Base Sequence|Computational Biology|Database Management Systems|Databases, Factual|Databases, Genetic|Gene Expression Profiling|Genome|Haemophilus influenzae|Helicobacter pylori|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Programming Languages|Promoter Regions, Genetic|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-111', '15869708', 'Searching for small tandem/disperse repetitive DNA sequences streamlines many biomedical research processes. For instance, whole genomic array analysis in yeast has revealed 22 PHO-regulated genes. The promoter regions of all but one of them contain at least one of the two core Pho4p binding sites, CACGTG and CACGTT. In humans, microsatellites play a role in a number of rare neurodegenerative diseases such as spinocerebellar ataxia type 1 (SCA1). SCA1 is a hereditary neurodegenerative disease caused by an expanded CAG repeat in the coding sequence of the gene. In bacterial pathogens, microsatellites are proposed to regulate expression of some virulence factors. For example, bacteria commonly generate intra-strain diversity through phase variation which is strongly associated with virulence determinants. A recent analysis of the complete sequences of the Helicobacter pylori strains 26695 and J99 has identified 46 putative phase-variable genes among the two genomes through their association with homopolymeric tracts and dinucleotide repeats. Life scientists are increasingly interested in studying the function of small sequences of DNA. However, current search algorithms often generate thousands of matches -- most of which are irrelevant to the researcher.'),
(90402, 2005, 'bmc', 'Confirmation of human protein interaction data by human expression data', 'Hahn, Andreas|Rahnenführer, Jörg|Talwar, Priti|Lengauer, Thomas', 'Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. andreas.hahn@mpi-sb.mpg.de|Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. andreas.hahn@mpi-sb.mpg.de|Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. andreas.hahn@mpi-sb.mpg.de|Max-Planck-Institute for Informatics, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. andreas.hahn@mpi-sb.mpg.de', 'Germany|Germany|Germany|Germany', 26, 'Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Protein Binding|Protein Interaction Mapping|Proteome', 'Journal Article', '10.1186/1471-2105-6-112', '15877815', 'With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.'),
(90403, 2005, 'bmc', 'Integration of the Gene Ontology into an object-oriented architecture', 'Shegogue, Daniel|Zheng, W Jim', 'Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Charleston, SC 29425, USA. shegogue@musc.edu|Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Charleston, SC 29425, USA. shegogue@musc.edu', 'United States|United States', 18, 'Algorithms|Animals|Computational Biology|Computer Graphics|Computer Simulation|Computer Systems|Computing Methodologies|Database Management Systems|Databases, Factual|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Models, Biological|Molecular Biology|Names|Programming Languages|Software|Software Design|Systems Integration|Terminology as Topic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-113', '15885145', 'To standardize gene product descriptions, a formal vocabulary defined as the Gene Ontology (GO) has been developed. GO terms have been categorized into biological processes, molecular functions, and cellular components. However, there is no single representation that integrates all the terms into one cohesive model. Furthermore, GO definitions have little information explaining the underlying architecture that forms these terms, such as the dynamic and static events occurring in a process. In contrast, object-oriented models have been developed to show dynamic and static events. A portion of the TGF-beta signaling pathway, which is involved in numerous cellular events including cancer, differentiation and development, was used to demonstrate the feasibility of integrating the Gene Ontology into an object-oriented model.'),
(90404, 2005, 'bmc', 'CAGER: classification analysis of gene expression regulation using multiple information sources', 'Ruan, Jianhua|Zhang, Weixiong', 'Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA. jruan@cse.wustl.edu|Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA. jruan@cse.wustl.edu', 'United States|United States', 6, 'Algorithms|Arabidopsis|Binding Sites|Chromatin Immunoprecipitation|Computer Graphics|DNA-Binding Proteins|Data Interpretation, Statistical|Down-Regulation|Gene Expression Profiling|Gene Expression Regulation|Genes, Fungal|Genes, Plant|Models, Biological|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Programming Languages|Promoter Regions, Genetic|Regulatory Sequences, Nucleic Acid|Response Elements|Software|Software Design|Transcription, Genetic|Up-Regulation|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-114', '15890068', 'Many classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a genes expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features.'),
(90405, 2005, 'bmc', 'Visualization methods for statistical analysis of microarray clusters', 'Hibbs, Matthew A|Dirksen, Nathaniel C|Li, Kai|Troyanskaya, Olga G', 'Computer Science Department, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA. mhibbs@cs.princeton.edu|Computer Science Department, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA. mhibbs@cs.princeton.edu|Computer Science Department, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA. mhibbs@cs.princeton.edu|Computer Science Department, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA. mhibbs@cs.princeton.edu', 'United States|United States|United States|United States', 45, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Graphics|Computers|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-115', '15890080', 'The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determine which clustering algorithm is most appropriate to apply, and it is difficult to verify the results of any algorithm due to the lack of a gold-standard. Appropriate data visualization tools can aid this analysis process, but existing visualization methods do not specifically address this issue.'),
(90406, 2005, 'bmc', 'Predicting functional sites with an automated algorithm suitable for heterogeneous datasets', 'La, David|Livesay, Dennis R', 'Department of Biological Sciences, California State Polytechnic University, Pomona, California 91768, USA. dla@csupomona.edu|Department of Biological Sciences, California State Polytechnic University, Pomona, California 91768, USA. dla@csupomona.edu', 'United States|United States', 20, 'Algorithms|Amino Acid Motifs|Automation|Binding Sites|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Conserved Sequence|Databases, Protein|Internet|Models, Molecular|Molecular Sequence Data|Phylogeny|Protein Binding|Protein Conformation|Protein Folding|RNA, Transfer|Sequence Alignment|Sequence Analysis, Protein|Software|Software Design|Triose-Phosphate Isomerase|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-116', '15890082', 'In a previous report (La et al., Proteins, 2005), we have demonstrated that the identification of phylogenetic motifs, protein sequence fragments conserving the overall familial phylogeny, represent a promising approach for sequence/function annotation. Across a structurally and functionally heterogeneous dataset, phylogenetic motifs have been demonstrated to correspond to a wide variety of functional site archetypes, including those defined by surface loops, active site clefts, and less exposed regions. However, in our original demonstration of the technique, phylogenetic motif identification is dependent upon a manually determined similarity threshold, prohibiting large-scale application of the technique.'),
(90407, 2005, 'bmc', 'Computing expectation values for RNA motifs using discrete convolutions', 'Lambert, André|Legendre, Matthieu|Fontaine, Jean-Fred|Gautheret, Daniel', 'CNRS UMR 6207, Université de la Méditerranée, Luminy Case 907, 13288 Marseille cedex 9, France. lambert@cpt.univ-mrs.fr|CNRS UMR 6207, Université de la Méditerranée, Luminy Case 907, 13288 Marseille cedex 9, France. lambert@cpt.univ-mrs.fr|CNRS UMR 6207, Université de la Méditerranée, Luminy Case 907, 13288 Marseille cedex 9, France. lambert@cpt.univ-mrs.fr|CNRS UMR 6207, Université de la Méditerranée, Luminy Case 907, 13288 Marseille cedex 9, France. lambert@cpt.univ-mrs.fr', 'France|France|France|France', 0, 'Algorithms|Animals|Base Sequence|Computational Biology|Data Interpretation, Statistical|Humans|Models, Statistical|Models, Theoretical|Nucleic Acid Conformation|RNA|Regulatory Sequences, Ribonucleic Acid|Selenoproteins|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1186/1471-2105-6-118', '15892887', 'Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations.'),
(90408, 2005, 'bmc', 'Robust detection of periodic time series measured from biological systems', 'Ahdesmäki, Miika|Lähdesmäki, Harri|Pearson, Ron|Huttunen, Heikki|Yli-Harja, Olli', 'Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. miika.ahdesmaki@tut.fi|Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland. miika.ahdesmaki@tut.fi', 'Finland|Finland|Finland|Finland|Finland', 95, 'Algorithms|Animals|Cluster Analysis|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Likelihood Functions|Models, Biological|Models, Statistical|Neural Networks (Computer)|Normal Distribution|Oligonucleotide Array Sequence Analysis|Sample Size|Software|Statistics, Nonparametric|Time Factors', 'Journal Article', '10.1186/1471-2105-6-117', '15892890', 'Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data.'),
(90409, 2005, 'bmc', 'Are scale-free networks robust to measurement errors?', 'Lin, Nan|Zhao, Hongyu', 'Department of Mathematics, Washington University, St. Louis, MO 63143, USA. nlin@math.wustl.edu|Department of Mathematics, Washington University, St. Louis, MO 63143, USA. nlin@math.wustl.edu', 'United States|United States', 13, 'Algorithms|Computational Biology|Computer Simulation|Databases, Protein|Fungal Proteins|Least-Squares Analysis|Models, Biological|Neural Networks (Computer)|Protein Interaction Mapping|Proteome|Proteomics|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software', 'Journal Article', '10.1186/1471-2105-6-119', '15904487', 'Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks.'),
(90410, 2005, 'bmc', 'The effects of normalization on the correlation structure of microarray data', 'Qiu, Xing|Brooks, Andrew I|Klebanov, Lev|Yakovlev, Ndrei', 'Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA. Xing_Qiu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA. Xing_Qiu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA. Xing_Qiu@urmc.rochester.edu|Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA. Xing_Qiu@urmc.rochester.edu', 'United States|United States|United States|United States', 82, 'Algorithms|Analysis of Variance|Computational Biology|Computer Simulation|Confidence Intervals|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Software|Statistics, Nonparametric|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-6-120', '15904488', 'Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test-statistics across genes. It is frequently assumed that dependence between genes (or tests) is sufficiently weak to justify the proposed methods of testing for differentially expressed genes. A potential impact of between-gene correlations on the performance of such methods has yet to be explored.'),
(90411, 2005, 'bmc', 'A multistep bioinformatic approach detects putative regulatory elements in gene promoters', 'Bortoluzzi, Stefania|Coppe, Alessandro|Bisognin, Andrea|Pizzi, Cinzia|Danieli, Gian Antonio', 'Department of Biology, University of Padova - Via Bassi 58/B, 35131, Padova, Italy. stefibo@bio.unipd.it|Department of Biology, University of Padova - Via Bassi 58/B, 35131, Padova, Italy. stefibo@bio.unipd.it|Department of Biology, University of Padova - Via Bassi 58/B, 35131, Padova, Italy. stefibo@bio.unipd.it|Department of Biology, University of Padova - Via Bassi 58/B, 35131, Padova, Italy. stefibo@bio.unipd.it|Department of Biology, University of Padova - Via Bassi 58/B, 35131, Padova, Italy. stefibo@bio.unipd.it', 'Italy|Italy|Italy|Italy|Italy', 16, 'Algorithms|Amino Acid Motifs|Cluster Analysis|Computational Biology|Conserved Sequence|Databases, Genetic|Databases, Protein|Expressed Sequence Tags|Gene Expression Profiling|Gene Expression Regulation|Genome, Human|Humans|Molecular Sequence Data|Promoter Regions, Genetic|Regulatory Elements, Transcriptional|Regulatory Sequences, Nucleic Acid|Response Elements|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-121', '15904489', 'Searching for approximate patterns in large promoter sequences frequently produces an exceedingly high numbers of results. Our aim was to exploit biological knowledge for definition of a sheltered search space and of appropriate search parameters, in order to develop a method for identification of a tractable number of sequence motifs.'),
(90412, 2005, 'bmc', 'arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays', 'Menten, Björn|Pattyn, Filip|De Preter, Katleen|Robbrecht, Piet|Michels, Evi|Buysse, Karen|Mortier, Geert|De Paepe, Anne|van Vooren, Steven|Vermeesch, Joris|Moreau, Yves|De Moor, Bart|Vermeulen, Stefan|Speleman, Frank|Vandesompele, Jo', 'Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>|Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, B-9000 Ghent, Belgium. bjorn.menten@ugent.be <bjorn.menten@ugent.be>', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belg', 72, 'Base Sequence|Chromosomes|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Genes, Neoplasm|Genes, Reporter|Genome, Human|Genomics|Humans|Information Storage and Retrieval|Internet|Neoplasms|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Programming Languages|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-124', '15910681', 'The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment.'),
(90413, 2005, 'bmc', 'Genome comparison without alignment using shortest unique substrings', 'Haubold, Bernhard|Pierstorff, Nora|Möller, Friedrich|Wiehe, Thomas', 'Department of Biotechnology and Bioinformatics, University of Applied Sciences, Weihenstephan, Germany. bernhard.haubold@fh-weihenstephan.de|Department of Biotechnology and Bioinformatics, University of Applied Sciences, Weihenstephan, Germany. bernhard.haubold@fh-weihenstephan.de|Department of Biotechnology and Bioinformatics, University of Applied Sciences, Weihenstephan, Germany. bernhard.haubold@fh-weihenstephan.de|Department of Biotechnology and Bioinformatics, University of Applied Sciences, Weihenstephan, Germany. bernhard.haubold@fh-weihenstephan.de', 'Germany|Germany|Germany|Germany', 55, 'Algorithms|Animals|Base Sequence|Caenorhabditis elegans|Computational Biology|DNA|Databases, Genetic|Databases, Protein|Genome|Genome, Human|Genomics|Humans|Mice|Models, Statistical|Molecular Sequence Data|Probability|Sequence Alignment|Sequence Analysis|Software|Species Specificity|Staphylococcus aureus', 'Journal Article', '10.1186/1471-2105-6-123', '15910684', 'Sequence comparison by alignment is a fundamental tool of molecular biology. In this paper we show how a number of sequence comparison tasks, including the detection of unique genomic regions, can be accomplished efficiently without an alignment step. Our procedure for nucleotide sequence comparison is based on shortest unique substrings. These are substrings which occur only once within the sequence or set of sequences analysed and which cannot be further reduced in length without losing the property of uniqueness. Such substrings can be detected using generalized suffix trees.'),
(90414, 2005, 'bmc', 'Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking', 'Hamahashi, Shugo|Onami, Shuichi|Kitano, Hiroaki', 'Kitano Symbiotic Systems Project, ERATO, Japan Science and Technology Corporation, M31 6A, 6-31-15 Jingumae, Shibuya, Tokyo 150-0001, Japan. shugo@so.bio.keio.ac.jp|Kitano Symbiotic Systems Project, ERATO, Japan Science and Technology Corporation, M31 6A, 6-31-15 Jingumae, Shibuya, Tokyo 150-0001, Japan. shugo@so.bio.keio.ac.jp|Kitano Symbiotic Systems Project, ERATO, Japan Science and Technology Corporation, M31 6A, 6-31-15 Jingumae, Shibuya, Tokyo 150-0001, Japan. shugo@so.bio.keio.ac.jp', 'Japan|Japan|Japan', 38, 'Algorithms|Animals|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Cell Division|Cell Nucleus|Computational Biology|Entropy|Image Interpretation, Computer-Assisted|Image Processing, Computer-Assisted|Information Storage and Retrieval|Microscopy, Interference|Sensitivity and Specificity|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-125', '15910690', 'The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D) Nomarski differential interference contrast (DIC) microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages.'),
(90415, 2005, 'bmc', 'Comparative analysis of chromatin landscape in regulatory regions of human housekeeping and tissue specific genes', 'Ganapathi, Mythily|Srivastava, Pragya|Das Sutar, Sushanta Kumar|Kumar, Kaushal|Dasgupta, Dipayan|Pal Singh, Gajinder|Brahmachari, Vani|Brahmachari, Samir K', 'Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in|Dr. B. R. Ambedkar Centre for Biomedical Research, University of Delhi, Delhi-110007, India. mythilyg@igib.res.in', 'India|India|India|India|India|India|India|India', 45, 'Cell Nucleus|Chromatin|Chromatin Assembly and Disassembly|Computational Biology|Databases, Genetic|Enhancer Elements, Genetic|Gene Expression Regulation|Genome, Human|Humans|Models, Genetic|Nucleosomes|Regulatory Sequences, Nucleic Acid|Repetitive Sequences, Nucleic Acid|Software|Tissue Distribution|Transcription, Genetic', 'Comparative Study', '10.1186/1471-2105-6-126', '15918906', 'Global regulatory mechanisms involving chromatin assembly and remodelling in the promoter regions of genes is implicated in eukaryotic transcription control especially for genes subjected to spatial and temporal regulation. The potential to utilise global regulatory mechanisms for controlling gene expression might depend upon the architecture of the chromatin in and around the gene. In-silico analysis can yield important insights into this aspect, facilitating comparison of two or more classes of genes comprising of a large number of genes within each group.'),
(90416, 2005, 'bmc', 'Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models', 'Hu, Pingzhao|Greenwood, Celia M T|Beyene, Joseph', 'The Hospital for Sick Children Research Institute, 555 University Ave,, Toronto, ON, M5G 1X8, Canada. phu@sickkids.ca|The Hospital for Sick Children Research Institute, 555 University Ave,, Toronto, ON, M5G 1X8, Canada. phu@sickkids.ca|The Hospital for Sick Children Research Institute, 555 University Ave,, Toronto, ON, M5G 1X8, Canada. phu@sickkids.ca', 'Canada|Canada|Canada', 40, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Lung Neoplasms|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Quality Control', 'Journal Article', '10.1186/1471-2105-6-128', '15921507', 'With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.'),
(90417, 2005, 'bmc', 'Phylogenetic reconstruction of ancestral character states for gene expression and mRNA splicing data', 'Rossnes, Roald|Eidhammer, Ingvar|Liberles, David A', 'Bergen Centre for Computational Science, University of Bergen, 5020 Bergen, Norway. roald@rossnes.org|Bergen Centre for Computational Science, University of Bergen, 5020 Bergen, Norway. roald@rossnes.org|Bergen Centre for Computational Science, University of Bergen, 5020 Bergen, Norway. roald@rossnes.org', 'Norway|Norway|Norway', 10, 'Alternative Splicing|Animals|Biological Evolution|Brain|Cell Lineage|Computational Biology|Evolution, Molecular|Gene Expression|Gene Expression Regulation|Genome|Genomics|Humans|Liver|Models, Genetic|Molecular Sequence Data|Pan troglodytes|Phylogeny|Pongo pygmaeus|RNA, Messenger|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-127', '15921519', 'As genomes evolve after speciation, gene content, coding sequence, gene expression, and splicing all diverge with time from ancestors with close relatives. A minimum evolution general method for continuous character analysis in a phylogenetic perspective is presented that allows for reconstruction of ancestral character states and for measuring along branch evolution.'),
(90418, 2005, 'bmc', 'Vestige: maximum likelihood phylogenetic footprinting', 'Wakefield, Matthew J|Maxwell, Peter|Huttley, Gavin A', 'John Curtin School of Medical Research, The Australian National University, Canberra 0200 ACT, Australia. matthew.wakefield@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra 0200 ACT, Australia. matthew.wakefield@anu.edu.au|John Curtin School of Medical Research, The Australian National University, Canberra 0200 ACT, Australia. matthew.wakefield@anu.edu.au', 'Australia|Australia|Australia', 8, 'Algorithms|Animals|BRCA1 Protein|Base Sequence|Codon|Computational Biology|Computer Simulation|DNA|DNA Repair|Data Interpretation, Statistical|Evolution, Molecular|Genome|Humans|Likelihood Functions|Models, Biological|Models, Statistical|Phylogeny|Programming Languages|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Species Specificity|Time Factors', 'Journal Article', '10.1186/1471-2105-6-130', '15921531', 'Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process.'),
(90419, 2005, 'bmc', 'Considerations when using the significance analysis of microarrays (SAM) algorithm', 'Larsson, Ola|Wahlestedt, Claes|Timmons, James A', 'Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se|Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius Väg, 35, 171 77 Stockholm, Sweden. ola.larsson@cgb.ki.se', 'Sweden|Sweden|Sweden', 102, 'Adult|Aged|Algorithms|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Microarray Analysis|Middle Aged|Models, Genetic|Models, Statistical|Muscle, Skeletal|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Research Design|Software|Software Design', 'Journal Article', '10.1186/1471-2105-6-129', '15921534', 'Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments. One of the most frequently utilised methods for gene expression data analysis is SAM (significance analysis of microarrays). The impact of selection thresholds, on the output from SAM, may critically alter the conclusion of a study, yet this consideration has not been systematically evaluated in any publication.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90420, 2005, 'bmc', 'Gene finding in the chicken genome', 'Eyras, Eduardo|Reymond, Alexandre|Castelo, Robert|Bye, Jacqueline M|Camara, Francisco|Flicek, Paul|Huckle, Elizabeth J|Parra, Genis|Shteynberg, David D|Wyss, Carine|Rogers, Jane|Antonarakis, Stylianos E|Birney, Ewan|Guigo, Roderic|Brent, Michael R', 'Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es|Institut Municipal dInvestigacio Medica, Universitat Pompeu Fabra, Centre de Regulacio Genomica, E08003 Barcelona, Catalonia, Spain. eeyras@imim.es', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain', 0, 'Animals|Chickens|Chromosome Mapping|Computational Biology|DNA Primers|DNA, Complementary|Data Interpretation, Statistical|Databases, Genetic|Exons|Expressed Sequence Tags|Gene Expression Profiling|Genome|Introns|Models, Statistical|Reverse Transcriptase Polymerase Chain Reaction|Sequence Alignment|Sequence Analysis, DNA|Software|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-6-131', '15924626', 'Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method.'),
(90421, 2005, 'bmc', 'SeqDoC: rapid SNP and mutation detection by direct comparison of DNA sequence chromatograms', 'Crowe, Mark L', 'Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia. m.crowe@imb.uq.edu.au', 'Australia', 30, 'Base Sequence|Chromatography|Computational Biology|Computer Graphics|DNA Mutational Analysis|DNA Primers|Database Management Systems|Databases, Genetic|Gene Deletion|Genotype|Heterozygote|Homozygote|Humans|Information Storage and Retrieval|Molecular Sequence Data|Mutagenesis|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Point Mutation|Polymorphism, Genetic|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-133', '15927052', 'This paper describes SeqDoC, a simple, web-based tool to carry out direct comparison of ABI sequence chromatograms. This allows the rapid identification of single nucleotide polymorphisms (SNPs) and point mutations without the need to install or learn more complicated analysis software.'),
(90422, 2005, 'bmc', 'Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method', 'Peters, Bjoern|Sette, Alessandro', 'La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. bjoern_peters@gmx.net|La Jolla Institute for Allergy and Immunology, 3030 Bunker Hill Street, Suite 326, San Diego, CA 92109, USA. bjoern_peters@gmx.net', 'United States|United States', 293, 'Algorithms|Amino Acid Sequence|Biology|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Protein|Models, Biological|Models, Statistical|Neural Networks (Computer)|Peptide Library|Peptides|Programming Languages|Protein Binding|Sensitivity and Specificity|Software', 'Journal Article', '10.1186/1471-2105-6-132', '15927070', 'Many processes in molecular biology involve the recognition of short sequences of nucleic-or amino acids, such as the binding of immunogenic peptides to major histocompatibility complex (MHC) molecules. From experimental data, a model of the sequence specificity of these processes can be constructed, such as a sequence motif, a scoring matrix or an artificial neural network. The purpose of these models is two-fold. First, they can provide a summary of experimental results, allowing for a deeper understanding of the mechanisms involved in sequence recognition. Second, such models can be used to predict the experimental outcome for yet untested sequences. In the past we reported the development of a method to generate such models called the Stabilized Matrix Method (SMM). This method has been successfully applied to predicting peptide binding to MHC molecules, peptide transport by the transporter associated with antigen presentation (TAP) and proteasomal cleavage of protein sequences.'),
(90423, 2005, 'bmc', 'Empirical codon substitution matrix', 'Schneider, Adrian|Cannarozzi, Gina M|Gonnet, Gaston H', 'Institute of Computational Science, Swiss Federal Institute of Technology, Zurich, Switzerland. schneadr@inf.ethz.ch <schneadr@inf.ethz.ch>|Institute of Computational Science, Swiss Federal Institute of Technology, Zurich, Switzerland. schneadr@inf.ethz.ch <schneadr@inf.ethz.ch>|Institute of Computational Science, Swiss Federal Institute of Technology, Zurich, Switzerland. schneadr@inf.ethz.ch <schneadr@inf.ethz.ch>', 'Switzerland|Switzerland|Switzerland', 59, 'Amino Acid Substitution|Animals|Base Sequence|Biological Evolution|Chickens|Codon|Computational Biology|Computer Simulation|Evolution, Molecular|Humans|Likelihood Functions|Mice|Models, Genetic|Models, Statistical|Mutation|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software|Species Specificity|Xenopus|Zebrafish', 'Journal Article', '10.1186/1471-2105-6-134', '15927081', 'Codon substitution probabilities are used in many types of molecular evolution studies such as determining Ka/Ks ratios, creating ancestral DNA sequences or aligning coding DNA. Until the recent dramatic increase in genomic data enabled construction of empirical matrices, researchers relied on parameterized models of codon evolution. Here we present the first empirical codon substitution matrix entirely built from alignments of coding sequences from vertebrate DNA and thus provide an alternative to parameterized models of codon evolution.'),
(90424, 2005, 'bmc', 'YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities', 'Schwarz, Roland|Musch, Patrick|von Kamp, Axel|Engels, Bernd|Schirmer, Heiner|Schuster, Stefan|Dandekar, Thomas', 'Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de|Dept of Bioinformatics, Biocenter, University of Würzburg, Germany. roland.schwarz@biozentrum.uni-wuerzburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 62, 'Algorithms|Animals|Cell Physiological Phenomena|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Enzymes|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Genomics|Glutathione Reductase|Humans|Internet|Models, Biological|Models, Statistical|Proteomics|Software|Software Design|Systems Integration|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-135', '15929789', 'A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest.'),
(90425, 2005, 'bmc', 'AVID: an integrative framework for discovering functional relationships among proteins', 'Jiang, Taijiao|Keating, Amy E', 'Department of Biology, Massachusetts Institute of Technology, Cambridge, USA. taijiao@moon.ibp.ac.cn|Department of Biology, Massachusetts Institute of Technology, Cambridge, USA. taijiao@moon.ibp.ac.cn', 'United States|United States', 29, 'Algorithms|Computational Biology|Databases, Genetic|Databases, Protein|Gene Expression Regulation, Fungal|Genes, Fungal|Genome, Fungal|Internet|Oligonucleotide Array Sequence Analysis|Programming Languages|Protein Interaction Mapping|Proteins|Proteomics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-136', '15929793', 'Determining the functions of uncharacterized proteins is one of the most pressing problems in the post-genomic era. Large scale protein-protein interaction assays, global mRNA expression analyses and systematic protein localization studies provide experimental information that can be used for this purpose. The data from such experiments contain many false positives and false negatives, but can be processed using computational methods to provide reliable information about protein-protein relationships and protein function. An outstanding and important goal is to predict detailed functional annotation for all uncharacterized proteins that is reliable enough to effectively guide experiments.'),
(90426, 2005, 'bmc', 'SplitTester: software to identify domains responsible for functional divergence in protein family', 'Gao, Xiang|Vander Velden, Kent A|Voytas, Daniel F|Gu, Xun', 'Department of Genetics, Development & Cell Biology, Iowa State University, Ames, Iowa 50011, USA. gaoxiang@iastate.edu|Department of Genetics, Development & Cell Biology, Iowa State University, Ames, Iowa 50011, USA. gaoxiang@iastate.edu|Department of Genetics, Development & Cell Biology, Iowa State University, Ames, Iowa 50011, USA. gaoxiang@iastate.edu|Department of Genetics, Development & Cell Biology, Iowa State University, Ames, Iowa 50011, USA. gaoxiang@iastate.edu', 'United States|United States|United States|United States', 8, 'Algorithms|Amino Acid Sequence|Base Sequence|Cluster Analysis|Computational Biology|Computer Graphics|DNA Primers|Databases, Protein|Evolution, Molecular|Gene Duplication|Models, Genetic|Molecular Sequence Data|Multigene Family|Phylogeny|Programming Languages|Protein Binding|Protein Structure, Tertiary|Proteins|RNA-Directed DNA Polymerase|Retroelements|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-6-137', '15929795', 'Many protein families have undergone functional divergence after gene duplications such that current subgroups of the family carry out overlapping but distinct biological roles. For the protein families with known functional subtypes (a functional split), we developed the software, SplitTester, to identify potential regions that are responsible for the observed distinct functional subtypes within the same protein family.'),
(90427, 2005, 'bmc', 'libcov: a C++ bioinformatic library to manipulate protein structures, sequence alignments and phylogeny', 'Butt, Davin|Roger, Andrew J|Blouin, Christian', 'Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada. davin@cs.dal.ca|Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada. davin@cs.dal.ca|Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada. davin@cs.dal.ca', 'Canada|Canada|Canada', 8, 'Algorithms|Cluster Analysis|Computational Biology|Computers|Data Interpretation, Statistical|Databases, Protein|Evolution, Molecular|Gene Library|Likelihood Functions|Phylogeny|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Software Design', 'Journal Article', '10.1186/1471-2105-6-138', '15938750', 'An increasing number of bioinformatics methods are considering the phylogenetic relationships between biological sequences. Implementing new methodologies using the maximum likelihood phylogenetic framework can be a time consuming task.'),
(90428, 2005, 'bmc', 'PentaPlot: a software tool for the illustration of genome mosaicism', 'Hamel, Lutz|Zhaxybayeva, Olga|Gogarten, J Peter', 'Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI 02881, USA. hamel@cs.uri.edu|Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI 02881, USA. hamel@cs.uri.edu|Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI 02881, USA. hamel@cs.uri.edu', 'United States|United States|United States', 4, 'Algorithms|Archaeoglobus fulgidus|Bacillus|Bacillus subtilis|Biological Evolution|Chlorobium|Chloroflexus|Computational Biology|Computer Graphics|Computer Simulation|Evolution, Molecular|Genome|Genome, Bacterial|Genomics|Models, Genetic|Phylogeny|Programming Languages|Rhodobacter capsulatus|Saccharomyces cerevisiae|Sequence Alignment|Software|Species Specificity|Sulfolobus solfataricus', 'Journal Article', '10.1186/1471-2105-6-139', '15938752', 'Dekapentagonal maps depict the phylogenetic relationships of five genomes in a visually appealing diagram and can be viewed as an alternative to a single evolutionary consensus tree. In particular, the generated maps focus attention on those gene families that significantly deviate from the consensus or plurality phylogeny. PentaPlot is a software tool that computes such dekapentagonal maps given an appropriate probability support matrix.'),
(90429, 2005, 'bmc', 'BMC Bioinformatics comes of age', 'Cockerill, Matthew J', 'BioMed Central Ltd, Middlesex House, 34-42 Cleveland Street, London, W1T 4LB, UK. matt@biomedcentral.com', 'United Kingdom', 3, 'Access to Information|Computational Biology|Databases as Topic|Genomics|Information Storage and Retrieval|Internet|Models, Statistical|Publishing|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-140', '15941469', '-'),
(90430, 2005, 'bmc', 'Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information', 'Cooper, James W|Kershenbaum, Aaron', 'Text Analytics, IBM Thomas J Watson Research Center, PO Box 704, Yorktown Heights, NY 10598, USA. jwcnmr@watson.ibm.com|Text Analytics, IBM Thomas J Watson Research Center, PO Box 704, Yorktown Heights, NY 10598, USA. jwcnmr@watson.ibm.com', 'United States|United States', 22, 'Artificial Intelligence|Binding Sites|Computational Biology|Computer Graphics|Database Management Systems|Humans|Information Storage and Retrieval|Linguistics|MEDLINE|Models, Statistical|Molecular Conformation|Natural Language Processing|Programming Languages|Protein Binding|Protein Interaction Mapping|Proteins|Proteomics|Software|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-143', '15941473', 'The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.'),
(90431, 2005, 'bmc', 'Chemistry in bioinformatics', 'Murray-Rust, Peter|Mitchell, John B O|Rzepa, Henry S', 'Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge. CB2 1EW, UK. pm286@cam.ac.uk <pm286@cam.ac.uk>|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge. CB2 1EW, UK. pm286@cam.ac.uk <pm286@cam.ac.uk>|Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge. CB2 1EW, UK. pm286@cam.ac.uk <pm286@cam.ac.uk>', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Access to Information|Chemistry|Computational Biology|Databases as Topic|Databases, Factual|Information Storage and Retrieval|Internet|Publishing|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-141', '15941476', 'Chemical information is now seen as critical for most areas of life sciences. But unlike Bioinformatics, where data is openly available and freely re-usable, most chemical information is closed and cannot be re-distributed without permission. This has led to a failure to adopt modern informatics and software techniques and therefore paucity of chemistry in bioinformatics. New technology, however, offers the hope of making chemical data (compounds and properties) free during the authoring process. We argue that the technology is already available, we require a collective agreement to enhance publication protocols.'),
(90432, 2005, 'bmc', 'Which gene did you mean?', 'Mons, Barend', 'Biosemantics Group Rotterdam, Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, P,O, Box 1738, NL-3000 DR Rotterdam, the Netherlands. b.mons@erasmusmc.nl', 'Netherlands', 29, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Computational Biology|Computers|Databases, Bibliographic|Genomics|Information Storage and Retrieval|Internet|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Publishing|Software|Systems Integration', 'Editorial', '10.1186/1471-2105-6-142', '15941477', 'Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing.'),
(90433, 2005, 'bmc', 'PAGE: parametric analysis of gene set enrichment', 'Kim, Seon-Young|Volsky, David J', 'Molecular Virology Division, St. Lukes-Roosevelt Hospital Center, Columbia University, New York, NY 10019, USA. kimsy@kribb.re.kr|Molecular Virology Division, St. Lukes-Roosevelt Hospital Center, Columbia University, New York, NY 10019, USA. kimsy@kribb.re.kr', 'United States|United States', 0, 'Computational Biology|Diabetes Mellitus|Energy Metabolism|Gene Expression Profiling|Humans|Models, Statistical|Muscle, Skeletal|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Comparative Study', '10.1186/1471-2105-6-144', '15941488', 'Gene set enrichment analysis (GSEA) is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. GSEA is especially useful when gene expression changes in a given microarray data set is minimal or moderate.'),
(90434, 2005, 'bmc', 'SuperLigands - a database of ligand structures derived from the Protein Data Bank', 'Michalsky, Elke|Dunkel, Mathias|Goede, Andrean|Preissner, Robert', 'BCB (Berlin Center for Genome Based Bioinformatics) at Institute of Biochemistry, Charité (University Medicine Berlin), Monbijoustr, 2, 10117 Berlin, Germany. elke.michalsky@charite.de|BCB (Berlin Center for Genome Based Bioinformatics) at Institute of Biochemistry, Charité (University Medicine Berlin), Monbijoustr, 2, 10117 Berlin, Germany. elke.michalsky@charite.de|BCB (Berlin Center for Genome Based Bioinformatics) at Institute of Biochemistry, Charité (University Medicine Berlin), Monbijoustr, 2, 10117 Berlin, Germany. elke.michalsky@charite.de|BCB (Berlin Center for Genome Based Bioinformatics) at Institute of Biochemistry, Charité (University Medicine Berlin), Monbijoustr, 2, 10117 Berlin, Germany. elke.michalsky@charite.de', 'Germany|Germany|Germany|Germany', 0, 'Animals|Binding Sites|Computational Biology|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Databases, Protein|Humans|Internet|Ligands|Models, Chemical|Models, Molecular|Molecular Conformation|Protein Binding|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-122', '15943884', 'Currently, the PDB contains approximately 29,000 protein structures comprising over 70,000 experimentally determined three-dimensional structures of over 5,000 different low molecular weight compounds. Information about these PDB ligands can be very helpful in the field of molecular modelling and prediction, particularly for the prediction of protein binding sites and function.'),
(90435, 2005, 'bmc', 'Satellog: a database for the identification and prioritization of satellite repeats in disease association studies', 'Missirlis, Perseus I|Mead, Carri-Lyn R|Butland, Stefanie L|Ouellette, B F Francis|Devon, Rebecca S|Leavitt, Blair R|Holt, Robert A', 'Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100, 570 West 7th Ave, Vancouver, BC, V5Z 4S6, Canada. perseusm@bcgsc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 14, '3 Untranslated Regions|Base Sequence|Biomedical Research|Cluster Analysis|Computational Biology|Databases as Topic|Databases, Genetic|Databases, Nucleic Acid|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genome, Human|Humans|Huntington Disease|Information Storage and Retrieval|Microsatellite Repeats|Polymorphism, Genetic|Schizophrenia|Sequence Alignment|Sequence Analysis, DNA|Software|Trinucleotide Repeat Expansion|Trinucleotide Repeats|Untranslated Regions', 'Journal Article', '10.1186/1471-2105-6-145', '15949044', 'To date, 35 human diseases, some of which also exhibit anticipation, have been associated with unstable repeats. Anticipation has been reported in a number of diseases in which repeat expansion may have a role in etiology. Despite the growing importance of unstable repeats in disease, currently no resource exists for the prioritization of repeats. Here we present Satellog, a database that catalogs all pure 1-16 repeat unit satellite repeats in the human genome along with supplementary data. Satellog analyzes each pure repeat in UniGene clusters for evidence of repeat polymorphism.'),
(90436, 2005, 'bmc', 'Visualization-based discovery and analysis of genomic aberrations in microarray data', 'Myers, Chad L|Chen, Xing|Troyanskaya, Olga G', 'Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ 08544, USA. clmyers@princeton.edu|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ 08544, USA. clmyers@princeton.edu|Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ 08544, USA. clmyers@princeton.edu', 'United States|United States|United States', 0, 'Algorithms|Breast Neoplasms|Chromosome Aberrations|Chromosome Mapping|Computational Biology|Computer Graphics|Gene Amplification|Gene Dosage|Gene Expression|Gene Expression Profiling|Genome, Human|Humans|Internet|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Phenotype|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-146', '15953389', 'Chromosomal copy number changes (aneuploidies) play a key role in cancer progression and molecular evolution. These copy number changes can be studied using microarray-based comparative genomic hybridization (array CGH) or gene expression microarrays. However, accurate identification of amplified or deleted regions requires a combination of visual and computational analysis of these microarray data.'),
(90437, 2005, 'bmc', 'Alkahest NuclearBLAST : a user-friendly BLAST management and analysis system', 'Diener, Stephen E|Houfek, Thomas D|Kalat, Sam E|Windham, D E|Burke, Mark|Opperman, Charles|Dean, Ralph A', 'Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu|Fungal Genomics Laboratory, Center for Integrated Fungal Research, North Carolina State University, Raleigh, NC 27695, USA. sediener@unity.ncsu.edu', 'United States|United States|United States|United States|United States|United States|United States', 8, 'Algorithms|Chromosomes, Artificial, Bacterial|Cluster Analysis|Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Expressed Sequence Tags|Gene Expression Profiling|Information Storage and Retrieval|Internet|Programming Languages|Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-147', '15958161', 'Sequencing of EST and BAC end datasets is no longer limited to large research groups. Drops in per-base pricing have made high throughput sequencing accessible to individual investigators. However, there are few options available which provide a free and user-friendly solution to the BLAST result storage and data mining needs of biologists.'),
(90438, 2005, 'bmc', 'Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes', 'Jirapech-Umpai, Thanyaluk|Aitken, Stuart', 'School of Informatics, The University of Edinburgh, Edinburgh EH8 9LE, United Kingdom. thanya@eng.cmu.ac.th|School of Informatics, The University of Edinburgh, Edinburgh EH8 9LE, United Kingdom. thanya@eng.cmu.ac.th', 'United Kingdom|United Kingdom', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Genetic|Models, Statistical|Multivariate Analysis|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-148', '15958165', 'In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. 1 and the NCI60 dataset of Ross et al. 2 present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed.'),
(90439, 2005, 'bmc', 'Thesaurus-based disambiguation of gene symbols', 'Schijvenaars, Bob J A|Mons, Barend|Weeber, Marc|Schuemie, Martijn J|van Mulligen, Erik M|Wain, Hester M|Kors, Jan A', 'Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl|Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands. r.schijvenaars@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 39, 'Algorithms|Databases, Genetic|Genes|Humans|Information Storage and Retrieval|Symbolism|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-149', '15958172', 'Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck.'),
(90440, 2005, 'bmc', 'Overview of BioCreAtIvE: critical assessment of information extraction for biology', 'Hirschman, Lynette|Yeh, Alexander|Blaschke, Christian|Valencia, Alfonso', 'The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org', 'United States|United States|United States|United States', 0, 'Computational Biology|Data Collection|Database Management Systems|Databases, Factual|Information Management|Internationality|Terminology as Topic', 'Congress', '10.1186/1471-2105-6-S1-S1', '15960821', 'The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28-31, 2004. The articles collected in this BMC Bioinformatics supplement entitled A critical assessment of text mining methods in molecular biology describe the BioCreAtIvE tasks, systems, results and their independent evaluation.'),
(90441, 2005, 'bmc', 'Text detective: a rule-based system for gene annotation in biomedical texts', 'Tamames, Javier', 'Alma Bioinformatics S,L, Ronda de Poniente 4, 28750 Tres Cantos, Madrid, Spain. tamames@almabioinfo.com', 'Spain', 20, 'Biomedical Research|Computational Biology|Dictionaries as Topic|Genes|Pattern Recognition, Automated|Sensitivity and Specificity|Textbooks as Topic|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S10', '15960822', 'The identification of mentions of gene or gene products in biomedical texts is a critical step in the development of text mining applications in biosciences. The complexity and ambiguity of gene nomenclature makes this a very difficult task.'),
(90442, 2005, 'bmc', 'Overview of BioCreAtIvE task 1B: normalized gene lists', 'Hirschman, Lynette|Colosimo, Marc|Morgan, Alexander|Yeh, Alexander', 'The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org', 'United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Bibliographic|Drosophila|Genes|Information Storage and Retrieval|Mice|Saccharomyces cerevisiae|Terminology as Topic', 'Evaluation Studies', '10.1186/1471-2105-6-S1-S11', '15960823', 'Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases. This paper summarizes the BioCreAtIvE task 1B, the Normalized Gene List task, which was inspired by the gene list supplied for each curated paper in a model organism database. The task was to produce the correct list of unique gene identifiers for the genes and gene products mentioned in sets of abstracts from three model organisms (Yeast, Fly, and Mouse).'),
(90443, 2005, 'bmc', 'Data preparation and interannotator agreement: BioCreAtIvE task 1B', 'Colosimo, Marc E|Morgan, Alexander A|Yeh, Alexander S|Colombe, Jeffrey B|Hirschman, Lynette', 'The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. mcolosimo@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. mcolosimo@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. mcolosimo@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. mcolosimo@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. mcolosimo@mitre.org', 'United States|United States|United States|United States|United States', 0, 'Animals|Computational Biology|Databases, Factual|Information Storage and Retrieval|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S12', '15960824', 'We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract, for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset.'),
(90444, 2005, 'bmc', 'Automatically annotating documents with normalized gene lists', 'Crim, Jeremiah|McDonald, Ryan|Pereira, Fernando', 'Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. jcrim@cis.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. jcrim@cis.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. jcrim@cis.upenn.edu', 'United States|United States|United States', 36, 'Animals|Databases, Genetic|Pattern Recognition, Automated|Writing', 'Comparative Study', '10.1186/1471-2105-6-S1-S13', '15960825', 'Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms.'),
(90445, 2005, 'bmc', 'ProMiner: rule-based protein and gene entity recognition', 'Hanisch, Daniel|Fundel, Katrin|Mevissen, Heinz-Theodor|Zimmer, Ralf|Fluck, Juliane', 'Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Daniel.Hanisch@aventis.com|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Daniel.Hanisch@aventis.com|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Daniel.Hanisch@aventis.com|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Daniel.Hanisch@aventis.com|Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Daniel.Hanisch@aventis.com', 'Germany|Germany|Germany|Germany|Germany', 168, 'Computational Biology|Genes|Pattern Recognition, Automated|Proteins|Recognition (Psychology)|Software', 'Journal Article', '10.1186/1471-2105-6-S1-S14', '15960826', 'Identification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, as well as names that may be ambiguous with other gene names or with general English words. The Gene List Task of the BioCreAtIvE challenge evaluation enables comparison of systems addressing the problem of protein and gene name identification on common benchmark data.'),
(90446, 2005, 'bmc', 'A simple approach for protein name identification: prospects and limits', 'Fundel, Katrin|Güttler, Daniel|Zimmer, Ralf|Apostolakis, Joannis', 'Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 Munich, Germany. katrin.fundel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 Munich, Germany. katrin.fundel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 Munich, Germany. katrin.fundel@bio.ifi.lmu.de|Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 Munich, Germany. katrin.fundel@bio.ifi.lmu.de', 'Germany|Germany|Germany|Germany', 0, 'Animals|Databases, Factual|Drosophila|Mice|Pattern Recognition, Automated|Proteins|Saccharomyces cerevisiae|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-6-S1-S15', '15960827', 'Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene product (protein) names and mapping these to unique database identifiers, it becomes possible to extract and integrate information from articles and various data sources. We present a simple and efficient approach that identifies gene and protein names in texts and returns database identifiers for matches. It has been evaluated in the recent BioCreAtIvE entity extraction and mention normalization task by an independent jury.'),
(90447, 2005, 'bmc', 'Evaluation of BioCreAtIvE assessment of task 2', 'Blaschke, Christian|Leon, Eduardo Andres|Krallinger, Martin|Valencia, Alfonso', 'Bioalma SL, Ronda de Poniente 4- 2nd floor, Tres Cantos, E-28760, Madrid, Spain. blaschke@almabioinfo.com|Bioalma SL, Ronda de Poniente 4- 2nd floor, Tres Cantos, E-28760, Madrid, Spain. blaschke@almabioinfo.com|Bioalma SL, Ronda de Poniente 4- 2nd floor, Tres Cantos, E-28760, Madrid, Spain. blaschke@almabioinfo.com|Bioalma SL, Ronda de Poniente 4- 2nd floor, Tres Cantos, E-28760, Madrid, Spain. blaschke@almabioinfo.com', 'Spain|Spain|Spain|Spain', 0, 'Computational Biology|Databases, Genetic|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic', 'Evaluation Studies', '10.1186/1471-2105-6-S1-S16', '15960828', 'Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed.'),
(90448, 2005, 'bmc', 'An evaluation of GO annotation retrieval for BioCreAtIvE and GOA', 'Camon, Evelyn B|Barrell, Daniel G|Dimmer, Emily C|Lee, Vivian|Magrane, Michele|Maslen, John|Binns, David|Apweiler, Rolf', 'European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk|European Molecular Biology Laboratory, European Bionformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. goa@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Computational Biology|Databases, Genetic|Genes|Information Storage and Retrieval|Pattern Recognition, Automated|Proteins', 'Evaluation Studies', '10.1186/1471-2105-6-S1-S17', '15960829', 'The Gene Ontology Annotation (GOA) database http://www.ebi.ac.uk/GOA aims to provide high-quality supplementary GO annotation to proteins in the UniProt Knowledgebase. Like many other biological databases, GOA gathers much of its content from the careful manual curation of literature. However, as both the volume of literature and of proteins requiring characterization increases, the manual processing capability can become overloaded. Consequently, semi-automated aids are often employed to expedite the curation process. Traditionally, electronic techniques in GOA depend largely on exploiting the knowledge in existing resources such as InterPro. However, in recent years, text mining has been hailed as a potentially useful tool to aid the curation process. To encourage the development of such tools, the GOA team at EBI agreed to take part in the functional annotation task of the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) challenge. BioCreAtIvE task 2 was an experiment to test if automatically derived classification using information retrieval and extraction could assist expert biologists in the annotation of the GO vocabulary to the proteins in the UniProt Knowledgebase. GOA provided the training corpus of over 9000 manual GO annotations extracted from the literature. For the test set, we provided a corpus of 200 new Journal of Biological Chemistry articles used to annotate 286 human proteins with GO terms. A team of experts manually evaluated the results of 9 participating groups, each of which provided highlighted sentences to support their GO and protein annotation predictions. Here, we give a biological perspective on the evaluation, explain how we annotate GO using literature and offer some suggestions to improve the precision of future text-retrieval and extraction techniques. Finally, we provide the results of the first inter-annotator agreement study for manual GO curation, as well as an assessment of our current electronic GO annotation strategies.'),
(90449, 2005, 'bmc', 'Learning statistical models for annotating proteins with function information using biomedical text', 'Ray, Soumya|Craven, Mark', 'Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA. sray@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA. sray@cs.wisc.edu', 'United States|United States', 9, 'Bayes Theorem|Biomedical Research|Computational Biology|Genes|Models, Statistical|Pattern Recognition, Automated|Periodicals as Topic|Proteins|Terminology as Topic|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S18', '15960830', 'The BioCreative text mining evaluation investigated the application of text mining methods to the task of automatically extracting information from text in biomedical research articles. We participated in Task 2 of the evaluation. For this task, we built a system to automatically annotate a given protein with codes from the Gene Ontology (GO) using the text of an article from the biomedical literature as evidence.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90450, 2005, 'bmc', 'A sentence sliding window approach to extract protein annotations from biomedical articles', 'Krallinger, Martin|Padron, Maria|Valencia, Alfonso', 'Protein Design Group, National Center of Biotechnology, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain. martink@cnb.uam.es|Protein Design Group, National Center of Biotechnology, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain. martink@cnb.uam.es|Protein Design Group, National Center of Biotechnology, CNB-CSIC, Cantoblanco, E-28049 Madrid, Spain. martink@cnb.uam.es', 'Spain|Spain|Spain', 0, 'Biomedical Research|Computational Biology|Genes|Language|Pattern Recognition, Automated|Periodicals as Topic|Proteins|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S19', '15960831', 'Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations.'),
(90451, 2005, 'bmc', 'BioCreAtIvE task 1A: gene mention finding evaluation', 'Yeh, Alexander|Morgan, Alexander|Colosimo, Marc|Hirschman, Lynette', 'The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. asy@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. asy@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. asy@mitre.org|The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. asy@mitre.org', 'United States|United States|United States|United States', 147, 'Computational Biology|Genes|Internationality|Markov Chains', 'Evaluation Studies', '10.1186/1471-2105-6-S1-S2', '15960832', 'The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. Finding mentions is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI).'),
(90452, 2005, 'bmc', 'Protein annotation as term categorization in the gene ontology using word proximity networks', 'Verspoor, Karin|Cohn, Judith|Joslyn, Cliff|Mniszewski, Sue|Rechtsteiner, Andreas|Rocha, Luis M|Simas, Tiago', 'Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov|Los Alamos National Laboratory, PO Box 1663, MS B256, Los Alamos, NM 87545, USA. verspoor@lanl.gov', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Databases, Genetic|Genes|Pattern Recognition, Automated|Proteins|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S20', '15960833', 'We participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a proteins document neighborhood into the GO.'),
(90453, 2005, 'bmc', 'Finding genomic ontology terms in text using evidence content', 'Couto, Francisco M|Silva, Mário J|Coutinho, Pedro M', 'Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal. fcouto@di.fc.ul.pt|Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal. fcouto@di.fc.ul.pt|Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal. fcouto@di.fc.ul.pt', 'Portugal|Portugal|Portugal', 119, 'Computational Biology|Genomics|Pattern Recognition, Automated|Periodicals as Topic|Terminology as Topic|Textbooks as Topic|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S21', '15960834', 'The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biological entities and properties in the text, and then decide which pairs represent valid annotations.'),
(90454, 2005, 'bmc', 'Mining protein function from text using term-based support vector machines', 'Rice, Simon B|Nenadic, Goran|Stapley, Benjamin J', 'Faculty of Life Sciences, University of Manchester, UK. S.Rice@postgrad.manchester.ac.uk|Faculty of Life Sciences, University of Manchester, UK. S.Rice@postgrad.manchester.ac.uk|Faculty of Life Sciences, University of Manchester, UK. S.Rice@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Pattern Recognition, Automated|Periodicals as Topic|Proteins|Terminology as Topic|Textbooks as Topic', 'Journal Article', '10.1186/1471-2105-6-S1-S22', '15960835', 'Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a proteins co-occurring terms that were automatically extracted from documents.'),
(90455, 2005, 'bmc', 'Data-poor categorization and passage retrieval for gene ontology annotation in Swiss-Prot', 'Ehrler, Frédéric|Geissbühler, Antoine|Jimeno, Antonio|Ruch, Patrick', 'Artificial Intelligence Laboratory, University of Geneva, Geneva, Switzerland.|Artificial Intelligence Laboratory, University of Geneva, Geneva, Switzerland.|Artificial Intelligence Laboratory, University of Geneva, Geneva, Switzerland.|Artificial Intelligence Laboratory, University of Geneva, Geneva, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland', 4, 'Computational Biology|Databases, Protein|Genes|Information Storage and Retrieval|Pattern Recognition, Automated|Terminology as Topic|Writing', 'Journal Article', '10.1186/1471-2105-6-S1-S23', '15960836', 'In the context of the BioCreative competition, where training data were very sparse, we investigated two complementary tasks: 1) given a Swiss-Prot triplet, containing a protein, a GO (Gene Ontology) term and a relevant article, extraction of a short passage that justifies the GO category assignment, 2) given a Swiss-Prot pair, containing a protein and a relevant article, automatic assignment of a set of categories.'),
(90456, 2005, 'bmc', 'GENETAG: a tagged corpus for gene/protein named entity recognition', 'Tanabe, Lorraine|Xie, Natalie|Thom, Lynne H|Matten, Wayne|Wilbur, W John', 'National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. tanabe@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. tanabe@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. tanabe@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. tanabe@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. tanabe@ncbi.nlm.nih.gov', 'United States|United States|United States|United States|United States', 134, 'Animals|Genes|Humans|MEDLINE|Proteins|Recognition (Psychology)|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-6-S1-S3', '15960837', 'Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition.'),
(90457, 2005, 'bmc', 'BioCreAtIvE task1A: entity identification with a stochastic tagger', 'Kinoshita, Shuhei|Cohen, K Bretonnel|Ogren, Philip V|Hunter, Lawrence', 'Center for Computational Pharmacology, University of Colorado School of Medicine, Denver, Colorado, USA. kino@strad.ssg.fujitsu.com|Center for Computational Pharmacology, University of Colorado School of Medicine, Denver, Colorado, USA. kino@strad.ssg.fujitsu.com|Center for Computational Pharmacology, University of Colorado School of Medicine, Denver, Colorado, USA. kino@strad.ssg.fujitsu.com|Center for Computational Pharmacology, University of Colorado School of Medicine, Denver, Colorado, USA. kino@strad.ssg.fujitsu.com', 'United States|United States|United States|United States', 0, 'Databases, Genetic|Stochastic Processes|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-S1-S4', '15960838', 'Our approach to Task 1A was inspired by Tanabe and Wilburs ABGene system. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Where their system uses the Brill tagger, we used TnT, the Trigrams n Tags HMM-based part-of-speech tagger. Based on careful error analysis, we implemented a set of post-processing rules to correct both false positives and false negatives. We participated in both the open and the closed divisions, for the open division, we made use of data from NCBI.'),
(90458, 2005, 'bmc', 'Exploring the boundaries: gene and protein identification in biomedical text', 'Finkel, Jenny|Dingare, Shipra|Manning, Christopher D|Nissim, Malvina|Alex, Beatrice|Grover, Claire', 'Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu|Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Biomedical Research|Computational Biology|Genes|Information Storage and Retrieval|Literature|Proteins|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-6-S1-S5', '15960839', 'Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.'),
(90459, 2005, 'bmc', 'Identifying gene and protein mentions in text using conditional random fields', 'McDonald, Ryan|Pereira, Fernando', 'Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. ryantm@cis.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA. ryantm@cis.upenn.edu', 'United States|United States', 133, 'Genes|Pattern Recognition, Automated|Probability|Proteins|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-6-S1-S6', '15960840', 'We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t/o) of a tag sequence given an observation sequence directly, and have previously been employed successfully for other tagging tasks. The mechanics of CRFs and their relationship to maximum entropy are discussed in detail.'),
(90460, 2005, 'bmc', 'Recognition of protein/gene names from text using an ensemble of classifiers', 'Zhou, GuoDong|Shen, Dan|Zhang, Jie|Su, Jian|Tan, SoonHeng', 'Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore. zhougd@i2r.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Biomedical Research|Computational Biology|Genes|Markov Chains|Medicine in Literature|Proteins|Random Allocation|Recognition (Psychology)|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-6-S1-S7', '15960841', 'This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognition task (Task 1A).'),
(90461, 2005, 'bmc', 'Gene/protein name recognition based on support vector machine using dictionary as features', 'Mitsumori, Tomohiro|Fation, Sevrani|Murata, Masaki|Doi, Kouichi|Doi, Hirohumi', 'Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0101, Japan. mitsumor@is.naist.jp|Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0101, Japan. mitsumor@is.naist.jp|Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0101, Japan. mitsumor@is.naist.jp|Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0101, Japan. mitsumor@is.naist.jp|Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0101, Japan. mitsumor@is.naist.jp', 'Japan|Japan|Japan|Japan|Japan', 38, 'Algorithms|Computational Biology|Dictionaries as Topic|Genes|Pattern Recognition, Automated|Proteins|Recognition (Psychology)', 'Journal Article', '10.1186/1471-2105-6-S1-S8', '15960842', 'Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition.'),
(90462, 2005, 'bmc', 'Systematic feature evaluation for gene name recognition', 'Hakenberg, Jörg|Bickel, Steffen|Plake, Conrad|Brefeld, Ulf|Zahn, Hagen|Faulstich, Lukas|Leser, Ulf|Scheffer, Tobias', 'Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de|Computer Science Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany. hakenberg@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 21, 'Computational Biology|Genes|Pattern Recognition, Automated|Recognition (Psychology)|Terminology as Topic', 'Evaluation Studies', '10.1186/1471-2105-6-S1-S9', '15960843', 'In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word classification system based on a sliding window approach with a Support Vector Machine, combined with a pattern-based post-processing for the recognition of phrases. The performance of such a system crucially depends on the type of features chosen for consideration by the classification method, such as pre- or postfixes, character n-grams, patterns of capitalization, or classification of preceding or following words. We present a systematic approach to evaluate the performance of different feature sets based on recursive feature elimination, RFE. Based on a systematic reduction of the number of features used by the system, we can quantify the impact of different feature sets on the results of the word classification problem. This helps us to identify descriptive features, to learn about the structure of the problem, and to design systems that are faster and easier to understand. We observe that the SVM is robust to redundant features. RFE improves the performance by 0.7%, compared to using the complete set of attributes. Moreover, a performance that is only 2.3% below this maximum can be obtained using fewer than 5% of the features.'),
(90463, 2005, 'bmc', 'AnovArray: a set of SAS macros for the analysis of variance of gene expression data', 'Hennequet-Antier, Christelle|Chiapello, Hélène|Piot, Karine|Degrelle, Séverine|Hue, Isabelle|Renard, Jean-Paul|Rodolphe, François|Robin, Stéphane', 'Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr|Unité Mathématique, Informatique et Génome, INRA, 78352 Jouy-en-Josas, France. Christelle.Hennequet-Antier@jouy.inra.fr', 'France|France|France|France|France|France|France|France', 0, 'Algorithms|Analysis of Variance|Animals|Cattle|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Gene Library|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Programming Languages|Quality Control|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Tissue Distribution', 'Journal Article', '10.1186/1471-2105-6-150', '15960854', 'Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment.'),
(90464, 2005, 'bmc', 'AutoFACT: an automatic functional annotation and classification tool', 'Koski, Liisa B|Gray, Michael W|Lang, B Franz|Burger, Gertraud', 'Robert-Cedergren Center for Bioinformatics and Genomics, Université de Montréal, Montréal, Quebec, Canada. lkoski@bch.umontreal.ca|Robert-Cedergren Center for Bioinformatics and Genomics, Université de Montréal, Montréal, Quebec, Canada. lkoski@bch.umontreal.ca|Robert-Cedergren Center for Bioinformatics and Genomics, Université de Montréal, Montréal, Quebec, Canada. lkoski@bch.umontreal.ca|Robert-Cedergren Center for Bioinformatics and Genomics, Université de Montréal, Montréal, Quebec, Canada. lkoski@bch.umontreal.ca', 'Canada|Canada|Canada|Canada', 155, 'Acanthamoeba castellanii|Animals|Computational Biology|Contig Mapping|DNA, Complementary|Databases, Genetic|Expressed Sequence Tags|Humans|Information Management|Internet|Phylogeny|Plasmodium falciparum|Rickettsia prowazekii|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA|Software|Software Validation', 'Journal Article', '10.1186/1471-2105-6-151', '15960857', 'Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets.'),
(90465, 2005, 'bmc', 'pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties', 'Sarda, Deepak|Chua, Gek Huey|Li, Kuo-Bin|Krishnan, Arun', 'Bioinformatics Institute, 30, Biopolis Street, #07-01, 138671, Singapore. deepak@bii.a-star.edu.sg|Bioinformatics Institute, 30, Biopolis Street, #07-01, 138671, Singapore. deepak@bii.a-star.edu.sg|Bioinformatics Institute, 30, Biopolis Street, #07-01, 138671, Singapore. deepak@bii.a-star.edu.sg|Bioinformatics Institute, 30, Biopolis Street, #07-01, 138671, Singapore. deepak@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Computational Biology|Databases, Protein|Gene Expression Profiling|Proteins|Sequence Analysis, Protein|Software Validation|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-6-152', '15963230', 'Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively.'),
(90466, 2005, 'bmc', 'Iterative approach to model identification of biological networks', 'Gadkar, Kapil G|Gunawan, Rudiyanto|Doyle, Francis J', 'Department of Chemical Engineering, University of California, Santa Barbara, CA, USA. gadkar@engr.ucsb.edu|Department of Chemical Engineering, University of California, Santa Barbara, CA, USA. gadkar@engr.ucsb.edu|Department of Chemical Engineering, University of California, Santa Barbara, CA, USA. gadkar@engr.ucsb.edu', 'United States|United States|United States', 115, 'Algorithms|Apoptosis|Caspase 8|Caspase 9|Caspases|Models, Biological|Signal Transduction', 'Journal Article', '10.1186/1471-2105-6-155', '15967022', 'Recent advances in molecular biology techniques provide an opportunity for developing detailed mathematical models of biological processes. An iterative scheme is introduced for model identification using available system knowledge and experimental measurements.'),
(90467, 2005, 'bmc', 'Effective ambiguity checking in biosequence analysis', 'Reeder, Janina|Steffen, Peter|Giegerich, Robert', 'InternationaI NRW Graduate School of Bioinformatics and Genome Research, Center of Biotechnology (CeBiTec), Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. janina@techfak.uni-bielefeld.de|InternationaI NRW Graduate School of Bioinformatics and Genome Research, Center of Biotechnology (CeBiTec), Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. janina@techfak.uni-bielefeld.de|InternationaI NRW Graduate School of Bioinformatics and Genome Research, Center of Biotechnology (CeBiTec), Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. janina@techfak.uni-bielefeld.de', 'Germany|Germany|Germany', 0, 'Algorithms|Language|Models, Molecular|Models, Statistical|Protein Structure, Secondary|RNA|Semantics|Sequence Analysis, Protein', 'Evaluation Studies', '10.1186/1471-2105-6-153', '15967024', 'Ambiguity is a problem in biosequence analysis that arises in various analysis tasks solved via dynamic programming, and in particular, in the modeling of families of RNA secondary structures with stochastic context free grammars. Several types of analysis are invalidated by the presence of ambiguity. As this problem inherits undecidability (as we show here) from the namely problem for context free languages, there is no complete algorithmic solution to the problem of ambiguity checking.'),
(90468, 2005, 'bmc', 'Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection', 'Reiss, David J|Avila-Campillo, Iliana|Thorsson, Vesteinn|Schwikowski, Benno|Galitski, Timothy', 'Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org|Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA. dreiss@systemsbiology.org', 'United States|United States|United States|United States|United States', 13, 'Computational Biology|Databases, Protein|Gene Expression Profiling|Genome|Hepacivirus|Hepatitis C|Humans|Internet|Liver|Models, Biological|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Programming Languages|Protein Interaction Mapping|Proteome|Software', 'Journal Article', '10.1186/1471-2105-6-154', '15967031', 'The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration.'),
(90469, 2005, 'bmc', 'transAlign: using amino acids to facilitate the multiple alignment of protein-coding DNA sequences', 'Bininda-Emonds, Olaf R P', 'Lehrstuhl für Tierzucht, Technical University of Munich, Hochfeldweg 1, 85354 Freising-Weihenstephan, Germany. Olaf.Bininda@tierzucht.tum.de', 'Germany', 145, 'Algorithms|Amino Acid Sequence|Amino Acids|Base Sequence|Computational Biology|DNA|Gene Expression Profiling|Genome|Genomics|Information Storage and Retrieval|Internet|Open Reading Frames|Phylogeny|Programming Languages|Protein Biosynthesis|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-156', '15969769', 'Alignments of homologous DNA sequences are crucial for comparative genomics and phylogenetic analysis. However, multiple alignment represents a computationally difficult problem. For protein-coding DNA sequences, it is more advantageous in terms of both speed and accuracy to align the amino-acid sequences specified by the DNA sequences rather than the DNA sequences themselves. Many implementations making use of this concept of translated alignments are incomplete in the sense that they require the user to manually translate the DNA sequences and to perform the amino-acid alignment. As such, they are not well suited to large-scale automated alignments of large and/or numerous DNA data sets.'),
(90470, 2005, 'bmc', 'Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation', 'Pahikkala, Tapio|Ginter, Filip|Boberg, Jorma|Järvinen, Jouni|Salakoski, Tapio', 'Department of Information Technology, University of Turku and Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, 20520 Turku, Finland. tapio.pahikkala@it.utu.fi|Department of Information Technology, University of Turku and Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, 20520 Turku, Finland. tapio.pahikkala@it.utu.fi|Department of Information Technology, University of Turku and Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, 20520 Turku, Finland. tapio.pahikkala@it.utu.fi|Department of Information Technology, University of Turku and Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, 20520 Turku, Finland. tapio.pahikkala@it.utu.fi|Department of Information Technology, University of Turku and Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14 A, 20520 Turku, Finland. tapio.pahikkala@it.utu.fi', 'Finland|Finland|Finland|Finland|Finland', 0, 'Algorithms|Area Under Curve|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Computational Biology|Computing Methodologies|Data Interpretation, Statistical|Databases, Bibliographic|Genome, Human|Humans|Models, Statistical|Names|Neural Networks (Computer)|Normal Distribution|Pattern Recognition, Automated|Proteins|ROC Curve|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-6-157', '15972097', 'The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task.'),
(90471, 2005, 'bmc', 'Sequence signature analysis of chromosome identity in three Drosophila species', 'Stenberg, Per|Pettersson, Fredrik|Saura, Anja O|Berglund, Anders|Larsson, Jan', 'UCMP, Umeå University, Umeå, Sweden. per.stenberg@molbiol.umu.se|UCMP, Umeå University, Umeå, Sweden. per.stenberg@molbiol.umu.se|UCMP, Umeå University, Umeå, Sweden. per.stenberg@molbiol.umu.se|UCMP, Umeå University, Umeå, Sweden. per.stenberg@molbiol.umu.se|UCMP, Umeå University, Umeå, Sweden. per.stenberg@molbiol.umu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden', 22, 'Animals|Chromosome Mapping|Chromosomes|Computational Biology|Computer Graphics|Drosophila|Drosophila Proteins|Evolution, Molecular|Female|Genes, Insect|Genome|Image Processing, Computer-Assisted|Least-Squares Analysis|Male|Multivariate Analysis|Principal Component Analysis|Reproducibility of Results|Salivary Glands|Sequence Analysis, DNA|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-6-158', '15975141', 'All eukaryotic organisms need to distinguish each of their chromosomes. A few protein complexes have been described that recognise entire, specific chromosomes, for instance dosage compensation complexes and the recently discovered autosome-specific Painting of Fourth (POF) protein in Drosophila. However, no sequences have been found that are chromosome-specific and distributed over the entire length of the respective chromosome. Here, we present a new, unbiased, exhaustive computational method that was used to probe three Drosophila genomes for chromosome-specific sequences.'),
(90472, 2005, 'bmc', 'Multiple sequence alignments of partially coding nucleic acid sequences', 'Stocsits, Roman R|Hofacker, Ivo L|Fried, Claudia|Stadler, Peter F', 'Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstrasse 16-18, D-04107 Leipzig, Germany. roman@bioinf.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstrasse 16-18, D-04107 Leipzig, Germany. roman@bioinf.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstrasse 16-18, D-04107 Leipzig, Germany. roman@bioinf.uni-leipzig.de|Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstrasse 16-18, D-04107 Leipzig, Germany. roman@bioinf.uni-leipzig.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Codon|Conserved Sequence|Homeodomain Proteins|Levivirus|Models, Molecular|Open Reading Frames|Protein Structure, Secondary|RNA|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1186/1471-2105-6-160', '15985156', 'High quality sequence alignments of RNA and DNA sequences are an important prerequisite for the comparative analysis of genomic sequence data. Nucleic acid sequences, however, exhibit a much larger sequence heterogeneity compared to their encoded protein sequences due to the redundancy of the genetic code. It is desirable, therefore, to make use of the amino acid sequence when aligning coding nucleic acid sequences. In many cases, however, only a part of the sequence of interest is translated. On the other hand, overlapping reading frames may encode multiple alternative proteins, possibly with intermittent non-coding parts. Examples are, in particular, RNA virus genomes.'),
(90473, 2005, 'bmc', 'FACT--a framework for the functional interpretation of high-throughput experiments', 'Kokocinski, Felix|Delhomme, Nicolas|Wrobel, Gunnar|Hummerich, Lars|Toedt, Grischa|Lichter, Peter', 'Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de|Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de|Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de|Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de|Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de|Molecular Genetics, Deutsches Krebsforschungszentrum, 69115 Heidelberg, Germany. F.Kokocinski@factweb.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 7, 'Algorithms|Cluster Analysis|Computer Simulation|Electronic Data Processing|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|Systems Integration', 'Evaluation Studies', '10.1186/1471-2105-6-161', '15985174', 'Interpreting the results of high-throughput experiments, such as those obtained from DNA-microarrays, is an often time-consuming task due to the high number of data-points that need to be analyzed in parallel. It is usually a matter of extensive testing and unknown beforehand, which of the possible approaches for the functional analysis will be the most informative.'),
(90474, 2005, 'bmc', 'Full cyclic coordinate descent: solving the protein loop closure problem in Calpha space', 'Boomsma, Wouter|Hamelryck, Thomas', 'Bioinformatics center, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, Building 10, DK-2100 Copenhagen, Denmark. wb@binf.ku.dk|Bioinformatics center, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, Building 10, DK-2100 Copenhagen, Denmark. wb@binf.ku.dk', 'Denmark|Denmark', 0, 'Algorithms|Biomechanical Phenomena|Computational Biology|Models, Molecular|Models, Statistical|Peptides, Cyclic|Protein Conformation|Proteins|Proteomics', 'Journal Article', '10.1186/1471-2105-6-159', '15985178', 'Various forms of the so-called loop closure problem are crucial to protein structure prediction methods. Given an N- and a C-terminal end, the problem consists of finding a suitable segment of a certain length that bridges the ends seamlessly. In homology modelling, the problem arises in predicting loop regions. In de novo protein structure prediction, the problem is encountered when implementing local moves for Markov Chain Monte Carlo simulations. Most loop closure algorithms keep the bond angles fixed or semi-fixed, and only vary the dihedral angles. This is appropriate for a full-atom protein backbone, since the bond angles can be considered as fixed, while the (phi, psi) dihedral angles are variable. However, many de novo structure prediction methods use protein models that only consist of Calpha atoms, or otherwise do not make use of all backbone atoms. These methods require a method that alters both bond and dihedral angles, since the pseudo bond angle between three consecutive Calpha atoms also varies considerably.'),
(90475, 2005, 'bmc', 'Theme discovery from gene lists for identification and viewing of multiple functional groups', 'Pehkonen, Petri|Wong, Garry|Törönen, Petri', 'Department of Neurobiology, A,I, Virtanen-Institute, University of Kuopio P,O, Box 1627, FIN-70211 Kuopio, Finland. petri.pehkonen@uku.fi|Department of Neurobiology, A,I, Virtanen-Institute, University of Kuopio P,O, Box 1627, FIN-70211 Kuopio, Finland. petri.pehkonen@uku.fi|Department of Neurobiology, A,I, Virtanen-Institute, University of Kuopio P,O, Box 1627, FIN-70211 Kuopio, Finland. petri.pehkonen@uku.fi', 'Finland|Finland|Finland', 0, 'Cluster Analysis|Data Interpretation, Statistical|Databases, Genetic|False Positive Reactions|Gene Expression Profiling|Genomics|Hydrogen Peroxide|Itraconazole|Oligonucleotide Array Sequence Analysis|Oxidative Stress|Software|Software Design|Yeasts', 'Comparative Study', '10.1186/1471-2105-6-162', '15987504', 'High throughput methods of the genome era produce vast amounts of data in the form of gene lists. These lists are large and difficult to interpret without advanced computational or bioinformatic tools. Most existing methods analyse a gene list as a single entity although it is comprised of multiple gene groups associated with separate biological functions. Therefore it is imperative to define and visualize gene groups with unique functionality within gene lists.'),
(90476, 2005, 'bmc', 'Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach', 'Lu, Jun|Tomfohr, John K|Kepler, Thomas B', 'Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina 27708, USA. lu000014@mc.duke.edu|Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina 27708, USA. lu000014@mc.duke.edu|Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina 27708, USA. lu000014@mc.duke.edu', 'United States|United States|United States', 55, 'Carcinoma, Pancreatic Ductal|Cell Line, Tumor|Gene Expression Profiling|Gene Library|Humans|Internet|Linear Models|Pancreatic Neoplasms|RNA, Messenger|ROC Curve|User-Computer Interface', 'Comparative Study', '10.1186/1471-2105-6-165', '15987513', 'In testing for differential gene expression involving multiple serial analysis of gene expression (SAGE) libraries, it is critical to account for both between and within library variation. Several methods have been proposed, including the t test, tw test, and an overdispersed logistic regression approach. The merits of these tests, however, have not been fully evaluated. Questions still remain on whether further improvements can be made.'),
(90477, 2005, 'bmc', 'Signal transduction pathway profiling of individual tumor samples', 'Breslin, Thomas|Krogh, Morten|Peterson, Carsten|Troein, Carl', 'Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. thomas@thep.lu.se|Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. thomas@thep.lu.se|Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. thomas@thep.lu.se|Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. thomas@thep.lu.se', 'Sweden|Sweden|Sweden|Sweden', 0, 'Breast Neoplasms|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Leukemia, Myeloid|Oligonucleotide Array Sequence Analysis|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Signal Transduction', 'Journal Article', '10.1186/1471-2105-6-163', '15987529', 'Signal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to analyze tumor gene expression data from microarrays in the context of such pathways.'),
(90478, 2005, 'bmc', 'Species-specific analysis of protein sequence motifs using mutual information', 'Hummel, Jan|Keshvari, Nima|Weckwerth, Wolfram|Selbig, Joachim', 'Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14424 Potsdam, Germany. hummel@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14424 Potsdam, Germany. hummel@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14424 Potsdam, Germany. hummel@mpimp-golm.mpg.de|Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14424 Potsdam, Germany. hummel@mpimp-golm.mpg.de', 'Germany|Germany|Germany|Germany', 0, 'Amino Acid Motifs|Animals|Arabidopsis|Conserved Sequence|Evolution, Molecular|Gene Expression Profiling|Humans|Mice|Murinae|Oryza|Peptide Mapping|Phylogeny|Rats|Saccharomyces cerevisiae|Sequence Alignment|Software|Species Specificity|Zinc Fingers', 'Journal Article', '10.1186/1471-2105-6-164', '15987530', 'Protein sequence motifs are by definition short fragments of conserved amino acids, often associated with a specific function. Accordingly protein sequence profiles derived from multiple sequence alignments provide an alternative description of functional motifs characterizing families of related sequences. Such profiles conveniently reflect functional necessities by pointing out proximity at conserved sequence positions as well as depicting distances at variable positions. Discovering significant conservation characteristics within the variable positions of profiles mirrors group-specific and, in particular, evolutionary features of the underlying sequences.'),
(90479, 2005, 'bmc', 'Quality assessment of microarrays: visualization of spatial artifacts and quantitation of regional biases', 'Reimers, Mark|Weinstein, John N', 'Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892, USA. reimersm@mail.nih.gov|Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892, USA. reimersm@mail.nih.gov', 'United States|United States', 0, 'Artifacts|Bias|Calibration|Image Interpretation, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Quality Control', 'Journal Article', '10.1186/1471-2105-6-166', '15992406', 'Quality-control is an important issue in the analysis of gene expression microarrays. One type of problem is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays does not address regional biases.'),
(90480, 2005, 'bmc', 'Prediction of twin-arginine signal peptides', 'Bendtsen, Jannick Dyrløv|Nielsen, Henrik|Widdick, David|Palmer, Tracy|Brunak, Søren', 'Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk|Center for Biological Sequence Analysis, BioCentrum-DTU, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark. jannick@cbs.dtu.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark', 358, 'Arginine|Bacterial Proteins|Membrane Transport Proteins|Neural Networks (Computer)|Protein Sorting Signals|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-6-167', '15992409', 'Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(90481, 2005, 'bmc', 'High-Throughput GoMiner, an industrial-strength integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID)', 'Zeeberg, Barry R|Qin, Haiying|Narasimhan, Sudarshan|Sunshine, Margot|Cao, Hong|Kane, David W|Reimers, Mark|Stephens, Robert M|Bryant, David|Burt, Stanley K|Elnekave, Eldad|Hari, Danielle M|Wynn, Thomas A|Cunningham-Rundles, Charlotte|Stewart, Donn M|Nelson, David|Weinstein, John N', 'Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov|Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 228, 'Binding Sites|Chromosome Mapping|Cluster Analysis|Common Variable Immunodeficiency|Data Display|Databases, Genetic|Electronic Data Processing|Gene Expression Profiling|Humans|Phenotype|Protein Array Analysis|Schistosomiasis|Software|Software Design|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-168', '15998470', 'We previously developed GoMiner, an application that organizes lists of interesting genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations.'),
(90482, 2005, 'bmc', 'DCD - a novel plant specific domain in proteins involved in development and programmed cell death', 'Tenhaken, Raimund|Doerks, Tobias|Bork, Peer', 'Plant Molecular Biology, University of Frankfurt, Marie-Curie-Str, 9, 60439 Frankfurt, Germany. R.Tenhaken@em.uni-frankfurt.de|Plant Molecular Biology, University of Frankfurt, Marie-Curie-Str, 9, 60439 Frankfurt, Germany. R.Tenhaken@em.uni-frankfurt.de|Plant Molecular Biology, University of Frankfurt, Marie-Curie-Str, 9, 60439 Frankfurt, Germany. R.Tenhaken@em.uni-frankfurt.de', 'Germany|Germany|Germany', 0, 'Amino Acid Motifs|Cell Death|Phylogeny|Plant Proteins|Protein Structure, Tertiary|Sequence Analysis, Protein|Soybeans', 'Journal Article', '10.1186/1471-2105-6-169', '16008837', 'Recognition of microbial pathogens by plants triggers the hypersensitive reaction, a common form of programmed cell death in plants. These dying cells generate signals that activate the plant immune system and alarm the neighboring cells as well as the whole plant to activate defense responses to limit the spread of the pathogen. The molecular mechanisms behind the hypersensitive reaction are largely unknown except for the recognition process of pathogens. We delineate the NRP-gene in soybean, which is specifically induced during this programmed cell death and contains a novel protein domain, which is commonly found in different plant proteins.'),
(90483, 2005, 'bmc', 'SeqX: a tool to detect, analyze and visualize residue co-locations in protein and nucleic acid structures', 'Biro, Jan C|Fördös, Gergely', 'Homulus Foundation, San Francisco, CA 94105-1649, USA. jan.biro@sbcglobal.net|Homulus Foundation, San Francisco, CA 94105-1649, USA. jan.biro@sbcglobal.net', 'United States|United States', 16, 'Amino Acid Sequence|Chromosome Mapping|Computational Biology|Data Display|False Positive Reactions|Peptide Fragments|Proteins|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-170', '16011796', 'The interacting residues of protein and nucleic acid sequences are close to each other - they are co-located. Structure databases (like Protein Data Bank, PDB and Nucleic Acid Data Bank, NDB) contain all information about these co-locations, however it is not an easy task to penetrate this complex information. We developed a JAVA tool, called SeqX for this purpose.'),
(90484, 2005, 'bmc', 'Systematic determination of the mosaic structure of bacterial genomes: species backbone versus strain-specific loops', 'Chiapello, H|Bourgait, I|Sourivong, F|Heuclin, G|Gendrault-Jacquemard, A|Petit, M-A|El Karoui, M', 'Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr|Mathématique, Informatique & Génome, INRA Domaine de Vilvert, 78352 Jouy-en-Josas Cedex, France. helene.chiapello@jouy.inra.fr', 'France|France|France|France|France|France|France', 39, 'Agrobacterium tumefaciens|Bacillus cereus|Bacteria|Chlamydophila pneumoniae|Chromosome Mapping|Conserved Sequence|Databases, Genetic|Escherichia coli|Evolution, Molecular|Genome, Bacterial|Information Systems|Internet|Sequence Alignment|Species Specificity', 'Journal Article', '10.1186/1471-2105-6-171', '16011797', 'Public databases now contain multitude of complete bacterial genomes, including several genomes of the same species. The available data offers new opportunities to address questions about bacterial genome evolution, a task that requires reliable fine comparison data of closely related genomes. Recent analyses have shown, using pairwise whole genome alignments, that it is possible to segment bacterial genomes into a common conserved backbone and strain-specific sequences called loops.'),
(90485, 2005, 'bmc', 'The PD-(D/E)XK superfamily revisited: identification of new members among proteins involved in DNA metabolism and functional predictions for domains of (hitherto) unknown function', 'Kosinski, Jan|Feder, Marcin|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. kosa@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. kosa@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. kosa@genesilico.pl', 'Poland|Poland|Poland', 57, 'Amino Acid Motifs|Conserved Sequence|DNA|Deoxyribonucleases, Type II Site-Specific|Models, Molecular|Protein Structure, Secondary|Protein Structure, Tertiary|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-6-172', '16011798', 'The PD-(D/E)XK nuclease superfamily, initially identified in type II restriction endonucleases and later in many enzymes involved in DNA recombination and repair, is one of the most challenging targets for protein sequence analysis and structure prediction. Typically, the sequence similarity between these proteins is so low, that most of the relationships between known members of the PD-(D/E)XK superfamily were identified only after the corresponding structures were determined experimentally. Thus, it is tempting to speculate that among the uncharacterized protein families, there are potential nucleases that remain to be discovered, but their identification requires more sensitive tools than traditional PSI-BLAST searches.'),
(90486, 2005, 'bmc', 'Normal uniform mixture differential gene expression detection for cDNA microarrays', 'Dean, Nema|Raftery, Adrian E', 'Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA. nemad@stat.washington.edu|Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA. nemad@stat.washington.edu', 'United States|United States', 46, 'Algorithms|CD4-Positive T-Lymphocytes|Computational Biology|DNA, Complementary|Data Interpretation, Statistical|False Negative Reactions|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation|HIV|Humans|Internet|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-6-173', '16011807', 'One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic.'),
(90487, 2005, 'bmc', 'Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines', 'Wang, Jiren|Sung, Wing-Kin|Krishnan, Arun|Li, Kuo-Bin', 'Bioinformatics Institute, Matrix, Singapore 138671. jiren@bii.a-star.edu.sg|Bioinformatics Institute, Matrix, Singapore 138671. jiren@bii.a-star.edu.sg|Bioinformatics Institute, Matrix, Singapore 138671. jiren@bii.a-star.edu.sg|Bioinformatics Institute, Matrix, Singapore 138671. jiren@bii.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 60, 'Algorithms|Cluster Analysis|Gram-Negative Bacteria|Models, Statistical|Predictive Value of Tests|Sequence Analysis, Protein|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-6-174', '16011808', 'Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria.'),
(90488, 2005, 'bmc', 'MPrime: efficient large scale multiple primer and oligonucleotide design for customized gene microarrays', 'Rouchka, Eric C|Khalyfa, Abdelnaby|Cooper, Nigel G F', 'Department of Computer Engineering and Computer Science, Speed School of Engineering, University of Louisville, Louisville, Kentucky, USA. eric.rouchka@louisville.edu|Department of Computer Engineering and Computer Science, Speed School of Engineering, University of Louisville, Louisville, Kentucky, USA. eric.rouchka@louisville.edu|Department of Computer Engineering and Computer Science, Speed School of Engineering, University of Louisville, Louisville, Kentucky, USA. eric.rouchka@louisville.edu', 'United States|United States|United States', 18, 'Animals|DNA Primers|Databases, Genetic|Humans|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Sensitivity and Specificity|Sequence Alignment|Software|Software Design|Software Validation|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-175', '16014168', 'Enhancements in sequencing technology have recently yielded assemblies of large genomes including rat, mouse, human, fruit fly, and zebrafish. The availability of large-scale genomic and genic sequence data coupled with advances in microarray technology have made it possible to study the expression of large numbers of sequence products under several different conditions in days where traditional molecular biology techniques might have taken months, or even years. Therefore, to efficiently study a number of gene products associated with a disease, pathway, or other biological process, it is necessary to be able to design primer pairs or oligonucleotides en masse rather than using a time consuming and laborious gene-by-gene method.'),
(90489, 2005, 'bmc', 'Quantitative analysis of EGR proteins binding to DNA: assessing additivity in both the binding site and the protein', 'Liu, Jiajian|Stormo, Gary D', 'Department of Genetics, Washington University School of Medicine, St, Louis, MO 63110, USA. jjliu@ural.wustl.edu|Department of Genetics, Washington University School of Medicine, St, Louis, MO 63110, USA. jjliu@ural.wustl.edu', 'United States|United States', 20, 'Algorithms|Amino Acids|Binding Sites|Computational Biology|DNA|Early Growth Response Transcription Factors|Models, Genetic|Models, Molecular|Protein Binding|Zinc Fingers', 'Journal Article', '10.1186/1471-2105-6-176', '16014175', 'Recognition codes for protein-DNA interactions typically assume that the interacting positions contribute additively to the binding energy. While this is known to not be precisely true, an additive model over the DNA positions can be a good approximation, at least for some proteins. Much less information is available about whether the protein positions contribute additively to the interaction.'),
(90490, 2005, 'bmc', 'Dissecting systems-wide data using mixture models: application to identify affected cellular processes', 'Svensson, J Peter|de Menezes, Renée X|Turesson, Ingela|Giphart-Gassler, Micheline|Vrieling, Harry', 'Department of Toxicogenetics, Leiden University Medical Centre, P.O. Box 9503, 2300 RA Leiden, The Netherlands. p.svensson@lumc.nl|Department of Toxicogenetics, Leiden University Medical Centre, P.O. Box 9503, 2300 RA Leiden, The Netherlands. p.svensson@lumc.nl|Department of Toxicogenetics, Leiden University Medical Centre, P.O. Box 9503, 2300 RA Leiden, The Netherlands. p.svensson@lumc.nl|Department of Toxicogenetics, Leiden University Medical Centre, P.O. Box 9503, 2300 RA Leiden, The Netherlands. p.svensson@lumc.nl|Department of Toxicogenetics, Leiden University Medical Centre, P.O. Box 9503, 2300 RA Leiden, The Netherlands. p.svensson@lumc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 2, 'Breast Neoplasms|Cell Cycle|Computational Biology|DNA-Binding Proteins|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Testing|Humans|Models, Genetic|Phosphorylation|Predictive Value of Tests|Protein Array Analysis', 'Journal Article', '10.1186/1471-2105-6-177', '16018805', 'Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives.'),
(90491, 2005, 'bmc', 'Approaching the taxonomic affiliation of unidentified sequences in public databases--an example from the mycorrhizal fungi', 'Nilsson, R Henrik|Kristiansson, Erik|Ryberg, Martin|Larsson, Karl-Henrik', 'Göteborg University, Botanical Institute, Box 461, 405 30 Göteborg, Sweden. henrik.nilsson@botany.gu.se|Göteborg University, Botanical Institute, Box 461, 405 30 Göteborg, Sweden. henrik.nilsson@botany.gu.se|Göteborg University, Botanical Institute, Box 461, 405 30 Göteborg, Sweden. henrik.nilsson@botany.gu.se|Göteborg University, Botanical Institute, Box 461, 405 30 Göteborg, Sweden. henrik.nilsson@botany.gu.se', 'Sweden|Sweden|Sweden|Sweden', 65, 'Classification|Databases, Genetic|Internet|Mycorrhizae|Software', 'Journal Article', '10.1186/1471-2105-6-178', '16022740', 'During the last few years, DNA sequence analysis has become one of the primary means of taxonomic identification of species, particularly so for species that are minute or otherwise lack distinct, readily obtainable morphological characters. Although the number of sequences available for comparison in public databases such as GenBank increases exponentially, only a minuscule fraction of all organisms have been sequenced, leaving taxon sampling a momentous problem for sequence-based taxonomic identification. When querying GenBank with a set of unidentified sequences, a considerable proportion typically lack fully identified matches, forming an ever-mounting pile of sequences that the researcher will have to monitor manually in the hope that new, clarifying sequences have been submitted by other researchers. To alleviate these concerns, a project to automatically monitor select unidentified sequences in GenBank for taxonomic progress through repeated local BLAST searches was initiated. Mycorrhizal fungi--a field where species identification often is prohibitively complex--and the much used ITS locus were chosen as test bed.'),
(90492, 2005, 'bmc', 'Proceedings of the second annual conference of the MidSouth Computational Biology and Bioinformatics Society 7-9 October 2004, Little Rock, Arkansas, USA', '-', '-', '-', 0, 'Computational Biology|Genomics|Proteomics', 'Congress', '10.1186/1471-2105-6-S2-S1', '16026594', '-'),
(90493, 2005, 'bmc', 'Reproducible clusters from microarray research: whither?', 'Garge, Nikhil R|Page, Grier P|Sprague, Alan P|Gorman, Bernard S|Allison, David B', 'Medical College of Georgia, 1120 15th Street, Ca4100, Augusta, GA 30912, USA, email: ngarge@mcg,edu|Medical College of Georgia, 1120 15th Street, Ca4100, Augusta, GA 30912, USA, email: ngarge@mcg,edu|Medical College of Georgia, 1120 15th Street, Ca4100, Augusta, GA 30912, USA, email: ngarge@mcg,edu|Medical College of Georgia, 1120 15th Street, Ca4100, Augusta, GA 30912, USA, email: ngarge@mcg,edu|Medical College of Georgia, 1120 15th Street, Ca4100, Augusta, GA 30912, USA, email: ngarge@mcg,edu', 'Georgia|Georgia|Georgia|Georgia|Georgia', 38, 'Cluster Analysis|Protein Array Analysis', 'Research Support, N.I.H., Extramural', '10.1186/1471-2105-6-S2-S10', '16026595', 'In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no right answer. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets.'),
(90494, 2005, 'bmc', 'Microarray scanner calibration curves: characteristics and implications', 'Shi, Leming|Tong, Weida|Su, Zhenqiang|Han, Tao|Han, Jing|Puri, Raj K|Fang, Hong|Frueh, Felix W|Goodsaid, Federico M|Guo, Lei|Branham, William S|Chen, James J|Xu, Z Alex|Harris, Stephen C|Hong, Huixiao|Xie, Qian|Perkins, Roger G|Fuscoe, James C', 'National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 67, 'Calibration|Protein Array Analysis|RNA, Messenger', 'Validation Studies', '10.1186/1471-2105-6-S2-S11', '16026596', 'Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear.'),
(90495, 2005, 'bmc', 'Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential', 'Shi, Leming|Tong, Weida|Fang, Hong|Scherf, Uwe|Han, Jing|Puri, Raj K|Frueh, Felix W|Goodsaid, Federico M|Guo, Lei|Su, Zhenqiang|Han, Tao|Fuscoe, James C|Xu, Z Alex|Patterson, Tucker A|Hong, Huixiao|Xie, Qian|Perkins, Roger G|Chen, James J|Casciano, Daniel A', 'National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 158, 'Databases, Genetic|Protein Array Analysis', 'Comparative Study', '10.1186/1471-2105-6-S2-S12', '16026597', 'The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630-631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676-5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology.'),
(90496, 2005, 'bmc', 'Genome-wide estimation of gender differences in the gene expression of human livers: statistical design and analysis', 'Delongchamp, Robert R|Velasco, Cruz|Dial, Stacey|Harris, Angela J', 'Division of Biometry and Risk Assessment, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. rdelongchamp@nctr.fda.gov|Division of Biometry and Risk Assessment, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. rdelongchamp@nctr.fda.gov|Division of Biometry and Risk Assessment, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. rdelongchamp@nctr.fda.gov|Division of Biometry and Risk Assessment, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. rdelongchamp@nctr.fda.gov', 'United States|United States|United States|United States', 23, 'Female|Gene Expression|Gene Expression Profiling|Genome, Human|Humans|Liver|Male|Research Design|Sex Characteristics', 'Comparative Study', '10.1186/1471-2105-6-S2-S13', '16026598', 'Gender differences in gene expression were estimated in liver samples from 9 males and 9 females. The study tested 31,110 genes for a gender difference using a design that adjusted for sources of variation associated with cDNA arrays, normalization, hybridizations and processing conditions.'),
(90497, 2005, 'bmc', 'Automating genomic data mining via a sequence-based matrix format and associative rule set', 'Wren, Jonathan D|Johnson, David|Gruenwald, Le', 'Advanced Center for Genome Technology, Department of Botany and Microbiology, 101 David L, Boren Blvd, Rm 2025. Jonathan.Wren@OU.edu|Advanced Center for Genome Technology, Department of Botany and Microbiology, 101 David L, Boren Blvd, Rm 2025. Jonathan.Wren@OU.edu|Advanced Center for Genome Technology, Department of Botany and Microbiology, 101 David L, Boren Blvd, Rm 2025. Jonathan.Wren@OU.edu', '-|-|-', 5, 'Base Sequence|CpG Islands|Databases, Genetic|Pattern Recognition, Automated|Repetitive Sequences, Nucleic Acid|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-6-S2-S2', '16026599', 'There is an enormous amount of information encoded in each genome--enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands.'),
(90498, 2005, 'bmc', 'CLU: a new algorithm for EST clustering', 'Ptitsyn, Andrey|Hide, Winston', 'Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge LA 70808, USA. ptitsyaa@pbrc.edu|Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge LA 70808, USA. ptitsyaa@pbrc.edu', 'United States|United States', 17, 'Algorithms|Cluster Analysis|Expressed Sequence Tags|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-6-S2-S3', '16026600', 'The continuous flow of EST data remains one of the richest sources for discoveries in modern biology. The first step in EST data mining is usually associated with EST clustering, the process of grouping of original fragments according to their annotation, similarity to known genomic DNA or each other. Clustered EST data, accumulated in databases such as UniGene, STACK and TIGR Gene Indices have proven to be crucial in research areas from gene discovery to regulation of gene expression.'),
(90499, 2005, 'bmc', 'Decision forest analysis of 61 single nucleotide polymorphisms in a case-control study of esophageal cancer, a novel method', 'Xie, Qian|Ratnasinghe, Luke D|Hong, Huixiao|Perkins, Roger|Tang, Ze-Zhong|Hu, Nan|Taylor, Philip R|Tong, Weida', 'Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov|Division of Bioinformatics, Z-tech at FDAs National Center for Toxicological Research, Jefferson, AR 72079, USA. QXie@nctr.fda.gov', 'United States|United States|United States|United States|United States|United States|United States|United States', 44, 'Algorithms|Case-Control Studies|Decision Support Techniques|Esophageal Neoplasms|Humans|Polymorphism, Single Nucleotide', 'Validation Studies', '10.1186/1471-2105-6-S2-S4', '16026601', 'Systematic evaluation and study of single nucleotide polymorphisms (SNPs) made possible by high throughput genotyping technologies and bioinformatics promises to provide breakthroughs in the understanding of complex diseases. Understanding how the millions of SNPs in the human genome are involved in conferring susceptibility or resistance to disease, or in rendering a drug efficacious or toxic in the individual is a major goal of the relatively new fields of pharmacogenomics. Esophageal squamous cell carcinoma is a high-mortality cancer with complex etiology and progression involving both genetic and environmental factors. We examined the association between esophageal cancer risk and patterns of 61 SNPs in a case-control study for a population from Shanxi Province in North Central China that has among the highest rates of esophageal squamous cell carcinoma in the world.'),
(90500, 2005, 'bmc', 'Quality control and quality assessment of data from surface-enhanced laser desorption/ionization (SELDI) time-of flight (TOF) mass spectrometry (MS)', 'Hong, Huixiao|Dragan, Yvonne|Epstein, Joshua|Teitel, Candee|Chen, Bangzheng|Xie, Qian|Fang, Hong|Shi, Leming|Perkins, Roger|Tong, Weida', 'Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov|Division of Bioinformatics, Z-Tech at FDAs National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. hhong@nctr.fda.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 54, 'Female|Humans|Liver Neoplasms|Male|Protein Array Analysis|Quality Control|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Address', '10.1186/1471-2105-6-S2-S5', '16026602', 'Proteomic profiling of complex biological mixtures by the ProteinChip technology of surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) is one of the most promising approaches in toxicological, biological, and clinic research. The reliable identification of protein expression patterns and associated protein biomarkers that differentiate disease from health or that distinguish different stages of a disease depends on developing methods for assessing the quality of SELDI-TOF mass spectra. The use of SELDI data for biomarker identification requires application of rigorous procedures to detect and discard low quality spectra prior to data analysis.'),
(90501, 2005, 'bmc', 'Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling', 'Fang, H|Tong, W|Perkins, R|Shi, L|Hong, H|Cao, X|Xie, Q|Yim, S H|Ward, J M|Pitot, H C|Dragan, Y P', 'Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov|Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079, USA. hfang@nctr.fda.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 22, 'Animals|Computational Biology|Gene Expression Profiling|Humans|Liver Neoplasms|Mice|Oligonucleotide Array Sequence Analysis|Rats|Species Specificity', 'Comparative Study', '10.1186/1471-2105-6-S2-S6', '16026603', 'The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer.'),
(90502, 2005, 'bmc', 'Evaluation of Glycine max mRNA clusters', 'Frank, Ronald L|Ercal, Fikret', 'Biological Sciences Department, University of Missouri-Rolla, Rolla, MO, USA. rfrank@umr.edu|Biological Sciences Department, University of Missouri-Rolla, Rolla, MO, USA. rfrank@umr.edu', 'United States|United States', 6, 'Cluster Analysis|RNA, Messenger|Soybeans', 'Evaluation Studies', '10.1186/1471-2105-6-S2-S7', '16026604', 'Clustering the ESTs from a large dataset representing a single species is a convenient starting point for a number of investigations into gene discovery, genome evolution, expression patterns, and alternatively spliced transcripts. Several methods have been developed to accomplish this, the most widely available being UniGene, a public domain collection of gene-oriented clusters for over 45 different species created and maintained by NCBI. The goal is for each cluster to represent a unique gene, but currently it is not known how closely the overall results represent that reality. UniGenes build procedure begins with initial mRNA clusters before joining ESTs. UniGenes results for soybean indicate a significant amount of redundancy among some sequences reported to be unique mRNAs. To establish a valid non-redundant known gene set for Glycine max we applied our algorithm to the clustering of only mRNA sequences. The mRNA dataset was run through the algorithm using two different matching stringencies. The resulting cluster compositions were compared to each other and to UniGene. Clusters exhibiting differences among the three methods were analyzed by 1) nucleotide and amino acid alignment and 2) submitting authors conclusions to determine whether members of a single cluster represented the same gene or not.'),
(90503, 2005, 'bmc', 'A microarray study of MPP+-treated PC12 Cells: Mechanisms of toxicity (MOT) analysis using bioinformatics tools', 'Xu, Zengjun|Patterson, Tucker A|Wren, Jonathan D|Han, Tao|Shi, Leming|Duhart, Helen|Ali, Syed F|Slikker, William', 'Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com|Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. axu@genelogic.com', 'United States|United States|United States|United States|United States|United States|United States|United States', 20, '1-Methyl-4-phenylpyridinium|Animals|Computational Biology|PC12 Cells|Protein Array Analysis|Rats', 'Comparative Study', '10.1186/1471-2105-6-S2-S8', '16026605', 'This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP+) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP+ depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP+-induced neurotoxicity is still unclear.'),
(90504, 2005, 'bmc', 'CLPM: a cross-linked peptide mapping algorithm for mass spectrometric analysis', 'Tang, Yong|Chen, Yingfeng|Lichti, Cheryl F|Hall, Roger A|Raney, Kevin D|Jennings, Steven F', 'Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas 72204, USA. YxTang2@UALR.edu', 'United States|United States|United States|United States|United States|United States', 31, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Peptide Mapping|Sequence Analysis, Protein|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-6-S2-S9', '16026606', 'Protein-protein, protein-DNA and protein-RNA interactions are of central importance in biological systems. Quadrapole Time-of-flight (Q-TOF) mass spectrometry is a sensitive, promising tool for studying these interactions. Combining this technique with chemical crosslinking, it is possible to identify the sites of interactions within these complexes. Due to the complexities of the mass spectrometric data of crosslinked proteins, new software is required to analyze the resulting products of these studies.'),
(90505, 2005, 'bmc', 'Processing methods for differential analysis of LC/MS profile data', 'Katajamaa, Mikko|Oresic, Matej', 'Turku Centre for Biotechnology, Tykistökatu 6, FIN-20521, Turku, Finland. mikko.katajamaa@btk.fi|Turku Centre for Biotechnology, Tykistökatu 6, FIN-20521, Turku, Finland. mikko.katajamaa@btk.fi', 'Finland|Finland', 293, 'Algorithms|Catharanthus|Cells, Cultured|Chromatography, Liquid|Computational Biology|Data Display|Electronic Data Processing|Internet|Mass Spectrometry|Sequence Alignment|Software|Software Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-6-179', '16026613', 'Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.');
