INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92242, 2008, 'bmc', 'AMYPdb: a database dedicated to amyloid precursor proteins', 'Pawlicki, Sandrine|Le Béchec, Antony|Delamarche, Christian', 'Université de Rennes I and CNRS UMR 6026, Equipe Structure et Dynamique des Macromolécules, Campus de Beaulieu, Nb 13, 35042 RENNES Cedex, France. sandrine.pawlicki@laposte.net|Université de Rennes I and CNRS UMR 6026, Equipe Structure et Dynamique des Macromolécules, Campus de Beaulieu, Nb 13, 35042 RENNES Cedex, France. sandrine.pawlicki@laposte.net|Université de Rennes I and CNRS UMR 6026, Equipe Structure et Dynamique des Macromolécules, Campus de Beaulieu, Nb 13, 35042 RENNES Cedex, France. sandrine.pawlicki@laposte.net', 'France|France|France', 0, 'Amino Acid Sequence|Amyloid beta-Protein Precursor|Animals|Central Nervous System Diseases|Databases, Protein|Diabetes Complications|Humans|Molecular Sequence Data|Protein Folding|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-9-273', '18544157', 'Misfolding and aggregation of proteins into ordered fibrillar structures is associated with a number of severe pathologies, including Alzheimers disease, prion diseases, and type II diabetes. The rapid accumulation of knowledge about the sequences and structures of these proteins allows using of in silico methods to investigate the molecular mechanisms of their abnormal conformational changes and assembly. However, such an approach requires the collection of accurate data, which are inconveniently dispersed among several generalist databases.'),
(92243, 2008, 'bmc', 'A tale of two symmetrical tails: structural and functional characteristics of palindromes in proteins', 'Sheari, Armita|Kargar, Mehdi|Katanforoush, Ali|Arab, Shahriar|Sadeghi, Mehdi|Pezeshk, Hamid|Eslahchi, Changiz|Marashi, Sayed-Amir', 'Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir|Bioinformatics Group, School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran. sheari@mail.ipm.ir', '-|-|-|-|-|-|-|-', 17, 'Amino Acid Sequence|Amino Acids|Binding Sites|Computational Biology|Conserved Sequence|Databases, Protein|Evolution, Molecular|Pattern Recognition, Automated|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Statistics, Nonparametric|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-274', '18547401', 'It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete.'),
(92244, 2008, 'bmc', 'Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles', 'Ge, Guangtao|Wong, G William', 'Department of Computer Science, Tufts University, Medford, MA 02155, USA. guge@eecs.tufts.edu|Department of Computer Science, Tufts University, Medford, MA 02155, USA. guge@eecs.tufts.edu', 'United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Decision Support Techniques|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Mass Spectrometry|Neoplasm Proteins|Pancreatic Neoplasms|Precancerous Conditions|Prognosis', 'Journal Article', '10.1186/1471-2105-9-275', '18547427', 'Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer.'),
(92245, 2008, 'bmc', 'Discovering functional interaction patterns in protein-protein interaction networks', 'Turanalp, Mehmet E|Can, Tolga', 'Department of Computer Engineering, Selcuk University, Alaaddin Keykubat Kampusu, 42075 Selcuklu, Konya, Turkey. eturanalp@selcuk.edu.tr|Department of Computer Engineering, Selcuk University, Alaaddin Keykubat Kampusu, 42075 Selcuklu, Konya, Turkey. eturanalp@selcuk.edu.tr', 'Turkey|Turkey', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-276', '18547430', 'In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks.'),
(92246, 2008, 'bmc', 'Semantic role labeling for protein transport predicates', 'Bethard, Steven|Lu, Zhiyong|Martin, James H|Hunter, Lawrence', 'Computer Science Department, University of Colorado at Boulder, Boulder, CO, USA. steven.bethard@colorado.edu|Computer Science Department, University of Colorado at Boulder, Boulder, CO, USA. steven.bethard@colorado.edu|Computer Science Department, University of Colorado at Boulder, Boulder, CO, USA. steven.bethard@colorado.edu|Computer Science Department, University of Colorado at Boulder, Boulder, CO, USA. steven.bethard@colorado.edu', 'United States|United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Animals|Classification|Computational Biology|Genes|Humans|Information Storage and Retrieval|Information Theory|Natural Language Processing|Pattern Recognition, Automated|Protein Transport|Semantics|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-277', '18547432', 'Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs - manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role.'),
(92247, 2008, 'bmc', 'Scipio: using protein sequences to determine the precise exon/intron structures of genes and their orthologs in closely related species', 'Keller, Oliver|Odronitz, Florian|Stanke, Mario|Kollmar, Martin|Waack, Stephan', 'Universität Göttingen, Institut für Informatik, Lotzestr. 16-18, 37083 Göttingen, Germany. keller@cs.uni-goettingen.de|Universität Göttingen, Institut für Informatik, Lotzestr. 16-18, 37083 Göttingen, Germany. keller@cs.uni-goettingen.de|Universität Göttingen, Institut für Informatik, Lotzestr. 16-18, 37083 Göttingen, Germany. keller@cs.uni-goettingen.de|Universität Göttingen, Institut für Informatik, Lotzestr. 16-18, 37083 Göttingen, Germany. keller@cs.uni-goettingen.de|Universität Göttingen, Institut für Informatik, Lotzestr. 16-18, 37083 Göttingen, Germany. keller@cs.uni-goettingen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Exons|Introns|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-278', '18554390', 'For many types of analyses, data about gene structure and locations of non-coding regions of genes are required. Although a vast amount of genomic sequence data is available, precise annotation of genes is lacking behind. Finding the corresponding gene of a given protein sequence by means of conventional tools is error prone, and cannot be completed without manual inspection, which is time consuming and requires considerable experience.'),
(92248, 2008, 'bmc', 'Identifying a few foot-and-mouth disease virus signature nucleotide strings for computational genotyping', 'Lin, Guohui|Cai, Zhipeng|Wu, Junfeng|Wan, Xiu-Feng|Xu, Lizhe|Goebel, Randy', 'Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca|Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. ghlin@cs.ualberta.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 6, 'Base Sequence|DNA, Viral|Foot-and-Mouth Disease Virus|Genotype|Molecular Sequence Data|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1186/1471-2105-9-279', '18554404', 'Serotypes of the Foot-and-Mouth disease viruses (FMDVs) were generally determined by biological experiments. The computational genotyping is not well studied even with the availability of whole viral genomes, due to uneven evolution among genes as well as frequent genetic recombination. Naively using sequence comparison for genotyping is only able to achieve a limited extent of success.'),
(92249, 2008, 'bmc', 'Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification', 'Zhu, Manli|Martinez, Aleix M', 'Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA. zhum.osu@gmail.com|Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA. zhum.osu@gmail.com', 'United States|United States', 5, 'Algorithms|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-280', '18554411', 'Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself.'),
(92250, 2008, 'bmc', 'PURE: a webserver for the prediction of domains in unassigned regions in proteins', 'Reddy, Chilamakuri C S|Shameer, Khader|Offmann, Bernard O|Sowdhamini, Ramanathan', 'National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore 560 065, India. csreddy@ncbs.res.in|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore 560 065, India. csreddy@ncbs.res.in|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore 560 065, India. csreddy@ncbs.res.in|National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore 560 065, India. csreddy@ncbs.res.in', 'India|India|India|India', 0, 'Adenylyl Cyclases|Amino Acid Motifs|Animals|Cluster Analysis|Computational Biology|Databases, Protein|Humans|Mycoplasma gallisepticum|Pattern Recognition, Automated|Protein Structure, Tertiary|Sequence Analysis, Protein|Software|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-9-281', '18554415', 'Protein domains are the structural and functional units of proteins. The ability to parse proteins into different domains is important for effective classification, understanding of protein structure, function, and evolution and is hence biologically relevant. Several computational methods are available to identify domains in the sequence. Domain finding algorithms often employ stringent thresholds to recognize sequence domains. Identification of additional domains can be tedious involving intense computation and manual intervention but can lead to better understanding of overall biological function. In this context, the problem of identifying new domains in the unassigned regions of a protein sequence assumes a crucial importance.'),
(92251, 2008, 'bmc', 'The combination approach of SVM and ECOC for powerful identification and classification of transcription factor', 'Zheng, Guangyong|Qian, Ziliang|Yang, Qing|Wei, Chaochun|Xie, Lu|Zhu, Yangyong|Li, Yixue', 'Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn|Department of Computing and Information Technology, Fudan University, 220 Handan Road, Shanghai 200433, PR China. zhenggy@sibs.ac.cn', 'China|China|China|China|China|China|China', 25, 'Algorithms|Artificial Intelligence|Base Sequence|Molecular Sequence Data|Open Reading Frames|Pattern Recognition, Automated|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-282', '18554421', 'Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.'),
(92252, 2008, 'bmc', 'Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions', 'Kim, Ki-Yeol|Ki, Dong Hyuk|Jeung, Hei-Cheul|Chung, Hyun Cheol|Rha, Sun Young', 'Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, South Korea. kky1004@yuhs.ac|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, South Korea. kky1004@yuhs.ac|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, South Korea. kky1004@yuhs.ac|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, South Korea. kky1004@yuhs.ac|Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, South Korea. kky1004@yuhs.ac', '-|-|-|-|-', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Proteome|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-283', '18554423', 'The information from different data sets experimented under different conditions may be inconsistent even though they are performed with the same research objectives. More than that, even when the data sets were generated from the same platform, the data agreement may be affected by the technical variation among the laboratories. In this case, it is necessary to use the combined data set after adjusting the differences between such data sets, for detecting the more reliable information.'),
(92253, 2008, 'bmc', 'Methods for evaluating gene expression from Affymetrix microarray datasets', 'Jiang, Ning|Leach, Lindsey J|Hu, Xiaohua|Potokina, Elena|Jia, Tianye|Druka, Arnis|Waugh, Robbie|Kearsey, Michael J|Luo, Zewei W', 'School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk|School of Biosciences, The University of Birmingham, Edgbaston Birmingham B15 2TT, England, UK. nxj677@bham.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Genetic|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Proteome|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-284', '18559105', 'Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.'),
(92254, 2008, 'bmc', 'Inferring modules of functionally interacting proteins using the Bond Energy Algorithm', 'Watanabe, Ryosuke L A|Morett, Enrique|Vallejo, Edgar E', 'ITESM Campus Estado de México, Carretera Lago de Guadalupe km 3,5, Atizapán de Zaragoza, 52926, México. A00455731@itesm.mx|ITESM Campus Estado de México, Carretera Lago de Guadalupe km 3,5, Atizapán de Zaragoza, 52926, México. A00455731@itesm.mx|ITESM Campus Estado de México, Carretera Lago de Guadalupe km 3,5, Atizapán de Zaragoza, 52926, México. A00455731@itesm.mx', '-|-|-', 0, 'Algorithms|Animals|Cell Physiological Phenomena|Cluster Analysis|Computational Biology|Databases, Genetic|Escherichia coli|Evolution, Molecular|Humans|Multigene Family|Pattern Recognition, Automated|Phylogeny|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1186/1471-2105-9-285', '18559112', 'Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required. In this paper, we used the Bond Energy Algorithm (BEA) to predict functionally related groups of proteins. With BEA we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.'),
(92255, 2008, 'bmc', 'Precise detection of rearrangement breakpoints in mammalian chromosomes', 'Lemaitre, Claire|Tannier, Eric|Gautier, Christian|Sagot, Marie-France', 'Université de Lyon, F-69000, Lyon, France. clemaitr@biomserv.univ-lyon1.fr|Université de Lyon, F-69000, Lyon, France. clemaitr@biomserv.univ-lyon1.fr|Université de Lyon, F-69000, Lyon, France. clemaitr@biomserv.univ-lyon1.fr|Université de Lyon, F-69000, Lyon, France. clemaitr@biomserv.univ-lyon1.fr', 'France|France|France|France', 32, 'Animals|Chromosome Breakage|Chromosomes, Mammalian|Dogs|Genome|Genome, Human|Humans|Mice|Sequence Homology, Nucleic Acid|Synteny', 'Comparative Study', '10.1186/1471-2105-9-286', '18564416', 'Genomes undergo large structural changes that alter their organisation. The chromosomal regions affected by these rearrangements are called breakpoints, while those which have not been rearranged are called synteny blocks. We developed a method to precisely delimit rearrangement breakpoints on a genome by comparison with the genome of a related species. Contrary to current methods which search for synteny blocks and simply return what remains in the genome as breakpoints, we propose to go further and to investigate the breakpoints themselves in order to refine them.'),
(92256, 2008, 'bmc', 'Partial mixture model for tight clustering of gene expression time-course', 'Yuan, Yinyin|Li, Chang-Tsun|Wilson, Roland', 'Department of Computer Science, University of Warwick, Coventry, UK. yina@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. yina@dcs.warwick.ac.uk|Department of Computer Science, University of Warwick, Coventry, UK. yina@dcs.warwick.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Cell Cycle|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression|Gene Expression Profiling|Genes, Fungal|Likelihood Functions|Models, Genetic|Multigene Family|Neural Networks (Computer)|Regression Analysis|Saccharomyces cerevisiae|Time Factors', 'Journal Article', '10.1186/1471-2105-9-287', '18564420', 'Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters. However, in the literature there is little work dedicated to this area of research. On the other hand, there has been extensive use of maximum likelihood techniques for model parameter estimation. By contrast, the minimum distance estimator has been largely ignored.'),
(92257, 2008, 'bmc', 'Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient', 'Yao, Jianchao|Chang, Chunqi|Salmi, Mari L|Hung, Yeung Sam|Loraine, Ann|Roux, Stanley J', 'Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu|Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu|Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu|Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu|Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu|Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA. jcyao@mail.utexas.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Confidence Intervals|Gene Expression|Gene Expression Profiling|Genomics|Germination|Oligonucleotide Array Sequence Analysis|Probability|Pteridaceae|Research Design|Saccharomyces cerevisiae', 'Journal Article', '10.1186/1471-2105-9-288', '18564431', 'Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data.'),
(92258, 2008, 'bmc', 'Stability of gene contributions and identification of outliers in multivariate analysis of microarray data', 'Baty, Florent|Jaeger, Daniel|Preiswerk, Frank|Schumacher, Martin M|Brutsche, Martin H', 'Pulmonary Gene Research, University Hospital Basel, Petersgraben 4, Basel, Switzerland. florent.baty@unibas.ch|Pulmonary Gene Research, University Hospital Basel, Petersgraben 4, Basel, Switzerland. florent.baty@unibas.ch|Pulmonary Gene Research, University Hospital Basel, Petersgraben 4, Basel, Switzerland. florent.baty@unibas.ch|Pulmonary Gene Research, University Hospital Basel, Petersgraben 4, Basel, Switzerland. florent.baty@unibas.ch|Pulmonary Gene Research, University Hospital Basel, Petersgraben 4, Basel, Switzerland. florent.baty@unibas.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Algorithms|Gene Expression Profiling|Genomic Instability|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Proteome', 'Journal Article', '10.1186/1471-2105-9-289', '18570644', 'Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes.'),
(92259, 2008, 'bmc', 'SNPAnalyzer 20: a web-based integrated workbench for linkage disequilibrium analysis and association analysis', 'Yoo, Jinho|Lee, Youngbok|Kim, Yujung|Rha, Sun Young|Kim, Yangseok', 'Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea. jino12@yonsei.ac.kr|Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea. jino12@yonsei.ac.kr|Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea. jino12@yonsei.ac.kr|Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea. jino12@yonsei.ac.kr|Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea. jino12@yonsei.ac.kr', '-|-|-|-|-', 79, 'Algorithms|Data Interpretation, Statistical|Haplotypes|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-9-290', '18570686', 'Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly.'),
(92260, 2008, 'bmc', 'Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease', 'Jelier, Rob|t Hoen, Peter A C|Sterrenburg, Ellen|den Dunnen, Johan T|van Ommen, Gert-Jan B|Kors, Jan A|Mons, Barend', 'Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl|Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. r.jelier@erasmusmc.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 0, 'Animals|Artificial Intelligence|Cluster Analysis|Gene Expression Profiling|Humans|MEDLINE|Meta-Analysis as Topic|Models, Animal|Muscle Development|Muscular Diseases|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Publications|Reproducibility of Results|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-291', '18577208', 'Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO) consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis) to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO.'),
(92261, 2008, 'bmc', 'Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models', 'Liu, Dawei|Ghosh, Debashis|Lin, Xihong', 'Center for Statistical Sciences, Brown University, Providence, RI 02912, USA. daweiliu@stat.brown.edu|Center for Statistical Sciences, Brown University, Providence, RI 02912, USA. daweiliu@stat.brown.edu|Center for Statistical Sciences, Brown University, Providence, RI 02912, USA. daweiliu@stat.brown.edu', 'United States|United States|United States', 113, 'Artificial Intelligence|Biometry|Feedback|Gene Expression Profiling|Genes|Humans|Linear Models|Logistic Models|Male|Models, Genetic|Prostatic Neoplasms|Research Design|Risk Assessment|Software|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-9-292', '18577223', 'Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.'),
(92262, 2008, 'bmc', 'Indel PDB: a database of structural insertions and deletions derived from sequence alignments of closely related proteins', 'Hsing, Michael|Cherkasov, Artem', 'Bioinformatics Graduate Program, Faculty of Graduate Studies, University of British Columbia, 100-570 West 7th Avenue, Vancouver, BC V5T 4S6, Canada. mhsing@interchange.ubc.ca|Bioinformatics Graduate Program, Faculty of Graduate Studies, University of British Columbia, 100-570 West 7th Avenue, Vancouver, BC V5T 4S6, Canada. mhsing@interchange.ubc.ca', 'Canada|Canada', 0, 'Amino Acid Sequence|Binding Sites|Databases, Protein|Mutagenesis, Insertional|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Deletion|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-293', '18578882', 'Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown.'),
(92263, 2008, 'bmc', 'Relating gene expression data on two-component systems to functional annotations in Escherichia coli', 'Denton, Anne M|Wu, Jianfei|Townsend, Megan K|Sule, Preeti|Prüss, Birgit M', 'Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu|Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu|Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu|Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu|Department of Computer Science and Operations Research, North Dakota State University, Fargo, ND 58105, USA. anne.denton@ndsu.edu', 'United States|United States|United States|United States|United States', 0, 'Adaptation, Biological|Algorithms|Artificial Intelligence|Chi-Square Distribution|Databases, Genetic|Escherichia coli|Gene Expression|Gene Expression Profiling|Gene Expression Regulation, Bacterial|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Protein Kinases|Signal Transduction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-294', '18578884', 'Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionally, this has been done in two consecutive steps. The first step identifies important genes through clustering or statistical techniques, while the second step assigns biological functions to the identified groups. Recently, techniques have been developed that identify such relationships in a single step.'),
(92264, 2008, 'bmc', 'Systems biology driven software design for the research enterprise', 'Boyle, John|Cavnor, Christopher|Killcoyne, Sarah|Shmulevich, Ilya', 'Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org|Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA. jboyle@systemsbiology.org', 'United States|United States|United States|United States', 16, 'Biomedical Research|Information Storage and Retrieval|Internet|Software Design|Systems Biology|Systems Integration|Technology Transfer', 'Journal Article', '10.1186/1471-2105-9-295', '18578887', 'In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.'),
(92265, 2008, 'bmc', 'Re-searcher: a system for recurrent detection of homologous protein sequences', 'Repsys, Valdemaras|Margelevicius, Mindaugas|Venclovas, Ceslovas', 'Institute of Biotechnology, Graicixc5xabno 8, LT-02241 Vilnius, Lithuania. valdemaras@gmail.com|Institute of Biotechnology, Graicixc5xabno 8, LT-02241 Vilnius, Lithuania. valdemaras@gmail.com|Institute of Biotechnology, Graicixc5xabno 8, LT-02241 Vilnius, Lithuania. valdemaras@gmail.com', 'Lithuania|Lithuania|Lithuania', 0, 'Animals|Databases, Protein|Humans|Proteins|Sequence Analysis, Protein|Sequence Homology|Software', 'Journal Article', '10.1186/1471-2105-9-296', '18588692', 'Sequence searches are routinely employed to detect and annotate related proteins. However, a rapid growth of databases necessitates a frequent repetition of sequence searches and subsequent analysis of obtained results. Although there are several automatic systems available for executing periodical sequence searches and reporting results, they all suffer either from a lack of sensitivity, restrictive database choice or limited flexibility in setting up search strategies. Here, a new sequence search and reporting software package designed to address these shortcomings is described.'),
(92266, 2008, 'bmc', 'Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information', 'Hu, Jing|Yan, Changhui', 'Department of Computer Science, Utah State University, Logan, UT 84322, USA. jinghu@cc.usu.edu|Department of Computer Science, Utah State University, Logan, UT 84322, USA. jinghu@cc.usu.edu', 'United States|United States', 0, 'Animals|Computational Biology|Humans|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-297', '18588693', 'As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.'),
(92267, 2008, 'bmc', 'Application of nonnegative matrix factorization to improve profile-profile alignment features for fold recognition and remote homolog detection', 'Jung, Inkyung|Lee, Jaehyung|Lee, Soo-Young|Kim, Dongsup', 'Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, South Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, South Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, South Korea. snowdrop83@gmail.com|Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, South Korea. snowdrop83@gmail.com', '-|-|-|-', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Databases, Protein|Likelihood Functions|Linear Models|Multivariate Analysis|Pattern Recognition, Automated|Protein Folding|Protein Structure, Secondary|Proteins|ROC Curve|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1186/1471-2105-9-298', '18590572', 'Nonnegative matrix factorization (NMF) is a feature extraction method that has the property of intuitive part-based representation of the original features. This unique ability makes NMF a potentially promising method for biological sequence analysis. Here, we apply NMF to fold recognition and remote homolog detection problems. Recent studies have shown that combining support vector machines (SVM) with profile-profile alignments improves performance of fold recognition and remote homolog detection remarkably. However, it is not clear which parts of sequences are essential for the performance improvement.'),
(92268, 2008, 'bmc', 'PseudoGeneQuest - service for identification of different pseudogene types in the human genome', 'Ortutay, Csaba|Vihinen, Mauno', 'Institute of Medical Technology, University of Tampere, FI-33014 Tampere, Finland. csaba.ortutay@uta.fi|Institute of Medical Technology, University of Tampere, FI-33014 Tampere, Finland. csaba.ortutay@uta.fi', 'Finland|Finland', 0, 'Computational Biology|Databases, Genetic|Evolution, Molecular|Genome, Human|Humans|Internet|Pseudogenes', 'Journal Article', '10.1186/1471-2105-9-299', '18597685', 'Pseudogenes, nonfunctional copies of genes, evolve fast due the lack of evolutionary pressures and thus appear in several different forms. PseudoGeneQuest is an online tool to search the human genome for a given query sequence and to identify different types of pseudogenes as well as novel genes and gene fragments.'),
(92269, 2008, 'bmc', 'A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data', 'Huang, Liping|Zhu, Wenying|Saunders, Christopher P|Macleod, James N|Zhou, Mai|Stromberg, Arnold J|Bathke, Arne C', 'Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu|Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40508-0027, USA. liping@ms.uky.edu', 'United States|United States|United States|United States|United States|United States|United States', 9, 'Animals|Biomarkers|Cartilage|Databases, Genetic|Gene Expression|Gene Expression Profiling|Horses|Linear Models|Oligonucleotide Array Sequence Analysis|Research Design|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-9-300', '18597687', 'Identification of biomarkers among thousands of genes arrayed for disease classification has been the subject of considerable research in recent years. These studies have focused on disease classification, comparing experimental groups of effected to normal patients. Related experiments can be done to identify tissue-restricted biomarkers, genes with a high level of expression in one tissue compared to other tissue types in the body.'),
(92270, 2008, 'bmc', 'Deducing topology of protein-protein interaction networks from experimentally measured sub-networks', 'Yang, Ling|Vondriska, Thomas M|Han, Zhangang|Maclellan, W Robb|Weiss, James N|Qu, Zhilin', 'Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu|Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu|Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu|Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu|Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu|Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California 90095, USA . lyang@mednet.ucla.edu', '-|-|-|-|-|-', 3, 'Animals|Cell Physiological Phenomena|Cluster Analysis|Computer Simulation|Databases, Protein|Humans|Neural Networks (Computer)|Protein Interaction Mapping|Proteomics|Sample Size|Systems Theory|Two-Hybrid System Techniques', 'Journal Article', '10.1186/1471-2105-9-301', '18598366', 'Protein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear.'),
(92271, 2008, 'bmc', '2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments', 'Allmer, Jens|Kuhlgert, Sebastian|Hippler, Michael', 'Institute for Plant Biochemistry and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, Germany. jens@allmer.de|Institute for Plant Biochemistry and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, Germany. jens@allmer.de|Institute for Plant Biochemistry and Biotechnology, University of Münster, Hindenburgplatz 55, Münster, Germany. jens@allmer.de', 'Germany|Germany|Germany', 0, 'Abstracting and Indexing as Topic|Algorithms|Animals|Artificial Intelligence|Data Compression|Database Management Systems|Databases, Protein|Humans|Mass Spectrometry|Peptide Mapping|Proteomics|Research Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-302', '18605993', 'The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed.'),
(92272, 2008, 'bmc', 'A unified approach to false discovery rate estimation', 'Strimmer, Korbinian', 'Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr, 16-18, 04107 Leipzig, Germany. strimmer@uni-leipzig.de', 'Germany', 243, 'Algorithms|Biometry|Breast Neoplasms|Confidence Intervals|Female|HIV|Humans|Likelihood Functions|Models, Statistical|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Sample Size|Software', 'Journal Article', '10.1186/1471-2105-9-303', '18613966', 'False discovery rate (FDR) methods play an important role in analyzing high-dimensional data. There are two types of FDR, tail area-based FDR and local FDR, as well as numerous statistical algorithms for estimating or controlling FDR. These differ in terms of underlying test statistics and procedures employed for statistical learning.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92273, 2008, 'bmc', 'Investigating selection on viruses: a statistical alignment approach', 'de Groot, Saskia|Mailund, Thomas|Lunter, Gerton|Hein, Jotun', 'Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, UK. sassysas@gmail.com|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, UK. sassysas@gmail.com|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, UK. sassysas@gmail.com|Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, UK. sassysas@gmail.com', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Biometry|Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Genome, Viral|HIV-2|Hepatitis B|Phylogeny|Predictive Value of Tests|Reading Frames|Selection, Genetic|Sequence Alignment|Sequence Analysis, DNA|Uncertainty|Viruses', 'Journal Article', '10.1186/1471-2105-9-304', '18616801', 'Two problems complicate the study of selection in viral genomes: Firstly, the presence of genes in overlapping reading frames implies that selection in one reading frame can bias our estimates of neutral mutation rates in another reading frame. Secondly, the high mutation rates we are likely to encounter complicate the inference of a reliable alignment of genomes. To address these issues, we develop a model that explicitly models selection in overlapping reading frames. We then integrate this model into a statistical alignment framework, enabling us to estimate selection while explicitly dealing with the uncertainty of individual alignments. We show that in this way we obtain un-biased selection parameters for different genomic regions of interest, and can improve in accuracy compared to using a fixed alignment.'),
(92274, 2008, 'bmc', 'MAID : an effect size based model for microarray data integration across laboratories and platforms', 'Borozan, Ivan|Chen, Limin|Paeper, Bryan|Heathcote, Jenny E|Edwards, Aled M|Katze, Michael|Zhang, Zhaolei|McGilvray, Ian D', 'Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca|Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada. ivan.borozan@utoronto.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 14, 'Artificial Intelligence|Data Collection|Data Compression|Databases, Genetic|Gene Expression Profiling|Hepatitis C, Chronic|Humans|Liver|Meta-Analysis as Topic|Oligonucleotide Array Sequence Analysis|Quality Control|Reference Standards|Sample Size|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-305', '18616827', 'Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.'),
(92275, 2008, 'bmc', 'Grammar-based distance in progressive multiple sequence alignment', 'Russell, David J|Otu, Hasan H|Sayood, Khalid', 'Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu|Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu|Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu', 'United States|United States|United States', 32, 'Animals|Computing Methodologies|Databases, Genetic|Genomics|Humans|Information Theory|Natural Language Processing|Semantics|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-306', '18616828', 'We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned.'),
(92276, 2008, 'bmc', 'Conditional variable importance for random forests', 'Strobl, Carolin|Boulesteix, Anne-Laure|Kneib, Thomas|Augustin, Thomas|Zeileis, Achim', 'Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de|Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de|Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de|Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de|Department of Statistics, Ludwig-Maximilians-Universität Munchen, Ludwigstrasse 33, D-80539 München, Germany. carolin.strobl@stat.uni-muenchen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Amino Acid Sequence|Binding Sites|Biometry|Computational Biology|Decision Trees|Factor Analysis, Statistical|Major Histocompatibility Complex|Regression Analysis|Research Design|Statistics, Nonparametric', 'Journal Article', '10.1186/1471-2105-9-307', '18620558', 'Random forests are becoming increasingly popular in many scientific fields because they can cope with small n large p problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables.'),
(92277, 2008, 'bmc', 'GenCLiP: a software program for clustering gene lists by literature profiling and constructing gene co-occurrence networks related to custom keywords', 'Huang, Zhong-Xi|Tian, Hui-Yong|Hu, Zhen-Fu|Zhou, Yi-Bo|Zhao, Jin|Yao, Kai-Tai', 'Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com|Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com|Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com|Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com|Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com|Cancer Institute, Southern Medical University, Guangzhou, 510515, PR China. huangzhongxi@gmail.com', 'China|China|China|China|China|China', 0, 'Cluster Analysis|Databases, Bibliographic|Gene Expression Profiling|Gene Regulatory Networks|Genetic Predisposition to Disease|Humans|Keloid|Multigene Family|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|Publications|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-308', '18620599', 'Biomedical researchers often want to explore pathogenesis and pathways regulated by abnormally expressed genes, such as those identified by microarray analyses. Literature mining is an important way to assist in this task. Many literature mining tools are now available. However, few of them allows the user to make manual adjustments to zero in on what he/she wants to know in particular.'),
(92278, 2008, 'bmc', 'PRESTO: rapid calculation of order statistic distributions and multiple-testing adjusted P-values via permutation for one and two-stage genetic association studies', 'Browning, Brian L', 'Department of Statistics, The University of Auckland, Auckland, New Zealand. b.browning@auckland.ac.nz', 'New Zealand', 31, 'Alleles|Biometry|Computational Biology|Crohn Disease|Genetic Markers|Genetic Predisposition to Disease|Genetic Testing|Genetics, Population|Genome, Human|Humans|Pedigree|Probability|Quantitative Trait Loci|Software|Statistical Distributions', 'Journal Article', '10.1186/1471-2105-9-309', '18620604', 'Large-scale genetic association studies can test hundreds of thousands of genetic markers for association with a trait. Since the genetic markers may be correlated, a Bonferroni correction is typically too stringent a correction for multiple testing. Permutation testing is a standard statistical technique for determining statistical significance when performing multiple correlated tests for genetic association. However, permutation testing for large-scale genetic association studies is computationally demanding and calls for optimized algorithms and software. PRESTO is a new software package for genetic association studies that performs fast computation of multiple-testing adjusted P-values via permutation of the trait.'),
(92279, 2008, 'bmc', 'Computational identification of ubiquitylation sites from protein sequences', 'Tung, Chun-Wei|Ho, Shinn-Ying', 'Institute of Bioinformatics, National Chiao Tung University, Hsinchu 300, Taiwan. cwtung@livemail.tw|Institute of Bioinformatics, National Chiao Tung University, Hsinchu 300, Taiwan. cwtung@livemail.tw', '-|-', 0, 'Amino Acid Sequence|Artificial Intelligence|Binding Sites|Chemical Phenomena|Chemistry, Physical|Classification|Databases, Protein|Decision Trees|Predictive Value of Tests|Protein Binding|Proteins|Proteomics|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software|Structure-Activity Relationship|Ubiquitination', 'Journal Article', '10.1186/1471-2105-9-310', '18625080', 'Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites.'),
(92280, 2008, 'bmc', 'Function2Gene: a gene selection tool to increase the power of genetic association studies by utilizing public databases and expert knowledge', 'Armstrong, Don L|Jacob, Chaim O|Zidovetzki, Raphael', 'Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92521, USA. don@donarmstrong.com|Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92521, USA. don@donarmstrong.com|Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92521, USA. don@donarmstrong.com', 'United States|United States|United States', 5, 'Computational Biology|Databases, Nucleic Acid|Genetic Predisposition to Disease|Humans|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-9-311', '18631403', 'Many common disorders have multiple genetic components which convey increased susceptibility. SNPs have been used to identify genetic components which are associated with a disease. Unfortunately, many studies using these methods suffer from low reproducibility due to lack of power.'),
(92281, 2008, 'bmc', 'MSDmotif: exploring protein sites and motifs', 'Golovin, Adel|Henrick, Kim', 'EMBL Outstation, The European Bioinformatics Institute, Welcome Trust Genome Campus, Hinxton, Cambridge, UK. golovin@ebi.ac.uk|EMBL Outstation, The European Bioinformatics Institute, Welcome Trust Genome Campus, Hinxton, Cambridge, UK. golovin@ebi.ac.uk', 'United Kingdom|United Kingdom', 0, 'Amino Acid Motifs|Binding Sites|Chemical Phenomena|Chemistry, Physical|Computer Graphics|Database Management Systems|Databases, Protein|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-312', '18637174', 'Protein structures have conserved features - motifs, which have a sufficient influence on the protein function. These motifs can be found in sequence as well as in 3D space. Understanding of these fragments is essential for 3D structure prediction, modelling and drug-design. The Protein Data Bank (PDB) is the source of this information however present search tools have limited 3D options to integrate protein sequence with its 3D structure.'),
(92282, 2008, 'bmc', 'SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies', 'Ryan, Michael C|Zeeberg, Barry R|Caplen, Natasha J|Cleland, James A|Kahn, Ari B|Liu, Hongfang|Weinstein, John N', 'Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com|Genomics & Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. mryan@tigerteamconsulting.com', 'United States|United States|United States|United States|United States|United States|United States', 28, 'Alternative Splicing|Biomedical Research|DNA Probes|Database Management Systems|Databases, Nucleic Acid|Information Dissemination|Oligonucleotide Array Sequence Analysis|Peptides|RNA Interference|RNA Splice Sites|RNA, Untranslated|Research Design|Reverse Transcriptase Polymerase Chain Reaction|Transcription, Genetic|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-313', '18638396', 'Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant.'),
(92283, 2008, 'bmc', 'Assessing probe-specific dye and slide biases in two-color microarray data', 'Lu, Ruixiao|Lee, Geun-Cheol|Shultz, Michael|Dardick, Chris|Jung, Kihong|Phetsom, Jirapa|Jia, Yi|Rice, Robert H|Goldberg, Zelanna|Schnable, Patrick S|Ronald, Pamela|Rocke, David M', 'Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com|Department of Data Analysis and Algorithm, Affymetrix, Inc., Santa Clara, California, USA. tinypenguin@gmail.com', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Algorithms|DNA Probes|Fluorescent Dyes|Image Interpretation, Computer-Assisted|Microscopy, Fluorescence, Multiphoton|Observer Variation|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Evaluation Studies', '10.1186/1471-2105-9-314', '18638416', 'A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue.'),
(92284, 2008, 'bmc', 'Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies', 'Ma, Li|Runesha, H Birali|Dvorkin, Daniel|Garbe, John R|Da, Yang', 'Department of Animal Science, University of Minnesota, USA. maxxx131@umn.edu|Department of Animal Science, University of Minnesota, USA. maxxx131@umn.edu|Department of Animal Science, University of Minnesota, USA. maxxx131@umn.edu|Department of Animal Science, University of Minnesota, USA. maxxx131@umn.edu|Department of Animal Science, University of Minnesota, USA. maxxx131@umn.edu', 'United States|United States|United States|United States|United States', 0, 'Epistasis, Genetic|Genomics|Polymorphism, Single Nucleotide|Quantitative Trait Loci|Software', 'Journal Article', '10.1186/1471-2105-9-315', '18644146', 'Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS.'),
(92285, 2008, 'bmc', 'PatternLab for proteomics: a tool for differential shotgun proteomics', 'Carvalho, Paulo C|Fischer, Juliana S G|Chen, Emily I|Yates, John R|Barbosa, Valmir C', 'Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br|Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. carvalhopc@cos.ufrj.br', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Artificial Intelligence|Calibration|Computer Graphics|Data Interpretation, Statistical|Databases, Protein|Gene Expression Profiling|Peptide Mapping|Proteins|Proteomics|Reference Values|Research Design|Software|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-9-316', '18644148', 'A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or spectral count, with its abundance in a mixture, however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.'),
(92286, 2008, 'bmc', 'Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis', 'Presson, Angela P|Sobel, Eric M|Pajukanta, Paivi|Plaisier, Christopher|Weeks, Daniel E|Aberg, Karolina|Papp, Jeanette C', 'Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu|Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. apresson@ucla.edu', 'United States|United States|United States|United States|United States|United States|United States', 3, 'Algorithms|Computational Biology|Gene Frequency|Genetic Linkage|Genotype|Methods|Microsatellite Repeats|Models, Statistical|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-9-317', '18644149', 'Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2).'),
(92287, 2008, 'bmc', 'Directed acyclic graph kernels for structural RNA analysis', 'Sato, Kengo|Mituyama, Toutai|Asai, Kiyoshi|Sakakibara, Yasubumi', 'Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp|Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp|Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp|Japan Biological Informatics Consortium (JBIC), 2-45 Aomi, Koto-ku, Tokyo 135-8073, Japan. sato-kengo@aist.go.jp', 'Japan|Japan|Japan|Japan', 0, 'Base Pairing|Base Sequence|Methods|Models, Molecular|Nucleic Acid Conformation|Probability|RNA, Untranslated|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-318', '18647390', 'Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.'),
(92288, 2008, 'bmc', 'A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification', 'Statnikov, Alexander|Wang, Lily|Aliferis, Constantin F', 'Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. alexander.statnikov@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. alexander.statnikov@vanderbilt.edu|Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. alexander.statnikov@vanderbilt.edu', 'United States|United States|United States', 374, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Computational Biology|Databases, Genetic|Decision Trees|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Random Allocation|Validation Studies as Topic', 'Comparative Study', '10.1186/1471-2105-9-319', '18647401', 'Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered best of class algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.'),
(92289, 2008, 'bmc', 'Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements', 'Yang, Qingwu|Sze, Sing-Hoi', 'Department of Computer Science, Texas A&M University, College Station, TX 77843, USA. qingwu-yang@neo.tamu.edu|Department of Computer Science, Texas A&M University, College Station, TX 77843, USA. qingwu-yang@neo.tamu.edu', 'United States|United States', 5, 'Bacterial Proteins|Computer Simulation|Genetic Variation|Models, Molecular|Protein Folding|Protein Structure, Secondary|Streptococcaceae', 'Journal Article', '10.1186/1471-2105-9-320', '18651953', 'Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins.'),
(92290, 2008, 'bmc', 'GBParsy: a GenBank flatfile parser library with high speed', 'Lee, Tae-Ho|Kim, Yeon-Ki|Nahm, Baek Hie', 'Division of Bioscience and Bioinformatics, MyongJi University, Yongin, Kyonggido, Republic of Korea. thlee@bio.mju.ac.kr|Division of Bioscience and Bioinformatics, MyongJi University, Yongin, Kyonggido, Republic of Korea. thlee@bio.mju.ac.kr|Division of Bioscience and Bioinformatics, MyongJi University, Yongin, Kyonggido, Republic of Korea. thlee@bio.mju.ac.kr', '-|-|-', 0, 'Base Sequence|Chromosomes|Computational Biology|Databases, Nucleic Acid|Gene Library|Genome|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-321', '18652706', 'GenBank flatfile (GBF) format is one of the most popular sequence file formats because of its detailed sequence features and ease of readability. To use the data in the file by a computer, a parsing process is required and is performed according to a given grammar for the sequence and the description in a GBF. Currently, several parser libraries for the GBF have been developed. However, with the accumulation of DNA sequence information from eukaryotic chromosomes, parsing a eukaryotic genome sequence with these libraries inevitably takes a long time, due to the large GBF file and its correspondingly large genomic nucleotide sequence and related feature information. Thus, there is significant need to develop a parsing program with high speed and efficient use of system memory.'),
(92291, 2008, 'bmc', 'PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships', 'Than, Cuong|Ruths, Derek|Nakhleh, Luay', 'Department of Computer Science, Rice University, 6100 Main Street, MS 132, Houston, TX, USA. cvthan@cs.rice.edu|Department of Computer Science, Rice University, 6100 Main Street, MS 132, Houston, TX, USA. cvthan@cs.rice.edu|Department of Computer Science, Rice University, 6100 Main Street, MS 132, Houston, TX, USA. cvthan@cs.rice.edu', 'United States|United States|United States', 188, 'Biological Evolution|Chimera|Gene Transfer, Horizontal|Genetic Speciation|Models, Genetic|Phylogeny|Recombination, Genetic|Software', 'Journal Article', '10.1186/1471-2105-9-322', '18662388', 'Phylogenies, i.e., the evolutionary histories of groups of taxa, play a major role in representing the interrelationships among biological entities. Many software tools for reconstructing and evaluating such phylogenies have been proposed, almost all of which assume the underlying evolutionary history to be a tree. While trees give a satisfactory first-order approximation for many families of organisms, other families exhibit evolutionary mechanisms that cannot be represented by trees. Processes such as horizontal gene transfer (HGT), hybrid speciation, and interspecific recombination, collectively referred to as reticulate evolutionary events, result in networks, rather than trees, of relationships. Various software tools have been recently developed to analyze reticulate evolutionary relationships, which include SplitsTree4, LatTrans, EEEP, HorizStory, and T-REX.'),
(92292, 2008, 'bmc', 'LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method', 'Antao, Tiago|Lopes, Ana|Lopes, Ricardo J|Beja-Pereira, Albano|Luikart, Gordon', 'Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. tiago.antao@liverpool.ac.uk|Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. tiago.antao@liverpool.ac.uk|Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. tiago.antao@liverpool.ac.uk|Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. tiago.antao@liverpool.ac.uk|Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK. tiago.antao@liverpool.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 863, 'Computational Biology|Computer Graphics|Computer Simulation|Databases, Genetic|Evolution, Molecular|Genetics, Population|Genomics|Selection, Genetic|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-323', '18662398', 'Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.'),
(92293, 2008, 'bmc', 'Baseline correction for NMR spectroscopic metabolomics data analysis', 'Xi, Yuanxin|Rocke, David M', 'Department of Applied Science, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA. yxi@bcm.edu|Department of Applied Science, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA. yxi@bcm.edu', 'United States|United States', 0, 'Data Interpretation, Statistical|Magnetic Resonance Spectroscopy|Metabolism', 'Journal Article', '10.1186/1471-2105-9-324', '18664284', 'We propose a statistically principled baseline correction method, derived from a parametric smoothing model. It uses a score function to describe the key features of baseline distortion and constructs an optimal baseline curve to maximize it. The parameters are determined automatically by using LOWESS (locally weighted scatterplot smoothing) regression to estimate the noise variance.'),
(92294, 2008, 'bmc', 'A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data', 'Zhou, Cong|Bowler, Lucas D|Feng, Jianfeng', 'Department of Computer Science and Mathematics, University of Warwick, Coventry CV4 7AL, UK. joecong2@yahoo.co.uk|Department of Computer Science and Mathematics, University of Warwick, Coventry CV4 7AL, UK. joecong2@yahoo.co.uk|Department of Computer Science and Mathematics, University of Warwick, Coventry CV4 7AL, UK. joecong2@yahoo.co.uk', 'United Kingdom|United Kingdom|United Kingdom', 27, 'Algorithms|Artificial Intelligence|Bayes Theorem|Neural Networks (Computer)|Peptides|Proteomics|Tandem Mass Spectrometry', 'Journal Article', '10.1186/1471-2105-9-325', '18664292', 'A better understanding of the mechanisms involved in gas-phase fragmentation of peptides is essential for the development of more reliable algorithms for high-throughput protein identification using mass spectrometry (MS). Current methodologies depend predominantly on the use of derived m/z values of fragment ions, and, the knowledge provided by the intensity information present in MS/MS spectra has not been fully exploited. Indeed spectrum intensity information is very rarely utilized in the algorithms currently in use for high-throughput protein identification.'),
(92295, 2008, 'bmc', 'A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition', 'Guescini, Michele|Sisti, Davide|Rocchi, Marco B L|Stocchi, Laura|Stocchi, Vilberto', 'Istituto di Ricerca sullAttività Motoria, Università degli Studi di Urbino Carlo Bo, Via I Maggetti, 26/2 - 61029 Urbino, Italy. michele.guescini@uniurb.it|Istituto di Ricerca sullAttività Motoria, Università degli Studi di Urbino Carlo Bo, Via I Maggetti, 26/2 - 61029 Urbino, Italy. michele.guescini@uniurb.it|Istituto di Ricerca sullAttività Motoria, Università degli Studi di Urbino Carlo Bo, Via I Maggetti, 26/2 - 61029 Urbino, Italy. michele.guescini@uniurb.it|Istituto di Ricerca sullAttività Motoria, Università degli Studi di Urbino Carlo Bo, Via I Maggetti, 26/2 - 61029 Urbino, Italy. michele.guescini@uniurb.it|Istituto di Ricerca sullAttività Motoria, Università degli Studi di Urbino Carlo Bo, Via I Maggetti, 26/2 - 61029 Urbino, Italy. michele.guescini@uniurb.it', 'Italy|Italy|Italy|Italy|Italy', 102, 'DNA|Kinetics|Methods|Nonlinear Dynamics|Polymerase Chain Reaction|Reference Standards|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-326', '18667053', 'Real-time PCR analysis is a sensitive DNA quantification technique that has recently gained considerable attention in biotechnology, microbiology and molecular diagnostics. Although, the cycle-threshold (Ct) method is the present gold standard, it is far from being a standard assay. Uniform reaction efficiency among samples is the most important assumption of this method. Nevertheless, some authors have reported that it may not be correct and a slight PCR efficiency decrease of about 4% could result in an error of up to 400% using the Ct method. This reaction efficiency decrease may be caused by inhibiting agents used during nucleic acid extraction or copurified from the biological sample. We propose a new method (Cy0) that does not require the assumption of equal reaction efficiency between unknowns and standard curve.'),
(92296, 2008, 'bmc', 'SCBB07 Proceedings of the Symposium of Computations in Bioinformatics and Bioscience, 13-15 August 2008, Iowa City, Iowa, USA', '-', '-', '-', 0, 'Computational Biology', 'Congress', '10.1186/1471-2105-9-326', '18668724', '-'),
(92297, 2008, 'bmc', 'Review of the selected proceedings of the Fifth International Workshop on Data Integration in the Life Sciences 2008', 'Bairoch, Amos|Cohen-Boulakia, Sarah|Froidevaux, Christine', 'Swiss Institute of Bioinformatics (SIB) and Department of Structural Biology and Bioinformatics, University of Geneva, Switzerland. Amos.Bairoch@isb-sib.ch|Swiss Institute of Bioinformatics (SIB) and Department of Structural Biology and Bioinformatics, University of Geneva, Switzerland. Amos.Bairoch@isb-sib.ch|Swiss Institute of Bioinformatics (SIB) and Department of Structural Biology and Bioinformatics, University of Geneva, Switzerland. Amos.Bairoch@isb-sib.ch', 'Switzerland|Switzerland|Switzerland', 0, 'Biological Science Disciplines|Computational Biology|Data Interpretation, Statistical', 'Introductory Journal Article', '10.1186/1471-2105-9-S8-S1', '18673525', '-'),
(92298, 2008, 'bmc', 'Integrating protein-protein interactions and text mining for protein function prediction', 'Jaeger, Samira|Gaudan, Sylvain|Leser, Ulf|Rebholz-Schuhmann, Dietrich', 'Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany. sjaeger@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany. sjaeger@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany. sjaeger@informatik.hu-berlin.de|Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany. sjaeger@informatik.hu-berlin.de', 'Germany|Germany|Germany|Germany', 29, 'Algorithms|Computational Biology|Data Mining|Databases, Protein|Proteins|Reproducibility of Results|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S8-S2', '18673526', 'Functional annotation of proteins remains a challenging task. Currently the scientific literature serves as the main source for yet uncurated functional annotations, but curation work is slow and expensive. Automatic techniques that support this work are still lacking reliability. We developed a method to identify conserved protein interaction graphs and to predict missing protein functions from orthologs in these graphs. To enhance the precision of the results, we furthermore implemented a procedure that validates all predictions based on findings reported in the literature.'),
(92299, 2008, 'bmc', 'Integrating biological data--the Distributed Annotation System', 'Jenkinson, Andrew M|Albrecht, Mario|Birney, Ewan|Blankenburg, Hagen|Down, Thomas|Finn, Robert D|Hermjakob, Henning|Hubbard, Tim J P|Jimenez, Rafael C|Jones, Philip|Kxc3xa4hxc3xa4ri, Andreas|Kulesha, Eugene|Macxc3xadas, Josxc3xa9 R|Reeves, Gabrielle A|Prlixc4x87, Andreas', 'European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk|European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD, UK. andy.jenkinson@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-S8-S3', '18673527', 'The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics.'),
(92300, 2008, 'bmc', 'Gene Ontology term overlap as a measure of gene functional similarity', 'Mistry, Meeta|Pavlidis, Paul', 'Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, British Columbia, Canada. mmistry@bioinformatics.ubc.ca|Department of Psychiatry and Centre for High-throughput Biology, University of British Columbia, British Columbia, Canada. mmistry@bioinformatics.ubc.ca', 'Canada|Canada', 133, 'Animals|Computational Biology|Databases, Genetic|Genes|Genomics|Information Storage and Retrieval|Mice|Natural Language Processing|Semantics|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-327', '18680592', 'The availability of various high-throughput experimental and computational methods allows biologists to rapidly infer functional relationships between genes. It is often necessary to evaluate these predictions computationally, a task that requires a reference database for functional relatedness. One such reference is the Gene Ontology (GO). A number of groups have suggested that the semantic similarity of the GO annotations of genes can serve as a proxy for functional relatedness. Here we evaluate a simple measure of semantic similarity, term overlap (TO).'),
(92301, 2008, 'bmc', 'A Java-based tool for the design of classification microarrays', 'Meng, Da|Broschat, Shira L|Call, Douglas R', 'School of Electrical Engineering and Computer Science, Washington State University, Pullman, USA. dmeng@eecs.wsu.edu|School of Electrical Engineering and Computer Science, Washington State University, Pullman, USA. dmeng@eecs.wsu.edu|School of Electrical Engineering and Computer Science, Washington State University, Pullman, USA. dmeng@eecs.wsu.edu', 'United States|United States|United States', 2, 'Bacteria|Cluster Analysis|Gene Expression Profiling|Genes, Bacterial|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Plasmids|Programming Languages', 'Journal Article', '10.1186/1471-2105-9-328', '18680597', 'Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria.'),
(92302, 2008, 'bmc', 'Evaluation of genomic island predictors using a comparative genomics approach', 'Langille, Morgan G I|Hsiao, William W L|Brinkman, Fiona S L', 'Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. mlangill@sfu.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. mlangill@sfu.ca|Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada. mlangill@sfu.ca', 'Canada|Canada|Canada', 0, 'Algorithms|Base Composition|Base Sequence|Genomic Islands|Genomics|Software', 'Comparative Study', '10.1186/1471-2105-9-329', '18680607', 'Genomic islands (GIs) are clusters of genes in prokaryotic genomes of probable horizontal origin. GIs are disproportionately associated with microbial adaptations of medical or environmental interest. Recently, multiple programs for automated detection of GIs have been developed that utilize sequence composition characteristics, such as G+C ratio and dinucleotide bias. To robustly evaluate the accuracy of such methods, we propose that a dataset of GIs be constructed using criteria that are independent of sequence composition-based analysis approaches.'),
(92303, 2008, 'bmc', 'NullHap--a versatile application to estimate haplotype frequencies from unphased genotypes in the presence of null alleles', 'Nowak, Robert M|Pxc5x82oski, Rafaxc5x82', 'Department of Electronics and Information Technology, Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland. r.m.nowak@elka.pw.edu.pl|Department of Electronics and Information Technology, Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland. r.m.nowak@elka.pw.edu.pl', 'Poland|Poland', 0, 'Algorithms|Alleles|Gene Frequency|Genetic Predisposition to Disease|Genotype|Haplotypes|Humans|Poland|Polymorphism, Genetic|Psoriasis|Receptors, KIR', 'Evaluation Studies', '10.1186/1471-2105-9-330', '18681957', 'Laboratory techniques used to determine haplotypes are often too expensive for large-scale studies and lack of phase information is commonly overcome using likelihood-based calculations. Whereas a number of programs are available for that purpose, none of them can handle loci with both multiple and null alleles.'),
(92304, 2008, 'bmc', 'HAPSIMU: a genetic simulation platform for population-based association studies', 'Zhang, Feng|Liu, Jianfeng|Chen, Jie|Deng, Hong-Wen', 'Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian 710049, PR China. fzhxjtu@gmail.com|Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian 710049, PR China. fzhxjtu@gmail.com|Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian 710049, PR China. fzhxjtu@gmail.com|Institute of Molecular Genetics, School of Life Science and Technology, Xian Jiaotong University, Xian 710049, PR China. fzhxjtu@gmail.com', 'China|China|China|China', 7, 'Computer Simulation|Genetic Predisposition to Disease|Genetic Variation|Genetics, Population|Haplotypes|Humans|Models, Genetic|Quantitative Trait Loci', 'Journal Article', '10.1186/1471-2105-9-331', '18681963', 'Population structure is an important cause leading to inconsistent results in population-based association studies (PBAS) of human diseases. Various statistical methods have been proposed to reduce the negative impact of population structure on PBAS. Due to lack of structural information in real populations, it is difficult to evaluate the impact of population structure on PBAS in real populations.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92305, 2008, 'bmc', 'Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores', 'Bastien, Olivier|Maréchal, Eric', 'UMR 5168 CNRS-CEA-INRA-Université J, Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. olivier.bastien@cea.fr|UMR 5168 CNRS-CEA-INRA-Université J, Fourier, Laboratoire de Physiologie Cellulaire Végétale, Département Réponse et Dynamique Cellulaire, CEA Grenoble, 17 rue des Martyrs, F-38054, Grenoble cedex 09, France. olivier.bastien@cea.fr', 'France|France', 0, 'Base Sequence|Computational Biology|Evolution, Molecular|Genomics|Models, Statistical|Monte Carlo Method|Sequence Alignment|Sequence Homology, Nucleic Acid|Time Factors', 'Journal Article', '10.1186/1471-2105-9-332', '18687111', 'Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2) following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support.'),
(92306, 2008, 'bmc', 'The SeqWord Genome Browser: an online tool for the identification and visualization of atypical regions of bacterial genomes through oligonucleotide usage', 'Ganesan, Hamilton|Rakitianskaia, Anna S|Davenport, Colin F|Tümmler, Burkhard|Reva, Oleg N', 'Dep of Biochemistry, Bioinformatics and Computational Biology Unit, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002, South Africa. ham.kzn@gmail.com|Dep of Biochemistry, Bioinformatics and Computational Biology Unit, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002, South Africa. ham.kzn@gmail.com|Dep of Biochemistry, Bioinformatics and Computational Biology Unit, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002, South Africa. ham.kzn@gmail.com|Dep of Biochemistry, Bioinformatics and Computational Biology Unit, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002, South Africa. ham.kzn@gmail.com|Dep of Biochemistry, Bioinformatics and Computational Biology Unit, University of Pretoria, Lynnwood road, Hillcrest, Pretoria, 0002, South Africa. ham.kzn@gmail.com', 'South Africa|South Africa|South Africa|South Africa|South Africa', 34, 'Bacteriophages|Base Composition|Base Sequence|Computational Biology|Genome, Bacterial|Internet|Oligonucleotides|Plasmids|Software', 'Journal Article', '10.1186/1471-2105-9-333', '18687122', 'Data mining in large DNA sequences is a major challenge in microbial genomics and bioinformatics. Oligonucleotide usage (OU) patterns provide a wealth of information for large scale sequence analysis and visualization. The purpose of this research was to make OU statistical analysis available as a novel web-based tool for functional genomics and annotation. The tool is also available as a downloadable package.'),
(92307, 2008, 'bmc', 'Performing statistical analyses on quantitative data in Taverna workflows: an example using R and maxdBrowse to identify differentially-expressed genes from microarray data', 'Li, Peter|Castrillo, Juan I|Velarde, Giles|Wassink, Ingo|Soiland-Reyes, Stian|Owen, Stuart|Withers, David|Oinn, Tom|Pocock, Matthew R|Goble, Carole A|Oliver, Stephen G|Kell, Douglas B', 'Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk|Manchester Centre for Integrative Systems Biology and School of Chemistry, Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess St, Manchester, M1 7DN, UK. peter.li@manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 37, 'Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1186/1471-2105-9-334', '18687127', 'There has been a dramatic increase in the amount of quantitative data derived from the measurement of changes at different levels of biological complexity during the post-genomic era. However, there are a number of issues associated with the use of computational tools employed for the analysis of such data. For example, computational tools such as R and MATLAB require prior knowledge of their programming languages in order to implement statistical analyses on data. Combining two or more tools in an analysis may also be problematic since data may have to be manually copied and pasted between separate user interfaces for each tool. Furthermore, this transfer of data may require a reconciliation step in order for there to be interoperability between computational tools.'),
(92308, 2008, 'bmc', 'Combining transcriptional datasets using the generalized singular value decomposition', 'Schreiber, Andreas W|Shirley, Neil J|Burton, Rachel A|Fincher, Geoffrey B', 'Australian Centre for Plant Functional Genomics, School of Agriculture and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA 5064, Australia. andreas.schreiber@adelaide.edu.au|Australian Centre for Plant Functional Genomics, School of Agriculture and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA 5064, Australia. andreas.schreiber@adelaide.edu.au|Australian Centre for Plant Functional Genomics, School of Agriculture and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA 5064, Australia. andreas.schreiber@adelaide.edu.au|Australian Centre for Plant Functional Genomics, School of Agriculture and Wine, University of Adelaide, Waite Campus, Glen Osmond, SA 5064, Australia. andreas.schreiber@adelaide.edu.au', 'Australia|Australia|Australia|Australia', 0, 'Algorithms|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Proteome|Reverse Transcriptase Polymerase Chain Reaction|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-335', '18687147', 'Both microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies while the latter is a more targeted technique. Because of platform-dependent systematic effects, simple comparisons or merging of datasets obtained by these technologies are difficult, even though they may often be desirable. These difficulties are exacerbated if there is only partial overlap between the experimental conditions and genes probed in the two datasets.'),
(92309, 2008, 'bmc', 'Determining significance of pairwise co-occurrences of events in bursty sequences', 'Haiminen, Niina|Mannila, Heikki|Terzi, Evimaria', 'HIIT, Department of Computer Science, PO Box 68, FI-00014 University of Helsinki, Finland. niina.haiminen@cs.helsinki.fi|HIIT, Department of Computer Science, PO Box 68, FI-00014 University of Helsinki, Finland. niina.haiminen@cs.helsinki.fi|HIIT, Department of Computer Science, PO Box 68, FI-00014 University of Helsinki, Finland. niina.haiminen@cs.helsinki.fi', 'Finland|Finland|Finland', 13, 'Algorithms|Data Interpretation, Statistical|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-336', '18691400', 'Event sequences where different types of events often occur close together arise, e.g., when studying potential transcription factor binding sites (TFBS, events) of certain transcription factors (TF, types) in a DNA sequence. These events tend to occur in bursts: in some genomic regions there are more genes and therefore potentially more binding sites, while in some, possibly very long regions, hardly any events occur. Also some types of events may occur in the sequence more often than others. Tendencies of co-occurrence of binding sites of two or more TFs are interesting, as they may imply a co-operative role between the TFs in regulatory processes. Determining a numerical value to summarize the tendency for co-occurrence between two TFs can be done in a number of ways. However, testing for the significance of such values should be done with respect to a relevant null model that takes into account the global sequence structure.'),
(92310, 2008, 'bmc', 'Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands', 'Dai, Wei|Teodoridis, Jens M|Graham, Janet|Zeller, Constanze|Huang, Tim H M|Yan, Pearlly|Vass, J Keith|Brown, Robert|Paul, Jim', 'Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk|Ovarian Cancer Action Centre and Section of Epigenetics, Department of Oncology, Imperial College, Hammersmith Hospital, London, UK. w.dai@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 14, 'Algorithms|Cell Line, Tumor|Cisplatin|CpG Islands|DNA Methylation|DNA Modification Methylases|Discriminant Analysis|Drug Resistance, Neoplasm|Female|Humans|Linear Models|Nucleic Acid Hybridization|Ovarian Neoplasms', 'Journal Article', '10.1186/1471-2105-9-337', '18691414', 'Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).'),
(92311, 2008, 'bmc', 'New time-scale criteria for model simplification of bio-reaction systems', 'Choi, Junwon|Yang, Kyung-won|Lee, Tai-yong|Lee, Sang Yup', 'Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, 335 Gwahangro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. martial@kaist.ac.kr|Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, 335 Gwahangro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. martial@kaist.ac.kr|Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, 335 Gwahangro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. martial@kaist.ac.kr|Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, 335 Gwahangro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. martial@kaist.ac.kr', '-|-|-|-', 9, 'Algorithms|Animals|Computer Simulation|Humans|Models, Biological|Proteome|Signal Transduction|Time Factors', 'Journal Article', '10.1186/1471-2105-9-338', '18694523', 'Quasi-steady state approximation (QSSA) based on time-scale analysis is known to be an effective method for simplifying metabolic reaction system, but the conventional analysis becomes time-consuming and tedious when the system is large. Although there are automatic methods, they are based on eigenvalue calculations of the Jacobian matrix and on linear transformations, which have a high computation cost. A more efficient estimation approach is necessary for complex systems.'),
(92312, 2008, 'bmc', 'A general modeling and visualization tool for comparing different members of a group: application to studying tau-mediated regulation of microtubule dynamics', 'Bhattacharya, Arnab|Levy, Sasha|LeBoeuf, Adria|Gaylord, Michelle|Wilson, Leslie|Singh, Ambuj K|Feinstein, Stuart C', 'Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in|Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India. arnabb@iitk.ac.in', 'India|India|India|India|India|India|India', 1, 'Computer Graphics|Image Processing, Computer-Assisted|Kinetics|Microscopy, Video|Microtubules|Models, Biological|Software|tau Proteins', 'Journal Article', '10.1186/1471-2105-9-339', '18700022', 'Innumerable biological investigations require comparing collections of molecules, cells or organisms to one another with respect to one or more of their properties. Almost all of these comparisons are performed manually, which can be susceptible to inadvertent bias as well as miss subtle effects. The development and application of computer-assisted analytical and interpretive tools could help address these issues and thereby dramatically improve these investigations.'),
(92313, 2008, 'bmc', 'RNA STRAND: the RNA secondary structure and statistical analysis database', 'Andronescu, Mirela|Bereg, Vera|Hoos, Holger H|Condon, Anne', 'Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada. andrones@cs.ubc.ca|Department of Computer Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada. andrones@cs.ubc.ca', 'Canada|Canada|Canada|Canada', 176, 'Computer Graphics|Computer Simulation|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|RNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-340', '18700982', 'The ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures.'),
(92314, 2008, 'bmc', 'SlowFaster, a user-friendly program for slow-fast analysis and its application on phylogeny of Blastocystis', 'Kostka, Martin|Uzlikova, Magdalena|Cepicka, Ivan|Flegr, Jaroslav', 'Department of Parasitology, Faculty of Science, Charles University, Vinicna 7, 128 44 Prague, Czech Republic. mkostka@centrum.cz|Department of Parasitology, Faculty of Science, Charles University, Vinicna 7, 128 44 Prague, Czech Republic. mkostka@centrum.cz|Department of Parasitology, Faculty of Science, Charles University, Vinicna 7, 128 44 Prague, Czech Republic. mkostka@centrum.cz|Department of Parasitology, Faculty of Science, Charles University, Vinicna 7, 128 44 Prague, Czech Republic. mkostka@centrum.cz', '-|-|-|-', 28, 'Animals|Blastocystis|Computational Biology|DNA, Protozoan|Phylogeny|Sequence Alignment|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-341', '18702831', 'Slow-fast analysis is a simple and effective method to reduce the influence of substitution saturation, one of the causes of phylogenetic noise and long branch attraction (LBA) artifacts. In several steps of increasing stringency, the slow-fast analysis omits the fastest substituting alignment positions from the analysed dataset and thus increases its signal/noise ratio.'),
(92315, 2008, 'bmc', 'SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool', 'Zi, Zhike|Zheng, Yanan|Rundell, Ann E|Klipp, Edda', 'Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de|Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de|Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de|Computational Systems Biology, Max Planck Institute for Molecular Genetics, Ihnestr, 73, 14195 Berlin, Germany. zhike_zi@molgen.mpg.de', 'Germany|Germany|Germany|Germany', 55, 'Algorithms|Computer Simulation|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1186/1471-2105-9-342', '18706080', 'It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.'),
(92316, 2008, 'bmc', 'Parameter estimation for robust HMM analysis of ChIP-chip data', 'Humburg, Peter|Bulger, David|Stone, Glenn', 'Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@csiro.au|Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@csiro.au|Department of Statistics, Macquarie University, North Ryde, NSW 2109, Australia. peter.humburg@csiro.au', 'Australia|Australia|Australia', 23, 'Algorithms|Artificial Intelligence|Chromatin Immunoprecipitation|Data Interpretation, Statistical|Databases, Genetic|Markov Chains|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-343', '18706106', 'Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis.'),
(92317, 2008, 'bmc', 'An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories', 'Sun, Hong|Ferhatosmanoglu, Hakan|Ota, Motonori|Wang, Yusu', 'Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu|Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. sun.82@osu.edu', 'United States|United States|United States|United States', 2, 'Algorithms|Cluster Analysis|Computer Simulation|Models, Chemical|Models, Molecular|Motion|Protein Conformation|Protein Folding|Proteins', 'Journal Article', '10.1186/1471-2105-9-344', '18710565', 'Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them.'),
(92318, 2008, 'bmc', 'VisualRepbase: an interface for the study of occurrences of transposable element families', 'Tempel, Sébastien|Jurka, Matthew|Jurka, Jerzy', 'Genetic Information Research Institute, 1925 Landings Dr, Mountain View, CA 94043, USA. seb@girinst.org|Genetic Information Research Institute, 1925 Landings Dr, Mountain View, CA 94043, USA. seb@girinst.org|Genetic Information Research Institute, 1925 Landings Dr, Mountain View, CA 94043, USA. seb@girinst.org', 'United States|United States|United States', 11, 'Chromosome Mapping|Computer Graphics|DNA Transposable Elements|Databases, Genetic|Repetitive Sequences, Nucleic Acid|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-345', '18710569', 'Repbase is a reference database of eukaryotic repetitive DNA, which includes prototypic sequences of repeats and basic information described in annotations. Repbase already has software for entering new sequence families and for comparing the users sequence with the database of consensus sequences.'),
(92319, 2008, 'bmc', 'AccPbFRET: an ImageJ plugin for semi-automatic, fully corrected analysis of acceptor photobleaching FRET images', 'Roszik, János|Szöllosi, János|Vereb, György', 'Department of Biophysics and Cell Biology, Research Center for Molecular Medicine, University of Debrecen, Debrecen, Hungary. janosr@dote.hu|Department of Biophysics and Cell Biology, Research Center for Molecular Medicine, University of Debrecen, Debrecen, Hungary. janosr@dote.hu|Department of Biophysics and Cell Biology, Research Center for Molecular Medicine, University of Debrecen, Debrecen, Hungary. janosr@dote.hu', 'Hungary|Hungary|Hungary', 41, 'Algorithms|Artifacts|Artificial Intelligence|Fluorescence Recovery After Photobleaching|Pattern Recognition, Automated|Programming Languages|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Software', 'Evaluation Studies', '10.1186/1471-2105-9-346', '18713453', 'The acceptor photobleaching fluorescence resonance energy transfer (FRET) method is widely used for monitoring molecular interactions in cells. This method of FRET, while among those with the simplest mathematics, is robust, self-controlled and independent of fluorophore amounts and ratios.'),
(92320, 2008, 'bmc', 'A robust linear regression based algorithm for automated evaluation of peptide identifications from shotgun proteomics by use of reversed-phase liquid chromatography retention time', 'Xu, Hua|Yang, Lanhao|Freitas, Michael A', 'Department of Molecular Virology Immunology and Medical Genetics, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, OH 43210, USA. xu.171@osu.edu|Department of Molecular Virology Immunology and Medical Genetics, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, OH 43210, USA. xu.171@osu.edu|Department of Molecular Virology Immunology and Medical Genetics, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, OH 43210, USA. xu.171@osu.edu', 'United States|United States|United States', 40, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Chromatography, Liquid|Computer Simulation|Linear Models|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Peptides|Proteome|Regression Analysis|Sequence Analysis, Protein|Time Factors', 'Journal Article', '10.1186/1471-2105-9-347', '18713471', 'Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.'),
(92321, 2008, 'bmc', 'Gene Vector Analysis (Geneva): a unified method to detect differentially-regulated gene sets and similar microarray experiments', 'Tanner, Stephen W|Agarwal, Pankaj', 'Bioinformatics program, University of California, San Diego, La Jolla, CA 92093-0419, USA. stanner@ucsd.edu|Bioinformatics program, University of California, San Diego, La Jolla, CA 92093-0419, USA. stanner@ucsd.edu', 'United States|United States', 11, 'Algorithms|Database Management Systems|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Software Validation', 'Journal Article', '10.1186/1471-2105-9-348', '18721468', 'Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.'),
(92322, 2008, 'bmc', 'Protein structure search and local structure characterization', 'Ku, Shih-Yen|Hu, Yuh-Jyh', 'Department of Computer Science, National Chiao Tung University, 1001 University Rd. Hsinchu, Taiwan. gis92622@cis.nctu.edu.tw|Department of Computer Science, National Chiao Tung University, 1001 University Rd. Hsinchu, Taiwan. gis92622@cis.nctu.edu.tw', '-|-', 13, 'Algorithms|Cluster Analysis|Databases, Protein|Methods|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-349', '18721472', 'Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.'),
(92323, 2008, 'bmc', 'Integration of relational and hierarchical network information for protein function prediction', 'Jiang, Xiaoyu|Nariai, Naoki|Steffen, Martin|Kasif, Simon|Kolaczyk, Eric D', 'Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. xiaoyu@math.bu.edu|Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. xiaoyu@math.bu.edu|Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. xiaoyu@math.bu.edu|Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. xiaoyu@math.bu.edu|Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA. xiaoyu@math.bu.edu', 'United States|United States|United States|United States|United States', 26, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-350', '18721473', 'In the current climate of high-throughput computational biology, the inference of a proteins function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.'),
(92324, 2008, 'bmc', 'SynBlast: assisting the analysis of conserved synteny information', 'Lehmann, Jörg|Stadler, Peter F|Prohaska, Sonja J', 'Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. joe@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. joe@bioinf.uni-leipzig.de|Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. joe@bioinf.uni-leipzig.de', 'Germany|Germany|Germany', 7, 'Algorithms|Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Gene Order|Genetic Linkage|Information Storage and Retrieval|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|Synteny', 'Journal Article', '10.1186/1471-2105-9-351', '18721485', 'In the last years more than 20 vertebrate genomes have been sequenced, and the rate at which genomic DNA information becomes available is rapidly accelerating. Gene duplication and gene loss events inherently limit the accuracy of orthology detection based on sequence similarity alone. Fully automated methods for orthology annotation do exist but often fail to identify individual members in cases of large gene families, or to distinguish missing data from traceable gene losses. This situation can be improved in many cases by including conserved synteny information.'),
(92325, 2008, 'bmc', 'Alignment of protein structures in the presence of domain motions', 'Mosca, Roberto|Brannetti, Barbara|Schneider, Thomas R', 'IFOM, FIRC Institute for Molecular Oncology Foundation, Via Adamello 16, 20139, Milan, Italy. roberto.mosca@embl-hamburg.de|IFOM, FIRC Institute for Molecular Oncology Foundation, Via Adamello 16, 20139, Milan, Italy. roberto.mosca@embl-hamburg.de|IFOM, FIRC Institute for Molecular Oncology Foundation, Via Adamello 16, 20139, Milan, Italy. roberto.mosca@embl-hamburg.de', 'Italy|Italy|Italy', 42, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Motion|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-352', '18727838', 'Structural alignment is an important step in protein comparison. Well-established methods exist for solving this problem under the assumption that the structures under comparison are considered as rigid bodies. However, proteins are flexible entities often undergoing movements that alter the positions of domains or subdomains with respect to each other. Such movements can impede the identification of structural equivalences when rigid aligners are used.'),
(92326, 2008, 'bmc', 'Identification and correction of abnormal, incomplete and mispredicted proteins in public databases', 'Nagy, Alinda|Hegyi, Hédi|Farkas, Krisztina|Tordai, Hedvig|Kozma, Evelin|Bányai, László|Patthy, László', 'Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu|Institute of Enzymology, Biological Research Center, Hungarian Academy of Sciences, H-1113 Budapest, Hungary. nagya@enzim.hu', 'Hungary|Hungary|Hungary|Hungary|Hungary|Hungary|Hungary', 49, 'Artifacts|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Natural Language Processing|Proteins|Quality Control|Sequence Analysis, Protein|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-353', '18752676', 'Despite significant improvements in computational annotation of genomes, sequences of abnormal, incomplete or incorrectly predicted genes and proteins remain abundant in public databases. Since the majority of incomplete, abnormal or mispredicted entries are not annotated as such, these errors seriously affect the reliability of these databases. Here we describe the MisPred approach that may provide an efficient means for the quality control of databases. The current version of the MisPred approach uses five distinct routines for identifying abnormal, incomplete or mispredicted entries based on the principle that a sequence is likely to be incorrect if some of its features conflict with our current knowledge about protein-coding genes and proteins: (i) conflict between the predicted subcellular localization of proteins and the absence of the corresponding sequence signals, (ii) presence of extracellular and cytoplasmic domains and the absence of transmembrane segments, (iii) co-occurrence of extracellular and nuclear domains, (iv) violation of domain integrity, (v) chimeras encoded by two or more genes located on different chromosomes.'),
(92327, 2008, 'bmc', 'Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration', 'Liang, Yulan|Kelemen, Arpad', 'Department of Organizational Systems and Adult Health, University of Maryland, 655 W, Lombard Street, Baltimore, MD 21201-1579, USA. ylian001@umaryland.edu|Department of Organizational Systems and Adult Health, University of Maryland, 655 W, Lombard Street, Baltimore, MD 21201-1579, USA. ylian001@umaryland.edu', 'United States|United States', 7, 'Animals|Bayes Theorem|Cluster Analysis|Confidence Intervals|Databases, Genetic|Gene Expression|Gene Expression Profiling|Kidney|Liver|Meta-Analysis as Topic|Methylprednisolone|Models, Genetic|Muscle, Skeletal|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Rats|Signal Transduction|Systems Biology|Time Factors', 'Journal Article', '10.1186/1471-2105-9-354', '18755028', 'This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug.'),
(92328, 2008, 'bmc', 'NITPICK: peak identification for mass spectrometry data', 'Renard, Bernhard Y|Kirchner, Marc|Steen, Hanno|Steen, Judith A J|Hamprecht, Fred A', 'Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de|Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany. bernhard.renard@iwr.uni-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Mass Spectrometry|Pattern Recognition, Automated|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-355', '18755032', 'The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments.'),
(92329, 2008, 'bmc', 'FERN - a Java framework for stochastic simulation and evaluation of reaction networks', 'Erhard, Florian|Friedel, Caroline C|Zimmer, Ralf', 'LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, München, Germany. erhardf@cip.ifi.lmu.de|LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, München, Germany. erhardf@cip.ifi.lmu.de|LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, München, Germany. erhardf@cip.ifi.lmu.de', 'Germany|Germany|Germany', 19, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Programming Languages|Proteome|Signal Transduction|Software|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-356', '18755046', 'Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary.'),
(92330, 2008, 'bmc', 'Impact of residue accessible surface area on the prediction of protein secondary structures', 'Momen-Roknabadi, Amir|Sadeghi, Mehdi|Pezeshk, Hamid|Marashi, Sayed-Amir', 'Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir|Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir|Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir|Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. roknabadi@khayam.ut.ac.ir', '-|-|-|-', 24, 'Amino Acid Sequence|Amino Acids|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Secondary|Proteins|Sequence Analysis, Protein|Surface Properties', 'Journal Article', '10.1186/1471-2105-9-357', '18759992', 'The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.'),
(92331, 2008, 'bmc', 'TOPS++FATCAT: fast flexible structural alignment using constraints derived from TOPS+ Strings Model', 'Veeramalai, Mallika|Ye, Yuzhen|Godzik, Adam', 'Joint Center for Molecular Modeling, Burnham Institute for Medical Research, La Jolla, CA 92037, USA. mallikav@burnham.org|Joint Center for Molecular Modeling, Burnham Institute for Medical Research, La Jolla, CA 92037, USA. mallikav@burnham.org|Joint Center for Molecular Modeling, Burnham Institute for Medical Research, La Jolla, CA 92037, USA. mallikav@burnham.org', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-358', '18759993', 'Protein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.'),
(92332, 2008, 'bmc', 'Methodology capture: discriminating between the best and the rest of community practice', 'Eales, James M|Pinney, John W|Stevens, Robert D|Robertson, David L', 'Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk|Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk|Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk|Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Artificial Intelligence|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Periodicals as Topic|Quality Control|Research', 'Journal Article', '10.1186/1471-2105-9-359', '18761740', 'The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field.'),
(92333, 2008, 'bmc', 'The C1C2: a framework for simultaneous model selection and assessment', 'Eklund, Martin|Spjuth, Ola|Wikberg, Jarl Es', 'Department of Pharmaceutical Pharmacology, Uppsala University, Box 591, BMC, SE-751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, Box 591, BMC, SE-751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se|Department of Pharmaceutical Pharmacology, Uppsala University, Box 591, BMC, SE-751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se', 'Sweden|Sweden|Sweden', 12, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Models, Statistical', 'Journal Article', '10.1186/1471-2105-9-360', '18761753', 'There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper methods for model selection and assessment. Here, we have addressed this issue by introducing a novel and general framework, the C1C2, for simultaneous model selection and assessment. The framework relies on a partitioning of the data in order to separate model choice from model assessment in terms of used data. Since the number of conceivable models in general is vast, it was also of interest to investigate the employment of two automatic search methods, a genetic algorithm and a brute-force method, for model choice. As a demonstration, the C1C2 was applied to simulated and real-world datasets. A penalized linear model was assumed to reasonably approximate the true relation between the dependent and independent variables, thus reducing the model choice problem to a matter of variable selection and choice of penalizing parameter. We also studied the impact of assuming prior knowledge about the number of relevant variables on model choice and generalization error estimates. The results obtained with the C1C2 were compared to those obtained by employing repeated K-fold cross-validation for choosing and assessing a model.'),
(92334, 2008, 'bmc', 'Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution', 'Moretti, Stefano|van Leeuwen, Danitsja|Gmuender, Hans|Bonassi, Stefano|van Delft, Joost|Kleinjans, Jos|Patrone, Fioravante|Merlo, Domenico Franco', 'Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it|Epidemiology and Biostatistics, National Cancer Research Institute, Genova, Italy. stefano.moretti@istge.it', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 0, 'Air Pollution|Algorithms|Biomarkers|Child|Computer Simulation|Czech Republic|Data Interpretation, Statistical|Environmental Exposure|Epidemiologic Methods|Gene Expression Profiling|Humans|Models, Biological|Models, Statistical|Proteome|Risk Assessment|Risk Factors', 'Evaluation Studies', '10.1186/1471-2105-9-361', '18764936', 'In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92335, 2008, 'bmc', 'Testing gene set enrichment for subset of genes: Sub-GSE', 'Yan, Xiting|Sun, Fengzhu', 'Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. xitingya@usc.edu|Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. xitingya@usc.edu', 'United States|United States', 0, 'Algorithms|Databases, Genetic|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Proteome|Signal Transduction|Software', 'Evaluation Studies', '10.1186/1471-2105-9-362', '18764941', 'Many methods have been developed to test the enrichment of genes related to certain phenotypes or cell states in gene sets. These approaches usually combine gene expression data with functionally related gene sets as defined in databases such as GeneOntology (GO), KEGG, or BioCarta. The results based on gene set analysis are generally more biologically interpretable, accurate and robust than the results based on individual gene analysis. However, while most available methods for gene set enrichment analysis test the enrichment of the entire gene set, it is more likely that only a subset of the genes in the gene set may be related to the phenotypes of interest.'),
(92336, 2008, 'bmc', 'Virtual screening of GPCRs: an in silico chemogenomics approach', 'Jacob, Laurent|Hoffmann, Brice|Stoven, Véronique|Vert, Jean-Philippe', 'Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honoré, F-77305, Fontainebleau, France. laurent.jacob@mines-paristech.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honoré, F-77305, Fontainebleau, France. laurent.jacob@mines-paristech.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honoré, F-77305, Fontainebleau, France. laurent.jacob@mines-paristech.fr|Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honoré, F-77305, Fontainebleau, France. laurent.jacob@mines-paristech.fr', 'France|France|France|France', 0, 'Binding Sites|Computer Simulation|Drug Delivery Systems|Models, Chemical|Models, Molecular|Pharmaceutical Preparations|Protein Binding|Protein Interaction Mapping|Receptors, G-Protein-Coupled', 'Journal Article', '10.1186/1471-2105-9-363', '18775075', 'The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies.'),
(92337, 2008, 'bmc', 'Fregene: simulation of realistic sequence-level data in populations and ascertained samples', 'Chadeau-Hyam, Marc|Hoggart, Clive J|OReilly, Paul F|Whittaker, John C|De Iorio, Maria|Balding, David J', 'Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk|Department of Epidemiology and Public Health, Imperial College, St Marys Campus, Norfolk Place, London, W2 1PG, UK. m.chadeau@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Models, Genetic|Molecular Sequence Data|Sample Size|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-364', '18778480', 'FREGENE simulates sequence-level data over large genomic regions in large populations. Because, unlike coalescent simulators, it works forwards through time, it allows complex scenarios of selection, demography, and recombination to be modelled simultaneously. Detailed tracking of sites under selection is implemented in FREGENE and provides the opportunity to test theoretical predictions and gain new insights into mechanisms of selection. We describe here main functionalities of both FREGENE and SAMPLE, a companion program that can replicate association study datasets.'),
(92338, 2008, 'bmc', 'Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions', 'Houseman, E Andres|Christensen, Brock C|Yeh, Ru-Fang|Marsit, Carmen J|Karagas, Margaret R|Wrensch, Margaret|Nelson, Heather H|Wiemels, Joseph|Zheng, Shichun|Wiencke, John K|Kelsey, Karl T', 'Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu|Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, 02115, USA. ahousema@hsph.harvard.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 134, 'Algorithms|Base Sequence|Chromosome Mapping|Cluster Analysis|DNA|DNA Methylation|Epigenesis, Genetic|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-365', '18782434', 'Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci, for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.'),
(92339, 2008, 'bmc', 'ReRep: computational detection of repetitive sequences in genome survey sequences (GSS)', 'Otto, Thomas D|Gomes, Leonardo H F|Alves-Ferreira, Marcelo|de Miranda, Antonio B|Degrave, Wim M', 'Laboratory for Functional Genomics and Bioinformatics, IOC, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, IOC, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, IOC, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, IOC, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br|Laboratory for Functional Genomics and Bioinformatics, IOC, Fiocruz, Rio de Janeiro, Brazil. otto@fiocruz.br', 'Brazil|Brazil|Brazil|Brazil|Brazil', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Genome|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-366', '18782453', 'Genome survey sequences (GSS) offer a preliminary global view of a genome since, unlike ESTs, they cover coding as well as non-coding DNA and include repetitive regions of the genome. A more precise estimation of the nature, quantity and variability of repetitive sequences very early in a genome sequencing project is of considerable importance, as such data strongly influence the estimation of genome coverage, library quality and progress in scaffold construction. Also, the elimination of repetitive sequences from the initial assembly process is important to avoid errors and unnecessary complexity. Repetitive sequences are also of interest in a variety of other studies, for instance as molecular markers.'),
(92340, 2008, 'bmc', 'IgTM: an algorithm to predict transmembrane domains and topology in proteins', 'Peris, Piedachu|López, Damián|Campos, Marcelino', 'Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camí de Vera s/n Valencia, Spain. pperis@dsic.upv.es|Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camí de Vera s/n Valencia, Spain. pperis@dsic.upv.es|Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camí de Vera s/n Valencia, Spain. pperis@dsic.upv.es', 'Spain|Spain|Spain', 0, 'Algorithms|Amino Acid Sequence|Cell Membrane|Computer Simulation|Membrane Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-367', '18783592', 'Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.'),
(92341, 2008, 'bmc', 'LOMA: a fast method to generate efficient tagged-random primers despite amplification bias of random PCR on pathogens', 'Lee, Wah Heng|Wong, Christopher W|Leong, Wan Yee|Miller, Lance D|Sung, Wing Kin', 'Genome Institute of Singapore, Genome, Singapore. leewhc@gis.a-star.edu.sg|Genome Institute of Singapore, Genome, Singapore. leewhc@gis.a-star.edu.sg|Genome Institute of Singapore, Genome, Singapore. leewhc@gis.a-star.edu.sg|Genome Institute of Singapore, Genome, Singapore. leewhc@gis.a-star.edu.sg|Genome Institute of Singapore, Genome, Singapore. leewhc@gis.a-star.edu.sg', 'Singapore|Singapore|Singapore|Singapore|Singapore', 4, 'Algorithms|Artifacts|Base Sequence|DNA Primers|DNA, Viral|Data Interpretation, Statistical|Molecular Sequence Data|Polymerase Chain Reaction|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-368', '18783594', 'Pathogen detection using DNA microarrays has the potential to become a fast and comprehensive diagnostics tool. However, since pathogen detection chips currently utilize random primers rather than specific primers for the RT-PCR step, bias inherent in random PCR amplification becomes a serious problem that causes large inaccuracies in hybridization signals.'),
(92342, 2008, 'bmc', 'Automatic detection of exonic splicing enhancers (ESEs) using SVMs', 'Mersch, Britta|Gepperth, Alexander|Suhai, Sándor|Hotz-Wagenblatt, Agnes', 'Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de|Department of Molecular Biophysics, German Cancer Research Center DKFZ, Im Neuenheimer Feld 580, Heidelberg, Germany. b.mersch@dkfz.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Exons|Molecular Sequence Data|Pattern Recognition, Automated|RNA Splice Sites|RNA Splicing|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-369', '18783607', 'Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins.'),
(92343, 2008, 'bmc', 'Mixture models for analysis of melting temperature data', 'Nellåker, Christoffer|Uhrzander, Fredrik|Tyrcha, Joanna|Karlsson, Håkan', 'Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se|Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se|Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se|Department of Neuroscience, Karolinska Institutet, Retzius Väg, Stockholm, Sweden. christoffer.nellaker@ki.se', 'Sweden|Sweden|Sweden|Sweden', 4, 'Algorithms|Computer Simulation|DNA|Models, Chemical|Models, Statistical|Reverse Transcriptase Polymerase Chain Reaction|Sequence Analysis, DNA|Transition Temperature', 'Journal Article', '10.1186/1471-2105-9-370', '18786251', 'In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (Tm) data. However, there is currently no convention on how to statistically analyze such high-resolution Tm data.'),
(92344, 2008, 'bmc', 'Kismeth: analyzer of plant methylation states through bisulfite sequencing', 'Gruntman, Eyal|Qi, Yijun|Slotkin, R Keith|Roeder, Ted|Martienssen, Robert A|Sachidanandam, Ravi', 'gruntman@cshl.edu|gruntman@cshl.edu|gruntman@cshl.edu|gruntman@cshl.edu|gruntman@cshl.edu|gruntman@cshl.edu', '-|-|-|-|-|-', 0, 'Algorithms|Base Sequence|DNA Methylation|DNA, Plant|Molecular Sequence Data|Sequence Analysis, DNA|Software|Sulfites', 'Journal Article', '10.1186/1471-2105-9-371', '18786255', 'There is great interest in probing the temporal and spatial patterns of cytosine methylation states in genomes of a variety of organisms. It is hoped that this will shed light on the biological roles of DNA methylation in the epigenetic control of gene expression. Bisulfite sequencing refers to the treatment of isolated DNA with sodium bisulfite to convert unmethylated cytosine to uracil, with PCR converting the uracil to thymidine followed by sequencing of the resultant DNA to detect DNA methylation. For the study of DNA methylation, plants provide an excellent model system, since they can tolerate major changes in their DNA methylation patterns and have long been studied for the effects of DNA methylation on transposons and epimutations. However, in contrast to the situation in animals, there arent many tools that analyze bisulfite data in plants, which can exhibit methylation of cytosines in a variety of sequence contexts (CG, CHG, and CHH).'),
(92345, 2008, 'bmc', 'Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast', 'Keich, Uri|Gao, Hong|Garretson, Jeffrey S|Bhaskar, Anand|Liachko, Ivan|Donato, Justin|Tye, Bik K', 'Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu|Department of Computer Science, Cornell University, Ithaca, NY 14853, USA. keich@cs.cornell.edu', 'United States|United States|United States|United States|United States|United States|United States', 7, 'Algorithms|Base Sequence|Binding Sites|Computer Simulation|DNA, Fungal|Data Interpretation, Statistical|Genetic Variation|Models, Genetic|Molecular Sequence Data|Protein Binding|Replication Origin|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-372', '18786274', 'In analyzing the stability of DNA replication origins in Saccharomyces cerevisiae we faced the question whether one set of sequences is significantly enriched in the number and/or the quality of the matches of a particular position weight matrix relative to another set.'),
(92346, 2008, 'bmc', 'Increasing the efficiency of bacterial transcription simulations: when to exclude the genome without loss of accuracy', 'Iafolla, Marco A J|Dong, Guang Qiang|McMillen, David R', 'Department of Chemical and Physical Sciences and Institute for Optical Sciences, University of Toronto Mississauga, 3359 Mississauga Rd N, Mississauga, ON, L5L 1C6, Canada. marco.iafolla@gmail.com|Department of Chemical and Physical Sciences and Institute for Optical Sciences, University of Toronto Mississauga, 3359 Mississauga Rd N, Mississauga, ON, L5L 1C6, Canada. marco.iafolla@gmail.com|Department of Chemical and Physical Sciences and Institute for Optical Sciences, University of Toronto Mississauga, 3359 Mississauga Rd N, Mississauga, ON, L5L 1C6, Canada. marco.iafolla@gmail.com', 'Canada|Canada|Canada', 2, 'Algorithms|Base Sequence|Chromosome Mapping|Escherichia coli|Genome, Bacterial|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-373', '18789148', 'Simulating the major molecular events inside an Escherichia coli cell can lead to a very large number of reactions that compose its overall behaviour. Not only should the model be accurate, but it is imperative for the experimenter to create an efficient model to obtain the results in a timely fashion. Here, we show that for many parameter regimes, the effect of the host cell genome on the transcription of a gene from a plasmid-borne promoter is negligible, allowing one to simulate the system more efficiently by removing the computational load associated with representing the presence of the rest of the genome. The key parameter is the on-rate of RNAP binding to the promoter (k_on), and we compare the total number of transcripts produced from a plasmid vector generated as a function of this rate constant, for two versions of our gene expression model, one incorporating the host cell genome and one excluding it. By sweeping parameters, we identify the k_on range for which the difference between the genome and no-genome models drops below 5%, over a wide range of doubling times, mRNA degradation rates, plasmid copy numbers, and gene lengths.'),
(92347, 2008, 'bmc', 'Large-scale identification of polymorphic microsatellites using an in silico approach', 'Tang, Jifeng|Baldwin, Samantha J|Jacobs, Jeanne Me|Linden, C Gerard van der|Voorrips, Roeland E|Leunissen, Jack Am|van Eck, Herman|Vosman, Ben', 'Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com|Laboratory of Bioinformatics, Wageningen University, PO Box 8128, 6700 ET Wageningen, the Netherlands. jifeng.tang@gmail.com', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 51, 'Algorithms|Base Sequence|Chromosome Mapping|Expressed Sequence Tags|Genetic Markers|Microsatellite Repeats|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-374', '18793407', 'Simple Sequence Repeat (SSR) or microsatellite markers are valuable for genetic research. Experimental methods to develop SSR markers are laborious, time consuming and expensive. In silico approaches have become a practicable and relatively inexpensive alternative during the last decade, although testing putative SSR markers still is time consuming and expensive. In many species only a relatively small percentage of SSR markers turn out to be polymorphic. This is particularly true for markers derived from expressed sequence tags (ESTs). In EST databases a large redundancy of sequences is present, which may contain information on length-polymorphisms in the SSR they contain, and whether they have been derived from heterozygotes or from different genotypes. Up to now, although a number of programs have been developed to identify SSRs in EST sequences, no software can detect putatively polymorphic SSRs.'),
(92348, 2008, 'bmc', 'Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements', 'Lange, Eva|Tautenhahn, Ralf|Neumann, Steffen|Gröpl, Clemens', 'Beatson Institute for Cancer Research, Proteomics and Mass Spectrometry Group, Scotland, UK. e.lange@beatson.gla.ac.uk|Beatson Institute for Cancer Research, Proteomics and Mass Spectrometry Group, Scotland, UK. e.lange@beatson.gla.ac.uk|Beatson Institute for Cancer Research, Proteomics and Mass Spectrometry Group, Scotland, UK. e.lange@beatson.gla.ac.uk|Beatson Institute for Cancer Research, Proteomics and Mass Spectrometry Group, Scotland, UK. e.lange@beatson.gla.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Chromatography, Liquid|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Proteome|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-375', '18793413', 'Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.'),
(92349, 2008, 'bmc', 'GenomeMatcher: a graphical user interface for DNA sequence comparison', 'Ohtsubo, Yoshiyuki|Ikeda-Ohtsubo, Wakako|Nagata, Yuji|Tsuda, Masataka', 'Department of Environmental Life Sciences, Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Sendai, Japan. yohtsubo@ige.tohoku.ac.jp|Department of Environmental Life Sciences, Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Sendai, Japan. yohtsubo@ige.tohoku.ac.jp|Department of Environmental Life Sciences, Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Sendai, Japan. yohtsubo@ige.tohoku.ac.jp|Department of Environmental Life Sciences, Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Sendai, Japan. yohtsubo@ige.tohoku.ac.jp', 'Japan|Japan|Japan|Japan', 155, 'Algorithms|Chromosome Mapping|Computer Graphics|DNA|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-376', '18793444', 'The number of available genome sequences is increasing, and easy-to-use software that enables efficient comparative analysis is needed.'),
(92350, 2008, 'bmc', 'Proceedings of the 2008 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference', 'Wren, Jonathan D|Wilkins, Dawn|Fuscoe, James C|Bridges, Susan|Winters-Hilt, Stephen|Gusev, Yuriy', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N.E. 13th Street, Oklahoma City, OK 73104-5005, USA. Jonathan.Wren@OMRF.org', 'United States|United States|United States|United States|United States|United States', 0, 'Computational Biology|Gene Expression Profiling|Genomics|Microarray Analysis', 'Congress', '10.1186/1471-2105-9-S9-S1', '18793454', '-'),
(92351, 2008, 'bmc', 'The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies', 'Shi, Leming|Jones, Wendell D|Jensen, Roderick V|Harris, Stephen C|Perkins, Roger G|Goodsaid, Federico M|Guo, Lei|Croner, Lisa J|Boysen, Cecilie|Fang, Hong|Qian, Feng|Amur, Shashi|Bao, Wenjun|Barbacioru, Catalin C|Bertholet, Vincent|Cao, Xiaoxi Megan|Chu, Tzu-Ming|Collins, Patrick J|Fan, Xiao-Hui|Frueh, Felix W|Fuscoe, James C|Guo, Xu|Han, Jing|Herman, Damir|Hong, Huixiao|Kawasaki, Ernest S|Li, Quan-Zhen|Luo, Yuling|Ma, Yunqing|Mei, Nan|Peterson, Ron L|Puri, Raj K|Shippy, Richard|Su, Zhenqiang|Sun, Yongming Andrew|Sun, Hongmei|Thorn, Brett|Turpaz, Yaron|Wang, Charles|Wang, Sue Jane|Warrington, Janet A|Willey, James C|Wu, Jie|Xie, Qian|Zhang, Liang|Zhang, Lu|Zhong, Sheng|Wolfinger, Russell D|Tong, Weida', 'National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov|National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. leming.shi@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|Uni', 163, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Genes|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-S9-S10', '18793455', 'Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.'),
(92352, 2008, 'bmc', 'Large-scale directional relationship extraction and resolution', 'Giles, Cory B|Wren, Jonathan D', 'Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, Oklahoma 73104-5005, USA. Cory-Giles@omrf.org|Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, 825 N,E, 13th Street, Oklahoma City, Oklahoma 73104-5005, USA. Cory-Giles@omrf.org', 'United States|United States', 27, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Pattern Recognition, Automated|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S9-S11', '18793456', 'Relationships between entities such as genes, chemicals, metabolites, phenotypes and diseases in MEDLINE are often directional. That is, one may affect the other in a positive or negative manner. Detection of causality and direction is key in piecing pathways together and in examining possible implications of experimental results. Because of the size and growth of biomedical literature, it is increasingly important to be able to automate this process as much as possible.'),
(92353, 2008, 'bmc', 'Hybrid MM/SVM structural sensors for stochastic sequential data', 'Roux, Brian|Winters-Hilt, Stephen', 'Department of Computer Science, University of New Orleans, LA 70148, USA. broux@cs.uno.edu|Department of Computer Science, University of New Orleans, LA 70148, USA. broux@cs.uno.edu', 'United States|United States', 7, 'Algorithms|Artificial Intelligence|Data Interpretation, Statistical|Markov Chains|Pattern Recognition, Automated|Sequence Analysis|Stochastic Processes', 'Journal Article', '10.1186/1471-2105-9-S9-S12', '18793457', 'In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5 and 3) splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison.'),
(92354, 2008, 'bmc', 'Clustering ionic flow blockade toggles with a mixture of HMMs', 'Churbanov, Alexander|Winters-Hilt, Stephen', 'The Research Institute for Children, 200 Henry Clay Ave, New Orleans, LA 70118, USA. atchourb@cs.uno.edu|The Research Institute for Children, 200 Henry Clay Ave, New Orleans, LA 70118, USA. atchourb@cs.uno.edu', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Biological Assay|DNA|Escherichia coli Proteins|Hemolysin Proteins|Ion Channel Gating|Ion Channels|Markov Chains|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S9-S13', '18793458', 'Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications for DNA sequencing. The alpha-Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by a complex ionic flow blockade pattern with readily distinguishable modes of toggling. Effective processing of such signals requires developing machine learning methods capable of learning the various blockade modes for classification and knowledge discovery purposes. Here we propose a method aimed to improve our stochastic analysis capabilities to better understand the discriminatory capabilities of the observed the nanopore channel interactions with analyte.'),
(92355, 2008, 'bmc', 'Time-dependent ARMA modeling of genomic sequences', 'Zielinski, Jerzy S|Bouaynaya, Nidhal|Schonfeld, Dan|ONeill, William', 'Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, USA. jszielinski@ualr.edu|Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, USA. jszielinski@ualr.edu|Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, USA. jszielinski@ualr.edu|Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, USA. jszielinski@ualr.edu', 'United States|United States|United States|United States', 9, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Models, Genetic|Models, Statistical|Molecular Sequence Data|Regression Analysis|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S9-S14', '18793459', 'Over the past decade, many investigators have used sophisticated time series tools for the analysis of genomic sequences. Specifically, the correlation of the nucleotide chain has been studied by examining the properties of the power spectrum. The main limitation of the power spectrum is that it is restricted to stationary time series. However, it has been observed over the past decade that genomic sequences exhibit non-stationary statistical behavior. Standard statistical tests have been used to verify that the genomic sequences are indeed not stationary. More recent analysis of genomic data has relied on time-varying power spectral methods to capture the statistical characteristics of genomic sequences. Techniques such as the evolutionary spectrum and evolutionary periodogram have been successful in extracting the time-varying correlation structure. The main difficulty in using time-varying spectral methods is that they are extremely unstable. Large deviations in the correlation structure results from very minor perturbations in the genomic data and experimental procedure. A fundamental new approach is needed in order to provide a stable platform for the non-stationary statistical analysis of genomic sequences.'),
(92356, 2008, 'bmc', 'An iterative block-shifting approach to retention time alignment that preserves the shape and area of gas chromatography-mass spectrometry peaks', 'Chae, Minho|Shmookler Reis, Robert J|Thaden, John J', 'UALR/UAMS Joint Graduate Program in Bioinformatics, University of Arkansas at Little Rock, Little Rock, AR 72204, USA. minho.chae@gmail.com|UALR/UAMS Joint Graduate Program in Bioinformatics, University of Arkansas at Little Rock, Little Rock, AR 72204, USA. minho.chae@gmail.com|UALR/UAMS Joint Graduate Program in Bioinformatics, University of Arkansas at Little Rock, Little Rock, AR 72204, USA. minho.chae@gmail.com', 'United States|United States|United States', 26, 'Algorithms|Artificial Intelligence|Biopolymers|Gas Chromatography-Mass Spectrometry|Pattern Recognition, Automated|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-S9-S15', '18793460', 'Metabolomics, petroleum and biodiesel chemistry, biomarker discovery, and other fields which rely on high-resolution profiling of complex chemical mixtures generate datasets which contain millions of detector intensity readings, each uniquely addressed along dimensions of time (e.g., retention time of chemicals on a chromatographic column), a spectral value (e.g., mass-to-charge ratio of ions derived from chemicals), and the analytical run number. They also must rely on data preprocessing techniques. In particular, inter-run variance in the retention time of chemical species poses a significant hurdle that must be cleared before feature extraction, data reduction, and knowledge discovery can ensue. Alignment methods, for calibrating retention reportedly (and in our experience) can misalign matching chemicals, falsely align distinct ones, be unduly sensitive to chosen values of input parameters, and result in distortions of peak shape and area.'),
(92357, 2008, 'bmc', 'Wanted: unique names for unique atom positions PDB-wide analysis of diastereotopic atom names of small molecules containing diphosphate', 'Bottoms, Christopher A|Xu, Dong', 'Department of Computer Science and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. ChristopherBottoms@mizzou.edu|Department of Computer Science and Christopher S, Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. ChristopherBottoms@mizzou.edu', 'United States|United States', 3, 'Algorithms|Database Management Systems|Databases, Protein|Diphosphates|Information Storage and Retrieval|Proteins|Software|Stereoisomerism|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-S9-S16', '18793461', 'Biological chemistry is very stereospecific. Nonetheless, the diastereotopic oxygen atoms of diphosphate-containing molecules in the Protein Data Bank (PDB) are often given names that do not uniquely distinguish them from each other due to the lack of standardization. This issue has largely not been addressed by the protein structure community.'),
(92358, 2008, 'bmc', 'Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples', 'Hong, Huixiao|Su, Zhenqiang|Ge, Weigong|Shi, Leming|Perkins, Roger|Fang, Hong|Xu, Joshua|Chen, James J|Han, Tao|Kaput, Jim|Fuscoe, James C|Tong, Weida', 'Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov|Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genome, Human|Genotype|Haplotypes|Humans|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1186/1471-2105-9-S9-S17', '18793462', 'Genome-wide association studies (GWAS) aim to identify genetic variants (usually single nucleotide polymorphisms [SNPs]) across the entire human genome that are associated with phenotypic traits such as disease status and drug response. Highly accurate and reproducible genotype calling are paramount since errors introduced by calling algorithms can lead to inflation of false associations between genotype and phenotype. Most genotype calling algorithms currently used for GWAS are based on multiple arrays. Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typically partitioned into batches containing subsets of the entire dataset for genotype calling. High call rates and accuracies have been achieved. However, the effects of batch size (i.e., number of chips analyzed together) and of batch composition (i.e., the choice of chips in a batch) on call rate and accuracy as well as the propagation of the effects into significantly associated SNPs identified have not been investigated. In this paper, we analyzed both the batch size and batch composition for effects on the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymetrix Human Mapping 500 K array set.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92359, 2008, 'bmc', 'Comprehensive analysis of circadian periodic pattern in plant transcriptome', 'Ptitsyn, Andrey', 'Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Campus delivery 1682 Fort Collins, CO 80523, USA. Andrey.Ptitsyn@colostate.edu', 'United States', 11, 'Arabidopsis|Arabidopsis Proteins|Biological Clocks|Circadian Rhythm|Gene Expression Profiling|Gene Expression Regulation, Plant|Proteome|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-S9-S18', '18793463', 'Circadian rhythm is a crucial factor in orchestration of plant physiology, keeping it in synchrony with the daylight cycle. Previous studies have reported that up to 16% of plant transcriptome are circadially expressed.'),
(92360, 2008, 'bmc', 'A structural approach for finding functional modules from large biological networks', 'Mete, Mutlu|Tang, Fusheng|Xu, Xiaowei|Yuruk, Nurcan', 'Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA. mxmete@ualr.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA. mxmete@ualr.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA. mxmete@ualr.edu|Department of Applied Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA. mxmete@ualr.edu', 'United States|United States|United States|United States', 56, 'Algorithms|Cluster Analysis|Computer Simulation|Models, Biological|Multigene Family|Proteome|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-S9-S19', '18793464', 'Biological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow.'),
(92361, 2008, 'bmc', 'Validation of an NSP-based (negative selection pattern) gene family identification strategy', 'Frank, Ronald L|Kandoth, Cyriac|Ercal, Fikret', 'Biological Sciences Department, Missouri S&T, Rolla, MO 65409, USA. rfrank@mst.edu|Biological Sciences Department, Missouri S&T, Rolla, MO 65409, USA. rfrank@mst.edu|Biological Sciences Department, Missouri S&T, Rolla, MO 65409, USA. rfrank@mst.edu', 'United States|United States|United States', 2, 'Arabidopsis|Arabidopsis Proteins|Chromosome Mapping|Expressed Sequence Tags|Genome, Plant|Multigene Family|Proteome|Selection, Genetic', 'Evaluation Studies', '10.1186/1471-2105-9-S9-S2', '18793465', 'Gene family identification from ESTs can be a valuable resource for analysis of genome evolution but presents unique challenges in organisms for which the entire genome is not yet sequenced. We have developed a novel gene family identification method based on negative selection patterns (NSP) between family members to screen EST-generated contigs. This strategy was tested on five known gene families in Arabidopsis to see if individual paralogs could be identified with accuracy from EST data alone when compared to the actual gene sequences in this fully sequenced genome.'),
(92362, 2008, 'bmc', 'Testing for treatment effects on gene ontology', 'Lee, Taewon|Desai, Varsha G|Velasco, Cruz|Reis, Robert J S|Delongchamp, Robert R', 'Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea. taewon70@korea.ac.kr|Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea. taewon70@korea.ac.kr|Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea. taewon70@korea.ac.kr|Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea. taewon70@korea.ac.kr|Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea. taewon70@korea.ac.kr', '-|-|-|-|-', 15, 'Biomarkers|DNA, Mitochondrial|Drug Evaluation|Drug Therapy|Gene Expression Profiling|Mitochondria|Oligonucleotide Array Sequence Analysis|Outcome Assessment (Health Care)|Pharmaceutical Preparations|Treatment Outcome', 'Journal Article', '10.1186/1471-2105-9-S9-S20', '18793466', 'In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, http://www.geneontology.org) because this facilitates the interpretation of effects and may markedly increase significance. A modified meta-analysis method to combine p-values was developed to measure the significance of an overall treatment effect on such functionally-defined groups of genes, taking into account the correlation structure among genes. For hypothesis testing that allows gene expression to change in both directions, p-values are calculated under the null distribution generated by a Monte Carlo method. As a test of this procedure, we attempted to distinguish altered pathways in microarray studies performed with Mitochips, oligonucleotide microarrays specific to mitochondrial DNA-encoded transcripts. We found that our analytic method improves the specificity of selection for altered pathways, due to incorporation of the inter-gene correlation structure in each pathway. It is thus a practical method to measure treatment effects on GO groups. In many actual applications, microarray experiments measure treatment effects under complicated design structures and with small sample sizes. For such applications to real data of limited statistical power, and also in computer simulations, we demonstrate that our method gives reasonable test results.'),
(92363, 2008, 'bmc', 'Exhaustive prediction of disease susceptibility to coding base changes in the human genome', 'Kulkarni, Vinayak|Errami, Mounir|Barber, Robert|Garner, Harold R', 'Mc Dermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA. vkullu@gmail.com|Mc Dermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA. vkullu@gmail.com|Mc Dermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA. vkullu@gmail.com|Mc Dermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA. vkullu@gmail.com', 'United States|United States|United States|United States', 11, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genetic Predisposition to Disease|Genetic Testing|Genome, Human|Humans|Molecular Sequence Data|Open Reading Frames|Polymorphism, Single Nucleotide|Quantitative Trait, Heritable|Sequence Analysis, DNA', 'Journal Article', '10.1186/1471-2105-9-S9-S3', '18793467', 'Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Bases are subject to various levels of selection pressure, reflected in their inter-species conservation.'),
(92364, 2008, 'bmc', 'From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells', 'Dozmorov, Mikhail G|Kyker, Kimberly D|Hauser, Paul J|Saban, Ricardo|Buethe, David D|Dozmorov, Igor|Centola, Michael B|Culkin, Daniel J|Hurst, Robert E', 'Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu|Department of Urology, Oklahoma University Health Sciences Centre, Oklahoma City, OK 73104, USA. Mikhail-Dozmorov@ouhsc.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 6, 'Biology|Biomarkers, Tumor|Computer Simulation|Data Interpretation, Statistical|Extracellular Matrix Proteins|Gene Expression Profiling|Models, Biological|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Phenotype|Signal Transduction|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-S9-S4', '18793468', 'A statistically robust and biologically-based approach for analysis of microarray data is described that integrates independent biological knowledge and data with a global F-test for finding genes of interest that minimizes the need for replicates when used for hypothesis generation. First, each microarray is normalized to its noise level around zero. The microarray dataset is then globally adjusted by robust linear regression. Second, genes of interest that capture significant responses to experimental conditions are selected by finding those that express significantly higher variance than those expressing only technical variability. Clustering expression data and identifying expression-independent properties of genes of interest including upstream transcriptional regulatory elements (TREs), ontologies and networks or pathways organizes the data into a biologically meaningful system. We demonstrate that when the number of genes of interest is inconveniently large, identifying a subset of beacon genes representing the largest changes will identify pathways or networks altered by biological manipulation. The entire dataset is then used to complete the picture outlined by the beacon genes. This allow construction of a structured model of a system that can generate biologically testable hypotheses. We illustrate this approach by comparing cells cultured on plastic or an extracellular matrix which organizes a dataset of over 2,000 genes of interest from a genome wide scan of transcription. The resulting model was confirmed by comparing the predicted pattern of TREs with experimental determination of active transcription factors.'),
(92365, 2008, 'bmc', 'ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining', 'Huan, Tianxiao|Sivachenko, Andrey Y|Harrison, Scott H|Chen, Jake Y', 'School of Informatics, Indiana University - Purdue University, Indianapolis, IN 46202, USA. huant@iupui.edu|School of Informatics, Indiana University - Purdue University, Indianapolis, IN 46202, USA. huant@iupui.edu|School of Informatics, Indiana University - Purdue University, Indianapolis, IN 46202, USA. huant@iupui.edu|School of Informatics, Indiana University - Purdue University, Indianapolis, IN 46202, USA. huant@iupui.edu', 'India|India|India|India', 31, 'Algorithms|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-S9-S5', '18793469', 'New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed.'),
(92366, 2008, 'bmc', 'MTAP: the motif tool assessment platform', 'Quest, Daniel|Dempsey, Kathryn|Shafiullah, Mohammad|Bastola, Dhundy|Ali, Hesham', 'College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu|College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu|College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu|College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu|College of Information Science & Technology, University of Nebraska at Omaha, Omaha NE, USA. djquest@unmc.edu', 'United States|United States|United States|United States|United States', 7, 'Algorithms|Base Sequence|Molecular Sequence Data|Regulatory Sequences, Nucleic Acid|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-S9-S6', '18793470', 'In recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest.'),
(92367, 2008, 'bmc', 'Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data', 'Rawat, Arun|Seifert, Georg J|Deng, Youping', 'University of Southern Mississippi, Hattiesburg, MS-39406, USA. arun.rawat@usm.edu|University of Southern Mississippi, Hattiesburg, MS-39406, USA. arun.rawat@usm.edu|University of Southern Mississippi, Hattiesburg, MS-39406, USA. arun.rawat@usm.edu', 'United States|United States|United States', 16, 'Algorithms|Carbohydrate Metabolism|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Multigene Family|Oligonucleotide Array Sequence Analysis|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-S9-S7', '18793471', 'Most existing transcriptional databases like Comprehensive Systems-Biology Database (CSB.DB) and Arabidopsis Microarray Database and Analysis Toolbox (GENEVESTIGATOR) help to seek a shared biological role (similar pathways and biosynthetic cycles) based on correlation. These utilize conventional methods like Pearson correlation and Spearman rank correlation to calculate correlation among genes. However, not all are genes expressed in all the conditions and this leads to their exclusion in these transcriptional databases that consist of experiments performed in varied conditions. This leads to incomplete studies of co-regulation among groups of genes that might be linked to the same or related biosynthetic pathway.'),
(92368, 2008, 'bmc', 'Systems biology approach to identification of biomarkers for metastatic progression in cancer', 'Ptitsyn, Andrey A|Weil, Michael M|Thamm, Douglas H', 'Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Colorado, USA. Andrey.Ptitsyn@colostate.edu|Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Colorado, USA. Andrey.Ptitsyn@colostate.edu|Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Colorado, USA. Andrey.Ptitsyn@colostate.edu', 'United States|United States|United States', 36, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Disease Progression|Gene Expression Profiling|Humans|Models, Biological|Neoplasm Metastasis|Neoplasm Proteins|Pattern Recognition, Automated|Signal Transduction|Systems Biology', 'Journal Article', '10.1186/1471-2105-9-S9-S8', '18793472', 'Metastases are responsible for the majority of cancer fatalities. The molecular mechanisms governing metastasis are poorly understood, hindering early diagnosis and treatment. Previous studies of gene expression patterns in metastasis have concentrated on selection of a small number of signature biomarkers.'),
(92369, 2008, 'bmc', 'Very Important Pool (VIP) genes--an application for microarray-based molecular signatures', 'Su, Zhenqiang|Hong, Huixiao|Fang, Hong|Shi, Leming|Perkins, Roger|Tong, Weida', 'Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov|Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov|Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov|Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov|Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov|Center for Toxicoinformatics, National Center for Toxicological Research (NCTR), U,S, Food and Drug Administration (FDA), 3900 NCTR Road, Jefferson, AR 72079, USA. zhenqiang.su@fda.hhs.gov', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Gene Expression Profiling|Gene Pool|Genes|Genetic Markers|Oligonucleotide Array Sequence Analysis|Proteome', 'Journal Article', '10.1186/1471-2105-9-S9-S9', '18793473', 'Advances in DNA microarray technology portend that molecular signatures from which microarray will eventually be used in clinical environments and personalized medicine. Derivation of biomarkers is a large step beyond hypothesis generation and imposes considerably more stringency for accuracy in identifying informative gene subsets to differentiate phenotypes. The inherent nature of microarray data, with fewer samples and replicates compared to the large number of genes, requires identifying informative genes prior to classifier construction. However, improving the ability to identify differentiating genes remains a challenge in bioinformatics.'),
(92370, 2008, 'bmc', 'CBESW: sequence alignment on the Playstation 3', 'Wirawan, Adrianto|Kwoh, Chee Keong|Hieu, Nim Tri|Schmidt, Bertil', 'School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg|School of Computer Engineering, Nanyang Technological University, Singapore. adri0004@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 36, 'Algorithms|Sequence Alignment|Sequence Analysis|Software|Video Games', 'Journal Article', '10.1186/1471-2105-9-377', '18798993', 'The exponential growth of available biological data has caused bioinformatics to be rapidly moving towards a data-intensive, computational science. As a result, the computational power needed by bioinformatics applications is growing exponentially as well. The recent emergence of accelerator technologies has made it possible to achieve an excellent improvement in execution time for many bioinformatics applications, compared to current general-purpose platforms. In this paper, we demonstrate how the PlayStation 3, powered by the Cell Broadband Engine, can be used as a computational platform to accelerate the Smith-Waterman algorithm.'),
(92371, 2008, 'bmc', 'SQUAT: A web tool to mine human, murine and avian SAGE data', 'Leyritz, Johan|Schicklin, Stéphane|Blachon, Sylvain|Keime, Céline|Robardet, Céline|Boulicaut, Jean-François|Besson, Jérémy|Pensa, Ruggero G|Gandrillon, Olivier', 'Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com|Equipe Bases Moléculaires de lAutorenouvellement et de ses Altérations, Université de Lyon, F-69622, Université Lyon 1, Villeurbanne, CNRS, UMR5534, Centre de Génétique Moléculaire et Cellualire, Lyon, France. jleyritz@gmail.com', 'France|France|France|France|France|France|France|France|France', 8, 'Algorithms|Animals|Birds|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Humans|Information Storage and Retrieval|Internet|Mice|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-378', '18801154', 'There is an increasing need in transcriptome research for gene expression data and pattern warehouses. It is of importance to integrate in these warehouses both raw transcriptomic data, as well as some properties encoded in these data, like local patterns.'),
(92372, 2008, 'bmc', 'MiMiR--an integrated platform for microarray data sharing, mining and analysis', 'Tomlinson, Chris|Thimma, Manjula|Alexandrakis, Stelios|Castillo, Tito|Dennis, Jayne L|Brooks, Anthony|Bradley, Thomas|Turnbull, Carly|Blaveri, Ekaterini|Barton, Geraint|Chiba, Norie|Maratou, Klio|Soutter, Pat|Aitman, Tim|Game, Laurence', 'Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk|Microarray Centre, MRC Clinical Sciences Centre and Imperial College, Hammersmith Hospital, London, UK. chris.tomlinson@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 17, 'Database Management Systems|Information Dissemination|Information Storage and Retrieval|Internet|Microarray Analysis|Research Design|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-379', '18801157', 'Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.'),
(92373, 2008, 'bmc', 'A Population Proportion approach for ranking differentially expressed genes', 'Gadgil, Mugdha', 'Chemical Engineering and Process Development, National Chemical Laboratory, Pune, India . mc.gadgil@ncl.res.in', 'India', 4, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1186/1471-2105-9-380', '18801167', 'DNA microarrays are used to investigate differences in gene expression between two or more classes of samples. Most currently used approaches compare mean expression levels between classes and are not geared to find genes whose expression is significantly different in only a subset of samples in a class. However, biological variability can lead to situations where key genes are differentially expressed in only a subset of samples. To facilitate the identification of such genes, a new method is reported.'),
(92374, 2008, 'bmc', 'MetWAMer: eukaryotic translation initiation site prediction', 'Sparks, Michael E|Brendel, Volker', 'Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. mespar1@iastate.edu|Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA. mespar1@iastate.edu', 'United States|United States', 11, 'Algorithms|Arabidopsis|Base Sequence|Bayes Theorem|Codon, Initiator|Computational Biology|Eukaryotic Cells|Markov Chains|Models, Statistical|Open Reading Frames|Predictive Value of Tests|Protein Biosynthesis|Protein Processing, Post-Translational|RNA, Messenger|ROC Curve|Reproducibility of Results|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1186/1471-2105-9-381', '18801175', 'Translation initiation site (TIS) identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for TIS prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations.'),
(92375, 2008, 'bmc', 'A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge', 'Cho, Young-Rae|Shi, Lei|Ramanathan, Murali|Zhang, Aidong', 'Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu|Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA. ycho8@cse.buffalo.edu', 'United States|United States|United States|United States', 17, 'Algorithms|Artificial Intelligence|Binding Sites|Computer Simulation|Data Interpretation, Statistical|Database Management Systems|Databases, Protein|Models, Biological|Models, Chemical|Models, Statistical|Protein Binding|Protein Interaction Mapping|Semantics|Structure-Activity Relationship|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-382', '18801191', 'The functional characterization of newly discovered proteins has been a challenge in the post-genomic era. Protein-protein interactions provide insights into the functional analysis because the function of unknown proteins can be postulated on the basis of their interaction evidence with known proteins. The protein-protein interaction data sets have been enriched by high-throughput experimental methods. However, the functional analysis using the interaction data has a limitation in accuracy because of the presence of the false positive data experimentally generated and the interactions that are a lack of functional linkage.'),
(92376, 2008, 'bmc', 'caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data', 'Zhu, Yitan|Li, Huai|Miller, David J|Wang, Zuyi|Xuan, Jianhua|Clarke, Robert|Hoffman, Eric P|Wang, Yue', 'Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic and State University, Arlington, VA 22203, USA. yitanzhu@vt.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Base Sequence|Chromosome Mapping|Cluster Analysis|Computer Graphics|Computer Simulation|Databases, Genetic|Models, Genetic|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-383', '18801195', 'The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables.'),
(92377, 2008, 'bmc', 'GlycomeDB - integration of open-access carbohydrate structure databases', 'Ranzinger, René|Herget, Stephan|Wetter, Thomas|von der Lieth, Claus-Wilhelm', 'German Cancer Research Center DKFZ, Core Facility Molecular Structural Analysis, Im Neuenheimer Feld 280, Heidelberg, Germany. r.ranzinger@dkfz.de|German Cancer Research Center DKFZ, Core Facility Molecular Structural Analysis, Im Neuenheimer Feld 280, Heidelberg, Germany. r.ranzinger@dkfz.de|German Cancer Research Center DKFZ, Core Facility Molecular Structural Analysis, Im Neuenheimer Feld 280, Heidelberg, Germany. r.ranzinger@dkfz.de|German Cancer Research Center DKFZ, Core Facility Molecular Structural Analysis, Im Neuenheimer Feld 280, Heidelberg, Germany. r.ranzinger@dkfz.de', 'Germany|Germany|Germany|Germany', 66, 'Carbohydrate Metabolism|Carbohydrate Sequence|Carbohydrates|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Molecular Sequence Data|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-384', '18803830', 'Although carbohydrates are the third major class of biological macromolecules, after proteins and DNA, there is neither a comprehensive database for carbohydrate structures nor an established universal structure encoding scheme for computational purposes. Funding for further development of the Complex Carbohydrate Structure Database (CCSD or CarbBank) ceased in 1997, and since then several initiatives have developed independent databases with partially overlapping foci. For each database, different encoding schemes for residues and sequence topology were designed. Therefore, it is virtually impossible to obtain an overview of all deposited structures or to compare the contents of the various databases.'),
(92378, 2008, 'bmc', 'Improving peptide-MHC class I binding prediction for unbalanced datasets', 'Sales, Ana Paula|Tomaras, Georgia D|Kepler, Thomas B', 'Center for Computational Immunology, Duke University, Durham, NC 27705, USA. ad44@duke.edu|Center for Computational Immunology, Duke University, Durham, NC 27705, USA. ad44@duke.edu|Center for Computational Immunology, Duke University, Durham, NC 27705, USA. ad44@duke.edu', 'United States|United States|United States', 0, 'Algorithms|Histocompatibility Antigens Class I|Humans|Peptides|Vaccines, Subunit', 'Journal Article', '10.1186/1471-2105-9-385', '18803836', 'Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI) is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms.'),
(92379, 2008, 'bmc', 'The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes', 'Meyer, F|Paarmann, D|DSouza, M|Olson, R|Glass, E M|Kubal, M|Paczian, T|Rodriguez, A|Stevens, R|Wilke, A|Wilkening, J|Edwards, R A', 'Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.gov|Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Avenue, Argonne, IL 60439, USA. folker@mcs.anl.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', 2147, 'Algorithms|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Phylogeny|Proteome|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-386', '18803844', 'Random community genomes (metagenomes) are now commonly used to study microbes in different environments. Over the past few years, the major challenge associated with metagenomics shifted from generating to analyzing sequences. High-throughput, low-cost next-generation sequencing has provided access to metagenomics to a wide range of researchers.'),
(92380, 2008, 'bmc', 'TOF2H: a precision toolbox for rapid, high density/high coverage hydrogen-deuterium exchange mass spectrometry via an LC-MALDI approach, covering the data pipeline from spectral acquisition to HDX rate analysis', 'Nikamanon, Pornpat|Pun, Elroy|Chou, Wayne|Koter, Marek D|Gershon, Paul D', 'Department of Molecular Biology & Biochemistry, UC-Irvine, Irvine, CA 92697, USA. pornpatn@gmail.com|Department of Molecular Biology & Biochemistry, UC-Irvine, Irvine, CA 92697, USA. pornpatn@gmail.com|Department of Molecular Biology & Biochemistry, UC-Irvine, Irvine, CA 92697, USA. pornpatn@gmail.com|Department of Molecular Biology & Biochemistry, UC-Irvine, Irvine, CA 92697, USA. pornpatn@gmail.com|Department of Molecular Biology & Biochemistry, UC-Irvine, Irvine, CA 92697, USA. pornpatn@gmail.com', 'United States|United States|United States|United States|United States', 27, 'Algorithms|Chromatography, Liquid|Deuterium Exchange Measurement|Information Storage and Retrieval|Protein Conformation|Proteins|Sensitivity and Specificity|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-9-387', '18803853', 'Protein-amide proton hydrogen-deuterium exchange (HDX) is used to investigate protein conformation, conformational changes and surface binding sites for other molecules. To our knowledge, software tools to automate data processing and analysis from sample fractionating (LC-MALDI) mass-spectrometry-based HDX workflows are not publicly available.'),
(92381, 2008, 'bmc', 'Sequence based residue depth prediction using evolutionary information and predicted secondary structure', 'Zhang, Hua|Zhang, Tuo|Chen, Ke|Shen, Shiyi|Ruan, Jishou|Kurgan, Lukasz', 'College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com|College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com|College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com|College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com|College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com|College of Mathematical Science and LPMC, Nankai University, Tianjin, PR China. zerohua@gmail.com', 'China|China|China|China|China|China', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Evolution, Molecular|Models, Chemical|Models, Molecular|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1186/1471-2105-9-388', '18803867', 'Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.'),
(92382, 2008, 'bmc', 'Combining classifiers for improved classification of proteins from sequence or structure', 'Melvin, Iain|Weston, Jason|Leslie, Christina S|Noble, William S', 'NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com|NEC Laboratories of America, Princeton, NJ, USA. iainmelvin@gmail.com', 'United States|United States|United States|United States', 20, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-389', '18808707', 'Predicting a proteins structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage.'),
(92383, 2008, 'bmc', 'R/parallel--speeding up bioinformatics analysis with R', 'Vera, Gonzalo|Jansen, Ritsert C|Suppi, Remo L', 'Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, Bellaterra, Spain. gonzalo.vera@rparallel.org|Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, Bellaterra, Spain. gonzalo.vera@rparallel.org|Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, Bellaterra, Spain. gonzalo.vera@rparallel.org', 'Spain|Spain|Spain', 24, 'Algorithms|Chromosome Mapping|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Programming Languages|Quantitative Trait Loci|Software Design', 'Journal Article', '10.1186/1471-2105-9-390', '18808714', 'R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.'),
(92384, 2008, 'bmc', 'SSMap: a new UniProt-PDB mapping resource for the curation of structural-related information in the UniProt/Swiss-Prot Knowledgebase', 'David, Fabrice P A|Yip, Yum L', 'Swiss Institute of Bioinformatics, Swiss-Prot group, 1 rue Michel Servet, 1211 Geneva, Switzerland. fabrice.david@isb-sib.ch|Swiss Institute of Bioinformatics, Swiss-Prot group, 1 rue Michel Servet, 1211 Geneva, Switzerland. fabrice.david@isb-sib.ch', 'Switzerland|Switzerland', 12, 'Algorithms|Amino Acid Sequence|Databases, Protein|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-391', '18811932', 'Sequences and structures provide valuable complementary information on protein features and functions. However, it is not always straightforward for users to gather information concurrently from the sequence and structure levels. The UniProt knowledgebase (UniProtKB) strives to help users on this undertaking by providing complete cross-references to Protein Data Bank (PDB) as well as coherent feature annotation using available structural information. In this study, SSMap - a new UniProt-PDB residue-residue level mapping - was generated. The primary objective of this mapping is not only to facilitate the two tasks mentioned above, but also to palliate a number of shortcomings of existent mappings. SSMap is the first isoform sequence-specific mapping resource and is up-to-date for UniProtKB annotation tasks. The method employed by SSMap differs from the other mapping resources in that it stresses on the correct reconstruction of the PDB sequence from structures, and on the correct attribution of a UniProtKB entry to each PDB chain by using a series of post-processing steps.'),
(92385, 2008, 'bmc', 'PredGPI: a GPI-anchor predictor', 'Pierleoni, Andrea|Martelli, Pier Luigi|Casadio, Rita', 'Biocomputing Group, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it|Biocomputing Group, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it|Biocomputing Group, Department of Biology, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy. andrea@biocomp.unibo.it', 'Italy|Italy|Italy', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Glycosylphosphatidylinositols|Models, Chemical|Molecular Sequence Data|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-392', '18811934', 'Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called omega-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92386, 2008, 'bmc', 'HECTAR: a method to predict subcellular targeting in heterokonts', 'Gschloessl, Bernhard|Guermeur, Yann|Cock, J Mark', 'UPMC Univ Paris 6, UMR 7139 Végétaux marins et Biomolécules, Station Biologique, F 29682, Roscoff, France. gschloesslb@gmail.com|UPMC Univ Paris 6, UMR 7139 Végétaux marins et Biomolécules, Station Biologique, F 29682, Roscoff, France. gschloesslb@gmail.com|UPMC Univ Paris 6, UMR 7139 Végétaux marins et Biomolécules, Station Biologique, F 29682, Roscoff, France. gschloesslb@gmail.com', 'France|France|France', 98, 'Algorithms|Artificial Intelligence|Eukaryotic Cells|Pattern Recognition, Automated|Proteome|Sequence Analysis, Protein|Software|Structure-Activity Relationship|Subcellular Fractions', 'Journal Article', '10.1186/1471-2105-9-393', '18811941', 'The heterokonts are a particularly interesting group of eukaryotic organisms, they include many key species of planktonic and coastal algae and several important pathogens. To understand the biology of these organisms, it is necessary to be able to predict the subcellular localisation of their proteins but this is not straightforward, particularly in photosynthetic heterokonts which possess a complex chloroplast, acquired as the result of a secondary endosymbiosis. This is because the bipartite target peptides that deliver proteins to these chloroplasts can be easily confused with the signal peptides of secreted proteins, causing currently available algorithms to make erroneous predictions. HECTAR, a subcellular targeting prediction method which takes into account the specific properties of heterokont proteins, has been developed to address this problem.'),
(92387, 2008, 'bmc', 'Comparison study on k-word statistical measures for protein: from sequence to sequence space', 'Dai, Qi|Wang, Tianming', 'Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. daiailiu2004@yahoo.com.cn|Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. daiailiu2004@yahoo.com.cn', 'China|China', 34, 'Algorithms|Amino Acid Sequence|Computer Simulation|Data Interpretation, Statistical|Models, Chemical|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1186/1471-2105-9-394', '18811946', 'Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using k-word frequencies of protein sequences. Given a protein sequence, the information on its related protein sequences hasnt been used for protein sequence comparison until now. This paper proposed a scheme to construct protein sequence space which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein sequence space or not. This paper also presented two statistical measures for protein: gre.k (generalized relative entropy) and gsm.k (gapped similarity measure).'),
(92388, 2008, 'bmc', 'GBNet: deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach', 'Shen, Li|Liu, Jie|Wang, Wei', 'Department of Chemistry and Biochemistry, University of California, San Diego, California, USA. shen@ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego, California, USA. shen@ucsd.edu|Department of Chemistry and Biochemistry, University of California, San Diego, California, USA. shen@ucsd.edu', 'United States|United States|United States', 0, 'Bayes Theorem|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Multigene Family|Proteome|Signal Transduction|Software|Transcriptional Activation', 'Journal Article', '10.1186/1471-2105-9-395', '18811979', 'Combinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.'),
(92389, 2008, 'bmc', 'FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects', 'Lagorce, David|Sperandio, Olivier|Galons, Hervé|Miteva, Maria A|Villoutreix, Bruno O', 'INSERM U648, MTi team, Paris Descartes University, Paris Diderot University, Paris, France. david.lagorce@univ-paris-diderot.fr|INSERM U648, MTi team, Paris Descartes University, Paris Diderot University, Paris, France. david.lagorce@univ-paris-diderot.fr|INSERM U648, MTi team, Paris Descartes University, Paris Diderot University, Paris, France. david.lagorce@univ-paris-diderot.fr|INSERM U648, MTi team, Paris Descartes University, Paris Diderot University, Paris, France. david.lagorce@univ-paris-diderot.fr|INSERM U648, MTi team, Paris Descartes University, Paris Diderot University, Paris, France. david.lagorce@univ-paris-diderot.fr', 'France|France|France|France|France', 185, 'Algorithms|Biochemistry|Computer Simulation|Drug Design|Models, Chemical|Pharmaceutical Preparations|Research|Software', 'Journal Article', '10.1186/1471-2105-9-396', '18816385', 'Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.'),
(92390, 2008, 'bmc', 'Predicting protein linkages in bacteria: which method is best depends on task', 'Karimpour-Fard, Anis|Leach, Sonia M|Gill, Ryan T|Hunter, Lawrence E', 'Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA. anis.karimpour-fard@uchsc.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA. anis.karimpour-fard@uchsc.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA. anis.karimpour-fard@uchsc.edu|Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA. anis.karimpour-fard@uchsc.edu', 'United States|United States|United States|United States', 15, 'Algorithms|Bacterial Proteins|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-397', '18816389', 'Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations.'),
(92391, 2008, 'bmc', 'Modifying the DPClus algorithm for identifying protein complexes based on new topological structures', 'Li, Min|Chen, Jian-er|Wang, Jian-xin|Hu, Bin|Chen, Gang', 'School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn|School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, PR China. limin@mail.csu.edu.cn', 'China|China|China|China|China', 179, 'Algorithms|Computer Simulation|Models, Biological|Multigene Family|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1186/1471-2105-9-398', '18816408', 'Identification of protein complexes is crucial for understanding principles of cellular organization and functions. As the size of protein-protein interaction set increases, a general trend is to represent the interactions as a network and to develop effective algorithms to detect significant complexes in such networks.'),
(92392, 2008, 'bmc', 'Presenting and exploring biological pathways with PathVisio', 'van Iersel, Martijn P|Kelder, Thomas|Pico, Alexander R|Hanspers, Kristina|Coort, Susan|Conklin, Bruce R|Evelo, Chris', 'Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl|Department of Bioinformatics, BiGCaT Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands. martijn.vaniersel@bigcat.unimaas.nl', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 260, 'Algorithms|Computer Graphics|Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-399', '18817533', 'Biological pathways are a useful abstraction of biological concepts, and software tools to deal with pathway diagrams can help biological research. PathVisio is a new visualization tool for biological pathways that mimics the popular GenMAPP tool with a completely new Java implementation that allows better integration with other open source projects. The GenMAPP MAPP file format is replaced by GPML, a new XML file format that provides seamless exchange of graphical pathway information among multiple programs.'),
(92393, 2008, 'bmc', 'Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction', 'Kuhn, Stefan|Egert, Björn|Neumann, Steffen|Steinbeck, Christoph', 'Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany. stefhk3@web.de|Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany. stefhk3@web.de|Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany. stefhk3@web.de|Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, Weinberg 3, 06120 Halle, Germany. stefhk3@web.de', 'Germany|Germany|Germany|Germany', 64, 'Algorithms|Artificial Intelligence|Linear Models|Magnetic Resonance Spectroscopy|Metabolomics|Models, Molecular|Molecular Structure|Pattern Recognition, Automated|Principal Component Analysis', 'Journal Article', '10.1186/1471-2105-9-400', '18817546', 'Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB.'),
(92394, 2008, 'bmc', 'Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem', 'Han, Lianyi|Wang, Yanli|Bryant, Stephen H', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. hanl@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. hanl@ncbi.nlm.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. hanl@ncbi.nlm.nih.gov', 'United States|United States|United States', 76, 'Artificial Intelligence|Biological Assay|Decision Support Techniques|Decision Trees|Drug Discovery|Information Storage and Retrieval|Pattern Recognition, Automated|Quantitative Structure-Activity Relationship|Ribonuclease H, Human Immunodeficiency Virus|Sensitivity and Specificity|Serotonin Plasma Membrane Transport Proteins', 'Journal Article', '10.1186/1471-2105-9-401', '18817552', 'Recent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.'),
(92395, 2008, 'bmc', 'Abbreviation definition identification based on automatic precision estimates', 'Sohn, Sunghwan|Comeau, Donald C|Kim, Won|Wilbur, W John', 'National Centre for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. s.sohn@yahoo.com|National Centre for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. s.sohn@yahoo.com|National Centre for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. s.sohn@yahoo.com|National Centre for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. s.sohn@yahoo.com', 'United States|United States|United States|United States', 43, 'Abbreviations as Topic|Abstracting and Indexing as Topic|Databases, Bibliographic|Humans|Natural Language Processing|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-9-402', '18817555', 'The rapid growth of biomedical literature presents challenges for automatic text processing, and one of the challenges is abbreviation identification. The presence of unrecognized abbreviations in text hinders indexing algorithms and adversely affects information retrieval and extraction. Automatic abbreviation definition identification can help resolve these issues. However, abbreviations and their definitions identified by an automatic process are of uncertain validity. Due to the size of databases such as MEDLINE only a small fraction of abbreviation-definition pairs can be examined manually. An automatic way to estimate the accuracy of abbreviation-definition pairs extracted from text is needed. In this paper we propose an abbreviation definition identification algorithm that employs a variety of strategies to identify the most probable abbreviation definition. In addition our algorithm produces an accuracy estimate, pseudo-precision, for each strategy without using a human-judged gold standard. The pseudo-precisions determine the order in which the algorithm applies the strategies in seeking to identify the definition of an abbreviation.'),
(92396, 2008, 'bmc', 'MetaMQAP: a meta-server for the quality assessment of protein models', 'Pawlowski, Marcin|Gajda, Michal J|Matlak, Ryszard|Bujnicki, Janusz M', 'Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. marcinp@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. marcinp@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. marcinp@genesilico.pl|Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology, Trojdena 4, PL-02-109 Warsaw, Poland. marcinp@genesilico.pl', 'Poland|Poland|Poland|Poland', 149, 'Artificial Intelligence|Computational Biology|Database Management Systems|Meta-Analysis as Topic|Models, Molecular|Multivariate Analysis|Observer Variation|Pattern Recognition, Automated|Protein Conformation|Proteins|Quality Control|Regression Analysis|Research Design', 'Journal Article', '10.1186/1471-2105-9-403', '18823532', 'Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors.'),
(92397, 2008, 'bmc', 'Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells', 'Niida, Atsushi|Smith, Andrew D|Imoto, Seiya|Tsutsumi, Shuichi|Aburatani, Hiroyuki|Zhang, Michael Q|Akiyama, Tetsu', 'Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp|Laboratory of Molecular and Genetic Information, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, 110-0032, Japan. niida@iam.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 29, 'Algorithms|Bayes Theorem|Breast Neoplasms|CCAAT-Binding Factor|Computational Biology|Consensus Sequence|Disease Progression|E2F4 Transcription Factor|Female|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Microarray Analysis|Nuclear Respiratory Factor 1|Nucleic Acid Conformation|Prognosis|Regulatory Elements, Transcriptional|Regulatory Sequences, Nucleic Acid|Transcription, Genetic|ets-Domain Protein Elk-1', 'Journal Article', '10.1186/1471-2105-9-404', '18823535', 'Microarray technology has unveiled transcriptomic differences among tumors of various phenotypes, and, especially, brought great progress in molecular understanding of phenotypic diversity of breast tumors. However, compared with the massive knowledge about the transcriptome, we have surprisingly little knowledge about regulatory mechanisms underling transcriptomic diversity.'),
(92398, 2008, 'bmc', 'iRefIndex: a consolidated protein interaction database with provenance', 'Razick, Sabry|Magklaras, George|Donaldson, Ian M', 'The Biotechnology Centre of Oslo, University of Oslo, PO Box 1125 Blindern, 0317 Oslo, Norway. sabry.razick@biotek.uio.no|The Biotechnology Centre of Oslo, University of Oslo, PO Box 1125 Blindern, 0317 Oslo, Norway. sabry.razick@biotek.uio.no|The Biotechnology Centre of Oslo, University of Oslo, PO Box 1125 Blindern, 0317 Oslo, Norway. sabry.razick@biotek.uio.no', 'Norway|Norway|Norway', 339, 'Abstracting and Indexing as Topic|Amino Acid Sequence|Animals|Data Compression|Database Management Systems|Databases, Protein|Humans|Neural Networks (Computer)|Protein Interaction Mapping|Proteins|Proteome|Proteomics', 'Journal Article', '10.1186/1471-2105-9-405', '18823568', 'Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.'),
(92399, 2008, 'bmc', 'BibGlimpse: the case for a light-weight reprint manager in distributed literature research', 'Tüchler, Thomas|Velez, Golda|Graf, Alexandra|Kreil, David P', 'Chair of Bioinformatics, Boku University, Vienna, Austria. thomas.tuechler@boku.ac.at|Chair of Bioinformatics, Boku University, Vienna, Austria. thomas.tuechler@boku.ac.at|Chair of Bioinformatics, Boku University, Vienna, Austria. thomas.tuechler@boku.ac.at|Chair of Bioinformatics, Boku University, Vienna, Austria. thomas.tuechler@boku.ac.at', 'Austria|Austria|Austria|Austria', 0, 'Abstracting and Indexing as Topic|Database Management Systems|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Publications|Research Design|Terminology as Topic', 'Journal Article', '10.1186/1471-2105-9-406', '18828894', 'While text-mining and distributed annotation systems both aim at capturing knowledge and presenting it in a standardized form, there have been few attempts to investigate potential synergies between these two fields. For instance, distributed annotation would be very well suited for providing topic focussed, expert knowledge enriched text corpora. A key limitation for this approach is the availability of literature annotation systems that can be routinely used by groups of collaborating researchers on a day to day basis, not distracting from the main focus of their work.'),
(92400, 2008, 'bmc', 'Towards a lightweight generic computational grid framework for biological research', 'Halling-Brown, Mark D|Moss, David S|Shepherd, Adrian J', 'Institute of Structural and Molecular Biology, School of Crystallography, Birkbeck College, Malet Street, London, WC1E 7HX, UK. m.halling-brown@mail.cryst.bbk.ac.uk|Institute of Structural and Molecular Biology, School of Crystallography, Birkbeck College, Malet Street, London, WC1E 7HX, UK. m.halling-brown@mail.cryst.bbk.ac.uk|Institute of Structural and Molecular Biology, School of Crystallography, Birkbeck College, Malet Street, London, WC1E 7HX, UK. m.halling-brown@mail.cryst.bbk.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 4, 'Biology|Computer Simulation|Immune System|Internet|Models, Immunological|Research Design|Systems Biology', 'Journal Article', '10.1186/1471-2105-9-407', '18831735', 'An increasing number of scientific research projects require access to large-scale computational resources. This is particularly true in the biological field, whether to facilitate the analysis of large high-throughput data sets, or to perform large numbers of complex simulations - a characteristic of the emerging field of systems biology.'),
(92401, 2008, 'bmc', 'Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analyzed with IMGT tools', 'Bleakley, Kevin|Lefranc, Marie-Paule|Biau, Gérard', 'Institut Curie, Centre de Recherche, Paris, F-75248, France. bleakley@math.univ-montp2.fr|Institut Curie, Centre de Recherche, Paris, F-75248, France. bleakley@math.univ-montp2.fr|Institut Curie, Centre de Recherche, Paris, F-75248, France. bleakley@math.univ-montp2.fr', 'France|France|France', 6, 'Base Sequence|Computational Biology|Confidence Intervals|Databases, Genetic|Gene Rearrangement, B-Lymphocyte|Gene Rearrangement, T-Lymphocyte|Genes, Immunoglobulin|Humans|Immunogenetics|Immunoglobulins|Models, Genetic|Nucleotides|Probability|Receptors, Antigen, T-Cell|Sequence Analysis, DNA|Statistical Distributions', 'Journal Article', '10.1186/1471-2105-9-408', '18831754', 'Nucleotides are trimmed from the ends of variable (V), diversity (D) and joining (J) genes during immunoglobulin (IG) and T cell receptor (TR) rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides) of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool output). However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed output represents a biased estimate of the true trimming process.'),
(92402, 2008, 'bmc', 'Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios', 'Staaf, Johan|Vallon-Christersson, Johan|Lindgren, David|Juliusson, Gunnar|Rosenquist, Richard|Höglund, Mattias|Borg, Ake|Ringnér, Markus', 'Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se|Department of Oncology, Clinical Sciences, Lund University, SE-22185 Lund, Sweden. johan.staaf@med.lu.se', 'Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden|Sweden', 103, 'Allelic Imbalance|Artifacts|DNA, Neoplasm|Gene Dosage|Gene Expression Profiling|Genome, Human|Genomics|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Polymorphism, Single Nucleotide|Reference Standards|Research Design|Subtraction Technique', 'Journal Article', '10.1186/1471-2105-9-409', '18831757', 'Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples.'),
(92403, 2008, 'bmc', 'Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments', 'Parodi, Stefano|Pistoia, Vito|Muselli, Marco', 'Epidemiology and Biostatistics Section, Scientific Directorate, G. Gaslini Childrens Hospital, Genoa, Italy. stefanoparodi@ospedale-gaslini.ge.it|Epidemiology and Biostatistics Section, Scientific Directorate, G. Gaslini Childrens Hospital, Genoa, Italy. stefanoparodi@ospedale-gaslini.ge.it|Epidemiology and Biostatistics Section, Scientific Directorate, G. Gaslini Childrens Hospital, Genoa, Italy. stefanoparodi@ospedale-gaslini.ge.it', 'Italy|Italy|Italy', 14, 'Animals|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Lymphoma|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Predictive Value of Tests|ROC Curve', 'Journal Article', '10.1186/1471-2105-9-410', '18834513', 'Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR). ABCR represents a more general approach than the standard area under the ROC curve (AUC), because it can identify both proper (i.e., concave) and not proper ROC curves (NPRC). In particular, NPRC may correspond to those genes that tend to escape standard selection methods.'),
(92404, 2008, 'bmc', 'Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method', 'Armutlu, Pelin|Ozdemir, Muhittin E|Uney-Yuksektepe, Fadime|Kavakli, I Halil|Turkay, Metin', 'Department of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey. parmutlu@ku.edu.tr|Department of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey. parmutlu@ku.edu.tr|Department of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey. parmutlu@ku.edu.tr|Department of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey. parmutlu@ku.edu.tr|Department of Industrial Engineering, Koç University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey. parmutlu@ku.edu.tr', 'Turkey|Turkey|Turkey|Turkey|Turkey', 19, 'Artificial Intelligence|Cholinesterase Inhibitors|Cyclooxygenase 2 Inhibitors|Databases, Factual|Drug Discovery|GABA-A Receptor Antagonists|Inhibitory Concentration 50|Least-Squares Analysis|Pharmaceutical Preparations|Programming, Linear|Protein Binding|Quantitative Structure-Activity Relationship|Tetrahydrofolate Dehydrogenase', 'Journal Article', '10.1186/1471-2105-9-411', '18834515', 'A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules.'),
(92405, 2008, 'bmc', 'Gene and pathway identification with Lp penalized Bayesian logistic regression', 'Liu, Zhenqiu|Gartenhaus, Ronald B|Tan, Ming|Jiang, Feng|Jiao, Xiaoli', 'Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu|Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. zliu@umm.edu', 'United States|United States|United States|United States|United States', 5, 'Algorithms|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Logistic Models|Models, Biological|Proteome|Regression Analysis|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-412', '18834526', 'Identifying genes and pathways associated with diseases such as cancer has been a subject of considerable research in recent years in the area of bioinformatics and computational biology. It has been demonstrated that the magnitude of differential expression does not necessarily indicate biological significance. Even a very small change in the expression of particular gene may have dramatic physiological consequences if the protein encoded by this gene plays a catalytic role in a specific cell function. Moreover, highly correlated genes may function together on the same pathway biologically. Finally, in sparse logistic regression with Lp (p < 1) penalty, the degree of the sparsity obtained is determined by the value of the regularization parameter. Usually this parameter must be carefully tuned through cross-validation, which is time consuming.'),
(92406, 2008, 'bmc', 'PhySIC_IST: cleaning source trees to infer more informative supertrees', 'Scornavacca, Celine|Berry, Vincent|Lefort, Vincent|Douzery, Emmanuel J P|Ranwez, Vincent', 'Institut des Sciences de lEvolution (ISEM, UMR 5554 CNRS), Université Montpellier II, Place E, Bataillon - CC 064 - 34095 Montpellier Cedex 5, France. scornava@lirmm.fr|Institut des Sciences de lEvolution (ISEM, UMR 5554 CNRS), Université Montpellier II, Place E, Bataillon - CC 064 - 34095 Montpellier Cedex 5, France. scornava@lirmm.fr|Institut des Sciences de lEvolution (ISEM, UMR 5554 CNRS), Université Montpellier II, Place E, Bataillon - CC 064 - 34095 Montpellier Cedex 5, France. scornava@lirmm.fr|Institut des Sciences de lEvolution (ISEM, UMR 5554 CNRS), Université Montpellier II, Place E, Bataillon - CC 064 - 34095 Montpellier Cedex 5, France. scornava@lirmm.fr|Institut des Sciences de lEvolution (ISEM, UMR 5554 CNRS), Université Montpellier II, Place E, Bataillon - CC 064 - 34095 Montpellier Cedex 5, France. scornava@lirmm.fr', 'France|France|France|France|France', 39, 'Algorithms|Animals|Biometry|Decision Trees|Information Storage and Retrieval|Models, Genetic|Neural Networks (Computer)|Phylogeny|Quality Control|Sensitivity and Specificity|Systems Integration', 'Journal Article', '10.1186/1471-2105-9-413', '18834542', 'Supertree methods combine phylogenies with overlapping sets of taxa into a larger one. Topological conflicts frequently arise among source trees for methodological or biological reasons, such as long branch attraction, lateral gene transfers, gene duplication/loss or deep gene coalescence. When topological conflicts occur among source trees, liberal methods infer supertrees containing the most frequent alternative, while veto methods infer supertrees not contradicting any source tree, i.e. discard all conflicting resolutions. When the source trees host a significant number of topological conflicts or have a small taxon overlap, supertree methods of both kinds can propose poorly resolved, hence uninformative, supertrees.'),
(92407, 2008, 'bmc', 'Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach', 'Anwar, Firoz|Baker, Syed Murtuza|Jabid, Taskeed|Mehedi Hasan, Md|Shoyaib, Mohammad|Khan, Haseena|Walshe, Ray', 'Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu|Department of Computer Science and Engineering, East West University, Bangladesh. fanwar@ewubd.edu', 'Bangladesh|Bangladesh|Bangladesh|Bangladesh|Bangladesh|Bangladesh|Bangladesh', 26, 'Animals|Artificial Intelligence|Databases, Nucleic Acid|Drosophila Proteins|Eukaryotic Cells|Genomics|Humans|Mice|Nucleic Acid Conformation|Promoter Regions, Genetic|RNA Polymerase II|Rats|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Structure-Activity Relationship', 'Journal Article', '10.1186/1471-2105-9-414', '18834544', 'Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.'),
(92408, 2008, 'bmc', 'BATS: a Bayesian user-friendly software for analyzing time series microarray experiments', 'Angelini, Claudia|Cutillo, Luisa|De Canditiis, Daniela|Mutarelli, Margherita|Pensky, Marianna', 'Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it|Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it|Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it|Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it|Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it', 'Italy|Italy|Italy|Italy|Italy', 0, 'Algorithms|Bayes Theorem|Gene Expression|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Research Design|Sample Size|Time Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-415', '18837969', 'Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a one-sample time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient.'),
(92409, 2008, 'bmc', 'Knowledge-guided multi-scale independent component analysis for biomarker identification', 'Chen, Li|Xuan, Jianhua|Wang, Chen|Shih, Ie-Ming|Wang, Yue|Zhang, Zhen|Hoffman, Eric|Clarke, Robert', 'Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu|Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA. lchen06@vt.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Biomarkers|Cell Cycle|Cluster Analysis|Database Management Systems|Databases, Genetic|Female|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Humans|Knowledge Bases|Meta-Analysis as Topic|Neural Networks (Computer)|Nuclear Proteins|Oligonucleotide Array Sequence Analysis|Ovarian Neoplasms|Pattern Recognition, Automated|Principal Component Analysis|Regulatory Sequences, Nucleic Acid|Saccharomyces cerevisiae|Sequence Analysis, DNA|Systems Biology|Trans-Activators', 'Journal Article', '10.1186/1471-2105-9-416', '18837990', 'Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.'),
(92410, 2008, 'bmc', 'A method for analyzing censored survival phenotype with gene expression data', 'Wu, Tongtong|Sun, Wei|Yuan, Shinsheng|Chen, Chun-Houh|Li, Ker-Chau', 'Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu|Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu|Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu|Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu|Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA. ttwu@umd.edu', 'United States|United States|United States|United States|United States', 11, 'Biomarkers, Tumor|Biometry|Breast Neoplasms|Female|Gene Expression|Gene Expression Profiling|Humans|Kaplan-Meier Estimate|Principal Component Analysis|Regression Analysis|Survival Rate', 'Journal Article', '10.1186/1471-2105-9-417', '18837994', 'Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.'),
(92411, 2008, 'bmc', 'Automatically extracting functionally equivalent proteins from SwissProt', 'McMillan, Lisa E M|Martin, Andrew C R', 'Research Department of Structural & Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK. mcmillan@biochem.ucl.ac.uk|Research Department of Structural & Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK. mcmillan@biochem.ucl.ac.uk', 'United Kingdom|United Kingdom', 11, 'Animals|Databases, Protein|Humans|Internet|Sequence Analysis, Protein|Software', 'Journal Article', '10.1186/1471-2105-9-418', '18838004', 'There is a frequent need to obtain sets of functionally equivalent homologous proteins (FEPs) from different species. While it is usually the case that orthology implies functional equivalence, this is not always true, therefore datasets of orthologous proteins are not appropriate. The information relevant to extracting FEPs is contained in databanks such as UniProtKB/Swiss-Prot and a manual analysis of these data allow FEPs to be extracted on a one-off basis. However there has been no resource allowing the easy, automatic extraction of groups of FEPs - for example, all instances of protein C.We have developed FOSTA, an automatically generated database of FEPs annotated as having the same function in UniProtKB/Swiss-Prot which can be used for large-scale analysis. The method builds a candidate list of homologues and filters out functionally diverged proteins on the basis of functional annotations using a simple text mining approach.'),
(92412, 2008, 'bmc', 'A database of phylogenetically atypical genes in archaeal and bacterial genomes, identified using the DarkHorse algorithm', 'Podell, Sheila|Gaasterland, Terry|Allen, Eric E', 'Marine Biology Research Division, Scripps Institution of Oceanography University of California at San Diego, La Jolla, CA 92093 USA. spodell@ucsd.edu|Marine Biology Research Division, Scripps Institution of Oceanography University of California at San Diego, La Jolla, CA 92093 USA. spodell@ucsd.edu|Marine Biology Research Division, Scripps Institution of Oceanography University of California at San Diego, La Jolla, CA 92093 USA. spodell@ucsd.edu', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Base Composition|Databases, Genetic|Evolution, Molecular|Gene Transfer, Horizontal|Genome, Archaeal|Genome, Bacterial|Neural Networks (Computer)|Phylogeny|Probability|Proteins|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-419', '18840280', 'The process of horizontal gene transfer (HGT) is believed to be widespread in Bacteria and Archaea, but little comparative data is available addressing its occurrence in complete microbial genomes. Collection of high-quality, automated HGT prediction data based on phylogenetic evidence has previously been impractical for large numbers of genomes at once, due to prohibitive computational demands. DarkHorse, a recently described statistical method for discovering phylogenetically atypical genes on a genome-wide basis, provides a means to solve this problem through lineage probability index (LPI) ranking scores. LPI scores inversely reflect phylogenetic distance between a test amino acid sequence and its closest available database matches. Proteins with low LPI scores are good horizontal gene transfer candidates, those with high scores are not.'),
(92413, 2008, 'bmc', 'XplorSeq: a software environment for integrated management and phylogenetic analysis of metagenomic sequence data', 'Frank, Daniel N', 'Department of Molecular, Cellular, and Developmental Biology, Mucosal and Vaccine Research Program Colorado, University of Colorado, Boulder, CO, USA 80309, USA. daniel.frank@colorado.edu', 'United States', 0, 'Artificial Intelligence|Base Sequence|Database Management Systems|Genes, rRNA|Genome|Information Storage and Retrieval|Meta-Analysis as Topic|Phylogeny|Sequence Analysis, DNA|Systems Integration|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-420', '18840282', 'Advances in automated DNA sequencing technology have accelerated the generation of metagenomic DNA sequences, especially environmental ribosomal RNA gene (rDNA) sequences. As the scale of rDNA-based studies of microbial ecology has expanded, need has arisen for software that is capable of managing, annotating, and analyzing the plethora of diverse data accumulated in these projects.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92414, 2008, 'bmc', 'Bayesian modeling of recombination events in bacterial populations', 'Marttinen, Pekka|Baldwin, Adam|Hanage, William P|Dowson, Chris|Mahenthiralingam, Eshwar|Corander, Jukka', 'Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi|Department of Mathematics and statistics, University of Helsinki, FIN-00014, Finland. pekka.marttinen@helsinki.fi', 'Finland|Finland|Finland|Finland|Finland|Finland', 21, 'Base Sequence|Bayes Theorem|Burkholderia|Chromosome Mapping|Information Storage and Retrieval|Pattern Recognition, Automated|Phylogeny|Recombination, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software|Uncertainty', 'Journal Article', '10.1186/1471-2105-9-421', '18840286', 'We consider the discovery of recombinant segments jointly with their origins within multilocus DNA sequences from bacteria representing heterogeneous populations of fairly closely related species. The currently available methods for recombination detection capable of probabilistic characterization of uncertainty have a limited applicability in practice as the number of strains in a data set increases.'),
(92415, 2008, 'bmc', 'SIGMA2: a system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and transcriptomes', 'Chari, Raj|Coe, Bradley P|Wedseltoft, Craig|Benetti, Marie|Wilson, Ian M|Vucic, Emily A|MacAulay, Calum|Ng, Raymond T|Lam, Wan L', 'Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca|Department of Cancer Genetics and Developmental Biology, BC Cancer Agency Research Centre, Vancouver, BC, Canada. rchari@bccrc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Animals|Computer Graphics|DNA, Neoplasm|Epigenesis, Genetic|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Neoplasms|Software|Systems Integration|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-422', '18840289', 'High throughput microarray technologies have afforded the investigation of genomes, epigenomes, and transcriptomes at unprecedented resolution. However, software packages to handle, analyze, and visualize data from these multiple omics disciplines have not been adequately developed.'),
(92416, 2008, 'bmc', 'LC-MSsim--a simulation software for liquid chromatography mass spectrometry data', 'Schulz-Trieglaff, Ole|Pfeifer, Nico|Gröpl, Clemens|Kohlbacher, Oliver|Reinert, Knut', 'International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de|International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de', 'Germany|Germany|Germany|Germany|Germany', 34, 'Algorithms|Artifacts|Chromatography, Liquid|Computational Biology|Computer Simulation|Data Compression|Humans|Ions|Mass Spectrometry|Peptides|Quality Control|Reference Values|Software|Spectrometry, Mass, Electrospray Ionization', 'Journal Article', '10.1186/1471-2105-9-423', '18842122', 'Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.'),
(92417, 2008, 'bmc', 'Detection of the dominant direction of information flow and feedback links in densely interconnected regulatory networks', 'Ispolatov, Iaroslav|Maslov, Sergei', 'Ariadne Inc., 9430 Key West Ave, Suite 113 Rockville, MD 20850, USA. slava@ariadnegenomics.com|Ariadne Inc., 9430 Key West Ave, Suite 113 Rockville, MD 20850, USA. slava@ariadnegenomics.com', 'United States|United States', 15, 'Algorithms|Computer Simulation|ErbB Receptors|Feedback, Physiological|Gene Regulatory Networks|Genome, Human|Humans|Models, Biological|Protein Processing, Post-Translational|Receptors, Antigen, B-Cell|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-424', '18842147', 'Finding the dominant direction of flow of information in densely interconnected regulatory or signaling networks is required in many applications in computational biology and neuroscience. This is achieved by first identifying and removing links which close up feedback loops in the original network and hierarchically arranging nodes in the remaining network. In mathematical language this corresponds to a problem of making a graph acyclic by removing as few links as possible and thus altering the original graph in the least possible way. The exact solution of this problem requires enumeration of all cycles and combinations of removed links, which, as an NP-hard problem, is computationally prohibitive even for modest-size networks.'),
(92418, 2008, 'bmc', 'Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems', 'Tsai, Yu-Shuen|Lin, Chin-Teng|Tseng, George C|Chung, I-Fang|Pal, Nikhil Ranjan', 'Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. g39528010@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. g39528010@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. g39528010@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. g39528010@ym.edu.tw|Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. g39528010@ym.edu.tw', '-|-|-|-|-', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Diagnosis, Computer-Assisted|Gene Expression Regulation, Neoplastic|Genes, Neoplasm|Genetic Markers|Humans|Neoplasms', 'Journal Article', '10.1186/1471-2105-9-425', '18842155', 'The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammons projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.'),
(92419, 2008, 'bmc', 'Mining protein networks for synthetic genetic interactions', 'Paladugu, Sri R|Zhao, Shan|Ray, Animesh|Raval, Alpan', 'Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. spaladug@kgi.edu|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. spaladug@kgi.edu|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. spaladug@kgi.edu|Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA 91711, USA. spaladug@kgi.edu', 'United States|United States|United States|United States', 0, 'Gene Expression Regulation, Fungal|Genes, Fungal|Genes, Lethal|Genes, Regulator|Genomics|Neural Networks (Computer)|Protein Interaction Mapping|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity', 'Journal Article', '10.1186/1471-2105-9-426', '18844977', 'The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.'),
(92420, 2008, 'bmc', 'Homology modelling of protein-protein complexes: a simple method and its possibilities and limitations', 'Launay, Guillaume|Simonson, Thomas', 'Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@irisa.fr|Laboratoire de Biochimie (UMR CNRS 7654), Department of Biology, Ecole Polytechnique, 91128, Palaiseau, France. guillaume.launay@irisa.fr', 'France|France', 0, 'Algorithms|Protein Binding|Protein Conformation|Protein Interaction Domains and Motifs|Proteins|Structural Homology, Protein', 'Journal Article', '10.1186/1471-2105-9-427', '18844985', 'Structure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most successful technique is homology modelling. We investigate a simple extension of this technique to protein-protein complexes. We consider a large set of complexes of known structures, involving pairs of single-domain proteins. The complexes are compared with each other to establish their sequence and structural similarities and the relation between the two. Compared to earlier studies, a simpler dataset, a simpler structural alignment procedure, and an additional energy criterion are used. Next, we compare the Xray structures to models obtained by threading the native sequence onto other, homologous complexes. An elementary requirement for a successful energy function is to rank the native structure above any threaded structure. We use the DFIREbeta energy function, whose quality and complexity are typical of the models used today. Finally, we compare near-native models to distinctly non-native models.'),
(92421, 2008, 'bmc', 'SPSmart: adapting population based SNP genotype databases for fast and comprehensive web access', 'Amigo, Jorge|Salas, Antonio|Phillips, Christopher|Carracedo, Angel', 'Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es|Spanish National Genotyping Center (CeGen), Genomic Medicine Group, CIBERER, University of Santiago de Compostela, Galicia, Spain. jorge.amigo@usc.es', 'Spain|Spain|Spain|Spain', 0, 'Genome, Human|Humans|Internet|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-428', '18847484', 'In the last five years large online resources of human variability have appeared, notably HapMap, Perlegen and the CEPH foundation. These databases of genotypes with population information act as catalogues of human diversity, and are widely used as reference sources for population genetics studies. Although many useful conclusions may be extracted by querying databases individually, the lack of flexibility for combining data from within and between each database does not allow the calculation of key population variability statistics.'),
(92422, 2008, 'bmc', 'Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure', 'Bystroff, Christopher|Webb-Robertson, Bobbie-Jo', 'Department of Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy NY, USA. bystrc@rpi.edu|Department of Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy NY, USA. bystrc@rpi.edu', 'United States|United States', 1, 'Amino Acid Motifs|Artificial Intelligence|Databases, Protein|Evolution, Molecular|Models, Statistical|Pattern Recognition, Automated|Protein Folding|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-429', '18847485', 'Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins. Profile models assume the statistical independence of each position in the sequence, but the energetics of protein folding is better captured in a scoring function that is based on pairwise interactions, like a force field.'),
(92423, 2008, 'bmc', 'Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments', 'Zheng, Ce|Kurgan, Lukasz', 'Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. czheng@ualberta.ca|Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. czheng@ualberta.ca', 'Canada|Canada', 42, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computational Biology|Databases, Protein|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Folding|Protein Structure, Secondary|Proteins|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-430', '18847492', 'beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor.'),
(92424, 2008, 'bmc', 'Probabilistic base calling of Solexa sequencing data', 'Rougemont, Jacques|Amzallag, Arnaud|Iseli, Christian|Farinelli, Laurent|Xenarios, Ioannis|Naef, Felix', 'School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch|School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch|School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch|School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch|School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch|School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. jacques.rougemont@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 0, 'Bacteriophage phi X 174|Base Sequence|Chromosome Mapping|Cluster Analysis|DNA, Viral|Expressed Sequence Tags|Pattern Recognition, Automated|Quality Control|Sequence Analysis, DNA|Software|Spectrometry, Fluorescence', 'Journal Article', '10.1186/1471-2105-9-431', '18851737', 'Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges, currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology.'),
(92425, 2008, 'bmc', 'easyExon--a Java-based GUI tool for processing and visualization of Affymetrix exon array data', 'Chang, Ting-Yu|Li, Yin-Yi|Jen, Chih-Hung|Yang, Tsun-Po|Lin, Chi-Hung|Hsu, Ming-Ta|Wang, Hsei-Wei', 'Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw|Institute of Microbiology and Immunology, National Yang-Ming University, and Department of Education and Research, Taipei City Hospital, Taipei, Taiwan. d49402002@ym.edu.tw', '-|-|-|-|-|-|-', 13, 'Alternative Splicing|Animals|Exons|Gene Expression Profiling|Humans|Information Storage and Retrieval|Mice|Oligonucleotide Array Sequence Analysis|Rats|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-432', '18851762', 'Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms.'),
(92426, 2008, 'bmc', 'Reranking candidate gene models with cross-species comparison for improved gene prediction', 'Liu, Qian|Crammer, Koby|Pereira, Fernando C N|Roos, David S', 'Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA. qianliu@seas.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA. qianliu@seas.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA. qianliu@seas.upenn.edu|Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA. qianliu@seas.upenn.edu', 'United States|United States|United States|United States', 0, 'Algorithms|Animals|Drosophila melanogaster|Genome|Models, Genetic', 'Journal Article', '10.1186/1471-2105-9-433', '18854050', 'Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features.'),
(92427, 2008, 'bmc', 'Comparative optimism in models involving both classical clinical and gene expression information', 'Truntzer, Caroline|Maucort-Boulch, Delphine|Roy, Pascal', 'Hospices Civils de Lyon, Service de Biostatistique, Lyon, France. caroline.truntzer@clipproteomic.fr|Hospices Civils de Lyon, Service de Biostatistique, Lyon, France. caroline.truntzer@clipproteomic.fr|Hospices Civils de Lyon, Service de Biostatistique, Lyon, France. caroline.truntzer@clipproteomic.fr', 'France|France|France', 11, 'Gene Expression Profiling|Humans|Multivariate Analysis|Neoplasms|Prognosis|Proportional Hazards Models', 'Journal Article', '10.1186/1471-2105-9-434', '18854055', 'In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: restricting their number and validating their significance. Thus, their contribution to prognosis is currently thought to be overestimated. The aim of this study was to determine to what extent optimism concerning current transcriptomic models may lead to overestimation of the contribution of transcriptomic variables to survival prognosis.'),
(92428, 2008, 'bmc', 'Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission', 'Roche, Benjamin|Guégan, Jean-François|Bousquet, François', 'UMR 2724 Génétique et Evolution des Maladies Infectieuses, IRD-CNRS-Université de Montpellier I, Montpellier, France. roche@mpl.ird.fr|UMR 2724 Génétique et Evolution des Maladies Infectieuses, IRD-CNRS-Université de Montpellier I, Montpellier, France. roche@mpl.ird.fr|UMR 2724 Génétique et Evolution des Maladies Infectieuses, IRD-CNRS-Université de Montpellier I, Montpellier, France. roche@mpl.ird.fr', 'France|France|France', 0, 'Algorithms|Animals|Biological Evolution|Computational Biology|Disease Transmission, Infectious|Disease Vectors|Ecosystem|Epidemiologic Methods|Geographic Information Systems|Host-Parasite Interactions|Models, Theoretical|Software|Unified Medical Language System', 'Journal Article', '10.1186/1471-2105-9-435', '18922166', 'Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems.'),
(92429, 2008, 'bmc', 'Prediction of TF target sites based on atomistic models of protein-DNA complexes', 'Angarica, Vladimir Espinosa|Pérez, Abel González|Vasconcelos, Ana T|Collado-Vides, Julio|Contreras-Moreira, Bruno', 'Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, España. vespinosa@gmail.com|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, España. vespinosa@gmail.com|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, España. vespinosa@gmail.com|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, España. vespinosa@gmail.com|Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Ciencias, Universidad de Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, España. vespinosa@gmail.com', '-|-|-|-|-', 38, 'Algorithms|Amino Acid Sequence|Area Under Curve|Base Sequence|Binding Sites|Computational Biology|Crystallography, X-Ray|DNA|DNA-Binding Proteins|Models, Biological|Models, Statistical|Protein Binding|Protein Structure, Secondary|Proteins|ROC Curve|Regression Analysis|Transcription Factors', 'Journal Article', '10.1186/1471-2105-9-436', '18922190', 'The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence.'),
(92430, 2008, 'bmc', 'OntoDas - a tool for facilitating the construction of complex queries to the Gene Ontology', 'ONeill, Kieran|Garcia, Alexander|Schwegmann, Anita|Jimenez, Rafael C|Jacobson, Dan|Hermjakob, Henning', 'Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca|Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca|Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca|Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca|Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca|Central Node, National Bioinformatics Network, Golf Park, Raapenberg Road, Cape Town, South Africa. koneill@bccrc.ca', 'South Africa|South Africa|South Africa|South Africa|South Africa|South Africa', 0, 'Computational Biology|Computer Graphics|Database Management Systems|Databases, Genetic|Natural Language Processing|Systems Integration|Terminology as Topic|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-437', '18925933', 'Ontologies such as the Gene Ontology can enable the construction of complex queries over biological information in a conceptual way, however existing systems to do this are too technical. Within the biological domain there is an increasing need for software that facilitates the flexible retrieval of information. OntoDas aims to fulfil this need by allowing the definition of queries by selecting valid ontology terms.'),
(92431, 2008, 'bmc', 'AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening', 'Pencheva, Tania|Lagorce, David|Pajeva, Ilza|Villoutreix, Bruno O|Miteva, Maria A', 'INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France. tania.pencheva@clbme.bas.bg|INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France. tania.pencheva@clbme.bas.bg|INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France. tania.pencheva@clbme.bas.bg|INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France. tania.pencheva@clbme.bas.bg|INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France. tania.pencheva@clbme.bas.bg', 'France|France|France|France|France', 36, 'Algorithms|Binding Sites|Drug Design|Ligands|Models, Molecular|Neuraminidase|Proteins|Receptors, Estrogen|Small Molecule Libraries|Software|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-438', '18925937', 'Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization.'),
(92432, 2008, 'bmc', 'CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data', 'Slawski, M|Daumer, M|Boulesteix, A-L', 'Sylvia Lawry Centre for Multiple Sclerosis Research, Munich, Germany. martin.slawski@campus.lmu.de|Sylvia Lawry Centre for Multiple Sclerosis Research, Munich, Germany. martin.slawski@campus.lmu.de|Sylvia Lawry Centre for Multiple Sclerosis Research, Munich, Germany. martin.slawski@campus.lmu.de', 'Germany|Germany|Germany', 72, 'Algorithms|Area Under Curve|Computational Biology|Computer Simulation|Discriminant Analysis|Internet|Least-Squares Analysis|Logistic Models|Microarray Analysis|Models, Statistical|Monte Carlo Method|Neural Networks (Computer)|Reproducibility of Results|Sensitivity and Specificity|Software|Statistics, Nonparametric|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-439', '18925941', 'For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called p > n setting where the number of predictors p by far exceeds the number of observations n, hence the term ill-posed-problem. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers.'),
(92433, 2008, 'bmc', 'PlasmoDraft: a database of Plasmodium falciparum gene function predictions based on postgenomic data', 'Bréhélin, Laurent|Dufayard, Jean-François|Gascuel, Olivier', 'Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM, Univ, Montpellier 2, CNRS, 161 rue Ada, 34392 MONTPELLIER, France. brehelin@lirmm.fr|Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM, Univ, Montpellier 2, CNRS, 161 rue Ada, 34392 MONTPELLIER, France. brehelin@lirmm.fr|Projet Méthodes et Algorithmes pour la Bioinformatique, LIRMM, Univ, Montpellier 2, CNRS, 161 rue Ada, 34392 MONTPELLIER, France. brehelin@lirmm.fr', 'France|France|France', 0, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Databases, Genetic|Gene Expression|Genes, Protozoan|Genomics|Humans|Information Storage and Retrieval|Plasmodium falciparum|Protozoan Proteins', 'Journal Article', '10.1186/1471-2105-9-440', '18925948', 'Of the 5,484 predicted proteins of Plasmodium falciparum, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes.'),
(92434, 2008, 'bmc', 'Structural assembly of two-domain proteins by rigid-body docking', 'Cheng, Tammy M K|Blundell, Tom L|Fernandez-Recio, Juan', 'Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. tammy@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. tammy@cryst.bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. tammy@cryst.bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 22, 'Biophysical Phenomena|Crystallography, X-Ray|Dimerization|Macromolecular Substances|Models, Molecular|Pattern Recognition, Automated|Protein Binding|Protein Structure, Tertiary|Proteins|Structural Homology, Protein|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-441', '18925951', 'Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requires ab initio methods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking.'),
(92435, 2008, 'bmc', 'Evading the annotation bottleneck: using sequence similarity to search non-sequence gene data', 'Gilchrist, Michael J|Christensen, Mikkel B|Harland, Richard|Pollet, Nicolas|Smith, James C|Ueno, Naoto|Papalopulu, Nancy', 'The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk|The Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, CB2 1QN, UK. m.gilchrist@gurdon.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Animals|Base Sequence|Conserved Sequence|Databases, Genetic|Humans|Information Storage and Retrieval|Xenopus', 'Journal Article', '10.1186/1471-2105-9-442', '18928517', 'Non-sequence gene data (images, literature, etc.) can be found in many different public databases. Access to these data is mostly by text based methods using gene names, however, gene annotation is neither complete, nor fully systematic between organisms, and is also not generally stable over time. This provides some challenges for text based access, especially for cross-species searches. We propose a method for non-sequence data retrieval based on sequence similarity, which removes dependence on annotation and text searches. This work was motivated by the need to provide better access to large numbers of in situ images, and the observation that such image data were usually associated with a specific gene sequence. Sequence similarity searches are found in existing gene oriented databases, but mostly give indirect access to non-sequence data via navigational links.'),
(92436, 2008, 'bmc', 'Peak intensity prediction in MALDI-TOF mass spectrometry: a machine learning study to support quantitative proteomics', 'Timm, Wiebke|Scherbart, Alexandra|Böcker, Sebastian|Kohlbacher, Oliver|Nattkemper, Tim W', 'Applied Neuroinformatics Group, Bielefeld University, Germany. wiebke.timm@childrens.harvard.edu|Applied Neuroinformatics Group, Bielefeld University, Germany. wiebke.timm@childrens.harvard.edu|Applied Neuroinformatics Group, Bielefeld University, Germany. wiebke.timm@childrens.harvard.edu|Applied Neuroinformatics Group, Bielefeld University, Germany. wiebke.timm@childrens.harvard.edu|Applied Neuroinformatics Group, Bielefeld University, Germany. wiebke.timm@childrens.harvard.edu', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Linear Models|Neural Networks (Computer)|Peptides|Proteins|Proteomics|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1186/1471-2105-9-443', '18937839', 'Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e.g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification.'),
(92437, 2008, 'bmc', 'Assessment of protein set coherence using functional annotations', 'Chagoyen, Monica|Carazo, Jose M|Pascual-Montano, Alberto', 'Centro Nacional de Biotecnología-CSIC, Madrid, Spain. monica.chagoyen@cnb.csic.es|Centro Nacional de Biotecnología-CSIC, Madrid, Spain. monica.chagoyen@cnb.csic.es|Centro Nacional de Biotecnología-CSIC, Madrid, Spain. monica.chagoyen@cnb.csic.es', 'Spain|Spain|Spain', 9, 'Candida albicans|Computational Biology|Databases, Protein|Fungal Proteins|Metabolic Networks and Pathways|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1186/1471-2105-9-444', '18937846', 'Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set.'),
(92438, 2008, 'bmc', 'An incremental approach to automated protein localisation', 'Tscherepanow, Marko|Jensen, Nickels|Kummert, Franz', 'Applied Computer Science, Faculty of Technology, Bielefeld University, Universitätsstrasse 25, D-33615 Bielefeld, Germany. marko@techfak.uni-bielefeld.de|Applied Computer Science, Faculty of Technology, Bielefeld University, Universitätsstrasse 25, D-33615 Bielefeld, Germany. marko@techfak.uni-bielefeld.de|Applied Computer Science, Faculty of Technology, Bielefeld University, Universitätsstrasse 25, D-33615 Bielefeld, Germany. marko@techfak.uni-bielefeld.de', 'Germany|Germany|Germany', 11, 'Algorithms|Animals|Cell Line|Cell Membrane|Computational Biology|Image Processing, Computer-Assisted|Intracellular Space|Microscopy, Fluorescence|Molecular Probes|Organelles|Proteins|Proteomics|Spodoptera', 'Journal Article', '10.1186/1471-2105-9-445', '18937856', 'The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches--i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards--has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected.'),
(92439, 2008, 'bmc', 'Envelope: interactive software for modeling and fitting complex isotope distributions', 'Sykes, Michael T|Williamson, James R', 'Department of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037, USA. sykes@scripps.edu|Department of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037, USA. sykes@scripps.edu', 'United States|United States', 9, 'Algorithms|Amino Acids|Computational Biology|Information Storage and Retrieval|Isotope Labeling|Isotopes|Mass Spectrometry|Models, Chemical|Oligonucleotides|Peptides|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-446', '18937869', 'An important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with in vivo metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species.'),
(92440, 2008, 'bmc', 'Identification of hot-spot residues in protein-protein interactions by computational docking', 'Grosdidier, Solène|Fernández-Recio, Juan', 'Life Sciences Department, Barcelona Supercomputing Center, Jordi Girona 29, 08034 Barcelona, Spain. solene.grosdidier@bsc.es|Life Sciences Department, Barcelona Supercomputing Center, Jordi Girona 29, 08034 Barcelona, Spain. solene.grosdidier@bsc.es', 'Spain|Spain', 0, 'Amino Acids|Binding Sites|Computational Biology|Information Storage and Retrieval|Protein Interaction Domains and Motifs|Protein Interaction Mapping|Static Electricity|Thermodynamics', 'Journal Article', '10.1186/1471-2105-9-447', '18939967', 'The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called hot-spots). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.'),
(92441, 2008, 'bmc', 'Volume-based solvation models out-perform area-based models in combined studies of wild-type and mutated protein-protein interfaces', 'Bougouffa, Salim|Warwicker, Jim', 'Faculty of Life Sciences, the Michael Smith Building, University of Manchester, Manchester, UK. salim.bougouffa@postgrad.manchester.ac.uk|Faculty of Life Sciences, the Michael Smith Building, University of Manchester, Manchester, UK. salim.bougouffa@postgrad.manchester.ac.uk', 'United Kingdom|United Kingdom', 7, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Mutant Proteins|Mutation|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Solvents', 'Journal Article', '10.1186/1471-2105-9-448', '18939984', 'Empirical binding models have previously been investigated for the energetics of protein complexation (DeltaG models) and for the influence of mutations on complexation (i.e. differences between wild-type and mutant complexes, DeltaDeltaG models). We construct binding models to directly compare these processes, which have generally been studied separately.'),
(92442, 2008, 'bmc', 'A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification', 'Wei, Ning|Flaschel, Erwin|Friehs, Karl|Nattkemper, Tim Wilhelm', 'Bielefeld University, Faculty of Technology, Fermentation Engineering, PO-Box 100131, 33501 Bielefeld, Germany. rieicp@gmail.com|Bielefeld University, Faculty of Technology, Fermentation Engineering, PO-Box 100131, 33501 Bielefeld, Germany. rieicp@gmail.com|Bielefeld University, Faculty of Technology, Fermentation Engineering, PO-Box 100131, 33501 Bielefeld, Germany. rieicp@gmail.com|Bielefeld University, Faculty of Technology, Fermentation Engineering, PO-Box 100131, 33501 Bielefeld, Germany. rieicp@gmail.com', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Cell Survival|Image Interpretation, Computer-Assisted|Microscopy|Pattern Recognition, Automated', 'Journal Article', '10.1186/1471-2105-9-449', '18939996', 'Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnological applications. Conventional methods for extracting information about cell viability usually need reagents to be applied on the targeted cells. These reagent-based techniques are reliable and versatile, however, some of them might be invasive and even toxic to the target cells. In support of automated noninvasive assessment of cell viability, a machine vision system has been developed.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(92443, 2008, 'bmc', 'Bluejay 10: genome browsing and comparison with rich customization provision and dynamic resource linking', 'Soh, Jung|Gordon, Paul Mk|Taschuk, Morgan L|Dong, Anguo|Ah-Seng, Andrew C|Turinsky, Andrei L|Sensen, Christoph W', 'University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca|University of Calgary, Faculty of Medicine, Sun Center of Excellence for Visual Genomics, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada. jsoh@ucalgary.ca', 'Canada|Canada|Canada|Canada|Canada|Canada|Canada', 7, 'Animals|Databases, Nucleic Acid|Genome|Humans|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-450', '18940007', 'The Bluejay genome browser has been developed over several years to address the challenges posed by the ever increasing number of data types as well as the increasing volume of data in genome research. Beginning with a browser capable of rendering views of XML-based genomic information and providing scalable vector graphics output, we have now completed version 1.0 of the system with many additional features. Our development efforts were guided by our observation that biologists who use both gene expression profiling and comparative genomics gain functional insights above and beyond those provided by traditional per-gene analyses.'),
(92444, 2008, 'bmc', 'Feature selection environment for genomic applications', 'Lopes, Fabrício Martins|Martins, David Corrêa|Cesar, Roberto M', 'Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil. fabricio@utfpr.edu.br|Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil. fabricio@utfpr.edu.br|Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil. fabricio@utfpr.edu.br', 'Brazil|Brazil|Brazil', 0, 'Algorithms|Bayes Theorem|Computational Biology|Data Interpretation, Statistical|Genomics|Internet|Markov Chains|Models, Genetic|Pattern Recognition, Automated|Reproducibility of Results|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-451', '18945362', 'Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer, or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.'),
(92445, 2008, 'bmc', 'Background correction using dinucleotide affinities improves the performance of GCRMA', 'Gharaibeh, Raad Z|Fodor, Anthony A|Gibas, Cynthia J', 'Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. rgharaib@uncc.edu|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. rgharaib@uncc.edu|Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. rgharaib@uncc.edu', 'United States|United States|United States', 0, 'Animals|Artifacts|Cluster Analysis|Computational Biology|Energy Transfer|Gene Expression Profiling|Humans|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Reproducibility of Results', 'Journal Article', '10.1186/1471-2105-9-452', '18947404', 'High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly.'),
(92446, 2008, 'bmc', 'Probe signal correction for differential methylation hybridization experiments', 'Potter, Dustin P|Yan, Pearlly|Huang, Tim H M|Lin, Shili', 'Human Cancer Genetics Program, OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. potterdp@gmail.com|Human Cancer Genetics Program, OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. potterdp@gmail.com|Human Cancer Genetics Program, OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. potterdp@gmail.com|Human Cancer Genetics Program, OSU Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA. potterdp@gmail.com', 'United States|United States|United States|United States', 0, 'Base Sequence|Breast Neoplasms|Carbocyanines|Cell Line, Tumor|DNA Methylation|DNA Probes|Female|Fluorescent Dyes|Gene Expression Profiling|Humans|Methylation|Models, Genetic|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Signal Transduction', 'Journal Article', '10.1186/1471-2105-9-453', '18947421', 'Non-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol.'),
(92447, 2008, 'bmc', 'In silico analysis of methyltransferase domains involved in biosynthesis of secondary metabolites', 'Ansari, Mohd Zeeshan|Sharma, Jyoti|Gokhale, Rajesh S|Mohanty, Debasisa', 'National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi-110067, India. zeeshan@nii.res.in|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi-110067, India. zeeshan@nii.res.in|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi-110067, India. zeeshan@nii.res.in|National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi-110067, India. zeeshan@nii.res.in', 'India|India|India|India', 0, 'Artificial Intelligence|Computational Biology|Drug Discovery|Methyltransferases|Models, Biological|Molecular Structure|Peptide Synthases|Polyketide Synthases|Protein Structure, Tertiary|Sequence Alignment', 'Journal Article', '10.1186/1471-2105-9-454', '18950525', 'Secondary metabolites biosynthesized by polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) family of enzymes constitute several classes of therapeutically important natural products like erythromycin, rapamycin, cyclosporine etc. In view of their relevance for natural product based drug discovery, identification of novel secondary metabolite natural products by genome mining has been an area of active research. A number of different tailoring enzymes catalyze a variety of chemical modifications to the polyketide or nonribosomal peptide backbone of these secondary metabolites to enhance their structural diversity. Therefore, development of powerful bioinformatics methods for identification of these tailoring enzymes and assignment of their substrate specificity is crucial for deciphering novel secondary metabolites by genome mining.'),
(92448, 2008, 'bmc', 'CompMoby: comparative MobyDick for detection of cis-regulatory motifs', 'Chaivorapol, Christina|Melton, Collin|Wei, Grace|Yeh, Ru-Fang|Ramalho-Santos, Miguel|Blelloch, Robert|Li, Hao', 'Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu|Department of Biochemistry and Biophysics, California Institute for Quantitative Biomedical Research, Graduate Program in Biological and Medical Informatics, University of California, San Francisco, CA 94143-2540, USA. cchaivor@genome.ucsf.edu', 'United States|United States|United States|United States|United States|United States|United States', 5, 'Algorithms|Amino Acid Motifs|Animals|Databases, Genetic|Embryonic Stem Cells|Gene Expression Regulation|Genomics|Humans|Mice|MicroRNAs|Promoter Regions, Genetic|Protein Structure, Tertiary|Regulatory Elements, Transcriptional|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-455', '18950538', 'The regulation of gene expression is complex and occurs at many levels, including transcriptional and post-transcriptional, in metazoans. Transcriptional regulation is mainly determined by sequence elements within the promoter regions of genes while sequence elements within the 3 untranslated regions of mRNAs play important roles in post-transcriptional regulation such as mRNA stability and translation efficiency. Identifying cis-regulatory elements, or motifs, in multicellular eukaryotes is more difficult compared to unicellular eukaryotes due to the larger intergenic sequence space and the increased complexity in regulation. Experimental techniques for discovering functional elements are often time consuming and not easily applied on a genome level. Consequently, computational methods are advantageous for genome-wide cis-regulatory motif detection. To decrease the search space in metazoans, many algorithms use cross-species alignment, although studies have demonstrated that a large portion of the binding sites for the same trans-acting factor do not reside in alignable regions. Therefore, a computational algorithm should account for both conserved and nonconserved cis-regulatory elements in metazoans.'),
(92449, 2008, 'bmc', 'FUNYBASE: a FUNgal phYlogenomic dataBASE', 'Marthey, Sylvain|Aguileta, Gabriela|Rodolphe, François|Gendrault, Annie|Giraud, Tatiana|Fournier, Elisabeth|Lopez-Villavicencio, Manuela|Gautier, Angélique|Lebrun, Marc-Henri|Chiapello, Hélène', 'UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr|UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France. sylvain.marthey@jouy.inra.fr', 'France|France|France|France|France|France|France|France|France|France', 0, 'Algorithms|Databases, Genetic|Databases, Protein|Evolution, Molecular|Fungi|Genes, Fungal|Genome, Fungal|Genomics|Information Storage and Retrieval|Phylogeny', 'Journal Article', '10.1186/1471-2105-9-456', '18954438', 'The increasing availability of fungal genome sequences provides large numbers of proteins for evolutionary and phylogenetic analyses. However the heterogeneity of data, including the quality of genome annotation and the difficulty of retrieving true orthologs, makes such investigations challenging. The aim of this study was to provide a reliable and integrated resource of orthologous gene families to perform comparative and phylogenetic analyses in fungi.'),
(92450, 2008, 'bmc', 'Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles', 'Meng, Hailong|Murrelle, Edward L|Li, Guoya', 'Scientific Division, ClearPoint Resources Inc., Richmond, VA 23219, USA. hlmeng@yahoo.com|Scientific Division, ClearPoint Resources Inc., Richmond, VA 23219, USA. hlmeng@yahoo.com|Scientific Division, ClearPoint Resources Inc., Richmond, VA 23219, USA. hlmeng@yahoo.com', 'United States|United States|United States', 9, 'Artificial Intelligence|Computational Biology|CpG Islands|DNA Methylation|Genetic Markers|Humans|Lung Neoplasms', 'Comparative Study', '10.1186/1471-2105-9-457', '18954440', 'DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases.'),
(92451, 2008, 'bmc', 'Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies', 'DiMaggio, Peter A|McAllister, Scott R|Floudas, Christodoulos A|Feng, Xiao-Jiang|Rabinowitz, Joshua D|Rabitz, Herschel A', 'Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu|Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu|Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu|Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu|Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu|Department of Chemical Engineering, Princeton University, Princeton, NJ, USA. pete@titan.princeton.edu', 'United States|United States|United States|United States|United States|United States', 47, 'Algorithms|Artificial Intelligence|Breast Neoplasms|Cluster Analysis|Colonic Neoplasms|Database Management Systems|Databases, Genetic|Escherichia coli|Humans|Image Processing, Computer-Assisted|Information Storage and Retrieval|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Saccharomyces cerevisiae|Systems Biology|Yeasts', 'Journal Article', '10.1186/1471-2105-9-458', '18954459', 'The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the best grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.'),
(92452, 2008, 'bmc', 'MetaMine--a tool to detect and analyse gene patterns in their environmental context', 'Bohnebeck, Uta|Lombardot, Thierry|Kottmann, Renzo|Glöckner, Frank O', 'ttz Bremerhaven, Bremerhaven, Germany. bohnebeck@ttz-bremerhaven.de|ttz Bremerhaven, Bremerhaven, Germany. bohnebeck@ttz-bremerhaven.de|ttz Bremerhaven, Bremerhaven, Germany. bohnebeck@ttz-bremerhaven.de|ttz Bremerhaven, Bremerhaven, Germany. bohnebeck@ttz-bremerhaven.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Genetic|Ecosystem|Multigene Family|Pattern Recognition, Automated|Software', 'Journal Article', '10.1186/1471-2105-9-459', '18957118', 'Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes.'),
(92453, 2008, 'bmc', 'A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics', 'Pérès, Sabine|Molina, Laurence|Salvetat, Nicolas|Granier, Claude|Molina, Franck', 'Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr|Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr', 'France|France|France|France|France', 0, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Electrophoresis, Gel, Two-Dimensional|Gene Expression Profiling|Humans|Models, Statistical|Pattern Recognition, Automated|Proteome|Proteomics|Software', 'Journal Article', '10.1186/1471-2105-9-460', '18957120', 'In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.'),
(92454, 2008, 'bmc', 'minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information', 'Meyer, Patrick E|Lafitte, Frédéric|Bontempi, Gianluca', 'Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium. pmeyer@ulb.ac.be|Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium. pmeyer@ulb.ac.be|Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium. pmeyer@ulb.ac.be', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Computational Biology|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|ROC Curve|Reproducibility of Results|Software|Transcription, Genetic', 'Journal Article', '10.1186/1471-2105-9-461', '18959772', 'This paper presents the R/Bioconductor package minet (version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset. Once fed with a microarray dataset, the package returns a network where nodes denote genes, edges model statistical dependencies between genes and the weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene-to-gene interaction. Four different entropy estimators are made available in the package minet (empirical, Miller-Madow, Schurmann-Grassberger and shrink) as well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also, the package integrates accuracy assessment tools, like F-scores, PR-curves and ROC-curves in order to compare the inferred network with a reference one.'),
(92455, 2008, 'bmc', 'Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer', 'Giancarlo, Raffaele|Scaturro, Davide|Utro, Filippo', 'Dipartimento di Matematica ed Applicazioni, Universitá di Palermo, Palermo, Italy. raffaele@math.unipa.it|Dipartimento di Matematica ed Applicazioni, Universitá di Palermo, Palermo, Italy. raffaele@math.unipa.it|Dipartimento di Matematica ed Applicazioni, Universitá di Palermo, Palermo, Italy. raffaele@math.unipa.it', 'Italy|Italy|Italy', 39, 'Algorithms|Animals|Benchmarking|Cluster Analysis|Computational Biology|Databases, Genetic|Humans|Oligonucleotide Array Sequence Analysis|Software|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-9-462', '18959783', 'Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data.'),
(92456, 2008, 'bmc', 'Information-based methods for predicting gene function from systematic gene knock-downs', 'Weirauch, Matthew T|Wong, Christopher K|Byrne, Alexandra B|Stuart, Joshua M', 'Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. weirauch@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. weirauch@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. weirauch@soe.ucsc.edu|Department of Biomolecular Engineering, University of California, Santa Cruz, CA 95064, USA. weirauch@soe.ucsc.edu', 'United States|United States|United States|United States', 0, 'Animals|Caenorhabditis elegans|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Knockdown Techniques|Gene Regulatory Networks|Genes|Genes, Helminth|Genomics|Humans|Information Storage and Retrieval|Models, Genetic|Phenotype|RNA Interference', 'Journal Article', '10.1186/1471-2105-9-463', '18959798', 'The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode Caenorhabditis elegans. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role.'),
(92457, 2008, 'bmc', 'Neutral network sizes of biological RNA molecules can be computed and are not atypically small', 'Jörg, Thomas|Martin, Olivier C|Wagner, Andreas', 'Inria Saclay, Ile-de-France, INRIA, Parc Orsay Université 4, Orsay Cedex, France. Thomas.Jorg@roma1.infn.it|Inria Saclay, Ile-de-France, INRIA, Parc Orsay Université 4, Orsay Cedex, France. Thomas.Jorg@roma1.infn.it|Inria Saclay, Ile-de-France, INRIA, Parc Orsay Université 4, Orsay Cedex, France. Thomas.Jorg@roma1.infn.it', 'France|France|France', 0, 'Algorithms|Databases, Nucleic Acid|Genotype|Monte Carlo Method|Neural Networks (Computer)|Nucleic Acid Conformation|Phenotype|RNA|RNA, Untranslated|Software', 'Journal Article', '10.1186/1471-2105-9-464', '18973652', 'Neutral networks or sets consist of all genotypes with a given phenotype. The size and structure of these sets has a strong influence on a biological systems robustness to mutations, and on its evolvability, the ability to produce phenotypic variation, in the few studied cases of molecular phenotypes, the larger this set, the greater both robustness and evolvability of phenotypes. Unfortunately, any one neutral set contains generally only a tiny fraction of genotype space. Thus, current methods cannot measure neutral set sizes accurately, except in the smallest genotype spaces.'),
(92458, 2008, 'bmc', 'QuantPrime--a flexible tool for reliable high-throughput primer design for quantitative PCR', 'Arvidsson, Samuel|Kwasniewski, Miroslaw|Riaño-Pachón, Diego Mauricio|Mueller-Roeber, Bernd', 'Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. arvid@uni-potsdam.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. arvid@uni-potsdam.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. arvid@uni-potsdam.de|Max-Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany. arvid@uni-potsdam.de', 'Germany|Germany|Germany|Germany', 0, 'Base Sequence|DNA Primers|Databases, Genetic|Internet|Molecular Sequence Data|Nucleic Acid Probes|Polymerase Chain Reaction|Sensitivity and Specificity|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-465', '18976492', 'Medium- to large-scale expression profiling using quantitative polymerase chain reaction (qPCR) assays are becoming increasingly important in genomics research. A major bottleneck in experiment preparation is the design of specific primer pairs, where researchers have to make several informed choices, often outside their area of expertise. Using currently available primer design tools, several interactive decisions have to be made, resulting in lengthy design processes with varying qualities of the assays.'),
(92459, 2008, 'bmc', 'Hierarchical structure of cascade of primary and secondary periodicities in Fourier power spectrum of alphoid higher order repeats', 'Paar, Vladimir|Pavin, Nenad|Basar, Ivan|Rosandixc4x87, Marija|Gluncixc4x87, Matko|Paar, Nils', 'Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr|Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr|Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr|Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr|Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr|Faculty of Science, University of Zagreb, Bijenicka 32, Zagreb, Croatia. paar@hazu.hr', 'Croatia|Croatia|Croatia|Croatia|Croatia|Croatia', 16, 'Algorithms|Animals|Base Sequence|Computational Biology|DNA|Databases, Nucleic Acid|Fourier Analysis|Humans|Nucleic Acid Conformation|Sequence Analysis, DNA|Tandem Repeat Sequences', 'Journal Article', '10.1186/1471-2105-9-466', '18980673', 'Identification of approximate tandem repeats is an important task of broad significance and still remains a challenging problem of computational genomics. Often there is no single best approach to periodicity detection and a combination of different methods may improve the prediction accuracy. Discrete Fourier transform (DFT) has been extensively used to study primary periodicities in DNA sequences. Here we investigate the application of DFT method to identify and study alphoid higher order repeats.'),
(92460, 2008, 'bmc', 'Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information', 'Luo, Weijun|Hankenson, Kurt D|Woolf, Peter J', 'Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu|Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA. luo@cshl.edu', 'United States|United States|United States', 46, 'Algorithms|Artificial Intelligence|Gene Expression|Gene Regulatory Networks|Systems Biology', 'Journal Article', '10.1186/1471-2105-9-467', '18980677', 'Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system.'),
(92461, 2008, 'bmc', 'A relation based measure of semantic similarity for Gene Ontology annotations', 'Sheehan, Brendan|Quigley, Aaron|Gaudin, Benoit|Dobson, Simon', 'Systems Research Group, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. brendan.sheehan@ucd.ie|Systems Research Group, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. brendan.sheehan@ucd.ie|Systems Research Group, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. brendan.sheehan@ucd.ie|Systems Research Group, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. brendan.sheehan@ucd.ie', 'Ireland|Ireland|Ireland|Ireland', 0, 'Algorithms|Classification|Computational Biology|Databases, Protein|Fuzzy Logic|Genes|Natural Language Processing|Proteins|Reproducibility of Results|Semantics|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-468', '18983678', 'Various measures of semantic similarity of terms in bio-ontologies such as the Gene Ontology (GO) have been used to compare gene products. Such measures of similarity have been used to annotate uncharacterized gene products and group gene products into functional groups. There are various ways to measure semantic similarity, either using the topological structure of the ontology, the instances (gene products) associated with terms or a mixture of both. We focus on an instance level definition of semantic similarity while using the information contained in the ontology, both in the graphical structure of the ontology and the semantics of relations between terms, to provide constraints on our instance level description.Semantic similarity of terms is extended to annotations by various approaches, either though aggregation operations such as min, max and average or through an extrapolative method. These approaches introduce assumptions about how semantic similarity of terms relates to the semantic similarity of annotations that do not necessarily reflect how terms relate to each other.'),
(92462, 2008, 'bmc', 'Gene set analyses for interpreting microarray experiments on prokaryotic organisms', 'Tintle, Nathan L|Best, Aaron A|DeJongh, Matthew|Van Bruggen, Dirk|Heffron, Fred|Porwollik, Steffen|Taylor, Ronald C', 'Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu|Department of Mathematics, Hope College, Holland, Michigan, USA. tintle@hope.edu', 'United States|United States|United States|United States|United States|United States|United States', 11, 'Computer Simulation|Escherichia coli K12|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Prokaryotic Cells|Salmonella typhimurium|Statistics as Topic', 'Journal Article', '10.1186/1471-2105-9-469', '18986519', 'Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fishers exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes.'),
(92463, 2008, 'bmc', 'MetaFIND: a feature analysis tool for metabolomics data', 'Bryan, Kenneth|Brennan, Lorraine|Cunningham, Pádraig', 'Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie|Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie|Complex & Adaptive Systems Laboratory (CASL), University College Dublin, Ireland. kenneth.bryan@ucd.ie', 'Ireland|Ireland|Ireland', 0, 'Computational Biology|Databases, Protein|Discriminant Analysis|Internet|Least-Squares Analysis|Mass Spectrometry|Metabolomics|Nuclear Magnetic Resonance, Biomolecular|Reproducibility of Results|Software|Statistics, Nonparametric|User-Computer Interface', 'Journal Article', '10.1186/1471-2105-9-470', '18986526', 'Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or features, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for post-feature selection correlation analysis of metabolomics data.'),
(92464, 2008, 'bmc', 'Psiscan: a computational approach to identify H/ACA-like and AGA-like non-coding RNA in trypanosomatid genomes', 'Myslyuk, Inna|Doniger, Tirza|Horesh, Yair|Hury, Avraham|Hoffer, Ran|Ziporen, Yaara|Michaeli, Shulamit|Unger, Ron', 'Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com|Faculty of Life Science, Bar-Ilan University, Ramat-Gan, Israel. myslyuk@hotmail.com', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 13, 'Animals|Artificial Intelligence|Computational Biology|Genomics|Models, Genetic|Mutagenesis|Protein Folding|Protein Structure, Secondary|RNA, Small Nuclear|Software|Trypanosoma', 'Journal Article', '10.1186/1471-2105-9-471', '18986541', 'Detection of non coding RNA (ncRNA) molecules is a major bioinformatics challenge. This challenge is particularly difficult when attempting to detect H/ACA molecules which are involved in converting uridine to pseudouridine on rRNA in trypanosomes, because these organisms have unique H/ACA molecules (termed H/ACA-like) that lack several of the features that characterize H/ACA molecules in most other organisms.'),
(92465, 2008, 'bmc', 'Evaluation of GO-based functional similarity measures using S cerevisiae protein interaction and expression profile data', 'Xu, Tao|Du, Linfang|Zhou, Yan', 'Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, PR China. xutao@chgc.sh.cn|Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, PR China. xutao@chgc.sh.cn|Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, PR China. xutao@chgc.sh.cn', 'China|China|China', 0, 'Algorithms|Artificial Intelligence|Computational Biology|Databases, Protein|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|ROC Curve|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Structure-Activity Relationship|Vocabulary, Controlled', 'Journal Article', '10.1186/1471-2105-9-472', '18986551', 'Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities.'),
(92466, 2008, 'bmc', 'Nuclear staining and relative distance for quantifying epidermal differentiation in biomarker expression profiling', 'Pommerencke, Thora|Steinberg, Thorsten|Dickhaus, Hartmut|Tomakidi, Pascal|Grabe, Niels', 'Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany. thora.pommerencke@bioquant.uni-heidelberg.de|Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany. thora.pommerencke@bioquant.uni-heidelberg.de|Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany. thora.pommerencke@bioquant.uni-heidelberg.de|Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany. thora.pommerencke@bioquant.uni-heidelberg.de|Hamamatsu Tissue Imaging and Analysis TIGA Center, BIOQUANT, University Heidelberg, Heidelberg, Germany. thora.pommerencke@bioquant.uni-heidelberg.de', 'Germany|Germany|Germany|Germany|Germany', 10, 'Cell Differentiation|Cell Nucleus|Epidermal Cells|Epidermis|Fluorescent Antibody Technique|Gene Expression Profiling|Genetic Markers|Keratinocytes', 'Comparative Study', '10.1186/1471-2105-9-473', '18990218', 'The epidermal physiology results from a complex regulated homeostasis of keratinocyte proliferation, differentiation and death and is tightly regulated by a specific protein expression during cellular maturation. Cellular in silico models are considered a promising and inevitable tool for the understanding of this complex system. Hence, we need to incorporate the information of the differentiation dependent protein expression in cell based systems biological models of tissue homeostasis. Such methods require measuring tissue differentiation quantitatively while correlating it with biomarker expression intensities.'),
(92467, 2008, 'bmc', 'Abstracts of the Fourth International Society for Computational Biology (ISCB) Student Council Symposium Toronto, Canada July 18, 2008', '-', '-', '-', 10, 'Computational Biology', 'Congress', '10.1186/1471-2105-9-473', '19007430', '-'),
(92468, 2008, 'bmc', 'RNAalifold: improved consensus structure prediction for RNA alignments', 'Bernhart, Stephan H|Hofacker, Ivo L|Will, Sebastian|Gruber, Andreas R|Stadler, Peter F', 'Department of Computer Science, University of Leipzig, Leipzig, Germany. berni@tbi.univie.ac.at|Department of Computer Science, University of Leipzig, Leipzig, Germany. berni@tbi.univie.ac.at|Department of Computer Science, University of Leipzig, Leipzig, Germany. berni@tbi.univie.ac.at|Department of Computer Science, University of Leipzig, Leipzig, Germany. berni@tbi.univie.ac.at|Department of Computer Science, University of Leipzig, Leipzig, Germany. berni@tbi.univie.ac.at', 'Germany|Germany|Germany|Germany|Germany', 347, 'Algorithms|Amino Acid Sequence|Computational Biology|Models, Chemical|Nucleic Acid Conformation|RNA|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-9-474', '19014431', 'The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.'),
(92469, 2008, 'bmc', 'Standardized high-throughput evaluation of cell-based compound screens', 'Frommolt, Peter|Thomas, Roman K', 'Institute of Medical Statistics, Informatics and Epidemiology, University of Köln, Köln, Germany. peter.frommolt@uni-koeln.de|Institute of Medical Statistics, Informatics and Epidemiology, University of Köln, Köln, Germany. peter.frommolt@uni-koeln.de', 'Germany|Germany', 9, 'Cell Physiological Phenomena|Computational Biology|Databases, Genetic|Drug Evaluation, Preclinical|Gene Expression Profiling|Pharmaceutical Preparations|Small Cell Lung Carcinoma|Software', 'Journal Article', '10.1186/1471-2105-9-475', '19014471', 'High-throughput screening of pharmaceutical compound activity in tissue culture experiments requires time-consuming repeated analysis of the large amounts of data generated. Automation of the evaluation procedure and assessment of measurement accuracy can save time and improve the comparability of results.'),
(92470, 2008, 'bmc', 'CoCoNUT: an efficient system for the comparison and analysis of genomes', 'Abouelhoda, Mohamed I|Kurtz, Stefan|Ohlebusch, Enno', 'Faculty of Engineering, Cairo University, Giza, Egypt. mabouelhoda@yahoo.com|Faculty of Engineering, Cairo University, Giza, Egypt. mabouelhoda@yahoo.com|Faculty of Engineering, Cairo University, Giza, Egypt. mabouelhoda@yahoo.com', 'Egypt|Egypt|Egypt', 20, 'Animals|Comparative Genomic Hybridization|Humans|Software|Statistics as Topic', 'Comparative Study', '10.1186/1471-2105-9-476', '19014477', 'Comparative genomics is the analysis and comparison of genomes from different species. This area of research is driven by the large number of sequenced genomes and heavily relies on efficient algorithms and software to perform pairwise and multiple genome comparisons.'),
(92471, 2008, 'bmc', 'Improved identification of conserved cassette exons using Bayesian networks', 'Sinha, Rileen|Hiller, Michael|Pudimat, Rainer|Gausmann, Ulrike|Platzer, Matthias|Backofen, Rolf', 'Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute, Jena, Germany. rsinha@fli-leibniz.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 5, 'Algorithms|Alternative Splicing|Artificial Intelligence|Bayes Theorem|Computational Biology|Exons|Nucleic Acid Conformation|RNA, Messenger', 'Journal Article', '10.1186/1471-2105-9-477', '19014490', 'Alternative splicing is a major contributor to the diversity of eukaryotic transcriptomes and proteomes. Currently, large scale detection of alternative splicing using expressed sequence tags (ESTs) or microarrays does not capture all alternative splicing events. Moreover, for many species genomic data is being produced at a far greater rate than corresponding transcript data, hence in silico methods of predicting alternative splicing have to be improved.'),
(92472, 2008, 'bmc', 'RIPCAL: a tool for alignment-based analysis of repeat-induced point mutations in fungal genomic sequences', 'Hane, James K|Oliver, Richard P', 'Australian Centre for Necrotrophic Fungal Pathogens, Faculty of Health Sciences, Murdoch University, Murdoch, Australia. j.hane@murdoch.edu.au|Australian Centre for Necrotrophic Fungal Pathogens, Faculty of Health Sciences, Murdoch University, Murdoch, Australia. j.hane@murdoch.edu.au', 'Australia|Australia', 100, 'Algorithms|Ascomycota|Computational Biology|Genomics|Molecular Sequence Data|Point Mutation|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Software', 'Journal Article', '10.1186/1471-2105-9-478', '19014496', 'Repeat-induced point mutation (RIP) is a fungal-specific genome defence mechanism that alters the sequences of repetitive DNA, thereby inactivating coding genes. Repeated DNA sequences align between mating and meiosis and both sequences undergo C:G to T:A transitions. In most fungi these transitions preferentially affect CpA di-nucleotides thus altering the frequency of certain di-nucleotides in the affected sequences. The majority of previously published in silico analyses were limited to the comparison of ratios of pre- and post-RIP di-nucleotides in putatively RIP-affected sequences - so-called RIP indices. The analysis of RIP is significantly more informative when comparing sequence alignments of repeated sequences. There is, however, a dearth of bioinformatics tools available to the fungal research community for alignment-based RIP analysis of repeat families.'),
(92473, 2008, 'bmc', 'Extension of the COG and arCOG databases by amino acid and nucleotide sequences', 'Meereis, Florian|Kaufmann, Michael', 'The Protein Chemistry Group, Witten/Herdecke University, Stockumer Str, 10, 58448 Witten, Germany. florian@meereis.com|The Protein Chemistry Group, Witten/Herdecke University, Stockumer Str, 10, 58448 Witten, Germany. florian@meereis.com', 'Germany|Germany', 5, 'Amino Acid Sequence|Base Sequence|Databases, Genetic|Genomics|Internet|Molecular Sequence Data', 'Journal Article', '10.1186/1471-2105-9-479', '19014535', 'The current versions of the COG and arCOG databases, both excellent frameworks for studies in comparative and functional genomics, do not contain the nucleotide sequences corresponding to their protein or protein domain entries.');
