INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72890, 2006, 'bio', 'Bayesian search of functionally divergent protein subgroups and their function specific residues', 'Marttinen, Pekka|Corander, Jukka|Törönen, Petri|Holm, Liisa', 'Department of Mathematics and Statistics, PO Box 68, 00014 University of Helsinki, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and Statistics, PO Box 68, 00014 University of Helsinki, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and Statistics, PO Box 68, 00014 University of Helsinki, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and Statistics, PO Box 68, 00014 University of Helsinki, Finland. pekka.marttinen@helsinki.fi', 'Finland|Finland|Finland|Finland', 38, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Bayes Theorem|Conserved Sequence|Evolution, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl411', '16870932', 'The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution.'),
(72891, 2006, 'bio', 'JVirGel 20: computational prediction of proteomes separated via two-dimensional gel electrophoresis under consideration of membrane and secreted proteins', 'Hiller, Karsten|Grote, Andreas|Maneck, Matthias|Münch, Richard|Jahn, Dieter', 'Institut für Mikrobiologie, Technische Universität, Braunschweig Spielmannstrasse 7, D-38106 Braunschweig, Germany.|Institut für Mikrobiologie, Technische Universität, Braunschweig Spielmannstrasse 7, D-38106 Braunschweig, Germany.|Institut für Mikrobiologie, Technische Universität, Braunschweig Spielmannstrasse 7, D-38106 Braunschweig, Germany.|Institut für Mikrobiologie, Technische Universität, Braunschweig Spielmannstrasse 7, D-38106 Braunschweig, Germany.|Institut für Mikrobiologie, Technische Universität, Braunschweig Spielmannstrasse 7, D-38106 Braunschweig, Germany.', 'Germany|Germany|Germany|Germany|Germany', 25, 'Algorithms|Amino Acid Sequence|Electrophoresis, Gel, Two-Dimensional|Membrane Proteins|Molecular Sequence Data|Peptide Mapping|Protein Sorting Signals|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl409', '16870933', 'After the publication of JVirGel 1.0 in 2003 we got many requests and suggestions from the proteomics community to further improve the performance of the software and to add additional useful new features.'),
(72892, 2006, 'bio', 'What should be expected from feature selection in small-sample settings', 'Sima, Chao|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.', 'United States|United States', 69, 'Artificial Intelligence|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl407', '16870934', 'High-throughput technologies for rapid measurement of vast numbers of biological variables offer the potential for highly discriminatory diagnosis and prognosis, however, high dimensionality together with small samples creates the need for feature selection, while at the same time making feature-selection algorithms less reliable. Feature selection must typically be carried out from among thousands of gene-expression features and in the context of a small sample (small number of microarrays). Two basic questions arise: (1) Can one expect feature selection to yield a feature set whose error is close to that of an optimal feature set? (2) If a good feature set is not found, should it be expected that good feature sets do not exist?'),
(72893, 2006, 'bio', 'Semi-supervised learning via penalized mixture model with application to microarray sample classification', 'Pan, Wei|Shen, Xiaotong|Jiang, Aixiang|Hebbel, Robert P', 'Division of Biostatistics, School of Public Health, University of Minnesota, MN, USA. weip@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, MN, USA. weip@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, MN, USA. weip@biostat.umn.edu|Division of Biostatistics, School of Public Health, University of Minnesota, MN, USA. weip@biostat.umn.edu', 'United States|United States|United States|United States', 8, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Proteome|Sample Size', 'Evaluation Studies', '10.1093/bioinformatics/btl393', '16870935', 'It is biologically interesting to address whether human blood outgrowth endothelial cells (BOECs) belong to or are closer to large vessel endothelial cells (LVECs) or microvascular endothelial cells (MVECs) based on global expression profiling. An earlier analysis using a hierarchical clustering and a small set of genes suggested that BOECs seemed to be closer to MVECs. By taking advantage of the two known classes, LVEC and MVEC, while allowing BOEC samples to belong to either of the two classes or to form their own new class, we take a semi-supervised learning approach, for high-dimensional data as encountered here, we propose a penalized mixture model with a weighted L1 penalty to realize automatic feature selection while fitting the model.'),
(72894, 2006, 'bio', 'Prediction of mRNA polyadenylation sites by support vector machine', 'Cheng, Yiming|Miura, Robert M|Tian, Bin', 'Department of Mathematical Sciences, New Jersey Institute of Technology Newark, NJ 07102, USA.|Department of Mathematical Sciences, New Jersey Institute of Technology Newark, NJ 07102, USA.|Department of Mathematical Sciences, New Jersey Institute of Technology Newark, NJ 07102, USA.', 'Jersey|Jersey|Jersey', 89, 'Artificial Intelligence|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Pattern Recognition, Automated|Poly A|Polyadenylation|RNA, Messenger|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl394', '16870936', 'mRNA polyadenylation is responsible for the 3 end formation of most mRNAs in eukaryotic cells and is linked to termination of transcription. Prediction of mRNA polyadenylation sites [poly(A) sites] can help identify genes, define gene boundaries, and elucidate regulatory mechanisms. Current methods for poly(A) site prediction achieve moderate sensitivity and specificity. Here, we present a method using support vector machine for poly(A) site prediction. Using 15 cis-regulatory elements that are over-represented in various regions surrounding poly(A) sites, this method achieves higher sensitivity and similar specificity when compared with polyadq, a common tool for poly(A) site prediction. In addition, we found that while the polyadenylation signal AAUAAA and U-rich elements are primary determinants for poly(A) site prediction, other elements contribute to both sensitivity and specificity of the prediction, indicating a combinatorial mechanism involving multiple elements when choosing poly(A) sites in human cells.'),
(72895, 2006, 'bio', 'Finding motifs from all sequences with and without binding sites', 'Leung, Henry C M|Chin, Francis Y L', 'Department of Computer Science, The University of Hong Kong Pokfulam, Hong Kong. cmleung2@cs.hku.hk|Department of Computer Science, The University of Hong Kong Pokfulam, Hong Kong. cmleung2@cs.hku.hk', 'Hong Kong|Hong Kong', 15, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Models, Statistical|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl371', '16870937', 'Finding common patterns, motifs, from a set of promoter regions of coregulated genes is an important problem in molecular biology. Most existing motif-finding algorithms consider a set of sequences bound by the transcription factor as the only input. However, we can get better results by considering sequences that are not bound by the transcription factor as an additional input.'),
(72896, 2006, 'bio', 'To permute or not to permute', 'Huang, Yifan|Xu, Haiyan|Calian, Violeta|Hsu, Jason C', 'H. Lee Moffitt Cancer Center & Research Institute, The University of South Florida Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center & Research Institute, The University of South Florida Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center & Research Institute, The University of South Florida Tampa, FL 33612, USA.|H. Lee Moffitt Cancer Center & Research Institute, The University of South Florida Tampa, FL 33612, USA.', 'United States|United States|United States|United States', 54, 'Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/btl383', '16870938', 'Permutation test is a popular technique for testing a hypothesis of no effect, when the distribution of the test statistic is unknown. To test the equality of two means, a permutation test might use a test statistic which is the difference of the two sample means in the univariate case. In the multivariate case, it might use a test statistic which is the maximum of the univariate test statistics. A permutation test then estimates the null distribution of the test statistic by permuting the observations between the two samples. We will show that, for such tests, if the two distributions are not identical (as for example when they have unequal variances, correlations or skewness), then a permutation test for equality of means based on difference of sample means can have an inflated Type I error rate even when the means are equal. Our results illustrate permutation testing should be confined to testing for non-identical distributions.'),
(72897, 2006, 'bio', 'Identification of function-associated loop motifs and application to protein function prediction', 'Espadaler, Jordi|Querol, Enrique|Aviles, Francesc X|Oliva, Baldo', 'Group de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra 08003 Barcelona, Catalonia, Spain.|Group de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra 08003 Barcelona, Catalonia, Spain.|Group de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra 08003 Barcelona, Catalonia, Spain.|Group de Bioinformàtica Estructural (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra 08003 Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 26, 'Amino Acid Sequence|Computer Simulation|Models, Biological|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl382', '16870939', 'The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations.'),
(72898, 2006, 'bio', 'Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles', 'Edelman, Elena|Porrello, Alessandro|Guinney, Justin|Balakumaran, Bala|Bild, Andrea|Febbo, Phillip G|Mukherjee, Sayan', 'Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States|United States|United States', 52, 'Algorithms|Chromosome Mapping|Gene Expression|Gene Expression Profiling|Models, Biological|Models, Statistical|Proteome|Sample Size|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl231', '16873460', 'Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies.'),
(72899, 2006, 'bio', 'Dense subgraph computation via stochastic search: application to detect transcriptional modules', 'Everett, Logan|Wang, Li-San|Hannenhalli, Sridhar', 'Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.|Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.|Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA.', 'United States|United States|United States', 14, 'Algorithms|Artificial Intelligence|Base Sequence|Computer Simulation|Models, Genetic|Molecular Sequence Data|Multigene Family|Pattern Recognition, Automated|Sequence Analysis, DNA|Signal Transduction|Stochastic Processes|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btl260', '16873461', 'In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors-a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed.'),
(72900, 2006, 'bio', 'Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle', 'Fauré, Adrien|Naldi, Aurélien|Chaouiya, Claudine|Thieffry, Denis', 'Institut de Biologie du Développement de Marseille-Luminy, Campus scientifique de Luminy, CNRS case 907, 13288 Marseille, France.|Institut de Biologie du Développement de Marseille-Luminy, Campus scientifique de Luminy, CNRS case 907, 13288 Marseille, France.|Institut de Biologie du Développement de Marseille-Luminy, Campus scientifique de Luminy, CNRS case 907, 13288 Marseille, France.|Institut de Biologie du Développement de Marseille-Luminy, Campus scientifique de Luminy, CNRS case 907, 13288 Marseille, France.', 'France|France|France|France', 395, 'Animals|Cell Cycle|Cell Cycle Proteins|Computer Simulation|Gene Expression Regulation|Humans|Kinetics|Logistic Models|Models, Biological|Regulatory Elements, Transcriptional|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl210', '16873462', 'To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks.'),
(72901, 2006, 'bio', 'Semi-supervised LC/MS alignment for differential proteomics', 'Fischer, Bernd|Grossmann, Jonas|Roth, Volker|Gruissem, Wilhelm|Baginsky, Sacha|Buhmann, Joachim M', 'Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch|Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 62, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Chromatography, Liquid|Mass Spectrometry|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Proteome|Proteomics|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl219', '16873463', 'Mass spectrometry (MS) combined with high-performance liquid chromatography (LC) has received considerable attention for high-throughput analysis of proteomes. Isotopic labeling techniques such as ICAT [5,6] have been successfully applied to derive differential quantitative information for two protein samples, however at the price of significantly increased complexity of the experimental setup. To overcome these limitations, we consider a label-free setting where correspondences between elements of two samples have to be established prior to the comparative analysis. The alignment between samples is achieved by nonlinear robust ridge regression. The correspondence estimates are guided in a semi-supervised fashion by prior information which is derived from sequenced tandem mass spectra.'),
(72902, 2006, 'bio', 'Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE', 'Foat, Barrett C|Morozov, Alexandre V|Bussemaker, Harmen J', 'Department of Biological Sciences, Columbia University New York, NY 10027, USA.|Department of Biological Sciences, Columbia University New York, NY 10027, USA.|Department of Biological Sciences, Columbia University New York, NY 10027, USA.', 'United States|United States|United States', 187, 'Algorithms|Base Sequence|Binding Sites|Biomechanical Phenomena|Chromosome Mapping|Computer Simulation|DNA|Models, Genetic|Models, Statistical|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl223', '16873464', 'Regulation of gene expression by a transcription factor requires physical interaction between the factor and the DNA, which can be described by a statistical mechanical model. Based on this model, we developed the MatrixREDUCE algorithm, which uses genome-wide occupancy data for a transcription factor (e.g. ChIP-chip) and associated nucleotide sequences to discover the sequence-specific binding affinity of the transcription factor. Advantages of our approach are that the information for all probes on the microarray is efficiently utilized because there is no need to delineate bound and unbound sequences, and that, unlike information content-based methods, it does not require a background sequence model.'),
(72903, 2006, 'bio', 'MotifCut: regulatory motifs finding with maximum density subgraphs', 'Fratkin, Eugene|Naughton, Brian T|Brutlag, Douglas L|Batzoglou, Serafim', 'Department of Computer Science, Stanford University, California 94305, USA. fratkin@cs.stanford.edu|Department of Computer Science, Stanford University, California 94305, USA. fratkin@cs.stanford.edu|Department of Computer Science, Stanford University, California 94305, USA. fratkin@cs.stanford.edu|Department of Computer Science, Stanford University, California 94305, USA. fratkin@cs.stanford.edu', 'United States|United States|United States|United States', 74, 'Algorithms|Base Sequence|Binding Sites|Chromosome Mapping|Computer Simulation|DNA|Models, Genetic|Models, Statistical|Molecular Sequence Data|Protein Binding|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl243', '16873465', 'DNA motif finding is one of the core problems in computational biology, for which several probabilistic and discrete approaches have been developed. Most existing methods formulate motif finding as an intractable optimization problem and rely either on expectation maximization (EM) or on local heuristic searches. Another challenge is the choice of motif model: simpler models such as the position-specific scoring matrix (PSSM) impose biologically unrealistic assumptions such as independence of the motif positions, while more involved models are harder to parametrize and learn.'),
(72904, 2006, 'bio', 'Bistable switching and excitable behaviour in the activation of Src at mitosis', 'Fuss, Hendrik|Dubitzky, Werner|Downes, Stephen|Kurth, Mary Jo', 'University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk|University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk|University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk|University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk', 'Ireland|Ireland|Ireland|Ireland', 14, 'Cell Cycle Proteins|Computer Simulation|Enzyme Activation|Gene Expression Profiling|Mitosis|Models, Biological|Multienzyme Complexes|Protein Interaction Mapping|Signal Transduction|src-Family Kinases', 'Journal Article', '10.1093/bioinformatics/btl201', '16873466', 'The protein tyrosine kinase Src is involved in a multitude of biochemical pathways and cellular functions. A complex network of interactions with other kinases and phosphatases obscures its precise mode of operation.'),
(72905, 2006, 'bio', 'DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations', 'Antes, Iris|Siu, Shirley W I|Lengauer, Thomas', 'MPI für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. antes@mpi-sb.mpg.de|MPI für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. antes@mpi-sb.mpg.de|MPI für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. antes@mpi-sb.mpg.de', 'Germany|Germany|Germany', 47, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Computer Simulation|Histocompatibility Antigens Class I|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Peptides|Protein Binding|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl216', '16873467', 'The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results.'),
(72906, 2006, 'bio', 'Context-specific independence mixture modeling for positional weight matrices', 'Georgi, Benjamin|Schliep, Alexander', 'Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. georgi@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Department of Computational Molecular Biology, Ihnestrasse 73, 14195 Berlin, Germany. georgi@molgen.mpg.de', 'Germany|Germany', 24, 'Algorithms|Animals|Base Sequence|Binding Sites|Computer Simulation|DNA|Humans|Mice|Models, Genetic|Models, Statistical|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl249', '16873468', 'A positional weight matrix (PWM) is a statistical representation of the binding pattern of a transcription factor estimated from known binding site sequences. Previous studies showed that for factors which bind to divergent binding sites, mixtures of multiple PWMs increase performance. However, estimating a conventional mixture distribution for each position will in many cases cause overfitting.'),
(72907, 2006, 'bio', 'Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design', 'Georgiev, Ivelin|Lilien, Ryan H|Donald, Bruce R', 'Dartmouth Computer Science Department, Hanover, NH 03755, USA.|Dartmouth Computer Science Department, Hanover, NH 03755, USA.|Dartmouth Computer Science Department, Hanover, NH 03755, USA.', 'United States|United States|United States', 34, 'Algorithms|Amino Acid Sequence|Computer Simulation|Drug Design|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Engineering|Proteins|Recombinant Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl220', '16873469', 'Structure-based protein redesign can help engineer proteins with desired novel function. Improving computational efficiency while still maintaining the accuracy of the design predictions has been a major goal for protein design algorithms. The combinatorial nature of protein design results both from allowing residue mutations and from the incorporation of protein side-chain flexibility. Under the assumption that a single conformation can model protein folding and binding, the goal of many algorithms is the identification of the Global Minimum Energy Conformation (GMEC). A dominant theorem for the identification of the GMEC is Dead-End Elimination (DEE). DEE-based algorithms have proven capable of eliminating the majority of candidate conformations, while guaranteeing that only rotamers not belonging to the GMEC are pruned. However, when the protein design process incorporates rotameric energy minimization, DEE is no longer provably-accurate. Hence, with energy minimization, the minimized-DEE (MinDEE) criterion must be used instead.'),
(72908, 2006, 'bio', 'Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks', 'Gevaert, Olivier|De Smet, Frank|Timmerman, Dirk|Moreau, Yves|De Moor, Bart', 'Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be|Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be', 'Belgium|Belgium|Belgium|Belgium|Belgium', 211, 'Bayes Theorem|Biomarkers, Tumor|Breast Neoplasms|Computer Simulation|Decision Support Systems, Clinical|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Logistic Models|Models, Biological|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Prognosis|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl230', '16873470', 'Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable.'),
(72909, 2006, 'bio', 'ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins', 'Granseth, Erik|Viklund, Håkan|Elofsson, Arne', 'Center for Biomembrane Research, Stockholm University, SE-106 91 Stockholm, Sweden.|Center for Biomembrane Research, Stockholm University, SE-106 91 Stockholm, Sweden.|Center for Biomembrane Research, Stockholm University, SE-106 91 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 45, 'Algorithms|Amino Acid Sequence|Cell Membrane|Computer Simulation|Markov Chains|Membrane Fluidity|Membrane Proteins|Models, Biological|Models, Chemical|Models, Molecular|Molecular Sequence Data|Neural Networks (Computer)|Protein Conformation|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl206', '16873471', 'Prediction methods are of great importance for membrane proteins as experimental information is harder to obtain than for globular proteins. As more membrane protein structures are solved it is clear that topology information only provides a simplified picture of a membrane protein. Here, we describe a novel challenge for the prediction of alpha-helical membrane proteins: to predict the distance between a residue and the center of the membrane, a measure we define as the Z-coordinate. Even though the traditional way of depicting membrane protein topology is useful, it is advantageous to have a measure that is based on a more physical property such as the Z-coordinate, since it implicitly contains information about re-entrant helices, interfacial helices, the tilt of a transmembrane helix and loop lengths.'),
(72910, 2006, 'bio', 'Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data', 'Hertel, Jana|Stadler, Peter F', 'Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. jana@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. jana@bioinf.uni-leipzig.de', 'Germany|Germany', 141, 'Algorithms|Animals|Artificial Intelligence|Base Sequence|Chromosome Mapping|Conserved Sequence|Databases, Genetic|Genomics|Humans|Information Storage and Retrieval|MicroRNAs|Molecular Sequence Data|Pattern Recognition, Automated|RNA Precursors|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl257', '16873472', 'Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes.'),
(72911, 2006, 'bio', 'Modelling sequential protein folding under kinetic control', 'Huard, Fabien P E|Deane, Charlotte M|Wood, Graham R', 'Department of Statistics, Macquarie University, NSW 2109, Australia. fhuard@efs.mq.edu.au|Department of Statistics, Macquarie University, NSW 2109, Australia. fhuard@efs.mq.edu.au|Department of Statistics, Macquarie University, NSW 2109, Australia. fhuard@efs.mq.edu.au', 'Australia|Australia|Australia', 22, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Crystallography|Kinetics|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl248', '16873473', 'This study presents a novel investigation of the effect of kinetic control on cotranslational protein folding. We demonstrate the effect using simple HP lattice models and show that the cotranslational folding of proteins under kinetic control has a significant impact on the final conformation. Differences arise if nature is not capable of pushing a partially folded protein back over a large energy barrier. For this reason we argue that such constraints should be incorporated into structure prediction techniques. We introduce a finite surmountable energy barrier which allows partially formed chains to partly unfold, and permits us to enumerate exhaustively all energy pathways.'),
(72912, 2006, 'bio', 'BNTagger: improved tagging SNP selection using Bayesian networks', 'Lee, Phil Hyoun|Shatkay, Hagit', 'School of Computing, Queens University, Kingston, ON, Canada. lee@cs.queensu.ca|School of Computing, Queens University, Kingston, ON, Canada. lee@cs.queensu.ca', 'Canada|Canada', 29, 'Algorithms|Base Sequence|Bayes Theorem|DNA Mutational Analysis|Expressed Sequence Tags|Logistic Models|Molecular Sequence Data|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl233', '16873474', 'Genetic variation analysis holds much promise as a basis for disease-gene association. However, due to the tremendous number of candidate single nucleotide polymorphisms (SNPs), there is a clear need to expedite genotyping by selecting and considering only a subset of all SNPs. This process is known as tagging SNP selection. Several methods for tagging SNP selection have been proposed, and have shown promising results. However, most of them rely on strong assumptions such as prior block-partitioning, bi-allelic SNPs, or a fixed number or location of tagging SNPs. We introduce BNTagger, a new method for tagging SNP selection, based on conditional independence among SNPs. Using the formalism of Bayesian networks (BNs), our system aims to select a subset of independent and highly predictive SNPs. Similar to previous prediction-based methods, we aim to maximize the prediction accuracy of tagging SNPs, but unlike them, we neither fix the number nor the location of predictive tagging SNPs, nor require SNPs to be bi-allelic. In addition, for newly-genotyped samples, BNTagger directly uses genotype data as input, while producing as output haplotype data of all SNPs. Using three public data sets, we compare the prediction performance of our method to that of three state-of-the-art tagging SNP selection methods. The results demonstrate that our method consistently improves upon previous methods in terms of prediction accuracy. Moreover, our method retains its good performance even when a very small number of tagging SNPs are used.'),
(72913, 2006, 'bio', 'Finding the evidence for protein-protein interactions from PubMed abstracts', 'Jang, Hyunchul|Lim, Jaesoo|Lim, Joon-Ho|Park, Soo-Jun|Lee, Kyu-Chul|Park, Seon-Hee', 'Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr|Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr|Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr|Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr|Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr|Bioinformatics Team, Electronics and Telecommunications Research Institute (ETRI), Gajeong-Dong, Yuseong-Gu, Daejeon, 305-350, Korea. janghc@etri.re.kr', '-|-|-|-|-|-', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Evidence-Based Medicine|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Protein Interaction Mapping|Proteins|PubMed|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl203', '16873475', 'Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers.'),
(72914, 2006, 'bio', 'Learning MHC I--peptide binding', 'Jojic, Nebojsa|Reyes-Gomez, Manuel|Heckerman, David|Kadie, Carl|Schueler-Furman, Ora', 'Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com|Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com|Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com|Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com|Microsoft Research, Redmond, WA 98052, USA. jojic@microsoft.com', 'United States|United States|United States|United States|United States', 71, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Computer Simulation|Histocompatibility Antigens Class I|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Peptides|Protein Binding|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl255', '16873476', 'Motivated by the ability of a simple threading approach to predict MHC I--peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were used in the original threading approach, we included three other sources of information on peptide-MHC binding: (1) MHC class I sequences, (2) known binding energies for a large number of MHC-peptide complexes, and (3) an even larger binary dataset that contains information about strong binders (epitopes) and non-binders (peptides that have a low affinity for a particular MHC molecule). Our model significantly outperforms the standard threading approach in binding energy prediction. In our approach, which we call adaptive double threading, the parameters of the threading model are learnable, and both MHC and peptide sequences can be threaded onto structures of other alleles. These two properties make our model appropriate for predicting binding for alleles for which very little data (if any) is available beyond just their sequence, including prediction for alleles for which 3D structures are not available. The ability of our model to generalize beyond the MHC types for which training data is available also separates our approach from epitope prediction methods which treat MHC alleles as symbolic types, rather than biological sequences. We used the trained binding energy predictor to study viral infections in 246 HIV patients from the West Australian cohort, and over 1000 sequences in HIV clade B from Los Alamos National Laboratory database, capturing the course of HIV evolution over the last 20 years. Finally, we illustrate short-, medium-, and long-term adaptation of HIV to the human immune system.'),
(72915, 2006, 'bio', 'Comparative genomics reveals unusually long motifs in mammalian genomes', 'Jones, Neil C|Pevzner, Pavel A', 'Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.|Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Animals|Binding Sites|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Humans|Protein Binding|Repressor Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Species Specificity|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btl265', '16873477', 'The recent discovery of the first small modulatory RNA (smRNA) presents the challenge of finding other molecules of similar length and conservation level. Unlike short interfering RNA (siRNA) and micro-RNA (miRNA), effective computational and experimental screening methods are not currently known for this species of RNA molecule, and the discovery of the one known example was partly fortuitous because it happened to be complementary to a well-studied DNA binding motif (the Neuron Restrictive Silencer Element).'),
(72916, 2006, 'bio', 'Distance based algorithms for small biomolecule classification and structural similarity search', 'Karakoc, Emre|Cherkasov, Artem|Sahinalp, S Cenk', 'School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. cenk@cs.sfu.ca|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. cenk@cs.sfu.ca|School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. cenk@cs.sfu.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Artificial Intelligence|Biopolymers|Computer Simulation|Drug Design|Models, Chemical|Models, Molecular|Pattern Recognition, Automated|Pharmaceutical Preparations|Quantitative Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl259', '16873478', 'Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds wrt the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds.'),
(72917, 2006, 'bio', 'ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains', 'Aoki-Kinoshita, Kiyoko F|Ueda, Nobuhisa|Mamitsuka, Hiroshi|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University.|Bioinformatics Center, Institute for Chemical Research, Kyoto University.|Bioinformatics Center, Institute for Chemical Research, Kyoto University.|Bioinformatics Center, Institute for Chemical Research, Kyoto University.', '-|-|-|-', 22, 'Algorithms|Binding Sites|Carbohydrate Sequence|Computer Simulation|Markov Chains|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Polysaccharides|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl244', '16873479', 'Carbohydrate sugar chains, or glycans, are considered the third major class of biomolecules after DNA and proteins. They consist of branching monosaccharides, starting from a single monosaccharide. They are extremely vital to the development and functioning of multicellular organisms because they are recognized by various proteins to allow them to perform specific functions. Our motivation is to study this recognition mechanism using informatics techniques from the data available. Previously, we introduced a probabilistic sibling-dependent tree Markov model (PSTMM), which we showed could be efficiently trained on sibling-dependent tree structures and return the most likely state paths. However, it had some limitations in that the extra dependency between siblings caused overfitting problems. The retrieval of the patterns from the trained model also involved manually extracting the patterns from the most likely state paths. Thus we introduce a profilePSTMM model which avoids these problems, incorporating a novel concept of different types of state transitions to handle parent-child and sibling dependencies differently.'),
(72918, 2006, 'bio', 'Rapid knot detection and application to protein structure prediction', 'Khatib, Firas|Weirauch, Matthew T|Rohl, Carol A', 'Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA. bort@soe.ucsc.edu|Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA. bort@soe.ucsc.edu|Department of Biomolecular Engineering, University of California at Santa Cruz, Santa Cruz, CA 95064, USA. bort@soe.ucsc.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl236', '16873480', 'Knots in polypeptide chains have been found in very few proteins, and consequently should be generally avoided in protein structure prediction methods. Most effective structure prediction methods do not model the protein folding process itself, but rather seek only to correctly obtain the final native state. Consequently, the mechanisms that prevent knots from occurring in native proteins are not relevant to the modeling process, and as a result, knots can occur with significantly higher frequency in protein models. Here we describe Knotfind, a simple algorithm for knot detection that is fast enough for structure prediction, where tens or hundreds of thousands of conformations may be sampled during the course of a prediction. We have used this algorithm to characterize knots in large populations of model structures generated for targets in CASP 5 and CASP 6 using the Rosetta homology-based modeling method.'),
(72919, 2006, 'bio', 'Annotating proteins by mining protein interaction networks', 'Kirac, Mustafa|Ozsoyoglu, Gultekin|Yang, Jiong', 'Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. kirac@case.edu|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. kirac@case.edu|Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA. kirac@case.edu', 'United States|United States|United States', 0, 'Amino Acid Sequence|Artificial Intelligence|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Molecular Sequence Data|Natural Language Processing|Pattern Recognition, Automated|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Signal Transduction|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl221', '16873481', 'In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72920, 2006, 'bio', 'A decompositional approach to parameter estimation in pathway modeling: a case study of the Akt and MAPK pathways and their crosstalk', 'Koh, Geoffrey|Teong, Huey Fern Carol|Clément, Marie-Véronique|Hsu, David|Thiagarajan, P S', 'Graduate School for Integrative Sciences and Engineering, National University of Singapore.|Graduate School for Integrative Sciences and Engineering, National University of Singapore.|Graduate School for Integrative Sciences and Engineering, National University of Singapore.|Graduate School for Integrative Sciences and Engineering, National University of Singapore.|Graduate School for Integrative Sciences and Engineering, National University of Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Feedback|Kinetics|MAP Kinase Signaling System|Mitogen-Activated Protein Kinases|Models, Biological|Multienzyme Complexes|Neural Networks (Computer)|Protein Interaction Mapping|Proto-Oncogene Proteins c-akt|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl264', '16873482', 'Parameter estimation is a critical problem in modeling biological pathways. It is difficult because of the large number of parameters to be estimated and the limited experimental data available. In this paper, we propose a decompositional approach to parameter estimation. It exploits the structure of a large pathway model to break it into smaller components, whose parameters can then be estimated independently. This leads to significant improvements in computational efficiency. We present our approach in the context of Hybrid Functional Petri Net modeling and evolutionary search for parameter value estimation. However, the approach can be easily extended to other modeling frameworks and is independent of the search method used. We have tested our approach on a detailed model of the Akt and MAPK pathways with two known and one hypothesized crosstalk mechanisms. The entire model contains 84 unknown parameters. Our simulation results exhibit good correlation with experimental data, and they yield positive evidence in support of the hypothesized crosstalk between the two pathways.'),
(72921, 2006, 'bio', 'Finding novel genes in bacterial communities isolated from the environment', 'Krause, Lutz|Diaz, Naryttza N|Bartels, Daniela|Edwards, Robert A|Pühler, Alfred|Rohwer, Forest|Meyer, Folker|Stoye, Jens', 'Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE|Bielefeld University, Center for Biotechnology (CeBiTec), D-33594 Bielefeld, Germany. Lutz.Krause@CeBiTec.Uni-Bielefeld.DE', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 16, 'Algorithms|Bacteria|Base Sequence|Chromosome Mapping|DNA, Bacterial|Environmental Microbiology|Environmental Monitoring|Genome, Bacterial|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btl247', '16873483', 'Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics.'),
(72922, 2006, 'bio', 'A combinatorial pattern discovery approach for the prediction of membrane dipping (re-entrant) loops', 'Lasso, Gorka|Antoniw, John F|Mullins, Jonathan G L', 'Membrane Proteins Structural Bioinformatics Group, School of Medicine, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK.|Membrane Proteins Structural Bioinformatics Group, School of Medicine, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK.|Membrane Proteins Structural Bioinformatics Group, School of Medicine, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK.', 'United Kingdom|United Kingdom|United Kingdom', 22, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Artificial Intelligence|Cell Membrane|Combinatorial Chemistry Techniques|Computer Simulation|Membrane Fluidity|Membrane Proteins|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl209', '16873484', 'Membrane dipping loops are sections of membrane proteins that reside in the membrane but do not traverse from one side to the other, rather they enter and leave the same side of the membrane. We applied a combinatorial pattern discovery approach to sets of sequences containing at least one characterised structure described as possessing a membrane dipping loop. Discovered patterns were found to be composed of residues whose biochemical role is known to be essential for function of the protein, thus validating our approach. TMLOOP (http://membraneproteins.swan.ac.uk/TMLOOP) was implemented to predict membrane dipping loops in polytopic membrane proteins. TMLOOP applies discovered patterns as weighted predictive rules in a collective motif method (a variation of the single motif method), to avoid inherent limitations of single motif methods in detecting distantly related proteins. The collective motif method applies several, partially overlapping patterns, which pertain to the same sequence region, allowing proteins containing small variations to be detected. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity. TMLOOP was applied to the Swiss-Prot database, identifying 1392 confirmed membrane dipping loops, 75 plausible membrane dipping loops hitherto uncharacterised by topology prediction methods or experimental approaches and 128 false positives (8.0%).'),
(72923, 2006, 'bio', 'Interpreting anonymous DNA samples from mass disasters--probabilistic forensic inference using genetic markers', 'Lin, Tien-Ho|Myers, Eugene W|Xing, Eric P', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.', 'United States|United States|United States', 11, 'Algorithms|Computer Simulation|DNA|DNA Fingerprinting|Disasters|Forensic Medicine|Genetic Markers|Humans|Models, Genetic|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl200', '16873485', 'The problem of identifying victims in a mass disaster using DNA fingerprints involves a scale of computation that requires efficient and accurate algorithms. In a typical scenario there are hundreds of samples taken from remains that must be matched to the pedigrees of the alleged victims surviving relatives. Moreover the samples are often degraded due to heat and exposure. To develop a competent method for this type of forensic inference problem, the complicated quality issues of DNA typing need to be handled appropriately, the matches between every sample and every family must be considered, and the confidence of matches need to be provided.'),
(72924, 2006, 'bio', 'ISMB 2006', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1093/bioinformatics/btl374', '16873486', '-'),
(72925, 2006, 'bio', 'Peptide sequence tag-based blind identification of post-translational modifications with point process model', 'Liu, Chunmei|Yan, Bo|Song, Yinglei|Xu, Ying|Cai, Liming', 'Department of Computer Science, University of Georgia, Athens, GA 30602, USA. chunmei@cs.uga.edu|Department of Computer Science, University of Georgia, Athens, GA 30602, USA. chunmei@cs.uga.edu|Department of Computer Science, University of Georgia, Athens, GA 30602, USA. chunmei@cs.uga.edu|Department of Computer Science, University of Georgia, Athens, GA 30602, USA. chunmei@cs.uga.edu|Department of Computer Science, University of Georgia, Athens, GA 30602, USA. chunmei@cs.uga.edu', 'Georgia|Georgia|Georgia|Georgia|Georgia', 28, 'Algorithms|Amino Acid Sequence|Computer Simulation|Expressed Sequence Tags|Models, Biological|Models, Chemical|Models, Statistical|Molecular Sequence Data|Peptide Mapping|Peptides|Protein Processing, Post-Translational|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl226', '16873487', 'An important but difficult problem in proteomics is the identification of post-translational modifications (PTMs) in a protein. In general, the process of PTM identification by aligning experimental spectra with theoretical spectra from peptides in a peptide database is very time consuming and may lead to high false positive rate. In this paper, we introduce a new approach that is both efficient and effective for blind PTM identification. Our work consists of the following phases. First, we develop a novel tree decomposition based algorithm that can efficiently generate peptide sequence tags (PSTs) from an extended spectrum graph. Sequence tags are selected from all maximum weighted antisymmetric paths in the graph and their reliabilities are evaluated with a score function. An efficient deterministic finite automaton (DFA) based model is then developed to search a peptide database for candidate peptides by using the generated sequence tags. Finally, a point process model-an efficient blind search approach for PTM identification, is applied to report the correct peptide and PTMs if there are any. Our tests on 2657 experimental tandem mass spectra and 2620 experimental spectra with one artificially added PTM show that, in addition to high efficiency, our ab-initio sequence tag selection algorithm achieves better or comparable accuracy to other approaches. Database search results show that the sequence tags of lengths 3 and 4 filter out more than 98.3% and 99.8% peptides respectively when applied to a yeast peptide database. With the dramatically reduced search space, the point process model achieves significant improvement in accuracy as well.'),
(72926, 2006, 'bio', 'Identifying cycling genes by combining sequence homology and expression data', 'Lu, Yong|Rosenfeld, Roni|Bar-Joseph, Ziv', 'School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.|School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.|School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Cell Cycle Proteins|Gene Expression Profiling|Humans|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl229', '16873488', 'The expression of genes during the cell division process has now been studied in many different species. An important goal of these studies is to identify the set of cycling genes. To date, this was done independently for each of the species studied. Due to noise and other data analysis problems, accurately deriving a set of cycling genes from expression data is a hard problem. This is especially true for some of the multicellular organisms, including humans.'),
(72927, 2006, 'bio', 'Quantification of transcription factor expression from Arabidopsis images', 'Mace, Daniel L|Lee, Ji-Young|Twigg, Richard W|Colinas, Juliette|Benfey, Philip N|Ohler, Uwe', 'Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.|Institute for Genome Science and Policy, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States|United States', 18, 'Arabidopsis Proteins|Cells, Cultured|Gene Expression|Gene Expression Profiling|Image Interpretation, Computer-Assisted|Microscopy, Confocal|Microscopy, Fluorescence|Transcription Factors', 'Comparative Study', '10.1093/bioinformatics/btl228', '16873489', 'Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples.'),
(72928, 2006, 'bio', 'Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees', 'Magori-Cohen, Reuma|Louzoun, Yoram|Kleinstein, Steven H', 'Math Department, Bar Ilan University, Ramat Gan, Israel.|Math Department, Bar Ilan University, Ramat Gan, Israel.|Math Department, Bar Ilan University, Ramat Gan, Israel.', 'Israel|Israel|Israel', 6, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Likelihood Functions|Linkage Disequilibrium|Models, Genetic|Phylogeny|Receptors, Antigen, B-Cell|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl239', '16873490', 'B cells responding to antigenic stimulation can fine-tune their binding properties through a process of affinity maturation composed of somatic hypermutation, affinity-selection and clonal expansion. The mutation rate of the B cell receptor DNA sequence, and the effect of these mutations on affinity and specificity, are of critical importance for understanding immune and autoimmune processes. Unbiased estimates of these properties are currently lacking due to the short time-scales involved and the small numbers of sequences available.'),
(72929, 2006, 'bio', 'Indel seeds for homology search', 'Mak, Denise|Gelfand, Yevgeniy|Benson, Gary', 'Graduate Program in Bioinformatics, Boston University, Boston, MA 02215, USA. dyfmak@bu.edu|Graduate Program in Bioinformatics, Boston University, Boston, MA 02215, USA. dyfmak@bu.edu|Graduate Program in Bioinformatics, Boston University, Boston, MA 02215, USA. dyfmak@bu.edu', 'United States|United States|United States', 18, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|DNA Transposable Elements|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Sequence Deletion|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btl263', '16873491', 'We are interested in detecting homologous genomic DNA sequences with the goal of locating approximate inverted, interspersed, and tandem repeats. Standard search techniques start by detecting small matching parts, called seeds, between a query sequence and database sequences. Contiguous seed models have existed for many years. Recently, spaced seeds were shown to be more sensitive than contiguous seeds without increasing the random hit rate. To determine the superiority of one seed model over another, a model of homologous sequence alignment must be chosen. Previous studies evaluating spaced and contiguous seeds have assumed that matches and mismatches occur within these alignments, but not insertions and deletions (indels). This is perhaps appropriate when searching for protein coding sequences (<5% of the human genome), but is inappropriate when looking for repeats in the majority of genomic sequence where indels are common. In this paper, we assume a model of homologous sequence alignment which includes indels and we describe a new seed model, called indel seeds, which explicitly allows indels. We present a waiting time formula for computing the sensitivity of an indel seed and show that indel seeds significantly outperform contiguous and spaced seeds when homologies include indels. We discuss the practical aspect of using indel seeds and finally we present results from a search for inverted repeats in the dog genome using both indel and spaced seeds.'),
(72930, 2006, 'bio', 'The iRMSD: a local measure of sequence alignment accuracy using structural information', 'Armougom, Fabrice|Moretti, Sébastien|Keduas, Vladimir|Notredame, Cedric', 'Laboratoire Information Génomique et Structurale, CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM) Parc Scientifique de Luminy, case 934, 163 Avenue de Luminy, FR-13288, Marseille cedex 09.|Laboratoire Information Génomique et Structurale, CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM) Parc Scientifique de Luminy, case 934, 163 Avenue de Luminy, FR-13288, Marseille cedex 09.|Laboratoire Information Génomique et Structurale, CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM) Parc Scientifique de Luminy, case 934, 163 Avenue de Luminy, FR-13288, Marseille cedex 09.|Laboratoire Information Génomique et Structurale, CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM) Parc Scientifique de Luminy, case 934, 163 Avenue de Luminy, FR-13288, Marseille cedex 09.', '-|-|-|-', 0, 'Algorithms|Conserved Sequence|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis|Sequence Homology|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl218', '16873492', 'We introduce the iRMSD, a new type of RMSD, independent from any structure superposition and suitable for evaluating sequence alignments of proteins with known structures.'),
(72931, 2006, 'bio', 'Efficient identification of DNA hybridization partners in a sequence database', 'Mann, Tobias P|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA. mann@gs.washington.edu|Department of Genome Sciences, University of Washington, Seattle, WA, USA. mann@gs.washington.edu', 'United States|United States', 6, 'Algorithms|Base Pairing|Base Sequence|Binding Sites|DNA|DNA Primers|Databases, Genetic|In Situ Hybridization|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btl240', '16873493', 'The specific hybridization of complementary DNA molecules underlies many widely used molecular biology assays, including the polymerase chain reaction and various types of microarray analysis. In order for such an assay to work well, the primer or probe must bind to its intended target, without also binding to additional sequences in the reaction mixture. For any given probe or primer, potential non-specific binding partners can be identified using state-of-the-art models of DNA binding stability. Unfortunately, these models rely on dynamic programming algorithms that are too slow to apply on a genomic scale.'),
(72932, 2006, 'bio', 'An experimental metagenome data management and analysis system', 'Markowitz, Victor M|Ivanova, Natalia|Palaniappan, Krishna|Szeto, Ernest|Korzeniewski, Frank|Lykidis, Athanasios|Anderson, Iain|Mavromatis, Konstantinos|Mavrommatis, Konstantinos|Kunin, Victor|Garcia Martin, Hector|Dubchak, Inna|Hugenholtz, Phil|Kyrpides, Nikos C', 'Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.gov|Biological Data Management and Technology Center, Lawrence Berkeley National Lab, USA. vmmarkowitz@lbl.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', 68, 'Bacterial Physiological Phenomena|Bacterial Proteins|Database Management Systems|Databases, Genetic|Genome, Bacterial|Information Storage and Retrieval|Models, Biological|Proteome|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl217', '16873494', 'The application of shotgun sequencing to environmental samples has revealed a new universe of microbial community genomes (metagenomes) involving previously uncultured organisms. Metagenome analysis, which is expected to provide a comprehensive picture of the gene functions and metabolic capacity for microbial communities, needs to be conducted in the context of a comprehensive data management and analysis system. We present in this paper IMG/M, an experimental metagenome data management and analysis system that is based on the Integrated Microbial Genomes (IMG) system. IMG/M provides tools and viewers for analyzing both metagenomes and isolate genomes individually or in a comparative context. IMG/M is available at http://img.jgi.doe.gov/m.'),
(72933, 2006, 'bio', 'An equilibrium partitioning model connecting gene expression and cis-motif content', 'Mellor, Joe|DeLisi, Charles', 'Program in Bioinformatics, Boston University, Boston, Massachusetts 02215, USA. mellor@bu.edu|Program in Bioinformatics, Boston University, Boston, Massachusetts 02215, USA. mellor@bu.edu', 'United States|United States', 0, 'Amino Acid Motifs|Base Sequence|Binding Sites|Computer Simulation|DNA|Gene Expression|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Protein Binding|Regulatory Elements, Transcriptional|Thermodynamics|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl253', '16873495', 'Thermodynamic favorability of transcription factor (TF) binding to DNA is a significant factor in the control of gene expression. Theoretical and in vitro measures link the relative equilibrium energy of a particular DNA binding protein to the sequence variation among binding sites in a genome. Extending this principle, we investigate whether biological variation in expression levels of active proteins leads to regulation of different sets of genes, based on inferred affinities of sites upstream of those genes. The TF-concentration-dependent variation in the repertoire of genes regulated by a particular TF is expected to follow patterns of chemical partitioning over DNA sites having differing affinity, and we develop a new modeling approach to test this hypothesis. Based on computational TF binding site discovery and genome-wide expression data available in Saccharomyces cerevisiae, we explore motif content for sets of genes and conditions having varying concentrations of different transcription factors which turn those genes on or off. We find cases of significant correlation between the level of intragenomic motif sequence variation and modeled TF protein levels that actuates regulation of corresponding sets of genes, and discuss the observed TF motif variants for several yeast transcription factors, as well as the potential biological functions of genes that are regulated by differential response to these high and low concentrations of particular TFs. These findings suggest that motif sequences of transcription factor binding sites may often be linked with the expression state of corresponding DNA-binding proteins.'),
(72934, 2006, 'bio', 'Identification of metabolic units induced by environmental signals', 'Nacher, Jose C|Schwartz, Jean-Marc|Kanehisa, Minoru|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji Kyoto 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji Kyoto 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji Kyoto 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji Kyoto 611-0011, Japan. nacher@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan', 4, 'Adaptation, Physiological|Algorithms|Computer Simulation|Environment|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Oxidative Stress|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl202', '16873496', 'Biological cells continually need to adapt the activity levels of metabolic functions to changes in their living environment. Although genome-wide transcriptional data have been gathered in a large variety of environmental conditions, the connections between the expression response to external changes and the induction or repression of specific metabolic functions have not been investigated at the genome scale.'),
(72935, 2006, 'bio', 'Informative priors based on transcription factor structural class improve de novo motif discovery', 'Narlikar, Leelavati|Gordân, Raluca|Ohler, Uwe|Hartemink, Alexander J', 'Department of Computer Science, Duke University, Durham, NC 27708, USA. lee@cs.duke.edu|Department of Computer Science, Duke University, Durham, NC 27708, USA. lee@cs.duke.edu|Department of Computer Science, Duke University, Durham, NC 27708, USA. lee@cs.duke.edu|Department of Computer Science, Duke University, Durham, NC 27708, USA. lee@cs.duke.edu', 'United States|United States|United States|United States', 48, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Computer Simulation|DNA|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl251', '16873497', 'An important problem in molecular biology is to identify the locations at which a transcription factor (TF) binds to DNA, given a set of DNA sequences believed to be bound by that TF. In previous work, we showed that information in the DNA sequence of a binding site is sufficient to predict the structural class of the TF that binds it. In particular, this suggests that we can predict which locations in any DNA sequence are more likely to be bound by certain classes of TFs than others. Here, we argue that traditional methods for de novo motif finding can be significantly improved by adopting an informative prior probability that a TF binding site occurs at each sequence location. To demonstrate the utility of such an approach, we present priority, a powerful new de novo motif finding algorithm.'),
(72936, 2006, 'bio', 'Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone', 'Ng, Patrick|Nagarajan, Niranjan|Jones, Neil|Keich, Uri', 'Department of Computer Science, Cornell University, Ithaca, NY, USA.|Department of Computer Science, Cornell University, Ithaca, NY, USA.|Department of Computer Science, Cornell University, Ithaca, NY, USA.|Department of Computer Science, Cornell University, Ithaca, NY, USA.', 'United States|United States|United States|United States', 6, 'Algorithms|Artificial Intelligence|Base Sequence|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl245', '16873498', 'Effective algorithms for finding relatively weak motifs are an important practical necessity while scanning long DNA sequences for regulatory elements. The success of such an algorithm hinges on the ability of its scoring function combined with a significance analysis test to discern real motifs from random noise.'),
(72937, 2006, 'bio', 'A model-based approach for mining membrane protein crystallization trials', 'Asur, Sitaram|Raman, Pichai|Otey, Matthew Eric|Parthasarathy, Srinivasan', 'Department of Computer Science and Engineering, Ohio State University, USA. srini@cse.ohio-state.edu|Department of Computer Science and Engineering, Ohio State University, USA. srini@cse.ohio-state.edu|Department of Computer Science and Engineering, Ohio State University, USA. srini@cse.ohio-state.edu|Department of Computer Science and Engineering, Ohio State University, USA. srini@cse.ohio-state.edu', 'United States|United States|United States|United States', 4, 'Algorithms|Computer Simulation|Crystallization|Crystallography|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Membrane Proteins|Models, Chemical|Models, Molecular|Multiprotein Complexes|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl225', '16873499', 'Membrane proteins are known to play crucial roles in various cellular functions. Information about their function can be derived from their structure, but knowledge of these proteins is limited, as their structures are difficult to obtain. Crystallization has proved to be an essential step in the determination of macromolecular structure. Unfortunately, the bottleneck is that the crystallization process is quite complex and extremely sensitive to experimental conditions, the selection of which is largely a matter of trial and error. Even under the best conditions, it can take a large amount of time, from weeks to years, to obtain diffraction-quality crystals. Other issues include the time and cost involved in taking multiple trials and the presence of very few positive samples in a wide and largely undetermined parameter space. Therefore, any help in directing scientists attention to the hot spots in the conceptual crystallization space would lead to increased efficiency in crystallization trials.'),
(72938, 2006, 'bio', 'Create and assess protein networks through molecular characteristics of individual proteins', 'Ofran, Yanay|Yachdav, Guy|Mozes, Eyal|Soong, Ta-tsen|Nair, Rajesh|Rost, Burkhard', 'Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu|Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street, New York, NY 10032, USA. ofran@cubic.bioc.columbia.edu', 'United States|United States|United States|United States|United States|United States', 4, 'Cell Physiological Phenomena|Computer Simulation|Databases, Protein|Gene Expression|Models, Biological|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl258', '16873500', 'The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes.'),
(72939, 2006, 'bio', 'BaCelLo: a balanced subcellular localization predictor', 'Pierleoni, Andrea|Martelli, Pier Luigi|Fariselli, Piero|Casadio, Rita', 'Biocomputing Group, Dept. of Biology University of Bologna, via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it|Biocomputing Group, Dept. of Biology University of Bologna, via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it|Biocomputing Group, Dept. of Biology University of Bologna, via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it|Biocomputing Group, Dept. of Biology University of Bologna, via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it', 'Italy|Italy|Italy|Italy', 231, 'Algorithms|Animals|Artificial Intelligence|Computer Simulation|Humans|Models, Biological|Pattern Recognition, Automated|Proteins|Sequence Analysis, Protein|Software|Structure-Activity Relationship|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btl222', '16873501', 'The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects.'),
(72940, 2006, 'bio', 'Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo', 'Qi, Yuan|Missiuro, Patrycja E|Kapoor, Ashish|Hunter, Craig P|Jaakkola, Tommi S|Gifford, David K|Ge, Hui', 'Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.|Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA.', 'United States|United States|United States|United States|United States|United States|United States', 4, 'Algorithms|Animals|Artificial Intelligence|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Computer Simulation|Evolution, Molecular|Gene Expression Profiling|Gene Expression Regulation, Developmental|Models, Biological|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl256', '16873502', 'Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods.'),
(72941, 2006, 'bio', 'Decoding non-unique oligonucleotide hybridization experiments of targets related by a phylogenetic tree', 'Schliep, Alexander|Rahmann, Sven', 'Dept. Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. alexander.schliep@molgen.mpg.de|Dept. Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. alexander.schliep@molgen.mpg.de', 'Germany|Germany', 6, 'Algorithms|Artificial Intelligence|Base Sequence|Bayes Theorem|Gene Targeting|In Situ Hybridization, Fluorescence|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Pattern Recognition, Automated|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl254', '16873503', 'The reliable identification of presence or absence of biological agents (targets), such as viruses or bacteria, is crucial for many applications from health care to biodiversity. If genomic sequences of targets are known, hybridization reactions between oligonucleotide probes and targets performed on suitable DNA microarrays will allow to infer presence or absence from the observed pattern of hybridization. Targets, for example all known strains of HIV, are often closely related and finding unique probes becomes impossible. The use of non-unique oligonucleotides with more advanced decoding techniques from statistical group testing allows to detect known targets with great success. Of great relevance, however, is the problem of identifying the presence of previously unknown targets or of targets that evolve rapidly.'),
(72942, 2006, 'bio', 'Integrating copy number polymorphisms into array CGH analysis using a robust HMM', 'Shah, Sohrab P|Xuan, Xiang|DeLeeuw, Ron J|Khojasteh, Mehrnoush|Lam, Wan L|Ng, Raymond|Murphy, Kevin P', 'Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca|Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. sshah@cs.ubc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 96, 'Artificial Intelligence|Chromosome Mapping|Computer Simulation|Gene Dosage|In Situ Hybridization, Fluorescence|Markov Chains|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Polymorphism, Genetic|Sequence Alignment|Sequence Analysis, DNA|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl238', '16873504', 'Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments, a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions.'),
(72943, 2006, 'bio', 'Relative contributions of structural designability and functional diversity in molecular evolution of duplicates', 'Shakhnovich, Boris E', 'Bioinformatics Program, 24 Cummington st., Boston University Boston, MA 02215, USA. borya@acs.bu.edu', 'United States', 7, 'Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genes, Duplicate|Genetic Variation|Genetics, Population|Models, Genetic|Selection, Genetic', 'Journal Article', '10.1093/bioinformatics/btl211', '16873505', 'Analysis of increasingly saturated sequence databases have shown that gene family sizes are highly skewed with many families being small and few containing many, far-diverged homologs. Additionally, recently published results have identified a structural determinant of mutational plasticity: designability that correlates strongly with gene family size. In this paper, we explore the possible links between the two observations, exploring the possible effect of designability on duplication and divergence. We show that designability has an inverse of expected relationship with strength of selection. More designable domains that should have more mutational plasticity evolve slower. However, we also present evidence that recently duplicated genes have variable probability of locus fixation correlated with strength of selection. As expected, paralogs under stronger evolutionary pressure have a lower failure rate. Finally, we show that probability of pseudogene formation from gene duplication can be directly tied to designability and functional flexibility of the family. We present evidence that gene families with higher designability have diverged farther because of lower probability of pseudogenization. Additionally, mutational plasticity may play an integral role by influencing pseudogenization rate. Either way, we show that considering the failure rate of duplications is integral in understanding the determinants and dynamics of molecular evolution.'),
(72944, 2006, 'bio', 'Integrating image data into biomedical text categorization', 'Shatkay, Hagit|Chen, Nawei|Blostein, Dorothea', 'School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca|School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca|School of Computing, Queens University, Kingston, Ontario, Canada. shatkay@cs.queensu.ca', 'Canada|Canada|Canada', 71, 'Artificial Intelligence|Computer Graphics|Database Management Systems|Databases, Factual|Documentation|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl235', '16873506', 'Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD02 text mining contest-where figure-captions proved to be an invaluable feature for identifying documents of interest-images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them-both alone and in combination with text-to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.'),
(72945, 2006, 'bio', 'On counting position weight matrix matches in a sequence, with application to discriminative motif finding', 'Sinha, Saurabh', 'Department of Computer Science, University of Illinois Urbana-Champaign, 201 N. Goodwin Ave, Urbana, IL 61801, USA. sinhas@cs.uiuc.edu', 'United States', 60, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Base Sequence|Binding Sites|Chromosome Mapping|DNA|Discriminant Analysis|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl227', '16873507', 'The position weight matrix (PWM) is a popular method to model transcription factor binding sites. A fundamental problem in cis-regulatory analysis is to count the occurrences of a PWM in a DNA sequence. We propose a novel probabilistic score to solve this problem of counting PWM occurrences. The proposed score has two important properties: (1) It gives appropriate weights to both strong and weak occurrences of the PWM, without using thresholds. (2) For any given PWM, this score can be computed while allowing for occurrences of other, a priori known PWMs, in a statistically sound framework. Additionally, the score is efficiently differentiable with respect to the PWM parameters, which has important consequences for designing search algorithms. The second problem we address is to find, ab initio, PWMs that have high counts in one set of sequences, and low counts in another. We develop a novel algorithm to solve this discriminative motif-finding problem, using the proposed score for counting a PWM in the sequences. The algorithm is a local search technique that exploits derivative information on an objective function to enhance speed and performance. It is extensively tested on synthetic data, and shown to perform better than other discriminative as well as non-discriminative PWM finding algorithms. It is then applied to cis-regulatory modules involved in development of the fruitfly embryo, to elicit known and novel motifs. We finally use the algorithm on genes predictive of social behavior in the honey bee, and find interesting motifs.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72946, 2006, 'bio', 'An ontology for a Robot Scientist', 'Soldatova, Larisa N|Clare, Amanda|Sparkes, Andrew|King, Ross D', 'Department of Computer Science, The University of Wales Aberystwyth, Penglais, Aberystwyth, SY23 3DB, Ceredigion, UK. lss@aber.ac.uk|Department of Computer Science, The University of Wales Aberystwyth, Penglais, Aberystwyth, SY23 3DB, Ceredigion, UK. lss@aber.ac.uk|Department of Computer Science, The University of Wales Aberystwyth, Penglais, Aberystwyth, SY23 3DB, Ceredigion, UK. lss@aber.ac.uk|Department of Computer Science, The University of Wales Aberystwyth, Penglais, Aberystwyth, SY23 3DB, Ceredigion, UK. lss@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 51, 'Artificial Intelligence|Cell Culture Techniques|Database Management Systems|Databases, Factual|Documentation|Information Storage and Retrieval|Natural Language Processing|Research|Research Design|Robotics|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Science|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl207', '16873508', 'A Robot Scientist is a physically implemented robotic system that can automatically carry out cycles of scientific experimentation. We are commissioning a new Robot Scientist designed to investigate gene function in S. cerevisiae. This Robot Scientist will be capable of initiating >1,000 experiments, and making >200,000 observations a day. Robot Scientists provide a unique test bed for the development of methodologies for the curation and annotation of scientific experiments: because the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process. This new ability brings with it significant technical challenges. To meet these we apply an ontology driven approach to the representation of all the Robot Scientists data and metadata.'),
(72947, 2006, 'bio', 'ARTS: accurate recognition of transcription starts in human', 'Sonnenburg, Sören|Zien, Alexander|Rätsch, Gunnar', 'Fraunhofer Institute, FIRST Kekuléstr. 7, Berlin, Germany.|Fraunhofer Institute, FIRST Kekuléstr. 7, Berlin, Germany.|Fraunhofer Institute, FIRST Kekuléstr. 7, Berlin, Germany.', 'Germany|Germany|Germany', 89, 'Algorithms|Base Sequence|DNA|Humans|Molecular Sequence Data|RNA Polymerase II|Sequence Alignment|Sequence Analysis, DNA|Software|Transcription Factors|Transcription Initiation Site', 'Journal Article', '10.1093/bioinformatics/btl250', '16873509', 'We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequence kernels, we achieve drastically higher prediction accuracies than state-of-the-art methods.'),
(72948, 2006, 'bio', 'A computational approach toward label-free protein quantification using predicted peptide detectability', 'Tang, Haixu|Arnold, Randy J|Alves, Pedro|Xun, Zhiyin|Clemmer, David E|Novotny, Milos V|Reilly, James P|Radivojac, Predrag', 'School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.|School of Informatics, Indiana University, Bloomington, IN, USA.', 'India|India|India|India|India|India|India|India', 139, 'Algorithms|Amino Acid Sequence|Computer Simulation|Mass Spectrometry|Models, Chemical|Models, Molecular|Molecular Sequence Data|Peptide Mapping|Peptides|Proteins|Sequence Analysis, Protein|Staining and Labeling', 'Journal Article', '10.1093/bioinformatics/btl237', '16873510', 'We propose here a new concept of peptide detectability which could be an important factor in explaining the relationship between a proteins quantity and the peptides identified from it in a high-throughput proteomics experiment. We define peptide detectability as the probability of observing a peptide in a standard sample analyzed by a standard proteomics routine and argue that it is an intrinsic property of the peptide sequence and neighboring regions in the parent protein. To test this hypothesis we first used publicly available data and data from our own synthetic samples in which quantities of model proteins were controlled. We then applied machine learning approaches to demonstrate that peptide detectability can be predicted from its sequence and the neighboring regions in the parent protein with satisfactory accuracy. The utility of this approach for protein quantification is demonstrated by peptides with higher detectability generally being identified at lower concentrations over those with lower detectability in the synthetic protein mixtures. These results establish a direct link between protein concentration and peptide detectability. We show that for each protein there exists a level of peptide detectability above which peptides are detected and below which peptides are not detected in an experiment. We call this level the minimum acceptable detectability for identified peptides (MDIP) which can be calibrated to predict protein concentration. Triplicate analysis of a biological sample showed that these MDIP values are consistent among the three data sets.'),
(72949, 2006, 'bio', 'An integrative approach for causal gene identification and gene regulatory pathway inference', 'Tu, Zhidong|Wang, Li|Arbeitman, Michelle N|Chen, Ting|Sun, Fengzhu', 'Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.|Molecular and Computational Biology Program, University of Southern California, Los Angeles, USA.', 'United States|United States|United States|United States|United States', 78, 'Chromosome Mapping|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Gene Targeting|Models, Biological|Protein Interaction Mapping|Regulatory Elements, Transcriptional|Signal Transduction|Systems Integration|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl234', '16873511', 'Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.'),
(72950, 2006, 'bio', 'Integrating structured biological data by Kernel Maximum Mean Discrepancy', 'Borgwardt, Karsten M|Gretton, Arthur|Rasch, Malte J|Kriegel, Hans-Peter|Schölkopf, Bernhard|Smola, Alex J', 'Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de|Institute for Computer Science, Ludwig-Maximilians-University, Munich, Germany. kb@dbs.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 447, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Databases, Factual|Information Storage and Retrieval|Models, Biological|Models, Statistical|Sample Size|Statistical Distributions|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl242', '16873512', 'Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology.'),
(72951, 2006, 'bio', 'Computational inference of the molecular logic for synaptic connectivity in C elegans', 'Varadan, Vinay|Miller, David M|Anastassiou, Dimitris', 'Center for Computational Biology and Bioinformatics (C2B2), and Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.|Center for Computational Biology and Bioinformatics (C2B2), and Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.|Center for Computational Biology and Bioinformatics (C2B2), and Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.', 'United States|United States|United States', 41, 'Animals|Brain|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Computer Simulation|Gene Expression Profiling|Logistic Models|Models, Neurological|Nerve Net|Nerve Tissue Proteins|Neural Pathways|Synapses|Synaptic Transmission', 'Journal Article', '10.1093/bioinformatics/btl224', '16873513', 'The nematode C. elegans is an ideal model organism in which to investigate the biomolecular mechanisms underlying the connectivity of neurons, because synaptic connections are described in a comprehensive wiring diagram and methods for defining gene expression profiles of individual neurons are now available.'),
(72952, 2006, 'bio', 'Novel unsupervised feature filtering of biological data', 'Varshavsky, Roy|Gottlieb, Assaf|Linial, Michal|Horn, David', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Israel. royke@cs.huji.ac.il|School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Israel. royke@cs.huji.ac.il|School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Israel. royke@cs.huji.ac.il|School of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Israel. royke@cs.huji.ac.il', 'Israel|Israel|Israel|Israel', 87, 'Artificial Intelligence|Biology|Biomarkers, Tumor|Computer Simulation|Gene Expression Profiling|Humans|Leukemia|Models, Biological|Neoplasm Proteins|Pattern Recognition, Automated|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl214', '16873514', 'Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR), forward selection by accumulating features according to which set produces highest entropy (FS1), forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2), backward elimination (BE) of features with the lowest CE.'),
(72953, 2006, 'bio', 'Constructing near-perfect phylogenies with multiple homoplasy events', 'Satya, Ravi Vijaya|Mukherjee, Amar|Alexe, Gabriela|Parida, Laxmi|Bhanot, Gyan', 'School of EECS, University of Central Florida, Orlando, FL 32816-2362, USA. rvijaya@cs.ucf.edu|School of EECS, University of Central Florida, Orlando, FL 32816-2362, USA. rvijaya@cs.ucf.edu|School of EECS, University of Central Florida, Orlando, FL 32816-2362, USA. rvijaya@cs.ucf.edu|School of EECS, University of Central Florida, Orlando, FL 32816-2362, USA. rvijaya@cs.ucf.edu|School of EECS, University of Central Florida, Orlando, FL 32816-2362, USA. rvijaya@cs.ucf.edu', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Base Sequence|Biological Evolution|Chromosome Mapping|DNA Mutational Analysis|Genome, Human|Haplotypes|Humans|Linkage Disequilibrium|Molecular Sequence Data|Phylogeny|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl262', '16873515', 'We explore the problem of constructing near-perfect phylogenies on bi-allelic haplotypes, where the deviation from perfect phylogeny is entirely due to homoplasy events. We present polynomial-time algorithms for restricted versions of the problem. We show that these algorithms can be extended to genotype data, in which case the problem is called the near-perfect phylogeny haplotyping (NPPH) problem. We present a near-optimal algorithm for the H1-NPPH problem, which is to determine if a given set of genotypes admit a phylogeny with a single homoplasy event. The time-complexity of our algorithm for the H1-NPPH problem is O(m2(n + m)), where n is the number of genotypes and m is the number of SNP sites. This is a significant improvement over the earlier O(n4) algorithm. We also introduce generalized versions of the problem. The H(1, q)-NPPH problem is to determine if a given set of genotypes admit a phylogeny with q homoplasy events, so that all the homoplasy events occur in a single site. We present an O(m(q+1)(n + m)) algorithm for the H(1,q)-NPPH problem.'),
(72954, 2006, 'bio', 'SNP Function Portal: a web database for exploring the function implication of SNP alleles', 'Wang, Pinglang|Dai, Manhong|Xuan, Weijian|McEachin, Richard C|Jackson, Anne U|Scott, Laura J|Athey, Brian|Watson, Stanley J|Meng, Fan', 'Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.|Molecular and Behavioral Neuroscience Institute and Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 63, 'Alleles|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Database Management Systems|Databases, Genetic|Genome, Human|Humans|Information Storage and Retrieval|Internet|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl241', '16873516', 'Finding the potential functional significance of SNPs is a major bottleneck in understanding genome-wide SNP scanning results, as the related functional data are distributed across many different databases. The SNP Function Portal is designed to be a clearing house for all public domain SNP functional annotation data, as well as in-house functional annotations derived from different data sources. It currently contains SNP functional annotations in six major categories including genomic elements, transcription regulation, protein function, pathway, disease and population genetics. Besides extensive SNP functional annotations, the SNP Function Portal includes a powerful search engine that accepts different types of genetic markers as input and identifies all genetically related SNPs based on the HapMap Phase II data as well as the relationship of different markers to known genes. As a result, our system allows users to identify the potential biological impact of genetic markers and complex relationships among genetic markers and genes, and it greatly facilitates knowledge discovery in genome-wide SNP scanning experiments.'),
(72955, 2006, 'bio', 'Protein classification using ontology classification', 'Wolstencroft, K|Lord, P|Tabernero, L|Brass, A|Stevens, R', 'School of Computer Science, University of Manchester, UK. KWolstencroft@cs.man.ac.uk|School of Computer Science, University of Manchester, UK. KWolstencroft@cs.man.ac.uk|School of Computer Science, University of Manchester, UK. KWolstencroft@cs.man.ac.uk|School of Computer Science, University of Manchester, UK. KWolstencroft@cs.man.ac.uk|School of Computer Science, University of Manchester, UK. KWolstencroft@cs.man.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 40, 'Algorithms|Amino Acid Sequence|Expert Systems|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl208', '16873517', 'The classification of proteins expressed by an organism is an important step in understanding the molecular biology of that organism. Traditionally, this classification has been performed by human experts. Human knowledge can recognise the functional properties that are sufficient to place an individual gene product into a particular protein family group. Automation of this task usually fails to meet the gold standard of the human annotator because of the difficult recognition stage. The growing number of genomes, the rapid changes in knowledge and the central role of classification in the annotation process, however, motivates the need to automate this process.'),
(72956, 2006, 'bio', 'Inferring functional pathways from multi-perturbation data', 'Yosef, Nir|Kaufman, Alon|Ruppin, Eytan', 'School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il|School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel. niryosef@post.tau.ac.il', 'Israel|Israel|Israel', 7, 'Algorithms|Computer Simulation|Databases, Factual|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl204', '16873518', 'Recently, a conceptually new approach for analyzing gene networks, the Functional Influence Network (FIN) was presented. The FIN approach uses the measured performance of a given cellular function under different multi-perturbations, to identify the main functional pathways and interactions underlying its processing. Here we present and study an iterative, extended version of FIN, the Functional Influence Network Extractor (FINE), which is specifically geared towards the accurate analysis of sparse cellular systems. We employ it to study a conceptually fundamental question of practical importance--how well should we know the system studied (such that we can predict its performance) so that we can understand its workings (i.e., chart its underlying functional network)?'),
(72957, 2006, 'bio', 'Accessing bioscience images from abstract sentences', 'Yu, Hong|Lee, Minsuk', 'Department of Health Sciences, University of Wisconsin-Milwaukee, Wisconsin, USA. hong.yu@dbmi.columbia.edu|Department of Health Sciences, University of Wisconsin-Milwaukee, Wisconsin, USA. hong.yu@dbmi.columbia.edu', 'United States|United States', 0, 'Abstracting and Indexing as Topic|Artificial Intelligence|Biological Science Disciplines|Computer Graphics|Image Interpretation, Computer-Assisted|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|PubMed|User-Computer Interface|Vocabulary, Controlled', 'Evaluation Studies', '10.1093/bioinformatics/btl261', '16873519', 'Images (e.g., figures) are important experimental results that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. On the other hand, biologists live in an age of information explosion. As thousands of biomedical articles are published every day, systems that help biologists efficiently access images in literature would greatly facilitate biomedical research. We hypothesize that much of image content reported in a full-text article can be summarized by the sentences in the abstract of the article. In our study, more than one hundred biologists had tested this hypothesis and more than 40 biologists had evaluated a novel user-interface BioEx that allows biologists to access images directly from abstract sentences. Our results show that 87.8% biologists were in favor of BioEx over two other baseline user-interfaces. We further developed systems that explored hierarchical clustering algorithms to automatically identify abstract sentences that summarize the images. One of the systems achieves a precision of 100% that corresponds to a recall of 4.6%.'),
(72958, 2006, 'bio', 'A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements', 'Zhang, Shaojie|Borovok, Ilya|Aharonowitz, Yair|Sharan, Roded|Bafna, Vineet', 'Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. shzhang@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. shzhang@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. shzhang@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. shzhang@cs.ucsd.edu|Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. shzhang@cs.ucsd.edu', 'United States|United States|United States|United States|United States', 25, 'Algorithms|Base Sequence|Molecular Sequence Data|RNA, Untranslated|Regulatory Elements, Transcriptional|Ribosomes|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl232', '16873520', 'Recent studies have uncovered an RNA world, in which non coding RNA (ncRNA) sequences play a central role in the regulation of gene expression. Computational studies on ncRNA have been directed toward developing detection methods for ncRNAs. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speedup the detection process.'),
(72959, 2006, 'bio', 'AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets', 'Bottegoni, Giovanni|Rocchia, Walter|Recanatini, Maurizio|Cavalli, Andrea', 'Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126, Bologna, Italy.|Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126, Bologna, Italy.|Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126, Bologna, Italy.|Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126, Bologna, Italy.', 'Italy|Italy|Italy|Italy', 37, 'Algorithms|Amino Acid Sequence|Amsacrine|Artificial Intelligence|Binding Sites|Cluster Analysis|Computer Simulation|Databases, Protein|Drug Delivery Systems|Drug Design|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pharmaceutical Preparations|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl212', '16873522', 'Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing.'),
(72960, 2006, 'bio', 'A top-level ontology of functions and its application in the Open Biomedical Ontologies', 'Burek, Patryk|Hoehndorf, Robert|Loebe, Frank|Visagie, Johann|Herre, Heinrich|Kelso, Janet', 'Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.|Department of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Augustusplatz 10-11, 04109 Leipzig.', '-|-|-|-|-|-', 40, 'Biomedical Engineering|Computational Biology|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Biological|Natural Language Processing|Proteins|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl266', '16873523', 'A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the communitys knowledge about biological functions.'),
(72961, 2006, 'bio', 'Comparative footprinting of DNA-binding proteins', 'Contreras-Moreira, Bruno|Collado-Vides, Julio', 'Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av.Universidad, s/n, 62210 Cuernavaca, Morelos, México. contrera@ccg.unam.mx|Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av.Universidad, s/n, 62210 Cuernavaca, Morelos, México. contrera@ccg.unam.mx', '-|-', 14, 'Algorithms|Binding Sites|Computer Simulation|DNA|DNA-Binding Proteins|Models, Chemical|Models, Molecular|Protein Binding|Protein Footprinting|Sequence Analysis, DNA|Sequence Analysis, Protein|Sequence Homology', 'Comparative Study', '10.1093/bioinformatics/btl215', '16873524', 'Comparative modelling is a computational method used to tackle a variety of problems in molecular biology and biotechnology. Traditionally it has been applied to model the structure of proteins on their own or bound to small ligands, although more recently it has also been used to model protein-protein interfaces. This work is the first to systematically analyze whether comparative models of protein-DNA complexes could be built and be useful for predicting DNA binding sites.'),
(72962, 2006, 'bio', 'A probabilistic approach to protein backbone tracing in electron density maps', 'DiMaio, Frank|Shavlik, Jude|Phillips, George N', 'Computer Sciences Dept., University of Wisconsin-Madison, USA. dimaio@cs.wisc.edu|Computer Sciences Dept., University of Wisconsin-Madison, USA. dimaio@cs.wisc.edu|Computer Sciences Dept., University of Wisconsin-Madison, USA. dimaio@cs.wisc.edu', 'United States|United States|United States', 17, 'Algorithms|Amino Acid Sequence|Computer Simulation|Crystallography, X-Ray|Data Interpretation, Statistical|Electron Probe Microanalysis|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl252', '16873525', 'One particularly time-consuming step in protein crystallography is interpreting the electron density map, that is, fitting a complete molecular model of the protein into a 3D image of the protein produced by the crystallographic process. In poor-quality electron density maps, the interpretation may require a significant amount of a crystallographers time. Our work investigates automating the time-consuming initial backbone trace in poor-quality density maps. We describe ACMI (Automatic Crystallographic Map Interpreter), which uses a probabilistic model known as a Markov field to represent the protein. Residues of the protein are modeled as nodes in a graph, while edges model pairwise structural interactions. Modeling the protein in this manner allows the model to be flexible, considering an almost infinite number of possible conformations, while rejecting any that are physically impossible. Using an efficient algorithm for approximate inference--belief propagation--allows the most probable trace of the proteins backbone through the density map to be determined. We test ACMI on a set of ten protein density maps (at 2.5 to 4.0 A resolution), and compare our results to alternative approaches. At these resolutions, ACMI offers a more accurate backbone trace than current approaches.'),
(72963, 2006, 'bio', 'Automatic clustering of orthologs and inparalogs shared by multiple proteomes', 'Alexeyenko, Andrey|Tamas, Ivica|Liu, Gang|Sonnhammer, Erik L L', 'Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.|Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 197, 'Algorithms|Animals|Artificial Intelligence|Cluster Analysis|Conserved Sequence|Evolution, Molecular|Humans|Pattern Recognition, Automated|Phylogeny|Proteome|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl213', '16873526', 'The complete sequencing of many genomes has made it possible to identify orthologous genes descending from a common ancestor. However, reconstruction of evolutionary history over long time periods faces many challenges due to gene duplications and losses. Identification of orthologous groups shared by multiple proteomes therefore becomes a clustering problem in which an optimal compromise between conflicting evidences needs to be found.'),
(72964, 2006, 'bio', 'CONTRAfold: RNA secondary structure prediction without physics-based models', 'Do, Chuong B|Woods, Daniel A|Batzoglou, Serafim', 'Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu|Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu|Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu', 'United States|United States|United States', 284, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Nucleic Acid Conformation|Physics|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btl246', '16873527', 'For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models.'),
(72965, 2006, 'bio', 'springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an information landscape', 'Ebbels, Timothy M D|Buxton, Bernard F|Jones, David T', 'Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT.|Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT.|Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT.', '-|-|-', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl205', '16873528', 'The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on spring embedding and an information landscape allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The information landscape highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.'),
(72966, 2006, 'bio', 'A multivariate approach for integrating genome-wide expression data and biological knowledge', 'Kong, Sek Won|Pu, William T|Park, Peter J', 'Department of Cardiology 300 Longwood Avenue, Boston, MA 02115, USA.|Department of Cardiology 300 Longwood Avenue, Boston, MA 02115, USA.|Department of Cardiology 300 Longwood Avenue, Boston, MA 02115, USA.', 'United States|United States|United States', 109, 'Algorithms|Biology|Chromosome Mapping|Computer Simulation|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Proteome|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl401', '16877751', 'Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions.'),
(72967, 2006, 'bio', 'Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays', 'Goldstein, Darlene R', 'Ecole Polytechnique Fédérale de Lausanne, Institut de mathématiques, Bâtiment MA, Station 8, CH-1015 Lausanne, Switzerland. Darlene.Goldstein@epfl.ch', 'Switzerland', 0, 'Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl402', '16877752', 'Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time.'),
(72968, 2006, 'bio', 'Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach', 'Motakis, E S|Nason, G P|Fryzlewicz, P|Rutter, G A', 'Department of Mathematics, University of Bristol Bristol, UK.|Department of Mathematics, University of Bristol Bristol, UK.|Department of Mathematics, University of Bristol Bristol, UK.|Department of Mathematics, University of Bristol Bristol, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 20, 'Algorithms|Colorimetry|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reference Values|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl412', '16877753', 'Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean-variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar-Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being distribution-free in the sense that no parametric model for the underlying microarray data is required to be specified or estimated, hence, DDHFm can be applied very generally, not just to microarray data.'),
(72969, 2006, 'bio', 'General framework for developing and evaluating database scoring algorithms using the TANDEM search engine', 'MacLean, Brendan|Eng, Jimmy K|Beavis, Ronald C|McIntosh, Martin', 'Fred Hutchinson Cancer Research Center Seattle, USA.|Fred Hutchinson Cancer Research Center Seattle, USA.|Fred Hutchinson Cancer Research Center Seattle, USA.|Fred Hutchinson Cancer Research Center Seattle, USA.', 'United States|United States|United States|United States', 171, 'Algorithms|Amino Acid Sequence|Computational Biology|Data Interpretation, Statistical|Databases, Factual|Databases, Protein|Information Storage and Retrieval|Mass Spectrometry|Peptides|Programming Languages|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btl379', '16877754', 'Tandem mass spectrometry (MS/MS) identifies protein sequences using database search engines, at the core of which is a score that measures the similarity between peptide MS/MS spectra and a protein sequence database. The TANDEM application was developed as a freely available database search engine for the proteomics research community. To extend TANDEM as a platform for further research on developing improved database scoring methods, we modified the software to allow users to redefine the scoring function and replace the native TANDEM scoring function while leaving the remaining core application intact. Redefinition is performed at run time so multiple scoring functions are available to be selected and applied from a single search engine binary. We introduce the implementation of the pluggable scoring algorithm and also provide implementations of two TANDEM compatible scoring functions, one previously described scoring function compatible with PeptideProphet and one very simple scoring function that quantitative researchers may use to begin their development. This extension builds on the open-source TANDEM project and will facilitate research into and dissemination of novel algorithms for matching MS/MS spectra to peptide sequences. The pluggable scoring schema is also compatible with related search applications P3 and Hunter, which are part of the X! suite of database matching algorithms. The pluggable scores and the X! suite of applications are all written in C++.'),
(72970, 2006, 'bio', 'SANTA domain: a novel conserved protein module in Eukaryota with potential involvement in chromatin regulation', 'Zhang, Dapeng|Martyniuk, Christopher J|Trudeau, Vance L', 'Centre for Advanced Research in Environmental Genomics, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.|Centre for Advanced Research in Environmental Genomics, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.|Centre for Advanced Research in Environmental Genomics, Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada.', 'Canada|Canada|Canada', 0, 'Amino Acid Sequence|Animals|Chromatin|Chromatin Assembly and Disassembly|Conserved Sequence|DNA-Binding Proteins|Eukaryotic Cells|Humans|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl414', '16877755', 'Since packaging of DNA in the chromatin structure restricts the accessibility for regulatory factors, chromatin remodeling is required to facilitate nuclear processes such as gene transcription, replication, and genome recombination. Many conserved non-enzymatic protein domains have been identified that contribute to the activities of multiprotein remodeling complexes. Here we identified a novel conserved protein domain in Eukaryota whose putative function may be in regulating chromatin remodeling. Since this domain is associated with a known SANT domain in several vertebrate proteins, we named it the SANTA (SANT Associated) domain. Sequence analysis showed that the SANTA domain is approximately a 90 amino acid module and likely composed of four central beta-sheets and three flanking alpha-helices. Many hydrophobic residues exhibited high conservation along the domain, implying a possible function in protein-protein interactions. The SANTA domain was identified in mammals, chicken, frog, fish, sea squirt, sea urchin, worms and plants. Furthermore, a phylogenetic tree of SANTA domains showed that one plant-specific duplication event happened in the Viridiplantae lineage.'),
(72971, 2006, 'bio', 'Hierarchical classification of hydrolases catalytic sites', 'Gariev, Igor A|Varfolomeev, Sergey D', 'School of Enzymology, Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, 119992, Russia. gariev@hotmail.com|School of Enzymology, Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, 119992, Russia. gariev@hotmail.com', '-|-', 0, 'Amino Acid Sequence|Binding Sites|Catalysis|Computer Simulation|Database Management Systems|Databases, Protein|Enzyme Activation|Hydrolases|Information Storage and Retrieval|Models, Molecular|Molecular Sequence Data|Protein Binding|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl413', '16877756', 'Universal ontology of catalytic sites is required to systematize enzyme catalytic sites, their evolution as well as relations between catalytic sites and protein families, organisms and chemical reactions. Here we present a classification of hydrolases catalytic sites based on hierarchical organization. The web-accessible database provides information on the catalytic sites, protein folds, EC numbers and source organisms of the enzymes and includes software allowing for analysis and visualization of the relations between them.'),
(72972, 2006, 'bio', 'Joint estimation of calibration and expression for high-density oligonucleotide arrays', 'Oberg, Ann L|Mahoney, Douglas W|Ballman, Karla V|Therneau, Terry M', 'Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu|Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu|Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu|Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine 200 First Street SW, Rochester, MN 55905, USA. oberg.ann@mayo.edu', 'United States|United States|United States|United States', 4, 'Algorithms|Calibration|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Evaluation Studies', '10.1093/bioinformatics/btl399', '16877757', 'The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step.'),
(72973, 2006, 'bio', 'Reliable gene signatures for microarray classification: assessment of stability and performance', 'Davis, Chad A|Gerick, Fabian|Hintermair, Volker|Friedel, Caroline C|Fundel, Katrin|Küffner, Robert|Zimmer, Ralf', 'Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.|Institute of Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17 80333 Munich, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 60, 'Algorithms|Biomarkers|Cartilage|Computer Simulation|Gene Expression|Gene Expression Profiling|Humans|Models, Biological|Models, Statistical|Neoplasms|Oligonucleotide Array Sequence Analysis|Osteoarthritis|Proteins|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btl400', '16882647', 'Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72974, 2006, 'bio', 'PYCHEM: a multivariate analysis package for python', 'Jarvis, Roger M|Broadhurst, David|Johnson, Helen|OBoyle, Noel M|Goodacre, Royston', 'School of Chemistry, The University of Manchester PO Box 88, Sackville Street, Manchester M60 1QD, UK. Roger.Jarvis@manchester.ac.uk|School of Chemistry, The University of Manchester PO Box 88, Sackville Street, Manchester M60 1QD, UK. Roger.Jarvis@manchester.ac.uk|School of Chemistry, The University of Manchester PO Box 88, Sackville Street, Manchester M60 1QD, UK. Roger.Jarvis@manchester.ac.uk|School of Chemistry, The University of Manchester PO Box 88, Sackville Street, Manchester M60 1QD, UK. Roger.Jarvis@manchester.ac.uk|School of Chemistry, The University of Manchester PO Box 88, Sackville Street, Manchester M60 1QD, UK. Roger.Jarvis@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 11, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl416', '16882648', 'We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines.'),
(72975, 2006, 'bio', 'Nemo: an evolutionary and population genetics programming framework', 'Guillaume, Frédéric|Rougemont, Jacques', 'Department of Ecology and Evolution, University of Lausanne Biofore, CH-1015 Lausanne, Switzerland. guillaum@zoology.ubc.ca|Department of Ecology and Evolution, University of Lausanne Biofore, CH-1015 Lausanne, Switzerland. guillaum@zoology.ubc.ca', 'Switzerland|Switzerland', 89, 'Biological Evolution|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genetics, Population|Models, Genetic|Quantitative Trait Loci|Software', 'Journal Article', '10.1093/bioinformatics/btl415', '16882649', 'Nemo is an individual-based, genetically explicit and stochastic population computer program for the simulation of population genetics and life-history trait evolution in a metapopulation context. It comes as both a C++ programming framework and an executable program file. Its object-oriented programming design gives it the flexibility and extensibility needed to implement a large variety of forward-time evolutionary models. It provides developers with abstract models allowing them to implement their own life-history traits and life-cycle events. Nemo offers a large panel of population models, from the Island model to lattice models with demographic or environmental stochasticity and a variety of already implemented traits (deleterious mutations, neutral markers and more), life-cycle events (mating, dispersal, aging, selection, etc.) and output operators for saving data and statistics. It runs on all major computer platforms including parallel computing environments.'),
(72976, 2006, 'bio', 'Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions', 'Sato, Tetsuya|Yamanishi, Yoshihiro|Horimoto, Katsuhisa|Kanehisa, Minoru|Toh, Hiroyuki', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan|Japan', 0, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btl419', '16882650', 'The computational prediction of protein-protein interactions is currently a major issue in bioinformatics. Recently, a variety of co-evolution-based methods have been investigated toward this goal. In this study, we introduced a partial correlation coefficient as a new measure for the degree of co-evolution between proteins, and proposed its use to predict protein-protein interactions.'),
(72977, 2006, 'bio', 'OSIRIS: a tool for retrieving literature about sequence variants', 'Bonis, Julio|Furlong, Laura Inés|Sanz, Ferran', 'Research Unit on Biomedical Informatics (GRIB), IMIM Universitat Pompeu Fabra, carrer Dr Aiguader 88, E-08003 Barcelona, Spain.|Research Unit on Biomedical Informatics (GRIB), IMIM Universitat Pompeu Fabra, carrer Dr Aiguader 88, E-08003 Barcelona, Spain.|Research Unit on Biomedical Informatics (GRIB), IMIM Universitat Pompeu Fabra, carrer Dr Aiguader 88, E-08003 Barcelona, Spain.', 'Spain|Spain|Spain', 0, 'Abstracting and Indexing as Topic|Base Sequence|Database Management Systems|Genetic Variation|Information Storage and Retrieval|Molecular Sequence Data|Natural Language Processing|Periodicals as Topic|Polymorphism, Single Nucleotide|PubMed|Sequence Analysis, DNA|Software|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl421', '16882651', 'Sequence variants, in particular single nucleotide polymorphisms (SNPs), are key elements for the identification of genes associated with complex diseases and with particular drug responses. The search for literature about sequence variation is hampered by the large number of allelic variants reported for many genes and by the variability in both gene and sequence variants nomenclatures. We describe OSIRIS, a search tool that integrates different sources of information with the aim to retrieve literature about sequence variation of a gene. In addition, it provides a method to link a dbSNP entry with the articles referring to it.'),
(72978, 2006, 'bio', 'Sebida: a database for the functional and evolutionary analysis of genes with sex-biased expression', 'Gnad, Florian|Parsch, John', 'Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany. gnad@biochem.mpg.de|Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany. gnad@biochem.mpg.de', 'Germany|Germany', 82, 'Animals|Biological Evolution|Database Management Systems|Databases, Genetic|Evolution, Molecular|Female|Gene Expression Profiling|Information Storage and Retrieval|Male|Oligonucleotide Array Sequence Analysis|Proteins|Sex Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl422', '16882652', 'We describe Sebida, a database of genes with sex-biased expression. The database integrates results from multiple, independent microarray studies comparing male and female gene expression in Drosophila melanogaster, Drosophila simulans and Anopheles gambiae. Sebida uses standard nomenclature, which allows individual genes to be compared across different microarray platforms and to be queried by gene name, symbol, or annotation number. In addition to ratios of male/female expression for each gene, Sebida also contains information useful for evolutionary studies, such as local recombination rate, degree of codon bias and interspecific divergence at synonymous and non-synonymous sites.'),
(72979, 2006, 'bio', 'Evaluation and comparison of gene clustering methods in microarray analysis', 'Thalamuthu, Anbupalam|Mukhopadhyay, Indranil|Zheng, Xiaojing|Tseng, George C', 'Department of Human Genetics, University of Pittsburgh, PA, USA.|Department of Human Genetics, University of Pittsburgh, PA, USA.|Department of Human Genetics, University of Pittsburgh, PA, USA.|Department of Human Genetics, University of Pittsburgh, PA, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software|Software Validation', 'Comparative Study', '10.1093/bioinformatics/btl406', '16882653', 'Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods.'),
(72980, 2006, 'bio', 'Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors', 'Ruan, Quansong|Dutta, Debojyoti|Schwalbach, Michael S|Steele, Joshua A|Fuhrman, Jed A|Sun, Fengzhu', 'Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.|Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.|Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.|Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.|Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.|Department of Mathematics, University of Southern California 3620 Vermont Avenue, KAP 108, Los Angeles, CA 90089-2532, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Bacteria|Bacterial Physiological Phenomena|Computer Simulation|Ecosystem|Environment|Marine Biology|Models, Biological|Plankton', 'Journal Article', '10.1093/bioinformatics/btl417', '16882654', 'Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these datasets which provide insight into microbial ecology. Pearson correlation coefficient (PCC) analysis is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ, thus, alternative analyses were explored.'),
(72981, 2006, 'bio', 'The HicAB cassette, a putative novel, RNA-targeting toxin-antitoxin system in archaea and bacteria', 'Makarova, Kira S|Grishin, Nick V|Koonin, Eugene V', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.', 'United States|United States|United States', 73, 'Antitoxins|Archaea|Bacteria|Bacterial Toxins|Binding Sites|Gene Targeting|Integrons|Protein Binding|RNA, Archaeal|RNA, Bacterial|RNA-Binding Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl418', '16895922', 'Toxin-antitoxin systems (TAS) are abundant, diverse, horizontally mobile gene modules that encode powerful resistance mechanisms in prokaryotes. We use the comparative-genomic approach to predict a new TAS that consists of a two-gene cassette encoding uncharacterized HicA and HicB proteins. Numerous bacterial and archaeal genomes encode from one to eight HicAB modules which appear to be highly prone to horizontal gene transfer. The HicB protein (COG1598/COG4226) has a partially degraded RNAse H fold, whereas HicA (COG1724) contains a double-stranded RNA-binding domain. The stable combination of these two domains suggests a link to RNA metabolism, possibly, via an RNA interference-type mechanism. In most HicB proteins, the RNAse H-like domain is fused to a DNA-binding domain, either of the ribbon-helix-helix or of the helix-turn-helix class, in other TAS, proteins containing these DNA-binding domains function as antitoxins. Thus, the HicAB module is predicted to be a novel TAS whose mechanism involves RNA-binding and, possibly, cleavage.'),
(72982, 2006, 'bio', 'Few crucial links assure checkpoint efficiency in the yeast cell-cycle network', 'Stoll, Gautier|Rougemont, Jacques|Naef, Félix', 'Swiss Institute of Experimental Cancer Research, ISREC, NCCR Molecular Oncology CH-1066 Epalinges, Switzerland.|Swiss Institute of Experimental Cancer Research, ISREC, NCCR Molecular Oncology CH-1066 Epalinges, Switzerland.|Swiss Institute of Experimental Cancer Research, ISREC, NCCR Molecular Oncology CH-1066 Epalinges, Switzerland.', 'Switzerland|Switzerland|Switzerland', 0, 'Cell Cycle|Cell Cycle Proteins|Computer Simulation|Gene Expression Regulation, Fungal|Genes, cdc|Models, Biological|Models, Statistical|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btl432', '16895923', 'The ability of cells to complete mitosis with high fidelity relies on elaborate checkpoint mechanisms. We study S- and M-phase checkpoint responses in silico in the budding yeast with a stochastic dynamical model for the cell-cycle. We aim to provide an unbiased functional classification of network interactions that reflect the contribution of each link to checkpoint efficiency in the presence of cellular fluctuations.'),
(72983, 2006, 'bio', 'MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression', 'He, Jingwu|Zelikovsky, Alexander', 'Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA.', 'Georgia|Georgia', 30, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Expressed Sequence Tags|Genotype|Linear Models|Models, Genetic|Molecular Sequence Data|Polymorphism, Single Nucleotide|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl420', '16895924', 'The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005).'),
(72984, 2006, 'bio', 'Robust inference of positive selection from recombining coding sequences', 'Scheffler, Konrad|Martin, Darren P|Seoighe, Cathal', 'Computational Biology Group, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch 7701, South Africa. konrad@cbio.uct.ac.za|Computational Biology Group, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch 7701, South Africa. konrad@cbio.uct.ac.za|Computational Biology Group, Institute of Infectious Disease and Molecular Medicine University of Cape Town, Private Bag, Rondebosch 7701, South Africa. konrad@cbio.uct.ac.za', 'South Africa|South Africa|South Africa', 150, 'Base Sequence|Biological Evolution|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genome, Viral|HIV-1|Likelihood Functions|Molecular Sequence Data|Open Reading Frames|Phylogeny|Recombination, Genetic|Selection, Genetic|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl427', '16895925', 'Accurate detection of positive Darwinian selection can provide important insights to researchers investigating the evolution of pathogens. However, many pathogens (particularly viruses) undergo frequent recombination and the phylogenetic methods commonly applied to detect positive selection have been shown to give misleading results when applied to recombining sequences. We propose a method that makes maximum likelihood inference of positive selection robust to the presence of recombination. This is achieved by allowing tree topologies and branch lengths to change across detected recombination breakpoints. Further improvements are obtained by allowing synonymous substitution rates to vary across sites.'),
(72985, 2006, 'bio', 'Branch and bound computation of exact p-values', 'Bejerano, Gill', 'Center for Biomolecular Science and Engineering, School of Engineering, 1156 High Street, University of California, Santa Cruz, CA 95064, USA. jill@soe.ucsc.edujill', 'United States', 1, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl357', '16895926', 'P-value computation is often used in bioinformatics to quantify the surprise, or significance, associated with a given observation. An implementation is provided that computes the exact p-value associated with any observed sample, against a null multinomial distribution, using the likelihood-ratio statistic. The efficient branch and bound code, far exceeding the full enumeration implemented by commercial packages, is especially useful with small sample, sparse data and rare events, common scenarios in bioinformatics, where approximations are often inaccurate and inappropriate. This code base can also be adapted to compute exact p-values of other statistics in diverse sampling scenarios.'),
(72986, 2006, 'bio', 'HiRes--a tool for comprehensive assessment and interpretation of metabolomic data', 'Zhao, Qi|Stoyanova, Radka|Du, Shuyan|Sajda, Paul|Brown, Truman R', 'Hatch Center for MR Research, Department of Radiology, Columbia University, New York, NY 10032 USA. qz2106@columbia.edu|Hatch Center for MR Research, Department of Radiology, Columbia University, New York, NY 10032 USA. qz2106@columbia.edu|Hatch Center for MR Research, Department of Radiology, Columbia University, New York, NY 10032 USA. qz2106@columbia.edu|Hatch Center for MR Research, Department of Radiology, Columbia University, New York, NY 10032 USA. qz2106@columbia.edu|Hatch Center for MR Research, Department of Radiology, Columbia University, New York, NY 10032 USA. qz2106@columbia.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Mass Spectrometry|Peptide Mapping|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl428', '16895927', 'The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other omics, a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval.'),
(72987, 2006, 'bio', 'Group testing for pathway analysis improves comparability of different microarray datasets', 'Manoli, Theodora|Gretz, Norbert|Gröne, Hermann-Josef|Kenzelmann, Marc|Eils, Roland|Brors, Benedikt', 'Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.|Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 114, 'Biomarkers, Tumor|Data Interpretation, Statistical|Databases, Factual|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl424', '16895928', 'The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different datasets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, e.g. metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis, functional class scoring (FCS), or global tests.'),
(72988, 2006, 'bio', 'UniGene Tabulator: a full parser for the UniGene format', 'Lenzi, Luca|Frabetti, Flavia|Facchin, Federica|Casadei, Raffaella|Vitale, Lorenza|Canaider, Silvia|Carinci, Paolo|Zannotti, Maria|Strippoli, Pierluigi', 'Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.|Department of Histology, Embryology and Applied Biology University of Bologna, 40126 Bologna, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 10, 'Base Sequence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl425', '16895929', 'UniGene Tabulator 1.0 provides a solution for full parsing of UniGene flat file format, it implements a structured graphical representation of each data field present in UniGene following import into a common database managing system usable in a personal computer. This database includes related tables for sequence, protein similarity, sequence-tagged site (STS) and transcript map interval (TXMAP) data, plus a summary table where each record represents a UniGene cluster. UniGene Tabulator enables full local management of UniGene data, allowing parsing, querying, indexing, retrieving, exporting and analysis of UniGene data in a relational database form, usable on Macintosh (OS X 10.3.9 or later) and Windows (2000, with service pack 4, XP, with service pack 2 or later) operating systems-based computers.'),
(72989, 2006, 'bio', 'Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information', 'Capriotti, E|Calabrese, R|Casadio, R', 'Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna via Irnerio 42, 40126 Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna via Irnerio 42, 40126 Bologna, Italy.|Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna via Irnerio 42, 40126 Bologna, Italy.', 'Italy|Italy|Italy', 190, 'Algorithms|Computational Biology|Databases, Protein|Evolution, Molecular|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genetic Variation|Humans|Mutation|Phenotype|Point Mutation|Polymorphism, Genetic|Polymorphism, Single Nucleotide|Probability|Proteins', 'Journal Article', '10.1093/bioinformatics/btl423', '16895930', 'Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Alzheimers, Parkinsons and Creutzfeldt-Jakobs diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence.'),
(72990, 2006, 'bio', 'A large quantity of novel human antisense transcripts detected by LongSAGE', 'Ge, Xijin|Wu, Qingfa|Jung, Yong-Chul|Chen, Jun|Wang, San Ming', 'Center for Functional Genomics, Division of Medical Genetics, Department of Medicine, ENH Research Institute, Northwestern University Feinberg School of Medicine 1001 University Place, Evanston, IL 60201 USA.|Center for Functional Genomics, Division of Medical Genetics, Department of Medicine, ENH Research Institute, Northwestern University Feinberg School of Medicine 1001 University Place, Evanston, IL 60201 USA.|Center for Functional Genomics, Division of Medical Genetics, Department of Medicine, ENH Research Institute, Northwestern University Feinberg School of Medicine 1001 University Place, Evanston, IL 60201 USA.|Center for Functional Genomics, Division of Medical Genetics, Department of Medicine, ENH Research Institute, Northwestern University Feinberg School of Medicine 1001 University Place, Evanston, IL 60201 USA.|Center for Functional Genomics, Division of Medical Genetics, Department of Medicine, ENH Research Institute, Northwestern University Feinberg School of Medicine 1001 University Place, Evanston, IL 60201 USA.', 'United States|United States|United States|United States|United States', 44, 'Base Sequence|Chromosome Mapping|DNA, Antisense|Expressed Sequence Tags|Gene Expression Profiling|Humans|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl429', '16895931', 'Taking advantage of the high sensitivity and specificity of LongSAGE tag for transcript detection and genome mapping, we analyzed the 632 813 unique human LongSAGE tags deposited in public databases to identify novel human antisense transcripts.'),
(72991, 2006, 'bio', 'COPA--cancer outlier profile analysis', 'MacDonald, James W|Ghosh, Debashis', 'University of Michigan Cancer Center, Ann Arbor, MI, USA. jmacdon@med.umich.edu|University of Michigan Cancer Center, Ann Arbor, MI, USA. jmacdon@med.umich.edu', 'United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl433', '16895932', 'Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t-test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset.'),
(72992, 2006, 'bio', 'ARFA: a program for annotating bacterial release factor genes, including prediction of programmed ribosomal frameshifting', 'Bekaert, Michaël|Atkins, John F|Baranov, Pavel V', 'Biosciences Institute, University College Cork, Cork, Ireland.|Biosciences Institute, University College Cork, Cork, Ireland.|Biosciences Institute, University College Cork, Cork, Ireland.', 'Ireland|Ireland|Ireland', 19, 'Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Escherichia coli|Escherichia coli Proteins|Frameshifting, Ribosomal|Genome, Bacterial|Information Storage and Retrieval|Molecular Sequence Data|Peptide Termination Factors|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl430', '16895933', 'Correct annotation of genes encoding release factors in bacterial genomes is often complicated by utilization of +1 programmed ribosomal frameshifting during synthesis of release factor 2, RF2. In the absence of robust computational approaches for predicting ribosomal frameshifting, the success of proper annotation depends on annotators familiarity with this phenomenon. Here we describe a novel computer tool that allows automatic discrimination of genes encoding class-I bacterial release factors, RF1, RF2 and RFH. Most usefully, this program identifies and automatically annotates +1 frameshifting in RF2 encoding genes. Comparison of ARFA performance with existing annotations of bacterial genomes revealed that only 20% of RF2 genes utilizing ribosomal frameshifting during their expression are annotated correctly.'),
(72993, 2006, 'bio', 'Solvated docking: introducing water into the modelling of biomolecular complexes', 'van Dijk, Aalt D J|Bonvin, Alexandre M J J', 'Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University 3584CH, Utrecht, The Netherlands.|Bijvoet Center for Biomolecular Research, Science Faculty, Utrecht University 3584CH, Utrecht, The Netherlands.', 'Netherlands|Netherlands', 111, 'Binding Sites|Biopolymers|Computer Simulation|Models, Chemical|Models, Molecular|Multiprotein Complexes|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Solutions|Solvents|Water', 'Journal Article', '10.1093/bioinformatics/btl395', '16899489', 'Interfacial water, which plays an important role in mediating biomolecular interactions, has been neglected in the modelling of biomolecular complexes.'),
(72994, 2006, 'bio', 'MASQOT-GUI: spot quality assessment for the two-channel microarray platform', 'Bylesjö, Max|Sjödin, Andreas|Eriksson, Daniel|Antti, Henrik|Moritz, Thomas|Jansson, Stefan|Trygg, Johan', 'Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se|Research group for Chemometrics, Department of Chemistry, Umeå University SE-901 87 Umeå, Sweden. max.bylesjo@chem.umu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Computer Graphics|In Situ Hybridization, Fluorescence|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Quality Control|Software|Spectrometry, Fluorescence|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl434', '16899490', 'MASQOT-GUI provides an open-source, platform-independent software pipeline for two-channel microarray spot quality control. This includes gridding, segmentation, quantification, quality assessment and data visualization. It hosts a set of independent applications, with interactions between the tools as well as import and export support for external software. The implementation of automated multivariate quality control assessment, which is a unique feature of MASQOT-GUI, is based on the previously documented and evaluated MASQOT methodology. Further abilities of the application are outlined and illustrated.'),
(72995, 2006, 'bio', 'Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation', 'Joung, Je-Gun|Shin, Dongho|Seong, Rho Hyun|Zhang, Byoung-Tak', 'Center for Bioinformation Technology, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea.|Center for Bioinformation Technology, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea.|Center for Bioinformation Technology, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea.|Center for Bioinformation Technology, Institute of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea.', '-|-|-|-', 22, 'Algorithms|Animals|Cell Differentiation|Cell Lineage|Cell Proliferation|Cluster Analysis|Computational Biology|Gene Expression Profiling|Genome|Humans|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Stem Cells', 'Journal Article', '10.1093/bioinformatics/btl343', '16899491', 'An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles.'),
(72996, 2006, 'bio', 'Significant sequence similarities in promoters and precursors of Arabidopsis thaliana non-conserved microRNAs', 'Wang, Yu|Hindemitt, Tobias|Mayer, Klaus F X', 'MIPS/Institute for Bioinformatics, GSF Research Centre for Environment and Health Ingolstaedter Landstrasse, 1 85758 Neuherberg, Germany.|MIPS/Institute for Bioinformatics, GSF Research Centre for Environment and Health Ingolstaedter Landstrasse, 1 85758 Neuherberg, Germany.|MIPS/Institute for Bioinformatics, GSF Research Centre for Environment and Health Ingolstaedter Landstrasse, 1 85758 Neuherberg, Germany.', 'Germany|Germany|Germany', 20, 'Arabidopsis|Base Sequence|Conserved Sequence|MicroRNAs|Molecular Sequence Data|Multigene Family|Promoter Regions, Genetic|RNA Precursors|RNA, Plant|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btl437', '16901935', 'Some plant microRNAs have been shown to be de novo generated by inverted duplication from their target genes. Subsequent duplication events potentially generate multigene microRNA families. Within this article we provide supportive evidence for the inverted duplication model of plant microRNA evolution. First, we report that the precursors of four Arabidopsis thaliana microRNA families, miR157, miR158, miR405 and miR447 share nearly identical nucleotide sequences throughout the whole miRNA precursor between the family members. The extent and degree of sequence conservation is suggestive of recent evolutionary duplication events. Furthermore we found that sequence similarities are not restricted to the transcribed part but extend into the promoter regions. Thus the duplication event most probably included the promoter regions as well. Conserved elements in upstream regions of miR163 and its targets were also detected. This implies that the inverted duplication of target genes, at least in certain cases, had included the promoters of the target genes. Sequence conservation within promoters of miRNA families as well as between miRNA and its potential progenitor gene can be exploited for understanding the regulation of microRNA genes.'),
(72997, 2006, 'bio', 'Meta-analysis based on control of false discovery rate: combining yeast ChIP-chip datasets', 'Pyne, Saumyadipta|Futcher, Bruce|Skiena, Steve', 'Department of Computer Science, Stony Brook University, NY 11794, USA. spyne@cs.sunysb.edu|Department of Computer Science, Stony Brook University, NY 11794, USA. spyne@cs.sunysb.edu|Department of Computer Science, Stony Brook University, NY 11794, USA. spyne@cs.sunysb.edu', 'United States|United States|United States', 0, 'Algorithms|Cell Cycle|Databases, Protein|False Positive Reactions|Gene Expression Profiling|Information Storage and Retrieval|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl439', '16908499', 'High-throughput microarray technology can be used to examine thousands of features, such as all the genes of an organism, and measure their expression. Two important issues of microarray bioinformatics are first, how to combine the significance values for each feature across experiments with high statistical power, and second, how to control the proportion of false positives. Existing methods address these issues separately, in spite of their linked usage.'),
(72998, 2006, 'bio', 'The ties problem resulting from counting-based error estimators and its impact on gene selection algorithms', 'Zhou, Xin|Mao, K Z', 'School of Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore.|School of Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore.', 'Singapore|Singapore', 20, 'Algorithms|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl438', '16908500', 'Feature selection approaches, such as filter and wrapper, have been applied to address the gene selection problem in the literature of microarray data analysis. In wrapper methods, the classification error is usually used as the evaluation criterion of feature subsets. Due to the nature of high dimensionality and small sample size of microarray data, however, counting-based error estimation may not necessarily be an ideal criterion for gene selection problem.'),
(72999, 2006, 'bio', 'Mining frequent stem patterns from unaligned RNA sequences', 'Hamada, Michiaki|Tsuda, Koji|Kudo, Taku|Kin, Taishin|Asai, Kiyoshi', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) 2-43 Aomi, Koto-ku, Tokyo, Japan. hamada-michiaki@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan', 35, 'Algorithms|Artificial Intelligence|Base Sequence|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|Pattern Recognition, Automated|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl431', '16908501', 'In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly.'),
(73000, 2006, 'bio', 'GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks', 'Vlasblom, James|Wu, Samuel|Pu, Shuye|Superina, Mark|Liu, Gina|Orsi, Chris|Wodak, Shoshana J', 'Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.|Structural Biology and Biochemistry Program, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 38, 'Algorithms|Computer Graphics|Computer Simulation|Computing Methodologies|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl356', '16921162', 'Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community.'),
(73001, 2006, 'bio', 'Systematic component selection for gene-network refinement', 'Radde, Nicole|Gebert, Jutta|Forst, Christian V', 'Center for Applied Computer Science, University of Cologne Weyertal 80, 50931 Cologne, Germany. {radde.gebert}@zpr.uni-koeln.de|Center for Applied Computer Science, University of Cologne Weyertal 80, 50931 Cologne, Germany. {radde.gebert}@zpr.uni-koeln.de|Center for Applied Computer Science, University of Cologne Weyertal 80, 50931 Cologne, Germany. {radde.gebert}@zpr.uni-koeln.de', 'Germany|Germany|Germany', 13, 'Algorithms|Bacterial Proteins|Computer Simulation|DNA Repair|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Mycobacterium tuberculosis|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/btl440', '16926218', 'A quantitative description of interactions between cell components is a major challenge in Computational Biology. As a method of choice, differential equations are used for this purpose, because they provide a detailed insight into the dynamic behavior of the system. In most cases, the number of time points of experimental time series is usually too small to estimate the parameters of a model of a whole gene regulatory network based on differential equations, such that one needs to focus on subnetworks consisting of only a few components. For most approaches, the set of components of the subsystem is given in advance and only the structure has to be estimated. However, the set of components that influence the system significantly are not always known in advance, making a method desirable that determines both, the components that are included into the model and the parameters.'),
(73002, 2006, 'bio', 'Text similarity: an alternative way to search MEDLINE', 'Lewis, James|Ossowski, Stephan|Hicks, Justin|Errami, Mounir|Garner, Harold R', 'University of Texas Southwestern Medical Center, Eugene McDermott Center for Human Growth and Development, Division for Translational Research 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.|University of Texas Southwestern Medical Center, Eugene McDermott Center for Human Growth and Development, Division for Translational Research 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.|University of Texas Southwestern Medical Center, Eugene McDermott Center for Human Growth and Development, Division for Translational Research 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.|University of Texas Southwestern Medical Center, Eugene McDermott Center for Human Growth and Development, Division for Translational Research 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.|University of Texas Southwestern Medical Center, Eugene McDermott Center for Human Growth and Development, Division for Translational Research 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.', 'United States|United States|United States|United States|United States', 72, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Database Management Systems|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl388', '16926219', 'The most widely used literature search techniques, such as those offered by NCBIs PubMed system, require significant effort on the part of the searcher, and inexperienced searchers do not use these systems as effectively as experienced users. Improved literature search engines can save researchers time and effort by making it easier to locate the most important and relevant literature.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73003, 2006, 'bio', 'Eigengene-based linear discriminant model for tumor classification using gene expression microarray data', 'Shen, Ronglai|Ghosh, Debashis|Chinnaiyan, Arul|Meng, Zhaoling', 'Department of Biostatistics, University of Michigan Ann Arbor, MI 48109-0602, USA.|Department of Biostatistics, University of Michigan Ann Arbor, MI 48109-0602, USA.|Department of Biostatistics, University of Michigan Ann Arbor, MI 48109-0602, USA.|Department of Biostatistics, University of Michigan Ann Arbor, MI 48109-0602, USA.', 'United States|United States|United States|United States', 32, 'Artificial Intelligence|Biomarkers, Tumor|Computer Simulation|Diagnosis, Computer-Assisted|Discriminant Analysis|Gene Expression Profiling|Humans|Linear Models|Models, Genetic|Multivariate Analysis|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btl442', '16926220', 'The nearest shrunken centroids classifier has become a popular algorithm in tumor classification problems using gene expression microarray data. Feature selection is an embedded part of the method to select top-ranking genes based on a univariate distance statistic calculated for each gene individually. The univariate statistics summarize gene expression profiles outside of the gene co-regulation network context, leading to redundant information being included in the selection procedure.'),
(73004, 2006, 'bio', 'SBML-PET: a Systems Biology Markup Language-based parameter estimation tool', 'Zi, Zhike|Klipp, Edda', 'Max Planck Institute for Molecular Genetics, Ihnestr. 73 14195 Berlin, Germany. zhike_zi@molgen.mpg.de|Max Planck Institute for Molecular Genetics, Ihnestr. 73 14195 Berlin, Germany. zhike_zi@molgen.mpg.de', 'Germany|Germany', 54, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Information Storage and Retrieval|Models, Biological|Programming Languages|Signal Transduction|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl443', '16926221', 'The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system.'),
(73005, 2006, 'bio', 'DOCKGROUND resource for studying protein-protein interfaces', 'Douguet, Dominique|Chen, Huei-Chi|Tovchigrechko, Andrey|Vakser, Ilya A', 'Centre de Biochimie Structurale, CNRS, U5048, Université Montpellier 1, INSERM, U554, 29, rue de Navacelles, Montpellier F-34090, France. douguet@cbs.cnrs.fr|Centre de Biochimie Structurale, CNRS, U5048, Université Montpellier 1, INSERM, U554, 29, rue de Navacelles, Montpellier F-34090, France. douguet@cbs.cnrs.fr|Centre de Biochimie Structurale, CNRS, U5048, Université Montpellier 1, INSERM, U554, 29, rue de Navacelles, Montpellier F-34090, France. douguet@cbs.cnrs.fr|Centre de Biochimie Structurale, CNRS, U5048, Université Montpellier 1, INSERM, U554, 29, rue de Navacelles, Montpellier F-34090, France. douguet@cbs.cnrs.fr', 'France|France|France|France', 67, 'Binding Sites|Computer Simulation|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl447', '16928732', 'Public resources for studying protein interfaces are necessary for better understanding of molecular recognition and developing intermolecular potentials, search procedures and scoring functions for the prediction of protein complexes.'),
(73006, 2006, 'bio', 'RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models', 'Stamatakis, Alexandros', 'Swiss Federal Institute of Technology Lausanne, School of Computer and Communication Sciences Lab Prof. Moret, STATION 14, CH-1015 Lausanne, Switzerland. Alexandros.Stamatakis@epfl.ch', 'Switzerland', 11521, 'Algorithms|Conserved Sequence|Evolution, Molecular|Models, Genetic|Models, Statistical|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Species Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btl446', '16928733', 'RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively.'),
(73007, 2006, 'bio', 'TESD: a transposable element dynamics simulation environment', 'Deceliere, Grégory|Letrillard, Yann|Charles, Sandrine|Biémont, Christian', 'Laboratoire de Biométrie et Biologie Evolutive (UMR 5558), CNRS, Université Claude Bernard Lyon 1 69622, Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Evolutive (UMR 5558), CNRS, Université Claude Bernard Lyon 1 69622, Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Evolutive (UMR 5558), CNRS, Université Claude Bernard Lyon 1 69622, Villeurbanne Cedex, France.|Laboratoire de Biométrie et Biologie Evolutive (UMR 5558), CNRS, Université Claude Bernard Lyon 1 69622, Villeurbanne Cedex, France.', 'France|France|France|France', 6, 'Algorithms|Chromosome Mapping|Computer Simulation|DNA Transposable Elements|Models, Genetic|Software', 'Journal Article', '10.1093/bioinformatics/btl454', '16928734', 'Various mathematical models have been used to explore the dynamics of transposable elements (TEs) within their host genomes. However, numerous factors can influence their dynamics, and we know only little about the dynamics of TEs when they first began to invade populations. In addition, the influence of population structuring has only recently been investigated. Transposable Element Simulator Dynamics, a population genomics simulation environment, has therefore been developed to provide a simple tool for analyzing the dynamics of TEs in a community based on (i) various TE parameters, such as the transposition and excision rates, the recombination rate and the coefficient of selection against TE insertions, and (ii) population parameters, such as population size and migration rates. The simulations can be used to illustrate the dynamic fate of TEs in structured populations, can be extended by using more specific molecular or demographic models, and can be useful for teaching population genetics and genomics.'),
(73008, 2006, 'bio', 'Anisotropic network model: systematic evaluation and a new web interface', 'Eyal, Eran|Yang, Lee-Wei|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15213, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15213, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 222, 'Algorithms|Anisotropy|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Proteins|Sequence Analysis, Protein|Software|Software Validation|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl448', '16928735', 'The Anisotropic Network Model (ANM) is a simple yet powerful model for normal mode analysis of proteins. Despite its broad use for exploring biomolecular collective motions, ANM has not been systematically evaluated to date. A lack of a convenient interface has been an additional obstacle for easy usage.'),
(73009, 2006, 'bio', 'Maximum likelihood of phylogenetic networks', 'Jin, Guohua|Nakhleh, Luay|Snir, Sagi|Tuller, Tamir', 'Department of Computer Science, Rice University Houston, TX, USA.|Department of Computer Science, Rice University Houston, TX, USA.|Department of Computer Science, Rice University Houston, TX, USA.|Department of Computer Science, Rice University Houston, TX, USA.', 'United States|United States|United States|United States', 80, 'Algorithms|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genetics, Population|Likelihood Functions|Models, Genetic|Models, Statistical|Phylogeny|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btl452', '16928736', 'Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications for human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative.'),
(73010, 2006, 'bio', 'MMM: a sequence-to-structure alignment protocol', 'Rai, Brajesh K|Madrid-Aliste, Carlos J|Fajardo, J Eduardo|Fiser, András', 'Department of Biochemistry and Seaver Center for Bioinformatics, Albert Einstein College of Medicine 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Biochemistry and Seaver Center for Bioinformatics, Albert Einstein College of Medicine 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Biochemistry and Seaver Center for Bioinformatics, Albert Einstein College of Medicine 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Biochemistry and Seaver Center for Bioinformatics, Albert Einstein College of Medicine 1300 Morris Park Avenue, Bronx, NY 10461, USA.', 'United States|United States|United States|United States', 31, 'Algorithms|Amino Acid Sequence|Computer Simulation|Internet|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Sequence Analysis, Protein|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl449', '16928737', 'Accurate alignment of a target sequence to a template structure continues to be a bottleneck in producing good quality comparative protein structure models.'),
(73011, 2006, 'bio', 'MARD: a new method to detect differential gene expression in treatment-control time courses', 'Cheng, Chao|Ma, Xiaotu|Yan, Xiting|Sun, Fengzhu|Li, Lei M', 'Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Calcium|Cell Cycle|Cell Cycle Proteins|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Software|Tacrolimus|Time Factors', 'Evaluation Studies', '10.1093/bioinformatics/btl451', '16928738', 'Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses, (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses.'),
(73012, 2006, 'bio', 'An application for assessing quality of RNA hybridized to Affymetrix GeneChips', 'Archer, Kellie J|Guennel, Tobias', 'Department of Biostatistics, Virginia Commonwealth University Richmond, VA 23298-0032, USA. kjarcher@vcu.edu|Department of Biostatistics, Virginia Commonwealth University Richmond, VA 23298-0032, USA. kjarcher@vcu.edu', 'United States|United States', 9, 'Algorithms|Base Sequence|DNA Probes|Equipment Failure Analysis|In Situ Hybridization|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Quality Control|RNA|Sequence Analysis, DNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btl459', '16935927', 'This paper describes a stand-alone application for estimating the 3 to 5 ratio by fitting a mixed effects model to the interior pixel intensities of perfect match probes for selected control probe sets from an Affymetrix *.DAT file. The effectiveness of this method was demonstrated previously by an application of the method to two microarray datasets for which external verification of RNA quality was known. This application provides a more objective assessment of sample quality in that both a point estimate and 95% confidence interval about the 3 to 5 ratio are provided.'),
(73013, 2006, 'bio', 'ArrayFusion: a web application for multi-dimensional analysis of CGH, SNP and microarray data', 'Yang, Tsun-Po|Chang, Ting-Yu|Lin, Chi-Hung|Hsu, Ming-Ta|Wang, Hsei-Wei', 'Microarray and Gene Expression Analysis Core Facility, VGH National Yang-Ming University Genome Research Center, Taipei, Taiwan.|Microarray and Gene Expression Analysis Core Facility, VGH National Yang-Ming University Genome Research Center, Taipei, Taiwan.|Microarray and Gene Expression Analysis Core Facility, VGH National Yang-Ming University Genome Research Center, Taipei, Taiwan.|Microarray and Gene Expression Analysis Core Facility, VGH National Yang-Ming University Genome Research Center, Taipei, Taiwan.|Microarray and Gene Expression Analysis Core Facility, VGH National Yang-Ming University Genome Research Center, Taipei, Taiwan.', '-|-|-|-|-', 21, 'Algorithms|Chromosome Mapping|Computer Graphics|Gene Expression Profiling|In Situ Hybridization|Internet|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl457', '16935928', 'ArrayFusion annotates conventional CGH results and various types of microarray data from a range of platforms (cDNA, expression, exon, SNP, array-CGH and ChIP-on-chip) and converts them into standard formats which can be visualized in genome browsers (Affymetrix Integrated Genome Browser and GBrowse in the HapMap Project). Converted files can then be imported simultaneously into a single genome browser to benefit a collective interpretation between different array results. ArrayFusion therefore provides a new type of tool facilitating the integration of CGH and array results to provide new experimental directions.'),
(73014, 2006, 'bio', 'Weighted quality estimates in machine learning', 'Budagyan, Levon|Abagyan, Ruben', 'Molsoft LLC, 3366 North Torrey Pines Court Suite 300, San Diego, CA 92037, USA. levon@molsoft.com|Molsoft LLC, 3366 North Torrey Pines Court Suite 300, San Diego, CA 92037, USA. levon@molsoft.com', 'United States|United States', 3, 'Algorithms|Artificial Intelligence|Data Interpretation, Statistical|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis', 'Evaluation Studies', '10.1093/bioinformatics/btl458', '16935929', 'Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the dataset is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information.'),
(73015, 2006, 'bio', 'Bio3d: an R package for the comparative analysis of protein structures', 'Grant, Barry J|Rodrigues, Ana P C|ElSawy, Karim M|McCammon, J Andrew|Caves, Leo S D', 'Department of Chemistry and Biochemistry, University of California, San Diego La Jolla, CA 92093, USA. bgrant@mccammon.ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego La Jolla, CA 92093, USA. bgrant@mccammon.ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego La Jolla, CA 92093, USA. bgrant@mccammon.ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego La Jolla, CA 92093, USA. bgrant@mccammon.ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego La Jolla, CA 92093, USA. bgrant@mccammon.ucsd.edu', 'United States|United States|United States|United States|United States', 0, 'Amino Acid Sequence|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl461', '16940322', 'An automated procedure for the analysis of homologous protein structures has been developed. The method facilitates the characterization of internal conformational differences and inter-conformer relationships and provides a framework for the analysis of protein structural evolution. The method is implemented in bio3d, an R package for the exploratory analysis of structure and sequence data.'),
(73016, 2006, 'bio', 'Model-based boosting in high dimensions', 'Hothorn, Torsten|Bühlmann, Peter', 'Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@R-project.org|Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@R-project.org', 'Germany|Germany', 26, 'Algorithms|Computational Biology|Computer Graphics|Gene Expression Profiling|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl462', '16940323', 'The R add-on package mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.'),
(73017, 2006, 'bio', 'iFold: a platform for interactive folding simulations of proteins', 'Sharma, Shantanu|Ding, Feng|Nie, Huifen|Watson, Daniel|Unnithan, Aditya|Lopp, Jameson|Pozefsky, Diane|Dokholyan, Nikolay V', 'Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.|Department of Biochemistry and Biophysics, University of North Carolina Chapel Hill, NC 27599, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Amino Acid Sequence|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Denaturation|Protein Folding|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl460', '16940324', 'We built a novel web-based platform for performing discrete molecular dynamics simulations of proteins. In silico protein folding involves searching for minimal frustration in the vast conformational landscape. Conventional approaches for simulating protein folding insufficiently address the problem of simulations in relevant time and length scales necessary for a mechanistic understanding of underlying biomolecular phenomena. Discrete molecular dynamics (DMD) offers an opportunity to bridge the size and timescale gaps and uncover the structural and biological properties of experimentally undetectable protein dynamics. The iFold server supports large-scale simulations of protein folding, thermal denaturation, thermodynamic scan, simulated annealing and p(fold) analysis using DMD and coarse-grained protein model with structure-based Gxc5x8d-interactions between amino acids.'),
(73018, 2006, 'bio', 'A new representation for protein secondary structure prediction based on frequent patterns', 'Birzele, Fabian|Kramer, Stefan', 'Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Amalienstrasse 17, D-80333 München, Germany.|Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Amalienstrasse 17, D-80333 München, Germany.', 'Germany|Germany', 46, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btl453', '16940325', 'A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using support vector machines (SVMs).'),
(73019, 2006, 'bio', 'Markov Chain Monte Carlo Algorithm based metabolic flux distribution analysis on Corynebacterium glutamicum', 'Kadirkamanathan, Visakan|Yang, Jing|Billings, Stephen A|Wright, Phillip C', 'Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK. visakan@sheffield.ac.uk|Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK. visakan@sheffield.ac.uk|Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK. visakan@sheffield.ac.uk|Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK. visakan@sheffield.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Algorithms|Bacterial Proteins|Computer Simulation|Corynebacterium glutamicum|Energy Metabolism|Gene Expression Profiling|Markov Chains|Metabolic Clearance Rate|Models, Biological|Models, Statistical|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl445', '16940326', 'Metabolic flux analysis via a (13)C tracer experiment has been achieved using a Monte Carlo method with the assumption of system noise as Gaussian noise. However, an unbiased flux analysis requires the estimation of fluxes and metabolites jointly without the restriction on the assumption of Gaussian noise. The flux distributions under such a framework can be freely obtained with various system noise and uncertainty models.'),
(73020, 2006, 'bio', 'Tropical--parameter estimation and simulation of reaction-diffusion models based on spatio-temporal microscopy images', 'Ulrich, Markus|Kappel, Constantin|Beaudouin, Joel|Hezel, Stefan|Ulrich, Jochen|Eils, Roland', 'Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.|Department of Theoretical Bioinformatics, German Cancer Research Center Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Diffusion|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence|Models, Biological|Protein Interaction Mapping|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl456', '16940327', 'Tropical is a software for simulation and parameter estimation of reaction-diffusion models. Based on spatio-temporal microscopy images, Tropical estimates reaction and diffusion coefficients for user-defined models. Tropical allows the investigation of systems with an inhomogeneous distribution of molecules, making it well suited for quantitative analyses of microscopy experiments such as fluorescence recovery after photobleaching (FRAP).'),
(73021, 2006, 'bio', 'IndexToolkit: an open source toolbox to index protein databases for high-throughput proteomics', 'Li, Dequan|Gao, Wen|Ling, Charles X|Wang, Xiaobiao|Sun, Ruixiang|He, Simin', 'Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn|Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China. dqli@jdl.ac.cn', 'China|China|China|China|China|China', 4, 'Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Proteomics|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl410', '16945944', 'A software package, IndexToolkit, aimed at overcoming the disadvantage of FASTA-format databases for frequent searching, is developed to utilize an indexing strategy to substantially accelerate sequence queries. IndexToolkit includes user-friendly tools and an Application Programming Interface (API) to facilitate indexing, storage and retrieval of protein sequence databases. As open source, it provides a sequence-retrieval developing framework, which is easily extensible for high-speed-request proteomic applications, such as database searching or modification discovering. We applied IndexToolkit to database searching engine pFind to demonstrate its effect. Experimental studies show that IndexToolkit is able to support significantly faster searches of protein database.'),
(73022, 2006, 'bio', 'PSoL: a positive sample only learning algorithm for finding non-coding RNA genes', 'Wang, Chunlin|Ding, Chris|Meraz, Richard F|Holbrook, Stephen R', 'Physical Biosciences Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA.|Physical Biosciences Division, Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA.', 'United States|United States|United States|United States', 75, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Escherichia coli|Molecular Sequence Data|Pattern Recognition, Automated|RNA, Bacterial|RNA, Untranslated|Sequence Alignment|Sequence Analysis, RNA', 'Evaluation Studies', '10.1093/bioinformatics/btl441', '16945945', 'Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well.'),
(73023, 2006, 'bio', 'A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions', 'Callegaro, A|Basso, D|Bicciato, S', 'Department of Chemical Process Engineering, University of Padua Via Marzolo 9, I-35131 Padua, Italy.|Department of Chemical Process Engineering, University of Padua Via Marzolo 9, I-35131 Padua, Italy.|Department of Chemical Process Engineering, University of Padua Via Marzolo 9, I-35131 Padua, Italy.', 'Italy|Italy|Italy', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Models, Genetic|Models, Statistical|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btl455', '16951291', 'The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.'),
(73024, 2006, 'bio', 'Discovering disease-genes by topological features in human protein-protein interaction network', 'Xu, Jianzhen|Li, Yongjin', 'Department of Bioinformatics, Harbin Medical University, Harbin 150086, China. jianzxu@hotmail.com|Department of Bioinformatics, Harbin Medical University, Harbin 150086, China. jianzxu@hotmail.com', 'China|China', 299, 'Chromosome Mapping|Cluster Analysis|Computational Biology|Computer Simulation|Databases, Genetic|Databases, Protein|Genetic Diseases, Inborn|Genome, Human|Humans|Models, Statistical|Phenotype|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btl467', '16954137', 'Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein-protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network.'),
(73025, 2006, 'bio', 'Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry', 'Eckel-Passow, J E|Oberg, A L|Therneau, T M|Mason, C J|Mahoney, D W|Johnson, K L|Olson, J E|Bergen, H R', 'Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu|Division of Biostatistics, Department of Health Sciences Research 200 First Street SW, Rochester, MN 55905, USA. eckel@mayo.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Humans|Isotopes|Kinetics|Mass Spectrometry|Multivariate Analysis|Oxygen Isotopes|Peptide Mapping|Proteins|Proteomics|Regression Analysis|Serum Albumin, Bovine|Transferrin|Trypsin', 'Journal Article', '10.1093/bioinformatics/btl464', '16954138', 'Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for (16)O/(18)O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of (16)O/(18)O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies.'),
(73026, 2006, 'bio', 'Targeted projection pursuit for visualizing gene expression data classifications', 'Faith, Joe|Mintram, Robert|Angelova, Maia', 'Northumbria University Newcastle, UK. joe.faith@unn.ac.uk|Northumbria University Newcastle, UK. joe.faith@unn.ac.uk|Northumbria University Newcastle, UK. joe.faith@unn.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Artificial Intelligence|Computer Graphics|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl463', '16954139', 'We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced, one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-orthogonal projections, respectively. The method is quantitatively and qualitatively compared with other dimension reduction techniques. It is shown to find 2D views that display the classification of cancers from gene expression data with a visual separation equal to, or better than, existing dimension reduction techniques.'),
(73027, 2006, 'bio', 'Synthetic biology--putting engineering into biology', 'Heinemann, Matthias|Panke, Sven', 'ETH Zurich, Bioprocess Laboratory, Institute of Process Engineering Universitätsstrasse 6, 8092 Zurich, Switzerland.|ETH Zurich, Bioprocess Laboratory, Institute of Process Engineering Universitätsstrasse 6, 8092 Zurich, Switzerland.', 'Switzerland|Switzerland', 0, 'Animals|Biotechnology|Computational Biology|DNA|Engineering|Genetic Engineering|Humans|Internet|Kinetics|Models, Biological|Protein Engineering|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btl469', '16954140', 'Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis-synthetic biologys system fabrication process-supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.'),
(73028, 2006, 'bio', 'Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network', 'Griffith, Mark|Courtney, Tod|Peccoud, Jean|Sanders, William H', 'Coordinated Science Laboratory, University of Illinois at Urbana-Champaign 1308 W. Main Street, Urbana, IL 61801, USA.|Coordinated Science Laboratory, University of Illinois at Urbana-Champaign 1308 W. Main Street, Urbana, IL 61801, USA.|Coordinated Science Laboratory, University of Illinois at Urbana-Champaign 1308 W. Main Street, Urbana, IL 61801, USA.|Coordinated Science Laboratory, University of Illinois at Urbana-Champaign 1308 W. Main Street, Urbana, IL 61801, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Products, tat|Kinetics|Models, Statistical|Monte Carlo Method|Proteomics|Software|Stochastic Processes|Time Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btl465', '16954141', 'The stochastic kinetics of a well-mixed chemical system, governed by the chemical Master equation, can be simulated using the exact methods of Gillespie. However, these methods do not scale well as systems become more complex and larger models are built to include reactions with widely varying rates, since the computational burden of simulation increases with the number of reaction events. Continuous models may provide an approximate solution and are computationally less costly, but they fail to capture the stochastic behavior of small populations of macromolecules.'),
(73029, 2006, 'bio', 'Probalign: multiple sequence alignment using partition function posterior probabilities', 'Roshan, Usman|Livesay, Dennis R', 'Department of Computer Science, New Jersey Institute of Technology GITC 4400, University Heights, NJ 07102, USA. usman@cs.njit.edu|Department of Computer Science, New Jersey Institute of Technology GITC 4400, University Heights, NJ 07102, USA. usman@cs.njit.edu', 'Jersey|Jersey', 159, 'Algorithms|Animals|Computational Biology|Computer Simulation|DNA|Databases, Genetic|Humans|Models, Statistical|Probability|Sequence Alignment|Sequence Analysis, Protein|Software|Software Design|Temperature|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btl472', '16954142', 'The maximum expected accuracy optimization criterion for multiple sequence alignment uses pairwise posterior probabilities of residues to align sequences. The partition function methodology is one way of estimating these probabilities. Here, we combine these two ideas for the first time to construct maximal expected accuracy sequence alignments.'),
(73030, 2006, 'bio', 'DUF283 domain of Dicer proteins has a double-stranded RNA-binding fold', 'Dlakixc4x87, Mensur', 'Department of Microbiology, Montana State University Bozeman, MT 59717, USA. mdlakic@montana.edu', 'United States', 0, 'Amino Acid Sequence|Animals|DEAD-box RNA Helicases|Endoribonucleases|Gene Silencing|Humans|Markov Chains|Mice|Models, Molecular|Molecular Sequence Data|Protein Folding|Protein Structure, Tertiary|RNA Interference|RNA, Double-Stranded|RNA-Binding Proteins|Ribonuclease III|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl468', '16954143', 'Two RNases, Dicer and Argonaute, are at the heart of the RNA interference (RNAi) molecular machinery responsible for gene silencing. Both RNases contain multiple domains, most of which have been characterized or have functions that can be predicted based on sequence comparisons. However, Dicers of higher eukaryotes contain the domain known as DUF283 which at present has no assigned role. Using sensitive profile-profile comparisons, we detected a divergent double-stranded RNA-binding domain coinciding with the DUF283 of Dicer. This finding has potential implications regarding the mechanistic role of Dicer in RNAi.'),
(73031, 2006, 'bio', 'SEBINI: Software Environment for BIological Network Inference', 'Taylor, Ronald C|Shah, Anuj|Treatman, Charles|Blevins, Meridith', 'Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov|Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov|Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov|Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov', 'United States|United States|United States|United States', 0, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Information Storage and Retrieval|Models, Biological|Programming Languages|Signal Transduction|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl444', '16954144', 'The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods, hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time.'),
(73032, 2006, 'bio', 'The-more-the-better and the-less-the-better', 'Li, Wentian', 'Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov', 'United States', 8, 'Computational Biology|Data Interpretation, Statistical|Databases, Factual|Genomics|Information Storage and Retrieval|Models, Biological', 'Editorial', '10.1093/bioinformatics/btl189', '16957134', '-'),
(73033, 2006, 'bio', 'Augur--a computational pipeline for whole genome microbial surface protein prediction and classification', 'Billion, A|Ghai, R|Chakraborty, T|Hain, T', 'Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany. Andre.Billion@mikrobio.med.uni-giessen.de|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany. Andre.Billion@mikrobio.med.uni-giessen.de|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany. Andre.Billion@mikrobio.med.uni-giessen.de|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany. Andre.Billion@mikrobio.med.uni-giessen.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Animals|Bacterial Proteins|Computational Biology|Databases, Protein|Electronic Data Processing|Genes, Bacterial|Genome, Bacterial|Markov Chains|Membrane Proteins|Models, Biological|Proteins|Proteomics|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl466', '16966358', 'The analysis of protein function is a challenge and a major bottleneck towards well-annotated and analysed microbial genomes. In particular, bacterial surface proteins present an opportunity for pharmacological intervention and vaccine development. We present Augur, an automatic prediction pipeline that integrates major surface prediction algorithms and enables comparative analysis, classification and visualization for gram-positive bacteria on a genomic scale.'),
(73034, 2006, 'bio', 'SCAssign: a sparky extension for the NMR resonance assignment of aliphatic side-chains of uniformly 13C,15N-labeled large proteins', 'Zhang, Lei|Yang, Daiwen', 'Graduate Program in Bioengineering, National University of Singapore 10 Medical Drive, 117597 Singapore.|Graduate Program in Bioengineering, National University of Singapore 10 Medical Drive, 117597 Singapore.', 'Singapore|Singapore', 4, 'Algorithms|Animals|Carbon Isotopes|Carrier Proteins|Computational Biology|Hemoglobins|Humans|Magnetic Resonance Spectroscopy|Maltose-Binding Proteins|Nitrogen Isotopes|Protein Conformation|Protein Structure, Secondary|Software', 'Journal Article', '10.1093/bioinformatics/btl477', '16966359', 'SCAssign (side-chain assignment) is a Sparky extension written in Python to assist the NMR resonance assignment of aliphatic side-chains of uniformly (13)C,(15)N-labeled large proteins. It is based on a general strategy recently developed in our laboratory that makes use of 4D (13)C,(15)N-edited NOESY, 3D MQ-(H)CC(m)H(m)-TOCSY, and prior backbone assignments. The program runs on all operating systems for which Sparky is available, and is easy to install, setup and use. Not only can it accelerate the assignment process, it also allows assignments of weak NOEs in 4D NOESY, which used to be very difficult with manual approach.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73035, 2006, 'bio', 'Protein-RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures', 'Morozova, N|Allers, J|Myers, J|Shamoo, Y', 'Department of Biochemistry and Cell Biology, Rice University 6100 Main Street, Houston, TX 77005, USA.|Department of Biochemistry and Cell Biology, Rice University 6100 Main Street, Houston, TX 77005, USA.|Department of Biochemistry and Cell Biology, Rice University 6100 Main Street, Houston, TX 77005, USA.|Department of Biochemistry and Cell Biology, Rice University 6100 Main Street, Houston, TX 77005, USA.', 'United States|United States|United States|United States', 42, 'Algorithms|Animals|Computational Biology|Databases, Protein|Humans|Hydrogen Bonding|Internet|Models, Molecular|Protein Conformation|Proteins|RNA|RNA-Binding Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btl470', '16966360', 'The recognition of specific RNA sequences and structures by proteins is critical to our understanding of RNA processing, gene expression and viral replication. The diversity of RNA structures suggests that RNA recognition is substantially different than that of DNA.'),
(73036, 2006, 'bio', 'SPEED: a molecular-evolution-based database of mammalian orthologous groups', 'Vallender, Eric J|Paschall, Justin E|Malcom, Christine M|Lahn, Bruce T|Wyckoff, Gerald J', 'Department of Human Genetics and Committee on Genetics, Howard Hughes Medical Institute, University of Chicago 920 East 58th Street, Chicago, IL 60637, USA.|Department of Human Genetics and Committee on Genetics, Howard Hughes Medical Institute, University of Chicago 920 East 58th Street, Chicago, IL 60637, USA.|Department of Human Genetics and Committee on Genetics, Howard Hughes Medical Institute, University of Chicago 920 East 58th Street, Chicago, IL 60637, USA.|Department of Human Genetics and Committee on Genetics, Howard Hughes Medical Institute, University of Chicago 920 East 58th Street, Chicago, IL 60637, USA.|Department of Human Genetics and Committee on Genetics, Howard Hughes Medical Institute, University of Chicago 920 East 58th Street, Chicago, IL 60637, USA.', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Animals|Computational Biology|Databases, Genetic|Databases, Protein|Evolution, Molecular|Genome|Humans|Phylogeny|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btl471', '16966361', 'The abundance of nucleotide sequence information available has expanded horizons of inquiry for molecular evolution, however, the full potential of whole-genome analysis has not been realized because of inadequate tools. Here, we present one of the first toolkits to aid multidisciplinary high-throughput analysis.'),
(73037, 2006, 'bio', 'Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities', 'Sanguinetti, Guido|Lawrence, Neil D|Rattray, Magnus', 'Department of Computer Science, Regent Court 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, Regent Court 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, Regent Court 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 74, 'Algorithms|Chromatin Immunoprecipitation|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Genes, Fungal|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Probability|Regulatory Elements, Transcriptional|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btl473', '16966362', 'Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques, such as Chromatin Immunoprecipitation (ChIP) provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data.'),
(73038, 2006, 'bio', 'Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure', 'Lewis, Darrin P|Jebara, Tony|Noble, William Stafford', 'Department of Computer Science, Columbia University, New York, NY, 10027.|Department of Computer Science, Columbia University, New York, NY, 10027.|Department of Computer Science, Columbia University, New York, NY, 10027.', '-|-|-', 0, 'Algorithms|Computational Biology|Databases, Protein|Fungal Proteins|Models, Statistical|Pattern Recognition, Automated|Proteins|Proteomics|ROC Curve|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl475', '16966363', 'Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Furthermore, several recently described extensions allow the SVM to assign relative weights to various datasets, depending upon their utilities in performing a given classification task.'),
(73039, 2006, 'bio', 'Clearcut: a fast implementation of relaxed neighbor joining', 'Sheneman, Luke|Evans, Jason|Foster, James A', 'Department of Biological Sciences, University of Idaho, Moscow, ID, USA. sheneman@hungry.com|Department of Biological Sciences, University of Idaho, Moscow, ID, USA. sheneman@hungry.com|Department of Biological Sciences, University of Idaho, Moscow, ID, USA. sheneman@hungry.com', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Computer Simulation|Evolution, Molecular|Models, Genetic|Models, Statistical|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl478', '16982706', 'Clearcut is an open source implementation for the relaxed neighbor joining (RNJ) algorithm. While traditional neighbor joining (NJ) remains a popular method for distance-based phylogenetic tree reconstruction, it suffers from a O(N(3)) time complexity, where N represents the number of taxa in the input. Due to this steep asymptotic time complexity, NJ cannot reasonably handle very large datasets. In contrast, RNJ realizes a typical-case time complexity on the order of N(2)logN without any significant qualitative difference in output. RNJ is particularly useful when inferring a very large tree or a large number of trees. In addition, RNJ retains the desirable property that it will always reconstruct the true tree given a matrix of additive pairwise distances. Clearcut implements RNJ as a C program, which takes either a set of aligned sequences or a pre-computed distance matrix as input and produces a phylogenetic tree. Alternatively, Clearcut can reconstruct phylogenies using an extremely fast standard NJ implementation.'),
(73040, 2006, 'bio', 'ADAM: another database of abbreviations in MEDLINE', 'Zhou, Wei|Torvik, Vetle I|Smalheiser, Neil R', 'Department of Psychiatry and Psychiatric Institute, MC912, University of Illinois at Chicago Chicago, IL 60612, USA.|Department of Psychiatry and Psychiatric Institute, MC912, University of Illinois at Chicago Chicago, IL 60612, USA.|Department of Psychiatry and Psychiatric Institute, MC912, University of Illinois at Chicago Chicago, IL 60612, USA.', 'United States|United States|United States', 0, 'Animals|Databases as Topic|Databases, Bibliographic|Databases, Factual|Dictionaries as Topic|Humans|Information Storage and Retrieval|Internet|Natural Language Processing|Pattern Recognition, Automated|Software|Subject Headings|Terminology as Topic|Unified Medical Language System|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl480', '16982707', 'Abbreviations are an important type of terminology in the biomedical domain. Although several groups have already created databases of biomedical abbreviations, these are either not public, or are not comprehensive, or focus exclusively on acronym-type abbreviations. We have created another abbreviation database, ADAM, which covers commonly used abbreviations and their definitions (or long-forms) within MEDLINE titles and abstracts, including both acronym and non-acronym abbreviations.'),
(73041, 2006, 'bio', 'RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis', 'Hong, Fangxin|Breitling, Rainer|McEntee, Connor W|Wittner, Ben S|Nemhauser, Jennifer L|Chory, Joanne', 'Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu|Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu|Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu|Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu|Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu|Plant Biology Laboratory La Jolla, CA, USA. fhong@salk.edu', 'United States|United States|United States|United States|United States|United States', 521, 'Computational Biology|DNA, Complementary|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Gene Expression Regulation|Internet|Meta-Analysis as Topic|Metabolism|Models, Statistical|Oligonucleotide Array Sequence Analysis|Plant Proteins|Proteomics|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btl476', '16982708', 'While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements.'),
(73042, 2006, 'bio', 'Hierarchical and multi-resolution representation of protein flexibility', 'Zhao, Yong|Stoffler, Daniel|Sanner, Michel', 'Department of Molecular Biology, TPC26, The Scripps Research Institute La Jolla, CA, USA.|Department of Molecular Biology, TPC26, The Scripps Research Institute La Jolla, CA, USA.|Department of Molecular Biology, TPC26, The Scripps Research Institute La Jolla, CA, USA.', 'United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Humans|Models, Molecular|Molecular Conformation|Protein Conformation|Protein Folding|Protein Structure, Tertiary|Proteins|Proteomics|p38 Mitogen-Activated Protein Kinases', 'Journal Article', '10.1093/bioinformatics/btl481', '16984893', 'Conformational rearrangements during molecular interactions are observed in a wide range of biological systems. However, computational methods that aim at simulating and predicting molecular interactions are still largely ignoring the flexible nature of biological macromolecules as the number of degrees of freedom is computationally intractable when using brute force representations.'),
(73043, 2006, 'bio', 'PromoterExplorer: an effective promoter identification method based on the AdaBoost algorithm', 'Xie, Xudong|Wu, Shuanhu|Lam, Kin-Man|Yan, Hong', 'Department of Electronic Engineering, City University of Hong Kong, Hong Kong.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong.', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 37, 'Algorithms|Animals|Computational Biology|CpG Islands|Data Interpretation, Statistical|Databases, Genetic|Databases, Protein|Humans|Pattern Recognition, Automated|Programming Languages|Promoter Regions, Genetic|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl482', '17000749', 'Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most informative or discriminating features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive.'),
(73044, 2006, 'bio', 'Software for dynamic analysis of tracer-based metabolomic data: estimation of metabolic fluxes and their statistical analysis', 'Selivanov, Vitaly A|Marin, Silvia|Lee, Paul W N|Cascante, Marta', 'Departamento de Bioquimica i Biologia Molecular, University of Barcelona Barcelona 08028, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, University of Barcelona Barcelona 08028, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, University of Barcelona Barcelona 08028, Catalunya, Spain.|Departamento de Bioquimica i Biologia Molecular, University of Barcelona Barcelona 08028, Catalunya, Spain.', 'Spain|Spain|Spain|Spain', 29, 'Algorithms|Animals|Carbon Isotopes|Computational Biology|Gas Chromatography-Mass Spectrometry|Humans|Isotope Labeling|Liver|Metabolism|Models, Biological|Models, Statistical|Monte Carlo Method|Programming Languages|Rats|Software', 'Journal Article', '10.1093/bioinformatics/btl484', '17000750', 'Metabolic flux analysis of biochemical reaction networks using isotope tracers requires software tools that can analyze the dynamics of isotopic isomer (isotopomer) accumulation in metabolites and reveal the underlying kinetic mechanisms of metabolism regulation. Since existing tools are restricted by the isotopic steady state and remain disconnected from the underlying kinetic mechanisms, we have recently developed a novel approach for the analysis of tracer-based metabolomic data that meets these requirements. The present contribution describes the last step of this development: implementation of (i) the algorithms for the determination of the kinetic parameters and respective metabolic fluxes consistent with the experimental data and (ii) statistical analysis of both fluxes and parameters, thereby lending it a practical application.'),
(73045, 2006, 'bio', 'GATHER: a systems approach to interpreting genomic signatures', 'Chang, Jeffrey T|Nevins, Joseph R', 'Department of Molecular Genetics and Microbiology, Duke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, NC 27710, USA.|Department of Molecular Genetics and Microbiology, Duke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, NC 27710, USA.', 'United States|United States', 0, 'Algorithms|Chromosome Mapping|DNA Fingerprinting|Quantitative Trait Loci|Sequence Analysis, DNA|Software|Systems Biology|Systems Theory', 'Journal Article', '10.1093/bioinformatics/btl483', '17000751', 'Understanding the full meaning of the biology captured in molecular profiles, within the context of the entire biological system, cannot be achieved with a simple examination of the individual genes in the signature. To facilitate such an understanding, we have developed GATHER, a tool that integrates various forms of available data to elucidate biological context within molecular signatures produced from high-throughput post-genomic assays.'),
(73046, 2006, 'bio', 'Peptide length-based prediction of peptide-MHC class II binding', 'Chang, Stewart T|Ghosh, Debashis|Kirschner, Denise E|Linderman, Jennifer J', 'Program in Bioinformatics, University of Michigan Ann Arbor, MI, USA.|Program in Bioinformatics, University of Michigan Ann Arbor, MI, USA.|Program in Bioinformatics, University of Michigan Ann Arbor, MI, USA.|Program in Bioinformatics, University of Michigan Ann Arbor, MI, USA.', 'United States|United States|United States|United States', 39, 'Algorithms|Computational Biology|Databases, Protein|Genes, MHC Class II|Histocompatibility Antigens Class II|Humans|Inhibitory Concentration 50|Models, Statistical|Pattern Recognition, Automated|Peptides|Protein Binding|Proteomics|Regression Analysis|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl479', '17000752', 'Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms.'),
(73047, 2006, 'bio', 'Modelling interaction sites in protein domains with interaction profile hidden Markov models', 'Friedrich, Torben|Pils, Birgit|Dandekar, Thomas|Schultz, Jörg|Müller, Tobias', 'Bioinformatik, Biozentrum, Am Hubland, Universität Würzburg 97074 Würzburg, Germany.|Bioinformatik, Biozentrum, Am Hubland, Universität Würzburg 97074 Würzburg, Germany.|Bioinformatik, Biozentrum, Am Hubland, Universität Würzburg 97074 Würzburg, Germany.|Bioinformatik, Biozentrum, Am Hubland, Universität Würzburg 97074 Würzburg, Germany.|Bioinformatik, Biozentrum, Am Hubland, Universität Würzburg 97074 Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Markov Chains|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btl486', '17000753', 'Due to the growing number of completely sequenced genomes, functional annotation of proteins becomes a more and more important issue. Here, we describe a method for the prediction of sites within protein domains, which are part of protein-ligand interactions. As recently demonstrated, these sites are not trivial to detect because of a varying degree of conservation of their location and type within a domain family.'),
(73048, 2006, 'bio', 'CAPS: coevolution analysis using protein sequences', 'Fares, Mario A|McNally, David', 'Evolutionary Genetics and Bioinformatics Laboratory, Department of Genetics Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin 2, Dublin, Ireland. faresm@tcd.ie|Evolutionary Genetics and Bioinformatics Laboratory, Department of Genetics Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin 2, Dublin, Ireland. faresm@tcd.ie', 'Ireland|Ireland', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Evolution, Molecular|Internet|Phylogeny|Programming Languages|Proteins|Proteomics|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl493', '17005535', 'Coevolution Analysis using Protein Sequences (CAPS) is a PERL based software that identifies co-evolution between amino acid sites. Blosum-corrected amino acid distances are used to identify amino acid co-variation. The phylogenetic sequence relationships are used to remove the phylogenetic and stochastic dependencies between sites. The 3D protein structure is used to identify the nature of the dependencies between co-evolving amino acid sites. Friendly interpretable output files are generated.'),
(73049, 2006, 'bio', 'Support vector machines for prediction of dihedral angle regions', 'Zimmermann, Olav|Hansmann, Ulrich H E', 'John v. Neumann Institute for Computing, FZ Jülich, 52425 Jülich, Germany.|John v. Neumann Institute for Computing, FZ Jülich, 52425 Jülich, Germany.', 'Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btl489', '17005536', 'Most secondary structure prediction programs target only alpha helix and beta sheet structures and summarize all other structures in the random coil pseudo class. However, such an assignment often ignores existing local ordering in so-called random coil regions. Signatures for such ordering are distinct dihedral angle pattern. For this reason, we propose as an alternative approach to predict directly dihedral regions for each residue as this leads to a higher amount of structural information.'),
(73050, 2006, 'bio', 'BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network', 'Chen, Xiaohui|Chen, Ming|Ning, Kaida', 'Department of Bioinformatics Zhejiang University, Hangzhou 310058, China.|Department of Bioinformatics Zhejiang University, Hangzhou 310058, China.|Department of Bioinformatics Zhejiang University, Hangzhou 310058, China.', 'China|China|China', 34, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl491', '17005537', 'BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant sub-modules of regulatory networks with high confidence are reconstructed by using our extended sub-network mining algorithm of directed graphs. BNArray can handle microarray datasets with missing data. To evaluate the statistical features of generated Bayesian networks, re-sampling procedures are utilized to yield collections of candidate 1st-order network sets for mining dense coherent sub-networks.'),
(73051, 2006, 'bio', 'A scalable method for integration and functional analysis of multiple microarray datasets', 'Huttenhower, Curtis|Hibbs, Matt|Myers, Chad|Troyanskaya, Olga G', 'Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.', 'United States|United States|United States|United States', 85, 'Algorithms|Bayes Theorem|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl492', '17005538', 'The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions.'),
(73052, 2007, 'bio', 'GenoProfiler: batch processing of high-throughput capillary fingerprinting data', 'You, Frank M|Luo, Ming-Cheng|Gu, Yong Qiang|Lazo, Gerard R|Deal, Karin|Dvorak, Jan|Anderson, Olin D', 'Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.|Department of Plant Sciences, University of California, Davis, CA 95616, USA.', 'United States|United States|United States|United States|United States|United States|United States', 23, 'Algorithms|Database Management Systems|Databases, Protein|Electrophoresis, Capillary|Gene Expression Profiling|Information Storage and Retrieval|Peptide Mapping|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl494', '17018534', 'High-throughput content fingerprinting techniques employing capillary electrophoresis place new demands on the editing of fingerprint files for the downstream contig assembly program, FPC. A cross-platform software application, GenoProfiler, was developed for automated editing of sized fingerprinting profiles generated by the ABI Genetic Analyzers. The batch-processing module extracts the sized fragment information directly from the ABI raw trace files, or from data files exported from GeneMapper or other size calling software, removes the background noise and undesired fragments, and generates fragment size files compatible with the FPC software.'),
(73053, 2006, 'bio', 'ProtBuD: a database of biological unit structures of protein families and superfamilies', 'Xu, Qifang|Canutescu, Adrian|Obradovic, Zoran|Dunbrack, Roland L', 'Institute for Cancer Research, Fox Chase Cancer Center 333 Cottman Avenue, Philadelphia, PA 19111 USA.|Institute for Cancer Research, Fox Chase Cancer Center 333 Cottman Avenue, Philadelphia, PA 19111 USA.|Institute for Cancer Research, Fox Chase Cancer Center 333 Cottman Avenue, Philadelphia, PA 19111 USA.|Institute for Cancer Research, Fox Chase Cancer Center 333 Cottman Avenue, Philadelphia, PA 19111 USA.', 'United States|United States|United States|United States', 38, 'Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl490', '17018535', 'Modeling of protein interactions is often possible from known structures of related complexes. It is often time-consuming to find the most appropriate template. Hypothesized biological units (BUs) often differ from the asymmetric units and it is usually preferable to model from the BUs.'),
(73054, 2007, 'bio', 'Sungear: interactive visualization and functional analysis of genomic datasets', 'Poultney, Christopher S|Gutiérrez, Rodrigo A|Katari, Manpreet S|Gifford, Miriam L|Paley, W Bradford|Coruzzi, Gloria M|Shasha, Dennis E', 'Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.|Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.', 'United States|United States|United States|United States|United States|United States|United States', 31, 'Algorithms|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Genetics, Population|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl496', '17018536', 'Sungear is a software system that supports a rapid, visually interactive and biologist-driven comparison of large datasets. The datasets can come from microarray experiments (e.g. genes induced in each experiment), from comparative genomics (e.g. genes present in each genome) or even from non-biological applications (e.g. demographics or baseball statistics). Sungear represents multiple datasets as vertices in a polygon. Each possible intersection among the sets is represented as a circle inside the polygon. The position of the circle is determined by the position of the vertices represented in the intersection and the area of the circle is determined by the number of elements in the intersection. Sungear shows which Gene Ontology terms are over-represented in a subset of circles or anchors. The intuitive Sungear interface has enabled biologists to determine quickly which dataset or groups of datasets play a role in a biological function of interest.'),
(73055, 2006, 'bio', 'A Novel algorithm for identifying low-complexity regions in a protein sequence', 'Li, Xuehui|Kahveci, Tamer', 'Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. xli@cise.ufl.edu|Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. xli@cise.ufl.edu', 'United States|United States', 22, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl495', '17018537', 'We consider the problem of identifying low-complexity regions (LCRs) in a protein sequence. LCRs are regions of biased composition, normally consisting of different kinds of repeats.'),
(73056, 2006, 'bio', 'P2BAT: a massive parallel implementation of PBAT for genome-wide association studies in R', 'Hoffmann, Thomas|Lange, Christoph', 'Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. thoffman@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. thoffman@hsph.harvard.edu', 'United States|United States', 13, 'Algorithms|Chromosome Mapping|Computer Graphics|Computing Methodologies|Genetics, Population|Programming Languages|Quantitative Trait Loci|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl507', '17021156', 'The software tool P2BAT provides a massive parallel and user friendly implementation of the PBAT-analysis tools for family-based association tests (FBATs) in large-scale studies, including genome-wide association studies with several thousand subjects. Built on the original PBAT-implementation of the Lange-Van Steen algorithm to bypass the multiple testing problem in family-based association studies, P2BAT integrates all PBAT-analysis tools for binary and complex traits into R and makes them accessible through a user-friendly GUI. The genome-wide analysis tools are fully automated and can be ran massively parallel directly through the GUI. P2BAT is fully documented and contains graphical output tools for time-to-onset analysis. P2BAT also features the ability to test for gene and environment/drug interaction.'),
(73057, 2006, 'bio', 'Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance', 'Deforche, K|Silander, T|Camacho, R|Grossman, Z|Soares, M A|Van Laethem, K|Kantor, R|Moreau, Y|Vandamme, A-M|non-B Workgroup', 'Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be|Rega Institute for Medical Research, Katholieke Universiteit Leuven Leuven, Belgium. koen.deforche@uz.kuleuven.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 59, 'Amino Acid Sequence|Amino Acid Substitution|Bayes Theorem|DNA Mutational Analysis|Drug Resistance, Viral|Gene Products, pol|HIV-1|Models, Genetic|Models, Statistical|Molecular Sequence Data|Mutation|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl508', '17021157', 'Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.'),
(73058, 2006, 'bio', 'TimeTree: a public knowledge-base of divergence times among organisms', 'Hedges, S Blair|Dudley, Joel|Kumar, Sudhir', 'Department of Biology, Pennsylvania State University, University Park, PA 16802-5301, USA. sbh1@psu.edu|Department of Biology, Pennsylvania State University, University Park, PA 16802-5301, USA. sbh1@psu.edu|Department of Biology, Pennsylvania State University, University Park, PA 16802-5301, USA. sbh1@psu.edu', 'United States|United States|United States', 710, 'Biological Evolution|Database Management Systems|Databases, Factual|Evolution, Molecular|Genetic Variation|Genetics, Population|Information Storage and Retrieval|Knowledge Bases|Phylogeny|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl505', '17021158', 'Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature.'),
(73059, 2006, 'bio', 'CREMOFAC--a database of chromatin remodeling factors', 'Shipra, Agrawal|Chetan, Kumar|Rao, M R S', 'Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560064, India.|Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560064, India.|Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560064, India.', 'India|India|India', 17, 'Animals|Chromatin|Chromatin Assembly and Disassembly|DNA-Binding Proteins|Database Management Systems|Databases, Genetic|Humans|Information Storage and Retrieval|Internet|Transcription Factors|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl509', '17021159', 'Chromatin-remodeling is an important event in the eukaryotic nucleus rendering nucleosomal DNA accessible for various transaction processes. Remodeling Factors facilitate the dynamic nature of chromatin through participation of the collective action of (i) ATP and (ii) Non-ATP dependent factors. Considering the importance of these factors in eukaryotes, we have developed, CREMOFAC, a dedicated and frequently updated web-database for chromatin-remodeling factors.'),
(73060, 2006, 'bio', 'The tYNA platform for comparative interactomics: a web tool for managing, comparing and mining multiple networks', 'Yip, Kevin Y|Yu, Haiyuan|Kim, Philip M|Schultz, Martin|Gerstein, Mark', 'Department of Computer Science, Yale University, New Haven, CT 06511, USA.|Department of Computer Science, Yale University, New Haven, CT 06511, USA.|Department of Computer Science, Yale University, New Haven, CT 06511, USA.|Department of Computer Science, Yale University, New Haven, CT 06511, USA.|Department of Computer Science, Yale University, New Haven, CT 06511, USA.', 'United States|United States|United States|United States|United States', 49, 'Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Protein Interaction Mapping|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl488', '17021160', 'Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories.'),
(73061, 2006, 'bio', 'FoldUnfold: web server for the prediction of disordered regions in protein chain', 'Galzitskaya, Oxana V|Garbuzynskiy, Sergiy O|Lobanov, Michail Yu', 'Institute of Protein Research, Russian Academy of Sciences 142290, Pushchino, Moscow Region, Russia. ogalzit@vega.protres.ru|Institute of Protein Research, Russian Academy of Sciences 142290, Pushchino, Moscow Region, Russia. ogalzit@vega.protres.ru|Institute of Protein Research, Russian Academy of Sciences 142290, Pushchino, Moscow Region, Russia. ogalzit@vega.protres.ru', '-|-|-', 110, 'Algorithms|Amino Acid Sequence|Computer Simulation|Internet|Models, Chemical|Molecular Sequence Data|Peptides|Protein Denaturation|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl504', '17021161', 'Identification of disordered regions in polypeptide chains is very important because such regions are essential for protein function. A new parameter, namely mean packing density of residues has been introduced to detect disordered regions in a protein sequence. We have demonstrated that regions with weak expected packing density would be responsible for the appearance of disordered regions. Our method (FoldUnfold) has been tested on datasets of globular proteins (559 proteins) and long disordered protein segments (129 proteins) and showed improved performance over some other widely used methods, such as DISOPRED, PONDR VL3H, IUPred and GlobPlot.'),
(73062, 2006, 'bio', 'Predicted function of the vaccinia virus G5R protein', 'Da Silva, Melissa|Shen, Ling|Tcherepanov, Vasily|Watson, Cristalle|Upton, Chris', 'Department of Biochemistry and Microbiology, University of Victoria Petch Building 207, Ring Road, Victoria, BC, V8P 5C2, Canada.|Department of Biochemistry and Microbiology, University of Victoria Petch Building 207, Ring Road, Victoria, BC, V8P 5C2, Canada.|Department of Biochemistry and Microbiology, University of Victoria Petch Building 207, Ring Road, Victoria, BC, V8P 5C2, Canada.|Department of Biochemistry and Microbiology, University of Victoria Petch Building 207, Ring Road, Victoria, BC, V8P 5C2, Canada.|Department of Biochemistry and Microbiology, University of Victoria Petch Building 207, Ring Road, Victoria, BC, V8P 5C2, Canada.', 'Canada|Canada|Canada|Canada|Canada', 14, 'Amino Acid Sequence|Computer Simulation|Evolution, Molecular|Flap Endonucleases|Models, Chemical|Models, Molecular|Protein Conformation|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Vaccinia virus|Viral Proteins', 'Journal Article', '10.1093/bioinformatics/btl506', '17021162', 'Of the approximately 200 proteins that have been identified for the vaccinia virus (VACV) genome, many are currently listed as having an unknown function, and seven of these are also found in all other poxvirus genomes that have been sequenced. The G5R protein of VACV is included in this list, and to date, very little is known about this essential and highly conserved protein. Conventional similarity searches of protein databases do not identify significantly similar proteins, and experimental approaches have been unsuccessful at determining protein function.'),
(73063, 2006, 'bio', 'Evaluating the performance of microarray segmentation algorithms', 'Lehmussola, Antti|Ruusuvuori, Pekka|Yli-Harja, Olli', 'Institute of Signal Processing, Tampere University of Technology PO Box 553, 33101 Tampere, Finland. antti.lehmussola@tut.fi|Institute of Signal Processing, Tampere University of Technology PO Box 553, 33101 Tampere, Finland. antti.lehmussola@tut.fi|Institute of Signal Processing, Tampere University of Technology PO Box 553, 33101 Tampere, Finland. antti.lehmussola@tut.fi', 'Finland|Finland|Finland', 33, 'Algorithms|Gene Expression Profiling|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software Validation', 'Comparative Study', '10.1093/bioinformatics/btl502', '17032673', 'Although numerous algorithms have been developed for microarray segmentation, extensive comparisons between the algorithms have acquired far less attention. In this study, we evaluate the performance of nine microarray segmentation algorithms. Using both simulated and real microarray experiments, we overcome the challenges in performance evaluation, arising from the lack of ground-truth information. The usage of simulated experiments allows us to analyze the segmentation accuracy on a single pixel level as is commonly done in traditional image processing studies. With real experiments, we indirectly measure the segmentation performance, identify significant differences between the algorithms, and study the characteristics of the resulting gene expression data.'),
(73064, 2006, 'bio', 'Large scale data mining approach for gene-specific standardization of microarray gene expression data', 'Yoon, Sukjoon|Yang, Young|Choi, Jiwon|Seong, Jeeweon', 'Department of Biological Sciences, Sookmyung Womens University Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742. yoonsj@sookmyung.ac.kr|Department of Biological Sciences, Sookmyung Womens University Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742. yoonsj@sookmyung.ac.kr|Department of Biological Sciences, Sookmyung Womens University Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742. yoonsj@sookmyung.ac.kr|Department of Biological Sciences, Sookmyung Womens University Hyochangwongil 52, Youngsan-gu, Seoul, Republic of Korea, 140-742. yoonsj@sookmyung.ac.kr', '-|-|-|-', 11, 'Algorithms|Biomarkers, Tumor|Database Management Systems|Databases, Genetic|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Information Storage and Retrieval|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1093/bioinformatics/btl500', '17032674', 'The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of genes behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples.'),
(73065, 2007, 'bio', 'KGraph: a system for visualizing and evaluating complex genetic associations', 'Kelly, Reagan J|Jacobsen, Douglas M|Sun, Yan V|Smith, Jennifer A|Kardia, Sharon L R', 'Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA. reagank@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA. reagank@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA. reagank@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA. reagank@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104, USA. reagank@umich.edu', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Computer Graphics|Computer Simulation|Genetic Predisposition to Disease|Genetics, Population|Humans|Information Storage and Retrieval|Linkage Disequilibrium|Models, Genetic|Software|Statistics as Topic|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl510', '17032675', 'The KGraph is a data visualization system that has been developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic factors, such as single nucleotide polymorphisms (SNPs). It allows for easy viewing and interpretation of genetic associations, correlations among covariates and SNPs, and information about the replication and cross-validation of the associations. The KGraph allows the user to more easily investigate multicollinearity and confounding through visualization of the multidimensional correlation structure underlying genetic associations. It emphasizes gene-environment and gene-gene interaction, both important components of any genetic system that are often overlooked in association frameworks.'),
(73066, 2006, 'bio', 'Robust method for detecting differential gene expression in twin studies', 'Begun, Alexander', 'Institute of Medical Informatics and Statistics, Kiel University Brunswiker Strasse 10, D-24105 Kiel, Germany. a.begun@ikmb.uni-kiel.de', 'Germany', 2, 'Algorithms|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Twin Studies as Topic|Twins', 'Journal Article', '10.1093/bioinformatics/btl501', '17032676', 'A steadily increasing number of experiments with microarrays stimulate the further development of the statistical methods of the analysis of gene expression data. One of the central problems in this area is detecting differential gene expression under two or more conditions. Unfortunately, up to now it has not been studied how the correlations between related individuals, such as twins influence the estimates of differential gene expression.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73067, 2006, 'bio', 'ChromoScan: a scan statistic application for identifying chromosomal regions in genomic studies', 'Sun, Yan V|Jacobsen, Douglas M|Kardia, Sharon L R', 'Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104-3028, USA. yansun@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104-3028, USA. yansun@umich.edu|Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48104-3028, USA. yansun@umich.edu', 'United States|United States|United States', 12, 'Algorithms|Chromosome Mapping|Data Interpretation, Statistical|Gene Targeting|Genome|Genomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl503', '17032677', 'ChromoScan is an implementation of a genome-based scan statistic that detects genomic regions, which are statistically significant for targeted measurements, such as genetic associations with disease, gene expression profiles, DNA copy number variations, as well as other genome-based measurements. A Java graphic user interface (GUI) is provided to allow users to select appropriate data transformations and thresholds for defining the significant events.'),
(73068, 2007, 'bio', 'DASS: efficient discovery and p-value calculation of substructures in unordered data', 'Hollunder, Jens|Friedel, Maik|Beyer, Andreas|Workman, Christopher T|Wilhelm, Thomas', 'Department of Theoretical Systems Biology, Leibniz Institute for Age Research--Fritz-Lipmann-Institute e. V. (former IMB Jena) Beutenbergstrasse 11, D-07745 Jena, Germany.|Department of Theoretical Systems Biology, Leibniz Institute for Age Research--Fritz-Lipmann-Institute e. V. (former IMB Jena) Beutenbergstrasse 11, D-07745 Jena, Germany.|Department of Theoretical Systems Biology, Leibniz Institute for Age Research--Fritz-Lipmann-Institute e. V. (former IMB Jena) Beutenbergstrasse 11, D-07745 Jena, Germany.|Department of Theoretical Systems Biology, Leibniz Institute for Age Research--Fritz-Lipmann-Institute e. V. (former IMB Jena) Beutenbergstrasse 11, D-07745 Jena, Germany.|Department of Theoretical Systems Biology, Leibniz Institute for Age Research--Fritz-Lipmann-Institute e. V. (former IMB Jena) Beutenbergstrasse 11, D-07745 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Escherichia coli Proteins|Gene Expression Profiling|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Protein Structure, Tertiary|Saccharomyces cerevisiae Proteins|Sequence Alignment|Sequence Analysis|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl511', '17032678', 'Pattern identification in biological sequence data is one of the main objectives of bioinformatics research. However, few methods are available for detecting patterns (substructures) in unordered datasets. Data mining algorithms mainly developed outside the realm of bioinformatics have been adapted for that purpose, but typically do not determine the statistical significance of the identified patterns. Moreover, these algorithms do not exploit the often modular structure of biological data.'),
(73069, 2006, 'bio', 'Integration of gel-based proteome data with pProRep', 'Laukens, Kris|Matthiesen, Rune|Lemière, Filip|Esmans, Eddy|Onckelen, Harry Van|Jensen, Ole Nørregaard|Witters, Erwin', 'Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be|Laboratory of Plant Physiology and Plant Biochemistry, University of Antwerp Groenenborgerlaan 171, B-2020 Antwerp, Belgium. kris.laukens@ua.ac.be', 'Belgium|Belgium|Belgium|Belgium|Belgium|Belgium|Belgium', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Databases, Protein|Electrophoresis, Gel, Two-Dimensional|Information Storage and Retrieval|Internet|Ions|Mass Spectrometry|Peptides|Programming Languages|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl487', '17032679', 'pProRep is a web application integrating electrophoretic and mass spectral data from proteome analyses into a relational database. The graphical web-interface allows users to upload, analyse and share experimental proteome data. It offers researchers the possibility to query all previously analysed datasets and can visualize selected features, such as the presence of a certain set of ions in a peptide mass spectrum, on the level of the two-dimensional gel.'),
(73070, 2006, 'bio', 'Survival analysis of longitudinal microarrays', 'Rajicic, Natasa|Finkelstein, Dianne M|Schoenfeld, David A|Inflammation Host Response to Injury Research Program Investigators', 'Massachusetts General Hospital, Biostatistics Unit 50 Staniford Street, Suite 560, Boston, MA, USA.|Massachusetts General Hospital, Biostatistics Unit 50 Staniford Street, Suite 560, Boston, MA, USA.|Massachusetts General Hospital, Biostatistics Unit 50 Staniford Street, Suite 560, Boston, MA, USA.|Massachusetts General Hospital, Biostatistics Unit 50 Staniford Street, Suite 560, Boston, MA, USA.', 'United States|United States|United States|United States', 8, 'Algorithms|Biomarkers|Diagnosis, Computer-Assisted|Gene Expression Profiling|Inflammation|Longitudinal Studies|Oligonucleotide Array Sequence Analysis|Proteins|Risk Assessment|Risk Factors|Survival Analysis|Survival Rate|Time Factors|Wounds and Injuries', 'Evaluation Studies', '10.1093/bioinformatics/btl450', '17032680', 'The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays.'),
(73071, 2006, 'bio', 'Meisetz and the birth of the KRAB motif', 'Birtle, Zoë|Ponting, Chris P', 'Department of Physiology, Anatomy and Genetics, MRC Functional Genetics Unit, University of Oxford South Parks Road, Oxford OX1 3QX, UK.|Department of Physiology, Anatomy and Genetics, MRC Functional Genetics Unit, University of Oxford South Parks Road, Oxford OX1 3QX, UK.', 'United Kingdom|United Kingdom', 0, 'Amino Acid Motifs|Animals|Base Sequence|Carrier Proteins|Chromosome Mapping|Evolution, Molecular|Histone Methyltransferases|Histone-Lysine N-Methyltransferase|Mice|Molecular Sequence Data|Nuclear Proteins|Protein Methyltransferases|Repressor Proteins|Sea Urchins|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl498', '17032681', 'The largest family of transcription factors in mammals is of Cys(2)His(2) zinc finger-proteins, each with an NH(2)-terminal KRAB motif. Extensive expansions of this family have occurred in separate mammalian lineages, with approximately 400 such genes known in the human genome. Despite their widespread occurrence, the evolutionary provenance of the KRAB motif is unclear since previously it has not been found outside of the tetrapod vertebrates. Here, we show that homologues of the histone methyltransferase Meisetz are present within the sea urchin (Strongylocentrotus purpuratus) genome. Sea urchin and mammalian Meisetz sequences each contain an N-terminal KRAB motif, which thereby establishes an early origin of the KRAB motif prior to the divergence of echinoderm and chordate lineages. Finally, we present evidence that KRAB motifs derive from a novel family of KRI (KRAB Interior) motifs that were present in the last common ancestor of animals, plants and fungi.'),
(73072, 2006, 'bio', 'Identifying cis-regulatory modules by combining comparative and compositional analysis of DNA', 'Pierstorff, Nora|Bergman, Casey M|Wiehe, Thomas', 'Institute for Genetics, University of Cologne Zuelpicher Strasse 47, 50674 Cologne, Germany. nora.pierstorff@uni-koeln.de|Institute for Genetics, University of Cologne Zuelpicher Strasse 47, 50674 Cologne, Germany. nora.pierstorff@uni-koeln.de|Institute for Genetics, University of Cologne Zuelpicher Strasse 47, 50674 Cologne, Germany. nora.pierstorff@uni-koeln.de', 'Germany|Germany|Germany', 30, 'Algorithms|Amino Acid Motifs|Animals|Base Sequence|DNA|Drosophila melanogaster|Molecular Sequence Data|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl499', '17032682', 'Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction.'),
(73073, 2006, 'bio', 'COPASI--a COmplex PAthway SImulator', 'Hoops, Stefan|Sahle, Sven|Gauges, Ralph|Lee, Christine|Pahle, Jürgen|Simus, Natalia|Singhal, Mudita|Xu, Liang|Mendes, Pedro|Kummer, Ursula', 'Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Programming Languages|Protein Interaction Mapping|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl485', '17032683', 'Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods.'),
(73074, 2006, 'bio', 'Structural genomics meets computational biology', 'Bateman, Alex|Valencia, Alfonso', 'Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.|Virginia Bioinformatics Institute, Virginia Tech, Washington St. 0477, Blacksburg, VA 24061, USA.', 'United States|United States', 4, 'Academies and Institutes|Computational Biology|Genomics|Interinstitutional Relations|Molecular Biology|Research|United States', 'Editorial', '10.1093/bioinformatics/btl426', '17032684', '-'),
(73075, 2006, 'bio', 'APDB: a web server to evaluate the accuracy of sequence alignments using structural information', 'Armougom, Fabrice|Poirot, Olivier|Moretti, Sébastien|Higgins, Desmond G|Bucher, Phillip|Keduas, Vladimir|Notredame, Cedric', 'CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.|CNRS UPR2589, Institute for Structural Biology and Microbiology (IBSM), Parc Scientifique de Luminy, 163 Avenue de Luminy, FR-13288, Marseille cedex 09, France.', 'France|France|France|France|France|France|France', 0, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Internet|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl404', '17032685', 'The APDB webserver uses structural information to evaluate the alignment of sequences with known structures. It returns a score correlated to the overall alignment accuracy as well as a local evaluation. Any sequence alignment can be analyzed with APDB provided it includes at least two proteins with known structures. Sequences without a known structure are simply ignored and do not contribute to the scoring procedure.'),
(73076, 2006, 'bio', 'Measuring covariation in RNA alignments: physical realism improves information measures', 'Lindgreen, S|Gardner, P P|Krogh, A', 'Bioinformatics Centre, Institute of Molecular Biology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen Ø, Denmark. stinus@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen Ø, Denmark. stinus@binf.ku.dk|Bioinformatics Centre, Institute of Molecular Biology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen Ø, Denmark. stinus@binf.ku.dk', 'Denmark|Denmark|Denmark', 38, 'Algorithms|Conserved Sequence|Evolution, Molecular|Genetic Variation|Information Theory|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl514', '17038338', 'The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures.'),
(73077, 2006, 'bio', 'A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge', 'Du, Jiang|Rozowsky, Joel S|Korbel, Jan O|Zhang, Zhengdong D|Royce, Thomas E|Schultz, Martin H|Snyder, Michael|Gerstein, Mark', 'Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, New Haven, CT 06520, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Artificial Intelligence|Biology|Chromosome Mapping|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Markov Chains|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA|Transcription Factors|Transcription, Genetic', 'Evaluation Studies', '10.1093/bioinformatics/btl515', '17038339', 'Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into active regions (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing.'),
(73078, 2006, 'bio', 'ssSNPer: identifying statistically similar SNPs to aid interpretation of genetic association studies', 'Nyholt, Dale R', 'Genetic Epidemiology Laboratory, QIMR 300 Herston Road, Brisbane, Queensland, 4006, Australia. daleN@qimr.edu.au', 'Australia', 0, 'Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Data Interpretation, Statistical|Genetic Linkage|Genetics, Population|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl518', '17038340', 'ssSNPer is a novel user-friendly web interface that provides easy determination of the number and location of untested HapMap SNPs, in the region surrounding a tested HapMap SNP, which are statistically similar and would thus produce comparable and perhaps more significant association results. Identification of ssSNPs can have crucial implications for the interpretation of the initial association results and the design of follow-up studies.'),
(73079, 2006, 'bio', 'NMPP: a user-customized NimbleGen microarray data processing pipeline', 'Wang, Xiangfeng|He, Hang|Li, Lei|Chen, Runsheng|Deng, Xing Wang|Li, Songgang', 'Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu|Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu|Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu|Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu|Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu|Center of Bioinformatics & Peking-Yale Joint Research Center of Plant Molecular Genetics and Agrobiotechnology, College of Life Sciences, Peking University, Beijing 100871, China. Xiangfeng.Wang@Yale.edu', 'China|China|China|China|China|China', 26, 'Algorithms|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl525', '17038341', 'NMPP package is a bundle of user-customized tools based on established algorithms and methods to process self-designed NimbleGen microarray data. It features a command-line-based integrative processing procedure that comprises five major functional components, namely the raw microarray data parsing and integrating module, the array spatial effect smoothing and visualization module, the probe-level multi-array normalization module, the gene expression intensity summarization module and the gene expression status inference module.'),
(73080, 2006, 'bio', 'Maximum likelihood inference of imprinting and allele-specific expression from EST data', 'Seoighe, Cathal|Nembaware, Victoria|Scheffler, Konrad', 'Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa. cathal@science.uct.ac.za|Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa. cathal@science.uct.ac.za|Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa. cathal@science.uct.ac.za', 'South Africa|South Africa|South Africa', 0, 'Alleles|Chromosome Mapping|Databases, Genetic|Expressed Sequence Tags|Gene Expression|Genomic Imprinting|Likelihood Functions|Models, Genetic|Models, Statistical|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Evaluation Studies', '10.1093/bioinformatics/btl521', '17038342', 'In a diploid organism the proportion of transcripts that are produced from the two parental alleles can differ substantially due, for example to epigenetic modification that causes complete or partial silencing of one parental allele or to cis acting polymorphisms that affect transcriptional regulation. Counts of SNP alleles derived from EST sequences have been used to identify both novel candidates for genomic imprinting as well as examples of genes with allelic differences in expression.'),
(73081, 2007, 'bio', 'THOR: targeted high-throughput ortholog reconstructor', 'Bainbridge, Matthew N|Warren, René L|He, An|Bilenky, Mikhail|Robertson, A Gordon|Jones, Steven J M', 'British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca|British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca|British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca|British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca|British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca|British Columbia Cancer Agency (BCCA) Genome Sciences Centre, 675 West 10th Avenue, Vancouver, BC, Canada. matthewb@bcgsc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Gene Targeting|Genome, Human|Humans|Molecular Sequence Data|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl513', '17038343', 'Low-coverage genomes (LCGs) are becoming an increasingly important source of data for phylogenetic studies. However, assembly of these genomes is time consuming, difficult and lags behind sequence generation. THOR is a fast, stringent application for targeted reconstruction of sequence orthologs in unassembled LCGs. Using a 4x coverage set of mouse whole-genome sequence reads, THOR could partially or completely reconstruct 416/1000 human promoter ortholog regions in approximately 7.3 min/promoter. THORs reconstruction rate improves markedly with both higher-coverage, and less divergent target species.'),
(73082, 2007, 'bio', 'Parameter estimation using Simulated Annealing for S-system models of biochemical networks', 'Gonzalez, Orland R|Küper, Christoph|Jung, Kirsten|Naval, Prospero C|Mendoza, Eduardo', 'Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de|Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de|Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de|Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de|Department of Computer Science University of the Philippines-Diliman, Munich, Germany. gonzalez@bio.ifi.lmu.de', 'Germany|Germany|Germany|Germany|Germany', 122, 'Biochemistry|Computer Simulation|Gene Expression Profiling|Models, Biological|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl522', '17038344', 'High-throughput technologies now allow the acquisition of biological data, such as comprehensive biochemical time-courses at unprecedented rates. These temporal profiles carry topological and kinetic information regarding the biochemical network from which they were drawn. Retrieving this information will require systematic application of both experimental and computational methods.'),
(73083, 2007, 'bio', 'Repseek, a tool to retrieve approximate repeats from large DNA sequences', 'Achaz, Guillaume|Boyer, Frédéric|Rocha, Eduardo P C|Viari, Alain|Coissac, Eric', 'Atelier de Bioinformatique, Université Pierre et Marie Curie-Paris 6 12, rue Cuvier, 75005 Paris, France. achaz@abi.snv.jussieu.fr|Atelier de Bioinformatique, Université Pierre et Marie Curie-Paris 6 12, rue Cuvier, 75005 Paris, France. achaz@abi.snv.jussieu.fr|Atelier de Bioinformatique, Université Pierre et Marie Curie-Paris 6 12, rue Cuvier, 75005 Paris, France. achaz@abi.snv.jussieu.fr|Atelier de Bioinformatique, Université Pierre et Marie Curie-Paris 6 12, rue Cuvier, 75005 Paris, France. achaz@abi.snv.jussieu.fr|Atelier de Bioinformatique, Université Pierre et Marie Curie-Paris 6 12, rue Cuvier, 75005 Paris, France. achaz@abi.snv.jussieu.fr', 'France|France|France|France|France', 53, 'Algorithms|Base Sequence|DNA|Information Storage and Retrieval|Information Systems|Sequence Analysis, DNA|Software|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btl519', '17038345', 'Chromosomes or other long DNA sequences contain many highly similar repeated sub-sequences. While there are efficient methods for detecting strict repeats or detecting already characterized repeats, there is no software available for detecting approximate repeats in large DNA sequences allowing for weighted substitutions and indels in a coherent statistical framework. Here, we present an implementation of a two-steps method (seed detection followed by their extension) that detects those approximate repeats. Our method is computationally efficient enough to handle large sequences and is flexible enough to account for influencing factors, such as sequence-composition biases both at the seed detection and alignment levels.'),
(73084, 2006, 'bio', 'Supporting the SBML layout extension', 'Deckard, Anastasia|Bergmann, Frank T|Sauro, Herbert M', 'Keck Graduate Institute, 535 Watson Dr, Claremont, CA 91711, USA.|Keck Graduate Institute, 535 Watson Dr, Claremont, CA 91711, USA.|Keck Graduate Institute, 535 Watson Dr, Claremont, CA 91711, USA.', 'United States|United States|United States', 17, 'Algorithms|Computer Simulation|Models, Biological|Programming Languages|Signal Transduction|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl520', '17038346', 'Researchers studying large or complex biochemical networks would benefit from the ability to automatically create lucid visualizations and store them in a portable and widely accepted format.'),
(73085, 2006, 'bio', 'Cell++--simulating biochemical pathways', 'Sanford, Chris|Yip, Matthew L K|White, Carl|Parkinson, John', 'Hospital for Sick Children, 555 University Avenue Toronto, ON, M5G 1X8, Canada.|Hospital for Sick Children, 555 University Avenue Toronto, ON, M5G 1X8, Canada.|Hospital for Sick Children, 555 University Avenue Toronto, ON, M5G 1X8, Canada.|Hospital for Sick Children, 555 University Avenue Toronto, ON, M5G 1X8, Canada.', 'Canada|Canada|Canada|Canada', 25, 'Biochemistry|Cell Physiological Phenomena|Computer Simulation|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl497', '17038347', 'With the generation of a wealth of information, detailing cellular components, their functions and interactions, there is a growing need for the development of new computational tools capable of interpreting these data within spatial and dynamic contexts. Here, we introduce Cell++, a novel stochastic simulation environment with the capacity to study a wide variety of biochemical processes within a spatial context.'),
(73086, 2007, 'bio', 'Inherent limitations in protein-protein docking procedures', 'Kowalsman, Noga|Eisenstein, Miriam', 'Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.|Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.', 'Israel|Israel', 24, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein', 'Comparative Study', '10.1093/bioinformatics/btl524', '17040920', 'The limited success rate of protein-protein docking procedures is generally attributed to structure differences between the bound and unbound states of the molecules. Herein we analyze a large dataset of protein-protein docking results and identify additional parameters that affect the performance of docking procedures.'),
(73087, 2006, 'bio', 'Design and implementation of a tool for translating SBML into the biochemical stochastic pi-calculus', 'Eccher, Claudio|Priami, Corrado', 'DIT-University of Trento Trento, Italy. cleccher@itc.it|DIT-University of Trento Trento, Italy. cleccher@itc.it', 'Italy|Italy', 5, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Models, Statistical|Programming Languages|Protein Interaction Mapping|Proteome|Signal Transduction|Software|Software Design|Stochastic Processes|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl516', '17046974', 'SBML is becoming a standard de-facto to represent and store biological models. Although SBML is very useful in defining ways of exchanging and storing biological information, it is not formal enough to allow direct translation into non ambiguous formal representation languages to perform analysis and simulation of models. We here suggest to map SBML models into process calculi representations.'),
(73088, 2006, 'bio', 'Extending MapMan: application to legume genome arrays', 'Goffard, Nicolas|Weiller, Georg', 'ARC Centre of Excellence for Integrative Legume Research and Bioinformatics Laboratory, Genomic Interactions Group, Research School of Biological Sciences, Australian National University GPO Box 475, Canberra, ACT 2601, Australia.|ARC Centre of Excellence for Integrative Legume Research and Bioinformatics Laboratory, Genomic Interactions Group, Research School of Biological Sciences, Australian National University GPO Box 475, Canberra, ACT 2601, Australia.', 'Australia|Australia', 27, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Chromosome Mapping|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl517', '17046975', 'Based on a gene classification into hierarchical categories (BINs), MapMan was originally developed to display Arabidopsis thaliana gene expression in a functional context. We have created a bioinformatics system to extend MapMan to any organism by using a new BIN structure based on the KEGG database. Gene sequences are assigned to this ontology by homology relationships in four reference databases: KEGG, COG, Swiss-Prot and Gene Ontology. We applied this system to tailor MapMan to the GeneChips of two model legumes, Glycine max and Medicago truncatula. We also developed a module to identify the most relevant pathways involved.'),
(73089, 2006, 'bio', 'sGAL: a computational method for finding surface exposed sites in proteins suitable for Cys-mediated cross-linking', 'Woolley, G Andrew|Lee, En-shiun|Zhang, Fuzhong', 'Department of Chemistry, University of Toronto 80 St. George Street, Toronto, ON M5S 3H6, Canada. awoolley@chem.utoronto.ca|Department of Chemistry, University of Toronto 80 St. George Street, Toronto, ON M5S 3H6, Canada. awoolley@chem.utoronto.ca|Department of Chemistry, University of Toronto 80 St. George Street, Toronto, ON M5S 3H6, Canada. awoolley@chem.utoronto.ca', 'Canada|Canada|Canada', 10, 'Algorithms|Binding Sites|Computer Simulation|Cross-Linking Reagents|Cysteine|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software|Surface Properties', 'Journal Article', '10.1093/bioinformatics/btl530', '17046976', 'sGAL is a computer program designed to find pairs of sites suitable for introducing chemical cross-links into proteins. sGAL takes a protein structure file in PDB format as input, truncates each residue sequentially to its gamma side chain atom to mimic mutation to Cys, and calculates the exposed surface area of the gamma atom. The user then inputs the minimum and maximum lengths of the cross-linker. sGAL provides as output pairs of residues that would have exposed gamma atom separations that fall within this range. Furthermore, if a line joining the pair of gamma atoms contacts more than a given number of buried atoms, that pair is discarded. In this way, sites for which the protein would sterically interfere with cross-linking are avoided.'),
(73090, 2006, 'bio', 'MetaQuant: a tool for the automatic quantification of GC/MS-based metabolome data', 'Bunk, Boyke|Kucklick, Martin|Jonas, Rochus|Münch, Richard|Schobert, Max|Jahn, Dieter|Hiller, Karsten', 'Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.|Institute of Microbiology, Technical University of Braunschweig, Spielmannstr. 7 D-38106 Braunschweig, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Gas Chromatography-Mass Spectrometry|Gene Expression Profiling|Peptide Mapping|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl526', '17046977', 'MetaQuant is a Java-based program for the automatic and accurate quantification of GC/MS-based metabolome data. In contrast to other programs MetaQuant is able to quantify hundreds of substances simultaneously with minimal manual intervention. The integration of a self-acting calibration function allows the parallel and fast calibration for several metabolites simultaneously. Finally, MetaQuant is able to import GC/MS data in the common NetCDF format and to export the results of the quantification into Systems Biology Markup Language (SBML), Comma Separated Values (CSV) or Microsoft Excel (XLS) format.'),
(73091, 2006, 'bio', 'Estimation of false discovery proportion under general dependence', 'Pawitan, Yudi|Calza, Stefano|Ploner, Alexander', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@ki.se|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@ki.se', 'Sweden|Sweden|Sweden', 26, 'Algorithms|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl527', '17046978', 'Wide-scale correlations between genes are commonly observed in gene expression data, due to both biological and technical reasons. These correlations increase the variability of the standard estimate of the false discovery rate (FDR). We highlight the false discovery proportion (FDP, instead of the FDR) as the suitable quantity for assessing differential expression in microarray data, demonstrate the deleterious effects of correlation on FDP estimation and propose an improved estimation method that accounts for the correlations.'),
(73092, 2007, 'bio', 'Computational prediction of novel components of lung transcriptional networks', 'Martinez, M Juanita|Smith, Andrew D|Li, Bilan|Zhang, Michael Q|Harrod, Kevin S', 'Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr SE, Albuquerque, NM 87108, USA.|Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr SE, Albuquerque, NM 87108, USA.|Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr SE, Albuquerque, NM 87108, USA.|Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr SE, Albuquerque, NM 87108, USA.|Lovelace Respiratory Research Institute, 2425 Ridgecrest Dr SE, Albuquerque, NM 87108, USA.', 'United States|United States|United States|United States|United States', 8, 'Algorithms|Amino Acid Motifs|Animals|Cell Differentiation|Cell Line|Cell Line, Tumor|Computational Biology|Epithelial Cells|Gene Expression Regulation, Developmental|Hepatocyte Nuclear Factor 3-beta|Humans|Lung|Metabolic Networks and Pathways|Mice|Morphogenesis|Nuclear Proteins|Species Specificity|Transcription Factors|Wnt Proteins', 'Journal Article', '10.1093/bioinformatics/btl531', '17050569', 'Little is known regarding the transcriptional mechanisms involved in forming and maintaining epithelial cell lineages of the mammalian respiratory tract.'),
(73093, 2007, 'bio', 'Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation', 'Letunic, Ivica|Bork, Peer', 'EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany.|EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany.', 'Germany|Germany', 731, 'Algorithms|Animals|Chromosome Mapping|Computational Biology|Data Display|Databases, Genetic|Decision Trees|Evolution, Molecular|Humans|Internet|Phylogeny|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl529', '17050570', 'Interactive Tree Of Life (iTOL) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. Trees can be interactively pruned and re-rooted. Various types of data such as genome sizes or protein domain repertoires can be mapped onto the tree. Export to several bitmap and vector graphics formats is supported.'),
(73094, 2006, 'bio', 'Building an abbreviation dictionary using a term recognition approach', 'Okazaki, Naoaki|Ananiadou, Sophia', 'Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan. okazaki@mi.ci.i.u-tokyo.ac.jp|Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan. okazaki@mi.ci.i.u-tokyo.ac.jp', 'Japan|Japan', 22, 'Abbreviations as Topic|Abstracting and Indexing as Topic|Artificial Intelligence|Database Management Systems|Dictionaries as Topic|Documentation|MEDLINE|Natural Language Processing|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btl534', '17050571', 'Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms.'),
(73095, 2006, 'bio', 'Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites', 'Mu, Fangping|Unkefer, Pat J|Unkefer, Clifford J|Hlavacek, William S', 'Theoretical Biology and Biophysics Group, Theoretical Division Los Alamos, NM 87545, USA.|Theoretical Biology and Biophysics Group, Theoretical Division Los Alamos, NM 87545, USA.|Theoretical Biology and Biophysics Group, Theoretical Division Los Alamos, NM 87545, USA.|Theoretical Biology and Biophysics Group, Theoretical Division Los Alamos, NM 87545, USA.', 'United States|United States|United States|United States', 18, 'Algorithms|Amino Acid Sequence|Binding Sites|Catalysis|Computer Simulation|Enzyme Activation|Models, Biological|Models, Chemical|Molecular Sequence Data|Oxidoreductases|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein|Structure-Activity Relationship|Substrate Specificity', 'Journal Article', '10.1093/bioinformatics/btl535', '17060354', 'Our knowledge of metabolism is far from complete, and the gaps in our knowledge are being revealed by metabolomic detection of small-molecules not previously known to exist in cells. An important challenge is to determine the reactions in which these compounds participate, which can lead to the identification of gene products responsible for novel metabolic pathways. To address this challenge, we investigate how machine learning can be used to predict potential substrates and products of oxidoreductase-catalyzed reactions.'),
(73096, 2006, 'bio', 'Helix Interaction Tool (HIT): a web-based tool for analysis of helix-helix interactions in proteins', 'Burba, Anne E Counterman|Lehnert, Ursula|Yu, Eric Z|Gerstein, Mark', 'Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT 06520, USA.|Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT 06520, USA.|Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT 06520, USA.|Department of Molecular Biophysics and Biochemistry, Yale University New Haven, CT 06520, USA.', 'United States|United States|United States|United States', 4, 'Algorithms|Amino Acid Motifs|Animals|Computational Biology|Data Interpretation, Statistical|Databases, Protein|Hydrogen Bonding|Internet|Molecular Conformation|Protein Conformation|Protein Structure, Secondary|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btl274', '17060355', 'In many proteins, helix-helix interactions can be critical to establishing protein conformation (folding) and dynamics, as well as determining associations between protein units. However, the determination of a set of rules that guide helix-helix interaction has been elusive. In order to gain further insight into the helix-helix interface, we have developed a comprehensive package of tools for analyzing helix-helix packing in proteins. These tools are available at http://helix.gersteinlab.org. They include quantitative measures of the helix interaction surface area and helix crossing angle, as well as several methods for visualizing the helical interaction. These methods can be used for analysis of individual protein conformations or to gain insight into dynamic changes in helix interactions. For the latter purpose, a direct interface from entries in the Molecular Motions Database to the HIT site has been provided.'),
(73097, 2006, 'bio', 'An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality', 'Yoon, Jeongah|Blumer, Anselm|Lee, Kyongbum', 'Department of Chemical and Biological Engineering, Medford, MA 02155, USA.|Department of Chemical and Biological Engineering, Medford, MA 02155, USA.|Department of Chemical and Biological Engineering, Medford, MA 02155, USA.', 'United States|United States|United States', 152, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl533', '17060356', 'Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the wiring of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data.'),
(73098, 2006, 'bio', 'High-resolution spatial normalization for microarrays containing embedded technical replicates', 'Yuan, Daniel S|Irizarry, Rafael A', 'Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD 21205, USA. dyuan@jhmi.edu|Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD 21205, USA. dyuan@jhmi.edu', 'United States|United States', 0, 'Algorithms|Artifacts|Data Interpretation, Statistical|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl542', '17060357', 'Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.'),
(73099, 2006, 'bio', 'SequenceLDhot: detecting recombination hotspots', 'Fearnhead, Paul', 'Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, UK. p.fearnhead@lancs.ac.uk', 'United Kingdom', 47, 'Algorithms|Base Sequence|Chromosome Mapping|Databases, Genetic|Genetics, Population|Molecular Sequence Data|Polymorphism, Genetic|Recombination, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl540', '17060358', 'There is much local variation in recombination rates across the human genome--with the majority of recombination occurring in recombination hotspots--short regions of around approximately 2 kb in length that have much higher recombination rates than neighbouring regions. Knowledge of this local variation is important, e.g. in the design and analysis of association studies for disease genes. Population genetic data, such as that generated by the HapMap project, can be used to infer the location of these hotspots. We present a new, efficient and powerful method for detecting recombination hotspots from population data.'),
(73100, 2007, 'bio', 'Combining functional and linkage disequilibrium information in the selection of tag SNPs', 'Sham, P C|Ao, S I|Kwan, J S H|Kao, P|Cheung, F|Fong, P Y|Ng, M K', 'Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.|Department of Psychiatry, Institute of Psychiatry, Kings College London, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Chromosome Mapping|Cluster Analysis|Genetic Markers|Internet|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Sequence Tagged Sites|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl532', '17060359', 'We have developed an online program, WCLUSTAG, for tag SNP selection that allows the user to specify variable tagging thresholds for different SNPs. Tag SNPs are selected such that a SNP with user-specified tagging threshold C will have a minimum R2 of C with at least one tag SNP. This flexible feature is useful for researchers who wish to prioritize genomic regions or SNPs in an association study.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73101, 2007, 'bio', 'Linkage analysis using sex-specific recombination fractions with GENEHUNTER-MODSCORE', 'Dietter, Johannes|Mattheisen, Manuel|Fürst, Robert|Rüschendorf, Franz|Wienker, Thomas F|Strauch, Konstantin', 'Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de|Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de|Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de|Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de|Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de|Institute of Medical Biometry and Epidemiology, Philipps University Marburg, 35032 Marburg, Germany. dietter@med.uni-marburg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Female|Gene Frequency|Humans|Lod Score|Male|Models, Genetic|Pedigree|Polymorphism, Single Nucleotide|Recombination, Genetic|Sex Factors|Software', 'Journal Article', '10.1093/bioinformatics/btl539', '17060360', 'Sex-specific marker maps have become increasingly available. We have implemented the usage of sex-specific recombination frequencies in the GENEHUNTER-MODSCORE program that performs multipoint linkage analysis. Furthermore, we have devised a consistent method to choose the combinations of male and female genetic positions at which linkage scores should be calculated. Marker coordinates can be read automatically from publicly available genetic maps.'),
(73102, 2006, 'bio', 'A Gibbs sampler for the identification of gene expression and network connectivity consistency', 'Brynildsen, Mark P|Tran, Linh M|Liao, James C', 'Department of Chemical and Biomolecular Engineering, University of California Los Angeles, CA 90095, USA.|Department of Chemical and Biomolecular Engineering, University of California Los Angeles, CA 90095, USA.|Department of Chemical and Biomolecular Engineering, University of California Los Angeles, CA 90095, USA.', 'United States|United States|United States', 0, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sample Size|Sensitivity and Specificity|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl541', '17060361', 'Data from DNA microarrays and ChIP-chip binding assays often form the basis of transcriptional regulatory analyses. However, experimental noise in both data types combined with environmental dependence and uncorrelation between binding and regulation in ChIP-chip binding data complicate analyses that utilize these complimentary data sources. Therefore, to minimize the impact of these inaccuracies on transcription analyses it is desirable to identify instances of gene expression-ChIP-chip agreement, under the premise that inaccuracies are less likely to be present when separate data sources corroborate each other. Current methods for such identification either make key assumptions that limit their applicability and/or yield high false positive and false negative rates. The goal of this work was to develop a method with a minimal amount of assumptions, and thus widely applicable, that can identify agreement between gene expression and ChIP-chip data at a higher confidence level than current methods.'),
(73103, 2007, 'bio', 'PROTERAN: animated terrain evolution for visual analysis of patterns in protein folding trajectory', 'Zhou, Ruhong|Parida, Laxmi|Kapila, Kush|Mudur, Sudhir', 'Computational Biology Center, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. ruhongz@us.ibm.com|Computational Biology Center, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. ruhongz@us.ibm.com|Computational Biology Center, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. ruhongz@us.ibm.com|Computational Biology Center, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. ruhongz@us.ibm.com', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Molecular|Pattern Recognition, Automated|Protein Folding|Software|TRPC Cation Channels', 'Journal Article', '10.1093/bioinformatics/btl538', '17062588', 'The mechanism of protein folding remains largely a mystery in molecular biology, despite the enormous effort from many groups in the past decades. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates such as the fraction of native contacts, the radius of gyration and so on. In this paper, we present an integrated approach towards understanding the folding process via visual analysis of patterns of these reaction coordinates. The three disparate processes (1) protein folding simulation, (2) pattern elicitation and (3) visualization of patterns, work in tandem. Thus as the protein folds, the changing landscape in the pattern space can be viewed via the visualization tool, PROTERAN, a program we developed for this purpose. We first present an incremental (on-line) trie-based pattern discovery algorithm to elicit the patterns and then describe the terrain metaphor based visualization tool. Using two example small proteins, a beta-hairpin and a designed protein Trp-cage, we next demonstrate that this combined pattern discovery and visualization approach extracts crucial information about protein folding intermediates and mechanism.'),
(73104, 2007, 'bio', 'SNiPer-HD: improved genotype calling accuracy by an expectation-maximization algorithm for high-density SNP arrays', 'Hua, Jianping|Craig, David W|Brun, Marcel|Webster, Jennifer|Zismann, Victoria|Tembe, Waibhav|Joshipura, Keta|Huentelman, Matthew J|Dougherty, Edward R|Stephan, Dietrich A', 'Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.|Computational Biology Division Phoenix, 445 N 5th Street, Phoenix, AZ, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Computational Biology|Databases, Genetic|European Continental Ancestry Group|False Positive Reactions|Gene Expression Profiling|Genotype|Humans|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Reproducibility of Results|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl536', '17062589', 'The technology to genotype single nucleotide polymorphisms (SNPs) at extremely high densities provides for hypothesis-free genome-wide scans for common polymorphisms associated with complex disease. However, we find that some errors introduced by commonly employed genotyping algorithms may lead to inflation of false associations between markers and phenotype.'),
(73105, 2006, 'bio', 'Hypothesis testing approaches to the exon prediction problem', 'Vilardell, Mireia|Sánchez-Pla, Alex', 'Statistics Department, University of Barcelona Barcelona, Spain.|Statistics Department, University of Barcelona Barcelona, Spain.', 'Spain|Spain', 1, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Exons|Genetic Code|Models, Genetic|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl544', '17065157', 'Many gene identification methods assign scores to gene elements prior to their assembly into predicted genes. The scoring system is often based on log-likelihood ratios. These methods usually perform well but it is difficult to interpret how significant a score is.'),
(73106, 2006, 'bio', 'Kalman filtering for disease-state estimation from microarray data', 'Kelemen, János Z|Kertész-Farkas, Attila|Kocsor, András|Puskás, László G', 'Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary. kelli@nucleus.szbk.u-szeged.hu|Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary. kelli@nucleus.szbk.u-szeged.hu|Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary. kelli@nucleus.szbk.u-szeged.hu|Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary. kelli@nucleus.szbk.u-szeged.hu', 'Hungary|Hungary|Hungary|Hungary', 0, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Systems Theory', 'Journal Article', '10.1093/bioinformatics/btl545', '17065158', 'In this paper, we propose using the Kalman filter (KF) as a pre-processing step in microarray-based molecular diagnosis. Incorporating the expression covariance between genes is important in such classification problems, since this represents the functional relationships that govern tissue state. Failing to fulfil such requirements may result in biologically implausible class prediction models. Here, we show that employing the KF to remove noise (while retaining meaningful covariance and thus being able to estimate the underlying biological state from microarray measurements) yields linearly separable data suitable for most classification algorithms.'),
(73107, 2006, 'bio', 'MUSA: a parameter free algorithm for the identification of biologically significant motifs', 'Mendes, Nuno D|Casimiro, Ana C|Santos, Pedro M|Sá-Correia, Isabel|Oliveira, Arlindo L|Freitas, Ana T', 'INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.|INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.|INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.|INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.|INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.|INESC-ID, Instituto Superior Técnico, Rua Alves Redol 9 1000-029 Lisboa, Portugal.', 'Portugal|Portugal|Portugal|Portugal|Portugal|Portugal', 20, 'Algorithms|Amino Acid Motifs|Base Sequence|Binding Sites|Cluster Analysis|Conserved Sequence|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Amino Acid|Software|Structure-Activity Relationship|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl537', '17068086', 'The ability to identify complex motifs, i.e. non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem is extremely important, not only because these motifs can accurately model biological phenomena but because its extraction is highly dependent upon the appropriate selection of numerous search parameters. Currently available combinatorial algorithms have proved to be highly efficient in exhaustively enumerating motifs (including complex motifs), which fulfill certain extraction criteria. However, one major problem with these methods is the large number of parameters that need to be specified.'),
(73108, 2007, 'bio', 'Simulated maximum likelihood method for estimating kinetic rates in gene expression', 'Tian, Tianhai|Xu, Songlin|Gao, Junbin|Burrage, Kevin', 'Advanced Computational Modelling Centre, University of Queensland Brisbane, QLD 4072, Australia. tian@maths.uq.edu.au|Advanced Computational Modelling Centre, University of Queensland Brisbane, QLD 4072, Australia. tian@maths.uq.edu.au|Advanced Computational Modelling Centre, University of Queensland Brisbane, QLD 4072, Australia. tian@maths.uq.edu.au|Advanced Computational Modelling Centre, University of Queensland Brisbane, QLD 4072, Australia. tian@maths.uq.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation|Kinetics|Likelihood Functions|Models, Genetic|Models, Statistical|Stochastic Processes|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btl552', '17068087', 'Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment.'),
(73109, 2007, 'bio', 'Oligonucleotide fingerprint identification for microarray-based pathogen diagnostic assays', 'Tembe, Waibhav|Zavaljevski, Nela|Bode, Elizabeth|Chase, Catherine|Geyer, Jeanne|Wasieloski, Leonard|Benson, Gary|Reifman, Jaques', 'Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.|Biotechnology HPC Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Ft. Detrick, MD Boston, MA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 21, 'Algorithms|DNA Fingerprinting|DNA, Bacterial|Francisella tularensis|Oligonucleotide Array Sequence Analysis|Plasmids|Software|Software Design|Yersinia pestis', 'Journal Article', '10.1093/bioinformatics/btl549', '17068088', 'Advances in DNA microarray technology and computational methods have unlocked new opportunities to identify DNA fingerprints, i.e. oligonucleotide sequences that uniquely identify a specific genome. We present an integrated approach for the computational identification of DNA fingerprints for design of microarray-based pathogen diagnostic assays. We provide a quantifiable definition of a DNA fingerprint stated both from a computational as well as an experimental point of view, and the analytical proof that all in silico fingerprints satisfying the stated definition are found using our approach.'),
(73110, 2007, 'bio', 'Recovering metabolic pathways via optimization', 'Beasley, John E|Planes, Francisco J', 'Mathematical Sciences, Brunel University Uxbridge, UB8 3PH, UK. john.beasley@brunel.ac.uk|Mathematical Sciences, Brunel University Uxbridge, UB8 3PH, UK. john.beasley@brunel.ac.uk', 'United Kingdom|United Kingdom', 0, 'Metabolic Networks and Pathways|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btl554', '17068089', 'A metabolic pathway is a coherent set of enzyme catalysed biochemical reactions by which a living organism transforms an initial (source) compound into a final (target) compound. Some of the different metabolic pathways adopted within organisms have been experimentally determined. In this paper, we show that a number of experimentally determined metabolic pathways can be recovered by a mathematical optimization model.'),
(73111, 2006, 'bio', 'Babels tower revisited: a universal resource for cross-referencing across annotation databases', 'Drxc4x83ghici, Sorin|Sellamuthu, Sivakumar|Khatri, Purvesh', 'Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. sorin@wayne.edu|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. sorin@wayne.edu|Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. sorin@wayne.edu', 'United States|United States|United States', 26, 'Database Management Systems|Databases, Genetic|Documentation|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl372', '17068090', 'Annotation databases are widely used as public repositories of biological knowledge. However, most of these resources have been developed by independent groups which used different designs and different identifiers for the same biological entities. As we show in this article, incoherent name spaces between various databases represent a serious impediment to using the existing annotations at their full potential. Navigating between various such name spaces by mapping IDs from one database to another is a very important issue which is not properly addressed at the moment.'),
(73112, 2006, 'bio', 'Adding sequence context to a Markov background model improves the identification of regulatory elements', 'Kim, Nak-Kyeong|Tharakaraman, Kannan|Spouge, John L', 'National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States', 0, 'Algorithms|Base Sequence|Computer Simulation|DNA|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Pattern Recognition, Automated|Regulatory Elements, Transcriptional|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl528', '17068091', 'Many computational methods for identifying regulatory elements use a likelihood ratio between motif and background models. Often, the methods use a background model of independent bases. At least two different Markov background models have been proposed with the aim of increasing the accuracy of predicting regulatory elements. Both Markov background models suffer theoretical drawbacks, so this article develops a third, context-dependent Markov background model from fundamental statistical principles.'),
(73113, 2007, 'bio', 'Dependence network modeling for biomarker identification', 'Qiu, Peng|Wang, Z Jane|Liu, K J Ray|Hu, Zhang-Zhi|Wu, Cathy H', 'Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA. qiupeng@umd.edu', 'United States|United States|United States|United States|United States', 20, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Mass Spectrometry|Models, Biological|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/btl553', '17077095', 'Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.'),
(73114, 2007, 'bio', 'Simple sequence repeats in organellar genomes of rice: frequency and distribution in genic and intergenic regions', 'Rajendrakumar, Passoupathy|Biswal, Akshaya Kumar|Balachandran, Sena M|Srinivasarao, Kommoju|Sundaram, Raman M', 'Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research Rajendranagar, Hyderabad-500030, India.|Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research Rajendranagar, Hyderabad-500030, India.|Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research Rajendranagar, Hyderabad-500030, India.|Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research Rajendranagar, Hyderabad-500030, India.|Biotechnology Laboratory, Crop Improvement Section, Directorate of Rice Research Rajendranagar, Hyderabad-500030, India.', 'India|India|India|India|India', 14, 'Base Sequence|Chloroplasts|DNA, Chloroplast|DNA, Intergenic|Dinucleotide Repeats|Gene Frequency|Genetic Markers|Minisatellite Repeats|Mitochondria|Oryza|Plant Leaves', 'Comparative Study', '10.1093/bioinformatics/btl547', '17077096', 'Simple sequence repeats (SSRs) are abundant across genomes. However, the significance of SSRs in organellar genomes of rice has not been completely understood. The availability of organellar genome sequences allows us to understand the organization of SSRs in their genic and intergenic regions.'),
(73115, 2007, 'bio', 'OmicBrowse: a browser of multidimensional omics annotations', 'Toyoda, Tetsuro|Mochizuki, Yoshiki|Player, Keith|Heida, Naohiko|Kobayashi, Norio|Sakaki, Yoshiyuki', 'Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp|Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp|Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp|Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp|Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp|Genomic Sciences Center, Yokohama Institute, RIKEN, 1-77-22, Suehiro, Tsurumi, Yokohama, Kanagawa, Japan. toyop@gsc.riken.go.jp', 'Japan|Japan|Japan|Japan|Japan|Japan', 19, 'Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Documentation|Information Storage and Retrieval|Internet|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl523', '17077097', 'OmicBrowse is a browser to explore multiple datasets coordinated in the multidimensional omic space integrating omics knowledge ranging from genomes to phenomes and connecting evolutional correspondences among multiple species. OmicBrowse integrates multiple data servers into a single omic space through secure peer-to-peer server communications, so that a user can easily obtain an integrated view of distributed data servers, e.g. an integrated view of numerous whole-genome tiling-array data retrieved from a users in-house private-data server, along with various genomic annotations from public internet servers. OmicBrowse is especially appropriate for positional-cloning purposes. It displays both genetic maps and genomic annotations within wide chromosomal intervals and assists a user to select candidate genes by filtering their annotations or associated documents against user-specified keywords or ontology terms. We also show that an omic-space chart effectively represents schemes for integrating multiple datasets of multiple species.'),
(73116, 2007, 'bio', 'GECO--linear visualization for comparative genomics', 'Kuenne, C T|Ghai, R|Chakraborty, T|Hain, T', 'Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany.|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany.|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany.|Institute of Medical Microbiology, Justus-Liebig-University Frankfurter Strasse 107, D-35392 Giessen, Germany.', 'Germany|Germany|Germany|Germany', 18, 'Algorithms|Data Display|Evolution, Molecular|Genes, Bacterial|Genetics, Microbial|Information Storage and Retrieval|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl556', '17077098', 'In order to understand and interpret phylogenetic and functional relationships between multiple prokaryotic species, qualitative and quantitative data must be correlated and displayed. GECO allows linear visualization of multiple genomes using a client/server based approach by dynamically creating .png- or .pdf-formatted images. It is able to display ortholog relations calculated using BLASTCLUST by color coding ortholog representations. Irregularities on the genomic level can be identified by anomalous G/C composition. Thus, this software will enable researchers to detect horizontally transferred genes, pseudogenes and insertions/deletions in related microbial genomes.'),
(73117, 2007, 'bio', 'Towards clustering of incomplete microarray data without the use of imputation', 'Kim, Dae-Won|Lee, Ki-Young|Lee, Kwang H|Lee, Doheon', 'School of Computer Science and Engineering, Chung-Ang University, Seoul City, Republic of Korea. dwkim@cau.ac.kr|School of Computer Science and Engineering, Chung-Ang University, Seoul City, Republic of Korea. dwkim@cau.ac.kr|School of Computer Science and Engineering, Chung-Ang University, Seoul City, Republic of Korea. dwkim@cau.ac.kr|School of Computer Science and Engineering, Chung-Ang University, Seoul City, Republic of Korea. dwkim@cau.ac.kr', '-|-|-|-', 17, 'Algorithms|Cluster Analysis|Computational Biology|Fuzzy Logic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btl555', '17077099', 'Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering, badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.'),
(73118, 2007, 'bio', 'Estimating p-values in small microarray experiments', 'Yang, Hyuna|Churchill, Gary', 'The Jackson Laboratory, Bar Harbor, ME 04609, USA.|The Jackson Laboratory, Bar Harbor, ME 04609, USA.', 'United States|United States', 47, 'Algorithms|Bayes Theorem|Computer Simulation|False Positive Reactions|Gene Expression Profiling|Genetic Testing|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl548', '17077100', 'Microarray data typically have small numbers of observations per gene, which can result in low power for statistical tests. Test statistics that borrow information from data across all of the genes can improve power, but these statistics have non-standard distributions, and their significance must be assessed using permutation analysis. When sample sizes are small, the number of distinct permutations can be severely limited, and pooling the permutation-derived test statistics across all genes has been proposed. However, the null distribution of the test statistics under permutation is not the same for equally and differentially expressed genes. This can have a negative impact on both p-value estimation and the power of information borrowing statistics.'),
(73119, 2006, 'bio', 'Application of a simple likelihood ratio approximant to protein sequence classification', 'Kaján, László|Kertész-Farkas, Attila|Franklin, Dino|Ivanova, Neli|Kocsor, András|Pongor, Sándor', 'Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.|Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.|Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.|Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.|Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.|Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology Padriciano 99, I-34012 Trieste, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Likelihood Functions|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl512', '17090576', 'Likelihood ratio approximants (LRA) have been widely used for model comparison in statistics. The present study was undertaken in order to explore their utility as a scoring (ranking) function in the classification of protein sequences.'),
(73120, 2007, 'bio', 'In silico grouping of peptide/HLA class I complexes using structural interaction characteristics', 'Tong, Joo Chuan|Tan, Tin Wee|Ranganathan, Shoba', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597.', 'Singapore|Singapore|Singapore', 9, 'Alleles|Artificial Intelligence|Binding Sites|Computer Simulation|Epitope Mapping|Histocompatibility Antigens Class I|Models, Chemical|Models, Immunological|Multigene Family|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl563', '17090577', 'Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored.'),
(73121, 2007, 'bio', 'Computing the maximum similarity bi-clusters of gene expression data', 'Liu, Xiaowen|Wang, Lusheng', 'Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.|Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.', 'Hong Kong|Hong Kong', 0, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Databases, Nucleic Acid|Female|Gene Expression Profiling|Humans|Models, Genetic|Models, Statistical', 'Journal Article', '10.1093/bioinformatics/btl560', '17090578', 'Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions.'),
(73122, 2007, 'bio', 'ECRbase: database of evolutionary conserved regions, promoters, and transcription factor binding sites in vertebrate genomes', 'Loots, Gabriela|Ovcharenko, Ivan', 'CMLS 7000 East Avenue, Livermore, CA, 94550, USA.|CMLS 7000 East Avenue, Livermore, CA, 94550, USA.', 'United States|United States', 74, 'Animals|Anura|Binding Sites|Chickens|Conserved Sequence|Databases, Factual|Dogs|Evolution, Molecular|Genome Components|Humans|Macaca mulatta|Mice|Opossums|Promoter Regions, Genetic|Rats|Species Specificity|Takifugu|Transcription Factors|Zebrafish', 'Journal Article', '10.1093/bioinformatics/btl546', '17090579', 'Evolutionary conservation of DNA sequences provides a tool for the identification of functional elements in genomes. We have created a database of evolutionary conserved regions (ECRs) in vertebrate genomes, entitled ECRbase, which is constructed from a collection of whole-genome alignments produced by the ECR Browser. ECRbase features a database of syntenic blocks that recapitulate the evolution of rearrangements in vertebrates and a comprehensive collection of promoters in all vertebrate genomes generated using multiple sources of gene annotation. The database also contains a collection of annotated transcription factor binding sites (TFBSs) in evolutionary conserved and promoter elements. ECRbase currently includes human, rhesus macaque, dog, opossum, rat, mouse, chicken, frog, zebrafish and fugu genomes. It is freely accessible at http://ecrbase.dcode.org.'),
(73123, 2007, 'bio', 'Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes', 'Barker, Daniel|Meade, Andrew|Pagel, Mark', 'Sir Harold Mitchell Building, School of Biology, University of St Andrews St Andrews, Fife, KY16 9TH, UK.|Sir Harold Mitchell Building, School of Biology, University of St Andrews St Andrews, Fife, KY16 9TH, UK.|Sir Harold Mitchell Building, School of Biology, University of St Andrews St Andrews, Fife, KY16 9TH, UK.', 'United Kingdom|United Kingdom|United Kingdom', 63, 'Algorithms|Allelic Imbalance|Animals|Computational Biology|Evolution, Molecular|Genetic Linkage|Likelihood Functions|Models, Genetic|Models, Statistical|Phylogeny|Saccharomyces cerevisiae|Sequence Homology|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/btl558', '17090580', 'We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data.'),
(73124, 2007, 'bio', 'An RNA conformational shift in recent H5N1 influenza A viruses', 'Gultyaev, Alexander P|Heus, Hans A|Olsthoorn, René C L', 'Leiden Institute of Biology, Leiden University, P.O.Box 9516, 2300 RA Leiden.|Leiden Institute of Biology, Leiden University, P.O.Box 9516, 2300 RA Leiden.|Leiden Institute of Biology, Leiden University, P.O.Box 9516, 2300 RA Leiden.', '-|-|-', 28, 'Base Sequence|Evolution, Molecular|Genetic Variation|Influenza A Virus, H5N1 Subtype|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA, Viral|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl559', '17090581', 'Recent outbreaks of avian influenza are being caused by unusually virulent H5N1 strains. It is unknown what makes these recent H5N1 strains more aggressive than previously circulating strains. Here, we have compared more than 3000 RNA sequences of segment 8 of type A influenza viruses and found a unique single nucleotide substitution typically associated with recent H5N1 strains. By phylogenetic analysis, biochemical and biophysical experiments, we demonstrate that this substitution dramatically affects the equilibrium between a hairpin and a pseudoknot conformation near the 3 splice-site of the NS gene. This conformational shift may have consequences for splicing regulation of segment 8 mRNA. Our data suggest that besides changes at the protein level, changes in RNA secondary structure should be seriously considered when attempting to explain influenza virus evolution.'),
(73125, 2007, 'bio', 'Neural network prediction of peptide separation in strong anion exchange chromatography', 'Oh, Cheolhwan|Zak, Stanislaw H|Mirzaei, Hamid|Buck, Charles|Regnier, Fred E|Zhang, Xiang', 'Bindley Bioscience Center, West Lafayette, IN 47907, USA.|Bindley Bioscience Center, West Lafayette, IN 47907, USA.|Bindley Bioscience Center, West Lafayette, IN 47907, USA.|Bindley Bioscience Center, West Lafayette, IN 47907, USA.|Bindley Bioscience Center, West Lafayette, IN 47907, USA.|Bindley Bioscience Center, West Lafayette, IN 47907, USA.', 'United States|United States|United States|United States|United States|United States', 12, 'Algorithms|Animals|Bacterial Proteins|Cattle|Chickens|Chromatography, Ion Exchange|Horses|Humans|Models, Molecular|Neural Networks (Computer)|Peptide Mapping|Peptides|Proteomics|Rabbits', 'Journal Article', '10.1093/bioinformatics/btl561', '17092987', 'The still emerging combination of technologies that enable description and characterization of all expressed proteins in a biological system is known as proteomics. Although many separation and analysis technologies have been employed in proteomics, it remains a challenge to predict peptide behavior during separation processes. New informatics tools are needed to model the experimental analysis method that will allow scientists to predict peptide separation and assist with required data mining steps, such as protein identification.'),
(73126, 2007, 'bio', 'AGScan: a pluggable microarray image quantification software based on the ImageJ library', 'Cathelin, R|Lopez, F|Klopp, Ch', 'INRA, Sigenae UR875 Biométrie et Intelligence Artificielle/Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan Cedex, France.|INRA, Sigenae UR875 Biométrie et Intelligence Artificielle/Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan Cedex, France.|INRA, Sigenae UR875 Biométrie et Intelligence Artificielle/Génétique Cellulaire, BP 52627, 31326 Castanet-Tolosan Cedex, France.', 'France|France|France', 0, 'Algorithms|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl564', '17092988', 'Many different programs are available to analyze microarray images. Most programs are commercial packages, some are free. In the latter group only few propose automatic grid alignment and batch mode. More often than not a program implements only one quantification algorithm. AGScan is an open source program that works on all major platforms. It is based on the ImageJ library [Rasband (1997-2006)] and offers a plug-in extension system to add new functions to manipulate images, align grid and quantify spots. It is appropriate for daily laboratory use and also as a framework for new algorithms.'),
(73127, 2007, 'bio', 'Inferential, robust non-negative matrix factorization analysis of microarray data', 'Fogel, Paul|Young, S Stanley|Hawkins, Douglas M|Ledirac, Nathalie', 'Consultant 4 rue Le Goff, F-75005, Paris, France.|Consultant 4 rue Le Goff, F-75005, Paris, France.|Consultant 4 rue Le Goff, F-75005, Paris, France.|Consultant 4 rue Le Goff, F-75005, Paris, France.', 'France|France|France|France', 47, 'Algorithms|Analysis of Variance|Computational Biology|Databases, Genetic|Gene Expression Regulation, Leukemic|Humans|Leukemia, Myeloid|Models, Genetic|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Precursor Cell Lymphoblastic Leukemia-Lymphoma|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl550', '17092989', 'Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory, even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis. We exploit two ideas: non-negative matrix factorization methods that create ordered sets of predictors, and statistical testing within ordered sets which is done sequentially, removing the need for correction for multiple testing within the set.'),
(73128, 2007, 'bio', 'Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions', 'Chung, Yujin|Lee, Seung Yeoun|Elston, Robert C|Park, Taesung', 'Department of Statistics, Seoul National University San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea.|Department of Statistics, Seoul National University San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea.|Department of Statistics, Seoul National University San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea.|Department of Statistics, Seoul National University San 56-1 Shillim-Dong, Kwanak-Gu, Seoul 151-747, Korea.', '-|-|-|-', 112, 'Fatigue Syndrome, Chronic|Genetic Variation|Humans|Membrane Transport Proteins|Models, Genetic|Models, Statistical|Odds Ratio|Polymorphism, Genetic|Protein Interaction Mapping|Risk Assessment|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl557', '17092990', 'The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk.'),
(73129, 2007, 'bio', 'Modular organization of protein interaction networks', 'Luo, Feng|Yang, Yunfeng|Chen, Chin-Fu|Chang, Roger|Zhou, Jizhong|Scheuermann, Richard H', 'Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu|Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu|Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu|Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu|Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu|Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA. luofeng@cs.clemson.edu', 'United States|United States|United States|United States|United States|United States', 132, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl562', '17092991', 'Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems.'),
(73130, 2006, 'bio', 'Pattern locator: a new tool for finding local sequence patterns in genomic DNA sequences', 'Mrázek, Jan|Xie, Shaohua', 'Department of Microbiology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. mrazek@uga.edu|Department of Microbiology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. mrazek@uga.edu', 'Georgia|Georgia', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btl551', '17095514', 'We present a new tool for finding local sequence patterns in long DNA sequences. The program, Pattern Locator, uses an intuitive syntax for pattern description, and provides more flexibility than existing programs by allowing combinations of specific nucleotide sequences, direct and inverted repeats, variable length tandem repeats of subpatterns, and a specified number of errors in any part of the pattern.'),
(73131, 2007, 'bio', 'CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data', 'Ma, Xiaotu|Lee, Hyunju|Wang, Li|Sun, Fengzhu', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl569', '17098772', 'Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem.');
