INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73953, 2008, 'bio', 'Complexity reduction of biochemical rate expressions', 'Schmidt, Henning|Madsen, Mads F|Danø, Sune|Cedersund, Gunnar', 'Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany. henning.schmidt@uni-rostock.de|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany. henning.schmidt@uni-rostock.de|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany. henning.schmidt@uni-rostock.de|Systems Biology and Bioinformatics Group, University of Rostock, Rostock, Germany. henning.schmidt@uni-rostock.de', 'Germany|Germany|Germany|Germany', 28, 'Algorithms|Computer Simulation|Kinetics|Metabolic Clearance Rate|Models, Biological|Models, Chemical|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn035', '18267948', 'The current trend in dynamical modelling of biochemical systems is to construct more and more mechanistically detailed and thus complex models. The complexity is reflected in the number of dynamic state variables and parameters, as well as in the complexity of the kinetic rate expressions. However, a greater level of complexity, or level of detail, does not necessarily imply better models, or a better understanding of the underlying processes. Data often does not contain enough information to discriminate between different model hypotheses, and such overparameterization makes it hard to establish the validity of the various parts of the model. Consequently, there is an increasing demand for model reduction methods.'),
(73954, 2008, 'bio', 'Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics', 'Kwon, Yung-Keun|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea.', '-|-', 0, 'Computer Simulation|Data Interpretation, Statistical|Feedback|Gene Expression Profiling|Kinetics|Models, Biological|Models, Statistical|Proteome|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn060', '18285369', 'It has been widely reported that biological networks are robust against perturbations such as mutations. On the contrary, it has also been known that biological networks are often fragile against unexpected mutations. There is a growing interest in these intriguing observations and the underlying design principle that causes such robust but fragile characteristics of biological networks. For relatively small networks, a feedback loop has been considered as an important motif for realizing the robustness. It is still, however, not clear how a number of coupled feedback loops actually affect the robustness of large complex biological networks. In particular, the relationship between fragility and feedback loops has not yet been investigated till now.'),
(73955, 2008, 'bio', 'Detecting hierarchical structure in molecular characteristics of disease using transitive approximations of directed graphs', 'Jacob, Juby|Jentsch, Marcel|Kostka, Dennis|Bentink, Stefan|Spang, Rainer', 'Computational Diagnostics Group, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. Juby.Jacob@klinik.uni-regensburg.de|Computational Diagnostics Group, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. Juby.Jacob@klinik.uni-regensburg.de|Computational Diagnostics Group, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. Juby.Jacob@klinik.uni-regensburg.de|Computational Diagnostics Group, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. Juby.Jacob@klinik.uni-regensburg.de|Computational Diagnostics Group, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany. Juby.Jacob@klinik.uni-regensburg.de', 'Germany|Germany|Germany|Germany|Germany', 7, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Humans|Lymphoma|Molecular Probe Techniques|Neoplasm Proteins|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn056', '18285370', 'Molecular diagnostics aims at classifying diseases into clinically relevant sub-entities based on molecular characteristics. Typically, the entities are split into subgroups, which might contain several variants yielding a hierarchical model of the disease. Recent years have introduced a plethora of new molecular screening technologies to molecular diagnostics. As a result molecular profiles of patients became complex and the classification task more difficult.'),
(73956, 2008, 'bio', 'ParCrys: a Parzen window density estimation approach to protein crystallization propensity prediction', 'Overton, Ian M|Padovani, Gianandrea|Girolami, Mark A|Barton, Geoffrey J', 'School of Life Sciences Research, University of Dundee, Dow Street, Dundee, DD1 5EH and Department of Computing Science, University of Glasgow, Glasgow, GL12 8QQ, UK.|School of Life Sciences Research, University of Dundee, Dow Street, Dundee, DD1 5EH and Department of Computing Science, University of Glasgow, Glasgow, GL12 8QQ, UK.|School of Life Sciences Research, University of Dundee, Dow Street, Dundee, DD1 5EH and Department of Computing Science, University of Glasgow, Glasgow, GL12 8QQ, UK.|School of Life Sciences Research, University of Dundee, Dow Street, Dundee, DD1 5EH and Department of Computing Science, University of Glasgow, Glasgow, GL12 8QQ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 47, 'Algorithms|Amino Acid Sequence|Computer Simulation|Crystallization|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn055', '18285371', 'The ability to rank proteins by their likely success in crystallization is useful in current Structural Biology efforts and in particular in high-throughput Structural Genomics initiatives. We present ParCrys, a Parzen Window approach to estimate a proteins propensity to produce diffraction-quality crystals. The Protein Data Bank (PDB) provided training data whilst the databases TargetDB and PepcDB were used to define feature selection data as well as test data independent of feature selection and training. ParCrys outperforms the OB-Score, SECRET and CRYSTALP on the data examined, with accuracy and Matthews correlation coefficient values of 79.1% and 0.582, respectively (74.0% and 0.227, respectively, on data with a real-world ratio of positive:negative examples). ParCrys predictions and associated data are available from www.compbio.dundee.ac.uk/parcrys.'),
(73957, 2008, 'bio', 'GOTreePlus: an interactive gene ontology browser', 'Lee, Bongshin|Brown, Kristy|Hathout, Yetrib|Seo, Jinwook', 'Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.|Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA.', 'United States|United States|United States|United States', 0, 'Computer Graphics|Database Management Systems|Databases, Protein|Gene Expression Profiling|Genes|Information Storage and Retrieval|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn068', '18292113', 'We developed an interactive gene ontology (GO) browser named GOTreePlus that superimposes annotation information over GO structures. It can facilitate the identification of important GO terms through interactive visualization of them in the GO structure. The interactive pie chart summarizing an annotation distribution for a selected GO term provides users with a succinct context-sensitive overview of their experimental results. We tested our GOTreePlus using a proteome profiling dataset obtained on differentiation of retinal pigment epithelial cells where 399 proteins were quantified.'),
(73958, 2008, 'bio', 'MMG: a probabilistic tool to identify submodules of metabolic pathways', 'Sanguinetti, Guido|Noirel, Josselin|Wright, Phillip C', 'Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Multigene Family|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn066', '18292114', 'A fundamental task in systems biology is the identification of groups of genes that are involved in the cellular response to particular signals. At its simplest level, this often reduces to identifying biological quantities (mRNA abundance, enzyme concentrations, etc.) which are differentially expressed in two different conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising from a mixture distribution. A common assumption of these approaches is that the data are independent and identically distributed, however, biological quantities are usually related through a complex (weighted) network of interactions, and often the more pertinent question is which subnetworks are differentially expressed, rather than which genes. Furthermore, in many interesting cases (such as high-throughput proteomics and metabolomics), only very partial observations are available, resulting in the need for efficient imputation techniques.'),
(73959, 2008, 'bio', 'PCCA: a program for phylogenetic canonical correlation analysis', 'Revell, Liam J|Harrison, Alexis S', 'Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. lrevell@fas.harvard.edu|Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. lrevell@fas.harvard.edu', 'United States|United States', 49, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Models, Genetic|Models, Statistical|Multivariate Analysis|Phylogeny|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btn065', '18292115', 'PCCA (phylogenetic canonical correlation analysis) is a new program for canonical correlation analysis of multivariate, continuously valued data from biological species. Canonical correlation analysis is a technique in which derived variables are obtained from two sets of original variables whereby the correlations between corresponding derived variables are maximized. It is a very useful multivariate statistical method for the calculation and analysis of correlations between character sets. The program controls for species non-independence due to phylogenetic history and computes canonical coefficients, correlations and scores, and conducts hypothesis tests on the canonical correlations. It can also compute a multivariate version of Pagels lambda, which can then be used in the phylogenetic transformation.'),
(73960, 2008, 'bio', 'Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models', 'Hirose, Osamu|Yoshida, Ryo|Imoto, Seiya|Yamaguchi, Rui|Higuchi, Tomoyuki|Charnock-Jones, D Stephen|Print, Cristin|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Cell Cycle|Cell Cycle Proteins|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btm639', '18292116', 'Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway.'),
(73961, 2008, 'bio', 'Tree-guided Bayesian inference of population structures', 'Zhang, Yu', 'Department of Statistics, The Pennsylvania State University, State College, PA, USA. yuzhang@stat.psu.edu', 'United States', 0, 'Algorithms|Base Sequence|Bayes Theorem|Computer Simulation|DNA Mutational Analysis|Genetics, Population|Models, Genetic|Molecular Sequence Data|Mutation|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn070', '18296461', 'Inferring population structures using genetic data sampled from a group of individuals is a challenging task. Many methods either consider a fixed population number or ignore the correlation between populations. As a result, they can lose sensitivity and specificity in detecting subtle stratifications. In addition, when a large number of genetic markers are used, many existing algorithms perform rather inefficiently.'),
(73962, 2008, 'bio', 'A comprehensive assessment of sequence-based and template-based methods for protein contact prediction', 'Wu, Sitao|Zhang, Yang', 'Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, 2030 Becker Dr, Lawrence, KS 66047, USA.|Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, 2030 Becker Dr, Lawrence, KS 66047, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Amino Acids|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn069', '18296462', 'Pair-wise residue-residue contacts in proteins can be predicted from both threading templates and sequence-based machine learning. However, most structure modeling approaches only use the template-based contact predictions in guiding the simulations, this is partly because the sequence-based contact predictions are usually considered to be less accurate than that by threading. With the rapid progress in sequence databases and machine-learning techniques, it is necessary to have a detailed and comprehensive assessment of the contact-prediction methods in different template conditions.'),
(73963, 2008, 'bio', 'CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms', 'Lai, Weil|Choudhary, Vidhu|Park, Peter J', 'Harvard-Partners Center for Genetics and Genomics and Informatics Program, Childrens Hospital, Boston, MA 02139, USA.|Harvard-Partners Center for Genetics and Genomics and Informatics Program, Childrens Hospital, Boston, MA 02139, USA.|Harvard-Partners Center for Genetics and Genomics and Informatics Program, Childrens Hospital, Boston, MA 02139, USA.', 'United States|United States|United States', 42, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Gene Dosage|In Situ Hybridization, Fluorescence|Internet|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn067', '18296463', 'Accurate estimation of DNA copy numbers from array comparative genomic hybridization (CGH) data is important for characterizing the cancer genome. An important part of this process is the segmentation of the log-ratios between the sample and control DNA along the chromosome into regions of different copy numbers. However, multiple algorithms are available in the literature for this procedure and the results can vary substantially among these. Thus, a visualization tool that can display the segmented profiles from a number of methods can be helpful to the biologist or the clinician to ascertain that a feature of interest did not arise as an artifact of the algorithm. Such a tool also allows the methodologist to easily contrast his method against others. We developed a web-based tool that applies a number of popular algorithms to a single array CGH profile entered by the user. It generates a heatmap panel of the segmented profiles for each method as well as a consensus profile. The clickable heatmap can be moved along the chromosome and zoomed in or out. It also displays the time that each algorithm took and provides numerical values of the segmented profiles for download. The web interface calls algorithms written in the statistical language R. We encourage developers of new algorithms to submit their routines to be incorporated into the website.'),
(73964, 2008, 'bio', 'fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control', 'Li, Leping|Bass, Robert L|Liang, Yu', 'Biostatistics Branch, National Institute of Environmental Health Sciences, NIH, DHHS, Research Triangle Park, NC 27709, USA. li3@niehs.nih.gov|Biostatistics Branch, National Institute of Environmental Health Sciences, NIH, DHHS, Research Triangle Park, NC 27709, USA. li3@niehs.nih.gov|Biostatistics Branch, National Institute of Environmental Health Sciences, NIH, DHHS, Research Triangle Park, NC 27709, USA. li3@niehs.nih.gov', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Motifs|Binding Sites|Models, Theoretical', 'Journal Article', '10.1093/bioinformatics/btn009', '18296465', 'Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered.'),
(73965, 2008, 'bio', 'Disease association tests by inferring ancestral haplotypes using a hidden markov model', 'Su, Shu-Yi|Balding, David J|Coin, Lachlan J M', 'Department of Epidemiology and Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Public Health, Imperial College, London W2 1PG, UK.|Department of Epidemiology and Public Health, Imperial College, London W2 1PG, UK.', 'United Kingdom|United Kingdom|United Kingdom', 19, 'Algorithms|Biological Evolution|Chromosome Mapping|Computer Simulation|Genetic Predisposition to Disease|Haplotypes|Markov Chains|Models, Genetic|Models, Statistical|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn071', '18296746', 'Most genome-wide association studies rely on single nucleotide polymorphism (SNP) analyses to identify causal loci. The increased stringency required for genome-wide analyses (with per-SNP significance threshold typically approximately 10(-7)) means that many real signals will be missed. Thus it is still highly relevant to develop methods with improved power at low type I error. Haplotype-based methods provide a promising approach, however, they suffer from statistical problems such as abundance of rare haplotypes and ambiguity in defining haplotype block boundaries.'),
(73966, 2008, 'bio', 'The effect of sequence quality on sequence alignment', 'Malde, Ketil', 'Institute of Marine Research, Bergen, Norway. ketil.malde@imr.no', 'Norway', 0, 'Algorithms|Base Sequence|Molecular Sequence Data|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btn052', '18296747', 'The nucleotide sequencing process produces not only the sequence of nucleotides, but also associated quality values. Quality values provide valuable information, but are primarily used only for trimming sequences and generally ignored in subsequent analyses.'),
(73967, 2008, 'bio', 'Comment on causality and pathway search in microarray time series experiment', 'Nagarajan, Radhakrishnan|Upreti, Meenakshi', 'Institute of Marine Research, Bergen, Norway. ketil.malde@imr.no|Institute of Marine Research, Bergen, Norway. ketil.malde@imr.no', 'Norway|Norway', 12, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction|Time Factors', 'Comment', '10.1093/bioinformatics/btm586', '18304931', '-'),
(73968, 2008, 'bio', 'SubSeqer: a graph-based approach for the detection and identification of repetitive elements in low-complexity sequences', 'He, David|Parkinson, John', 'Program in Molecular Structure and Function, Hospital for Sick Children, University of Toronto, Toronto, Canada.|Program in Molecular Structure and Function, Hospital for Sick Children, University of Toronto, Toronto, Canada.', 'Canada|Canada', 1, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Proteins|Repetitive Sequences, Amino Acid|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn073', '18304932', 'Low-complexity, repetitive protein sequences with a limited amino acid palette are abundant in nature, and many of them play an important role in the structure and function of certain types of proteins. However, such repetitive sequences often do not have rigidly defined motifs. Consequently, the identification of these low-complexity repetitive elements has proven challenging for existing pattern-matching algorithms. Here we introduce a new web-tool SubSeqer (http://compsysbio.org/subseqer/) which uses graphical visualization methods borrowed from protein interaction studies to identify and characterize repetitive elements in low-complexity sequences. Given their abundance, we suggest that SubSeqer represents a valuable resource for the study of typically neglected low-complexity sequences.'),
(73969, 2008, 'bio', 'AIMIE: a web-based environment for detection and interpretation of significant sequence motifs in prokaryotic genomes', 'Mrázek, Jan|Xie, Shaohua|Guo, Xiangxue|Srivastava, Anuj', 'Department of Microbiology, University of Georgia, Athens, GA 30602-2605, USA. mrazek@uga.edu|Department of Microbiology, University of Georgia, Athens, GA 30602-2605, USA. mrazek@uga.edu|Department of Microbiology, University of Georgia, Athens, GA 30602-2605, USA. mrazek@uga.edu|Department of Microbiology, University of Georgia, Athens, GA 30602-2605, USA. mrazek@uga.edu', 'Georgia|Georgia|Georgia|Georgia', 0, 'Animals|Base Sequence|Chromosome Mapping|Conserved Sequence|Humans|Internet|Molecular Sequence Data|Prokaryotic Cells|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btn077', '18304933', 'Genomes contain biologically significant information that extends beyond that encoded in genes. Some of this information relates to various short dispersed repeats distributed throughout the genome. The goal of this work was to combine tools for detection of statistically significant dispersed repeats in DNA sequences with tools to aid development of hypotheses regarding their possible physiological functions in an easy-to-use web-based environment.'),
(73970, 2008, 'bio', 'FTFD: an informatics pipeline supporting phylogenomic analysis of fungal transcription factors', 'Park, Jongsun|Park, Jaejin|Jang, Suwang|Kim, Seryun|Kong, Sunghyung|Choi, Jaeyoung|Ahn, Kyohun|Kim, Juhyeon|Lee, Seungmin|Kim, Sunggon|Park, Bongsoo|Jung, Kyongyong|Kim, Soonok|Kang, Seogchan|Lee, Yong-Hwan', 'Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.|Fungal Bioinformatics Laboratory, Seoul National University, Seoul 151-921, Korea.', '-|-|-|-|-|-|-|-|-|-|-|-|-|-|-', 98, 'Chromosome Mapping|Database Management Systems|Databases, Protein|Evolution, Molecular|Fungal Proteins|Genomics|Multigene Family|Phylogeny|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn058', '18304934', 'Genomes of more than 60 fungal species have been sequenced to date, yet there has been no systematic approach to analyze fungal transcription factors (TFs) kingdom widely. We developed a standardized pipeline for annotating TFs in fungal genomes. Resulting data have been archived in a new database termed the Fungal Transcription Factor Database (FTFD). In FTFD, 31,832 putative fungal TFs, identified from 62 fungal and 3 Oomycete species, were classified into 61 families and phylogenetically analyzed. The FTFD will serve as a community resource supporting comparative analyses of the distribution and domain structure of TFs within and across species.'),
(73971, 2008, 'bio', 'DeconMSn: a software tool for accurate parent ion monoisotopic mass determination for tandem mass spectra', 'Mayampurath, Anoop M|Jaitly, Navdeep|Purvine, Samuel O|Monroe, Matthew E|Auberry, Kenneth J|Adkins, Joshua N|Smith, Richard D', 'Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.|Pacific Northwest National Laboratory, Richland, WA 99354, USA.', 'United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btn063', '18304935', 'DeconMSn accurately determines the monoisotopic mass and charge state of parent ions from high-resolution tandem mass spectrometry data, offering significant improvement for LTQ_FT and LTQ_Orbitrap instruments over the commercially delivered Thermo Fisher Scientifics extract_msn tool. Optimal parent ion mass tolerance values can be determined using accurate mass information, thus improving peptide identifications for high-mass measurement accuracy experiments. For low-resolution data from LCQ and LTQ instruments, DeconMSn incorporates a support-vector-machine-based charge detection algorithm that identifies the most likely charge of a parent species through peak characteristics of its fragmentation pattern.'),
(73972, 2008, 'bio', 'Tracing evolutionary pressure', 'Ye, Kai|Vriend, Gert|IJzerman, Adriaan P', 'Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333CC, Leiden, The Netherlands. k.ye@lacdr.leidenuniv.nl|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333CC, Leiden, The Netherlands. k.ye@lacdr.leidenuniv.nl|Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333CC, Leiden, The Netherlands. k.ye@lacdr.leidenuniv.nl', 'Netherlands|Netherlands|Netherlands', 25, 'Algorithms|Base Sequence|Biological Evolution|Computer Simulation|DNA Mutational Analysis|Evolution, Molecular|Models, Genetic|Molecular Sequence Data|Phylogeny|Proteins|Selection, Genetic|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn057', '18304936', 'Recent advances in sequencing techniques have yielded enormous amounts of protein sequence data from various species. This large dataset allows sequence comparison between paralogous and orthologous proteins to identify motifs or functional positions that account for the differences of functional subgroups (specificity positions). Algorithms such as SDPpred and the two-entropies analysis (TEA) have been developed to detect such specificity positions from a multiple sequence alignment (MSA) grouped into classes according to certain biological functions. Other algorithms such as TreeDet compute a classification and then predict specificity positions associated with it. However, there are still many unresolved questions: Was the optimal subdivision of a protein family achieved? Do the definitions at different levels of the phylogenetic tree affect the prediction of specificity positions? Can the whole phylogenetic tree be used instead of only one level in it to predict specificity positions?'),
(73973, 2008, 'bio', 'From pull-down data to protein interaction networks and complexes with biological relevance', 'Zhang, Bing|Park, Byung-Hoon|Karpinets, Tatiana|Samatova, Nagiza F', 'Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.|Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Biology|Computer Simulation|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Peptide Mapping|Protein Interaction Mapping|Proteins|Signal Transduction|Structure-Activity Relationship|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn036', '18304937', 'Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance.'),
(73974, 2008, 'bio', 'Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis', 'Futschik, Matthias E|Herzel, Hanspeter', 'Institute for Theoretical Biology, Charité, Humboldt-Universität, Invalidenstrasse 43, 10115 Berlin, Germany. matthias.futschik@charite.de|Institute for Theoretical Biology, Charité, Humboldt-Universität, Invalidenstrasse 43, 10115 Berlin, Germany. matthias.futschik@charite.de', 'Germany|Germany', 27, 'Artifacts|Cell Cycle|Cell Cycle Proteins|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn072', '18310054', 'Periodic processes play fundamental roles in organisms. Prominent examples are the cell cycle and the circadian clock. Microarray array technology has enabled us to screen complete sets of transcripts for possible association with such fundamental periodic processes on a system-wide level. Frequently, quite large numbers of genes have been detected as periodically expressed. However, the small overlap between genes identified in different studies has cast some doubts on the reliability of the periodic expression detected.'),
(73975, 2008, 'bio', 'ISD: a software package for Bayesian NMR structure calculation', 'Rieping, Wolfgang|Nilges, Michael|Habeck, Michael', 'Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. wolfgang.rieping@bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. wolfgang.rieping@bioc.cam.ac.uk|Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. wolfgang.rieping@bioc.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Bayes Theorem|Biopolymers|Computer Simulation|Crystallography|Magnetic Resonance Spectroscopy|Models, Chemical|Models, Molecular|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn062', '18310055', 'The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The inferential structure determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need to be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology.'),
(73976, 2008, 'bio', 'In silico Biochemical Reaction Network Analysis (IBRENA): a package for simulation and analysis of reaction networks', 'Liu, Gang|Neelamegham, Sriram', 'Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.|Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY 14260, USA.', 'United States|United States', 3, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn061', '18310056', 'We present In silico Biochemical Reaction Network Analysis (IBRENA), a software package which facilitates multiple functions including cellular reaction network simulation and sensitivity analysis (both forward and adjoint methods), coupled with principal component analysis, singular-value decomposition and model reduction. The software features a graphical user interface that aids simulation and plotting of in silico results. While the primary focus is to aid formulation, testing and reduction of theoretical biochemical reaction networks, the program can also be used for analysis of high-throughput genomic and proteomic data.'),
(73977, 2008, 'bio', 'Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline', 'Dowsey, Andrew W|Dunn, Michael J|Yang, Guang-Zhong', 'Institute of Biomedical Engineering, Imperial College London, United Kingdom.|Institute of Biomedical Engineering, Imperial College London, United Kingdom.|Institute of Biomedical Engineering, Imperial College London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom', 30, 'Algorithms|Amino Acid Sequence|Electrophoresis, Gel, Two-Dimensional|Image Interpretation, Computer-Assisted|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Proteome|Proteomics|Sequence Analysis, Protein|Subtraction Technique|Systems Integration', 'Evaluation Studies', '10.1093/bioinformatics/btn059', '18310057', 'The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka shotgun proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline.'),
(73978, 2008, 'bio', 'Network-constrained regularization and variable selection for analysis of genomic data', 'Li, Caiyan|Li, Hongzhe', 'Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.|Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.', 'United States|United States', 0, 'Algorithms|Biomarkers, Tumor|Chromosome Mapping|Gene Expression Profiling|Glioblastoma|Humans|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Signal Transduction|Survival Analysis|Survival Rate', 'Journal Article', '10.1093/bioinformatics/btn081', '18310618', 'Graphs or networks are common ways of depicting information. In biology in particular, many different biological processes are represented by graphs, such as regulatory networks or metabolic pathways. This kind of a priori information gathered over many years of biomedical research is a useful supplement to the standard numerical genomic data such as microarray gene-expression data. How to incorporate information encoded by the known biological networks or graphs into analysis of numerical data raises interesting statistical challenges. In this article, we introduce a network-constrained regularization procedure for linear regression analysis in order to incorporate the information from these graphs into an analysis of the numerical data, where the network is represented as a graph and its corresponding Laplacian matrix. We define a network-constrained penalty function that penalizes the L(1)-norm of the coefficients but encourages smoothness of the coefficients on the network.'),
(73979, 2008, 'bio', 'Reference descriptions of cellular electrophysiology models', 'Nickerson, David P|Corrias, Alberto|Buist, Martin L', 'Division of Bioengineering, National University of Singapore, Singapore. david.nickerson@nus.edu.sg|Division of Bioengineering, National University of Singapore, Singapore. david.nickerson@nus.edu.sg|Division of Bioengineering, National University of Singapore, Singapore. david.nickerson@nus.edu.sg', 'Singapore|Singapore|Singapore', 11, 'Cell Membrane|Computer Simulation|Electrophysiology|Ion Channels|Membrane Potentials|Models, Biological|Software', 'Journal Article', '10.1093/bioinformatics/btn080', '18310619', 'In recent years there has been much development of the fundamental ideas underlying mathematical model curation in regard to models of biology. While much has been achieved in the realms of systems biology and bioinformatics, little progress has been made in relation to cellular electrophysiology modeling. The primary reason for slow progress in this field is the lack of a consistent and machine-readable reference description for a given model. CellML has been widely used to describe mathematical models of cellular electrophysiology in an unambiguous, machine-readable format. Through the use of well-annotated CellML models we propose a standard by which reference descriptions of cellular electrophysiology models can be similarly defined in an unambiguous, software independent, and machine-readable format. Adoption of this standard will provide a consistent technology by which cellular electrophysiology models can be curated.'),
(73980, 2008, 'bio', 'Identifying trait clusters by linkage profiles: application in genetical genomics', 'Sampson, Joshua N|Self, Steven G', 'Department of Biostatistics, University of Washington and Statistical Center for HIV/AIDS Research and Prevention, Seattle, WA, USA. joshua.sampson@yale.edu|Department of Biostatistics, University of Washington and Statistical Center for HIV/AIDS Research and Prevention, Seattle, WA, USA. joshua.sampson@yale.edu', 'United States|United States', 0, 'Algorithms|Chromosome Mapping|Cluster Analysis|Computer Simulation|Genomics|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Multigene Family|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btn064', '18310620', 'Genes often regulate multiple traits. Identifying clusters of traits influenced by a common group of genes helps elucidate regulatory networks and can improve linkage mapping.'),
(73981, 2008, 'bio', 'Robustified MANOVA with applications in detecting differentially expressed genes from oligonucleotide arrays', 'Xu, Jin|Cui, Xinping', 'Department of Statistics, East China Normal University, Shanghai 200241, China.|Department of Statistics, East China Normal University, Shanghai 200241, China.', 'China|China', 0, 'Analysis of Variance|Artifacts|Data Interpretation, Statistical|Gene Expression Profiling|Multivariate Analysis|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btn053', '18316342', 'Oligonucleotide arrays such as Affymetrix GeneChips use multiple probes, or a probe set, to measure the abundance of mRNA of every gene of interest. Some analysis methods attempt to summarize the multiple observations into one single score before conducting further analysis such as detecting differentially expressed genes (DEG), clustering and classification. However, there is a risk of losing a significant amount of information and consequently reaching inaccurate or even incorrect conclusions during this data reduction.'),
(73982, 2008, 'bio', 'PedMine--a simulated annealing algorithm to identify maximally unrelated individuals in population isolates', 'Douglas, Julie A|Sandefur, Conner I', 'Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA. jddoug@umich.edu|Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA. jddoug@umich.edu', 'United States|United States', 0, 'Algorithms|Computer Simulation|Genetic Linkage|Genetics, Population|Humans|Linkage Disequilibrium|Models, Genetic|Pedigree|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btn087', '18321883', 'In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a genetic study in the Old Order Amish of Lancaster County, Pennsylvania, a population isolate derived from a modest number of founders. Given one or more pedigrees, our program automatically and rapidly extracts a fixed number of maximally unrelated individuals.'),
(73983, 2008, 'bio', 'e-LiSe--an online tool for finding needles in the (Medline) haystack', 'Gladki, Arek|Siedlecki, Pawel|Kaczanowski, Szymon|Zielenkiewicz, Piotr', 'Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106, Warszawa, Poland.|Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106, Warszawa, Poland.|Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106, Warszawa, Poland.|Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106, Warszawa, Poland.', 'Poland|Poland|Poland|Poland', 9, 'Database Management Systems|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Online Systems|Periodicals as Topic|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn086', '18321884', 'Using literature databases one can find not only known and true relations between processes but also less studied, non-obvious associations. The main problem with discovering such type of relevant biological information is selection. The ability to distinguish between a true correlation (e.g. between different types of biological processes) and random chance that this correlation is statistically significant is crucial for any bio-medical research, literature mining being no exception. This problem is especially visible when searching for information which has not been studied and described in many publications. Therefore, a novel bio-linguistic statistical method is required, capable of selecting true correlations, even when they are low-frequency associations. In this article, we present such statistical approach based on Z-score and implemented in a web-based application e-LiSe.'),
(73984, 2008, 'bio', 'BicOverlapper: a tool for bicluster visualization', 'Santamaría, Rodrigo|Therón, Roberto|Quintales, Luis', 'Departamento de Informática y Automática, Pz. de Los Caídos S/N, 37008 Salamanca, Spain. rodri@usal.es|Departamento de Informática y Automática, Pz. de Los Caídos S/N, 37008 Salamanca, Spain. rodri@usal.es|Departamento de Informática y Automática, Pz. de Los Caídos S/N, 37008 Salamanca, Spain. rodri@usal.es', 'Spain|Spain|Spain', 49, 'Cluster Analysis|Computer Graphics|Computer Simulation|Data Display|Models, Biological|Multigene Family|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn076', '18321885', 'BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions.'),
(73985, 2008, 'bio', 'Inference of biochemical network models in S-system using multiobjective optimization approach', 'Liu, Pang-Kai|Wang, Feng-Sheng', 'Department of Chemical Engineering, National Chung Cheng University, Chiayi 621-02, Taiwan, ROC.|Department of Chemical Engineering, National Chung Cheng University, Chiayi 621-02, Taiwan, ROC.', '-|-', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn075', '18321886', 'The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied sums of magnitude of kinetic orders as a penalty term in the fitness evaluation. How to tune a penalty weight to yield a realizable model structure is the main issue for the inverse problem. No guideline has been published for tuning a suitable penalty weight to infer a suitable model structure of biochemical networks.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(73986, 2008, 'bio', 'Predicting proteolytic sites in extracellular proteins: only halfway there', 'Kliger, Yossef|Gofer, Eyal|Wool, Assaf|Toporik, Amir|Apatoff, Avihay|Olshansky, Moshe', 'Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il|Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il|Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il|Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il|Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il|Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il', 'Israel|Israel|Israel|Israel|Israel|Israel', 0, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Extracellular Matrix Proteins|Models, Chemical|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn084', '18321887', 'Many secretory proteins are synthesized as inactive precursors that must undergo post-translational proteolysis in order to mature and become active. In the current study, we address the challenge of sequence-based discovery of proteolytic sites in secreted proteins using machine learning.'),
(73987, 2008, 'bio', 'Consensus generation and variant detection by Celera Assembler', 'Denisov, Gennady|Walenz, Brian|Halpern, Aaron L|Miller, Jason|Axelrod, Nelson|Levy, Samuel|Sutton, Granger', 'J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org|J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. gdenisov@jcvi.org', 'United States|United States|United States|United States|United States|United States|United States', 54, 'Algorithms|Base Sequence|Chromosome Mapping|Consensus Sequence|DNA Mutational Analysis|Gene Frequency|Genetic Variation|Genome, Human|Haploidy|Humans|Molecular Sequence Data|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn074', '18321888', 'We present an algorithm to identify allelic variation given a Whole Genome Shotgun (WGS) assembly of haploid sequences, and to produce a set of haploid consensus sequences rather than a single consensus sequence. Existing WGS assemblers take a column-by-column approach to consensus generation, and produce a single consensus sequence which can be inconsistent with the underlying haploid alleles, and inconsistent with any of the aligned sequence reads. Our new algorithm uses a dynamic windowing approach. It detects alleles by simultaneously processing the portions of aligned reads spanning a region of sequence variation, assigns reads to their respective alleles, phases adjacent variant alleles and generates a consensus sequence corresponding to each confirmed allele. This algorithm was used to produce the first diploid genome sequence of an individual human. It can also be applied to assemblies of multiple diploid individuals and hybrid assemblies of multiple haploid organisms.'),
(73988, 2008, 'bio', 'W-AlignACE: an improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data', 'Chen, Xin|Guo, Lingqiong|Fan, Zhaocheng|Jiang, Tao', 'School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg|School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg|School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg|School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. chenxin@ntu.edu.sg', 'Singapore|Singapore|Singapore|Singapore', 30, 'Algorithms|Artificial Intelligence|Base Sequence|Chromatin Immunoprecipitation|Data Interpretation, Statistical|Gene Expression Profiling|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Software', 'Evaluation Studies', '10.1093/bioinformatics/btn088', '18325926', 'Position weight matrices (PWMs) are widely used to depict the DNA binding preferences of transcription factors (TFs) in computational molecular biology and regulatory genomics. Thus, learning an accurate PWM to characterize the binding sites of a specific TF is a fundamental problem that plays an important role in modeling regulatory motifs and also in discovering the regulatory targets of TFs.'),
(73989, 2008, 'bio', 'Merging two gene-expression studies via cross-platform normalization', 'Shabalin, Andrey A|Tjelmeland, Håkon|Fan, Cheng|Perou, Charles M|Nobel, Andrew B', 'Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu|Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu|Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu|Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu|Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Reference Values', 'Journal Article', '10.1093/bioinformatics/btn083', '18325927', 'Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms.'),
(73990, 2009, 'bio', 'MiSearch adaptive pubMed search tool', 'States, David J|Ade, Alex S|Wright, Zachary C|Bookvich, Aaron V|Athey, Brian D', 'Department of Human Genetics, National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. dstates@umich.edu|Department of Human Genetics, National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. dstates@umich.edu|Department of Human Genetics, National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. dstates@umich.edu|Department of Human Genetics, National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. dstates@umich.edu|Department of Human Genetics, National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, MI 48109, USA. dstates@umich.edu', 'United States|United States|United States|United States|United States', 19, 'Algorithms|Computational Biology|Databases, Factual|Information Storage and Retrieval|Internet|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn033', '18326507', 'MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking.'),
(73991, 2008, 'bio', '3D-Garden: a system for modelling protein-protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm', 'Lesk, Victor I|Sternberg, Michael J E', 'Division of Molecular Biosciences, Imperial College London, South Kensington, London SW72AZ, UK. v.lesk@ic.ac.uk|Division of Molecular Biosciences, Imperial College London, South Kensington, London SW72AZ, UK. v.lesk@ic.ac.uk', 'United Kingdom|United Kingdom', 45, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein Interaction Mapping|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn093', '18326508', 'Reliable structural modelling of protein-protein complexes has widespread application, from drug design to advancing our knowledge of protein interactions and function. This work addresses three important issues in protein-protein docking: implementing backbone flexibility, incorporating prior indications from experiment and bioinformatics, and providing public access via a server. 3D-Garden (Global And Restrained Docking Exploration Nexus), our benchmarked and server-ready flexible docking system, allows sophisticated programming of surface patches by the user via a facet representation of the interactors molecular surfaces (generated with the marching cubes algorithm). Flexibility is implemented as a weighted exhaustive conformer search for each clashing pair of molecular branches in a set of 5000 models filtered from around approximately 340,000 initially.'),
(73992, 2008, 'bio', 'A nearest neighbor approach for automated transporter prediction and categorization from protein sequences', 'Li, Haiquan|Dai, Xinbin|Zhao, Xuechun', 'Bioinformatics Lab, Plant Biology Division, The Samuel Roberts Noble Foundation, Inc., 2510 Sam Noble Parkway, Ardmore, OK 73401, USA.|Bioinformatics Lab, Plant Biology Division, The Samuel Roberts Noble Foundation, Inc., 2510 Sam Noble Parkway, Ardmore, OK 73401, USA.|Bioinformatics Lab, Plant Biology Division, The Samuel Roberts Noble Foundation, Inc., 2510 Sam Noble Parkway, Ardmore, OK 73401, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Membrane Transport Proteins|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn099', '18337257', 'Membrane transport proteins play a crucial role in the import and export of ions, small molecules or macromolecules across biological membranes. Currently, there are a limited number of published computational tools which enable the systematic discovery and categorization of transporters prior to costly experimental validation. To approach this problem, we utilized a nearest neighbor method which seamlessly integrates homologous search and topological analysis into a machine-learning framework.'),
(73993, 2008, 'bio', 'A pattern recognition approach to infer time-lagged genetic interactions', 'Chuang, Cheng-Long|Jen, Chih-Hung|Chen, Chung-Ming|Shieh, Grace S', 'Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.|Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.', '-|-|-|-', 27, 'Algorithms|Artificial Intelligence|Binding Sites|Gene Expression Profiling|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Protein Binding|Signal Transduction|Software|Time Factors|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn098', '18337258', 'For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass.'),
(73994, 2008, 'bio', 'Optimizing the size of the sequence profiles to increase the accuracy of protein sequence alignments generated by profile-profile algorithms', 'Poleksic, Aleksandar|Fienup, Mark', 'Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614, USA. poleksic@cs.uni.edu|Department of Computer Science, University of Northern Iowa, Cedar Falls, IA 50614, USA. poleksic@cs.uni.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Gene Expression Profiling|Molecular Sequence Data|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein|Software', 'Comparative Study', '10.1093/bioinformatics/btn097', '18337259', 'Profile-based protein homology detection algorithms are valuable tools in genome annotation and protein classification. By utilizing information present in the sequences of homologous proteins, profile-based methods are often able to detect extremely weak relationships between protein sequences, as evidenced by the large-scale benchmarking experiments such as CASP and LiveBench.'),
(73995, 2008, 'bio', 'PlasmoGF: an integrated system for comparative genomics and phylogenetic analysis of Plasmodium gene families', 'Xu, Xiang|Wu, Jinyu|Xiao, Jian|Tan, Yi|Bao, Qiyu|Zhao, Fangqing|Li, Xiaokun', 'School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.|School of Pharmaceutical Science/Zhejiang Provincial Key Laboratory of Biotechnology Pharmaceutical Engineering, Wenzhou Medical College, Wenzhou 325035, PR China.', 'China|China|China|China|China|China|China', 4, 'Animals|Base Sequence|Chromosome Mapping|Computer Graphics|DNA|Genomics|Molecular Sequence Data|Multigene Family|Phylogeny|Plasmodium|Sequence Analysis, DNA|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn092', '18337260', 'Malaria, one of the worlds most common diseases, is caused by the intracellular protozoan parasite known as Plasmodium. Recently, with the arrival of several malaria parasite genomes, we established an integrated system named PlasmoGF for comparative genomics and phylogenetic analysis of Plasmodium gene families. Gene families were clustered using the Markov Cluster algorithm implemented in TribeMCL program and could be searched using keywords, gene-family information, domain composition, Gene Ontology and BLAST. Moreover, a number of useful bioinformatics tools were implemented to facilitate the analysis of these putative Plasmodium gene families, including gene retrieval, annotation, sequence alignment, phylogeny construction and visualization. In the current version, PlasmoGF contained 8980 sets of gene families derived from six malaria parasite genomes: Plasmodium. falciparum, P. berghei, P. knowlesi, P. chabaudi, P. vivax and P. yoelii. The availability of such a highly integrated system would be of great interest for the community of researchers working on malaria parasite phylogenomics.'),
(73996, 2008, 'bio', 'The Taverna Interaction Service: enabling manual interaction in workflows', 'Lanzén, Anders|Oinn, Tom', 'Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, 5008 Bergen, Norway.|Computational Biology Unit, Bergen Center for Computational Science, University of Bergen, 5008 Bergen, Norway.', 'Norway|Norway', 22, 'Algorithms|Biological Science Disciplines|Computational Biology|Computer Graphics|Database Management Systems|Databases, Factual|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn082', '18337261', 'Taverna is an application that eases the integration of tools and databases for life science research by the construction of workflows. The Taverna Interaction Service extends the functionality of Taverna by defining human interaction within a workflow and acting as a mediation layer between the automated workflow engine and one or more users.'),
(73997, 2008, 'bio', 'ChIPCodis: mining complex regulatory systems in yeast by concurrent enrichment analysis of chip-on-chip data', 'Abascal, Federico|Carmona-Saez, Pedro|Carazo, Jose-Maria|Pascual-Montano, Alberto', 'BioComputing Unit, National Center of Biotechnology (CSIC), Madrid, Spain.|BioComputing Unit, National Center of Biotechnology (CSIC), Madrid, Spain.|BioComputing Unit, National Center of Biotechnology (CSIC), Madrid, Spain.|BioComputing Unit, National Center of Biotechnology (CSIC), Madrid, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Chromatin Immunoprecipitation|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Regulatory Sequences, Nucleic Acid|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn094', '18339638', 'Eukaryotic genes are often regulated by multiple transcription factors (TFs). Depending on the interactions among different TFs the expression of a gene can be tuned to respond to diverse environmental conditions. Chip-on-chip experiments provide a snapshot of which TF are in vivo bound to which genes in a particular condition, and have been applied to characterize the regulatory code of yeast under several experimental settings. ChIPCodis mines this data to provide new insights about how the expression of a particular group of genes is regulated. For a given list of yeast genes ChIPCodis determines which combinations of TFs are significantly over-represented in a series of environmental conditions.'),
(73998, 2008, 'bio', 'Biological sequence classification utilizing positive and unlabeled data', 'Xiao, Yuanyuan|Segal, Mark R', 'Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA.|Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA.', 'United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Genetic Markers|Molecular Sequence Data|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software|Staining and Labeling', 'Journal Article', '10.1093/bioinformatics/btn089', '18344247', 'In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data.'),
(73999, 2008, 'bio', 'Fitting a geometric graph to a protein-protein interaction network', 'Higham, Desmond J|Rasajski, Marija|Przulj, Natasa', 'Department of Mathematics, University of Strathclyde, Glasgow G1 1XH, UK.|Department of Mathematics, University of Strathclyde, Glasgow G1 1XH, UK.|Department of Mathematics, University of Strathclyde, Glasgow G1 1XH, UK.', 'United Kingdom|United Kingdom|United Kingdom', 76, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Models, Statistical|Multigene Family|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn079', '18344248', 'Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs. We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N(2)) where N is the number of proteins.'),
(74000, 2008, 'bio', 'GlycoBase and autoGU: tools for HPLC-based glycan analysis', 'Campbell, Matthew P|Royle, Louise|Radcliffe, Catherine M|Dwek, Raymond A|Rudd, Pauline M', 'Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. matthew.campbell@nibrt.ie|Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. matthew.campbell@nibrt.ie|Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. matthew.campbell@nibrt.ie|Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. matthew.campbell@nibrt.ie|Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. matthew.campbell@nibrt.ie', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Chromatography, High Pressure Liquid|Computer Simulation|Models, Chemical|Polysaccharides|Software', 'Journal Article', '10.1093/bioinformatics/btn090', '18344517', 'The development of robust high-performance liquid chromatography (HPLC) technologies continues to improve the detailed analysis and sequencing of glycan structures released from glycoproteins. Here, we present a database (GlycoBase) and analytical tool (autoGU) to assist the interpretation and assignment of HPLC-glycan profiles. GlycoBase is a relational database which contains the HPLC elution positions for over 350 2-AB labelled N-glycan structures together with predicted products of exoglycosidase digestions. AutoGU assigns provisional structures to each integrated HPLC peak and, when used in combination with exoglycosidase digestions, progressively assigns each structure automatically based on the footprint data. These tools are potentially very promising and facilitate basic research as well as the quantitative high-throughput analysis of low concentrations of glycans released from glycoproteins.'),
(74001, 2008, 'bio', 'Unequal group variances in microarray data analyses', 'Demissie, Meaza|Mascialino, Barbara|Calza, Stefano|Pawitan, Yudi', 'Department of Statistics, University of Orebro, Sweden.|Department of Statistics, University of Orebro, Sweden.|Department of Statistics, University of Orebro, Sweden.|Department of Statistics, University of Orebro, Sweden.', 'Sweden|Sweden|Sweden|Sweden', 19, 'Algorithms|Analysis of Variance|Artifacts|Data Interpretation, Statistical|Gene Expression Profiling|Genetic Variation|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn100', '18344518', 'In searching for differentially expressed (DE) genes in microarray data, we often observe a fraction of the genes to have unequal variability between groups. This is not an issue in large samples, where a valid test exists that uses individual variances separately. The problem arises in the small-sample setting, where the approximately valid Welch test lacks sensitivity, while the more sensitive moderated t-test assumes equal variance.'),
(74002, 2008, 'bio', 'Celestial3D: a novel method for 3D visualization of familial data', 'Loh, Angeline M|Wiltshire, Steven|Emery, Jon|Carter, Kim W|Palmer, Lyle J', 'Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, The University of Western Australia, Perth, Australia. celestial3d@genepi.org.au|Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, The University of Western Australia, Perth, Australia. celestial3d@genepi.org.au|Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, The University of Western Australia, Perth, Australia. celestial3d@genepi.org.au|Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, The University of Western Australia, Perth, Australia. celestial3d@genepi.org.au|Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, The University of Western Australia, Perth, Australia. celestial3d@genepi.org.au', 'Australia|Australia|Australia|Australia|Australia', 9, 'Computer Graphics|Computer Simulation|Data Display|Database Management Systems|Databases, Genetic|Family|Humans|Imaging, Three-Dimensional|Information Storage and Retrieval|Models, Genetic|Pedigree|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn091', '18346980', 'Traditional two-dimensional (2D) software programs for drawing pedigrees are limited when dealing with extended pedigrees. In successive generations, the number of individuals grows exponentially, leading to an unworkable amount of space required in the horizontal direction for 2D displays. In addition, it is not always possible to place closely related individuals near each other due to the lack of space in 2Ds. To address these issues we have developed three-dimensional (3D) pedigree drawing techniques to enable clearer visualization of extended pedigrees. Currently no other methods are available for displaying extended pedigrees in 3Ds. We have made freely available a software tool--Celestial3D--that implements these novel techniques.'),
(74003, 2008, 'bio', 'Integrating ARC grid middleware with Taverna workflows', 'Krabbenhöft, Hajo N|Möller, Steffen|Bayer, Daniel', 'Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany.|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany.|Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Programming Languages|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn095', '18353787', 'This work presents two independent approaches for a seamless integration of computational grids with the bioinformatics workflow suite Taverna. These are supported by a unique relational database to link applications with grid resources and presents those as workflow elements. A web portal facilitates its collaborative maintenance. The first approach implements a gateway service to handle authentication certificates and all communication with the grid. It reads the database to spawn web services for workflow elements which are in turn used by Taverna. The second approach lets Taverna communicate with the grid on its own, by means of a newly developed plug-in. It reads the database and executes the needed tasks directly on the grid. While the gateway service is non-intrusive, the plug-in has technical advantages, e.g. by allowing data to remain on the grid while being passed between workflow elements.'),
(74004, 2008, 'bio', 'Combining statistical alignment and phylogenetic footprinting to detect regulatory elements', 'Satija, Rahul|Pachter, Lior|Hein, Jotun', 'Department of Statistics, Oxford University, Oxford, UK. satija@stats.ox.ac.uk|Department of Statistics, Oxford University, Oxford, UK. satija@stats.ox.ac.uk|Department of Statistics, Oxford University, Oxford, UK. satija@stats.ox.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 27, 'Algorithms|Base Sequence|DNA Footprinting|Data Interpretation, Statistical|Molecular Sequence Data|Phylogeny|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn104', '18353788', 'Traditional alignment-based phylogenetic footprinting approaches make predictions on the basis of a single assumed alignment. The predictions are therefore highly sensitive to alignment errors or regions of alignment uncertainty. Alternatively, statistical alignment methods provide a framework for performing phylogenetic analyses by examining a distribution of alignments.'),
(74005, 2008, 'bio', 'An analytical pipeline for genomic representations used for cytosine methylation studies', 'Thompson, Reid F|Reimers, Mark|Khulan, Batbayar|Gissot, Mathieu|Richmond, Todd A|Chen, Quan|Zheng, Xin|Kim, Kami|Greally, John M', 'Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.|Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 43, 'Artifacts|Chromosome Mapping|Cytosine|DNA Methylation|Oligonucleotide Array Sequence Analysis|Polymerase Chain Reaction|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn096', '18353789', 'Representations of the genome can be generated by the selection of a subpopulation of restriction fragments using ligation-mediated PCR. Such representations form the basis for a number of high-throughput assays, including the HELP assay to study cytosine methylation. We find that HELP data analysis is complicated not only by PCR amplification heterogeneity but also by a complex and variable distribution of cytosine methylation. To address this, we created an analytical pipeline and novel normalization approach that improves concordance between microarray-derived data and single locus validation results, demonstrating the value of the analytical approach. A major influence on the PCR amplification is the size of the restriction fragment, requiring a quantile normalization approach that reduces the influence of fragment length on signal intensity. Here we describe all of the components of the pipeline, which can also be applied to data derived from other assays based on genomic representations.'),
(74006, 2008, 'bio', 'A suite of Perl modules for handling microarray data', 'Morris, James A|Gayther, Simon A|Jacobs, Ian J|Jones, Christopher', 'Translational Research Laboratories, UCL EGA Institute for Womens Health, University College London, London, United Kingdom.|Translational Research Laboratories, UCL EGA Institute for Womens Health, University College London, London, United Kingdom.|Translational Research Laboratories, UCL EGA Institute for Womens Health, University College London, London, United Kingdom.|Translational Research Laboratories, UCL EGA Institute for Womens Health, University College London, London, United Kingdom.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn085', '18353790', 'We describe PerlMAT, a Perl microarray toolkit providing easy to use object-oriented methods for the simplified manipulation, management and analysis of microarray data. The toolkit provides objects for the encapsulation of microarray spots and reporters, several common microarray data file formats and GAL files. In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data. This important addition to the Perl developers library will facilitate more widespread use of Perl for microarray application development within the bioinformatics community. The coherent interface and well-documented code enables rapid analysis by even inexperienced Perl developers.'),
(74007, 2008, 'bio', 'A noise model for mass spectrometry based proteomics', 'Du, Peicheng|Stolovitzky, Gustavo|Horvatovich, Peter|Bischoff, Rainer|Lim, Jihyeon|Suits, Frank', 'IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com|IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com|IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com|IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com|IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com|IBM Computational Biology Center, P.O. Box 218, Yorktown Heights, NY 10598, USA. pdu@us.ibm.com', 'United States|United States|United States|United States|United States|United States', 43, 'Artifacts|Computer Simulation|Models, Chemical|Models, Statistical|Proteins|Proteomics|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Spectrometry, Mass, Electrospray Ionization|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btn078', '18353791', 'Mass spectrometry data are subjected to considerable noise. Good noise models are required for proper detection and quantification of peptides. We have characterized noise in both quadrupole time-of-flight (Q-TOF) and ion trap data, and have constructed models for the noise.'),
(74008, 2008, 'bio', 'Scaffolding and validation of bacterial genome assemblies using optical restriction maps', 'Nagarajan, Niranjan|Read, Timothy D|Pop, Mihai', 'University of Maryland, College Park, MD 20742, USA.|University of Maryland, College Park, MD 20742, USA.|University of Maryland, College Park, MD 20742, USA.', 'United States|United States|United States', 74, 'Algorithms|Base Sequence|Chromosome Mapping|Genome, Bacterial|Molecular Sequence Data|Optics and Photonics|Restriction Mapping|Sequence Alignment|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btn102', '18356192', 'New, high-throughput sequencing technologies have made it feasible to cheaply generate vast amounts of sequence information from a genome of interest. The computational reconstruction of the complete sequence of a genome is complicated by specific features of these new sequencing technologies, such as the short length of the sequencing reads and absence of mate-pair information. In this article we propose methods to overcome such limitations by incorporating information from optical restriction maps.'),
(74009, 2008, 'bio', 'Bayesian inference of the sites of perturbations in metabolic pathways via Markov chain Monte Carlo', 'Jayawardhana, Bayu|Kell, Douglas B|Rattray, Magnus', 'Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester M1 7DN, UK. bayujw@ieee.org|Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester M1 7DN, UK. bayujw@ieee.org|Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester M1 7DN, UK. bayujw@ieee.org', 'United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computer Simulation|Data Interpretation, Statistical|Markov Chains|Models, Biological|Monte Carlo Method|Pattern Recognition, Automated|Proteome|Reproducibility of Results|Sensitivity and Specificity|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn103', '18356193', 'Genetic modifications or pharmaceutical interventions can influence multiple sites in metabolic pathways, and often these are distant from the primary effect. In this regard, the ability to identify target and off-target effects of a specific compound or gene therapy is both a major challenge and critical in drug discovery.'),
(74010, 2008, 'bio', 'Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data', 'Kim, Jongrae|Bates, Declan G|Postlethwaite, Ian|Heslop-Harrison, Pat|Cho, Kwang-Hyun', 'Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.|Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.|Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.|Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.|Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Computer Simulation|Feedback|Gene Expression Profiling|Gene Expression Regulation|Linear Models|Models, Biological|Nonlinear Dynamics|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn107', '18367478', 'Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.'),
(74011, 2008, 'bio', 'Visualization of unfavorable interactions in protein folds', 'Weichenberger, Christian X|Byzia, Piotr|Sippl, Manfred J', 'Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria.', 'Austria|Austria|Austria', 8, 'Algorithms|Artifacts|Binding Sites|Computer Graphics|Computer Simulation|Internet|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Folding|Protein Interaction Mapping|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn108', '18375963', 'Three dimensional structures of proteins contain errors which often originate from limitations of the experimental techniques employed. Such errors frequently result in unfavorable atomic interactions. Here we present a new web service, called Interaction Viewer, for the visualization and correction of such errors. We show how the Interaction Viewer is used in combination with the NQ-Flipper service to spot strained asparagine and glutamine rotamers and we emphasize the convenience of this service in correcting such errors.'),
(74012, 2008, 'bio', 'Analysis of correlated mutations in HIV-1 protease using spectral clustering', 'Liu, Ying|Eyal, Eran|Bahar, Ivet', 'Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15232, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15232, USA.|Department of Computational Biology, School of Medicine, University of Pittsburgh, PA 15232, USA.', 'United States|United States|United States', 18, 'Algorithms|Amino Acid Substitution|Base Sequence|Cluster Analysis|DNA Mutational Analysis|Evolution, Molecular|HIV Protease|Molecular Sequence Data|Mutation|Sequence Alignment|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btn110', '18375964', 'The ability of human immunodeficiency virus-1 (HIV-1) protease to develop mutations that confer multi-drug resistance (MDR) has been a major obstacle in designing rational therapies against HIV. Resistance is usually imparted by a cooperative mechanism that can be elucidated by a covariance analysis of sequence data. Identification of such correlated substitutions of amino acids may be obscured by evolutionary noise.'),
(74013, 2008, 'bio', 'CompariMotif: quick and easy comparisons of sequence motifs', 'Edwards, Richard J|Davey, Norman E|Shields, Denis C', 'UCD Complex and Adaptive Systems Laboratory and UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland. r.edwards@southampton.ac.uk|UCD Complex and Adaptive Systems Laboratory and UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland. r.edwards@southampton.ac.uk|UCD Complex and Adaptive Systems Laboratory and UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland. r.edwards@southampton.ac.uk', 'Ireland|Ireland|Ireland', 14, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Evaluation Studies', '10.1093/bioinformatics/btn105', '18375965', 'CompariMotif is a novel tool for making motif-motif comparisons, identifying and describing similarities between regular expression motifs. CompariMotif can identify a number of different relationships between motifs, including exact matches, variants of degenerate motifs and complex overlapping motifs. Motif relationships are scored using shared information content, allowing the best matches to be easily identified in large comparisons. Many input and search options are available, enabling a list of motifs to be compared to itself (to identify recurring motifs) or to datasets of known motifs.'),
(74014, 2008, 'bio', 'Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection', 'Damoulas, Theodoros|Girolami, Mark A', 'Department of Computing Science, University of Glasgow, S. A. W. Building, G12 8QQ, UK. theo@dcs.gla.ac.uk|Department of Computing Science, University of Glasgow, S. A. W. Building, G12 8QQ, UK. theo@dcs.gla.ac.uk', 'United Kingdom|United Kingdom', 22, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Data Interpretation, Statistical|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Folding|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btn112', '18378524', 'The problems of protein fold recognition and remote homology detection have recently attracted a great deal of interest as they represent challenging multi-feature multi-class problems for which modern pattern recognition methods achieve only modest levels of performance. As with many pattern recognition problems, there are multiple feature spaces or groups of attributes available, such as global characteristics like the amino-acid composition (C), predicted secondary structure (S), hydrophobicity (H), van der Waals volume (V), polarity (P), polarizability (Z), as well as attributes derived from local sequence alignment such as the Smith-Waterman scores. This raises the need for a classification method that is able to assess the contribution of these potentially heterogeneous object descriptors while utilizing such information to improve predictive performance. To that end, we offer a single multi-class kernel machine that informatively combines the available feature groups and, as is demonstrated in this article, is able to provide the state-of-the-art in performance accuracy on the fold recognition problem. Furthermore, the proposed approach provides some insight by assessing the significance of recently introduced protein features and string kernels. The proposed method is well-founded within a Bayesian hierarchical framework and a variational Bayes approximation is derived which allows for efficient CPU processing times.'),
(74015, 2008, 'bio', 'An improved physico-chemical model of hybridization on high-density oligonucleotide microarrays', 'Ono, Naoaki|Suzuki, Shingo|Furusawa, Chikara|Agata, Tomoharu|Kashiwagi, Akiko|Shimizu, Hiroshi|Yomo, Tetsuya', 'Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.|Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan|Japan', 0, 'Algorithms|Computer Simulation|DNA|DNA Probes|Gene Targeting|In Situ Hybridization|Models, Chemical|Models, Genetic|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn109', '18378525', 'High-density DNA microarrays provide useful tools to analyze gene expression comprehensively. However, it is still difficult to obtain accurate expression levels from the observed microarray data because the signal intensity is affected by complicated factors involving probe-target hybridization, such as non-linear behavior of hybridization, non-specific hybridization, and folding of probe and target oligonucleotides. Various methods for microarray data analysis have been proposed to address this problem. In our previous report, we presented a benchmark analysis of probe-target hybridization using artificially synthesized oligonucleotides as targets, in which the effect of non-specific hybridization was negligible. The results showed that the preceding models explained the behavior of probe-target hybridization only within a narrow range of target concentrations. More accurate models are required for quantitative expression analysis.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74016, 2008, 'bio', 'FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps', 'Vassura, Marco|Margara, Luciano|Di Lena, Pietro|Medri, Filippo|Fariselli, Piero|Casadio, Rita', 'Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it|Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it|Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it|Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it|Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it|Department of Computer Science and Department of Biology, Biocomputing group, University of Bologna, Bologna, Italy. vassura@cs.unibo.it', 'Italy|Italy|Italy|Italy|Italy|Italy', 45, 'Algorithms|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Reproducibility of Results|Sensitivity and Specificity|Software', 'Journal Article', '10.1093/bioinformatics/btn115', '18381401', 'Fault Tolerant Contact Map Reconstruction (FT-COMAR) is a heuristic algorithm for the reconstruction of the protein three-dimensional structure from (possibly) incomplete (i.e. containing unknown entries) and noisy contact maps. FT-COMAR runs within minutes, allowing its application to a large-scale number of predictions.'),
(74017, 2008, 'bio', 'Expected gene-order distances and model selection in bacteria', 'Dalevi, Daniel|Eriksen, Niklas', 'Department of Computing Science and Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden.|Department of Computing Science and Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden.', 'Sweden|Sweden', 0, 'Base Sequence|Biological Evolution|Chromosome Mapping|Computer Simulation|DNA, Bacterial|Evolution, Molecular|Genome, Bacterial|Linkage Disequilibrium|Models, Genetic|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn111', '18381402', 'The evolutionary distance inferred from gene-order comparisons of related bacteria is dependent on the model. Therefore, it is highly important to establish reliable assumptions before inferring its magnitude.'),
(74018, 2008, 'bio', 'Assigning functional linkages to proteins using phylogenetic profiles and continuous phenotypes', 'Gonzalez, Orland|Zimmer, Ralf', 'Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333 Munich, Germany. orlandgonzalez@gmail.com|Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333 Munich, Germany. orlandgonzalez@gmail.com', 'Germany|Germany', 0, 'Chromosome Mapping|Conserved Sequence|Evolution, Molecular|Linkage Disequilibrium|Phenotype|Phylogeny|Proteins|Quantitative Trait Loci', 'Journal Article', '10.1093/bioinformatics/btn106', '18381403', 'A class of non-homology-based methods for protein function prediction relies on the assumption that genes linked to a phenotypic trait are preferentially conserved among organisms that share the trait. These methods typically compare pairs of binary strings, where one string encodes the phylogenetic distribution of a trait and the other of a protein. In this work, we extended the approach to automatically deal with continuous phenotypes.'),
(74019, 2008, 'bio', 'GeneTrack--a genomic data processing and visualization framework', 'Albert, Istvan|Wachi, Shinichiro|Jiang, Cizhong|Pugh, B Franklin', 'Huck Institutes for the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, 16802, USA. iual@psu.edu|Huck Institutes for the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, 16802, USA. iual@psu.edu|Huck Institutes for the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, 16802, USA. iual@psu.edu|Huck Institutes for the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, 16802, USA. iual@psu.edu', 'United States|United States|United States|United States', 71, 'Algorithms|Chromatin Immunoprecipitation|Chromosome Mapping|Computer Graphics|Genome, Human|Humans|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn119', '18388141', 'High-throughput ChIP-chip and ChIP-seq methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com'),
(74020, 2008, 'bio', 'MTMDAT: Automated analysis and visualization of mass spectrometry data for tertiary and quaternary structure probing of proteins', 'Hennig, Janosch|Hennig, Klaus D M|Sunnerhagen, Maria', 'Department of Physics, Chemistry, and Biology, Linköping University, SE-581 83 Linköping, Sweden. janhe@ifm.liu.se|Department of Physics, Chemistry, and Biology, Linköping University, SE-581 83 Linköping, Sweden. janhe@ifm.liu.se|Department of Physics, Chemistry, and Biology, Linköping University, SE-581 83 Linköping, Sweden. janhe@ifm.liu.se', 'Sweden|Sweden|Sweden', 0, 'Algorithms|Amino Acid Sequence|Mass Spectrometry|Molecular Sequence Data|Protein Structure, Quaternary|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn116', '18388142', 'In structural biology and -genomics, nuclear magnetic resonance (NMR) spectroscopy and crystallography are the methods of choice, but sample requirements can be hard to fulfil. Valuable structural information can also be obtained by using a combination of limited proteolysis and mass spectrometry, providing not only knowledge of how to improve sample conditions for crystallization trials or NMR spectrosopy by gaining insight into subdomain identities but also probing tertiary and quaternary structure, folding and stability, ligand binding, protein interactions and the location of post-translational modifications. For high-throughput studies and larger proteins, however, this experimentally fast and easy approach produces considerable amounts of data, which until now has made the evaluation exceedingly laborious if at all manually possible. MTMDAT, equipped with a browser-like graphical user interface, accelerates this evaluation manifold by automated peak picking, assignment, data processing and visualization.'),
(74021, 2008, 'bio', 'Prediction of the translocon-mediated membrane insertion free energies of protein sequences', 'Park, Yungki|Helms, Volkhard', 'Center for Bioinformatics, Saarland University, Germany.|Center for Bioinformatics, Saarland University, Germany.', 'Germany|Germany', 7, 'Amino Acid Sequence|Cell Membrane|Computer Simulation|Energy Transfer|Membrane Proteins|Models, Chemical|Molecular Sequence Data|Sequence Analysis, Protein|Structure-Activity Relationship|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btn114', '18388143', 'Helical membrane proteins (HMPs) play crucial roles in a variety of cellular processes. Unlike water-soluble proteins, HMPs need not only to fold but also get inserted into the membrane to be fully functional. This process of membrane insertion is mediated by the translocon complex. Thus, it is of great interest to develop computational methods for predicting the translocon-mediated membrane insertion free energies of protein sequences.'),
(74022, 2008, 'bio', 'ASPicDB: a database resource for alternative splicing analysis', 'Castrignanò, T|DAntonio, M|Anselmo, A|Carrabino, D|DOnorio De Meo, A|DErchia, A M|Licciulli, F|Mangiulli, M|Mignone, F|Pavesi, G|Picardi, E|Riva, A|Rizzi, R|Bonizzoni, P|Pesole, G', 'Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.|Consorzio Interuniversitario per le Applicazioni di Supercalcolo per Università e Ricerca, CASPUR, Rome, Italy.', 'Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy|Italy', 37, 'Base Sequence|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|RNA Splice Sites|Sequence Alignment|Sequence Analysis, DNA|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn113', '18388144', 'Alternative splicing has recently emerged as a key mechanism responsible for the expansion of transcriptome and proteome complexity in human and other organisms. Although several online resources devoted to alternative splicing analysis are available they may suffer from limitations related both to the computational methodologies adopted and to the extent of the annotations they provide that prevent the full exploitation of the available data. Furthermore, current resources provide limited query and download facilities.'),
(74023, 2008, 'bio', 'UniProtJAPI: a remote API for accessing UniProt data', 'Patient, Samuel|Wieser, Daniela|Kleen, Michael|Kretschmann, Ernst|Jesus Martin, Maria|Apweiler, Rolf', 'The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk|The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk|The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk|The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk|The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk|The European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK. spatient@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Databases, Protein|Internet|Programming Languages|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn122', '18390879', 'Programmatic access to the UniProt Knowledgebase (UniProtKB) is essential for many bioinformatics applications dealing with protein data. We have created a Java library named UniProtJAPI, which facilitates the integration of UniProt data into Java-based software applications. The library supports queries and similarity searches that return UniProtKB entries in the form of Java objects. These objects contain functional annotations or sequence information associated with a UniProt entry. Here, we briefly describe the UniProtJAPI and demonstrate its usage.'),
(74024, 2008, 'bio', 'Peak bagging for peptide mass fingerprinting', 'He, Zengyou|Yang, Can|Yu, Weichuan', 'Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. eezyhe@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. eezyhe@ust.hk|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. eezyhe@ust.hk', 'China|China|China', 0, 'Algorithms|Amino Acid Sequence|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Proteome|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn123', '18397892', 'Mass Spectrometry (MS)-based protein identification via peptide mass fingerprinting (PMF) is a key component in high-throughput proteome research. While PMF was the first commonly used protein identification method, provided higher throughput than the tandem MS-based method, its accuracy is lower than that of the tandem MS method. Thus, it is desirable to develop PMF-based algorithm with higher protein identification accuracy to facilitate proteome research.'),
(74025, 2008, 'bio', 'siRNA specificity searching incorporating mismatch tolerance data', 'Chalk, Alistair M|Sonnhammer, Erik L L', 'Department of Cell and Molecular Biology, Karolinska Institutet, S-171 77 Stockholm, Sweden.|Department of Cell and Molecular Biology, Karolinska Institutet, S-171 77 Stockholm, Sweden.', 'Sweden|Sweden', 14, 'Algorithms|Base Sequence|Databases, Genetic|Gene Silencing|Gene Targeting|Information Storage and Retrieval|Molecular Sequence Data|RNA, Small Interfering|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btn121', '18397893', 'Artificially synthesized short interfering RNAs (siRNAs) are widely used in functional genomics to knock down specific target genes. One ongoing challenge is to guarantee that the siRNA does not elicit off-target effects. Initial reports suggested that siRNAs were highly sequence-specific, however, subsequent data indicates that this is not necessarily the case. It is still uncertain what level of similarity and other rules are required for an off-target effect to be observed, and scoring schemes have not been developed to look beyond simple measures such as the number of mismatches or the number of consecutive matching bases present. We created design rules for predicting the likelihood of a non-specific effect and present a web server that allows the user to check the specificity of a given siRNA in a flexible manner using a combination of methods. The server finds potential off-target matches in the corresponding RefSeq database and ranks them according to a scoring system based on experimental studies of specificity.'),
(74026, 2008, 'bio', 'Domain annotation of trimeric autotransporter adhesins--daTAA', 'Szczesny, Pawel|Lupas, Andrei', 'Department of Protein Evolution, Max-Planck Institute for Developmental Biology, Spemannstr 35, 72076 Tuebingen, Germany.|Department of Protein Evolution, Max-Planck Institute for Developmental Biology, Spemannstr 35, 72076 Tuebingen, Germany.', 'Germany|Germany', 78, 'Adhesins, Bacterial|Algorithms|Amino Acid Sequence|Dimerization|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btn118', '18397894', 'Trimeric autotransporter adhesins (TAAs), such as Yersinia YadA, Neisseria NadA, Moraxella UspAs, Haemophilus Hia and Bartonella BadA, are important pathogenicity factors of proteobacteria. Their high sequence diversity and distinct mosaic-like structure lead to difficulties in the annotation of their sequences. These stem from the large number of short repeats, the presence of compositionally unusual coiled-coils, fuzzy domain boundaries and regions of seemingly low sequence complexity.'),
(74027, 2008, 'bio', 'adegenet: a R package for the multivariate analysis of genetic markers', 'Jombart, Thibaut', 'Université de Lyon, Université Lyon 1, CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, France. jombart@biomserv.univ-lyon1.fr', 'France', 0, 'Algorithms|Chromosome Mapping|Genetic Markers|Multivariate Analysis|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn129', '18397895', 'The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization.'),
(74028, 2008, 'bio', 'Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data', 'Chang, Chunqi|Ding, Zhi|Hung, Yeung Sam|Fung, Peter Chin Wan', 'Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA. cqchang@eee.hku.hk|Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA. cqchang@eee.hku.hk|Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA. cqchang@eee.hku.hk|Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA. cqchang@eee.hku.hk', 'Hong Kong|Hong Kong|Hong Kong|Hong Kong', 59, 'Algorithms|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Principal Component Analysis|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn131', '18400771', 'Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations.'),
(74029, 2008, 'bio', 'EPO-KB: a searchable knowledge base of biomarker to protein links', 'Lustgarten, Jonathan L|Kimmel, Chad|Ryberg, Henrik|Hogan, William', 'Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave M-183 Parkvale, Pittsburgh, PA 15260, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave M-183 Parkvale, Pittsburgh, PA 15260, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave M-183 Parkvale, Pittsburgh, PA 15260, USA. JLL47@pitt.edu|Department of Biomedical Informatics, University of Pittsburgh, 200 Meyran Ave M-183 Parkvale, Pittsburgh, PA 15260, USA. JLL47@pitt.edu', 'United States|United States|United States|United States', 10, 'Algorithms|Artificial Intelligence|Biomarkers|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Pattern Recognition, Automated|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn125', '18400772', 'The knowledge base EPO-KB (Empirical Proteomic Ontology Knowledge Base) is based on an OWL ontology that represents current knowledge linking mass-to-charge (m/z) ratios to proteins on multiple platforms including Matrix Assisted Laser/Desorption Ionization (MALDI) and Surface Enhanced Laser/Desorption Ionization (SELDI)--Time of Flight (TOF). At present, it contains information on m/z ratio to protein links that were extracted from 120 published research papers. It has a web interface that allows researchers to query and retrieve putative proteins that correspond to a user-specified m/z ratio. EPO-KB also allows automated entry of additional m/z ratio to protein links and is expandable to the addition of gene to protein and protein to disease links.'),
(74030, 2008, 'bio', 'MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline', 'Kim, Jung-Jae|Pezik, Piotr|Rebholz-Schuhmann, Dietrich', 'EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. kim@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. kim@ebi.ac.uk|EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB101SD, UK. kim@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 43, 'Abstracting and Indexing as Topic|Artificial Intelligence|Database Management Systems|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn117', '18400773', 'Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results.'),
(74031, 2008, 'bio', 'A system for generating transcription regulatory networks with combinatorial control of transcription', 'Roy, Sushmita|Werner-Washburne, Margaret|Lane, Terran', 'Department of Computer Science and Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Computer Science and Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.|Department of Computer Science and Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA.', 'Mexico|Mexico|Mexico', 0, 'Combinatorial Chemistry Techniques|Computer Simulation|Gene Expression Regulation|Models, Biological|Protein Interaction Mapping|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btn126', '18400774', 'We have developed a new software system, REgulatory Network generator with COmbinatorial control (RENCO), for automatic generation of differential equations describing pre-transcriptional combinatorics in artificial regulatory networks. RENCO has the following benefits: (a) it explicitly models protein-protein interactions among transcription factors, (b) it captures combinatorial control of transcription factors on target genes and (c) it produces output in Systems Biology Markup Language (SBML) format, which allows these equations to be directly imported into existing simulators. Explicit modeling of the protein interactions allows RENCO to incorporate greater mechanistic detail of the transcription machinery compared to existing models and can provide a better assessment of algorithms for regulatory network inference.'),
(74032, 2008, 'bio', 'Simple is beautiful: a straightforward approach to improve the delineation of true and false positives in PSI-BLAST searches', 'Lee, Marianne M|Chan, Michael K|Bundschuh, Ralf', 'The Ohio State Biophysics Program, Ohio State University, 484 W 12th Av., Columbus OH 43210-1117, USA.|The Ohio State Biophysics Program, Ohio State University, 484 W 12th Av., Columbus OH 43210-1117, USA.|The Ohio State Biophysics Program, Ohio State University, 484 W 12th Av., Columbus OH 43210-1117, USA.', 'United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Databases, Genetic|False Negative Reactions|False Positive Reactions|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis', 'Evaluation Studies', '10.1093/bioinformatics/btn130', '18403442', 'The deluge of biological information from different genomic initiatives and the rapid advancement in biotechnologies have made bioinformatics tools an integral part of modern biology. Among the widely used sequence alignment tools, BLAST and PSI-BLAST are arguably the most popular. PSI-BLAST, which uses an iterative profile position specific score matrix (PSSM)-based search strategy, is more sensitive than BLAST in detecting weak homologies, thus making it suitable for remote homolog detection. Many refinements have been made to improve PSI-BLAST, and its computational efficiency and high specificity have been much touted. Nevertheless, corruption of its profile via the incorporation of false positive sequences remains a major challenge.'),
(74033, 2008, 'bio', 'Towards patterns tree of gene coexpression in eukaryotic species', 'Wang, Haiyun|Wang, Qi|Li, Xia|Shen, Bairong|Ding, Min|Shen, Ziyin', 'School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.|School of Life Science and Technology, Tongji University, Shanghai 200092, China.', 'China|China|China|China|China|China', 8, 'Algorithms|Animals|Computer Simulation|Eukaryotic Cells|Gene Expression Profiling|Humans|Models, Biological|Multigene Family|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn134', '18407921', 'Cellular pathways behave coordinated regulation activity, and some reported works also have affirmed that genes in the same pathway have similar expression pattern. However, the complexity of biological systems regulation actually causes expression relationships between genes to display multiple patterns, such as linear, non-linear, local, global, linear with time-delayed, non-linear with time-delayed, monotonic and non-monotonic, which should be the explicit representation of cellular inner regulation mechanism in mRNA level. To investigate the relationship between different patterns, our work aims to systematically reveal gene-expression relationship patterns in cellular pathways and to check for the existence of dominating gene-expression pattern. By a large scale analysis of genes expression in three eukaryotic species, Saccharomyces cerevisiae, Caenorhabditis elegans and Human, we constructed gene coexpression patterns tree to systematically and hierarchically illustrate the different patterns and their interrelations.'),
(74034, 2008, 'bio', 'Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles', 'Bhattacharya, Anindya|De, Rajat K', 'Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia and Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.|Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia and Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.', 'India|India', 49, 'Algorithms|Artificial Intelligence|Cluster Analysis|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn133', '18407922', 'Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others.'),
(74035, 2008, 'bio', 'URL decay in MEDLINE--a 4-year follow-up study', 'Wren, Jonathan D', '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', 55, 'Bibliometrics|Follow-Up Studies|Information Dissemination|Information Storage and Retrieval|Internet|MEDLINE|Time Factors', 'Journal Article', '10.1093/bioinformatics/btn127', '18413326', 'Internet-based electronic resources, as given by Uniform Resource Locators (URLs), are being increasingly used in scientific publications but are also becoming inaccessible in a time-dependant manner, a phenomenon documented across disciplines. Initial reports brought attention to the problem, spawning methods of effectively preserving URL content while some journals adopted policies regarding URL publication and begun storing supplementary information on journal websites. Thus, a reexamination of URL growth and decay in the literature is merited to see if the problem has grown or been mitigated by any of these changes.'),
(74036, 2008, 'bio', 'Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface', 'Guillot, Gilles|Santos, Filipe|Estoup, Arnaud', 'Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O Box 1066 Blindern, 0316 Oslo, Norway. gilles.guillot@bio.uio.no|Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O Box 1066 Blindern, 0316 Oslo, Norway. gilles.guillot@bio.uio.no|Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, P.O Box 1066 Blindern, 0316 Oslo, Norway. gilles.guillot@bio.uio.no', 'Norway|Norway|Norway', 217, 'Algorithms|Computer Graphics|Computer Simulation|Databases, Genetic|Gene Frequency|Genetics, Population|Geographic Information Systems|Models, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn136', '18413327', 'We introduce a new algorithm to account for the presence of null alleles in inferences of populations clusters from individual multilocus genetic data. We show by simulations that the presence of null alleles can affect the accuracy of inferences if not properly accounted for and that our algorithm improve signficantly their accuracy.'),
(74037, 2008, 'bio', 'Implementation of a regulatory gene network to simulate the TH1/2 differentiation in an agent-based model of hypersensitivity reactions', 'Santoni, Daniele|Pedicini, Marco|Castiglione, Filippo', 'Institute for Computing Applications M. Picone, National Research Council (CNR), V.le del Policlinico, 13700161 Rome, Italy.|Institute for Computing Applications M. Picone, National Research Council (CNR), V.le del Policlinico, 13700161 Rome, Italy.|Institute for Computing Applications M. Picone, National Research Council (CNR), V.le del Policlinico, 13700161 Rome, Italy.', 'Italy|Italy|Italy', 51, 'Animals|Cell Differentiation|Computer Simulation|Cytokines|Gene Expression Regulation|Humans|Hypersensitivity|Models, Immunological|Signal Transduction|Th1 Cells|Th2 Cells|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btn135', '18413328', 'An unbalanced differentiation of T helper cells from precursor type TH0 to the TH1 or TH2 phenotype in immune responses often leads to a pathological condition. In general, immune reactions biased toward TH1 responses may result in auto-immune diseases, while enhanced TH2 responses may cause allergic reactions. The aim of this work is to integrate a gene network of the TH differentiation in an agent-based model of the hyper-sensitivity reaction. The implementation of such a system introduces a second level of description beyond the mesoscopic level of the inter-cellular interaction of the agent-based model. The intra-cellular level consists in the cell internal dynamics of gene activation and transcription. The gene regulatory network includes genes-related molecules that have been found to be involved in the differentiation process in TH cells.'),
(74038, 2008, 'bio', 'Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers', 'Lundegaard, Claus|Lund, Ole|Nielsen, Morten', 'Center for Biological Sequence Analysis--CBS, Department of Systems Biology, The Technical University of Denmark--DTU, Kemitorvet Build. 208, 2800 Lyngby, Denmark. lunde@cbs.dtu.dk|Center for Biological Sequence Analysis--CBS, Department of Systems Biology, The Technical University of Denmark--DTU, Kemitorvet Build. 208, 2800 Lyngby, Denmark. lunde@cbs.dtu.dk|Center for Biological Sequence Analysis--CBS, Department of Systems Biology, The Technical University of Denmark--DTU, Kemitorvet Build. 208, 2800 Lyngby, Denmark. lunde@cbs.dtu.dk', 'Denmark|Denmark|Denmark', 141, 'Algorithms|Artificial Intelligence|Binding Sites|Histocompatibility Antigens Class I|Peptides|Protein Binding|Protein Interaction Mapping|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn128', '18413329', 'Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides.'),
(74039, 2008, 'bio', 'Forward-time simulations of non-random mating populations using simuPOP', 'Peng, Bo|Amos, Christopher I', 'Department of Epidemiology, The University of Texas, M. D. Anderson Cancer Center, 1155 Pressler Blvd, Houston, TX 77030, USA. bpeng@mdanderson.org|Department of Epidemiology, The University of Texas, M. D. Anderson Cancer Center, 1155 Pressler Blvd, Houston, TX 77030, USA. bpeng@mdanderson.org', 'United States|United States', 36, 'Algorithms|Animals|Computer Simulation|Ecosystem|Humans|Models, Biological|Population Dynamics|Reproductive Behavior|Sexual Behavior, Animal|Software', 'Journal Article', '10.1093/bioinformatics/btn179', '18417488', 'Computer simulations play an important role in studies of non-random mating populations. Because of implementation difficulties, only very limited types of non-random mating schemes are provided in the currently available simulation programs. Starting with version 0.8.5, simuPOP provides a few mating schemes that can be used to simulate arbitrary non-random mating models. This article describes the concepts and methods behind these mating schemes and demonstrates their uses in a few examples, including partial self-mating, positive assortative mating, non-random outbreeding, and simulation of overlapping generations in age-structured populations.'),
(74040, 2008, 'bio', 'IDMap: facilitating the detection of potential leads with therapeutic targets', 'Ha, Soyang|Seo, Young-Ju|Kwon, Min-Seok|Chang, Byung-Ha|Han, Cheol-Kyu|Yoon, Jeong-Hyeok', 'BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.|BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.|BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.|BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.|BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.|BT Solutions Team, R&D Center, Equispharm Co., Ltd, Gyeonggi Bio-Center Bld. 11Fl., 864-1, Iui-dong, Yeongtong-gu, Suwon, Gyeonggi-do 443-766, Republic of Korea.', '-|-|-|-|-|-', 29, 'Artificial Intelligence|Database Management Systems|Databases, Factual|Drug Design|Drug Therapy|Information Storage and Retrieval|Pharmaceutical Preparations|Software', 'Journal Article', '10.1093/bioinformatics/btn138', '18417489', 'Pharmaceutical industry has been striving to reduce the costs of drug development and increase productivity. Among the many different attempts, drug repositioning (retargeting existing drugs) comes into the spotlight because of its financial efficiency. We introduce IDMap which predicts novel relationships between targets and chemicals and thus is capable of repositioning the marketed drugs by using text mining and chemical structure information. Also capable of mapping commercial chemicals to possible drug targets and vice versa, IDMap creates convenient environments for identifying the potential lead and its targets, especially in the field of drug repositioning.'),
(74041, 2008, 'bio', 'Fast grid layout algorithm for biological networks with sweep calculation', 'Kojima, Kaname|Nagasaki, Masao|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.|Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.', 'Japan|Japan|Japan', 13, 'Algorithms|Computer Graphics|Computer Simulation|Information Storage and Retrieval|Models, Biological|Proteome|Signal Transduction|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn196', '18424458', 'Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations, thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks.'),
(74042, 2008, 'bio', 'A note on the false discovery rate and inconsistent comparisons between experiments', 'Higdon, Roger|van Belle, Gerald|Kolker, Eugene', 'Seattle Childrens Research Institute, Seattle, WA 98101, USA.|Seattle Childrens Research Institute, Seattle, WA 98101, USA.|Seattle Childrens Research Institute, Seattle, WA 98101, USA.', 'United States|United States|United States', 22, 'Algorithms|Artifacts|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn120', '18424815', 'The false discovery rate (FDR) has been widely adopted to address the multiple comparisons issue in high-throughput experiments such as microarray gene-expression studies. However, while the FDR is quite useful as an approach to limit false discoveries within a single experiment, like other multiple comparison corrections it may be an inappropriate way to compare results across experiments. This article uses several examples based on gene-expression data to demonstrate the potential misinterpretations that can arise from using FDR to compare across experiments. Researchers should be aware of these pitfalls and wary of using FDR to compare experimental results. FDR should be augmented with other measures such as p-values and expression ratios. It is worth including standard error and variance information for meta-analyses and, if possible, the raw data for re-analyses. This is especially important for high-throughput studies because data are often re-used for different objectives, including comparing common elements across many experiments. No single error rate or data summary may be appropriate for all of the different objectives.'),
(74043, 2008, 'bio', 'Comparative conservation analysis of the human mitotic phosphoproteome', 'Malik, Rainer|Nigg, Erich A|Körner, Roman', 'Department of Cell Biology, Max Planck Institute of Biochemistry, Martinsried D-82152, Germany. malik@biochem.mpg.de|Department of Cell Biology, Max Planck Institute of Biochemistry, Martinsried D-82152, Germany. malik@biochem.mpg.de|Department of Cell Biology, Max Planck Institute of Biochemistry, Martinsried D-82152, Germany. malik@biochem.mpg.de', 'Germany|Germany|Germany', 44, 'Amino Acid Sequence|Animals|Binding Sites|Conserved Sequence|Enzyme Activation|Humans|Mitosis|Molecular Sequence Data|Phosphorylation|Phosphotransferases|Protein Binding|Proteome|Sequence Alignment|Sequence Analysis, Protein|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/btn197', '18426804', 'A key challenge in phosphoproteomic studies is to distinguish functionally relevant phosphorylation sites from potentially silent phosphorylation. Considering that relevant phosphorylation sites are expected to be better conserved during evolution than overall Serine, Threonine and Tyrosine (S/ T/ Y) residues, we asked whether this can be directly demonstrated through statistic analysis, using a large experimental dataset.'),
(74044, 2008, 'bio', 'Prediction of disordered regions in proteins based on the meta approach', 'Ishida, Takashi|Kinoshita, Kengo', 'Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. t-ishida@hgc.jp|Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. t-ishida@hgc.jp', 'Japan|Japan', 199, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn195', '18426805', 'Intrinsically disordered regions in proteins have no unique stable structures without their partner molecules, thus these regions sometimes prevent high-quality structure determination. Furthermore, proteins with disordered regions are often involved in important biological processes, and the disordered regions are considered to play important roles in molecular interactions. Therefore, identifying disordered regions is important to obtain high-resolution structural information and to understand the functional aspects of these proteins.'),
(74045, 2008, 'bio', 'Eukaryotic transcription factor binding sites--modeling and integrative search methods', 'Hannenhalli, Sridhar', 'Penn Center for Bioinformatics and Department of Genetics, University of Pennsylvania, Philadelphia, USA. sridharh@pcbi.upenn.edu', 'United States', 0, 'Algorithms|Base Sequence|Binding Sites|Computer Simulation|DNA|Models, Genetic|Molecular Sequence Data|Protein Binding|Sequence Analysis, DNA|Systems Integration|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn198', '18426806', 'A comprehensive knowledge of transcription factor binding sites (TFBS) is important for a mechanistic understanding of transcriptional regulation as well as for inferring gene regulatory networks. Because the DNA motif recognized by a transcription factor is typically short and degenerate, computational approaches for identifying binding sites based only on the sequence motif inevitably suffer from high error rates. Current state-of-the-art techniques for improving computational identification of binding sites can be broadly categorized into two classes: (1) approaches that aim to improve binding motif models by extracting maximal sequence information from experimentally determined binding sites and (2) approaches that supplement binding motif models with additional genomic or other attributes (such as evolutionary conservation). In this review we will discuss recent attempts to improve computational identification of TFBS through these two types of approaches and conclude with thoughts on future development.'),
(74046, 2008, 'bio', 'OnD-CRF: predicting order and disorder in proteins using [corrected] conditional random fields', 'Wang, Lixiao|Sauer, Uwe H', 'Umeå Centre for Molecular Pathogenesis, UCMP, and Centre for Chemical Biology, KBC, Umeå University, SE-901 87 Umeå, Sweden.|Umeå Centre for Molecular Pathogenesis, UCMP, and Centre for Chemical Biology, KBC, Umeå University, SE-901 87 Umeå, Sweden.', 'Sweden|Sweden', 57, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Statistical|Molecular Sequence Data|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn132', '18430742', 'Order and Disorder prediction using Conditional Random Fields (OnD-CRF) is a new method for accurately predicting the transition between structured and mobile or disordered regions in proteins. OnD-CRF applies CRFs relying on features which are generated from the amino acids sequence and from secondary structure prediction. Benchmarking results based on CASP7 targets, and evaluation with respect to several CASP criteria, rank the OnD-CRF model highest among the fully automatic server group.'),
(74047, 2008, 'bio', 'TOPDOM: database of domains and motifs with conservative location in transmembrane proteins', 'Tusnády, Gábor E|Kalmár, Lajos|Hegyi, Hédi|Tompa, Péter|Simon, István', 'Institue of Enzymology, BRC, Hungarian Academy of Sciences, H-1113 Karolina út 29, Budapest, Hungary. tusi@enzim.hu|Institue of Enzymology, BRC, Hungarian Academy of Sciences, H-1113 Karolina út 29, Budapest, Hungary. tusi@enzim.hu|Institue of Enzymology, BRC, Hungarian Academy of Sciences, H-1113 Karolina út 29, Budapest, Hungary. tusi@enzim.hu|Institue of Enzymology, BRC, Hungarian Academy of Sciences, H-1113 Karolina út 29, Budapest, Hungary. tusi@enzim.hu|Institue of Enzymology, BRC, Hungarian Academy of Sciences, H-1113 Karolina út 29, Budapest, Hungary. tusi@enzim.hu', 'Hungary|Hungary|Hungary|Hungary|Hungary', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Conserved Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Membrane Proteins|Molecular Sequence Data|Protein Structure, Tertiary|Sequence Alignment|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn202', '18434342', 'The TOPDOM database is a collection of domains and sequence motifs located consistently on the same side of the membrane in alpha-helical transmembrane proteins. The database was created by scanning well-annotated transmembrane protein sequences in the UniProt database by specific domain or motif detecting algorithms. The identified domains or motifs were added to the database if they were uniformly annotated on the same side of the membrane of the various proteins in the UniProt database. The information about the location of the collected domains and motifs can be incorporated into constrained topology prediction algorithms, like HMMTOP, increasing the prediction accuracy.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74048, 2008, 'bio', 'A global pathway crosstalk network', 'Li, Yong|Agarwal, Pankaj|Rajagopalan, Dilip', 'Computational Biology, GlaxoSmithKline R&D, 709 Swedeland Road, UMW2230, King of Prussia, PA 19406, USA. yong.2.li@gsk.com|Computational Biology, GlaxoSmithKline R&D, 709 Swedeland Road, UMW2230, King of Prussia, PA 19406, USA. yong.2.li@gsk.com|Computational Biology, GlaxoSmithKline R&D, 709 Swedeland Road, UMW2230, King of Prussia, PA 19406, USA. yong.2.li@gsk.com', 'United States|United States|United States', 109, 'Algorithms|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn200', '18434343', 'Given the complex nature of biological systems, pathways often need to function in a coordinated fashion in order to produce appropriate physiological responses to both internal and external stimuli. Therefore, understanding the interaction and crosstalk between pathways is important for understanding the function of both cells and more complex systems.'),
(74049, 2008, 'bio', 'RNAplex: a fast tool for RNA-RNA interaction search', 'Tafer, Hakim|Hofacker, Ivo L', 'Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, Währingerstrasse 17, A-1090 Vienna, Austria.', 'Austria|Austria', 200, 'Binding Sites|Computational Biology|Humans|RNA|Sensitivity and Specificity|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btn193', '18434344', 'Regulatory RNAs often unfold their action via RNA-RNA interaction. Transcriptional gene silencing by means of siRNAs and miRNA as well as snoRNA directed RNA editing rely on this mechanism. Additionally ncRNA regulation in bacteria is mainly based upon RNA duplex formation. Finding putative target sites for newly discovered ncRNAs is a lengthy task as tools for cofolding RNA molecules like RNAcofold and RNAup are too slow for genome-wide search. Tools like RNAhybrid that neglects intramolecular interactions have runtimes proportional to O(m x n), albeit with a large prefactor. Still in many cases the need for even faster methods exists.'),
(74050, 2008, 'bio', 'Annotation-Modules: a tool for finding significant combinations of multisource annotations for gene lists', 'Hackenberg, Michael|Matthiesen, Rune', 'Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain. mlhackenberg@gmail.com|Bioinformatics Group, CIC bioGUNE, CIBER-HEPAD, Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain. mlhackenberg@gmail.com', 'Spain|Spain', 21, 'Algorithms|Database Management Systems|Databases, Genetic|Documentation|Genes|Information Storage and Retrieval|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn178', '18434345', 'The ontological analysis of the gene lists obtained from DNA microarray experiments constitutes an important step in understanding the underlying biology of the analyzed system. Over the last years, many other high-throughput techniques emerged, covering now basically all omics fields. However, for some of these techniques the generally used functional ontologies might not be sufficient to describe the biological system represented by the derived gene lists. For a more complete and correct interpretation of these experiments, it is important to extend substantially the number of annotations, adapting the ontological analysis to the new emerging techniques.'),
(74051, 2008, 'bio', 'swissPIT: a novel approach for pipelined analysis of mass spectrometry data', 'Quandt, Andreas|Hernandez, Patricia|Masselot, Alexandre|Hernandez, Céline|Maffioletti, Sergio|Pautasso, Cesare|Appel, Ron D|Lisacek, Frederique', 'Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch|Swiss Institute of Bioinformatics, Proteome Informatics Group, Geneva, Geneva Bioinformatics (GeneBio) S.A., Geneva, Switzerland. andreas.quandt@isb-sib.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 6, 'Algorithms|Amino Acid Sequence|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn139', '18436540', 'The identification and characterization of peptides from tandem mass spectrometry (MS/MS) data represents a critical aspect of proteomics. Today, tandem MS analysis is often performed by only using a single identification program achieving identification rates between 10-50% (Elias and Gygi, 2007). Beside the development of new analysis tools, recent publications describe also the pipelining of different search programs to increase the identification rate (Hartler et al., 2007, Keller et al., 2005). The Swiss Protein Identification Toolbox (swissPIT) follows this approach, but goes a step further by providing the user an expandable multi-tool platform capable of executing workflows to analyze tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identification of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel, but also the meaningful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at http://swisspit.cscs.ch upon request for account.'),
(74052, 2008, 'bio', 'MAMOT: hidden Markov modeling tool', 'Schütz, Frédéric|Delorenzi, Mauro', 'Swiss Institute of Bioinformatics and Bioinformatics Core Facility of the NCCR Molecular Oncology, CH-1015 Lausanne, Switzerland.|Swiss Institute of Bioinformatics and Bioinformatics Core Facility of the NCCR Molecular Oncology, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 12, 'Algorithms|Computer Simulation|Markov Chains|Models, Biological|Models, Statistical|Pattern Recognition, Automated|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn201', '18440999', 'Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding.'),
(74053, 2008, 'bio', 'fdrtool: a versatile R package for estimating local and tail area-based false discovery rates', 'Strimmer, Korbinian', 'Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany. strimmer@uni-leipzig.de', 'Germany', 333, 'Algorithms|Confidence Intervals|Data Interpretation, Statistical|False Positive Reactions|Genomics|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btn209', '18441000', 'False discovery rate (FDR) methodologies are essential in the study of high-dimensional genomic and proteomic data. The R package fdrtool facilitates such analyses by offering a comprehensive set of procedures for FDR estimation. Its distinctive features include: (i) many different types of test statistics are allowed as input data, such as P-values, z-scores, correlations and t-scores, (ii) simultaneously, both local FDR and tail area-based FDR values are estimated for all test statistics and (iii) empirical null models are fit where possible, thereby taking account of potential over- or underdispersion of the theoretical null. In addition, fdrtool provides readily interpretable graphical output, and can be applied to very large scale (in the order of millions of hypotheses) multiple testing problems. Consequently, fdrtool implements a flexible FDR estimation scheme that is unified across different test statistics and variants of FDR.'),
(74054, 2008, 'bio', 'gpDB: a database of GPCRs, G-proteins, effectors and their interactions', 'Theodoropoulou, Margarita C|Bagos, Pantelis G|Spyropoulos, Ioannis C|Hamodrakas, Stavros J', 'Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece.|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece.|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece.|Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 157 01, Greece.', 'Greece|Greece|Greece|Greece', 24, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Conserved Sequence|Database Management Systems|Databases, Protein|GTP-Binding Proteins|Information Storage and Retrieval|Molecular Sequence Data|Protein Interaction Mapping|Protein Structure, Tertiary|Receptors, G-Protein-Coupled|Sequence Alignment|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn206', '18441001', 'gpDB is a publicly accessible, relational database, containing information about G-proteins, G-protein coupled receptors (GPCRs) and effectors, as well as information concerning known interactions between these molecules. The sequences are classified according to a hierarchy of different classes, families and subfamilies based on literature search. The main innovation besides the classification of G-proteins, GPCRs and effectors is the relational model of the database, describing the known coupling specificity of GPCRs to their respective alpha subunits of G-proteins, and also the specific interaction between G-proteins and their effectors, a unique feature not available in any other database.'),
(74055, 2008, 'bio', 'Positive selection drives a correlation between non-synonymous/synonymous divergence and functional divergence', 'Tennessen, Jacob A', 'Department of Zoology, 3029 Cordley Hall, Oregon State University, Corvallis, OR 97331, USA. tennessj@science.oregonstate.edu', 'United States', 17, 'Amino Acid Sequence|Antimicrobial Cationic Peptides|Base Sequence|Computer Simulation|Evolution, Molecular|Genetic Variation|Models, Genetic|Molecular Sequence Data|Mutation|Selection, Genetic|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btn205', '18443017', 'Functional divergence among proteins is often assumed to be strongly influenced by natural selection, as inferred from the ratio of non-synonymous nucleotide divergence (d(N)) to synonymous nucleotide divergence (d(S)). That is, the more a mutation changes protein function, the more likely it is to be either selected against or selectively favored, and because the d(N)/d(S) ratio is a measure of natural selection, this ratio can be used to predict the degree of functional divergence (d(F)). However, these hypotheses have rarely been experimentally tested.'),
(74056, 2008, 'bio', 'PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure', 'Sweredoski, Michael J|Baldi, Pierre', 'Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, California 92697-3435, USA.|Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine, California 92697-3435, USA.', 'United States|United States', 143, 'Algorithms|Amino Acid Sequence|Epitope Mapping|Epitopes, B-Lymphocyte|Molecular Sequence Data|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn199', '18443018', 'Accurate prediction of B-cell epitopes is an important goal of computational immunology. Up to 90% of B-cell epitopes are discontinuous in nature, yet most predictors focus on linear epitopes. Even when the tertiary structure of the antigen is available, the accurate prediction of B-cell epitopes remains challenging.'),
(74057, 2008, 'bio', 'Cytoscape ESP: simple search of complex biological networks', 'Ashkenazi, Maital|Bader, Gary D|Kuchinsky, Allan|Moshelion, Menachem|States, David J', 'Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il|Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il|Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il|Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il|Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il', 'Israel|Israel|Israel|Israel|Israel', 10, 'Algorithms|Computer Graphics|Computer Simulation|Information Storage and Retrieval|Models, Biological|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn208', '18445605', 'Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/'),
(74058, 2008, 'bio', 'jSquid: a Java applet for graphical on-line network exploration', 'Klammer, Martin|Roopra, Sanjit|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden.|Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden.', 'Sweden|Sweden|Sweden', 14, 'Algorithms|Computer Graphics|Computer Simulation|Information Storage and Retrieval|Models, Biological|Programming Languages|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn213', '18445606', 'jSquid is a graph visualization tool for exploring graphs from protein-protein interaction or functional coupling networks. The tool was designed for the FunCoup web site, but can be used for any similar network exploring purpose. The program offers various visualization and graph manipulation techniques to increase the utility for the user.'),
(74059, 2008, 'bio', 'nuScore: a web-interface for nucleosome positioning predictions', 'Tolstorukov, Michael Y|Choudhary, Vidhu|Olson, Wilma K|Zhurkin, Victor B|Park, Peter J', 'Harvard-Partners Center for Genetics and Genomics, Brigham and Womens Hospital, Boston, MA 02115, USA. tolstorukov@gmail.com|Harvard-Partners Center for Genetics and Genomics, Brigham and Womens Hospital, Boston, MA 02115, USA. tolstorukov@gmail.com|Harvard-Partners Center for Genetics and Genomics, Brigham and Womens Hospital, Boston, MA 02115, USA. tolstorukov@gmail.com|Harvard-Partners Center for Genetics and Genomics, Brigham and Womens Hospital, Boston, MA 02115, USA. tolstorukov@gmail.com|Harvard-Partners Center for Genetics and Genomics, Brigham and Womens Hospital, Boston, MA 02115, USA. tolstorukov@gmail.com', 'United States|United States|United States|United States|United States', 44, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Internet|Models, Genetic|Molecular Sequence Data|Nucleosomes|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn212', '18445607', 'Sequence-directed mapping of nucleosome positions is of major biological interest. Here, we present a web-interface for estimation of the affinity of the histone core to DNA and prediction of nucleosome arrangement on a given sequence. Our approach is based on assessment of the energy cost of imposing the deformations required to wrap DNA around the histone surface. The interface allows the user to specify a number of options such as selecting from several structural templates for threading calculations and adding random sequences to the analysis.'),
(74060, 2008, 'bio', 'quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation', 'Neuenschwander, Samuel|Hospital, Frédéric|Guillaume, Frédéric|Goudet, Jérôme', 'Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland. samuel.neuenschwander@unil.ch|Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland. samuel.neuenschwander@unil.ch|Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland. samuel.neuenschwander@unil.ch|Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland. samuel.neuenschwander@unil.ch', 'Switzerland|Switzerland|Switzerland|Switzerland', 85, 'Algorithms|Animals|Chromosome Mapping|Computer Simulation|Genetics, Population|Humans|Models, Genetic|Quantitative Trait Loci|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btn219', '18450810', 'quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform.'),
(74061, 2008, 'bio', 'Characterization and prediction of residues determining protein functional specificity', 'Capra, John A|Singh, Mona', 'Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.|Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.', 'United States|United States', 80, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn214', '18450811', 'Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each proteins particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs, however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs.'),
(74062, 2008, 'bio', 'A correction for estimating error when using the Local Pooled Error Statistical Test', 'Murie, Carl|Nadon, Robert', 'McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, Canada.|McGill University and Genome Quebec Innovation Centre, 740 avenue du Docteur Penfield, Montreal, Quebec, Canada.', 'Canada|Canada', 4, 'Algorithms|Artifacts|Data Interpretation, Statistical|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn211', '18450812', 'Jain et al. introduced the Local Pooled Error (LPE) statistical test designed for use with small sample size microarray gene-expression data. Based on an asymptotic proof, the test multiplicatively adjusts the standard error for a test of differences between two classes of observations by pi/2 due to the use of medians rather than means as measures of central tendency. The adjustment is upwardly biased at small sample sizes, however, producing fewer than expected small P-values with a consequent loss of statistical power. We present an empirical correction to the adjustment factor which removes the bias and produces theoretically expected P-values when distributional assumptions are met. Our adjusted LPE measure should prove useful to ongoing methodological studies designed to improve the LPEs, performance for microarray and proteomics applications and for future work for other high-throughput biotechnologies.'),
(74063, 2008, 'bio', 'A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics', 'Webb-Robertson, Bobbie-Jo M|Cannon, William R|Oehmen, Christopher S|Shah, Anuj R|Gurumoorthi, Vidhya|Lipton, Mary S|Waters, Katrina M', 'Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov|Computational Biology & Bioinformatics, Pacific Northwest National Laboratory. bj@pnl.gov', '-|-|-|-|-|-|-', 39, 'Algorithms|Artificial Intelligence|Bacterial Proteins|Computer Simulation|Models, Chemical|Pattern Recognition, Automated|Peptide Mapping|Peptides|Proteome|Proteomics|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btn218', '18453551', 'The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic).'),
(74064, 2008, 'bio', 'DAnTE: a statistical tool for quantitative analysis of -omics data', 'Polpitiya, Ashoka D|Qian, Wei-Jun|Jaitly, Navdeep|Petyuk, Vladislav A|Adkins, Joshua N|Camp, David G|Anderson, Gordon A|Smith, Richard D', 'Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|Databases, Protein|Peptide Mapping|Proteome|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btn217', '18453552', 'Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly.'),
(74065, 2008, 'bio', 'High-performance hardware implementation of a parallel database search engine for real-time peptide mass fingerprinting', 'Bogdán, István A|Rivers, Jenny|Beynon, Robert J|Coca, Daniel', 'Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.|Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.|Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.|Department of Automatic Control & Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 13, 'Computer Systems|Database Management Systems|Databases, Factual|Equipment Design|Equipment Failure Analysis|Information Storage and Retrieval|Mass Spectrometry|Peptide Mapping', 'Journal Article', '10.1093/bioinformatics/btn216', '18453553', 'Peptide mass fingerprinting (PMF) is a method for protein identification in which a protein is fragmented by a defined cleavage protocol (usually proteolysis with trypsin), and the masses of these products constitute a fingerprint that can be searched against theoretical fingerprints of all known proteins. In the first stage of PMF, the raw mass spectrometric data are processed to generate a peptide mass list. In the second stage this protein fingerprint is used to search a database of known proteins for the best protein match. Although current software solutions can typically deliver a match in a relatively short time, a system that can find a match in real time could change the way in which PMF is deployed and presented. In a paper published earlier we presented a hardware design of a raw mass spectra processor that, when implemented in Field Programmable Gate Array (FPGA) hardware, achieves almost 170-fold speed gain relative to a conventional software implementation running on a dual processor server. In this article we present a complementary hardware realization of a parallel database search engine that, when running on a Xilinx Virtex 2 FPGA at 100 MHz, delivers 1800-fold speed-up compared with an equivalent C software routine, running on a 3.06 GHz Xeon workstation. The inherent scalability of the design means that processing speed can be multiplied by deploying the design on multiple FPGAs. The database search processor and the mass spectra processor, running on a reconfigurable computing platform, provide a complete real-time PMF protein identification solution.'),
(74066, 2008, 'bio', 'An efficient method to identify differentially expressed genes in microarray experiments', 'Qin, Huaizhen|Feng, Tao|Harding, Scott A|Tsai, Chung-Jui|Zhang, Shuanglin', 'Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.|Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.|Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.|Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.|Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genes, Plant|Humans|Models, Theoretical|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis|Programming Languages|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btn215', '18453554', 'Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.'),
(74067, 2008, 'bio', 'Mireval: a web tool for simple microRNA prediction in genome sequences', 'Ritchie, William|Théodule, François-Xavier|Gautheret, Daniel', 'INSERM U928, Technologies Avancées pour le Génome et la Clinique, Luminy Case 906, 13288 Marseille, Cedex 09, France. W.Ritchie@centenary.org.au|INSERM U928, Technologies Avancées pour le Génome et la Clinique, Luminy Case 906, 13288 Marseille, Cedex 09, France. W.Ritchie@centenary.org.au|INSERM U928, Technologies Avancées pour le Génome et la Clinique, Luminy Case 906, 13288 Marseille, Cedex 09, France. W.Ritchie@centenary.org.au', 'France|France|France', 37, 'Algorithms|Base Sequence|Chromosome Mapping|Internet|MicroRNAs|Molecular Sequence Data|Sequence Analysis, RNA|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btn137', '18453555', 'We have developed an online tool called mirEval which can search sequences of up to 10 000 nt for novel microRNAs in multiple organisms. It is a comprehensive tool, easy to use and very informative. It will allow users with no prior knowledge of in-silico detection of microRNAs to take advantage of the most successful approaches to investigate sequences of interest.'),
(74068, 2008, 'bio', 'SYCAMORE--a systems biology computational analysis and modeling research environment', 'Weidemann, Andreas|Richter, Stefan|Stein, Matthias|Sahle, Sven|Gauges, Ralph|Gabdoulline, Razif|Surovtsova, Irina|Semmelrock, Nils|Besson, Bruno|Rojas, Isabel|Wade, Rebecca|Kummer, Ursula', 'Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.|Scientific Databases and Visualization Group, EML Research, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 27, 'Algorithms|Computer Graphics|Computer Simulation|Internet|Models, Biological|Research Design|Signal Transduction|Software|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn207', '18463116', 'SYCAMORE is a browser-based application that facilitates construction, simulation and analysis of kinetic models in systems biology. Thus, it allows e.g. database supported modelling, basic model checking and the estimation of unknown kinetic parameters based on protein structures. In addition, it offers some guidance in order to allow non-expert users to perform basic computational modelling tasks.'),
(74069, 2008, 'bio', 'Identification of OBO nonalignments and its implications for OBO enrichment', 'Bada, Michael|Hunter, Lawrence', 'Department of Pharmacology, University of Colorado at Denver, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6400A, PO Box 6511, Aurora, CO 80045, USA. mike.bada@uchsc.edu|Department of Pharmacology, University of Colorado at Denver, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6400A, PO Box 6511, Aurora, CO 80045, USA. mike.bada@uchsc.edu', 'United States|United States', 0, 'Algorithms|Artificial Intelligence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Natural Language Processing|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn194', '18463117', 'Existing projects that focus on the semiautomatic addition of links between existing terms in the Open Biomedical Ontologies can take advantage of reasoners that can make new inferences between terms that are based on the added formal definitions and that reflect nonalignments between the linked terms. However, these projects require that these definitions be necessary and sufficient, a strong requirement that often does not hold. If such definitions cannot be added, the reasoners cannot point to the nonalignments through the suggestion of new inferences.'),
(74070, 2008, 'bio', 'PatMaN: rapid alignment of short sequences to large databases', 'Prüfer, Kay|Stenzel, Udo|Dannemann, Michael|Green, Richard E|Lachmann, Michael|Kelso, Janet', 'Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de|Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de|Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de|Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de|Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de|Max-Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany. pruefer@eva.mpg.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Sequence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn223', '18467344', 'We present a tool suited for searching for many short nucleotide sequences in large databases, allowing for a predefined number of gaps and mismatches. The commandline-driven program implements a non-deterministic automata matching algorithm on a keyword tree of the search strings. Both queries with and without ambiguity codes can be searched. Search time is short for perfect matches, and retrieval time rises exponentially with the number of edits allowed.'),
(74071, 2008, 'bio', 'Evolutionary design principles of modules that control cellular differentiation: consequences for hysteresis and multistationarity', 'Kim, Junil|Kim, Tae-Geon|Jung, Sung Hoon|Kim, Jeong-Rae|Park, Taesung|Heslop-Harrison, Pat|Cho, Kwang-Hyun', 'Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.|Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea.', '-|-|-|-|-|-|-', 0, 'Cell Differentiation|Computer Simulation|Evolution, Molecular|Feedback|Gene Expression Regulation, Developmental|Models, Genetic|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn229', '18467345', 'Gene regulatory networks (GRNs) govern cellular differentiation processes and enable construction of multicellular organisms from single cells. Although such networks are complex, there must be evolutionary design principles that shape the network to its present form, gaining complexity from simple modules.'),
(74072, 2008, 'bio', 'Discerning static and causal interactions in genome-wide reverse engineering problems', 'Zampieri, Mattia|Soranzo, Nicola|Altafini, Claudio', 'SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.|SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.|SISSA-ISAS, International School for Advanced Studies, via Beirut 2-4, 34014 Trieste, Italy.', 'Italy|Italy|Italy', 24, 'Chromosome Mapping|Computer Simulation|Genetic Engineering|Models, Genetic|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn220', '18467346', 'In the past years devising methods for discovering gene regulatory mechanisms at a genome-wide level has become a fundamental topic in the field of systems biology. The aim is to infer gene-gene interactions in an increasingly sophisticated and reliable way through the continuous improvement of reverse engineering algorithms exploiting microarray data.'),
(74073, 2008, 'bio', 'Microbial genotype-phenotype mapping by class association rule mining', 'Tamura, Makio|Dhaeseleer, Patrik', 'Lawrence Livermore National Laboratory, Computing Applications and Research Department/Chemistry, Materials, Earth and Life Sciences Department, Microbial Systems Biology Group, Livermore, CA 94550, USA. makio323@gmail.com|Lawrence Livermore National Laboratory, Computing Applications and Research Department/Chemistry, Materials, Earth and Life Sciences Department, Microbial Systems Biology Group, Livermore, CA 94550, USA. makio323@gmail.com', 'United States|United States', 34, 'Algorithms|Bacteria|Bacterial Proteins|Computer Simulation|Information Storage and Retrieval|Models, Genetic|Multigene Family|Phylogeny|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn210', '18467347', 'Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient class association rule mining algorithm, netCAR, in order to extract sets of COGs (clusters of orthologous groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. netCAR takes into account the phylogenetic co-occurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation.'),
(74074, 2008, 'bio', 'lumi: a pipeline for processing Illumina microarray', 'Du, Pan|Kibbe, Warren A|Lin, Simon M', 'Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu|Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA. dupan@northwestern.edu', 'United States|United States|United States', 0, 'Algorithms|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn224', '18467348', 'Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data.'),
(74075, 2008, 'bio', 'HSEpred: predict half-sphere exposure from protein sequences', 'Song, Jiangning|Tan, Hao|Takemoto, Kazuhiro|Akutsu, Tatsuya', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. sjn@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. sjn@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. sjn@kuicr.kyoto-u.ac.jp|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. sjn@kuicr.kyoto-u.ac.jp', 'Japan|Japan|Japan|Japan', 46, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn222', '18467349', 'Half-sphere exposure (HSE) is a newly developed two-dimensional solvent exposure measure. By conceptually separating an amino acids sphere in a protein structure into two half spheres which represent its distinct spatial neighborhoods in the upward and downward directions, the HSE-up and HSE-down measures show superior performance compared with other measures such as accessible surface area, residue depth and contact number. However, currently there is no existing method for the prediction of HSE measures from sequence data.'),
(74076, 2008, 'bio', 'FunSiP: a modular and extensible classifier for the prediction of functional sites in DNA', 'Van Bel, Michiel|Saeys, Yvan|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.', 'Belgium|Belgium|Belgium', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Conserved Sequence|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn225', '18474505', 'Many problems in genome annotation are tackled by using a classification model to predict functional sites such as splice sites, translation start sites or stop codons. Locating the correct position of these sites remains one of the most important but also one of the most difficult issues in the structural annotation of genomes. Most of the software currently in use is written for a very specific problem, thereby limiting the possibilities for reuse.'),
(74077, 2008, 'bio', 'Malin: maximum likelihood analysis of intron evolution in eukaryotes', 'Csurös, Miklós', 'Department of Computer Science and Operations Research, University of Montréal, Montréal, Québec, Canada. csuros@iro.umontreal.ca', 'Canada', 20, 'Algorithms|Animals|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Humans|Introns|Likelihood Functions|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn226', '18474506', 'Malin is a software package for the analysis of eukaryotic gene structure evolution. It provides a graphical user interface for various tasks commonly used to infer the evolution of exon-intron structure in protein-coding orthologs. Implemented tasks include the identification of conserved homologous intron sites in protein alignments, as well as the estimation of ancestral intron content, lineage-specific intron losses and gains. Estimates are computed either with parsimony, or with a probabilistic model that incorporates rate variation across lineages and intron sites.'),
(74078, 2008, 'bio', 'OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar', 'Viklund, Håkan|Elofsson, Arne', 'Department of Biochemistry and Biophysics/Center for Biomembrane Research/Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.|Department of Biochemistry and Biophysics/Center for Biomembrane Research/Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.', 'Sweden|Sweden', 255, 'Algorithms|Amino Acid Sequence|Computer Simulation|Membrane Proteins|Models, Chemical|Models, Molecular|Molecular Sequence Data|Neural Networks (Computer)|Pattern Recognition, Automated|Protein Conformation|Reproducibility of Results|Semantics|Sensitivity and Specificity|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btn221', '18474507', 'As alpha-helical transmembrane proteins constitute roughly 25% of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins are important.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74079, 2008, 'bio', 'DupTree: a program for large-scale phylogenetic analyses using gene tree parsimony', 'Wehe, André|Bansal, Mukul S|Burleigh, J Gordon|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States|United States|United States', 93, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|DNA Mutational Analysis|Evolution, Molecular|Models, Genetic|Molecular Sequence Data|Phylogeny|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn230', '18474508', 'DupTree is a new software program for inferring rooted species trees from collections of gene trees using the gene tree parsimony approach. The program implements a novel algorithm that significantly improves upon the run time of standard search heuristics for gene tree parsimony, and enables the first truly genome-scale phylogenetic analyses. In addition, DupTree allows users to examine alternate rootings and to weight the reconciliation costs for gene trees. DupTree is an open source project written in C++.'),
(74080, 2008, 'bio', 'A distance metric for a class of tree-sibling phylogenetic networks', 'Cardona, Gabriel|Llabrés, Mercè|Rosselló, Francesc|Valiente, Gabriel', 'Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es|Department of Mathematics and Computer Science, University of the Balearic Islands, E-07122 Palma de Mallorca, Spain. gabriel.cardona@uib.es', 'Spain|Spain|Spain|Spain', 38, 'Chromosome Mapping|Computer Simulation|Evolution, Molecular|Models, Genetic|Phylogeny|Proteome|Sequence Analysis, DNA|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn231', '18477576', 'The presence of reticulate evolutionary events in phylogenies turn phylogenetic trees into phylogenetic networks. These events imply in particular that there may exist multiple evolutionary paths from a non-extant species to an extant one, and this multiplicity makes the comparison of phylogenetic networks much more difficult than the comparison of phylogenetic trees. In fact, all attempts to define a sound distance measure on the class of all phylogenetic networks have failed so far. Thus, the only practical solutions have been either the use of rough estimates of similarity (based on comparison of the trees embedded in the networks), or narrowing the class of phylogenetic networks to a certain class where such a distance is known and can be efficiently computed. The first approach has the problem that one may identify two networks as equivalent, when they are not, the second one has the drawback that there may not exist algorithms to reconstruct such networks from biological sequences.'),
(74081, 2008, 'bio', 'ProBias: a web-server for the identification of user-specified types of compositionally biased segments in protein sequences', 'Kuznetsov, Igor B', 'Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, One Discovery Drive, University at Albany, Rensselaer, NY 12144, USA. IKuznetsov@albany.edu', 'United States', 3, 'Algorithms|Amino Acid Sequence|Internet|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn233', '18480099', 'Most proteins contain compositionally biased segments (CBS) in which one or more amino acid types are significantly overrepresented. CBS that contain amino acids with similar chemical properties can have functional and structural importance. This article describes ProBias, a web-server that searches a protein sequence for CBS composed of user-specified amino acid types. ProBias utilizes the discrete scan statistics to estimate statistical significance of CBS and is able to detect even subtle local deviations from the random independence model. The web-server also analyzes the global compositional bias of the input sequence. In the case of novel proteins that lack functional annotation, statistically significant CBS reported by ProBias can be used to guide the search for potential functionally important sites or domains.'),
(74082, 2008, 'bio', 'Computing chemical organizations in biological networks', 'Centler, Florian|Kaleta, Christoph|di Fenizio, Pietro Speroni|Dittrich, Peter', 'Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB) and Department of Mathematics and Computer Science, Friedrich-Schiller-University Jena, D-07743 Jena, Germany.|Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB) and Department of Mathematics and Computer Science, Friedrich-Schiller-University Jena, D-07743 Jena, Germany.|Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB) and Department of Mathematics and Computer Science, Friedrich-Schiller-University Jena, D-07743 Jena, Germany.|Bio Systems Analysis Group, Jena Centre for Bioinformatics (JCB) and Department of Mathematics and Computer Science, Friedrich-Schiller-University Jena, D-07743 Jena, Germany.', 'Germany|Germany|Germany|Germany', 30, 'Algorithms|Animals|Biology|Computational Biology|Computer Simulation|Escherichia coli|Genome|Genome, Bacterial|Humans|Models, Biological|Models, Chemical|Software|Software Design|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn228', '18480100', 'Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states.'),
(74083, 2008, 'bio', 'An overview of the wcd EST clustering tool', 'Hazelhurst, Scott|Hide, Winston|Lipták, Zsuzsanna|Nogueira, Ramon|Starfield, Richard', 'Wits Bioinformatics, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za|Wits Bioinformatics, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za|Wits Bioinformatics, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za|Wits Bioinformatics, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za|Wits Bioinformatics, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za', 'South Africa|South Africa|South Africa|South Africa|South Africa', 27, 'Algorithms|Base Sequence|Cluster Analysis|Expressed Sequence Tags|Internet|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn203', '18480101', 'The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d(2) distance function or edit distance, improving existing implementations of d(2). It supports merging, refinement and reclustering of clusters. It is drop in compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server).'),
(74084, 2008, 'bio', 'qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis', 'Ritz, Christian|Spiess, Andrej-Nikolai', 'Statistics Group, Department of Natural Sciences, Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark.|Statistics Group, Department of Natural Sciences, Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark.', 'Denmark|Denmark', 217, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Programming Languages|Reverse Transcriptase Polymerase Chain Reaction|Software', 'Journal Article', '10.1093/bioinformatics/btn227', '18482995', 'The qpcR library is an add-on to the free R statistical environment performing sigmoidal model selection in real-time quantitative polymerase chain reaction (PCR) data analysis. Additionally, the package implements the most commonly used algorithms for real-time PCR data analysis and is capable of extensive statistical comparison for the selection and evaluation of the different models based on several measures of goodness of fit.'),
(74085, 2008, 'bio', 'Penalized estimation of haplotype frequencies', 'Ayers, Kristin L|Lange, Kenneth', 'Department of Biomathematics, Department of Human Genetics and Department of Statistics, University of California, Los Angeles, CA 90095, USA. kayers@ucla.edu|Department of Biomathematics, Department of Human Genetics and Department of Statistics, University of California, Los Angeles, CA 90095, USA. kayers@ucla.edu', 'United States|United States', 15, 'Algorithms|Chromosomes, Human, X|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Gene Frequency|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Male|Models, Statistical|Models, Theoretical|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btn236', '18487240', 'Low haplotype diversity and linkage disequilibrium are the rule in short genomic segments. This fact suggests that parsimony should be enforced in estimation of haplotype frequencies. The current article introduces a diversity penalty that automatically discards potential haplotypes with low explanatory power. The standard EM algorithm for haplotype frequency estimation can accommodate the penalty if one passes over to a more general minorize-maximize (MM) scheme for estimation.'),
(74086, 2008, 'bio', 'PhyloWidget: web-based visualizations for the tree of life', 'Jordan, Gregory E|Piel, William H', 'EMBL-EBI, Hinxton, Cambridge, CB10 1SD, UK and Peabody Museum of Natural History, Yale University, P.O. Box 208118, New Haven, CT 06520, USA.|EMBL-EBI, Hinxton, Cambridge, CB10 1SD, UK and Peabody Museum of Natural History, Yale University, P.O. Box 208118, New Haven, CT 06520, USA.', 'United States|United States', 94, 'Algorithms|Animals|Biological Evolution|Computational Biology|Computer Graphics|Computers|Data Display|Databases, Genetic|Evolution, Molecular|Humans|Internet|Phylogeny|Programming Languages|Software|Software Design', 'Journal Article', '10.1093/bioinformatics/btn235', '18487241', 'PhyloWidget is a web-based tool for the visualization and manipulation of phylogenetic tree data. It can be accessed online or downloaded as a standalone application. A simple URL-based API allows databases to easily link to and customize PhyloWidget for interactively viewing medium- to large-sized trees.'),
(74087, 2008, 'bio', 'Swelfe: a detector of internal repeats in sequences and structures', 'Abraham, Anne-Laure|Rocha, Eduardo P C|Pothier, Joël', 'UPMC Univ Paris 06, Atelier de BioInformatique, F75005 Paris, France. annela@abi.snv.jussieu.fr|UPMC Univ Paris 06, Atelier de BioInformatique, F75005 Paris, France. annela@abi.snv.jussieu.fr|UPMC Univ Paris 06, Atelier de BioInformatique, F75005 Paris, France. annela@abi.snv.jussieu.fr', 'France|France|France', 38, 'Algorithms|Internet|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn234', '18487242', 'Intragenic duplications of genetic material have important biological roles because of their protein sequence and structural consequences. We developed Swelfe to find internal repeats at three levels. Swelfe quickly identifies statistically significant internal repeats in DNA and amino acid sequences and in 3D structures using dynamic programming. The associated web server also shows the relationships between repeats at each level and facilitates visualization of the results.'),
(74088, 2008, 'bio', 'MOCSphaser: a haplotype inference tool from a mixture of copy number variation and single nucleotide polymorphism data', 'Kato, Mamoru|Nakamura, Yusuke|Tsunoda, Tatsuhiko', 'SNP Research Center, RIKEN, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.|SNP Research Center, RIKEN, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.|SNP Research Center, RIKEN, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.', 'Japan|Japan|Japan', 13, 'Algorithms|Alleles|Computational Biology|Gene Dosage|Gene Frequency|Genetic Variation|Genetics, Population|Genotype|Haplotypes|Humans|Linkage Disequilibrium|Phenotype|Polymerase Chain Reaction|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btn242', '18492685', 'Detailed analyses of the population-genetic nature of copy number variations (CNVs) and the linkage disequilibrium between CNV and single nucleotide polymorphism (SNP) loci from high-throughput experimental data require a computational tool to accurately infer alleles of CNVs and haplotypes composed of both CNV alleles and SNP alleles. Here we developed a new tool to infer population frequencies of such alleles and haplotypes from observed copy numbers and SNP genotypes, using the expectation-maximization algorithm. This tool can also handle copy numbers ambiguously determined, such as 2 or 3 copies, due to experimental noise.'),
(74089, 2008, 'bio', 'Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments', 'Shinoda, Kosaku|Tomita, Masaru|Ishihama, Yasushi', 'Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.|Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0017, Japan.', 'Japan|Japan|Japan', 17, 'Algorithms|Arabidopsis|Artificial Intelligence|Chromatography, Liquid|Computational Biology|Genes, Plant|Mass Spectrometry|Models, Genetic|Peptides|Plant Proteins|Proteome|Proteomics|Signal Processing, Computer-Assisted|Software|Solvents', 'Journal Article', '10.1093/bioinformatics/btn240', '18492686', 'Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool in proteomics studies, but when peptide retention information is used for identification purposes, it remains challenging to compare multiple LC-MS/MS runs or to match observed and predicted retention times, because small changes of LC conditions unavoidably lead to variability in retention times. In addition, non-contiguous retention data obtained with different LC-MS instruments or in different laboratories must be aligned to confirm and utilize rapidly accumulating published proteomics data.'),
(74090, 2008, 'bio', 'EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis', 'Lee, Sophia S F|Sun, Lei|Kustra, Rafal|Bull, Shelley B', 'Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.|Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.|Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.|Department of Public Health Sciences, University of Toronto, Toronto M5T3M7, Canada.', 'Canada|Canada|Canada|Canada', 15, 'Algorithms|Chromosome Mapping|Computational Biology|Data Interpretation, Statistical|Genetic Linkage|Genotype|Humans|Lod Score|Models, Genetic|Models, Statistical|Phenotype|Programming Languages|Quantitative Trait, Heritable|Random Allocation|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btn239', '18499695', 'We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.'),
(74091, 2008, 'bio', 'The Sleipnir library for computational functional genomics', 'Huttenhower, Curtis|Schroeder, Mark|Chikina, Maria D|Troyanskaya, Olga G', 'Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540, USA.|Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540, USA.|Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540, USA.|Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540, USA.', 'United States|United States|United States|United States', 48, 'Algorithms|Artificial Intelligence|Computational Biology|Database Management Systems|Databases, Genetic|Genomics|Information Storage and Retrieval|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn237', '18499696', 'Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory and processing resources carefully.'),
(74092, 2008, 'bio', 'A Bayesian estimator of protein-protein association probabilities', 'Gilmore, Jason M|Auberry, Deanna L|Sharp, Julia L|White, Amanda M|Anderson, Kevin K|Daly, Don S', 'Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.|Pacific Northwest National Laboratory, Battelle Boulevard, Richland, WA 99352, USA.', 'United States|United States|United States|United States|United States|United States', 6, 'Algorithms|Bayes Theorem|Binding Sites|Data Interpretation, Statistical|Models, Statistical|Protein Binding|Protein Interaction Mapping|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btn238', '18499697', 'The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments.'),
(74093, 2008, 'bio', 'Estimating dynamic models for gene regulation networks', 'Cao, Jiguo|Zhao, Hongyu', 'Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada.|Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada.', 'Canada|Canada', 0, 'Algorithms|Computational Biology|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Gene Regulatory Networks|Genetic Vectors|Models, Biological|Models, Statistical|Models, Theoretical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Oligonucleotides|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btn246', '18505754', 'Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data.'),
(74094, 2008, 'bio', 'InteroPORC: automated inference of highly conserved protein interaction networks', 'Michaut, Magali|Kerrien, Samuel|Montecchi-Palazzi, Luisa|Chauvat, Franck|Cassier-Chauvat, Corinne|Aude, Jean-Christophe|Legrain, Pierre|Hermjakob, Henning', 'CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com|CEA, IBITECS, Gif sur Yvette, F-91191, France. michaut.bioinfo@gmail.com', 'France|France|France|France|France|France|France|France', 24, 'Algorithms|Automation|Computational Biology|Computer Simulation|Cyanobacteria|Genome, Bacterial|Internet|Models, Biological|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Signal Processing, Computer-Assisted|Software|Synechocystis|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn249', '18508856', 'Protein-protein interaction networks provide insights into the relationships between the proteins of an organism thereby contributing to a better understanding of cellular processes. Nevertheless, large-scale interaction networks are available for only a few model organisms. Thus, interologs are useful for a systematic transfer of protein interaction networks between organisms. However, no standard tool is available so far for that purpose.'),
(74095, 2008, 'bio', 'Using inferred residue contacts to distinguish between correct and incorrect protein models', 'Miller, Christopher S|Eisenberg, David', 'UCLA-DOE Institute for Genomics & Proteomics, Molecular Biology Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA.|UCLA-DOE Institute for Genomics & Proteomics, Molecular Biology Institute, Box 951570, UCLA, Los Angeles, CA 90095, USA.', 'United States|United States', 33, 'Algorithms|Amino Acid Sequence|Bayes Theorem|Computational Biology|Databases, Protein|Genome|Genomics|Models, Statistical|Molecular Sequence Data|Probability|Protein Conformation|Proteins|Reproducibility of Results|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btn248', '18511466', 'The de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure?'),
(74096, 2008, 'bio', 'Efficient functional clustering of protein sequences using the Dirichlet process', 'Brown, Duncan P', 'Department of Bioengineering, UC Berkeley and Merck & Co, Inc, 1700 Owens St, San Francisco, CA 94158, USA. duncan_brown@merck.com', 'United States', 0, 'Algorithms|Amino Acid Sequence|Data Interpretation, Statistical|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn244', '18511467', 'Automatic clustering of protein sequences is an important problem in computational biology. The recent explosion in genome sequences has given biological researchers a vast number of novel protein sequences. However, the majority of these sequences have no experimental evidence for their molecular function in the cell, and the responsibility for correctly annotating these sequences falls upon the bioinformatics community. Ideally, we would like to be able to group sequences of similar or identical molecular function in an automatic fashion, without relying on experimental evidence.'),
(74097, 2008, 'bio', 'Ontologizer 20--a multifunctional tool for GO term enrichment analysis and data exploration', 'Bauer, Sebastian|Grossmann, Steffen|Vingron, Martin|Robinson, Peter N', 'Institute of Medical Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.bauer@charite.de|Institute of Medical Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.bauer@charite.de|Institute of Medical Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.bauer@charite.de|Institute of Medical Genetics, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. sebastian.bauer@charite.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Computational Biology|Databases, Genetic|Databases, Protein|Internet|Models, Biological|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Programming Languages|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn250', '18511468', 'The Ontologizer is a Java application that can be used to perform statistical analysis for overrepresentation of Gene Ontology (GO) terms in sets of genes or proteins derived from an experiment. The Ontologizer implements the standard approach to statistical analysis based on the one-sided Fishers exact test, the novel parent-child method, as well as topology-based algorithms. A number of multiple-testing correction procedures are provided. The Ontologizer allows users to visualize data as a graph including all significantly overrepresented GO terms and to explore the data by linking GO terms to all genes/proteins annotated to the term and by linking individual terms to child terms.'),
(74098, 2008, 'bio', 'CellTrack: an open-source software for cell tracking and motility analysis', 'Sacan, Ahmet|Ferhatosmanoglu, Hakan|Coskun, Huseyin', 'Computer Engineering Department, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu|Computer Engineering Department, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu|Computer Engineering Department, Middle East Technical University, Ankara, Turkey. sacan@cse.ohio-state.edu', 'Turkey|Turkey|Turkey', 67, 'Animals|Automation|Cell Movement|Computational Biology|Computer Simulation|Humans|Models, Biological|Models, Statistical|Models, Theoretical|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn247', '18511469', 'Cell motility is a critical part of many important biological processes. Automated and sensitive cell tracking is essential to cell motility studies where the tracking results can be used for diagnostic or curative decisions and where mathematical models can be developed to deepen our understanding of the mechanisms underlying cell motility.'),
(74099, 2008, 'bio', 'Sparse kernel methods for high-dimensional survival data', 'Evers, Ludger|Messow, Claudia-Martina', 'Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, United Kingdom. l.evers@bris.ac.uk|Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, United Kingdom. l.evers@bris.ac.uk', 'United Kingdom|United Kingdom', 0, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computational Biology|Computer Simulation|Computing Methodologies|Gene Expression Profiling|Humans|Lung Neoplasms|Lymphoma|Models, Statistical|Pattern Recognition, Automated|Proportional Hazards Models|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn253', '18515276', 'Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Coxs proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be kernelized. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches.'),
(74100, 2008, 'bio', 'Web-based design and evaluation of T-cell vaccine candidates', 'Thurmond, James|Yoon, Hyejin|Kuiken, Carla|Yusim, Karina|Perkins, Simon|Theiler, James|Bhattacharya, Tanmoy|Korber, Bette|Fischer, Will', 'Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.|Los Alamos National Laboratory, Los Alamos, NM 87545, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 37, 'AIDS Vaccines|Algorithms|Antigens|Computers|Drug Design|Epitopes|Epitopes, T-Lymphocyte|HIV Infections|HIV-1|Humans|Internet|Software|T-Lymphocytes|Technology, Pharmaceutical|Vaccines', 'Journal Article', '10.1093/bioinformatics/btn251', '18515277', 'We present a suite of on-line tools to design candidate vaccine proteins, and to assess antigen potential, using coverage of k-mers (as proxies for potential T-cell epitopes) as a metric. The vaccine design tool uses the recently published mosaic method to generate protein sequences optimized for coverage of high-frequency k-mers, the coverage-assessment tools facilitate coverage comparisons for any potential antigens. To demonstrate these tools, we designed mosaic protein sets for B-clade HIV-1 Gag, Pol and Nef, and compared them to antigens used in a recent human vaccine trial.'),
(74101, 2008, 'bio', 'SNP analysis to results (SNPator): a web-based environment oriented to statistical genomics analyses upon SNP data', 'Morcillo-Suarez, Carlos|Alegre, Josep|Sangros, Ricardo|Gazave, Elodie|de Cid, Rafael|Milne, Roger|Amigo, Jorge|Ferrer-Admetlla, Anna|Moreno-Estrada, Andrés|Gardner, Michelle|Casals, Ferran|Pérez-Lezaun, Anna|Comas, David|Bosch, Elena|Calafell, Francesc|Bertranpetit, Jaume|Navarro, Arcadi', 'Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.|Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spain|Spai', 0, 'Algorithms|Animals|Biological Evolution|Computational Biology|Evolution, Molecular|Genomics|Genotype|Humans|Internet|Models, Statistical|Phenotype|Polymorphism, Single Nucleotide|Quality Control|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btn241', '18515823', 'Single nucleotide polymorphisms (SNPs) are the most widely used marker in studies to assess associations between genetic variants and complex traits or diseases. They are also becoming increasingly important in the study of the evolution and history of humans and other species. The analysis and processing of SNPs obtained thanks to high-throughput technologies imply the time consuming and costly use of different, complex and usually format-incompatible software. SNPator is a user-friendly web-based SNP data analysis suite that integrates, among many other algorithms, the most common steps of a SNP association study. It frees the user from the need to have large computer facilities and an in depth knowledge of genetic software installation and management. Genotype data is directly read from the output files of the usual genotyping platforms. Phenotypic data on the samples can also be easily uploaded. Many different quality control and analysis procedures can be performed either by using built-in SNPator algorithms or by calling standard genetic software.'),
(74102, 2008, 'bio', 'LOT: a tool for linkage analysis of ordinal traits for pedigree data', 'Zhang, Meizhuo|Feng, Rui|Chen, Xiang|Hu, Buqu|Zhang, Heping', 'Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Yale University School of Medicine, New Haven, CT 06520-8034, USA.|Yale University School of Medicine, New Haven, CT 06520-8034, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Genetic Linkage|Models, Genetic|Pedigree|Software', 'Journal Article', '10.1093/bioinformatics/btn258', '18535081', 'Existing linkage-analysis methods address binary or quantitative traits. However, many complex diseases and human conditions, particularly behavioral disorders, are rated on ordinal scales. Herein, we introduce, LOT, a tool that performs linkage analysis of ordinal traits for pedigree data. It implements a latent-variable proportional-odds logistic model that relates inheritance patterns to the distribution of the ordinal trait. The likelihood-ratio test is used for testing evidence of linkage.'),
(74103, 2008, 'bio', 'Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification', 'Martin, Christian|Diaz, Naryttza N|Ontrup, Jörg|Nattkemper, Tim W', 'AG Applied Neuroinformatics, Technical Faculty, Bielefeld University and Graduate School Bioinformatics and Genome Research, CeBiTec, Bielefeld University, Bielefeld, Germany. christian.martin@uni-bielefeld.de|AG Applied Neuroinformatics, Technical Faculty, Bielefeld University and Graduate School Bioinformatics and Genome Research, CeBiTec, Bielefeld University, Bielefeld, Germany. christian.martin@uni-bielefeld.de|AG Applied Neuroinformatics, Technical Faculty, Bielefeld University and Graduate School Bioinformatics and Genome Research, CeBiTec, Bielefeld University, Bielefeld, Germany. christian.martin@uni-bielefeld.de|AG Applied Neuroinformatics, Technical Faculty, Bielefeld University and Graduate School Bioinformatics and Genome Research, CeBiTec, Bielefeld University, Bielefeld, Germany. christian.martin@uni-bielefeld.de', 'Germany|Germany|Germany|Germany', 19, 'Algorithms|Archaeal Proteins|Artificial Intelligence|Bacterial Proteins|Classification|Cluster Analysis|Computational Biology|DNA|Databases, Genetic|Models, Genetic|Models, Statistical|Multigene Family|Oligonucleotides|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn257', '18535082', 'Modern high-throughput sequencing technologies enable the simultaneous analysis of organisms in an environment. The analysis of species diversity and the binning of DNA fragments of non-sequenced species for assembly are two major challenges in sequence analysis. To achieve reasonable binnings and classifications, DNA fragment structure has to be represented appropriately, so it can be processed by machine learning algorithms.'),
(74104, 2008, 'bio', 'Modeling promoter grammars with evolving hidden Markov models', 'Won, Kyoung-Jae|Sandelin, Albin|Marstrand, Troels Torben|Krogh, Anders', 'The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.|The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.|The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.|The Bioinformatics Centre, Department of Biology & Biotech Research and Innovation Centre, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.', 'Denmark|Denmark|Denmark|Denmark', 0, 'Base Sequence|Binding Sites|Computer Simulation|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Semantics|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn254', '18535083', 'Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.'),
(74105, 2008, 'bio', 'Optimal vaccination schedules using simulated annealing', 'Pennisi, Marzio|Catanuto, Roberto|Pappalardo, Francesco|Motta, Santo', 'Department of Mathematics and Computer Science, University of Catania, University of Catania, Catania, Italy.|Department of Mathematics and Computer Science, University of Catania, University of Catania, Catania, Italy.|Department of Mathematics and Computer Science, University of Catania, University of Catania, Catania, Italy.|Department of Mathematics and Computer Science, University of Catania, University of Catania, Catania, Italy.', 'Italy|Italy|Italy|Italy', 28, 'Communicable Disease Control|Computer Simulation|Decision Support Systems, Clinical|Drug Administration Schedule|Drug Therapy, Computer-Assisted|Humans|Models, Biological|Vaccination|Vaccines', 'Journal Article', '10.1093/bioinformatics/btn260', '18535084', 'Since few years the problem of finding optimal solutions for drug or vaccine protocols have been tackled using system biology modeling. These approaches are usually computationally expensive. Our previous experiences in optimizing vaccine or drug protocols using genetic algorithms required the use of a high performance computing infrastructure for a couple of days. In the present article we show that by an appropriate use of a different optimization algorithm, the simulated annealing, we have been able to downsize the computational effort by a factor 10(2). The new algorithm requires computational effort that can be achieved by current generation personal computers.'),
(74106, 2008, 'bio', 'Knowledge-based gene expression classification via matrix factorization', 'Schachtner, R|Lutter, D|Knollmüller, P|Tomé, A M|Theis, F J|Schmitz, G|Stetter, M|Vilda, P Gómez|Lang, E W', 'CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.|CIML/Biophysics, University of Regensburg, D-93040 Regensburg, Germany.', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany|Germany', 30, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn245', '18535085', 'Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.'),
(74107, 2008, 'bio', 'Retroviral promoters in the human genome', 'Conley, Andrew B|Piriyapongsa, Jittima|Jordan, I King', 'School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta, GA 30306, USA.|School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta, GA 30306, USA.|School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta, GA 30306, USA.', 'Georgia|Georgia|Georgia', 81, '5 Untranslated Regions|Animals|Computational Biology|Databases, Genetic|Gene Expression Profiling|Genome, Human|Humans|Promoter Regions, Genetic|RNA, Messenger|Retroviridae|Sequence Analysis, DNA|Terminal Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btn243', '18535086', 'Endogenous retrovirus (ERV) elements have been shown to contribute promoter sequences that can initiate transcription of adjacent human genes. However, the extent to which retroviral sequences initiate transcription within the human genome is currently unknown. We analyzed genome sequence and high-throughput expression data to systematically evaluate the presence of retroviral promoters in the human genome.'),
(74108, 2008, 'bio', 'Enriched transcription factor binding sites in hypermethylated gene promoters in drug resistant cancer cells', 'Li, Meng|Paik, Hyun-il Henry|Balch, Curt|Kim, Yoosung|Li, Lang|Huang, Tim H-M|Nephew, Kenneth P|Kim, Sun', 'Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.|Medical Sciences, School of Medicine, Indiana University, Bloomington, IN 47404, USA.', 'India|India|India|India|India|India|India|India', 11, 'Animals|Binding Sites|DNA Methylation|Drug Resistance, Neoplasm|Gene Expression Regulation, Neoplastic|Humans|Neoplasms|Promoter Regions, Genetic|Protein Binding|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn256', '18540022', 'In the human genome, CpG islands, CG-rich regions located in or near gene promoters, are normally unmethylated. However, in cancer cells, CpG islands frequently gain methylation, resulting in silencing of growth-limiting tumor suppressor genes. To our knowledge, the potential relationship between CpG island hypermethylation, transcription factor (TF) binding in local promoter regions and transcriptional control has not been previously explored in a genome-wide context.'),
(74109, 2008, 'bio', 'Sequence-specific reconstruction from fragmentary databases using seed sequences: implementation and validation on SAGE, proteome and generic sequencing data', 'Sobreira, Tiago J P|Gruber, Arthur', 'Instituto do Coração - USP, Av. Prof. Enéas de Carvalho Aguiar 44, São Paulo SP, 05403-000, Brazil.|Instituto do Coração - USP, Av. Prof. Enéas de Carvalho Aguiar 44, São Paulo SP, 05403-000, Brazil.', 'Brazil|Brazil', 5, 'Algorithms|Amino Acid Sequence|Base Sequence|DNA|Database Management Systems|Databases, Genetic|Molecular Sequence Data|Proteome|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn283', '18544546', 'DNA assembly programs classically perform an all-against-all comparison of reads to identify overlaps, followed by a multiple sequence alignment and generation of a consensus sequence. If the aim is to assemble a particular segment, instead of a whole genome or transcriptome, a target-specific assembly is a more sensible approach. GenSeed is a Perl program that implements a seed-driven recursive assembly consisting of cycles comprising a similarity search, read selection and assembly. The iterative process results in a progressive extension of the original seed sequence. GenSeed was tested and validated on many applications, including the reconstruction of nuclear genes or segments, full-length transcripts, and extrachromosomal genomes. The robustness of the method was confirmed through the use of a variety of DNA and protein seeds, including short sequences derived from SAGE and proteome projects.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74110, 2008, 'bio', 'Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value', 'Boulesteix, Anne-Laure|Porzelius, Christine|Daumer, Martin', 'Sylvia Lawry Centre for MS Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org|Sylvia Lawry Centre for MS Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org|Sylvia Lawry Centre for MS Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org', 'Germany|Germany|Germany', 69, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Evaluation Studies', '10.1093/bioinformatics/btn262', '18544547', 'In the context of clinical bioinformatics methods are needed for assessing the additional predictive value of microarray data compared to simple clinical parameters alone. Such methods should also provide an optimal prediction rule making use of all potentialities of both types of data: they should ideally be able to catch subtypes which are not identified by clinical parameters alone. Moreover, they should address the question of the additional predictive value of microarray data in a fair framework.'),
(74111, 2008, 'bio', 'DNAlive: a tool for the physical analysis of DNA at the genomic scale', 'Goñi, J Ramon|Fenollosa, Carlos|Pérez, Alberto|Torrents, David|Orozco, Modesto', 'Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain.|Joint IRB-BSC Program on Computational Biology, Institute of Research in Biomedicine, Parc Científic de Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain.', 'Spain|Spain|Spain|Spain|Spain', 20, 'Algorithms|Base Sequence|Chromosome Mapping|DNA|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn259', '18544548', 'DNAlive is a tool for the analysis and graphical display of structural and physical characteristics of genomic DNA. The web server implements a wide repertoire of metrics to derive physical information from DNA sequences with a powerful interface to derive 3D information on large sequences of both naked and protein-bound DNAs. Furthermore, it implements a mesoscopic Metropolis code which allows the inexpensive study of the dynamic properties of chromatin fibers. In addition, our server also surveys other protein and genomic databases allowing the user to combine and explore the physical properties of selected DNA in the context of functional features annotated on those regions.'),
(74112, 2008, 'bio', 'MINS2: revisiting the molecular code for transmembrane-helix recognition by the Sec61 translocon', 'Park, Yungki|Helms, Volkhard', 'Center for Bioinformatics, Saarland University, Germany.|Center for Bioinformatics, Saarland University, Germany.', 'Germany|Germany', 4, 'Amino Acid Sequence|Binding Sites|Cell Membrane|Membrane Proteins|Molecular Sequence Data|Protein Binding|SEC Translocation Channels|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btn255', '18544549', 'To be fully functional, membrane proteins should not only fold, but also get inserted into the membrane, which is mediated by the Sec61 translocon. Recent experimental studies have attempted to elucidate how the Sec61 translocon accomplishes this delicate task by measuring the translocon-mediated membrane insertion free energies of 357 systematically designed peptides. On the basis of this data set, we have developed MINS2, a novel sequence-based computational method for predicting the membrane insertion free energies of protein sequences. A benchmark analysis of MINS2 shows that MINS2 signi.cantly outperforms previously proposed methods. Importantly, the application of MINS2 to known membrane protein structures shows that a better prediction of membrane insertion free energies does not lead to a better prediction of transmembrane segments of polytopic membrane proteins.'),
(74113, 2008, 'bio', 'MPIDB: the microbial protein interaction database', 'Goll, Johannes|Rajagopala, Seesandra V|Shiau, Shen C|Wu, Hank|Lamb, Brian T|Uetz, Peter', 'The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org|The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org|The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org|The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org|The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org|The J. Craig Venter Institute, Rockville, MD 20850, USA. jgoll@jcvi.org', 'United States|United States|United States|United States|United States|United States', 90, 'Bacterial Proteins|Binding Sites|Database Management Systems|Databases, Protein|Internet|Models, Biological|Models, Chemical|Models, Molecular|Protein Binding|Protein Interaction Mapping|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn285', '18556668', 'The microbial protein interaction database (MPIDB) aims to collect and provide all known physical microbial interactions. Currently, 22,530 experimentally determined interactions among proteins of 191 bacterial species/strains can be browsed and downloaded. These microbial interactions have been manually curated from the literature or imported from other databases (IntAct, DIP, BIND, MINT) and are linked to 24,060 experimental evidences (PubMed ID, PSI-MI methods). In contrast to these databases, interactions in MPIDB are further supported by 8150 additional evidences based on interaction conservation, co-purification and 3D domain contacts (iPfam, 3did).'),
(74114, 2008, 'bio', 'Modeling immune system control of atherogenesis', 'Pappalardo, Francesco|Musumeci, Salvatore|Motta, Santo', 'Faculty of Pharmacy, Department of Mathematics and Computer Science, University of Catania, Catania, Italy. francesco@dmi.unict.it|Faculty of Pharmacy, Department of Mathematics and Computer Science, University of Catania, Catania, Italy. francesco@dmi.unict.it|Faculty of Pharmacy, Department of Mathematics and Computer Science, University of Catania, Catania, Italy. francesco@dmi.unict.it', 'Italy|Italy|Italy', 39, 'Arteries|Atherosclerosis|Autoantibodies|Computer Simulation|Humans|Immunity, Innate|Immunologic Factors|Lipoproteins, LDL|Models, Cardiovascular|Models, Immunological', 'Journal Article', '10.1093/bioinformatics/btn306', '18556669', 'Atherosclerosis is a disease that is present in almost all humans, typically beginning in early adolescence. It is a human disease broadly investigated, that is amenable to quantitative analysis. Oxidized low-density lipoproteins (LDLs) and their autoantibodies are involved in the development of atherosclerosis in animal models, but their role in humans is still not clear. Computer models may represent a virtual environment to perform experiments not possible in human volunteers that can provide a useful instrument for monitoring both the evolution of atherosclerotic lesions and to quantify the efficacy of treatments, including vaccines, oriented to reduce the LDLs and their oxidized fraction.'),
(74115, 2008, 'bio', 'Ensemble non-negative matrix factorization methods for clustering protein-protein interactions', 'Greene, Derek|Cagney, Gerard|Krogan, Nevan|Cunningham, Pádraig', 'School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. derek.greene@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. derek.greene@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. derek.greene@ucd.ie|School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. derek.greene@ucd.ie', 'Ireland|Ireland|Ireland|Ireland', 61, 'Algorithms|Cluster Analysis|Computer Simulation|Models, Chemical|Pattern Recognition, Automated|Protein Binding|Protein Interaction Mapping|Proteins', 'Journal Article', '10.1093/bioinformatics/btn286', '18556670', 'When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a soft hierarchy of clusters.'),
(74116, 2008, 'bio', 'Memory-efficient dynamic programming backtrace and pairwise local sequence alignment', 'Newberg, Lee A', 'New York State Department of Health, Center for Bioinformatics, Wadsworth Center, Albany, NY 12208-3425, USA. leen@cs.rpi.edu', 'United States', 0, 'Algorithms|Information Storage and Retrieval|Numerical Analysis, Computer-Assisted|Sequence Alignment|Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn308', '18558620', 'A backtrace through a dynamic programming algorithms intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward-backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis.'),
(74117, 2008, 'bio', 'Predicting functional regulatory polymorphisms', 'Torkamani, Ali|Schork, Nicholas J', 'Department of Molecular and Experimental Medicine, Scripps Genomic Medicine and the Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA.|Department of Molecular and Experimental Medicine, Scripps Genomic Medicine and the Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA.', 'United States|United States', 19, 'Base Sequence|Chromosome Mapping|Gene Expression Regulation|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn311', '18562267', 'Limited availability of data has hindered the development of algorithms that can identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given the large number of common polymorphisms known to reside in the human genome, the identification of functional rSNPs via laboratory assays will be costly and time-consuming. Therefore appropriate bioinformatics strategies for predicting functional rSNPs are necessary. Recent data from the Encyclopedia of DNA Elements (ENCODE) Project has significantly expanded the amount of available functional information relevant to non-coding regions of the genome, and, importantly, led to the conclusion that many functional elements in the human genome are not conserved.'),
(74118, 2008, 'bio', 'Predicting sub-Golgi localization of type II membrane proteins', 'van Dijk, A D J|Bosch, D|ter Braak, C J F|van der Krol, A R|van Ham, R C H J', 'Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.|Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.|Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.|Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.|Applied Bioinformatics, PRI, Wageningen UR, Wageningen, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 30, 'Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Golgi Apparatus|Models, Biological|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|SNARE Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn309', '18562268', 'Recent research underlines the importance of finegrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood.'),
(74119, 2008, 'bio', 'Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data', 'Oh, Jung Hun|Kim, Young Bun|Gurnani, Prem|Rosenblatt, Kevin P|Gao, Jean X', 'Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.|Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA.', 'United States|United States|United States|United States|United States', 14, 'Biomarkers, Tumor|Carcinoma, Hepatocellular|Disease Progression|Gene Expression Profiling|Humans|Liver Cirrhosis|Liver Neoplasms|Reproducibility of Results|Sensitivity and Specificity|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btn316', '18562269', 'Diseases normally progress through several stages. Therefore, biomarkers corresponding to each stage may exist. To deal with such a multi-category problem, including sample stage prediction and biomarker selection, we propose methods for classification and feature selection. The proposed classification method is based on two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). The final decision for a test sample prediction is an integration of these two schemes. The biomarker pattern for distinguishing each disease category from another one is achieved by the development of an extended Markov blanket (EMB) feature selection method.'),
(74120, 2008, 'bio', 'Spidermonkey: rapid detection of co-evolving sites using Bayesian graphical models', 'Poon, Art F Y|Lewis, Fraser I|Frost, Simon D W|Kosakovsky Pond, Sergei L', 'Division of Comparative Pathology and Medicine, Department of Pathology, University of California, San Diego, CA 92103, USA. afpoon@ucsd.edu|Division of Comparative Pathology and Medicine, Department of Pathology, University of California, San Diego, CA 92103, USA. afpoon@ucsd.edu|Division of Comparative Pathology and Medicine, Department of Pathology, University of California, San Diego, CA 92103, USA. afpoon@ucsd.edu|Division of Comparative Pathology and Medicine, Department of Pathology, University of California, San Diego, CA 92103, USA. afpoon@ucsd.edu', 'United States|United States|United States|United States', 45, 'Amino Acid Sequence|Base Sequence|Bayes Theorem|Computer Simulation|DNA|DNA Mutational Analysis|Evolution, Molecular|Models, Chemical|Models, Genetic|Molecular Sequence Data|Proteins|Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btn313', '18562270', 'Spidermonkey is a new component of the Datamonkey suite of phylogenetic tools that provides methods for detecting coevolving sites from a multiple alignment of homologous nucleotide or amino acid sequences. It reconstructs the substitution history of the alignment by maximum likelihood-based phylogenetic methods, and then analyzes the joint distribution of substitution events using Bayesian graphical models to identify significant associations among sites.'),
(74121, 2008, 'bio', 'On E-values for tandem MS scoring schemes', 'Segal, Mark R', 'Division of Comparative Pathology and Medicine, Department of Pathology, University of California, San Diego, CA 92103, USA. afpoon@ucsd.edu', 'United States', 2, 'Algorithms|Calibration|Computational Biology|Data Interpretation, Statistical|Markov Chains|Models, Statistical|Models, Theoretical|Peptides|Proteomics|Software|Tandem Mass Spectrometry', 'Comment', '10.1093/bioinformatics/btn232', '18562272', '-'),
(74122, 2008, 'bio', 'Sequence analysis of GerM and SpoVS, uncharacterized bacterial sporulation proteins with widespread phylogenetic distribution', 'Rigden, Daniel J|Galperin, Michael Y', 'School of Biological Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK. drigden@liverpool.ac.uk|School of Biological Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK. drigden@liverpool.ac.uk', 'United Kingdom|United Kingdom', 20, 'Amino Acid Motifs|Amino Acid Sequence|Bacterial Proteins|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Phylogeny|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn314', '18562273', 'Sporulation in low-G+C gram-positive bacteria (Firmicutes) is an important survival mechanism that involves up to 150 genes, acting in a highly regulated manner. Many sporulation genes have close homologs in non-sporulating bacteria, including cyanobacteria, proteobacteria and spirochaetes, indicating that their products play a wider biological role. Most of them have been characterized as regulatory proteins or enzymes of peptidoglycan turnover, functions of others remain unknown but they are likely to have a general role in cell division and/or development. We have compiled a list of such widely conserved sporulation and germination proteins with poorly characterized functions, ranked them by the width of their phylogenetic distribution, and performed detailed sequence analysis and, where possible, structural modeling aimed at estimating their potential functions. Here we report the results of sequence analysis of Bacillus subtilis spore germination protein GerM, suggesting that it is a widespread cell development protein, whose function might involve binding to peptidoglycan. GerM consists of two tandem copies of a new domain (designated the GERMN domain) that forms phylum-specific fusions with two other newly described domains, GERMN-associated domains 1 and 2 (GMAD1 and GMAD2). Fold recognition reveals a beta-propeller fold for GMAD1, while ab initio modeling suggests that GMAD2 adopts a fibronectin type III fold. SpoVS is predicted to adopt the AlbA archaeal chromatin protein fold, which suggests that it is a DNA-binding protein, most likely a novel transcriptional regulator.'),
(74123, 2008, 'bio', 'ForSim: a tool for exploring the genetic architecture of complex traits with controlled truth', 'Lambert, Brian W|Terwilliger, Joseph D|Weiss, Kenneth M', 'Department of Anthropology, Penn State University, University Park, PA, USA. bwl1@psu.edu|Department of Anthropology, Penn State University, University Park, PA, USA. bwl1@psu.edu|Department of Anthropology, Penn State University, University Park, PA, USA. bwl1@psu.edu', 'United States|United States|United States', 41, 'Artifacts|Chromosome Mapping|Evolution, Molecular|Models, Genetic|Quantitative Trait Loci|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btn317', '18565989', 'Many important problems in biology involve complex traits affected by multiple coding or regulatory parts of the genome. How well the underlying genetic architecture can be inferred by statistical methods such as mapping and association studies are active research areas. ForSim is a flexible forward evolutionary simulation tool for exploring the consequences of evolution by phenotype, whereby demographic, chance, behavioral and selective effects mold genetic architecture. Simulation is useful for exploring these issues as well as the choice of study design inferential methods.'),
(74124, 2008, 'bio', 'Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence', 'Saccone, Scott F|Saccone, Nancy L|Swan, Gary E|Madden, Pamela A F|Goate, Alison M|Rice, John P|Bierut, Laura J', 'Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu|Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri 63110, USA. ssaccone@wustl.edu', 'United States|United States|United States|United States|United States|United States|United States', 57, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genetic Predisposition to Disease|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA|Tobacco Use Disorder', 'Journal Article', '10.1093/bioinformatics/btn315', '18565990', 'A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype-phenotype correlation with a priori evidence of biological relevance.'),
(74125, 2008, 'bio', 'Database indexing for production MegaBLAST searches', 'Morgulis, Aleksandr|Coulouris, George|Raytselis, Yan|Madden, Thomas L|Agarwala, Richa|Schäffer, Alejandro A', 'Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.|Department of Health and Human Services, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States|United States|United States', 492, 'Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn322', '18567917', 'The BLAST software package for sequence comparison speeds up homology search by preprocessing a query sequence into a lookup table. Numerous research studies have suggested that preprocessing the database instead would give better performance. However, production usage of sequence comparison methods that preprocess the database has been limited to programs such as BLAT and SSAHA that are designed to find matches when query and database subsequences are highly similar.'),
(74126, 2008, 'bio', 'PoooL: an efficient method for estimating haplotype frequencies from large DNA pools', 'Zhang, Han|Yang, Hsin-Chou|Yang, Yaning', 'Department of Statistics and Finance, University of Science and Technology of China, Anhui, Taiwan. zhanghan@mail.ustc.edu.cn|Department of Statistics and Finance, University of Science and Technology of China, Anhui, Taiwan. zhanghan@mail.ustc.edu.cn|Department of Statistics and Finance, University of Science and Technology of China, Anhui, Taiwan. zhanghan@mail.ustc.edu.cn', 'China|China|China', 17, 'Algorithms|Base Sequence|DNA|Gene Frequency|Gene Pool|Haplotypes|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn324', '18573795', 'Pooling DNA is a cost-effective alternative to individual genotyping method. It is often used for initial screening in genome-wide association analysis. In some studies, large pools with sizes up to several hundreds were applied in order to significantly reduce genotyping cost. However, method for estimating haplotype frequencies from large DNA pools has not been available due to computational complexity involved.'),
(74127, 2008, 'bio', 'On correcting the overestimation of the permutation-based false discovery rate estimator', 'Jiao, Shuo|Zhang, Shunpu', 'Department of Statistics, University of Nebraska Lincoln, Lincoln, NE 68526, USA.|Department of Statistics, University of Nebraska Lincoln, Lincoln, NE 68526, USA.', 'United States|United States', 7, 'Algorithms|Artifacts|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn310', '18573796', 'Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR), which is defined as the expected percentage of the number of false positive genes among the claimed significant genes. As a consequence, the accuracy of the FDR estimators will be important for correctly controlling FDR. Xie et al. found that the standard permutation method of estimating FDR is biased and proposed to delete the predicted differentially expressed (DE) genes in the estimation of FDR for one-sample comparison. However, we notice that the formula of the FDR used in their paper is incorrect. This makes the comparison results reported in their paper unconvincing. Other problems with their method include the biased estimation of FDR caused by over- or under-deletion of DE genes in the estimation of FDR and by the implicit use of an unreasonable estimator of the true proportion of equivalently expressed (EE) genes. Due to the great importance of accurate FDR estimation in microarray data analysis, it is necessary to point out such problems and propose improved methods.'),
(74128, 2008, 'bio', 'FIRMA: a method for detection of alternative splicing from exon array data', 'Purdom, E|Simpson, K M|Robinson, M D|Conboy, J G|Lapuk, A V|Speed, T P', 'Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu|Department of Statistics, University of California at Berkeley, 367 Evans Hall #3860, Berkeley, CA 94720-3860, USA. epurdom@stat.berkeley.edu', 'United States|United States|United States|United States|United States|United States', 94, 'Algorithms|Chromosome Mapping|Databases, Genetic|Expressed Sequence Tags|Oligonucleotide Array Sequence Analysis|RNA Splice Sites|Reverse Transcriptase Polymerase Chain Reaction|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn284', '18573797', 'Analyses of EST data show that alternative splicing is much more widespread than once thought. The advent of exon and tiling microarrays means that researchers now have the capacity to experimentally measure alternative splicing on a genome wide level. New methods are needed to analyze the data from these arrays.'),
(74129, 2008, 'bio', 'Computational design of synthetic gene circuits with composable parts', 'Marchisio, M A|Stelling, J', 'Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland. mario.marchisio@bsse.ethz.ch|Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland. mario.marchisio@bsse.ethz.ch', 'Switzerland|Switzerland', 0, 'Biomimetics|Computer Simulation|Computer-Aided Design|Genes, Synthetic|Genetic Engineering|Models, Genetic|Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn330', '18579565', 'In principle, novel genetic circuits can be engineered using standard parts with well-understood functionalities. However, no model based on the simple composition of these parts has become a standard, mainly because it is difficult to define signal exchanges between biological units as unambiguously as in electrical engineering. Corresponding concepts and computational tools for easy circuit design in biology are missing.'),
(74130, 2008, 'bio', 'iFoldRNA: three-dimensional RNA structure prediction and folding', 'Sharma, Shantanu|Ding, Feng|Dokholyan, Nikolay V', 'Department of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Biochemistry & Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.', 'United States|United States|United States', 140, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btn328', '18579566', 'Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2-5 A root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses.'),
(74131, 2008, 'bio', 'Intrinsic disorder prediction from the analysis of multiple protein fold recognition models', 'McGuffin, Liam J', 'School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK. l.j.mcguf.n@reading.ac.uk', 'United Kingdom', 0, 'Amino Acid Sequence|Caspase 7|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Folding|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn326', '18579567', 'Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified.'),
(74132, 2008, 'bio', 'PRIMEGENS-v2: genome-wide primer design for analyzing DNA methylation patterns of CpG islands', 'Srivastava, Gyan P|Guo, Juyuan|Shi, Huidong|Xu, Dong', 'Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.|Computer Science Department and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|CpG Islands|DNA Methylation|DNA Primers|Molecular Sequence Data|Promoter Regions, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn320', '18579568', 'DNA methylation plays important roles in biological processes and human diseases, especially cancers. High-throughput bisulfite genomic sequencing based on new generation of sequencers, such as the 454-sequencing system provides an efficient method for analyzing DNA methylation patterns. The successful implementation of this approach depends on the use of primer design software capable of performing genome-wide scan for optimal primers from in silico bisulfite-treated genome sequences. We have developed a method, which fulfills this requirement and conduct primer design for sequences including regions of given promoter CpG islands.'),
(74133, 2008, 'bio', 'Comparing simulation results of SBML capable simulators', 'Bergmann, Frank T|Sauro, Herbert M', 'University of Washington, Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA. fbergman@u.washington.edu|University of Washington, Department of Bioengineering, William H. Foege Building, Box 355061, Seattle, WA 98195-5061, USA. fbergman@u.washington.edu', 'United States|United States', 26, 'Computer Simulation|Models, Biological|Proteome|Signal Transduction|Software|Software Validation', 'Comparative Study', '10.1093/bioinformatics/btn319', '18579569', 'Simulations are an essential tool when analyzing biochemical networks. Researchers and developers seeking to refine simulation tools or develop new ones would benefit greatly from being able to compare their simulation results.'),
(74134, 2008, 'bio', 'Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data', 'Li, Huai|Zhan, Ming', 'Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD 21224, USA.|Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD 21224, USA.', 'United States|United States', 23, 'Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Signal Transduction|Systems Integration|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btn332', '18586698', 'Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge.'),
(74135, 2008, 'bio', 'Context-dependent DNA recognition code for C2H2 zinc-finger transcription factors', 'Liu, Jiajian|Stormo, Gary D', 'Department of Genetics, Washington University School of Medicine, 660 S Euclid, Box 8232, St. Louis, MO 63110, USA.|Department of Genetics, Washington University School of Medicine, 660 S Euclid, Box 8232, St. Louis, MO 63110, USA.', 'United States|United States', 37, 'Base Sequence|Binding Sites|Computer Simulation|DNA|DNA-Binding Proteins|Models, Genetic|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Transcription Factors|Zinc Fingers', 'Journal Article', '10.1093/bioinformatics/btn331', '18586699', 'Modeling and identifying the DNA-protein recognition code is one of the most challenging problems in computational biology. Several quantitative methods have been developed to model DNA-protein interactions with specific focus on the C(2)H(2) zinc-finger proteins, the largest transcription factor family in eukaryotic genomes. In many cases, they performed well. But the overall the predictive accuracy of these methods is still limited. One of the major reasons is all these methods used weight matrix models to represent DNA-protein interactions, assuming all base-amino acid contacts contribute independently to the total free energy of binding.'),
(74136, 2008, 'bio', 'FlexStem: improving predictions of RNA secondary structures with pseudoknots by reducing the search space', 'Chen, Xiang|He, Si-Min|Bu, Dongbo|Zhang, Fa|Wang, Zhiyong|Chen, Runsheng|Gao, Wen', 'Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn|Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. xchen@jdl.ac.cn', 'China|China|China|China|China|China|China', 30, 'Algorithms|Computational Biology|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid', 'Evaluation Studies', '10.1093/bioinformatics/btn327', '18586700', 'RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is proved to be NP-hard. Due to kinetic reasons the real RNA secondary structure often has local instead of global minimum free energy. This implies that we may improve the performance of RNA secondary structure prediction by taking kinetics into account and minimize free energy in a local area.'),
(74137, 2008, 'bio', 'A model of higher accuracy for the individual haplotyping problem based on weighted SNP fragments and genotype with errors', 'Xie, Minzhu|Wang, Jianxin|Chen, Jianer', 'School of Information Science and Engineering, Central South University, Changsha 410083, China.|School of Information Science and Engineering, Central South University, Changsha 410083, China.|School of Information Science and Engineering, Central South University, Changsha 410083, China.', 'China|China|China', 0, 'Base Sequence|Chromosome Mapping|Computer Simulation|Genetic Linkage|Genotype|Haplotypes|Models, Genetic|Molecular Sequence Data|Polymorphism, Single Nucleotide|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn147', '18586702', 'In genetic studies of complex diseases, haplotypes provide more information than genotypes. However, haplotyping is much more difficult than genotyping using biological techniques. Therefore effective computational techniques have been in demand. The individual haplotyping problem is the computational problem of inducing a pair of haplotypes from an individuals aligned SNP fragments. Based on various optimal criteria and including different extra information, many models for the problem have been proposed. Higher accuracy of the models has been an important issue in the study of haplotype reconstruction.'),
(74138, 2008, 'bio', 'Estimating true evolutionary distances under the DCJ model', 'Lin, Yu|Moret, Bernard M E', 'Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland.|Laboratory for Computational Biology and Bioinformatics, Swiss Federal Institute of Technology (EPFL), EPFL-IIS-LCBB, INJ 230, Station 14, CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 39, 'Base Sequence|Chromosome Mapping|Computer Simulation|Evolution, Molecular|Genome|Linkage Disequilibrium|Models, Genetic|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn148', '18586703', 'Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome, such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes?'),
(74139, 2008, 'bio', 'Efficient inference of bacterial strain trees from genome-scale multilocus data', 'Than, C|Sugino, R|Innan, H|Nakhleh, L', 'Department of Computer Science, Rice University, Houston, TX 77005, USA.|Department of Computer Science, Rice University, Houston, TX 77005, USA.|Department of Computer Science, Rice University, Houston, TX 77005, USA.|Department of Computer Science, Rice University, Houston, TX 77005, USA.', 'United States|United States|United States|United States', 15, 'Algorithms|Base Sequence|Biological Evolution|Chromosome Mapping|DNA Mutational Analysis|Databases, Genetic|Evolution, Molecular|Genome, Bacterial|Molecular Sequence Data|Sequence Analysis, DNA|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btn149', '18586704', 'In bacterial evolution, inferring a strain tree, which is the evolutionary history of different strains of the same bacterium, plays a major role in analyzing and understanding the evolution of strongly isolated populations, population divergence and various evolutionary events, such as horizontal gene transfer and homologous recombination. Inferring a strain tree from multilocus data of these strains is exceptionally hard since, at this scale of evolution, processes such as homologous recombination result in a very high degree of gene tree incongruence.'),
(74140, 2008, 'bio', 'The multiple gene duplication problem revisited', 'Bansal, Mukul S|Eulenstein, Oliver', 'Department of Computer Science, Iowa State University, Ames, IA 50011, USA.|Department of Computer Science, Iowa State University, Ames, IA 50011, USA.', 'United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Gene Duplication|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn150', '18586705', 'Deciphering the location of gene duplications and multiple gene duplication episodes on the Tree of Life is fundamental to understanding the way gene families and genomes evolve. The multiple gene duplication problem provides a framework for placing gene duplication events onto nodes of a given species tree, and detecting episodes of multiple gene duplication. One version of the multiple gene duplication problem was defined by Guigó et al. in 1996. Several heuristic solutions have since been proposed for this problem, but no exact algorithms were known.'),
(74141, 2008, 'bio', 'Nucleosome positioning from tiling microarray data', 'Yassour, Moran|Kaplan, Tommy|Jaimovich, Ariel|Friedman, Nir', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 91904, Israel.', 'Israel|Israel|Israel|Israel', 14, 'Artificial Intelligence|Base Sequence|Markov Chains|Models, Genetic|Molecular Sequence Data|Nucleosomes|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn151', '18586706', 'The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5-10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.'),
(74142, 2008, 'bio', 'Alignment and classification of time series gene expression in clinical studies', 'Lin, Tien-ho|Kaminski, Naftali|Bar-Joseph, Ziv', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States', 46, 'Algorithms|Artificial Intelligence|Biomedical Research|Gene Expression Profiling|Pattern Recognition, Automated|Sequence Alignment|Time Factors|Tissue Array Analysis', 'Journal Article', '10.1093/bioinformatics/btn152', '18586707', 'Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class).'),
(74143, 2008, 'bio', 'The effectiveness of position- and composition-specific gap costs for protein similarity searches', 'Stojmirovixc4x87, Aleksandar|Gertz, E Michael|Altschul, Stephen F|Yu, Yi-Kuo', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Markov Chains|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn171', '18586708', 'The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74144, 2008, 'bio', 'Inferring differentiation pathways from gene expression', 'Costa, Ivan G|Roepcke, Stefan|Hafemeister, Christoph|Schliep, Alexander', 'Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. filho@molgen.mpg.de|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. filho@molgen.mpg.de|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. filho@molgen.mpg.de|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany. filho@molgen.mpg.de', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Cell Cycle Proteins|Cell Differentiation|Computer Simulation|Gene Expression Profiling|Models, Biological|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn153', '18586709', 'The regulation of proliferation and differentiation of embryonic and adult stem cells into mature cells is central to developmental biology. Gene expression measured in distinguishable developmental stages helps to elucidate underlying molecular processes. In previous work we showed that functional gene modules, which act distinctly in the course of development, can be represented by a mixture of trees. In general, the similarities in the gene expression programs of cell populations reflect the similarities in the differentiation path.'),
(74145, 2008, 'bio', 'Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences', 'Ward, Lucas D|Bussemaker, Harmen J', 'Department of Biological Sciences, Columbia University, New York, NY 10027, USA.|Department of Biological Sciences, Columbia University, New York, NY 10027, USA.', 'United States|United States', 45, 'Algorithms|Binding Sites|Promoter Regions, Genetic|Protein Binding|Sequence Alignment|Sequence Analysis|Sequence Homology, Nucleic Acid|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn154', '18586710', 'The identification of transcription factor (TF) binding sites and the regulatory circuitry that they define is currently an area of intense research. Data from whole-genome chromatin immunoprecipitation (ChIP-chip), whole-genome expression microarrays, and sequencing of multiple closely related genomes have all proven useful. By and large, existing methods treat the interpretation of functional data as a classification problem (between bound and unbound DNA), and the analysis of comparative data as a problem of local alignment (to recover phylogenetic footprints of presumably functional elements). Both of these approaches suffer from the inability to model and detect low-affinity binding sites, which have recently been shown to be abundant and functional.'),
(74146, 2008, 'bio', 'Combinatorial influence of environmental parameters on transcription factor activity', 'Knijnenburg, T A|Wessels, L F A|Reinders, M J T', 'Information and Communication Theory Group, Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. t.a.knijnenburg@tudelft.nl|Information and Communication Theory Group, Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. t.a.knijnenburg@tudelft.nl|Information and Communication Theory Group, Department of Mediamatics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. t.a.knijnenburg@tudelft.nl', 'Netherlands|Netherlands|Netherlands', 0, 'Cell Culture Techniques|Computer Simulation|Data Interpretation, Statistical|Environment|Gene Expression Profiling|Gene Expression Regulation, Fungal|Models, Biological|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn155', '18586711', 'Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference.'),
(74147, 2008, 'bio', 'Designing succinct structural alphabets', 'Li, Shuai Cheng|Bu, Dongbo|Gao, Xin|Xu, Jinbo|Li, Ming', 'David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont. N2L 3G1, Canada. scli@uwaterloo.ca|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont. N2L 3G1, Canada. scli@uwaterloo.ca|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont. N2L 3G1, Canada. scli@uwaterloo.ca|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont. N2L 3G1, Canada. scli@uwaterloo.ca|David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ont. N2L 3G1, Canada. scli@uwaterloo.ca', 'Canada|Canada|Canada|Canada|Canada', 0, 'Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn165', '18586712', 'The 3D structure of a protein sequence can be assembled from the substructures corresponding to small segments of this sequence. For each small sequence segment, there are only a few more likely substructures. We call them the structural alphabet for this segment. Classical approaches such as ROSETTA used sequence profile and secondary structure information, to predict structural fragments. In contrast, we utilize more structural information, such as solvent accessibility and contact capacity, for finding structural fragments.'),
(74148, 2008, 'bio', 'Predicting protein thermostability changes from sequence upon multiple mutations', 'Montanucci, Ludovica|Fariselli, Piero|Martelli, Pier Luigi|Casadio, Rita', 'Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.|Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy.', 'Italy|Italy|Italy|Italy', 0, 'Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Mutation|Protein Conformation|Protein Denaturation|Protein Folding|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship|Temperature', 'Journal Article', '10.1093/bioinformatics/btn166', '18586713', 'A basic question in protein science is to which extent mutations affect protein thermostability. This knowledge would be particularly relevant for engineering thermostable enzymes. In several experimental approaches, this issue has been serendipitously addressed. It would be therefore convenient providing a computational method that predicts when a given protein mutant is more thermostable than its corresponding wild-type.'),
(74149, 2008, 'bio', 'Algorithm for backrub motions in protein design', 'Georgiev, Ivelin|Keedy, Daniel|Richardson, Jane S|Richardson, David C|Donald, Bruce R', 'Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.|Department of Computer Science, Duke University, Durham, NC 27708, USA.', 'United States|United States|United States|United States|United States', 55, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Mutation|Protein Engineering|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn169', '18586714', 'The Backrub is a small but kinematically efficient side-chain-coupled local backbone motion frequently observed in atomic-resolution crystal structures of proteins. A backrub shifts the C(alpha)-C(beta) orientation of a given side-chain by rigid-body dipeptide rotation plus smaller individual rotations of the two peptides, with virtually no change in the rest of the protein. Backrubs can therefore provide a biophysically realistic model of local backbone flexibility for structure-based protein design. Previously, however, backrub motions were applied via manual interactive model-building, so their incorporation into a protein design algorithm (a simultaneous search over mutation and backbone/side-chain conformation space) was infeasible.'),
(74150, 2008, 'bio', 'Contact replacement for NMR resonance assignment', 'Xiong, Fei|Pandurangan, Gopal|Bailey-Kellogg, Chris', 'Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.|Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.', 'United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computer Simulation|Magnetic Resonance Spectroscopy|Models, Chemical|Molecular Sequence Data|Peptide Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn167', '18586716', 'Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize protein-protein and protein-ligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This article develops a novel approach to address that bottleneck, exploiting an available X-ray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments.'),
(74151, 2008, 'bio', 'A computational framework to empower probabilistic protein design', 'Fromer, Menachem|Yanover, Chen', 'School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel. fromer@cs.huji.ac.il|School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel. fromer@cs.huji.ac.il', 'Israel|Israel', 17, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Statistical|Molecular Sequence Data|Mutation|Protein Engineering|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn168', '18586717', 'The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult.'),
(74152, 2008, 'bio', 'Identifying functional modules in protein-protein interaction networks: an integrated exact approach', 'Dittrich, Marcus T|Klau, Gunnar W|Rosenwald, Andreas|Dandekar, Thomas|Müller, Tobias', 'Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.|Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.', 'Germany|Germany|Germany|Germany|Germany', 330, 'Algorithms|Gene Expression Profiling|Humans|Lymphoma|Protein Interaction Mapping|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn161', '18586718', 'With the exponential growth of expression and protein-protein interaction (PPI) data, the frontier of research in systems biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sharing common cellular function beyond the scope of classical pathways, by means of detecting differentially expressed regions in PPI networks. This requires on the one hand an adequate scoring of the nodes in the network to be identified and on the other hand the availability of an effective algorithm to find the maximally scoring network regions. Various heuristic approaches have been proposed in the literature.'),
(74153, 2008, 'bio', 'Prediction of drug-target interaction networks from the integration of chemical and genomic spaces', 'Yamanishi, Yoshihiro|Araki, Michihiro|Gutteridge, Alex|Honda, Wataru|Kanehisa, Minoru', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr|Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. Yoshihiro.Yamanishi@ensmp.fr', 'Japan|Japan|Japan|Japan|Japan', 0, 'Binding Sites|Chromosome Mapping|Computer Simulation|Drug Delivery Systems|Models, Chemical|Models, Genetic|Pharmaceutical Preparations|Protein Binding|Protein Interaction Mapping|Proteins|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btn162', '18586719', 'The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently.'),
(74154, 2008, 'bio', 'ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles', 'Abeel, Thomas|Saeys, Yvan|Rouzé, Pierre|Van de Peer, Yves', 'Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.|Department of Plant Systems Biology, VIB, 9052 Gent, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Cluster Analysis|Genome, Human|Humans|Molecular Sequence Data|Pattern Recognition, Automated|Promoter Regions, Genetic|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btn172', '18586720', 'More and more genomes are being sequenced, and to keep up with the pace of sequencing projects, automated annotation techniques are required. One of the most challenging problems in genome annotation is the identification of the core promoter. Because the identification of the transcription initiation region is such a challenging problem, it is not yet a common practice to integrate transcription start site prediction in genome annotation projects. Nevertheless, better core promoter prediction can improve genome annotation and can be used to guide experimental work.'),
(74155, 2008, 'bio', 'Biomolecular network motif counting and discovery by color coding', 'Alon, Noga|Dao, Phuong|Hajirasouliha, Iman|Hormozdiari, Fereydoun|Sahinalp, S Cenk', 'School of Mathematical Sciences, Tel Aviv University, Ramat Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Ramat Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Ramat Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Ramat Aviv, Israel.|School of Mathematical Sciences, Tel Aviv University, Ramat Aviv, Israel.', 'Israel|Israel|Israel|Israel|Israel', 103, 'Algorithms|Color|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn163', '18586721', 'Protein-protein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks. Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with k < or = 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with k > or = 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others. In this article, we show how to apply the color coding technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif G with k vertices in a network G with n vertices in time polynomial with n, provided k = O(log n). We use our algorithm to obtain treelet distributions for k < or = 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the duplication model but are quite different from that of the preferential attachment model. The treelet distribution is robust w.r.t. sparsification with bait/edge coverage of 70% but differences can be observed when bait/edge coverage drops to 50%.'),
(74156, 2008, 'bio', 'Protein complex identification by supervised graph local clustering', 'Qi, Yanjun|Balem, Fernanda|Faloutsos, Christos|Klein-Seetharaman, Judith|Bar-Joseph, Ziv', 'School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.|School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computer Simulation|Models, Biological|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn164', '18586722', 'Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure.'),
(74157, 2008, 'bio', 'Annotation-based inference of transporter function', 'Lee, Thomas J|Paulsen, Ian|Karp, Peter', 'Artificial Intelligence Center, SRI International, Menlo Park, CA, USA. tomlee@ai.sri.com|Artificial Intelligence Center, SRI International, Menlo Park, CA, USA. tomlee@ai.sri.com|Artificial Intelligence Center, SRI International, Menlo Park, CA, USA. tomlee@ai.sri.com', 'United States|United States|United States', 0, 'Computer Simulation|Databases, Protein|Information Storage and Retrieval|Membrane Transport Proteins|Models, Biological|Natural Language Processing|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn180', '18586723', 'We present a method for inferring and constructing transport reactions for transporter proteins based primarily on the analysis of the names of individual proteins in the genome annotation of an organism. Transport reactions are declarative descriptions of transporter activities, and thus can be manipulated computationally, unlike free-text protein names. Once transporter activities are encoded as transport reactions, a number of computational analyses are possible including database queries by transporter activity, inclusion of transporters into an automatically generated metabolic-map diagram that can be painted with omics data to aid in their interpretation, detection of anomalies in the metabolic and transport networks, such as substrates that are transported into the cell but are not inputs to any metabolic reaction or pathway, and comparative analyses of the transport capabilities of different organisms.'),
(74158, 2008, 'bio', 'Detection of IUPAC and IUPAC-like chemical names', 'Klinger, Roman|Kolárik, Corinna|Fluck, Juliane|Hofmann-Apitius, Martin|Friedrich, Christoph M', 'Fraunhofer Institute Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53574 Sankt Augustin, Germany. roman.klinger@scai.fraunhofer.de|Fraunhofer Institute Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53574 Sankt Augustin, Germany. roman.klinger@scai.fraunhofer.de|Fraunhofer Institute Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53574 Sankt Augustin, Germany. roman.klinger@scai.fraunhofer.de|Fraunhofer Institute Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53574 Sankt Augustin, Germany. roman.klinger@scai.fraunhofer.de|Fraunhofer Institute Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53574 Sankt Augustin, Germany. roman.klinger@scai.fraunhofer.de', 'Germany|Germany|Germany|Germany|Germany', 82, 'Abstracting and Indexing as Topic|Artificial Intelligence|Dictionaries, Chemical as Topic|MEDLINE|Natural Language Processing|Pattern Recognition, Automated|Pharmaceutical Preparations|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn181', '18586724', 'Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools.'),
(74159, 2008, 'bio', 'Identifying gene-disease associations using centrality on a literature mined gene-interaction network', 'Ozgür, Arzucan|Vu, Thuy|Erkan, Günes|Radev, Dragomir R', 'Electrical Engineering and Computer Science and School of Information, University of Michigan, Ann Arbor, MI 48109, USA.|Electrical Engineering and Computer Science and School of Information, University of Michigan, Ann Arbor, MI 48109, USA.|Electrical Engineering and Computer Science and School of Information, University of Michigan, Ann Arbor, MI 48109, USA.|Electrical Engineering and Computer Science and School of Information, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States|United States|United States', 240, 'Database Management Systems|Genetic Predisposition to Disease|Humans|Natural Language Processing|Periodicals as Topic|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn182', '18586725', 'Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network.'),
(74160, 2008, 'bio', 'Integrating high dimensional bi-directional parsing models for gene mention tagging', 'Hsu, Chun-Nan|Chang, Yu-Ming|Kuo, Cheng-Ju|Lin, Yu-Shi|Huang, Han-Shen|Chung, I-Fang', 'Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw|Institute of Information Science, Academia Sinica, Taipei, Taiwan. chunnan@iis.sinica.edu.tw', '-|-|-|-|-|-', 63, 'Artificial Intelligence|Computer Simulation|Genes|Genetic Markers|Models, Genetic|Natural Language Processing|Periodicals as Topic|Systems Integration|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btn183', '18586726', 'Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results.'),
(74161, 2008, 'bio', 'The EXACT description of biomedical protocols', 'Soldatova, Larisa N|Aubrey, Wayne|King, Ross D|Clare, Amanda', 'Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth, SY23 3DB, Wales, UK. lss@aber.ac.uk|Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth, SY23 3DB, Wales, UK. lss@aber.ac.uk|Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth, SY23 3DB, Wales, UK. lss@aber.ac.uk|Department of Computer Science, Aberystwyth University, Penglais, Aberystwyth, SY23 3DB, Wales, UK. lss@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Database Management Systems|Databases, Factual|Documentation|Information Storage and Retrieval|Internet|Research', 'Journal Article', '10.1093/bioinformatics/btn156', '18586727', 'Many published manuscripts contain experiment protocols which are poorly described or deficient in information. This means that the published results are very hard or impossible to repeat. This problem is being made worse by the increasing complexity of high-throughput/automated methods. There is therefore a growing need to represent experiment protocols in an efficient and unambiguous way.'),
(74162, 2008, 'bio', 'Towards the use of argumentation in bioinformatics: a gene expression case study', 'McLeod, Kenneth|Burger, Albert', 'Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kcm1@hw.ac.uk|Department of Computer Science, Heriot-Watt University, Edinburgh, UK. kcm1@hw.ac.uk', 'United Kingdom|United Kingdom', 7, 'Algorithms|Artificial Intelligence|Biomimetics|Computational Biology|Gene Expression Profiling', 'Journal Article', '10.1093/bioinformatics/btn157', '18586728', 'Due to different experimental setups and various interpretations of results, the data contained in online bioinformatics resources can be inconsistent, therefore, making it more difficult for users of these resources to assess the suitability and correctness of the answers to their queries. This work investigates the role of argumentation systems to help users evaluate such answers. More specifically, it looks closely at a gene expression case study, creating an appropriate representation of the underlying data and series of rules that are used by a third-party argumentation engine to reason over the query results provided by the mouse gene expression database EMAGE.'),
(74163, 2008, 'bio', 'The ontology of biological taxa', 'Schulz, Stefan|Stenzhorn, Holger|Boeker, Martin', 'Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany. stschulz@uni-freiburg.de|Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany. stschulz@uni-freiburg.de', 'Germany|Germany|Germany', 22, 'Algorithms|Animals|Classification|Humans|Information Storage and Retrieval|Software|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btn158', '18586729', 'The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa.'),
(74164, 2008, 'bio', 'Optimal pooling for genome re-sequencing with ultra-high-throughput short-read technologies', 'Hajirasouliha, Iman|Hormozdiari, Fereydoun|Sahinalp, S Cenk|Birol, Inanc', 'Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.|Lab for Computational Biology, Simon Fraser University, Burnaby, BC, Canada.', 'Canada|Canada|Canada|Canada', 16, 'Algorithms|Base Sequence|Chromosome Mapping|Chromosomes, Artificial, Bacterial|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn173', '18586730', 'New generation sequencing technologies offer unique opportunities and challenges for re-sequencing studies. In this article, we focus on re-sequencing experiments using the Solexa technology, based on bacterial artificial chromosome (BAC) clones, and address an experimental design problem. In these specific experiments, approximate coordinates of the BACs on a reference genome are known, and fine-scale differences between the BAC sequences and the reference are of interest. The high-throughput characteristics of the sequencing technology makes it possible to multiplex BAC sequencing experiments by pooling BACs for a cost-effective operation. However, the way BACs are pooled in such re-sequencing experiments has an effect on the downstream analysis of the generated data, mostly due to subsequences common to multiple BACs. The experimental design strategy we develop in this article offers combinatorial solutions based on approximation algorithms for the well-known max n-cut problem and the related max n-section problem on hypergraphs. Our algorithms, when applied to a number of sample cases give more than a 2-fold performance improvement over random partitioning.'),
(74165, 2008, 'bio', 'GenoQuery: a new querying module for functional annotation in a genomic warehouse', 'Lemoine, Frédéric|Labedan, Bernard|Froidevaux, Christine', 'Institut de Génétique et Microbiologie, Université Paris-Sud XI, 91405 Orsay Cedex, France.|Institut de Génétique et Microbiologie, Université Paris-Sud XI, 91405 Orsay Cedex, France.|Institut de Génétique et Microbiologie, Université Paris-Sud XI, 91405 Orsay Cedex, France.', 'France|France|France', 7, 'Chromosome Mapping|Database Management Systems|Databases, Genetic|Documentation|Information Storage and Retrieval|Internet|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn159', '18586731', 'We have to cope with both a deluge of new genome sequences and a huge amount of data produced by high-throughput approaches used to exploit these genomic features. Crossing and comparing such heterogeneous and disparate data will help improving functional annotation of genomes. This requires designing elaborate integration systems such as warehouses for storing and querying these data.'),
(74166, 2008, 'bio', 'Assessing the functional structure of genomic data', 'Huttenhower, C|Troyanskaya, O G', 'Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.|Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08540, USA.', 'United States|United States', 10, 'Algorithms|Base Sequence|Chromosome Mapping|Database Management Systems|Databases, Genetic|Molecular Sequence Data|Saccharomyces cerevisiae|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn160', '18586732', 'The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries.'),
(74167, 2008, 'bio', 'Matching isotopic distributions from metabolically labeled samples', 'McIlwain, Sean|Page, David|Huttlin, Edward L|Sussman, Michael R', 'Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu|Department of Computer Sciences, University of Wisconsin, Madison, WI, USA. mcilwain@cs.wisc.edu', 'United States|United States|United States|United States', 4, 'Amino Acid Sequence|Isotope Labeling|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn190', '18586733', 'In recent years stable isotopic labeling has become a standard approach for quantitative proteomic analyses. Among the many available isotopic labeling strategies, metabolic labeling is attractive for the excellent internal control it provides. However, analysis of data from metabolic labeling experiments can be complicated because the spacing between labeled and unlabeled forms of each peptide depends on its sequence, and is thus variable from analyte to analyte. As a result, one generally needs to know the sequence of a peptide to identify its matching isotopic distributions in an automated fashion. In some experimental situations it would be necessary or desirable to match pairs of labeled and unlabeled peaks from peptides of unknown sequence. This article addresses this largely overlooked problem in the analysis of quantitative mass spectrometry data by presenting an algorithm that not only identifies isotopic distributions within a mass spectrum, but also annotates matches between natural abundance light isotopic distributions and their metabolically labeled counterparts. This algorithm is designed in two stages: first we annotate the isotopic peaks using a modified version of the IDM algorithm described last year, then we use a probabilistic classifier that is supplemented by dynamic programming to find the metabolically labeled matched isotopic pairs. Such a method is needed for high-throughput quantitative proteomic metabolomic experiments measured via mass spectrometry.'),
(74168, 2008, 'bio', 'Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification', 'Klammer, Aaron A|Reynolds, Sheila M|Bilmes, Jeff A|MacCoss, Michael J|Noble, William Stafford', 'Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.|Department of Genome Sciences, University of Washington, Seattle, WA, USA.', 'United States|United States|United States|United States|United States', 46, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Bayes Theorem|Mass Spectrometry|Molecular Sequence Data|Pattern Recognition, Automated|Peptide Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn189', '18586734', 'Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms.'),
(74169, 2008, 'bio', 'BLASTing small molecules--statistics and extreme statistics of chemical similarity scores', 'Baldi, Pierre|Benz, Ryan W', 'Department of Computer Science, University of California, Irvine, CA 92697-3435, USA. pfbaldi@ics.uci.edu|Department of Computer Science, University of California, Irvine, CA 92697-3435, USA. pfbaldi@ics.uci.edu', 'United States|United States', 11, 'Algorithms|Chemistry Techniques, Analytical|Data Interpretation, Statistical|Databases, Factual|Organic Chemicals|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btn187', '18586735', 'Small organic molecules, from nucleotides and amino acids to metabolites and drugs, play a fundamental role in chemistry, biology and medicine. As databases of small molecules continue to grow and become more open, it is important to develop the tools to search them efficiently. In order to develop a BLAST-like tool for small molecules, one must first understand the statistical behavior of molecular similarity scores.'),
(74170, 2008, 'bio', 'A maximum common substructure-based algorithm for searching and predicting drug-like compounds', 'Cao, Yiqun|Jiang, Tao|Girke, Thomas', 'Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ycao@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ycao@cs.ucr.edu|Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. ycao@cs.ucr.edu', 'United States|United States|United States', 86, 'Algorithms|Artificial Intelligence|Drug Design|Pattern Recognition, Automated|Pharmaceutical Preparations', 'Journal Article', '10.1093/bioinformatics/btn186', '18586736', 'The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics. Many computational methods in this area focus on measuring the structural similarities between chemical structures. However, traditional similarity measures are often too rigid or consider only global similarities between structures. The maximum common substructure (MCS) approach provides a more promising and flexible alternative for predicting bioactive compounds.'),
(74171, 2008, 'bio', 'Classification of arrayCGH data using fused SVM', 'Rapaport, Franck|Barillot, Emmanuel|Vert, Jean-Philippe', 'Institut Curie, Centre de Recherche, INSERM, U900, Paris, F-75248 France. franck.rapaport@curie.fr|Institut Curie, Centre de Recherche, INSERM, U900, Paris, F-75248 France. franck.rapaport@curie.fr|Institut Curie, Centre de Recherche, INSERM, U900, Paris, F-75248 France. franck.rapaport@curie.fr', 'France|France|France', 55, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Gene Dosage|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn188', '18586737', 'Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are starting to be used as markers to improve prognosis or diagnosis of cancer, which implies that methods for automated supervised classification of arrayCGH data are needed. Like gene expression profiles, arrayCGH profiles are characterized by a large number of variables usually measured on a limited number of samples. However, arrayCGH profiles have a particular structure of correlations between variables, due to the spatial organization of bacterial artificial chromosomes along the genome. This suggests that classical classification methods, often based on the selection of a small number of discriminative features, may not be the most accurate methods and may not produce easily interpretable prediction rules.'),
(74172, 2008, 'bio', 'Assessment and improvement of the Plasmodium yoelii yoelii genome annotation through comparative analysis', 'Vaughan, Ashley|Chiu, Sum-Ying|Ramasamy, Gowthaman|Li, Ling|Gardner, Malcolm J|Tarun, Alice S|Kappe, Stefan H I|Peng, Xinxia', 'Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.|Seattle Biomedical Research Institute, Seattle, WA 98109, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 9, 'Algorithms|Animals|Chromosome Mapping|Genome, Protozoan|Plasmodium yoelii|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn140', '18586738', 'The sequencing of the Plasmodium yoelii genome, a model rodent malaria parasite, has greatly facilitated research for the development of new drug and vaccine candidates against malaria. Unfortunately, only preliminary gene models were annotated on the partially sequenced genome, mostly by in silico gene prediction, and there has been no major improvement of the annotation since 2002.'),
(74173, 2008, 'bio', 'Differential variability analysis of gene expression and its application to human diseases', 'Ho, Joshua W K|Stefani, Maurizio|dos Remedios, Cristobal G|Charleston, Michael A', 'School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. joshua@it.usyd.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. joshua@it.usyd.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. joshua@it.usyd.edu.au|School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. joshua@it.usyd.edu.au', 'Australia|Australia|Australia|Australia', 84, 'Computer Simulation|Gene Expression Profiling|Genetic Markers|Genetic Predisposition to Disease|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btn142', '18586739', 'Current microarray analyses focus on identifying sets of genes that are differentially expressed (DE) or differentially coexpressed (DC) in different biological states (e.g. diseased versus non-diseased). We observed that in many human diseases, some genes have a significant increase or decrease in expression variability (variance). As these observed changes in expression variability may be caused by alteration of the underlying expression dynamics, such differential variability (DV) patterns are also biologically interesting.'),
(74174, 2008, 'bio', 'Selecting anti-HIV therapies based on a variety of genomic and clinical factors', 'Rosen-Zvi, Michal|Altmann, Andre|Prosperi, Mattia|Aharoni, Ehud|Neuvirth, Hani|Sönnerborg, Anders|Schülter, Eugen|Struck, Daniel|Peres, Yardena|Incardona, Francesca|Kaiser, Rolf|Zazzi, Maurizio|Lengauer, Thomas', 'Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com|Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com', 'Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel|Israel', 50, 'Anti-HIV Agents|Chromosome Mapping|Decision Support Systems, Clinical|Genetic Predisposition to Disease|HIV Infections|Humans|Outcome Assessment (Health Care)|Pharmacogenetics', 'Journal Article', '10.1093/bioinformatics/btn141', '18586740', 'Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(74175, 2008, 'bio', 'Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model', 'Wang, Yuan|Zhou, Xiaobo|Wang, Honghui|Li, King|Yao, Lixiu|Wong, Stephen T C', 'Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.|Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.', 'United States|United States|United States|United States|United States|United States', 14, 'Artificial Intelligence|Computer Simulation|Data Interpretation, Statistical|Markov Chains|Models, Chemical|Models, Statistical|Monte Carlo Method|Pattern Recognition, Automated|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btn143', '18586741', 'Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.'),
(74176, 2008, 'bio', 'Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space', 'Loewenstein, Yaniv|Portugaly, Elon|Fromer, Menachem|Linial, Michal', 'School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.|School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.|School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.|School of Computer Science and Engineering, Institute of Life Sciences, The Hebrew University of Jerusalem, Israel.', 'Israel|Israel|Israel|Israel', 82, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Cluster Analysis|Databases, Protein|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Reproducibility of Results|Sensitivity and Specificity|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btn174', '18586742', 'UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets.'),
(74177, 2008, 'bio', 'MicroRNA prediction with a novel ranking algorithm based on random walks', 'Xu, Yunpen|Zhou, Xuefeng|Zhang, Weixiong', 'Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.|Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.|Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA.', 'United States|United States|United States', 61, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Data Interpretation, Statistical|MicroRNAs|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btn175', '18586744', 'MicroRNA (miRNAs) play essential roles in post-transcriptional gene regulation in animals and plants. Several existing computational approaches have been developed to complement experimental methods in discovery of miRNAs that express restrictively in specific environmental conditions or cell types. These computational methods require a sufficient number of characterized miRNAs as training samples, and rely on genome annotation to reduce the number of predicted putative miRNAs. However, most sequenced genomes have not been well annotated and many of them have a very few experimentally characterized miRNAs. As a result, the existing methods are not effective or even feasible for identifying miRNAs in these genomes. Aiming at identifying miRNAs from genomes with a few known miRNA and/or little annotation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks- based ranking algorithm. We first tested our method on Homo sapiens genome, using a very few known human miRNAs as samples, our method achieved a prediction accuracy greater than 95%. We then applied our method to predict 200 miRNAs in Anopheles gambiae, which is the most important vector of malaria in Africa. Our further study showed that 78 out of the 200 putative miRNA precursors encode mature miRNAs that are conserved in at least one other animal species. These conserved putative miRNAs are good candidates for further experimental study to understand malaria infection.'),
(74178, 2008, 'bio', 'A robust framework for detecting structural variations in a genome', 'Lee, Seunghak|Cheran, Elango|Brudno, Michael', 'Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. seunghak@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. seunghak@cs.toronto.edu|Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada. seunghak@cs.toronto.edu', 'Canada|Canada|Canada', 47, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Genetic Variation|Genome|Models, Genetic|Models, Statistical|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn176', '18586745', 'Recently, structural genomic variants have come to the forefront as a significant source of variation in the human population, but the identification of these variants in a large genome remains a challenge. The complete sequencing of a human individual is prohibitive at current costs, while current polymorphism detection technologies, such as SNP arrays, are not able to identify many of the large scale events. One of the most promising methods to detect such variants is the computational mapping of clone-end sequences to a reference genome.'),
(74179, 2008, 'bio', 'POIMs: positional oligomer importance matrices--understanding support vector machine-based signal detectors', 'Sonnenburg, Sören|Zien, Alexander|Philips, Petra|Rätsch, Gunnar', 'Fraunhofer Institute FIRST, Department IDA, Kekulèstr. 7, 12489 Berlin, Germany. Soeren.Sonnenburg@first.fraunhofer.de|Fraunhofer Institute FIRST, Department IDA, Kekulèstr. 7, 12489 Berlin, Germany. Soeren.Sonnenburg@first.fraunhofer.de|Fraunhofer Institute FIRST, Department IDA, Kekulèstr. 7, 12489 Berlin, Germany. Soeren.Sonnenburg@first.fraunhofer.de|Fraunhofer Institute FIRST, Department IDA, Kekulèstr. 7, 12489 Berlin, Germany. Soeren.Sonnenburg@first.fraunhofer.de', 'Germany|Germany|Germany|Germany', 42, 'Algorithms|Artificial Intelligence|Base Sequence|DNA|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn170', '18586746', 'At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts.'),
(74180, 2008, 'bio', 'A max-margin model for efficient simultaneous alignment and folding of RNA sequences', 'Do, Chuong B|Foo, Chuan-Sheng|Batzoglou, Serafim', 'Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu|Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu|Computer Science Department, Stanford University, Stanford, CA 94305, USA. chuongdo@cs.stanford.edu', 'United States|United States|United States', 56, 'Algorithms|Base Sequence|Computer Simulation|Consensus Sequence|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btn177', '18586747', 'The need for accurate and efficient tools for computational RNA structure analysis has become increasingly apparent over the last several years: RNA folding algorithms underlie numerous applications in bioinformatics, ranging from microarray probe selection to de novo non-coding RNA gene prediction. In this work, we present RAF (RNA Alignment and Folding), an efficient algorithm for simultaneous alignment and consensus folding of unaligned RNA sequences. Algorithmically, RAF exploits sparsity in the set of likely pairing and alignment candidates for each nucleotide (as identified by the CONTRAfold or CONTRAlign programs) to achieve an effectively quadratic running time for simultaneous pairwise alignment and folding. RAFs fast sparse dynamic programming, in turn, serves as the inference engine within a discriminative machine learning algorithm for parameter estimation.'),
(74181, 2008, 'bio', 'MACHOS: Markov clusters of homologous subsequences', 'Wong, Simon|Ragan, Mark A', 'ARC Centre of Excellence in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.|ARC Centre of Excellence in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia', 15, 'Algorithms|Amino Acid Sequence|Cluster Analysis|Computer Simulation|Markov Chains|Models, Chemical|Models, Statistical|Molecular Sequence Data|Protein Structure, Tertiary|Proteome|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btn144', '18586748', 'The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein may share common ancestry with distinct, even mutually exclusive, sets of homologs, thereby creating an intricate web of homologous relationships if full-length sequences are taken as the unit of evolution. We attempt to disentangle this web by developing a fully automated pipeline to delineate protein subsequences that represent sensible units for homology inference, and clustering them into putatively homologous families using the Markov clustering algorithm.'),
(74182, 2008, 'bio', 'Classification and feature selection algorithms for multi-class CGH data', 'Liu, Jun|Ranka, Sanjay|Kahveci, Tamer', 'Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. juliu@cise.ufl.edu|Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. juliu@cise.ufl.edu|Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. juliu@cise.ufl.edu', 'United States|United States|United States', 32, 'Algorithms|Artificial Intelligence|Chromosome Mapping|Gene Dosage|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn145', '18586749', 'Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses and gains. In this article, we develop novel SVM-based methods for classification and feature selection of CGH data. For classification, we developed a novel similarity kernel that is shown to be more effective than the standard linear kernel used in SVM. For feature selection, we propose a novel method based on the new kernel that iteratively selects features that provides the maximum benefit for classification. We compared our methods against the best wrapper-based and filter-based approaches that have been used for feature selection of large dimensional biological data. Our results on datasets generated from the Progenetix database, suggests that our methods are considerably superior to existing methods.'),
(74183, 2008, 'bio', 'Guided genome halving: hardness, heuristics and the history of the Hemiascomycetes', 'Zheng, Chunfang|Zhu, Qian|Adam, Zaky|Sankoff, David', 'Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.|Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.', 'Canada|Canada|Canada|Canada', 28, 'Algorithms|Ascomycota|Base Sequence|Chromosome Mapping|Gene Duplication|Genome, Fungal|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn146', '18586750', 'Some present day species have incurred a whole genome doubling event in their evolutionary history, and this is reflected today in patterns of duplicated segments scattered throughout their chromosomes. These duplications may be used as data to halve the genome, i.e. to reconstruct the ancestral genome at the moment of doubling, but the solution is often highly nonunique. To resolve this problem, we take account of outgroups, external reference genomes, to guide and narrow down the search.'),
(74184, 2008, 'bio', 'Automated mapping of large-scale chromatin structure in ENCODE', 'Lian, Heng|Thompson, William A|Thurman, Robert|Stamatoyannopoulos, John A|Noble, William Stafford|Lawrence, Charles E', 'Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.|Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.|Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.|Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.|Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.|Division of Mathematical Sciences, SPMS, Nanyang Technological University, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 22, 'Base Sequence|Chromatin|Chromosome Mapping|Computer Simulation|Models, Chemical|Models, Genetic|Molecular Sequence Data|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn335', '18591192', 'A recently developed DNaseI assay has given us our first genome-wide view of chromatin structure. In addition to cataloging DNaseI hypersensitive sites, these data allows us to more completely characterize overall features of chromatin accessibility. We employed a Bayesian hierarchical change-point model (CPM), a generalization of a hidden Markov Model (HMM), to characterize tiled microarray DNaseI sensitivity data available from the ENCODE project.'),
(74185, 2008, 'bio', 'Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions', 'Ritchie, David W|Kozakov, Dima|Vajda, Sandor', 'Department of Computing Science, University of Aberdeen, Aberdeen, Scotland, UK. d.w.ritchie@abdn.ac.uk|Department of Computing Science, University of Aberdeen, Aberdeen, Scotland, UK. d.w.ritchie@abdn.ac.uk|Department of Computing Science, University of Aberdeen, Aberdeen, Scotland, UK. d.w.ritchie@abdn.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 136, 'Algorithms|Binding Sites|Computer Simulation|Fourier Analysis|Models, Chemical|Models, Molecular|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Rotation|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btn334', '18591193', 'Predicting how proteins interact at the molecular level is a computationally intensive task. Many protein docking algorithms begin by using fast Fourier transform (FFT) correlation techniques to find putative rigid body docking orientations. Most such approaches use 3D Cartesian grids and are therefore limited to computing three dimensional (3D) translational correlations. However, translational FFTs can speed up the calculation in only three of the six rigid body degrees of freedom, and they cannot easily incorporate prior knowledge about a complex to focus and hence further accelerate the calculation. Furthemore, several groups have developed multi-term interaction potentials and others use multi-copy approaches to simulate protein flexibility, which both add to the computational cost of FFT-based docking algorithms. Hence there is a need to develop more powerful and more versatile FFT docking techniques.'),
(74186, 2008, 'bio', 'Predicting protein function from domain content', 'Forslund, Kristoffer|Sonnhammer, Erik L L', 'Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden. Kristoffer.Forslund@sbc.su.se|Stockholm Bioinformatics Centre, Stockholm University, 10691 Stockholm, Sweden. Kristoffer.Forslund@sbc.su.se', 'Sweden|Sweden', 52, 'Amino Acid Sequence|Computer Simulation|Models, Biological|Models, Chemical|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btn312', '18591194', 'Computational assignment of protein function may be the single most vital application of bioinformatics in the post-genome era. These assignments are made based on various protein features, where one is the presence of identifiable domains. The relationship between protein domain content and function is important to investigate, to understand how domain combinations encode complex functions.'),
(74187, 2008, 'bio', 'PuReD-MCL: a graph-based PubMed document clustering methodology', 'Theodosiou, T|Darzentas, N|Angelis, L|Ouzounis, C A', 'Department of Informatics, Aristotle University of Thessalonica, P.O. Box 54124, Thessalonica, Greece. theodos@csd.auth.gr|Department of Informatics, Aristotle University of Thessalonica, P.O. Box 54124, Thessalonica, Greece. theodos@csd.auth.gr|Department of Informatics, Aristotle University of Thessalonica, P.O. Box 54124, Thessalonica, Greece. theodos@csd.auth.gr|Department of Informatics, Aristotle University of Thessalonica, P.O. Box 54124, Thessalonica, Greece. theodos@csd.auth.gr', 'Greece|Greece|Greece|Greece', 35, 'Algorithms|Artificial Intelligence|Cluster Analysis|Database Management Systems|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|PubMed|Software', 'Journal Article', '10.1093/bioinformatics/btn318', '18593717', 'Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed.'),
(74188, 2008, 'bio', 'BioMAJ: a flexible framework for databanks synchronization and processing', 'Filangi, Olivier|Beausse, Yoann|Assi, Anthony|Legrand, Ludovic|Larré, Jean-Marc|Martin, Véronique|Collin, Olivier|Caron, Christophe|Leroy, Hugues|Allouche, David', 'IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.|IRISA/INRIA, Symbiose Project, Unité de Biométrie et Intelligence Artificielle, UR875, INRA, F-31320 Castanet-Tolosan, France.', 'France|France|France|France|France|France|France|France|France|France', 0, 'Algorithms|Computational Biology|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Programming Languages|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn325', '18593718', 'Large- and medium-scale computational molecular biology projects require accurate bioinformatics software and numerous heterogeneous biological databanks, which are distributed around the world. BioMAJ provides a flexible, robust, fully automated environment for managing such massive amounts of data. The JAVA application enables automation of the data update cycle process and supervision of the locally mirrored data repository. We have developed workflows that handle some of the most commonly used bioinformatics databases. A set of scripts is also available for post-synchronization data treatment consisting of indexation or format conversion (for NCBI blast, SRS, EMBOSS, GCG, etc.). BioMAJ can be easily extended by personal homemade processing scripts. Source history can be kept via html reports containing statements of locally managed databanks.'),
(74189, 2008, 'bio', 'Coherent coupling of feedback loops: a design principle of cell signaling networks', 'Kwon, Yung-Keun|Cho, Kwang-Hyun', 'School of Computer Science and Information Technology, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan, Republic of Korea.|School of Computer Science and Information Technology, University of Ulsan, San 29, Mugeo 2-dong, Nam-gu, Ulsan, Republic of Korea.', '-|-', 49, 'Algorithms|Computer Simulation|Feedback|Models, Biological|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btn337', '18596076', 'It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together.'),
(74190, 2008, 'bio', 'ChemmineR: a compound mining framework for R', 'Cao, Yiqun|Charisi, Anna|Cheng, Li-Chang|Jiang, Tao|Girke, Thomas', 'Department of Computer Science and Engineering, University of California, Riverside, California, USA.|Department of Computer Science and Engineering, University of California, Riverside, California, USA.|Department of Computer Science and Engineering, University of California, Riverside, California, USA.|Department of Computer Science and Engineering, University of California, Riverside, California, USA.|Department of Computer Science and Engineering, University of California, Riverside, California, USA.', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Biopolymers|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btn307', '18596077', 'Software applications for structural similarity searching and clustering of small molecules play an important role in drug discovery and chemical genomics. Here, we present the first open-source compound mining framework for the popular statistical programming environment R. The integration with a powerful statistical environment maximizes the flexibility, expandability and programmability of the provided analysis functions.'),
(74191, 2008, 'bio', 'Watermarking sexually reproducing diploid organisms', 'Heider, Dominik|Kessler, Daniel|Barnekow, Angelika', 'Department of Experimental Tumorbiology, University of Muenster, Badestrasse 9, 48149 Muenster, Germany.|Department of Experimental Tumorbiology, University of Muenster, Badestrasse 9, 48149 Muenster, Germany.|Department of Experimental Tumorbiology, University of Muenster, Badestrasse 9, 48149 Muenster, Germany.', 'Germany|Germany|Germany', 17, 'Algorithms|Base Sequence|DNA, Mitochondrial|Genetic Code|Information Storage and Retrieval|Molecular Sequence Data|Product Labeling|Sequence Analysis, DNA|Signal Processing, Computer-Assisted|Software', 'Journal Article', '10.1093/bioinformatics/btn342', '18599517', 'DNA watermarks are used for hiding messages or for authenticating genetically modified organisms. Recently, we presented an algorithm called DNA-Crypt for generating DNA-based watermarks that can be integrated into the genome by using the characteristics of the degenerative genetic code. DNA-Crypt generates the watermark by replacing single bases and thus creating synonymous codons that encrypt the hidden information. Mutations within the integrated DNA sequence can be corrected using several mutation correction codes, to keep the hidden information intact. This method has successfully been tested in asexually replicating organisms like bacteria or yeast, where the watermark is duplicated with every cell division. It has been shown that DNA watermarks produced by DNA-Crypt do not influence the transcription or translation of a protein. In sexually reproducing diploid organisms, additional problems can occur, e.g. recombination events can destroy hidden information. Using population predictions as well as statistical analyses we identified a coupled Y-chromosomal/mitochondrial DNA watermarking procedure as the most appropriate for diploid organisms. We developed a mitochondria adapted version of DNA-Crypt, which is called Project Mito that can be used in combination with the original program.'),
(74192, 2008, 'bio', 'FindPeaks 31: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology', 'Fejes, Anthony P|Robertson, Gordon|Bilenky, Mikhail|Varhol, Richard|Bainbridge, Matthew|Jones, Steven J M', 'Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca|Genome Sciences Centre, BC Cancer Agency, Suite 100 570 West 7th Avenue, Vancouver, British Columbia, Canada. afejes@bcgsc.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 0, 'Algorithms|Binding Sites|Chromatin Immunoprecipitation|Chromosome Mapping|Pattern Recognition, Automated|Sequence Analysis, DNA|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btn305', '18599518', 'Next-generation sequencing can provide insight into protein-DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom WIG track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed, typically 4 GB permits processing of up to 40 million reads.'),
(74193, 2008, 'bio', 'Bioimage informatics: a new area of engineering biology', 'Peng, Hanchuan', 'Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. pengh@janelia.hhmi.org', 'United States', 234, 'Biomedical Engineering|Computational Biology|Computer Graphics|Computer Simulation|Diagnostic Imaging|Models, Biological|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn346', '18603566', 'In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called bioimage informatics. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources.'),
(74194, 2008, 'bio', 'TRITON: a graphical tool for ligand-binding protein engineering', 'Prokop, Martin|Adam, Jan|Kríz, Zdenek|Wimmerová, Michaela|Koca, Jaroslav', 'National Centre for Biomolecular Research and Department of Biochemistry, Faculty of Science, Masaryk University, Kotlárská 2, 611 37 Brno, Czech Republic.|National Centre for Biomolecular Research and Department of Biochemistry, Faculty of Science, Masaryk University, Kotlárská 2, 611 37 Brno, Czech Republic.|National Centre for Biomolecular Research and Department of Biochemistry, Faculty of Science, Masaryk University, Kotlárská 2, 611 37 Brno, Czech Republic.|National Centre for Biomolecular Research and Department of Biochemistry, Faculty of Science, Masaryk University, Kotlárská 2, 611 37 Brno, Czech Republic.|National Centre for Biomolecular Research and Department of Biochemistry, Faculty of Science, Masaryk University, Kotlárská 2, 611 37 Brno, Czech Republic.', '-|-|-|-|-', 23, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|Drug Design|Ligands|Models, Chemical|Models, Molecular|Protein Binding|Protein Engineering|Proteins|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn344', '18603567', 'The new version of the TRITON program provides user-friendly graphical tools for modeling protein mutants using the external program MODELLER and for docking ligands into the mutants using the external program AutoDock. TRITON can now be used to design ligand-binding proteins, to study protein-ligand binding mechanisms or simply to dock any ligand to a protein.'),
(74195, 2008, 'bio', 'A probe-density-based analysis method for array CGH data: simulation, normalization and centralization', 'Chen, Hung-I Harry|Hsu, Fang-Han|Jiang, Yuan|Tsai, Mong-Hsun|Yang, Pan-Chyr|Meltzer, Paul S|Chuang, Eric Y|Chen, Yidong', 'Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.|Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 106, Republic of China.', 'China|China|China|China|China|China|China|China', 22, 'Base Sequence|Chromosome Mapping|Computer Simulation|DNA Probes|Gene Dosage|Models, Genetic|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn321', '18603568', 'Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results.'),
(74196, 2009, 'bio', 'A new modeling method in feature construction for the HSQC spectra screening problem', 'Arai, Hiromi|Watanabe, Satoru|Kigawa, Takanori|Yamamura, Masayuki', 'Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan. arai@es.dis.titech.ac.jp|Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan. arai@es.dis.titech.ac.jp|Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan. arai@es.dis.titech.ac.jp|Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama, Japan. arai@es.dis.titech.ac.jp', 'Japan|Japan|Japan|Japan', 2, 'Algorithms|Artificial Intelligence|Databases, Protein|Models, Molecular|Nuclear Magnetic Resonance, Biomolecular|Pattern Recognition, Automated|Protein Conformation|Proteins', 'Journal Article', '10.1093/bioinformatics/btn345', '18603650', 'Large-scale biological analyses produce huge amounts of data. As a consequence, automation in the data analysis process is needed. Sample screening problems in NMR high-throughput protein structure analysis are the typical examples. Especially, screening by protein (1)H-(15)N heteronuclear single quantum coherence (HSQC) spectra must be done quantitatively by a human expert. One popular solution for this problem is data mining. Machine learning methods can automatically extract rules and achieve high accuracy in prediction when a good quality training dataset is prepared. However, they tend to be a black box and the learned machines suffer the risk of overfitting to the dataset.'),
(74197, 2008, 'bio', 'ProteoWizard: open source software for rapid proteomics tools development', 'Kessner, Darren|Chambers, Matt|Burke, Robert|Agus, David|Mallick, Parag', 'Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, USA. darren@proteowizard.org|Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, USA. darren@proteowizard.org|Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, USA. darren@proteowizard.org|Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, USA. darren@proteowizard.org|Spielberg Family Center for Applied Proteomics, Cedars-Sinai Medical Center, USA. darren@proteowizard.org', 'United States|United States|United States|United States|United States', 894, 'Databases, Protein|Proteins|Proteome|Proteomics|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn323', '18606607', 'The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses, the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and commercial projects. In addition to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.'),
(74198, 2008, 'bio', 'Discovering regulatory motifs in the Plasmodium genome using comparative genomics', 'Wu, Jie|Sieglaff, Douglas H|Gervin, Joshua|Xie, Xiaohui S', 'Department of Computer Sciences, University of California, Irvine, CA 92697, CODA Genomics, Laguna Hills, CA 92656, USA.|Department of Computer Sciences, University of California, Irvine, CA 92697, CODA Genomics, Laguna Hills, CA 92656, USA.|Department of Computer Sciences, University of California, Irvine, CA 92697, CODA Genomics, Laguna Hills, CA 92656, USA.|Department of Computer Sciences, University of California, Irvine, CA 92697, CODA Genomics, Laguna Hills, CA 92656, USA.', 'United States|United States|United States|United States', 27, 'Algorithms|Animals|Base Sequence|Chromosome Mapping|Molecular Sequence Data|Plasmodium|Regulatory Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Species Specificity', 'Comparative Study', '10.1093/bioinformatics/btn348', '18611947', 'Understanding gene regulation in Plasmodium, the causative agent of malaria, is an important step in deciphering its complex life cycle as well as leading to possible new targets for therapeutic applications. Very little is known about gene regulation in Plasmodium, and in particular, few regulatory elements have been identified. Such discovery has been significantly hampered by the high A-T content of some of the genomes of Plasmodium species, as well as the challenge in associating discovered regulatory elements to gene regulatory cascades due to Plasmodiums complex life cycle.'),
(74199, 2008, 'bio', 'Dynamical modeling and multi-experiment fitting with PottersWheel', 'Maiwald, Thomas|Timmer, Jens', 'Freiburg Center for Data Analysis and Modeling, Freiburg University, Freiburg, Germany. maiwald@fdm.uni-freiburg.de|Freiburg Center for Data Analysis and Modeling, Freiburg University, Freiburg, Germany. maiwald@fdm.uni-freiburg.de', 'Germany|Germany', 159, 'Computer Simulation|Programming Languages|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btn350', '18614583', 'Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization.'),
(74200, 2008, 'bio', 'Yale Image Finder (YIF): a new search engine for retrieving biomedical images', 'Xu, Songhua|McCusker, James|Krauthammer, Michael', 'Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06520, USA.|Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06520, USA.', 'United States|United States|United States', 83, 'Algorithms|Database Management Systems|Documentation|Information Storage and Retrieval|Internet|Natural Language Processing|Radiology Information Systems|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btn340', '18614584', 'Yale Image Finder (YIF) is a publicly accessible search engine featuring a new way of retrieving biomedical images and associated papers based on the text carried inside the images. Image queries can also be issued against the image caption, as well as words in the associated paper abstract and title. A typical search scenario using YIF is as follows: a user provides few search keywords and the most relevant images are returned and presented in the form of thumbnails. Users can click on the image of interest to retrieve the high resolution image. In addition, the search engine will provide two types of related images: those that appear in the same paper, and those from other papers with similar image content. Retrieved images link back to their source papers, allowing users to find related papers starting with an image of interest. Currently, YIF has indexed over 140 000 images from over 34 000 open access biomedical journal papers.'),
(74201, 2008, 'bio', 'Synchronous versus asynchronous modeling of gene regulatory networks', 'Garg, Abhishek|Di Cara, Alessandro|Xenarios, Ioannis|Mendoza, Luis|De Micheli, Giovanni', 'Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch|Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 164, 'Algorithms|Computer Simulation|Gene Expression Regulation|Logistic Models|Models, Genetic|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btn336', '18614585', 'In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.'),
(74202, 2008, 'bio', 'Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies', 'Homer, Nils|Tembe, Waibhav D|Szelinger, Szabolcs|Redman, Margot|Stephan, Dietrich A|Pearson, John V|Nelson, Stanley F|Craig, David', 'Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.|Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 15, 'Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genetic Linkage|Genetic Markers|Haplotypes|Linkage Disequilibrium|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btn333', '18617537', 'For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r(2) provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling.');
