INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72618, 2006, 'bio', 'Identification of a novel putative mitogen-activated kinase cascade on human chromosome 21 by computational approaches', 'Pellegrini-Calace, Marialuisa|Tramontano, Anna', 'Department of Biochemical Sciences, Rossi-Fanelli, University La Sapienza, Rome, Italy. anna.tramontano@uniroma1.it|Department of Biochemical Sciences, Rossi-Fanelli, University La Sapienza, Rome, Italy. anna.tramontano@uniroma1.it', 'Italy|Italy', 9, 'Amino Acid Sequence|Chromosomes, Human, Pair 21|Computational Biology|Down Syndrome|Humans|MAP Kinase Signaling System|Markov Chains|Models, Biological|Molecular Sequence Data|Sequence Alignment', 'Journal Article', '10.1093/bioinformatics/btl006', '16428264', 'Down syndrome (DS) is the most frequent form of mental retardation and is caused by chromosome 21 (HSA21) trisomy. Despite the number of known genes involved in DS and its high therapeutic interest, biological mechanisms leading to the DS phenotype are not fully clear. We present a functional hypothesis based on fold recognition and hidden Markov model techniques for four HSA21 genes located in the DS Candidate Region (DSCR). More specifically, we propose that they are members of a novel mitogen-activated protein kinase pathway with DYRK1A, SNF1LK and RIPK4 gene products being elements of the kinase cascade and the DSCR3 acting as structural scaffold for their interaction. This hypothesis finds support in various biochemical studies concerning the biological behavior and features of the involved HSA21 proteins. Our analysis calls for specifically designed experiments to validate our prediction and establish its relevance in terms of therapeutic approaches to the disease.'),
(72619, 2006, 'bio', 'MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition', 'Höglund, Annette|Dönnes, Pierre|Blum, Torsten|Adolph, Hans-Werner|Kohlbacher, Oliver', 'Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de|Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de', 'Germany|Germany|Germany|Germany|Germany', 255, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Biological|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|Proteome|Sequence Analysis, Protein|Software|Subcellular Fractions', 'Comparative Study', '10.1093/bioinformatics/btl002', '16428265', 'Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a proteins subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage.'),
(72620, 2006, 'bio', 'Automatic extension of Gene Ontology with flexible identification of candidate terms', 'Lee, Jin-Bok|Kim, Jung-jae|Park, Jong C', 'Computer Science Division and AITrc, KAIST 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, South Korea.|Computer Science Division and AITrc, KAIST 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, South Korea.|Computer Science Division and AITrc, KAIST 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, South Korea.', '-|-|-', 16, 'Algorithms|Artificial Intelligence|Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Proteins|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl010', '16428805', 'Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones.'),
(72621, 2006, 'bio', 'The MGED Ontology: a resource for semantics-based description of microarray experiments', 'Whetzel, Patricia L|Parkinson, Helen|Causton, Helen C|Fan, Liju|Fostel, Jennifer|Fragoso, Gilberto|Game, Laurence|Heiskanen, Mervi|Morrison, Norman|Rocca-Serra, Philippe|Sansone, Susanna-Assunta|Taylor, Chris|White, Joseph|Stoeckert, Christian J', 'Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.|Center for Bioinformatics and Department of Genetics, University of Pennsylvania School of Medicine, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 154, 'Computational Biology|Computer Simulation|Database Management Systems|Gene Expression Profiling|Information Storage and Retrieval|Models, Biological|Oligonucleotide Array Sequence Analysis|Programming Languages|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl005', '16428806', 'The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards.'),
(72622, 2006, 'bio', 'VISSA: a program to visualize structural features from structure sequence alignment', 'Li, Weizhong|Godzik, Adam', 'Burnham Institute for Medical Research, La Jolla, CA 92037, USA. liwz@burnham.org|Burnham Institute for Medical Research, La Jolla, CA 92037, USA. liwz@burnham.org', 'United States|United States', 9, 'Computational Biology|Computer Graphics|Conserved Sequence|Protein Conformation|Proteins|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl019', '16434438', 'Multiple sequence alignment is an important tool to understand and analyze functions of homologous proteins. However, the logic of residue conservation/variation is usually apparent only in three-dimensional (3D) space, not on a primary sequence level. Thus, in a traditional multiple alignment it is often difficult to directly visualize and analyze key residues because they are masked by other residues along the alignment. Here we present an integrated multiple alignment and 3D structure visualization program that can (1) map and highlight residues from a 1D alignment onto a 3D structure and vice versa and (2) display only the alignment of preselected, key residues. This program, called Visualize Structure Sequence Alignment, also has many other built-in tools that can help analyze multiple sequence alignments.'),
(72623, 2006, 'bio', 'Polynomial model approach for resynchronization analysis of cell-cycle gene expression data', 'Qiu, Peng|Wang, Z Jane|Liu, K J Ray', 'Department of Electrical and Computer Engineering, University of Maryland, College Park, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, USA. qiupeng@umd.edu|Department of Electrical and Computer Engineering, University of Maryland, College Park, USA. qiupeng@umd.edu', 'United States|United States|United States', 9, 'Algorithms|Cell Cycle Proteins|Computer Simulation|Gene Expression Profiling|Models, Biological|Numerical Analysis, Computer-Assisted|Oligonucleotide Array Sequence Analysis|Periodicity|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins', 'Journal Article', '10.1093/bioinformatics/btl017', '16434439', 'Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by raw microarray gene expression data directly is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements and extracting synchronized expression will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes.'),
(72624, 2006, 'bio', 'Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering', 'Hakamada, Kazumi|Okamoto, Masahiro|Hanai, Taizo', 'Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.|Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.|Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.', 'Japan|Japan|Japan', 15, 'Algorithms|Cluster Analysis|Data Interpretation, Statistical|Gene Expression Profiling|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Saccharomyces cerevisiae', 'Comparative Study', '10.1093/bioinformatics/btl016', '16434440', 'Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t+1. Here, we propose a method designated mathematical model-based clustering (MMBC).'),
(72625, 2006, 'bio', 'CSS-Palm: palmitoylation site prediction with a clustering and scoring strategy (CSS)', 'Zhou, Fengfeng|Xue, Yu|Yao, Xuebiao|Xu, Ying', 'Computational Systems Biology Laboratory, Department of Biochemical and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemical and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemical and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.|Computational Systems Biology Laboratory, Department of Biochemical and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.', 'Georgia|Georgia|Georgia|Georgia', 97, 'Algorithms|Amino Acid Sequence|Animals|Cluster Analysis|Computational Biology|Databases, Protein|Humans|Mice|Molecular Sequence Data|Palmitates|Phosphorylation|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl013', '16434441', 'Palmitoylation is an important post-translational lipid modification of proteins. Unlike prenylation and myristoylation, palmitoylation is a reversible covalent modification, allowing for dynamic regulation of multiple complex cellular systems. However, in vivo or in vitro identification of palmitoylation sites is usually time-consuming and labor-intensive. So in silico predictions could help to narrow down the possible palmitoylation sites, which can be used to guide further experimental design. Previous studies suggested that there is no unique canonical motif for palmitoylation sites, so we hypothesize that the bona fide pattern might be compromised by heterogeneity of multiple structural determinants with different features. Based on this hypothesis, we partition the known palmitoylation sites into three clusters and score the similarity between the query peptide and the training ones based on BLOSUM62 matrix. We have implemented a computer program for palmitoylation site prediction, Clustering and Scoring Strategy for Palmitoylation Sites Prediction (CSS-Palm) system, and found that the programs prediction performance is encouraging with highly positive Jack-Knife validation results (sensitivity 82.16% and specificity 83.17% for cut-off score 2.6). Our analyses indicate that CSS-Palm could provide a powerful and effective tool to studies of palmitoylation sites.'),
(72626, 2006, 'bio', 'Discontinuous epitope prediction based on mimotope analysis', 'Moreau, Violaine|Granier, Claude|Villard, Sylvie|Laune, Daniel|Molina, Franck', 'CNRS UMR 5160, Centre de Pharmacologie et Biotechnologie pour la Santé Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491, 34093 Montpellier Cedex 5, France.|CNRS UMR 5160, Centre de Pharmacologie et Biotechnologie pour la Santé Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491, 34093 Montpellier Cedex 5, France.|CNRS UMR 5160, Centre de Pharmacologie et Biotechnologie pour la Santé Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491, 34093 Montpellier Cedex 5, France.|CNRS UMR 5160, Centre de Pharmacologie et Biotechnologie pour la Santé Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491, 34093 Montpellier Cedex 5, France.|CNRS UMR 5160, Centre de Pharmacologie et Biotechnologie pour la Santé Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491, 34093 Montpellier Cedex 5, France.', 'France|France|France|France|France', 77, 'Algorithms|Amino Acid Sequence|Binding Sites|Epitope Mapping|Epitopes|Molecular Sequence Data|Protein Binding|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl012', '16434442', 'Phage display is a widespread technique used to obtain peptide mimotopes selected by binding to a given monoclonal antibody in a similar way as the native epitope. However, the localization of the interaction site mimicked by the mimotopes on the surface of the antigen is not always a straightforward task. MIMOP is a computational tool developed with the aim of helping experimentalists to analyze a set of mimotope sequences and guide them in the identification of the mimicked region.'),
(72627, 2006, 'bio', 'Incorporating gene functions as priors in model-based clustering of microarray gene expression data', 'Pan, Wei', 'Division of Biostatistics, MMC 303, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0392, USA. weip@biostat.umn.edu', 'United States', 75, 'Algorithms|Cluster Analysis|Computer Simulation|Gene Expression Profiling|Models, Statistical|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl011', '16434443', 'Cluster analysis of gene expression profiles has been widely applied to clustering genes for gene function discovery. Many approaches have been proposed. The rationale is that the genes with the same biological function or involved in the same biological process are more likely to co-express, hence they are more likely to form a cluster with similar gene expression patterns. However, most existing methods, including model-based clustering, ignore known gene functions in clustering.'),
(72628, 2006, 'bio', 'COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations', 'Jothi, Raja|Zotenko, Elena|Tasneem, Asba|Przytycka, Teresa M', 'National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. jothi@mail.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. jothi@mail.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. jothi@mail.nih.gov|National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. jothi@mail.nih.gov', 'United States|United States|United States|United States', 48, 'Algorithms|Animals|Cluster Analysis|Computational Biology|DNA-Directed RNA Polymerases|Evolution, Molecular|Glutamate-tRNA Ligase|Humans|Models, Biological|Models, Genetic|Phylogeny|Protein Subunits|Ribonuclease H|Sequence Homology|Serine Endopeptidases', 'Journal Article', '10.1093/bioinformatics/btl009', '16434444', 'Determining orthology relations among genes across multiple genomes is an important problem in the post-genomic era. Identifying orthologous genes can not only help predict functional annotations for newly sequenced or poorly characterized genomes, but can also help predict new protein-protein interactions. Unfortunately, determining orthology relation through computational methods is not straightforward due to the presence of paralogs. Traditional approaches have relied on pairwise sequence comparisons to construct graphs, which were then partitioned into putative clusters of orthologous groups. These methods do not attempt to preserve the non-transitivity and hierarchic nature of the orthology relation.'),
(72629, 2006, 'bio', 'Computation of recurrent minimal genomic alterations from array-CGH data', 'Rouveirol, C|Stransky, N|Hupé, Ph|Rosa, Ph La|Viara, E|Barillot, E|Radvanyi, F', 'LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr|LRI, UMR CNRS 8623, Université Paris Sud, bât 490 91405 Orsay cedex, France. celine@lri.fr', 'France|France|France|France|France|France|France', 70, 'Algorithms|Breast Neoplasms|Chromosome Mapping|Colonic Neoplasms|Computer Simulation|Databases, Genetic|Female|Gene Expression Profiling|Humans|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btl004', '16434445', 'The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH data has been studied extensively, but subsequent steps in the analysis require flexible, efficient algorithms, particularly if the number of available profiles exceeds a few tens or the number of array probes exceeds a few thousands.'),
(72630, 2006, 'bio', 'ProteomeCommonsorg JAF: reference information and tools for proteomics', 'Falkner, J A|Falkner, J W|Andrews, P C', 'Department Biological Chemistry, University of Michigan, Ann Arbor, MI 48104, USA. jfalkner@umich.edu|Department Biological Chemistry, University of Michigan, Ann Arbor, MI 48104, USA. jfalkner@umich.edu|Department Biological Chemistry, University of Michigan, Ann Arbor, MI 48104, USA. jfalkner@umich.edu', 'United States|United States|United States', 11, 'Database Management Systems|Databases, Protein|Documentation|Peptide Mapping|Proteins|Proteomics|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btk015', '16434446', 'Analysis of proteomics data, specifically mass spectrometry data, commonly relies on libraries of known information such as atomic masses, known stable isotopes, atomic compositions of amino acids, observed modifications of known amino acids and ion masses that directly correspond to known amino acid sequences. The Java Analysis Framework (JAF) for proteomics provides a freely usable, open-source library of Java code that abstracts all of the aforementioned data, enabling more rapid development of proteomics tools. The JAF also includes several user tools that can be run directly from a web browser.'),
(72631, 2006, 'bio', 'Enhancing instance-based classification with local density: a new algorithm for classifying unbalanced biomedical data', 'Plant, Claudia|Böhm, Christian|Tilg, Bernhard|Baumgartner, Christian', 'Research Group for Clinical Bioinformatics, Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria.|Research Group for Clinical Bioinformatics, Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria.|Research Group for Clinical Bioinformatics, Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria.|Research Group for Clinical Bioinformatics, Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria.', 'Austria|Austria|Austria|Austria', 14, 'Algorithms|Artificial Intelligence|Cluster Analysis|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Pattern Recognition, Automated', 'Comparative Study', '10.1093/bioinformatics/btl027', '16443633', 'Classification is an important data mining task in biomedicine. In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. Even more important than the overall accuracy is the balance of a classifier, particularly if datasets of unbalanced class size are examined.'),
(72632, 2006, 'bio', 'A novel domain suggests a ciliary function for ASPM, a brain size determining gene', 'Ponting, Chris P', 'MRC Functional Genetics Unit, University of Oxford, Department of Human Anatomy and Genetics South Parks Road, Oxford OX1 3QX, UK. chris.ponting@anat.ox.ac.uk', 'United Kingdom', 77, 'Amino Acid Sequence|Animals|Body Patterning|Brain|Cilia|Humans|Molecular Sequence Data|Nerve Tissue Proteins|Organ Size|Protein Structure, Tertiary|Quantitative Trait Loci|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl022', '16443634', 'The N-terminal domain of abnormal spindle-like microcephaly-associated protein (ASPM) is identified as a member of a novel family of ASH (ASPM, SPD-2, Hydin) domains. These domains are present in proteins associated with cilia, flagella, the centrosome and the Golgi complex, and in Hydin and OCRL whose deficiencies are associated with hydrocephalus and Lowe oculocerebrorenal syndrome, respectively. Genes encoding ASH domains thus represent good candidates for primary ciliary dyskinesias. ASPM has been proposed to function in neurogenesis and to be a major determinant of cerebral cortical size in humans. Support for this hypothesis stems from associations between mutations in ASPM and primary microcephaly, and from the rapid evolution of ASPM during recent hominid evolution. The identification of the ASH domain family instead indicates possible roles for ASPM in sperm flagellar or in ependymal cells cilia. ASPMs rapid evolution may thus reflect selective pressures on ciliary function, rather than pressures on mitosis during neurogenesis.'),
(72633, 2006, 'bio', 'Genomic island identification in Vibrio vulnificus reveals significant genome plasticity in this human pathogen', 'Quirke, A M|Reen, F Jerry|Claesson, Marcus J|Boyd, E Fidelma', 'Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.|Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.|Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.|Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.', 'Ireland|Ireland|Ireland|Ireland', 38, 'Adaptation, Physiological|Chromosome Mapping|DNA, Bacterial|Evolution, Molecular|Genetic Variation|Genomic Islands|Humans|Sequence Analysis, DNA|Species Specificity|Vibrio Infections|Vibrio vulnificus', 'Journal Article', '10.1093/bioinformatics/btl015', '16443635', 'Genomic islands (GIs) are large chromosomal regions present in a subset of bacterial strains that increase the fitness of the organism under specific conditions. We compared the complete genome sequences of two Vibrio vulnificus strains YJ016 and CMCP6 and identified 14 regions (ranging in size from 14 to 117 kb), which had the characteristics of GIs. Bioinformatic analysis of these 14 GI regions identified the presence of phage-like integrase genes, aberrant GC content and genome signature (dinucleotide frequency) within each GI compared with the core genome indicating that these regions were acquired from an anomalous source. We examined the distribution of the nine GIs from strain YJ016 among 27 V. vulnificus isolates and found that most GIs were absent from the majority of these isolates. The chromosomal insertion sites of three GIs were adjacent to tRNA sites, which contained novel horizontally acquired DNA in all six available sequenced Vibrionaceae genomes.'),
(72634, 2006, 'bio', 'Synthetic biology: challenges ahead', 'de Lorenzo, Victor|Serrano, Luis|Valencia, Alfonso', 'Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.|Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.|Department of Microbiology, University College Cork, National University of Ireland, Cork, Ireland.', 'Ireland|Ireland|Ireland', 15, 'Biomimetics|Computational Biology|Genetic Engineering|Molecular Biology|Systems Biology', 'Editorial', '10.1093/bioinformatics/btk018', '16443636', '-'),
(72635, 2006, 'bio', 'HaptenDB: a comprehensive database of haptens, carrier proteins and anti-hapten antibodies', 'Singh, Mahender Kumar|Srivastava, Shilpy|Raghava, G P S|Varshney, Grish C', 'Institute of Microbial Technology Chandigarh, India.|Institute of Microbial Technology Chandigarh, India.|Institute of Microbial Technology Chandigarh, India.|Institute of Microbial Technology Chandigarh, India.', 'India|India|India|India', 34, 'Antibodies|Carrier Proteins|Database Management Systems|Databases, Protein|Drug Design|Haptens|India|Information Storage and Retrieval|Systems Integration|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/bti692', '16443637', 'The key requirement for successful immunochemical assay is the availability of antibodies with high specificity and desired affinity. Small molecules, when used as haptens, are not immunogenic. However, on conjugating with carrier molecule they elicit antibody response. The production of anti-hapten antibodies of desired specificity largely depends on the hapten design (preserving greatly the chemical structure and spatial conformation of target compound), selection of the appropriate carrier protein and the conjugation method. This manuscript describes a curated database HaptenDB, where information is collected from published literature and web resources. The current version of the database has 2021 entries for 1087 haptens and 25 carrier proteins, where each entry provides comprehensive details about (1) nature of the hapten, (2) 2D and 3D structures of haptens, (3) carrier proteins, (4) coupling method, (5) method of anti-hapten antibody production, (6) assay method (used for characterization) and (7) specificities of antibodies. The current version of HaptenDB covers a wide array of haptens including pesticides, herbicides, insecticides, drugs, vitamins, steroids, hormones, toxins, dyes, explosives, etc. It provides internal and external links to various databases/resources to obtain further information about the nature of haptens, carriers and respective antibodies. For structure similarity comparison of haptens, the database also integrates tools like JME Editor and JMOL for sketching, displaying and manipulating hapten 2D/3D structures online. So the database would be of great help in identifying functional group(s) in smaller molecules using antibodies as well as for the development of immunodiagnostics/therapeutics by providing data and procedures available so far for the generation of specific or cross-reactive antibodies.'),
(72636, 2006, 'bio', 'siRecords: an extensive database of mammalian siRNAs with efficacy ratings', 'Ren, Yongliang|Gong, Wuming|Xu, Qiqi|Zheng, Xin|Lin, Dong|Wang, Yejun|Li, Tongbin', 'Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.|Department of Neuroscience, University of Minnesota Minneapolis, MN 55455, USA.', 'United States|United States|United States|United States|United States|United States|United States', 35, 'Abstracting and Indexing as Topic|Databases, Genetic|Gene Silencing|Gene Targeting|Internet|Natural Language Processing|PubMed|RNA, Small Interfering', 'Journal Article', '10.1093/bioinformatics/btl026', '16443930', 'Short interfering RNAs (siRNAs) have been gaining popularity as the gene knock-down tool of choice by many researchers because of the clean nature of their workings as well as the technical simplicity and cost efficiency in their applications. We have constructed siRecords, a database of siRNAs experimentally tested by researchers with consistent efficacy ratings. This database will help siRNA researchers develop more reliable siRNA design rules, in the mean time, siRecords will benefit experimental researchers directly by providing them with information about the siRNAs that have been experimentally tested against the genes of their interest. Currently, more than 4100 carefully annotated siRNA sequences obtained from more than 1200 published siRNA studies are hosted in siRecords. This database will continue to expand as more experimentally tested siRNAs are published.'),
(72637, 2006, 'bio', 'Thermodynamics of RNA-RNA binding', 'Mückstein, Ulrike|Tafer, Hakim|Hackermüller, Jörg|Bernhart, Stephan H|Stadler, Peter F|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.|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.|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|Austria|Austria|Austria|Austria', 235, 'Algorithms|Base Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Statistical|Molecular Sequence Data|RNA, Messenger|RNA, Small Interfering|Sequence Alignment|Sequence Analysis, RNA|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btl024', '16446276', 'Reliable prediction of RNA-RNA binding energies is crucial, e.g. for the understanding on RNAi, microRNA-mRNA binding and antisense interactions. The thermodynamics of such RNA-RNA interactions can be understood as the sum of two energy contributions: (1) the energy necessary to open the binding site and (2) the energy gained from hybridization.'),
(72638, 2006, 'bio', 'AMiGA: the arthropodan mitochondrial genomes accessible database', 'Feijão, Pedro Cipriano|Neiva, Lissiene Silva|de Azeredo-Espin, Ana Maria Lima|Lessinger, Ana Cláudia', 'Laboratório de Genética Animal, Centro de Biologia Molecular e Engenharia Genética (CBMEG), Universidade Estadual de Campinas (UNICAMP), CP 6010, CEP 13035-875, Campinas, São Paulo, Brazil.|Laboratório de Genética Animal, Centro de Biologia Molecular e Engenharia Genética (CBMEG), Universidade Estadual de Campinas (UNICAMP), CP 6010, CEP 13035-875, Campinas, São Paulo, Brazil.|Laboratório de Genética Animal, Centro de Biologia Molecular e Engenharia Genética (CBMEG), Universidade Estadual de Campinas (UNICAMP), CP 6010, CEP 13035-875, Campinas, São Paulo, Brazil.|Laboratório de Genética Animal, Centro de Biologia Molecular e Engenharia Genética (CBMEG), Universidade Estadual de Campinas (UNICAMP), CP 6010, CEP 13035-875, Campinas, São Paulo, Brazil.', 'Brazil|Brazil|Brazil|Brazil', 45, 'Animals|Arthropods|Computational Biology|DNA, Mitochondrial|Database Management Systems|Databases, Genetic|Genomics', 'Journal Article', '10.1093/bioinformatics/btl021', '16446277', 'The Arthropodan Mitochondrial Genomes Accessible database (AMiGA) is a relational database developed to help in managing access to the increasing amount of data arising from developments in arthropodan mitochondrial genomics (136 mitochondrial genomes as of September 2005). The strengths of AMiGA include (1) a more accessible and up-to-date database containing a more comprehensive set of mitochondrial genomes for this phylum, (2) the provision of flexible search options for retrieving detailed information such as bibliographical data, genomic graphics, FASTA sequences and taxonomical status, (3) the possibility of enhanced comparative analyses by multiple alignment of single or concatenated sets of genes, (4) more accurate and updated information resulting from a specific curation process called AMiGA Notes and (5) the possibility of including unpublished sequences in a password-restricted area for comparative analysis with the other sequences stored in the database.'),
(72639, 2006, 'bio', 'Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale', 'Li, Haiquan|Li, Jinyan|Wong, Limsoon', 'Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.', 'Singapore|Singapore|Singapore', 41, 'Algorithms|Amino Acid Motifs|Binding Sites|Databases, Protein|Information Storage and Retrieval|Protein Binding|Protein Interaction Mapping|Proteins|Proteome|Sequence Analysis, Protein', 'Evaluation Studies', '10.1093/bioinformatics/btl020', '16446278', 'Protein-protein interaction, mediated by protein interaction sites, is intrinsic to many functional processes in the cell. In this paper, we propose a novel method to discover patterns in protein interaction sites. We observed from protein interaction networks that there exist a kind of significant substructures called interacting protein group pairs, which exhibit an all-versus-all interaction between the two protein-sets in such a pair. The full-interaction between the pair indicates a common interaction mechanism shared by the proteins in the pair, which can be referred as an interaction type. Motif pairs at the interaction sites of the protein group pairs can be used to represent such interaction type, with each motif derived from the sequences of a protein group by standard motif discovery algorithms. The systematic discovery of all pairs of interacting protein groups from large protein interaction networks is a computationally challenging problem. By a careful and sophisticated problem transformation, the problem is solved using efficient algorithms for mining frequent patterns, a problem extensively studied in data mining.'),
(72640, 2006, 'bio', 'Capturing expert knowledge with argumentation: a case study in bioinformatics', 'Jefferys, Benjamin R|Kelley, Lawrence A|Sergot, Marek J|Fox, John|Sternberg, Michael J E', 'Division of Molecular Biosciences, Imperial College, London SW7 2AZ, UK.|Division of Molecular Biosciences, Imperial College, London SW7 2AZ, UK.|Division of Molecular Biosciences, Imperial College, London SW7 2AZ, UK.|Division of Molecular Biosciences, Imperial College, London SW7 2AZ, UK.|Division of Molecular Biosciences, Imperial College, London SW7 2AZ, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 15, 'Amino Acid Sequence|Computational Biology|Computer Simulation|Expert Systems|Models, Chemical|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl018', '16446279', 'The output of a bioinformatic tool such as BLAST must usually be interpreted by an expert before reliable conclusions can be drawn. This may be based upon the experts experience, additional data and statistical analysis. Often the process is laborious, goes unrecorded and may be biased. Argumentation is an established technique for reasoning about situations where absolute truth or precise probability is impossible to determine.'),
(72641, 2006, 'bio', 'MAMMOT--a set of tools for the design, management and visualization of genomic tiling arrays', 'Ryder, E|Jackson, R|Ferguson-Smith, A|Russell, S', 'Department of Genetics, University of Cambridge CB2 3EH, UK. e.ryder@gen.cam.ac.uk|Department of Genetics, University of Cambridge CB2 3EH, UK. e.ryder@gen.cam.ac.uk|Department of Genetics, University of Cambridge CB2 3EH, UK. e.ryder@gen.cam.ac.uk|Department of Genetics, University of Cambridge CB2 3EH, UK. e.ryder@gen.cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 10, 'Animals|Chromosome Mapping|Computational Biology|Database Management Systems|Drosophila|Information Dissemination|Internet|Mice|Microarray Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl031', '16452111', 'The MAMMOT software suite is a collection of Perl and PHP scripts for designing, annotating and visualizing genome tiling arrays to, for example, facilitate studies into the epigenetics of gene regulation. The web design allows rapid experimental data entry from multiple users, and results can easily be shared between groups and individuals.'),
(72642, 2006, 'bio', 'Efficient estimation of graphlet frequency distributions in protein-protein interaction networks', 'Przulj, N|Corneil, D G|Jurisica, I', 'Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada.|Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada.|Department of Computer Science, University of Toronto, Toronto M5S 3G4, Canada.', 'Canada|Canada|Canada', 76, 'Algorithms|Computer Graphics|Computer Simulation|Data Interpretation, Statistical|Models, Biological|Models, Statistical|Protein Interaction Mapping|Proteins|Signal Transduction|Statistical Distributions', 'Journal Article', '10.1093/bioinformatics/btl030', '16452112', 'Algorithmic and modeling advances in the area of protein-protein interaction (PPI) network analysis could contribute to the understanding of biological processes. Local structure of networks can be measured by the frequency distribution of graphlets, small connected non-isomorphic induced subgraphs. This measure of local structure has been used to show that high-confidence PPI networks have local structure of geometric random graphs. Finding graphlets exhaustively in a large network is computationally intensive. More complete PPI networks, as well as PPI networks of higher organisms, will thus require efficient heuristic approaches.'),
(72643, 2006, 'bio', 'Structured polychotomous machine diagnosis of multiple cancer types using gene expression', 'Koo, Ja-Yong|Sohn, Insuk|Kim, Sujong|Lee, Jae Won', 'Department of Statistics, Korea University, Seoul 136-701, Korea. jykoo@korea.ac.kr|Department of Statistics, Korea University, Seoul 136-701, Korea. jykoo@korea.ac.kr|Department of Statistics, Korea University, Seoul 136-701, Korea. jykoo@korea.ac.kr|Department of Statistics, Korea University, Seoul 136-701, Korea. jykoo@korea.ac.kr', '-|-|-|-', 16, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl029', '16452113', 'The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine (SVM). The latter has been shown (through simulations) to be superior in comparison with other methods, such as classical discriminant analysis, however, SVM suffers from the drawback that the solution is implicit and therefore is difficult to interpret. In order to remedy this difficulty, an analysis of variance decomposition using structured kernels is proposed and is referred to as the structured polychotomous machine. This technique utilizes Newton-Raphson to find estimates of coefficients followed by the Rao and Wald tests, respectively, for addition and deletion of import vectors.'),
(72644, 2006, 'bio', 'Memory efficient folding algorithms for circular RNA secondary structures', 'Hofacker, Ivo L|Stadler, Peter F', 'Institute for Theoretical Chemistry, University of Vienna Währingerstr. 17, A-1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna Währingerstr. 17, A-1090 Vienna, Austria.', 'Austria|Austria', 120, 'Algorithms|Base Sequence|Computer Simulation|Computing Methodologies|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btl023', '16452114', 'A small class of RNA molecules, in particular the tiny genomes of viroids, are circular. Yet most structure prediction algorithms handle only linear RNAs. The most straightforward approach is to compute circular structures from internal and external substructures separated by a base pair. This is incompatible, however, with the memory-saving approach of the Vienna RNA Package which builds a linear RNA structure from shorter (internal) structures only.'),
(72645, 2006, 'bio', 'FANMOD: a tool for fast network motif detection', 'Wernicke, Sebastian|Rasche, Florian', 'Institut für Informatik, Friedrich-Schiller-Universität Jena Ernst-Abbe-Platz 2, 07743 Jena, Germany. wernicke@minet.uni-jena.de|Institut für Informatik, Friedrich-Schiller-Universität Jena Ernst-Abbe-Platz 2, 07743 Jena, Germany. wernicke@minet.uni-jena.de', 'Germany|Germany', 453, 'Amino Acid Motifs|Cell Physiological Phenomena|Computer Graphics|Computer Simulation|Models, Biological|Protein Interaction Mapping|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl038', '16455747', 'Motifs are small connected subnetworks that a network displays in significantly higher frequencies than would be expected for a random network. They have recently gathered much attention as a concept to uncover structural design principles of complex biological networks. FANMOD is a tool for fast network motif detection, it relies on recently developed algorithms to improve the efficiency of network motif detection by some orders of magnitude over existing tools. This facilitates the detection of larger motifs in bigger networks than previously possible. Additional benefits of FANMOD are the ability to analyze colored networks, a graphical user interface and the ability to export results to a variety of machine- and human-readable file formats including comma-separated values and HTML.'),
(72646, 2006, 'bio', 'A deterministic motif finding algorithm with application to the human genome', 'Hon, Lawrence S|Jain, Ajay N', 'UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California San Francisco, CA, USA.|UCSF Cancer Research Institute and Comprehensive Cancer Center, University of California San Francisco, CA, USA.', 'United States|United States', 16, 'Algorithms|Amino Acid Motifs|Base Sequence|Chromosome Mapping|Genome, Human|Humans|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl037', '16455748', 'We present a novel algorithm, MaMF, for identifying transcription factor (TF) binding site motifs. The method is deterministic and depends on an indexing technique to optimize the search process. On common yeast datasets, MaMF performs competitively with other methods. We also present results on a challenging group of eight sets of human genes known to be responsive to a diverse group of TFs. In every case, MaMF finds the annotated motif among the top scoring putative motifs. We compared MaMF against other motif finders on a larger human group of 21 gene sets and found that MaMF performs better than other algorithms. We analyzed the remaining high scoring motifs and show that many correspond to other TFs that are known to co-occur with the annotated TF motifs. The significant and frequent presence of co-occurring transcription factor binding sites explains in part the difficulty of human motif finding. MaMF is a very fast algorithm, suitable for application to large numbers of interesting gene sets.'),
(72647, 2006, 'bio', 'The LCB Data Warehouse', 'Ameur, Adam|Yankovski, Vladimir|Enroth, Stefan|Spjuth, Ola|Komorowski, Jan', 'The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.|The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.|The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.|The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.|The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 35, 'Computer Graphics|Database Management Systems|Databases, Genetic|Gene Expression Profiling|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl036', '16455749', 'The Linnaeus Centre for Bioinformatics Data Warehouse (LCB-DWH) is a web-based infrastructure for reliable and secure microarray gene expression data management and analysis that provides an online service for the scientific community. The LCB-DWH is an effort towards a complete system for storage (using the BASE system), analysis and publication of microarray data. Important features of the system include: access to established methods within R/Bioconductor for data analysis, built-in connection to the Gene Ontology database and a scripting facility for automatic recording and re-play of all the steps of the analysis. The service is up and running on a high performance server. At present there are more than 150 registered users.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72648, 2006, 'bio', 'Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model', 'Broët, Philippe|Richardson, Sylvia', 'Faculté de Médecine, Université Paris-XI IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr|Faculté de Médecine, Université Paris-XI IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr', 'France|France', 0, 'Algorithms|Chromosome Mapping|Computer Simulation|Gene Dosage|In Situ Hybridization|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btl035', '16455750', 'Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them.'),
(72649, 2006, 'bio', 'PROFbval: predict flexible and rigid residues in proteins', 'Schlessinger, Avner|Yachdav, Guy|Rost, Burkhard', 'CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. profbval@rostlab.org|CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. profbval@rostlab.org|CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. profbval@rostlab.org', 'United States|United States|United States', 95, 'Computational Biology|Databases, Protein|Protein Conformation|Proteins|Sequence Alignment|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl032', '16455751', 'The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid.'),
(72650, 2006, 'bio', 'affylmGUI: a graphical user interface for linear modeling of single channel microarray data', 'Wettenhall, James M|Simpson, Ken M|Satterley, Keith|Smyth, Gordon K', 'Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, Parkville 3050, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, Parkville 3050, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, Parkville 3050, Australia.|Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, Parkville 3050, Australia.', 'Australia|Australia|Australia|Australia', 172, 'Bayes Theorem|Computer Graphics|Gene Expression Profiling|Linear Models|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl025', '16455752', 'affylmGUI is a graphical user interface (GUI) to an integrated workflow for Affymetrix microarray data. The user is able to proceed from raw data (CEL files) to QC and pre-processing, and eventually to analysis of differential expression using linear models with empirical Bayes smoothing. Output of the analysis (tables and figures) can be exported to an HTML report. The GUI provides user-friendly access to state-of-the-art methods embodied in the Bioconductor software repository.'),
(72651, 2006, 'bio', 'Predicting interactions in protein networks by completing defective cliques', 'Yu, Haiyuan|Paccanaro, Alberto|Trifonov, Valery|Gerstein, Mark', 'Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.|Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.|Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.|Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.', 'United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Databases, Protein|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Comparative Study', '10.1093/bioinformatics/btl014', '16455753', 'Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/'),
(72652, 2006, 'bio', 'CellML2SBML: conversion of CellML into SBML', 'Schilstra, Maria J|Li, Lu|Matthews, Joanne|Finney, Andrew|Hucka, Michael|Le Novère, Nicolas', 'Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk|Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk|Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk|Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk|Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk|Biological and Neural Computation Group, STRI, University of Hertfordshire, Hatfield AL10 9AB, UK. m.j.1.schilstra@herts.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Cell Physiological Phenomena|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Biology|Programming Languages|Proteins', 'Journal Article', '10.1093/bioinformatics/btl047', '16473867', 'CellML and SBML are XML-based languages for storage and exchange of molecular biological and physiological reaction models. They use very similar subsets of MathML to specify the mathematical aspects of the models. CellML2SBML is implemented as a suite of XSLT stylesheets that, when applied consecutively, convert models expressed in CellML into SBML without significant loss of information. The converter is based on the most recent stable versions of the languages (CellML version 1.1, SBML Level 2 Version 1), and the XSLT used in the stylesheets adheres to the XSLT version 1.0 specification. Of all 306 models in the CellML repository in April 2005, CellML2SBML converted 91% automatically into SBML. Minor manual changes to the unit definitions in the originals raised the percentage of successful conversions to 96%.'),
(72653, 2006, 'bio', 'Development and validation of a consistency based multiple structure alignment algorithm', 'Ebert, Jessica|Brutlag, Douglas', 'Program in Biophysics and Department of Biochemistry, Stanford University Stanford, CA 94305, USA.|Program in Biophysics and Department of Biochemistry, Stanford University Stanford, CA 94305, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Amino Acid Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Evaluation Studies', '10.1093/bioinformatics/btl046', '16473868', 'We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments (MSTAs). Consistency-based alignment (CBA) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBAs alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif.'),
(72654, 2006, 'bio', 'Comparison of P-RnaPredict and mfold--algorithms for RNA secondary structure prediction', 'Wiese, Kay C|Hendriks, Andrew', 'School of Computing Science and InfoNet Media Centre, Simon Fraser University, 15th Floor, Central City Tower, 13450 102nd Avenue, Surrey, BC, Canada V3T 5X3. wiese@cs.sfu.ca|School of Computing Science and InfoNet Media Centre, Simon Fraser University, 15th Floor, Central City Tower, 13450 102nd Avenue, Surrey, BC, Canada V3T 5X3. wiese@cs.sfu.ca', 'Canada|Canada', 50, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|Sequence Alignment|Sequence Analysis, RNA|Software', 'Comparative Study', '10.1093/bioinformatics/btl043', '16473869', 'Ribonucleic acid is vital in numerous stages of protein synthesis, it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure.'),
(72655, 2006, 'bio', 'Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations', 'Kel, A|Konovalova, T|Waleev, T|Cheremushkin, E|Kel-Margoulis, O|Wingender, E', 'BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com|BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com|BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com|BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com|BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com|BIOBASE GmbH Halchtersche Str. 33, D-38304 Wolfenbüttel, Germany. alexander.kel@biobase-international.com', 'Germany|Germany|Germany|Germany|Germany|Germany', 42, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Sequence Analysis, Protein|Software|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl041', '16473870', 'Functionally related genes involved in the same molecular-genetic, biochemical or physiological process are often regulated coordinately. Such regulation is provided by precisely organized binding of a multiplicity of special proteins [transcription factors (TFs)] to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression.'),
(72656, 2006, 'bio', 'Generation of 3D templates of active sites of proteins with rigid prosthetic groups', 'Nebel, Jean-Christophe', 'Faculty of Computing, Information Systems & Mathematics, Kingston University Kingston-upon-Thames, Surrey KT1 2EE, UK. j.nebel@kingston.ac.uk', 'United Kingdom', 12, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl040', '16473871', 'With the increasing availability of protein structures, the generation of biologically meaningful 3D patterns from the simultaneous alignment of several protein structures is an exciting prospect: active sites could be better understood, protein functions and protein 3D structures could be predicted more accurately. Although patterns can already be generated at the fold and topological levels, no system produces high-resolution 3D patterns including atom and cavity positions. To address this challenge, our research focuses on generating patterns from proteins with rigid prosthetic groups. Since these groups are key elements of protein active sites, the generated 3D patterns are expected to be biologically meaningful.'),
(72657, 2006, 'bio', 'CFinder: locating cliques and overlapping modules in biological networks', 'Adamcsek, Balázs|Palla, Gergely|Farkas, Illés J|Derényi, Imre|Vicsek, Tamás', 'Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary.|Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary.|Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary.|Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary.|Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary.', 'Hungary|Hungary|Hungary|Hungary|Hungary', 633, 'Biology|Cell Physiological Phenomena|Computer Graphics|Databases, Protein|Information Storage and Retrieval|Proteins|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl039', '16473872', 'Most cellular tasks are performed not by individual proteins, but by groups of functionally associated proteins, often referred to as modules. In a protein association network modules appear as groups of densely interconnected nodes, also called communities or clusters. These modules often overlap with each other and form a network of their own, in which nodes (links) represent the modules (overlaps). We introduce CFinder, a fast program locating and visualizing overlapping, densely interconnected groups of nodes in undirected graphs, and allowing the user to easily navigate between the original graph and the web of these groups. We show that in gene (protein) association networks CFinder can be used to predict the function(s) of a single protein and to discover novel modules. CFinder is also very efficient for locating the cliques of large sparse graphs.'),
(72658, 2006, 'bio', 'Non-linear tests for identifying differentially expressed genes or genetic networks', 'Xiong, Hao', 'Department of Computer Science, Texas A&M University, 301 Harvey R. Bright Bldg, College Station, TX 77843-3112, USA. hxiong@cs.tamu.edu', 'United States', 19, 'Algorithms|Chromosome Mapping|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Genetic|Models, Statistical|Nonlinear Dynamics|Oligonucleotide Array Sequence Analysis|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl034', '16473873', 'One of the recently developed statistics for identifying differentially expressed genetic networks is Hotelling T2 statistic, which is a quadratic form of difference in linear functions of means of gene expressions between two types of tissue samples, and so their power is limited.'),
(72659, 2006, 'bio', 'A new summarization method for Affymetrix probe level data', 'Hochreiter, Sepp|Clevert, Djork-Arné|Obermayer, Klaus', 'Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany. hochreit@bioinf.jku.at|Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany. hochreit@bioinf.jku.at|Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany. hochreit@bioinf.jku.at', 'Germany|Germany|Germany', 174, 'Algorithms|Computer Simulation|DNA Probes|Gene Expression Profiling|Models, Genetic|Oligonucleotide Array Sequence Analysis|Sequence Analysis, DNA', 'Evaluation Studies', '10.1093/bioinformatics/btl033', '16473874', 'We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. Thereafter, the RNA concentration is estimated from the model. In contrast to previous methods our new method called Factor Analysis for Robust Microarray Summarization (FARMS) supplies both P-values indicating interesting information and signal intensity values.'),
(72660, 2006, 'bio', 'Look-Align: an interactive web-based multiple sequence alignment viewer with polymorphism analysis support', 'Canaran, Payan|Stein, Lincoln|Ware, Doreen', 'Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.|Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.|Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA.', 'United States|United States|United States', 8, 'Databases, Genetic|Polymorphism, Genetic|Sequence Alignment|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl028', '16473875', 'We have developed Look-Align, an interactive web-based viewer to display pre-computed multiple sequence alignments. Although initially developed to support the visualization needs of the maize diversity website Panzea (http://www.panzea.org), the viewer is a generic stand-alone tool that can be easily integrated into other websites.'),
(72661, 2006, 'bio', 'maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments', 'Conesa, Ana|Nueda, María José|Ferrer, Alberto|Talón, Manuel', 'Centro de Genómica. Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113 Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica. Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113 Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica. Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113 Moncada, Valencia, Spain. aconesa@ivia.es|Centro de Genómica. Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113 Moncada, Valencia, Spain. aconesa@ivia.es', 'Spain|Spain|Spain|Spain', 231, 'Algorithms|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Software|Time Factors', 'Journal Article', '10.1093/bioinformatics/btl056', '16481333', 'Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis.'),
(72662, 2006, 'bio', 'An ensemble of K-local hyperplanes for predicting protein-protein interactions', 'Nanni, Loris|Lumini, Alessandra', 'DEIS, IEIIT, CNR, Università di Bologna Viale Risorgimento 2, 40136 Bologna, Italy. lnanni@deis.unibo.it|DEIS, IEIIT, CNR, Università di Bologna Viale Risorgimento 2, 40136 Bologna, Italy. lnanni@deis.unibo.it', 'Italy|Italy', 147, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Biological|Models, Chemical|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl055', '16481334', 'Prediction of protein-protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the Sum rule enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset.'),
(72663, 2006, 'bio', 'Adapters, shims, and glue--service interoperability for in silico experiments', 'Radetzki, U|Leser, U|Schulze-Rauschenbach, S C|Zimmermann, J|Lüssem, J|Bode, T|Cremers, A B', 'Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de|Department of Computer Science, University of Bonn D-53117 Bonn, Germany. ur@iai.uni-bonn.de', 'Germany|Germany|Germany|Germany|Germany|Germany|Germany', 24, 'Computational Biology|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Software|Software Design|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl054', '16481335', 'Computationally, in silico experiments in biology are workflows describing the collaboration of people, data and methods. The Grid and Web services are proposed to be the next generation infrastructure supporting the deployment of bioinformatics workflows. But the growing number of autonomous and heterogeneous services pose challenges to the used middleware w.r.t. composition, i.e. discovery and interoperability of services required within in silico experiments. In the IRIS project, we handle the problem of service interoperability by a semi-automatic procedure for identifying and placing customizable adapters into workflows built by service composition.'),
(72664, 2006, 'bio', 'Functional bioinformatics for Arabidopsis thaliana', 'Clare, A|Karwath, A|Ougham, H|King, R D', 'Department of Computer Science, University of Wales Aberystwyth SY23 3DB, UK. afc@aber.ac.uk|Department of Computer Science, University of Wales Aberystwyth SY23 3DB, UK. afc@aber.ac.uk|Department of Computer Science, University of Wales Aberystwyth SY23 3DB, UK. afc@aber.ac.uk|Department of Computer Science, University of Wales Aberystwyth SY23 3DB, UK. afc@aber.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 23, 'Algorithms|Arabidopsis|Arabidopsis Proteins|Artificial Intelligence|Computational Biology|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Natural Language Processing|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl051', '16481336', 'The genome of Arabidopsis thaliana, which has the best understood plant genome, still has approximately one-third of its genes with no functional annotation at all from either MIPS or TAIR. We have applied our Data Mining Prediction (DMP) method to the problem of predicting the functional classes of these protein sequences. This method is based on using a hybrid machine-learning/data-mining method to identify patterns in the bioinformatic data about sequences that are predictive of function. We use data about sequence, predicted secondary structure, predicted structural domain, InterPro patterns, sequence similarity profile and expressions data.'),
(72665, 2006, 'bio', 'The DIMA web resource--exploring the protein domain network', 'Pagel, Philipp|Oesterheld, Matthias|Stümpflen, Volker|Frishman, Dmitrij', 'Department of Genome Oriented Bioinformatics, Technical University of Munich, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. p.pagel@gsf.de|Department of Genome Oriented Bioinformatics, Technical University of Munich, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. p.pagel@gsf.de|Department of Genome Oriented Bioinformatics, Technical University of Munich, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. p.pagel@gsf.de|Department of Genome Oriented Bioinformatics, Technical University of Munich, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. p.pagel@gsf.de', 'Germany|Germany|Germany|Germany', 17, 'Algorithms|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl050', '16481337', 'Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria.'),
(72666, 2006, 'bio', 'Pedigree-drawing with R and graphviz', 'Zhao, Jing Hua', 'MRC Epidemiology Unit, Strangeways Research Laboratory Cambridge CB1 8RN, UK. jinghua.zhao@mrc-epid.cam.ac.uk', 'United Kingdom', 15, 'Computer Graphics|Documentation|Female|Humans|Information Storage and Retrieval|Male|Pedigree|Programming Languages|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl058', '16488908', 'Two functions for pedigree-drawing available in R (http://www.r-project.org): plot.pedigree in kinship and pedtodot in gap are described. The latter requires graphviz (http://www.graphviz.org). They can produce many pedigree diagrams quickly into a single file, serving as alternatives to programs that only offer interactive use.'),
(72667, 2006, 'bio', 'Recco: recombination analysis using cost optimization', 'Maydt, Jochen|Lengauer, Thomas', 'Max-Planck Institut für Informatik Saarbrücken, Germany. jmaydt@mpi-inf.mpg.de|Max-Planck Institut für Informatik Saarbrücken, Germany. jmaydt@mpi-inf.mpg.de', 'Germany|Germany', 89, 'Algorithms|Amino Acid Sequence|Evolution, Molecular|Molecular Sequence Data|Phylogeny|Proteins|Recombination, Genetic|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl057', '16488909', 'Recombination plays an important role in the evolution of many pathogens, such as HIV or malaria. Despite substantial prior work, there is still a pressing need for efficient and effective methods of detecting recombination and analyzing recombinant sequences.'),
(72668, 2006, 'bio', 'Structural bioinformatics of DNA: a web-based tool for the analysis of molecular dynamics results and structure prediction', 'Dixit, Surjit B|Beveridge, David L', 'Department of Chemistry and Molecular Biophysics Program Hall-Atwater Laboratories, Wesleyan University, Middletown, CT 06459, USA. sdixit@wesleyan.edu|Department of Chemistry and Molecular Biophysics Program Hall-Atwater Laboratories, Wesleyan University, Middletown, CT 06459, USA. sdixit@wesleyan.edu', 'United States|United States', 18, 'Base Sequence|Computational Biology|Computer Simulation|DNA|Internet|Kinetics|Models, Chemical|Models, Molecular|Molecular Biology|Molecular Sequence Data|Nucleic Acid Conformation|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl059', '16489208', 'We report here the release of a web-based tool (MDDNA) to study and model the fine structural details of DNA on the basis of data extracted from a set of molecular dynamics (MD) trajectories of DNA sequences involving all the unique tetranucleotides. The dynamic web interface can be employed to analyze the first neighbor sequence context effects on the 10 unique dinucleotide steps of DNA. Functionality is included to build all atom models of any user-defined sequence based on the MD results. The backend of this interface is a relational database storing the conformational details of DNA obtained in 39 different MD simulation trajectories comprising all the 136 unique tetranucleotide steps. Examples of the use of this data to predict DNA structures are included.'),
(72669, 2006, 'bio', 'Matched-pairs tests of homogeneity with applications to homologous nucleotide sequences', 'Ababneh, Faisal|Jermiin, Lars S|Ma, Chunsheng|Robinson, John', 'School of Mathematics and Statistics, University of Sydney NSW 2006, Australia.|School of Mathematics and Statistics, University of Sydney NSW 2006, Australia.|School of Mathematics and Statistics, University of Sydney NSW 2006, Australia.|School of Mathematics and Statistics, University of Sydney NSW 2006, Australia.', 'Australia|Australia|Australia|Australia', 18, 'Base Pair Mismatch|Base Pairing|Base Sequence|Conserved Sequence|Molecular Sequence Data|Nucleotides|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btl064', '16492684', 'Most phylogenetic methods assume that the sequences of nucleotides or amino acids have evolved under stationary, reversible and homogeneous conditions. When these assumptions are violated by the data, there is an increased probability of errors in the phylogenetic estimates. Methods to examine aligned sequences for these violations are available, but they are rarely used, possibly because they are not widely known or because they are poorly understood.'),
(72670, 2006, 'bio', 'Assessing semantic similarity measures for the characterization of human regulatory pathways', 'Guo, Xiang|Liu, Rongxiang|Shriver, Craig D|Hu, Hai|Liebman, Michael N', 'Windber Research Institute, Windber, PA 15963, USA. s.guo@wriwindber.org|Windber Research Institute, Windber, PA 15963, USA. s.guo@wriwindber.org|Windber Research Institute, Windber, PA 15963, USA. s.guo@wriwindber.org|Windber Research Institute, Windber, PA 15963, USA. s.guo@wriwindber.org|Windber Research Institute, Windber, PA 15963, USA. s.guo@wriwindber.org', 'United States|United States|United States|United States|United States', 138, 'Databases, Protein|Gene Expression Regulation|Humans|Information Storage and Retrieval|Natural Language Processing|Protein Interaction Mapping|Proteins|Semantics|Signal Transduction', 'Comparative Study', '10.1093/bioinformatics/btl042', '16492685', 'Pathway modeling requires the integration of multiple data including prior knowledge. In this study, we quantitatively assess the application of Gene Ontology (GO)-derived similarity measures for the characterization of direct and indirect interactions within human regulatory pathways. The characterization would help the integration of prior pathway knowledge for the modeling.'),
(72671, 2006, 'bio', 'Avoiding model selection bias in small-sample genomic datasets', 'Berrar, Daniel|Bradbury, Ian|Dubitzky, Werner', 'School of Biomedical Sciences, University of Ulster at Coleraine Northern Ireland. dp.berrar@ulster.ac.uk|School of Biomedical Sciences, University of Ulster at Coleraine Northern Ireland. dp.berrar@ulster.ac.uk|School of Biomedical Sciences, University of Ulster at Coleraine Northern Ireland. dp.berrar@ulster.ac.uk', 'Ireland|Ireland|Ireland', 0, 'Algorithms|Bias|Biomarkers, Tumor|Chromosome Mapping|Computer Simulation|Databases, Genetic|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Models, Genetic|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sample Size|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl066', '16500931', 'Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived from such studies are often optimistically biased, because the apparent differences in performance are usually not controlled in a statistically stringent framework taking into account the adopted sampling strategy. We investigate this problem by means of a comparison of various classifiers in the context of multiclass microarray data.'),
(72672, 2006, 'bio', 'Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data', 'Huang, Desheng|Pan, Wei', 'Department of Mathematics, China Medical University Shenyang, China.|Department of Mathematics, China Medical University Shenyang, China.', 'China|China', 91, 'Algorithms|Biology|Cluster Analysis|Databases, Genetic|Expert Systems|Gene Expression Profiling|Knowledge Bases|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btl065', '16500932', 'Because co-expressed genes are likely to share the same biological function, cluster analysis of gene expression profiles has been applied for gene function discovery. Most existing clustering methods ignore known gene functions in the process of clustering.'),
(72673, 2006, 'bio', 'Refinement of optical map assemblies', 'Valouev, Anton|Zhang, Yu|Schwartz, David C|Waterman, Michael S', 'MCB 1050 Childs Way, Los Angeles, CA 90089-2910, USA. valouev@usc.edu|MCB 1050 Childs Way, Los Angeles, CA 90089-2910, USA. valouev@usc.edu|MCB 1050 Childs Way, Los Angeles, CA 90089-2910, USA. valouev@usc.edu|MCB 1050 Childs Way, Los Angeles, CA 90089-2910, USA. valouev@usc.edu', 'United States|United States|United States|United States', 23, 'Algorithms|Computer Simulation|DNA|DNA Mutational Analysis|Image Interpretation, Computer-Assisted|Microfluidic Analytical Techniques|Microscopy, Fluorescence|Models, Genetic|Optics and Photonics|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl063', '16500933', 'Genomic mutations and variations provide insightful information about the functionality of sequence elements and their association with human diseases. Traditionally, variations are identified through analysis of short DNA sequences, usually shorter than 1000 bp per fragment. Optical maps provide both faster and more cost-efficient means for detecting such differences, because a single map can span over 1 million bp. Optical maps are assembled to cover the whole genome, and the accuracy of assembly is critical.'),
(72674, 2006, 'bio', 'START: an automated tool for serial analysis of chromatin occupancy data', 'Marinescu, Voichita D|Kohane, Isaac S|Kim, Tae-Kyung|Harmin, David A|Greenberg, Michael E|Riva, Alberto', 'Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org|Childrens Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA. vdmarinescu@chip.org', 'United States|United States|United States|United States|United States|United States', 0, 'Artificial Intelligence|Chromatin|Chromosome Mapping|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl062', '16500934', 'The serial analysis of chromatin occupancy technique (SACO) promises to become a widely used method for the unbiased genome-wide experimental identification of loci bound by a transcription factor of interest. We describe the first web-based automatic tool, termed sequence tag analysis and reporting tool (START), for processing SACO data generated by experiments performed for the yeast, fruit fly, mouse, rat or human genomes. The program uses as input sequences of inserts from a SACO library from which it extracts all SACO tags, maps them to genomic locations and annotates them. START returns detailed information about these tags including the genes, the genomic elements and the miRNA precursors found in their vicinity, and makes use of the MAPPER database to identify putative transcription factor binding sites located close to the tags.'),
(72675, 2006, 'bio', 'Mass analysis peptide sequence prediction (MAPSP)', 'Eisenacher, Martin|de Braaf, Jürgen|König, Simone', 'Integrated Functional Genomics, Interdisciplinary Center for Clinical Research (IZKF), Westfalian Wilhelms-University of Muenster, Roentgenstr. 21, D-48149 Muenster, Germany.|Integrated Functional Genomics, Interdisciplinary Center for Clinical Research (IZKF), Westfalian Wilhelms-University of Muenster, Roentgenstr. 21, D-48149 Muenster, Germany.|Integrated Functional Genomics, Interdisciplinary Center for Clinical Research (IZKF), Westfalian Wilhelms-University of Muenster, Roentgenstr. 21, D-48149 Muenster, Germany.', 'Germany|Germany|Germany', 3, 'Algorithms|Amino Acid Sequence|Database Management Systems|Internet|Mass Spectrometry|Molecular Sequence Data|Peptide Mapping|Peptides|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl052', '16500935', 'The software tool MAPSP allows the combinatorial prediction of novel short peptides such as hormones with common sequence features. In addition, it assists in de novo sequencing in general. The tool was designed for use in conjunction with the analytical identification method of mass spectrometry (MS) and it can considerably speed-up the analysis of unknowns.'),
(72676, 2006, 'bio', 'A novel sensitive method for the detection of user-defined compositional bias in biological sequences', 'Kuznetsov, Igor B|Hwang, Seungwoo', 'Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, State University of New York One Discovery Drive, Rensselaer, NY 12144, USA. ikuznetsov@albany.edu|Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, State University of New York One Discovery Drive, Rensselaer, NY 12144, USA. ikuznetsov@albany.edu', 'United States|United States', 12, 'Algorithms|Artificial Intelligence|Bias|Conserved Sequence|Data Interpretation, Statistical|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btl049', '16500936', 'Most biological sequences contain compositionally biased segments in which one or more residue types are significantly overrepresented. The function and evolution of these segments are poorly understood. Usually, all types of compositionally biased segments are masked and ignored during sequence analysis. However, it has been shown for a number of proteins that biased segments that contain amino acids with similar chemical properties are involved in a variety of molecular functions and human diseases. A detailed large-scale analysis of the functional implications and evolutionary conservation of different compositionally biased segments requires a sensitive method capable of detecting user-specified types of compositional bias.'),
(72677, 2006, 'bio', 'The UCSC Known Genes', 'Hsu, Fan|Kent, W James|Clawson, Hiram|Kuhn, Robert M|Diekhans, Mark|Haussler, David', 'Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu|Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz Santa Cruz, CA 95064, USA. fanhsu@soe.ucsc.edu', 'United States|United States|United States|United States|United States|United States', 371, 'Base Sequence|California|Chromosome Mapping|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Molecular Sequence Data|Proteome|RNA, Messenger|Universities|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl048', '16500937', 'The University of California Santa Cruz (UCSC) Known Genes dataset is constructed by a fully automated process, based on protein data from Swiss-Prot/TrEMBL (UniProt) and the associated mRNA data from Genbank. The detailed steps of this process are described. Extensive cross-references from this dataset to other genomic and proteomic data were constructed. For each known gene, a details page is provided containing rich information about the gene, together with extensive links to other relevant genomic, proteomic and pathway data. As of July 2005, the UCSC Known Genes are available for human, mouse and rat genomes. The Known Genes serves as a foundation to support several key programs: the Genome Browser, Proteome Browser, Gene Sorter and Table Browser offered at the UCSC website. All the associated data files and program source code are also available. They can be accessed at http://genome.ucsc.edu. The genomic coverage of UCSC Known Genes, RefSeq, Ensembl Genes, H-Invitational and CCDS is analyzed. Although UCSC Known Genes offers the highest genomic and CDS coverage among major human and mouse gene sets, more detailed analysis suggests all of them could be further improved.'),
(72678, 2006, 'bio', 'Paloverde: an OpenGL 3D phylogeny browser', 'Sanderson, Michael J', 'Section of Evolution and Ecology, University of California, Davis, CA, USA. mjsanderson@ucdavis.edu', 'United States', 20, 'Computer Graphics|Documentation|Imaging, Three-Dimensional|Phylogeny|Programming Languages|Trees|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl044', '16500938', 'Paloverde is a new program designed to help visualize the phylogenetic structure of moderately large trees--trees on the scale of 100-2500 leaf nodes. The program embeds the user in an interactive virtual 3D world in which a large tree presented in various layouts can be manipulated through a mouse interface. The program implements radial 2D layouts, and true 3D spiral, conical and hemispherical (i.e. truly tree-like) layouts. Subclades can be defined in the input file (using standard node-based definitions) and displayed collapsed as new leaf nodes, or left intact but annotated with names around the periphery of the tree. A search tool lets the user zoom to any selected leaf node. Paloverde is an open source project written in ANSI C using the OpenGL library for 3D visualization.'),
(72679, 2006, 'bio', 'Mayday--a microarray data analysis workbench', 'Dietzsch, Janko|Gehlenborg, Nils|Nieselt, Kay', 'Center for Bioinformatics Tübingen, Department of Information and Cognitive Sciences, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, Department of Information and Cognitive Sciences, University of Tübingen, Sand 14, 72076 Tübingen, Germany.|Center for Bioinformatics Tübingen, Department of Information and Cognitive Sciences, University of Tübingen, Sand 14, 72076 Tübingen, Germany.', 'Germany|Germany|Germany', 40, 'Computer Graphics|Database Management Systems|Databases, Genetic|Documentation|Gene Expression Profiling|Information Storage and Retrieval|Internet|Oligonucleotide Array Sequence Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl070', '16500939', 'Mayday is a workbench for visualization, analysis and storage of microarray data. It features a graphical user interface and supports the development and integration of existing and new analysis methods. Besides the infrastructural core functionality, Mayday offers a variety of plug-ins, such as various interactive viewers, a connection to the R statistical environment, a connection to SQL-based databases and different data mining methods, including WEKA-library based methods for classification and various clustering methods. In addition, so-called meta information objects are provided for annotation of the microarray data allowing integration of data from different sources, which is a feature that, for instance, is employed in the enhanced heatmap visualization.'),
(72680, 2006, 'bio', 'Automated classification of alternative splicing and transcriptional initiation and construction of visual database of classified patterns', 'Nagasaki, Hideki|Arita, Masanori|Nishizawa, Tatsuya|Suwa, Makiko|Gotoh, Osamu', 'Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology Tokyo 135-0064, Japan. h-nagasaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology Tokyo 135-0064, Japan. h-nagasaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology Tokyo 135-0064, Japan. h-nagasaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology Tokyo 135-0064, Japan. h-nagasaki@aist.go.jp|Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology Tokyo 135-0064, Japan. h-nagasaki@aist.go.jp', 'Japan|Japan|Japan|Japan|Japan', 28, 'Algorithms|Alternative Splicing|Artificial Intelligence|Base Sequence|Computer Graphics|Databases, Genetic|Molecular Sequence Data|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA|Transcription Initiation Site|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl067', '16500940', 'Large-scale detection and classification of alternative splicing and transcriptional initiation (ASTI) is the first step towards detailed studies of the functional implication and mechanisms of these phenomena.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72681, 2006, 'bio', 'A systematic comparison and evaluation of biclustering methods for gene expression data', 'Prelixc4x87, Amela|Bleuler, Stefan|Zimmermann, Philip|Wille, Anja|Bxc3xbchlmann, Peter|Gruissem, Wilhelm|Hennig, Lars|Thiele, Lothar|Zitzler, Eckart', 'Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.|Computer Engineering and Networks Laboratory, ETH Zurich, 8092 Zurich, Switzerland.', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Switzerland|Swit', 601, 'Algorithms|Artificial Intelligence|Cluster Analysis|Databases, Genetic|Gene Expression|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Comparative Study', '10.1093/bioinformatics/btl060', '16500941', 'In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature, however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available.'),
(72682, 2006, 'bio', 'Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes', 'Prieto, C|Rivas, M J|Sánchez, J M|López-Fidalgo, J|De Las Rivas, J', 'Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC), Salamanca, Spain.|Bioinformatics and Functional Genomics Research Group, Cancer Research Center (CIC USAL-CSIC), Salamanca, Spain.', 'Spain|Spain|Spain|Spain|Spain', 20, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genetic Predisposition to Disease|Genetic Variation|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis', 'Journal Article', '10.1093/bioinformatics/btl053', '16500942', 'Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better.'),
(72683, 2006, 'bio', 'Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes', 'Rantanen, Ari|Mielikäinen, Taneli|Rousu, Juho|Maaheimo, Hannu|Ukkonen, Esko', 'Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) 00014 University of Helsinki Finland. ajrantan@cs.helsinki.fi|Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) 00014 University of Helsinki Finland. ajrantan@cs.helsinki.fi|Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) 00014 University of Helsinki Finland. ajrantan@cs.helsinki.fi|Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) 00014 University of Helsinki Finland. ajrantan@cs.helsinki.fi|Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) 00014 University of Helsinki Finland. ajrantan@cs.helsinki.fi', 'Finland|Finland|Finland|Finland|Finland', 23, 'Algorithms|Carbon|Computer Simulation|Diagnostic Techniques, Radioisotope|Magnetic Resonance Spectroscopy|Mass Spectrometry|Models, Biological|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl069', '16504982', 'Flux estimation using isotopomer information of metabolites is currently the most reliable method to obtain quantitative estimates of the activity of metabolic pathways. However, the development of isotopomer measurement techniques for intermediate metabolites is a demanding task. Careful planning of isotopomer measurements is thus needed to maximize the available flux information while minimizing the experimental effort.'),
(72684, 2006, 'bio', 'GALGO: an R package for multivariate variable selection using genetic algorithms', 'Trevino, Victor|Falciani, Francesco', 'School of Biosciences, University of Birmingham Birmingham, B15 2TT, UK.|School of Biosciences, University of Birmingham Birmingham, B15 2TT, UK.', 'United Kingdom|United Kingdom', 92, 'Algorithms|Data Interpretation, Statistical|Models, Biological|Models, Genetic|Models, Statistical|Multivariate Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl074', '16510496', 'The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.'),
(72685, 2006, 'bio', 'Family specific rates of protein evolution', 'Luz, Hannes|Vingron, Martin', 'Max Planck Institute for Molecular Genetics Ihnestrasse 73, 14195 Berlin, Germany. luz@molgen.mpg.de|Max Planck Institute for Molecular Genetics Ihnestrasse 73, 14195 Berlin, Germany. luz@molgen.mpg.de', 'Germany|Germany', 20, 'Algorithms|Amino Acid Sequence|Animals|Computer Simulation|Conserved Sequence|Evolution, Molecular|Humans|Models, Genetic|Molecular Sequence Data|Proteome|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl073', '16510497', 'Amino acid changing mutations in proteins are contstrained by purifying selection and accumulate at different rates. We estimate evolutionary rates on multiple alignments of eukaryotic protein families in a maximum likelihood framework and spot sets of slow and fast evolving proteins.'),
(72686, 2006, 'bio', 'PIANA: protein interactions and network analysis', 'Aragues, Ramon|Jaeggi, Daniel|Oliva, Baldo', 'Structural Bioinformatics Group (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, C/Doctor Aiguader, 83, Barcelona 08003, Catalonia, Spain. ramon.aragues@upf.edu|Structural Bioinformatics Group (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, C/Doctor Aiguader, 83, Barcelona 08003, Catalonia, Spain. ramon.aragues@upf.edu|Structural Bioinformatics Group (GRIB-IMIM), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, C/Doctor Aiguader, 83, Barcelona 08003, Catalonia, Spain. ramon.aragues@upf.edu', 'Spain|Spain|Spain', 0, 'Computer Graphics|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Proteins|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl072', '16510498', 'We present a software framework and tool called Protein Interactions And Network Analysis (PIANA) that facilitates working with protein interaction networks by (1) integrating data from multiple sources, (2) providing a library that handles graph-related tasks and (3) automating the analysis of protein-protein interaction networks. PIANA can also be used as a stand-alone application to create protein interaction networks and perform tasks such as predicting protein interactions and helping to identify spots in a 2D electrophoresis gel.'),
(72687, 2006, 'bio', 'Prediction of HLA-DQ32beta ligands: evidence of multiple registers in class II binding peptides', 'Tong, Joo Chuan|Zhang, Guang Lan|Tan, Tin Wee|August, J Thomas|Brusic, Vladimir|Ranganathan, Shoba', 'Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.|Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore 8 Medical Drive, Singapore 117597, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 0, 'Antibodies|Binding Sites|Computer Simulation|Databases, Protein|Epitope Mapping|HLA-DQ Antigens|Immunoassay|Models, Chemical|Models, Immunological|Peptides|Protein Binding|Protein Interaction Mapping|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl071', '16510499', 'While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models.'),
(72688, 2006, 'bio', 'Constructing quantitative models from qualitative mutant phenotypes: preferences in selecting sensory organ precursors', 'Hsu, Chao-Ping|Lee, Pei-Hsuan|Chang, Ching-Wei|Lee, Cheng-Tsung', 'Institute of Chemistry, Academia Sinica, 128 Section, 2 Academia Road, Nankang, Taipei 115, Taiwan.|Institute of Chemistry, Academia Sinica, 128 Section, 2 Academia Road, Nankang, Taipei 115, Taiwan.|Institute of Chemistry, Academia Sinica, 128 Section, 2 Academia Road, Nankang, Taipei 115, Taiwan.|Institute of Chemistry, Academia Sinica, 128 Section, 2 Academia Road, Nankang, Taipei 115, Taiwan.', '-|-|-|-', 0, 'Animals|Cluster Analysis|Computational Biology|Drosophila|Evolution, Molecular|Gene Expression Regulation|Gene Expression Regulation, Developmental|Models, Biological|Mutation|Neurons|Phenotype|Sense Organs|Systems Biology|Time Factors', 'Journal Article', '10.1093/bioinformatics/btl082', '16522667', 'To study biology from the systems level, mathematical models that describe the time-evolution of the system offer useful insights. Quantitative information is required for constructing such models, but such information is rarely provided.'),
(72689, 2006, 'bio', 'MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data', 'Walley, David C|Tripp, Ben W|Song, Young C|Walley, Keith R|Tebbutt, Scott J', 'James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Pauls Hospital, University of British Columbia Vancouver, V6Z 1Y6, Canada.|James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Pauls Hospital, University of British Columbia Vancouver, V6Z 1Y6, Canada.|James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Pauls Hospital, University of British Columbia Vancouver, V6Z 1Y6, Canada.|James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Pauls Hospital, University of British Columbia Vancouver, V6Z 1Y6, Canada.|James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research, St. Pauls Hospital, University of British Columbia Vancouver, V6Z 1Y6, Canada.', 'Canada|Canada|Canada|Canada|Canada', 5, 'Algorithms|Artificial Intelligence|Base Sequence|Cluster Analysis|Genotype|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl080', '16522668', 'Multi-dimensional Automated Clustering Genotyping Tool (MACGT) is a Java application that clusters complex multi-dimensional vector data derived from single nucleotide polymorphism (SNP) genotyping experiments using mini-sequencing based microarray chemistries such as arrayed primer extension (APEX). Spot intensity output files from microarray experiments across multiple samples are imported into MACGT. The datasets can include four channels of intensity data for each spot, replica spots for each SNP probe and multiple probe types (APEX and allele-specific APEX probes) on both DNA strands for each SNP. MACGT automatically clusters these multi-dimensionality datasets for each SNP across multiple samples. Incorporation of additional array datasets from known samples that have previously validated SNP genotype calls allows unknown samples to be automatically assigned a genotype based on the clustering, along with numerical measures of confidence for each genotype call. Calling accuracy by MACGT exceeds 98% when applied to genotyping data from APEX microarrays, and can be increased to >99.5% by applying thresholds to the confidence measures.'),
(72690, 2006, 'bio', 'Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces', 'Burgoyne, Nicholas J|Jackson, Richard M', 'Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.|Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.', 'United Kingdom|United Kingdom', 0, 'Binding Sites|Computational Biology|Databases, Protein|False Positive Reactions|Ligands|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Proteomics|ROC Curve|Software|Static Electricity', 'Journal Article', '10.1093/bioinformatics/btl079', '16522669', 'Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding hot-spots, and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex.'),
(72691, 2006, 'bio', 'Convergence of the proteomic pattern in cancer', 'Müller, Ute|Ernst, Günther|Melle, Christian|Guthke, Reinhard|von Eggeling, Ferdinand', 'Core Unit Chip Application (CUCA), Institute of Human Genetics and Anthropology, Friedrich-Schiller-University, 07740 Jena, Germany.|Core Unit Chip Application (CUCA), Institute of Human Genetics and Anthropology, Friedrich-Schiller-University, 07740 Jena, Germany.|Core Unit Chip Application (CUCA), Institute of Human Genetics and Anthropology, Friedrich-Schiller-University, 07740 Jena, Germany.|Core Unit Chip Application (CUCA), Institute of Human Genetics and Anthropology, Friedrich-Schiller-University, 07740 Jena, Germany.|Core Unit Chip Application (CUCA), Institute of Human Genetics and Anthropology, Friedrich-Schiller-University, 07740 Jena, Germany.', 'Germany|Germany|Germany|Germany|Germany', 5, 'Cell Differentiation|Colon|Colorectal Neoplasms|Computational Biology|Disease Progression|Gene Expression Regulation, Neoplastic|Humans|Models, Statistical|Neoplasms|Protein Array Analysis|Proteomics', 'Journal Article', '10.1093/bioinformatics/btl077', '16522670', 'On the histological level the differentiation of normal epithelial tissues is well known. The phenomenon of dedifferentiation, which occurs as cells develop towards malignancy is also well described. To identify an epithelial tumor-specific proteomic profile as well as to measure the proximities between we used data from tumor tissue and adjacent normal tissue microdissected from head and neck and colon cancer samples which were analyzed using ProteinChip technology and performed a bioinformatic meta-analysis on the resulting four complex datasets.'),
(72692, 2006, 'bio', 'Detection of a tandem BRCT in Nbs1 and Xrs2 with functional implications in the DNA damage response', 'Becker, Emmanuelle|Meyer, Vincent|Madaoui, Hocine|Guerois, Raphaël', 'Service de Biophysique des Fonctions Membranaires, URA CNRS 2096, Département de Biologie Joliot-Curie, CEA Saclay, 91191 Gif-Sur-Yvette Cedex, France.|Service de Biophysique des Fonctions Membranaires, URA CNRS 2096, Département de Biologie Joliot-Curie, CEA Saclay, 91191 Gif-Sur-Yvette Cedex, France.|Service de Biophysique des Fonctions Membranaires, URA CNRS 2096, Département de Biologie Joliot-Curie, CEA Saclay, 91191 Gif-Sur-Yvette Cedex, France.|Service de Biophysique des Fonctions Membranaires, URA CNRS 2096, Département de Biologie Joliot-Curie, CEA Saclay, 91191 Gif-Sur-Yvette Cedex, France.', 'France|France|France|France', 46, 'Amino Acid Sequence|Cell Cycle Proteins|Computational Biology|DNA Damage|DNA Repair|Models, Molecular|Molecular Sequence Data|Mutation|Nuclear Proteins|Protein Structure, Tertiary|Repetitive Sequences, Nucleic Acid|Saccharomyces cerevisiae Proteins|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl075', '16522671', 'Human Nbs1 and its homolog Xrs2 in Saccharomyces cerevisiae are part of the conserved MRN complex (MRX in yeast) which plays a crucial role in maintaining genomic stability. NBS1 corresponds to the gene mutated in the Nijmegen breakage syndrome (NBS) known as a radiation hyper-sensitive disease. Despite the conservation and the importance of the MRN complex, the high sequence divergence between Nbs1 and Xrs2 precluded the identification of common domains downstream of the N-terminal Fork-Head Associated (FHA) domain.'),
(72693, 2006, 'bio', 'A calibration method for estimating absolute expression levels from microarray data', 'Engelen, Kristof|Naudts, Bart|De Moor, Bart|Marchal, Kathleen', 'BIOI@SCD, Department of Electrical Engineering K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.|BIOI@SCD, Department of Electrical Engineering K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.|BIOI@SCD, Department of Electrical Engineering K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.|BIOI@SCD, Department of Electrical Engineering K.U.Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 7, 'Algorithms|Calibration|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|In Situ Hybridization, Fluorescence|Models, Genetic|Reference Values|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl068', '16522672', 'We describe an approach to normalize spotted microarray data, based on a physically motivated calibration model. This model consists of two major components, describing the hybridization of target transcripts to their corresponding probes on the one hand, and the measurement of fluorescence from the hybridized, labeled target on the other hand. The model parameters and error distributions are estimated from external control spikes.'),
(72694, 2006, 'bio', 'Rosetta error model for gene expression analysis', 'Weng, Lee|Dai, Hongyue|Zhan, Yihui|He, Yudong|Stepaniants, Sergey B|Bassett, Douglas E', 'Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com|Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com|Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com|Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com|Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com|Rosetta Inpharmatics LLC 401 Terry Avenue North, Seattle, WA 98109, USA. lee_weng@rosettabio.com', 'United States|United States|United States|United States|United States|United States', 257, 'Algorithms|Analysis of Variance|Computer Simulation|Data Interpretation, Statistical|Gene Expression|Gene Expression Profiling|Genetic Variation|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl045', '16522673', 'In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent.'),
(72695, 2006, 'bio', 'A generalization of the PST algorithm: modeling the sparse nature of protein sequences', 'Leonardi, Florencia G', 'Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão 1010, CEP 05508-090, São Paulo, Brazil. leonardi@ime.usp.br', 'Brazil', 11, 'Algorithms|Computational Biology|Genomics|Internet|Markov Chains|Models, Statistical|Probability|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl088', '16527830', 'A central problem in genomics is to determine the function of a protein using the information contained in its amino acid sequence. Variable length Markov chains (VLMC) are a promising class of models that can effectively classify proteins into families and they can be estimated in linear time and space.'),
(72696, 2006, 'bio', 'Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms', 'Lelandais, Gaëlle|Vincens, Pierre|Badel-Chagnon, Anne|Vialette, Stéphane|Jacq, Claude|Hazout, Serge', 'Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr|Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr|Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr|Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr|Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr|Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue dUlm, 75230 Paris cedex 05, France. lelandais@biologie.ens.fr', 'France|France|France|France|France|France', 15, 'Computational Biology|Evolution, Molecular|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genome|Models, Statistical|Saccharomyces cerevisiae|Schizosaccharomyces|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl087', '16527831', 'Molecular evolution, which is classically assessed by comparison of individual proteins or genes between species, can now be studied by comparing co-expressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by genome-wide expression analyses. However, it requires new methodologies to represent the data in a more accessible way for cross-species comparisons.'),
(72697, 2006, 'bio', 'The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks', 'Machné, Rainer|Finney, Andrew|Müller, Stefan|Lu, James|Widder, Stefanie|Flamm, Christoph', 'Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.|Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.', 'Austria|Austria|Austria|Austria|Austria|Austria', 70, 'Algorithms|Computational Biology|Computer Simulation|Computers|Gene Library|Models, Biological|Models, Theoretical|Programming Languages|Software Design|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btl086', '16527832', 'The SBML ODE Solver Library (SOSlib) is a programming library for symbolic and numerical analysis of chemical reaction network models encoded in the Systems Biology Markup Language (SBML). It is written in ISO C and distributed under the open source LGPL license. The package employs libSBML structures for formula representation and associated functions to construct a system of ordinary differential equations, their Jacobian matrix and other derivatives. SUNDIALS CVODES is incorporated for numerical integration and sensitivity analysis. Preliminary benchmarking results give a rough overview on the behavior of different tools and are discussed in the Supplementary Material. The native application program interface provides fine-grained interfaces to all internal data structures, symbolic operations and numerical routines, enabling the construction of very efficient analytic applications and hybrid or multi-scale solvers with interfaces to SBML and non SBML data sources. Optional modules based on XMGrace and Graphviz allow quick inspection of structure and dynamics.'),
(72698, 2006, 'bio', 'Progressive peak clustering in GC-MS Metabolomic experiments applied to Leishmania parasites', 'De Souza, David P|Saunders, Eleanor C|McConville, Malcolm J|Likixc4x87, Vladimir A', 'Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.|Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.|Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.|Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.', 'Australia|Australia|Australia|Australia', 51, 'Animals|Cluster Analysis|Data Interpretation, Statistical|Gas Chromatography-Mass Spectrometry|Gene Expression Profiling|Leishmania mexicana|Metabolism|Proteomics|Software', 'Journal Article', '10.1093/bioinformatics/btl085', '16527833', 'A common problem in the emerging field of metabolomics is the consolidation of signal lists derived from metabolic profiling of different cell/tissue/fluid states where a number of replicate experiments was collected on each state.'),
(72699, 2006, 'bio', 'Semantic Mining in Biomedicine (Introduction to the papers selected from the SMBM 2005 Symposium, Hinxton, UK, April 2005)', 'Hahn, Udo|Valencia, Alfonso', 'Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.|Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville 3010, Australia.', 'Australia|Australia', 2, 'Biomedical Engineering|Computational Biology|Database Management Systems|Databases, Bibliographic|Documentation|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Semantics', 'Editorial', '10.1093/bioinformatics/btl084', '16527834', '-'),
(72700, 2006, 'bio', 'BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data', 'Marioni, J C|Thorne, N P|Tavaré, S', 'Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk|Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk|Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk', '-|-|-', 109, 'Algorithms|Artificial Intelligence|Base Sequence|Chromosome Mapping|Gene Dosage|In Situ Hybridization|Markov Chains|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl089', '16533818', 'We have developed a new method (BioHMM) for segmenting array comparative genomic hybridization data into states with the same underlying copy number. By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biological factors (e.g. the distance between adjacent clones) in the segmentation process.'),
(72701, 2006, 'bio', 'Graph-based analysis and visualization of experimental results with ONDEX', 'Köhler, Jacob|Baumbach, Jan|Taubert, Jan|Specht, Michael|Skusa, Andre|Rüegg, Alexander|Rawlings, Chris|Verrier, Paul|Philippi, Stephan', 'Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk|Division of Biomathematics and Bioinformatics, Rothamsted Research, AL5 2JQ Harpenden, UK. Jacob.Koehler@bbsrc.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom|United Kingdom', 131, 'Algorithms|Arabidopsis|Automation|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl081', '16533819', 'Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise.'),
(72702, 2006, 'bio', 'A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly', 'Wainreb, Gilad|Haspel, Nurit|Wolfson, Haim J|Nussinov, Ruth', 'Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.|Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.', 'Israel|Israel|Israel|Israel', 5, 'Algorithms|Amino Acid Sequence|Animals|Cluster Analysis|Computational Biology|Humans|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl098', '16543273', 'Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly.'),
(72703, 2006, 'bio', 'CAFE: a computational tool for the study of gene family evolution', 'De Bie, Tijl|Cristianini, Nello|Demuth, Jeffery P|Hahn, Matthew W', 'K.U.Leuven, OKP Research Group Tiensestraat 102, 3000 Leuven, Belgium.|K.U.Leuven, OKP Research Group Tiensestraat 102, 3000 Leuven, Belgium.|K.U.Leuven, OKP Research Group Tiensestraat 102, 3000 Leuven, Belgium.|K.U.Leuven, OKP Research Group Tiensestraat 102, 3000 Leuven, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 378, 'Algorithms|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetic Variation|Multigene Family|Phylogeny|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl097', '16543274', 'We present CAFE (Computational Analysis of gene Family Evolution), a tool for the statistical analysis of the evolution of the size of gene families. It uses a stochastic birth and death process to model the evolution of gene family sizes over a phylogeny. For a specified phylogenetic tree, and given the gene family sizes in the extant species, CAFE can estimate the global birth and death rate of gene families, infer the most likely gene family size at all internal nodes, identify gene families that have accelerated rates of gain and loss (quantified by a p-value) and identify which branches cause the p-value to be small for significant families.'),
(72704, 2006, 'bio', 'Cyanobacterial response regulator PatA contains a conserved N-terminal domain (PATAN) with an alpha-helical insertion', 'Makarova, Kira S|Koonin, Eugene V|Haselkorn, Robert|Galperin, Michael Y', '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', 26, 'Amino Acid Sequence|Anabaena|Bacterial Physiological Phenomena|Bacterial Proteins|Cell Differentiation|Cell Movement|Computational Biology|Conserved Sequence|Helix-Turn-Helix Motifs|Molecular Sequence Data|Protein Binding|Protein Structure, Secondary|Protein Structure, Tertiary|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl096', '16543275', 'The cyanobacterium Anabaena (Nostoc) PCC 7120 responds to starvation for nitrogen compounds by differentiating approximately every 10th cell in the filament into nitrogen-fixing cells called heterocysts. Heterocyst formation is subject to complex regulation, which involves an unusual response regulator PatA that contains a CheY-like phosphoacceptor (receiver, REC) domain at its C-terminus. PatA-like response regulators are widespread in cyanobacteria, one of them regulates phototaxis in Synechocystis PCC 6803. Sequence analysis of PatA revealed, in addition to the REC domain, a previously undetected, conserved domain, which we named PATAN (after PatA N-terminus), and a potential helix-turn-helix (HTH) domain. PATAN domains are encoded in a variety of environmental bacteria and archaea, often in several copies per genome, and are typically associated with REC, Roadblock and other signal transduction domains, or with DNA-binding HTH domains. Many PATAN domains contain insertions of a small additional domain, termed alpha-clip, which is predicted to form a four-helix bundle. PATAN domains appear to participate in protein-protein interactions that regulate gliding motility and processes of cell development and differentiation in cyanobacteria and some proteobacteria, such as Myxococcus xanthus and Geobacter sulfurreducens.'),
(72705, 2006, 'bio', 'Prediction of viable circular permutants using a graph theoretic approach', 'Paszkiewicz, Konrad H|Sternberg, Michael J E|Lappe, Michael', 'Structural Bioinformatics Group, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. konrad.paszkiewicz@imperial.ac.uk|Structural Bioinformatics Group, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. konrad.paszkiewicz@imperial.ac.uk|Structural Bioinformatics Group, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. konrad.paszkiewicz@imperial.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 14, 'Binding Sites|Computational Biology|Computer Graphics|Computer Simulation|Escherichia coli|False Positive Reactions|Models, Molecular|Protein Conformation|Protein Folding|Protein Interaction Mapping|Protein Structure, Secondary|Proteomics|ROC Curve|Tetrahydrofolate Dehydrogenase', 'Journal Article', '10.1093/bioinformatics/btl095', '16543276', 'In recent years graph-theoretic descriptions have been applied to aid the analysis of a number of complex biological systems. However, such an approach has only just begun to be applied to examine protein structures and the network of interactions between residues with promising results. Here we examine whether a graph measure known as closeness is capable of predicting regions where a protein can be split to form a viable circular permutant. Circular permutants are a powerful experimental tool to probe folding mechanisms and more recently have been used to design split enzyme reporter proteins.'),
(72706, 2006, 'bio', 'Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier', 'Yousef, Malik|Nebozhyn, Michael|Shatkay, Hagit|Kanterakis, Stathis|Showe, Louise C|Showe, Michael K', 'The Wistar Institute, Philadelphia, PA 19104, USA.|The Wistar Institute, Philadelphia, PA 19104, USA.|The Wistar Institute, Philadelphia, PA 19104, USA.|The Wistar Institute, Philadelphia, PA 19104, USA.|The Wistar Institute, Philadelphia, PA 19104, USA.|The Wistar Institute, Philadelphia, PA 19104, USA.', 'United States|United States|United States|United States|United States|United States', 147, 'Algorithms|Animals|Artificial Intelligence|Base Sequence|Bayes Theorem|Computational Biology|Humans|MicroRNAs|Molecular Sequence Data|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl094', '16543277', 'Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species.'),
(72707, 2006, 'bio', 'WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks', 'Lee, Dong-Yup|Yun, Choamun|Cho, Ayoun|Hou, Bo Kyeng|Park, Sunwon|Lee, Sang Yup', 'Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.|Department of Chemical and Biomolecular Engineering, Metabolic and Biomolecular Engineering National Research Laboratory 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Korea.', '-|-|-|-|-|-', 27, 'Cell Physiological Phenomena|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Internet|Kinetics|Models, Biological|Proteome|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl091', '16543278', 'WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.'),
(72708, 2006, 'bio', 'An effective structure learning method for constructing gene networks', 'Chen, Xue-Wen|Anantha, Gopalakrishna|Wang, Xinkun', 'Bioinformatics and Computational Life Sciences Laboratory, Electrical Engineering and Computer Science Department, 1520 West 15th Street, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu|Bioinformatics and Computational Life Sciences Laboratory, Electrical Engineering and Computer Science Department, 1520 West 15th Street, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu|Bioinformatics and Computational Life Sciences Laboratory, Electrical Engineering and Computer Science Department, 1520 West 15th Street, The University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu', 'United States|United States|United States', 85, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computational Biology|Fungal Proteins|Gene Expression Profiling|Gene Expression Regulation, Fungal|Genes, Fungal|Neural Networks (Computer)|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl090', '16543279', 'Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery.'),
(72709, 2006, 'bio', 'Analysis of internal loops within the RNA secondary structure in almost quadratic time', 'Ogurtsov, Aleksey Y|Shabalina, Svetlana A|Kondrashov, Alexey S|Roytberg, Mikhail A', '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|Base Composition|Computational Biology|Nucleic Acid Conformation|Programming Languages|RNA|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btl083', '16543280', 'Evaluating all possible internal loops is one of the key steps in predicting the optimal secondary structure of an RNA molecule. The best algorithm available runs in time O(L(3)), L is the length of the RNA.'),
(72710, 2006, 'bio', 'A machine learning information retrieval approach to protein fold recognition', 'Cheng, Jianlin|Baldi, Pierre', 'Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.|Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA.', 'United States|United States', 0, 'Algorithms|Animals|Artificial Intelligence|Computational Biology|Databases, Protein|Humans|Information Storage and Retrieval|Models, Molecular|Models, Statistical|Normal Distribution|Protein Folding|Proteins|Sensitivity and Specificity|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btl102', '16547073', 'Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein.'),
(72711, 2006, 'bio', 'Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients', 'Broët, Philippe|Kuznetsov, Vladimir A|Bergh, Jonas|Liu, Edison T|Miller, Lance D', 'Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr|Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr|Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr|Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr|Faculté de Médecine-Université Paris-XI, IFR69, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France. broet@vjf.inserm.fr', 'France|France|France|France|France', 18, 'Biomarkers, Tumor|Breast Neoplasms|Computer Simulation|Disease Progression|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Models, Statistical|Pattern Recognition, Automated|Proportional Hazards Models|Recurrence|Time Factors|Treatment Outcome', 'Journal Article', '10.1093/bioinformatics/btl110', '16551658', 'In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72712, 2006, 'bio', 'DRTF: a database of rice transcription factors', 'Gao, Ge|Zhong, Yingfu|Guo, Anyuan|Zhu, Qihui|Tang, Wen|Zheng, Weimou|Gu, Xiaocheng|Wei, Liping|Luo, Jingchu', 'Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.|Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, Peoples Republic of China.', 'China|China|China|China|China|China|China|China|China', 0, 'Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Oryza|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl107', '16551659', 'DRTF contains 2025 putative transcription factors (TFs) in Oryza sativa L. ssp. indica and 2384 in ssp. japonica, distributed in 63 families, identified by computational prediction and manual curation. It includes detailed annotations of each TF including sequence features, functional domains, Gene Ontology assignment, chromosomal localization, EST and microarray expression information, as well as multiple sequence alignment of the DNA-binding domains for each TF family. The database can be browsed and searched with a user-friendly web interface.'),
(72713, 2006, 'bio', 'UniSave: the UniProtKB sequence/annotation version database', 'Leinonen, Rasko|Nardone, Francesco|Zhu, Weimin|Apweiler, Rolf', 'EMBL Outstation, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.|EMBL Outstation, The European Bioinformatics Institute (EBI) Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Database Management Systems|Databases, Protein|Documentation|Information Storage and Retrieval|Internet|Proteins|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl105', '16551660', 'The UniProtKB Sequence/Annotation Version database (UniSave) is a comprehensive archive of UniProtKB/Swiss-Prot and UniProtKB/TrEMBL entry versions. All changed Swiss-Prot and TrEMBL entries are loaded into the UniSave as part of the public bi-weekly UniProtKB releases. Unlike the UniProtKB, which contains only the latest Swiss-Prot and TrEMBL entry versions, the UniSave provides access to previous versions of these entries.'),
(72714, 2006, 'bio', 'Phylogeny-independent detection of functional residues', 'Pazos, Florencio|Rausell, Antonio|Valencia, Alfonso', 'Protein Design Group, National Centre for Biotechnology (CNB-CSIC) C/Darwin, 3. Campus U. Autónoma, 28049 Cantoblanco, Madrid, Spain. pazos@cnb.uam.es|Protein Design Group, National Centre for Biotechnology (CNB-CSIC) C/Darwin, 3. Campus U. Autónoma, 28049 Cantoblanco, Madrid, Spain. pazos@cnb.uam.es|Protein Design Group, National Centre for Biotechnology (CNB-CSIC) C/Darwin, 3. Campus U. Autónoma, 28049 Cantoblanco, Madrid, Spain. pazos@cnb.uam.es', 'Spain|Spain|Spain', 52, 'Amino Acid Sequence|Computational Biology|Evolution, Molecular|Models, Genetic|Models, Molecular|Models, Statistical|Molecular Sequence Data|Multivariate Analysis|Phylogeny|Protein Conformation|Protein Structure, Tertiary|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl104', '16551661', 'Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family.'),
(72715, 2006, 'bio', 'XQTav: an XQuery processor for Taverna environment', 'Sroka, Jacek|Kaczor, Grzegorz|Tyszkiewicz, Jerzy|Kierzek, Andrzej M', 'Warsaw University Warsaw, Poland. sroka@mimuw.edu.pl|Warsaw University Warsaw, Poland. sroka@mimuw.edu.pl|Warsaw University Warsaw, Poland. sroka@mimuw.edu.pl|Warsaw University Warsaw, Poland. sroka@mimuw.edu.pl', 'Poland|Poland|Poland|Poland', 0, 'Algorithms|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Software|Systems Biology|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl101', '16551662', 'Taverna workbench is an environment for construction, visualization and execution of bioinformatic workflows that integrate specialized tools available through the internet. It is gaining popularity fast, because of supporting the most important bioinformatic services and its simple, yet robust graphical notation. Here we present XQTav-an extension of Taverna that provides full integration with XQuery (the query language for XML) engine. XQTav allows execution of XQuery scripts in Taverna workflow diagrams. All existing Taverna processors can be accessed in the XQuery scripts. This provides an alternative way of specifying subworkflows in Taverna and is useful when one deals with query-like algorithms (e.g. filters and inner joins). Moreover, XQtav may be used to automatically generate an XQuery script that is equivalent to Tavernas workflow. This constitutes another way of creating and enacting bioinformatic workflows: overall structure of a diagram is drawn in Taverna environment, XQuery code is generated and possibly adjusted by hand. It can be executed by XQuery engines or incorporated into other software environments.'),
(72716, 2006, 'bio', 'Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis', 'Li, Wentian|Wang, Mingyi|Irigoyen, Patricia|Gregersen, Peter K', 'The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System 350 Community Drive, Manhasset, NY, USA. wli@nslij-genetics.org|The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System 350 Community Drive, Manhasset, NY, USA. wli@nslij-genetics.org|The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System 350 Community Drive, Manhasset, NY, USA. wli@nslij-genetics.org|The Robert S Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Shore LIJ Health System 350 Community Drive, Manhasset, NY, USA. wli@nslij-genetics.org', 'United States|United States|United States|United States', 0, 'Arthritis, Rheumatoid|Biomarkers|Computational Biology|Epitopes|Gene Expression Profiling|Genetic Predisposition to Disease|Genotype|Humans|Models, Statistical|Phenotype', 'Journal Article', '10.1093/bioinformatics/btl100', '16551663', 'Genetic association analysis is based on statistical correlations which do not assign any cause-to-effect arrows between the two correlated variables. Normally, such assignment of cause and effect label is not necessary in genetic analysis since genes are always the cause and phenotypes are always the effect. However, among intermediate phenotypes and biomarkers, assigning cause and effect becomes meaningful, and causal inference can be useful.'),
(72717, 2006, 'bio', 'BicAT: a biclustering analysis toolbox', 'Barkow, Simon|Bleuler, Stefan|Prelic, Amela|Zimmermann, Philip|Zitzler, Eckart', 'Reverse Engineering Group: Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Zurich ETH Zentrum, 8092 Zurich, Switzerland. barkow@tik.ee.ethz.ch|Reverse Engineering Group: Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Zurich ETH Zentrum, 8092 Zurich, Switzerland. barkow@tik.ee.ethz.ch|Reverse Engineering Group: Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Zurich ETH Zentrum, 8092 Zurich, Switzerland. barkow@tik.ee.ethz.ch|Reverse Engineering Group: Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Zurich ETH Zentrum, 8092 Zurich, Switzerland. barkow@tik.ee.ethz.ch|Reverse Engineering Group: Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology Zurich ETH Zentrum, 8092 Zurich, Switzerland. barkow@tik.ee.ethz.ch', 'Switzerland|Switzerland|Switzerland|Switzerland|Switzerland', 203, 'Algorithms|Artificial Intelligence|Cluster Analysis|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl099', '16551664', 'Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments.'),
(72718, 2006, 'bio', 'CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data', 'Kaderali, Lars|Zander, Thomas|Faigle, Ulrich|Wolf, Jürgen|Schultze, Joachim L|Schrader, Rainer', 'Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de|Cologne University Bioinformatics Center and Center for Applied Computer Science Weyertal 80, 50931 Köln, Germany. kaderali@zpr.uni-koeln.de', 'Germany|Germany|Germany|Germany|Germany|Germany', 0, 'Bayes Theorem|Cell Line, Tumor|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Lung Neoplasms|Multivariate Analysis|Neoplasms|Oligonucleotide Array Sequence Analysis|Prognosis|Proportional Hazards Models|ROC Curve|Regression Analysis|Sensitivity and Specificity|Time Factors', 'Journal Article', '10.1093/bioinformatics/btl103', '16554338', 'DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction.'),
(72719, 2006, 'bio', 'GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition', 'Ortuso, Francesco|Langer, Thierry|Alcaro, Stefano', 'Dipartimento di Scienze Farmacobiologiche Università di Catanzaro Magna Graecia Complesso Ninì Barbieri I-88021, Roccelletta di Borgia (CZ), Italy.|Dipartimento di Scienze Farmacobiologiche Università di Catanzaro Magna Graecia Complesso Ninì Barbieri I-88021, Roccelletta di Borgia (CZ), Italy.|Dipartimento di Scienze Farmacobiologiche Università di Catanzaro Magna Graecia Complesso Ninì Barbieri I-88021, Roccelletta di Borgia (CZ), Italy.', 'Italy|Italy|Italy', 75, 'Algorithms|Animals|Automation|Cluster Analysis|Computational Biology|Humans|Interleukin-8|Models, Molecular|Molecular Conformation|Pharmaceutical Preparations|Proteins|Receptors, Drug|Software|X-Linked Inhibitor of Apoptosis Protein', 'Journal Article', '10.1093/bioinformatics/btl115', '16567363', 'Automatic procedures to obtain pharmacophore models from experimentally determined macromolecular complexes can help in the drug discovery process, especially when protein-protein recognition plays an important biological role.'),
(72720, 2006, 'bio', 'A dynamic programming algorithm for binning microbial community profiles', 'Ruan, Quansong|Steele, Joshua A|Schwalbach, Michael S|Fuhrman, Jed A|Sun, Fengzhu', 'Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.|Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.|Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.|Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.|Department of Mathematics, University of Southern California 3620 South Vermont Avenue, KAP 108, Los Angeles, California 90089-253, USA.', 'United States|United States|United States|United States|United States', 16, 'Algorithms|Biodiversity|Cluster Analysis|Computational Biology|DNA, Ribosomal Spacer|Genes, Bacterial|Models, Biological|Models, Theoretical|Mutation|Programming Languages|RNA, Ribosomal, 16S|RNA, Ribosomal, 23S|Seawater|Software', 'Journal Article', '10.1093/bioinformatics/btl114', '16567364', 'A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Owing to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We have developed a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time.'),
(72721, 2006, 'bio', 'Finding regions of significance in SELDI measurements for identifying protein biomarkers', 'Tan, Chuen Seng|Ploner, Alexander|Quandt, Andreas|Lehtiö, Janne|Pawitan, Yudi', 'Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Stockholm, Sweden.|Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Stockholm, Sweden.', 'Sweden|Sweden|Sweden|Sweden|Sweden', 0, 'Algorithms|Animals|Biomarkers|Biomarkers, Tumor|Cell Line, Tumor|Humans|Models, Statistical|Prognosis|Programming Languages|Proteins|Proteomics|ROC Curve|Reproducibility of Results|Software|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Journal Article', '10.1093/bioinformatics/btl106', '16567365', 'There is a well-recognized potential of protein expression profiling using the surface-enhanced laser desorption and ionization technology for discovering biomarkers that can be applied in clinical diagnosis, prognosis and therapy prediction. The pre-processing of the raw data, however, is still problematic.'),
(72722, 2006, 'bio', 'PseudoPipe: an automated pseudogene identification pipeline', 'Zhang, Zhaolei|Carriero, Nicholas|Zheng, Deyou|Karro, John|Harrison, Paul M|Gerstein, Mark', 'Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.|Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.|Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.|Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.|Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.|Banting and Best Department of Medical Research, Donnelly CCBR, University of Toronto 160 College Street, Toronto, ON M5S 3E1, Canada.', 'Canada|Canada|Canada|Canada|Canada|Canada', 96, 'Algorithms|Animals|Automation|Computational Biology|Evolution, Molecular|Genome|Humans|Models, Genetic|Pseudogenes|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btl116', '16574694', 'Mammalian genomes contain many genomic fossils i.e. pseudogenes. These are disabled copies of functional genes that have been retained in the genome by gene duplication or retrotransposition events. Pseudogenes are important resources in understanding the evolutionary history of genes and genomes.'),
(72723, 2006, 'bio', 'GenRGenS: software for generating random genomic sequences and structures', 'Ponty, Yann|Termier, Michel|Denise, Alain', 'LRI, UMR CNRS 8623, Université Paris-Sud 11 F91405 Orsay cedex, France.|LRI, UMR CNRS 8623, Université Paris-Sud 11 F91405 Orsay cedex, France.|LRI, UMR CNRS 8623, Université Paris-Sud 11 F91405 Orsay cedex, France.', 'France|France|France', 0, 'Computational Biology|Computer Simulation|Genome|Markov Chains|Models, Statistical|Nucleic Acid Conformation|Open Reading Frames|Programming Languages|RNA|Software', 'Journal Article', '10.1093/bioinformatics/btl113', '16574695', 'GenRGenS is a software tool dedicated to randomly generating genomic sequences and structures. It handles several classes of models useful for sequence analysis, such as Markov chains, hidden Markov models, weighted context-free grammars, regular expressions and PROSITE expressions. GenRGenS is the only program that can handle weighted context-free grammars, thus allowing the user to model and to generate structured objects (such as RNA secondary structures) of any given desired size. GenRGenS also allows the user to combine several of these different models at the same time.'),
(72724, 2006, 'bio', 'SBMLToolbox: an SBML toolbox for MATLAB users', 'Keating, Sarah M|Bornstein, Benjamin J|Finney, Andrew|Hucka, Michael', 'Science and Technology Research Institute, University of Hertfordshire Hatfield, UK. s.m.keating@herts.ac.uk|Science and Technology Research Institute, University of Hertfordshire Hatfield, UK. s.m.keating@herts.ac.uk|Science and Technology Research Institute, University of Hertfordshire Hatfield, UK. s.m.keating@herts.ac.uk|Science and Technology Research Institute, University of Hertfordshire Hatfield, UK. s.m.keating@herts.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 96, 'Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Models, Chemical|Programming Languages|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btl111', '16574696', 'We present SBMLToolbox, a toolbox that facilitates importing and exporting models represented in the Systems Biology Markup Language (SBML) in and out of the MATLAB environment and provides functionality that enables an experienced user of either SBML or MATLAB to combine the computing power of MATLAB with the portability and exchangeability of an SBML model. SBMLToolbox supports all levels and versions of SBML.'),
(72725, 2006, 'bio', 'Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments', 'Gao, Xin', 'Department of Mathematics and Statistics, York University 4700 Keele Street, Toronto, ON M3J 1P3, Canada. xingao@mathstat.yorku.ca', 'Canada', 15, 'Algorithms|Animals|Computer Simulation|Data Interpretation, Statistical|Drosophila melanogaster|False Positive Reactions|Gene Expression Profiling|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btl109', '16574697', 'The parametric F-test has been widely used in the analysis of factorial microarray experiments to assess treatment effects. However, the normality assumption is often untenable for microarray experiments with small replications. Therefore, permutation-based methods are called for help to assess the statistical significance. The distribution of the F-statistics across all the genes on the array can be regarded as a mixture distribution with a proportion of statistics generated from the null distribution of no differential gene expression whereas the other proportion of statistics generated from the alternative distribution of genes differentially expressed. This results in the fact that the permutation distribution of the F-statistics may not approximate well to the true null distribution of the F-statistics. Therefore, the construction of a proper null statistic to better approximate the null distribution of F-statistic is of great importance to the permutation-based multiple testing in microarray data analysis.'),
(72726, 2006, 'bio', 'SScore: an R package for detecting differential gene expression without gene expression summaries', 'Kennedy, Richard E|Kerns, Robnet T|Kong, Xiangrong|Archer, Kellie J|Miles, Michael F', 'Department of Biostatistics, Virginia Commonwealth University Box 980032, Richmond, VA 23298-0032, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University Box 980032, Richmond, VA 23298-0032, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University Box 980032, Richmond, VA 23298-0032, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University Box 980032, Richmond, VA 23298-0032, USA. rkennedy@vcu.edu|Department of Biostatistics, Virginia Commonwealth University Box 980032, Richmond, VA 23298-0032, USA. rkennedy@vcu.edu', 'United States|United States|United States|United States|United States', 26, 'Algorithms|Artificial Intelligence|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Software|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btl108', '16574698', 'SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available.'),
(72727, 2006, 'bio', 'PowQ: a user-friendly package for the design of variance component multipoint linkage analysis studies', 'Falchi, Mario|Borlino, Cesare Cappio', 'Twin Research and Genetic Epidemiology Unit, St Thomas Hospital London, UK. mario.falchi@kcl.ac.uk|Twin Research and Genetic Epidemiology Unit, St Thomas Hospital London, UK. mario.falchi@kcl.ac.uk', 'United Kingdom|United Kingdom', 0, 'Alleles|Computational Biology|Computers|Genetic Linkage|Humans|Models, Genetic|Programming Languages|Quantitative Trait Loci|Quantitative Trait, Heritable|Software', 'Journal Article', '10.1093/bioinformatics/btl124', '16585065', 'A user-friendly, graphical package for power evaluation and enhancement planning through variance component linkage analysis in a multipoint framework.'),
(72728, 2006, 'bio', 'Identification of biochemical networks by S-tree based genetic programming', 'Cho, Dong-Yeon|Cho, Kwang-Hyun|Zhang, Byoung-Tak', 'School of Computer Science and Engineering, Seoul National University Seoul 151-742, Korea.|School of Computer Science and Engineering, Seoul National University Seoul 151-742, Korea.|School of Computer Science and Engineering, Seoul National University Seoul 151-742, Korea.', '-|-|-', 76, 'Algorithms|Biochemistry|Computational Biology|Computer Simulation|DNA Repair|Escherichia coli|Fermentation|Gene Expression Profiling|Genes, Bacterial|Genes, Fungal|Models, Genetic|Models, Statistical|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btl122', '16585066', 'Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system.'),
(72729, 2006, 'bio', 'Global dynamics of biological systems from time-resolved omics experiments', 'Grigorov, Martin G', 'Nestlé Research Center, BioAnalytical Science CH-1000 Lausanne 26, Switzerland. martin.grigorov@rdls.nestle.com', 'Switzerland', 7, 'Algorithms|Animals|Biomedical Research|Cell Cycle|Computational Biology|Computer Simulation|Drosophila melanogaster|Fungal Proteins|Gene Expression Regulation, Developmental|Genomics|Models, Genetic|Proteomics|Systems Biology|Time Factors', 'Journal Article', '10.1093/bioinformatics/btl119', '16585068', 'The emergent properties of biological systems, organized around complex networks of irregularly connected elements, limit the applications of the direct scientific method to their study. The current lack of knowledge opens new perspectives to the inverse scientific paradigm where observations are accumulated and analysed by advanced data-mining techniques to enable a better understanding and the formulation of testable hypotheses about the structure and functioning of these systems. The current technology allows for the wide application of omics analytical methods in the determination of time-resolved molecular profiles of biological samples. Here it is proposed that the theory of dynamical systems could be the natural framework for the proper analysis and interpretation of such experiments. A new method is described, based on the techniques of non-linear time series analysis, which is providing a global view on the dynamics of biological systems probed with time-resolved omics experiments.'),
(72730, 2006, 'bio', 'A fast coarse filtering method for peptide identification by mass spectrometry', 'Ramakrishnan, Smriti R|Mao, Rui|Nakorchevskiy, Aleksey A|Prince, John T|Willard, Willard S|Xu, Weijia|Marcotte, Edward M|Miranker, Daniel P', 'Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu|Department of Computer Sciences, The University of Texas at Austin Austin, Texas 78712, USA. smriti@cs.utexas.edu', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Databases, Protein|Escherichia coli|Genetic Vectors|Mass Spectrometry|Peptide Mapping|Peptides|Programming Languages|Proteins|Proteomics|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl118', '16585069', 'We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme.'),
(72731, 2006, 'bio', 'Computational recognition of potassium channel sequences', 'Heil, Burkhard|Ludwig, Jost|Lichtenberg-Fraté, Hella|Lengauer, Thomas', 'Universität Bonn, IZMB Kirschallee 1, 53115 Bonn, Germany. Burkhard.Heil@gmail.com|Universität Bonn, IZMB Kirschallee 1, 53115 Bonn, Germany. Burkhard.Heil@gmail.com|Universität Bonn, IZMB Kirschallee 1, 53115 Bonn, Germany. Burkhard.Heil@gmail.com|Universität Bonn, IZMB Kirschallee 1, 53115 Bonn, Germany. Burkhard.Heil@gmail.com', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Amino Acid Sequence|Animals|Computational Biology|Genome|Genome, Fungal|Humans|Molecular Sequence Data|Potassium Channels|Saccharomyces cerevisiae|Sequence Homology, Amino Acid|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl132', '16595554', 'Potassium channels are mainly known for their role in regulating and maintaining the membrane potential. Since this is one of the key mechanisms of signal transduction, malfunction of these potassium channels leads to a wide variety of severe diseases. Thus potassium channels are priority targets of research for new drugs, despite the fact that this protein family is highly variable and closely related to other channels, which makes it very difficult to identify new types of potassium channel sequences.'),
(72732, 2006, 'bio', 'ALTree: association detection and localization of susceptibility sites using haplotype phylogenetic trees', 'Bardel, Claire|Danjean, Vincent|Génin, Emmanuelle', 'Unité de recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. bardel@vjf.inserm.fr|Unité de recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. bardel@vjf.inserm.fr|Unité de recherche en Génétique Epidémiologique et Structure des Populations Humaines, INSERM U535, Villejuif, France. bardel@vjf.inserm.fr', 'France|France|France', 0, 'Computational Biology|Genetic Diseases, Inborn|Genetic Predisposition to Disease|Genetics, Population|Haplotypes|Humans|Models, Genetic|Models, Statistical|Phylogeny|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl131', '16595555', 'Finding the genes involved in complex diseases susceptibility and among those genes, localizing the variant sites explaining this susceptibility is a major goal of genetic epidemiology. In this context, haplotypic methods that use the joint information on several markers may be of particular interest. When the number of haplotypes is large, a grouping may be required. Phylogenetic trees allow such groupings of haplotypes based on their evolutionary history and may help in the detection and localization of disease susceptibility sites. In this paper, we present a new software to perform phylogeny-based association and localization analysis.'),
(72733, 2006, 'bio', 'Integrating multi-attribute similarity networks for robust representation of the protein space', 'Camoglu, Orhan|Can, Tolga|Singh, Ambuj K', 'Department of Computer Science, University of California Santa Barbara, 93106, USA. orhan@cs.ucsb.edu|Department of Computer Science, University of California Santa Barbara, 93106, USA. orhan@cs.ucsb.edu|Department of Computer Science, University of California Santa Barbara, 93106, USA. orhan@cs.ucsb.edu', 'United States|United States|United States', 0, 'Algorithms|Automation|Bayes Theorem|Cluster Analysis|Computational Biology|Databases, Protein|Evolution, Molecular|Models, Statistical|Probability|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl130', '16595556', 'A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure. A more robust representation of the protein space can be realized if multiple sources of information are used.'),
(72734, 2006, 'bio', 'NQ-Flipper: validation and correction of asparagine/glutamine amide rotamers in protein crystal structures', 'Weichenberger, Christian X|Sippl, Manfred J', 'Center of Applied Molecular Engineering, University of Salzburg, Jakob Haringerstrasse 5, 5020 Salzburg, Austria.|Center of Applied Molecular Engineering, University of Salzburg, Jakob Haringerstrasse 5, 5020 Salzburg, Austria.', 'Austria|Austria', 25, 'Asparagine|Crystallography|Data Interpretation, Statistical|Databases, Protein|Escherichia coli|Glutamine|Internet|Models, Molecular|Molecular Conformation|Protein Conformation|Protein Folding|Reproducibility of Results|Software|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btl128', '16595557', 'The error rate of asparagine (Asn) and glutamine (Gln) amide rotamers in protein crystal structures is in the order of 20% and as a consequence the current Protein Database (PDB) contains approximately half a million incorrect Asn and Gln side-chain rotamers. Here we present NQ-Flipper, a web service based on knowledge-based potentials of mean force to automatically detect and correct erroneous rotamers. We achieve excellent agreement with expert curated data.'),
(72735, 2006, 'bio', 'Effective similarity measures for expression profiles', 'Yona, Golan|Dirks, William|Rahman, Shafquat|Lin, David M', 'Department of Computer Science, Cornell University, NY, USA. golan@cs.technion.ac.il|Department of Computer Science, Cornell University, NY, USA. golan@cs.technion.ac.il|Department of Computer Science, Cornell University, NY, USA. golan@cs.technion.ac.il|Department of Computer Science, Cornell University, NY, USA. golan@cs.technion.ac.il', 'United States|United States|United States|United States', 0, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Fungal|Genes, Fungal|Mutation|Open Reading Frames|Pattern Recognition, Automated|Saccharomyces cerevisiae|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl127', '16595558', 'It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners, they can participate in the same pathway without interacting directly, they can perform similar functions, or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/'),
(72736, 2006, 'bio', 'HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data', 'Makarenkov, Vladimir|Kevorkov, Dmytro|Zentilli, Pablo|Gagarin, Andrei|Malo, Nathalie|Nadon, Robert', 'Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca|Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca|Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca|Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca|Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca|Departement dinformatique, Université du Québec à Montreal, C.P.8888 suc.Centre-Ville, Montreal, QC, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca', 'Canada|Canada|Canada|Canada|Canada|Canada', 25, 'Algorithms|Computational Biology|Computer Simulation|Computers|Drug Design|Drug Evaluation, Preclinical|Drug Industry|Models, Statistical|Quality Control|Reproducibility of Results|Software|Technology, Pharmaceutical', 'Journal Article', '10.1093/bioinformatics/btl126', '16595559', 'High-throughput screening (HTS) plays a central role in modern drug discovery, allowing for testing of >100,000 compounds per screen. The aim of our work was to develop and implement methods for minimizing the impact of systematic error in the analysis of HTS data. To the best of our knowledge, two new data correction methods included in HTS-Corrector are not available in any existing commercial software or freeware.'),
(72737, 2006, 'bio', 'Pvclust: an R package for assessing the uncertainty in hierarchical clustering', 'Suzuki, Ryota|Shimodaira, Hidetoshi', 'Department of Mathematical and Computing Sciences, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan. ryota.suzuki@is.titech.ac.jp|Department of Mathematical and Computing Sciences, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan. ryota.suzuki@is.titech.ac.jp', 'Japan|Japan', 1282, 'Algorithms|Cluster Analysis|Computational Biology|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Humans|Internet|Models, Statistical|Oligonucleotide Array Sequence Analysis|Probability|Programming Languages|Software|Uncertainty', 'Journal Article', '10.1093/bioinformatics/btl117', '16595560', 'Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstrap resampling techniques. Two types of p-values are available: approximately unbiased (AU) p-value and bootstrap probability (BP) value. Multiscale bootstrap resampling is used for the calculation of AU p-value, which has superiority in bias over BP value calculated by the ordinary bootstrap resampling. In addition the computation time can be enormously decreased with parallel computing option.'),
(72738, 2006, 'bio', 'ProMAT: protein microarray analysis tool', 'White, Amanda M|Daly, Don S|Varnum, Susan M|Anderson, Kevin K|Bollinger, Nikki|Zangar, Richard C', 'Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov|Statistical Sciences, Pacific Northwest National Laboratory Richland, WA 99354, USA. amanda.white@pnl.gov', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Data Interpretation, Statistical|Enzyme-Linked Immunosorbent Assay|Protein Array Analysis|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl093', '16595561', 'ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code.'),
(72739, 2006, 'bio', 'Using mRNAs lengths to accurately predict the alternatively spliced gene products in Caenorhabditis elegans', 'Agrawal, Ritesh|Stormo, Gary D', 'Department of Genetics, Washington University School of Medicine 660 S. Euclid, Campus Box 8232, St. Louis, MO 63110, USA.|Department of Genetics, Washington University School of Medicine 660 S. Euclid, Campus Box 8232, St. Louis, MO 63110, USA.', 'United States|United States', 2, 'Algorithms|Alternative Splicing|Animals|Base Sequence|Caenorhabditis elegans|Caenorhabditis elegans Proteins|Chromosome Mapping|Computer Simulation|Gene Expression|Models, Genetic|Molecular Sequence Data|RNA, Messenger|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl076', '16595562', 'Computational gene prediction methods are an important component of whole genome analyses. While ab initio gene finders have demonstrated major improvements in accuracy, the most reliable methods are evidence-based gene predictors. These algorithms can rely on several different sources of evidence including predictions from multiple ab initio gene finders, matches to known proteins, sequence conservation and partial cDNAs to predict the final product. Despite the success of these algorithms, prediction of complete gene structures, especially for alternatively spliced products, remains a difficult task.'),
(72740, 2006, 'bio', 'GRAST: a new way of genome reduction analysis using comparative genomics', 'Toft, Christina|Fares, Mario A', 'Molecular Evolution and Bioinformatics Laboratory, Department of Biology, National University of Ireland Maynooth.|Molecular Evolution and Bioinformatics Laboratory, Department of Biology, National University of Ireland Maynooth.', 'Ireland|Ireland', 3, 'Algorithms|Animals|Aphids|Computational Biology|Databases, Genetic|Evolution, Molecular|Genome|Genome, Bacterial|Genomics|Models, Genetic|Models, Statistical|Multigene Family|Software', 'Journal Article', '10.1093/bioinformatics/btl139', '16601001', 'Establishment of intra-cellular life involved a profound re-configuration of the genetic characteristics of bacteria, including genome reduction and rearrangements. Understanding the mechanisms underlying these phenomena will shed light on the genome rearrangements essential for the development of an intra-cellular lifestyle. Comparison of genomes with differences in their sizes poses statistical as well as computational problems. Little efforts have been made to develop flexible computational tools with which to analyse genome reduction and rearrangements.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72741, 2006, 'bio', 'Intrinsically disordered C-terminal segments of voltage-activated potassium channels: a possible fishing rod-like mechanism for channel binding to scaffold proteins', 'Magidovich, Elhanan|Fleishman, Sarel J|Yifrach, Ofer', 'Department of Life Sciences and the Zlotowski Center for Neurosciences, Ben Gurion University of the Negev Beer Sheva, Israel.|Department of Life Sciences and the Zlotowski Center for Neurosciences, Ben Gurion University of the Negev Beer Sheva, Israel.|Department of Life Sciences and the Zlotowski Center for Neurosciences, Ben Gurion University of the Negev Beer Sheva, Israel.', 'Israel|Israel|Israel', 0, 'Amino Acid Motifs|Animals|Computational Biology|Disks Large Homolog 4 Protein|Humans|Intracellular Signaling Peptides and Proteins|Membrane Proteins|Models, Biological|Phylogeny|Potassium Channels|Protein Binding|Protein Denaturation|Protein Structure, Tertiary|Shaker Superfamily of Potassium Channels', 'Journal Article', '10.1093/bioinformatics/btl137', '16601002', 'Membrane-embedded voltage-activated potassium channels (Kv) bind intracellular scaffold proteins, such as the Post Synaptic Density 95 (PSD-95) protein, using a conserved PDZ-binding motif located at the channels C-terminal tip. This interaction underlies Kv-channel clustering, and is important for the proper assembly and functioning of the synapse. Here we demonstrate that the C-terminal segments of Kv channels adjacent to the PDZ-binding motif are intrinsically disordered. Phylogenetic analysis of the Kv channel family reveals a cluster of channel sequences belonging to three out of the four main channel families, for which an association is demonstrated between the presence of the consensus terminal PDZ-binding motif and the intrinsically disordered nature of the immediately adjacent C-terminal segment. Our observations, combined with a structural analogy to the N-terminal intra-molecular ball-and-chain mechanism for Kv channel inactivation, suggest that the C-terminal disordered segments of these channel families encode an inter-molecular fishing rod-like mechanism for K(+) channel binding to scaffold proteins.'),
(72742, 2006, 'bio', 'SNAP: Combine and Map modules for multilocus population genetic analysis', 'Aylor, David L|Price, Eric W|Carbone, Ignazio', 'Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA.|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA.|Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA.', 'United States|United States|United States', 0, 'Algorithms|Computational Biology|Gene Expression Profiling|Genetics, Population|Genomics|Haplotypes|Models, Genetic|Phenotype|Programming Languages|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl136', '16601003', 'We have added two software tools to our Suite of Nucleotide Analysis Programs (SNAP) for working with DNA sequences sampled from populations. SNAP Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map includes novel features such as recoding insertions or deletions, including or excluding variable sites that violate an infinite-sites model and the option of collapsing sequences with corresponding phenotypic information, important in testing for significant haplotype-phenotype associations. SNAP Combine merges multiple DNA sequence alignments into a single multiple alignment file. The resulting file can be the union or intersection of the input files. SNAP Combine currently reads from and writes to several sequence alignment file formats including both sequential and interleaved formats. Combine also keeps track of the start and end positions of each separate alignment file allowing the user to exclude variable sites or taxa, important in creating input files for multilocus analyses.'),
(72743, 2006, 'bio', 'Gene fusion/fission is a major contributor to evolution of multi-domain bacterial proteins', 'Pasek, Sophie|Risler, Jean-Loup|Brézellec, Pierre', 'Laboratoire Statistique et Génome 523 Place des Terrasses, 91034 Evry cedex, France. pasek@genopole.cnrs.fr|Laboratoire Statistique et Génome 523 Place des Terrasses, 91034 Evry cedex, France. pasek@genopole.cnrs.fr|Laboratoire Statistique et Génome 523 Place des Terrasses, 91034 Evry cedex, France. pasek@genopole.cnrs.fr', 'France|France|France', 0, 'Bacterial Proteins|Biological Evolution|Computational Biology|Evolution, Molecular|Gene Deletion|Gene Fusion|Genes, Bacterial|Genome, Bacterial|Internet|Phylogeny|Protein Structure, Tertiary|Proteins', 'Journal Article', '10.1093/bioinformatics/btl135', '16601004', 'Most proteins comprise one or several domains. New domain architectures can be created by combining previously existing domains. The elementary events that create new domain architectures may be categorized into three classes, namely domain(s) insertion or deletion (indel), exchange and repetition. Using DomainTeam, a tool dedicated to the search for microsyntenies of domains, we quantified the relative contribution of these events. This tool allowed us to collect homologous bacterial genes encoding proteins that have obviously evolved by modular assembly of domains. We show that indels are the most frequent elementary events and that they occur in most cases at either the N- or C-terminus of the proteins. As revealed by the genomic neighbourhood/context of the corresponding genes, we show that a substantial number of these terminal indels are the consequence of gene fusions/fissions. We provide evidence showing that the contribution of gene fusion/fission to the evolution of multi-domain bacterial proteins is lower-bounded by 27% and upper-bounded by 64%. We conclude that gene fusion/fission is a major contributor to the evolution of multi-domain bacterial proteins.'),
(72744, 2006, 'bio', 'Analysing the ability to retain sidechain hydrogen-bonds in mutant proteins', 'Cuff, Alison L|Janes, Robert W|Martin, Andrew C R', 'School of Animal and Microbial Sciences, University of Reading Whiteknights, PO Box 228, Reading RG6 6AJ, UK.|School of Animal and Microbial Sciences, University of Reading Whiteknights, PO Box 228, Reading RG6 6AJ, UK.|School of Animal and Microbial Sciences, University of Reading Whiteknights, PO Box 228, Reading RG6 6AJ, UK.', 'United Kingdom|United Kingdom|United Kingdom', 9, 'Algorithms|Animals|Asparagine|Computational Biology|Humans|Hydrogen Bonding|Models, Molecular|Mutant Proteins|Mutation|Nitrogen|Software|Thermodynamics|Tumor Suppressor Protein p53|Tyrosine', 'Journal Article', '10.1093/bioinformatics/btl120', '16601005', 'Hydrogen bonds are one of the most important inter-atomic interactions in biology. Previous experimental, theoretical and bioinformatics analyses have shown that the hydrogen bonding potential of amino acids is generally satisfied and that buried unsatisfied hydrogen-bond-capable residues are destabilizing. When studying mutant proteins, or introducing mutations to residues involved in hydrogen bonding, one needs to know whether a hydrogen bond can be maintained. Our aim, therefore, was to develop a rapid method to evaluate whether a sidechain can form a hydrogen-bond.'),
(72745, 2006, 'bio', 'Improved scoring of functional groups from gene expression data by decorrelating GO graph structure', 'Alexa, Adrian|Rahnenführer, Jörg|Lengauer, Thomas', 'Max-Planck-Institute for Informatics Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. alexa@mpi-sb.mpg.de|Max-Planck-Institute for Informatics Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. alexa@mpi-sb.mpg.de|Max-Planck-Institute for Informatics Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. alexa@mpi-sb.mpg.de', 'Germany|Germany|Germany', 952, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation|Gene Expression Regulation, Neoplastic|Humans|Leukemia|Models, Statistical|Oligonucleotide Array Sequence Analysis|Protein Folding', 'Journal Article', '10.1093/bioinformatics/btl140', '16606683', 'The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance.'),
(72746, 2006, 'bio', 'Analysis assistant for single-molecule force spectroscopy data on membrane proteins--MPTV', 'Mueller, Frank|Muller, Daniel J|Labudde, Dirk', 'Department of Cellular Machines, Center of Biotechnology, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany.|Department of Cellular Machines, Center of Biotechnology, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany.|Department of Cellular Machines, Center of Biotechnology, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany.', 'Germany|Germany|Germany', 0, 'Algorithms|Computer Simulation|Elasticity|Membrane Proteins|Microscopy, Atomic Force|Models, Chemical|Models, Molecular|Sequence Analysis, Protein|Software|Stress, Mechanical', 'Journal Article', '10.1093/bioinformatics/btl138', '16606684', 'Force-distance (F-D) curves of single membrane proteins reveal information on inter- and intramolecular interactions occurring within a protein and between proteins. However, the analysis of single-molecule force spectroscopy data is a time consuming and complex process requiring objective criteria. In most cases the user requires additional information to interpret F-D curves. Therefore we developed a software assistant representing the force or molecular interaction pattern and the topology or the 3D structure of the membrane protein. This representation establishes a basis for detailed interpretation of the protein structure and its underlying molecular interactions. Various integrated bioinformatic features further assist in the interpretation of measured and assigned molecular interactions that determine membrane protein folding, structure, stability and function. Web queries and programs about the topology are directly linked. Motifs, helix types, representation of Venn diagrams and the complete functionality of the program Jmol belong to it.'),
(72747, 2006, 'bio', 'ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles', 'Yoshida, Ryo|Higuchi, Tomoyuki|Imoto, Seiya|Miyano, Satoru', 'Human Genome Center, Institute of Medical Science, University of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yoshidar@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yoshidar@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yoshidar@ims.u-tokyo.ac.jp|Human Genome Center, Institute of Medical Science, University of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yoshidar@ims.u-tokyo.ac.jp', 'Japan|Japan|Japan|Japan', 22, 'Bayes Theorem|Cluster Analysis|Computational Biology|Gene Expression Profiling|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl129', '16606685', 'One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.'),
(72748, 2006, 'bio', 'AuberGene--a sensitive genome alignment tool', 'Szklarczyk, Radek|Heringa, Jaap', 'Centre for Integrative Bioinformatics VU (IBIVU), Faculty of Sciences and Faculty of Earth and Life Sciences, Vrije Universiteit De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.|Centre for Integrative Bioinformatics VU (IBIVU), Faculty of Sciences and Faculty of Earth and Life Sciences, Vrije Universiteit De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.', 'Netherlands|Netherlands', 8, 'Algorithms|Animals|Computational Biology|Evolution, Molecular|Exons|Humans|Mice|Open Reading Frames|Phylogeny|Sequence Alignment|Sequence Analysis, Protein|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl112', '16606686', 'The accumulation of genome sequences will only accelerate in the coming years. We aim to use this abundance of data to improve the quality of genomic alignments and devise a method which is capable of detecting regions evolving under weak or no evolutionary constraints.'),
(72749, 2006, 'bio', 'Ribosomal RNA as molecular barcodes: a simple correlation analysis without sequence alignment', 'Chu, K H|Li, C P|Qi, J', 'Department of Biology, The Chinese University of Hong Kong, Hong Kong, China. kahouchu@cuhk.edu.hk|Department of Biology, The Chinese University of Hong Kong, Hong Kong, China. kahouchu@cuhk.edu.hk|Department of Biology, The Chinese University of Hong Kong, Hong Kong, China. kahouchu@cuhk.edu.hk', 'China|China|China', 0, 'Algorithms|Analysis of Variance|Base Sequence|DNA, Ribosomal|Data Interpretation, Statistical|Feasibility Studies|Molecular Sequence Data|RNA, Ribosomal|Sequence Analysis, RNA|Software|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btl146', '16613905', 'We explored the feasibility of using unaligned rRNA gene sequences as DNA barcodes, based on correlation analysis of composition vectors (CVs) derived from nucleotide strings. We tested this method with seven rRNA (including 12, 16, 18, 26 and 28S) datasets from a wide variety of organisms (from archaea to tetrapods) at taxonomic levels ranging from class to species.'),
(72750, 2006, 'bio', 'VizStruct for visualization of genome-wide SNP analyses', 'Bhasi, Kavitha|Zhang, Li|Brazeau, Daniel|Zhang, Aidong|Ramanathan, Murali', 'Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.|Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.|Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.|Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.|Department of Pharmaceutical Sciences, State University of New York Buffalo, NY 14260, USA.', 'United States|United States|United States|United States|United States', 10, 'Animals|Anthozoa|Chromosomes, Human, Pair 21|Computational Biology|Fourier Analysis|Genome|Genotype|Haplotypes|Humans|Imaging, Three-Dimensional|Lipoprotein Lipase|Models, Statistical|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btl144', '16613906', 'The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets.'),
(72751, 2006, 'bio', 'FlyTF: a systematic review of site-specific transcription factors in the fruit fly Drosophila melanogaster', 'Adryan, Boris|Teichmann, Sarah A', 'MRC Laboratory of Molecular Biology Cambridge CB2 2QH, UK. boris@mrc-lmb.cam.ac.uk|MRC Laboratory of Molecular Biology Cambridge CB2 2QH, UK. boris@mrc-lmb.cam.ac.uk', 'United Kingdom|United Kingdom', 71, 'Animals|Computational Biology|DNA|DNA-Binding Proteins|Data Interpretation, Statistical|Drosophila Proteins|Drosophila melanogaster|Protein Structure, Tertiary|Software|Transcription Factors|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btl143', '16613907', 'We present a manually annotated catalogue of site-specific transcription factors (TFs) in the fruit fly Drosophila melanogaster. These were identified from a list of candidate proteins with transcription-related Gene Ontology (Go) annotation as well as structural DNA-binding domain assignments. For all 1052 candidate proteins, a defined set of rules was applied to classify information from the literature and computational data sources with respect to both DNA-binding and transcriptional regulatory properties. We propose a set of 753 TFs in the fruit fly, of which 23 are confident novel predictions of this function for previously uncharacterized proteins.'),
(72752, 2006, 'bio', 'STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time', 'Dalli, Deniz|Wilm, Andreas|Mainz, Indra|Steger, Gerhard', 'Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie D-40225 Düsseldorf, Germany.|Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie D-40225 Düsseldorf, Germany.|Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie D-40225 Düsseldorf, Germany.|Heinrich-Heine-Universität Düsseldorf, Institut für Physikalische Biologie D-40225 Düsseldorf, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Base Pairing|Base Sequence|Computational Biology|Models, Statistical|Molecular Sequence Data|Nucleic Acid Conformation|Phylogeny|Probability|RNA|RNA, Untranslated|Sequence Alignment|Software|Time', 'Journal Article', '10.1093/bioinformatics/btl142', '16613908', 'Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem.'),
(72753, 2006, 'bio', 'Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules', 'Zhu, Shanfeng|Udaka, Keiko|Sidney, John|Sette, Alessandro|Aoki-Kinoshita, Kiyoko F|Mamitsuka, Hiroshi', 'Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.|Bioinformatics Center, Institute for Chemical Research, Kyoto University Gokasho, Uji 611-0011, Japan.', 'Japan|Japan|Japan|Japan|Japan|Japan', 33, 'Algorithms|Alleles|Computational Biology|Databases, Genetic|Entropy|Genes, MHC Class I|HLA-A Antigens|Histocompatibility Antigens|Humans|Models, Statistical|Oligopeptides|Peptides|Protein Binding|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btl141', '16613909', 'Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules.'),
(72754, 2006, 'bio', 'Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures', 'Chowdhary, Rajesh|Tan, Sin Lam|Ali, R Ayesha|Boerlage, Brent|Wong, Limsoon|Bajic, Vladimir B', 'Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.|Knowledge Extraction Lab, Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore 119613, Singapore.', 'Singapore|Singapore|Singapore|Singapore|Singapore|Singapore', 5, 'Algorithms|Bayes Theorem|Chromosome Mapping|Computer Simulation|Models, Genetic|Models, Statistical|Pattern Recognition, Automated|Promoter Regions, Genetic|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl125', '16613910', 'Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers.'),
(72755, 2006, 'bio', 'Using hidden Markov models and observed evolution to annotate viral genomes', 'McCauley, Stephen|Hein, Jotun', 'Department of Statistics, Oxford University, Oxford, UK. mccauley@stats.ox.ac.uk|Department of Statistics, Oxford University, Oxford, UK. mccauley@stats.ox.ac.uk', 'United Kingdom|United Kingdom', 10, 'Algorithms|Codon|Computational Biology|Evolution, Molecular|Genome, Viral|Genomics|HIV-2|Markov Chains|Models, Statistical|Mutation|Open Reading Frames|Phylogeny|RNA, Viral|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl092', '16613911', 'ssRNA (single stranded) viral genomes are generally constrained in length and utilize overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximize coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are 6-fold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic hidden Markov model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterized, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation.'),
(72756, 2006, 'bio', 'Modified signal-to-noise: a new simple and practical gene filtering approach based on the concept of projective adaptive resonance theory (PART) filtering method', 'Takahashi, Hiro|Honda, Hiroyuki', 'Department of Biotechnology, School of Engineering, Nagoya University, Nagoya 464-8603, Japan.|Department of Biotechnology, School of Engineering, Nagoya University, Nagoya 464-8603, Japan.', 'Japan|Japan', 7, 'Algorithms|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Humans|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Software', 'Comparative Study', '10.1093/bioinformatics/btl156', '16632489', 'Considering the recent advances in and the benefits of DNA microarray technologies, many gene filtering approaches have been employed for the diagnosis and prognosis of diseases. In our previous study, we developed a new filtering method, namely, the projective adaptive resonance theory (PART) filtering method. This method was effective in subclass discrimination. In the PART algorithm, the genes with a low variance in gene expression in either class, not both classes, were selected as important genes for modeling. Based on this concept, we developed novel simple filtering methods such as modified signal-to-noise (S2N) in the present study. The discrimination model constructed using these methods showed higher accuracy with higher reproducibility as compared with many conventional filtering methods, including the t-test, S2N, NSC and SAM. The reproducibility of prediction was evaluated based on the correlation between the sets of U-test p-values on randomly divided datasets. With respect to leukemia, lymphoma and breast cancer, the correlation was high, a difference of >0.13 was obtained by the constructed model by using <50 genes selected by S2N. Improvement was higher in the smaller genes and such higher correlation was observed when t-test, NSC and SAM were used. These results suggest that these modified methods, such as S2N, have high potential to function as new methods for marker gene selection in cancer diagnosis using DNA microarray data.'),
(72757, 2006, 'bio', 'A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription', 'Sanguinetti, Guido|Rattray, Magnus|Lawrence, Neil D', 'Department of Computer Science, Regent Court, 211 Portobello Road, Sheffield S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, Regent Court, 211 Portobello Road, Sheffield S1 4DP, UK. guido@dcs.shef.ac.uk|Department of Computer Science, Regent Court, 211 Portobello Road, Sheffield S1 4DP, UK. guido@dcs.shef.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 30, 'Computer Simulation|Gene Expression|Gene Expression Profiling|Gene Expression Regulation|Kinetics|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Regulatory Elements, Transcriptional|Transcription Factors|Transcription, Genetic|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btl154', '16632490', 'Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes.'),
(72758, 2006, 'bio', 'GeneRecon--a coalescent based tool for fine-scale association mapping', 'Mailund, Thomas|Schierup, Mikkel H|Pedersen, Christian N S|Madsen, Jesper N|Hein, Jotun|Schauser, Leif', 'Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk|Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk|Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk|Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk|Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk|Bioinformatics Research Center, University of Aarhus Høegh-Guldbergs Gade 10, DK-8000 Arhus C, Denmark. mailund@birc.au.dk', 'Denmark|Denmark|Denmark|Denmark|Denmark|Denmark', 8, 'Algorithms|Animals|Chromosome Mapping|DNA Mutational Analysis|Genetic Linkage|Genetic Predisposition to Disease|Genetics, Population|Humans|Linkage Disequilibrium|Models, Genetic|Models, Statistical|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl153', '16632491', 'GeneRecon is a tool for fine-scale association mapping using a coalescence model. GeneRecon takes as input case-control data from phased or unphased SNP and microsatellite genotypes. The posterior distribution of disease locus position is obtained by Metropolis-Hastings sampling in the state space of genealogies. Input format, search strategy and the sampled statistics can be configured through the Guile Scheme programming language embedded in GeneRecon, making GeneRecon highly configurable.'),
(72759, 2006, 'bio', 'Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments', 'Vacic, Vladimir|Iakoucheva, Lilia M|Radivojac, Predrag', 'Computer Science and Engineering Department, University of California Riverside, CA, USA.|Computer Science and Engineering Department, University of California Riverside, CA, USA.|Computer Science and Engineering Department, University of California Riverside, CA, USA.', 'United States|United States|United States', 266, 'Algorithms|Alternative Splicing|Base Sequence|Computational Biology|Computer Graphics|Exons|Humans|Internet|Introns|Models, Statistical|Molecular Sequence Data|Programming Languages|Sequence Alignment|Thermodynamics', 'Journal Article', '10.1093/bioinformatics/btl151', '16632492', 'Two Sample Logo is a web-based tool that detects and displays statistically significant differences in position-specific symbol compositions between two sets of multiple sequence alignments. In a typical scenario, two groups of aligned sequences will share a common motif but will differ in their functional annotation. The inclusion of the background alignment provides an appropriate underlying amino acid or nucleotide distribution and addresses intersite symbol correlations. In addition, the difference detection process is sensitive to the sizes of the aligned groups. Two Sample Logo extends WebLogo, a widely-used sequence logo generator. The source code is distributed under the MIT Open Source license agreement and is available for download free of charge.'),
(72760, 2006, 'bio', 'Molecular complexes at a glance: automated generation of two-dimensional complex diagrams', 'Stierand, Katrin|Maass, Patrick C|Rarey, Matthias', 'Center for Bioinformatics (ZBH), University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany.|Center for Bioinformatics (ZBH), University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany.|Center for Bioinformatics (ZBH), University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany.', 'Germany|Germany|Germany', 164, 'Algorithms|Binding Sites|Computer Simulation|Ligands|Models, Chemical|Models, Molecular|Multiprotein Complexes|Protein Binding|Protein Conformation|Proteins|Sequence Analysis, Protein|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl150', '16632493', 'In this paper a new algorithmic approach is presented, which automatically generates structure diagrams of molecular complexes. A complex diagram contains the ligand, the amino acids of the protein interacting with the ligand and the hydrophilic interactions schematized as dashed lines between the corresponding atoms. The algorithm is based on a combinatorial optimization strategy which solves parts of the layout problem non-heuristically. The depicted molecules are represented as structure diagrams according to the chemical nomenclature. Due to the frequent usage of complex diagrams in the scientific literature as well as in text books dealing with structural biology, biochemistry and medicinal chemistry, the new algorithm is a key element for computer applications in these areas.'),
(72761, 2006, 'bio', 'A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays', 'McLachlan, G J|Bean, R W|Jones, L Ben-Tovim', 'Department of Mathematics, University of Queensland St Lucia, Brisbane 4072, Australia. gjm@maths.uq.edu.au|Department of Mathematics, University of Queensland St Lucia, Brisbane 4072, Australia. gjm@maths.uq.edu.au|Department of Mathematics, University of Queensland St Lucia, Brisbane 4072, Australia. gjm@maths.uq.edu.au', 'Australia|Australia|Australia', 116, 'Algorithms|Bayes Theorem|Breast Neoplasms|Colonic Neoplasms|Computational Biology|Data Interpretation, Statistical|Gene Expression Profiling|HIV Infections|Humans|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results', 'Journal Article', '10.1093/bioinformatics/btl148', '16632494', 'An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive.'),
(72762, 2006, 'bio', 'GAME: detecting cis-regulatory elements using a genetic algorithm', 'Wei, Zhi|Jensen, Shane T', 'Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine Philadelphia, 19104, USA. zhiwei@mail.med.upenn.edu|Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine Philadelphia, 19104, USA. zhiwei@mail.med.upenn.edu', 'United States|United States', 0, 'Algorithms|Amino Acid Motifs|Base Sequence|Bayes Theorem|Binding Sites|Computational Biology|Escherichia coli|Gene Expression Regulation|Models, Genetic|Molecular Sequence Data|Pattern Recognition, Automated|Regulatory Elements, Transcriptional|Software', 'Journal Article', '10.1093/bioinformatics/btl147', '16632495', 'Identification of a transcription factor binding sites is an important aspect of the analysis of genetic regulation. Many programs have been developed for the de novo discovery of a binding motif (collection of binding sites). Recently, a scoring function formulation was derived that allows for the comparison of discovered motifs from different programs [S.T. Jensen, X.S. Liu, Q. Zhou and J.S. Liu (2004) Stat. Sci., 19, 188-204.] A simple program, BioOptimizer, was proposed in [S.T. Jensen and J.S. Liu (2004) Bioinformatics, 20, 1557-1564.] that improved discovered motifs by optimizing a scoring function. However, BioOptimizer is a very simple algorithm that can only make local improvements upon an already discovered motif and so BioOptimizer can only be used in conjunction with other motif-finding software.'),
(72763, 2006, 'bio', 'Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions', 'Chua, Hon Nian|Sung, Wing-Kin|Wong, Limsoon', 'Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore. g0306417@nus.edu.sg.|Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore. g0306417@nus.edu.sg.|Graduate School for Integrated Sciences and Engineering, National University of Singapore, Singapore. g0306417@nus.edu.sg.', 'Singapore|Singapore|Singapore', 0, 'Algorithms|Cluster Analysis|Computational Biology|Databases, Protein|Fungal Proteins|Gene Expression Regulation, Fungal|Genes, Fungal|Models, Statistical|Protein Folding|Protein Interaction Mapping|Saccharomyces cerevisiae', 'Journal Article', '10.1093/bioinformatics/btl145', '16632496', 'Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that functional similarity between a protein and its neighbours from the two different levels arise from two distinct forms of functional association, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested in finding out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.'),
(72764, 2006, 'bio', 'G8: a novel domain associated with polycystic kidney disease and non-syndromic hearing loss', 'He, Quan-yuan|Liu, Xiang-hua|Li, Qiang|Studholme, David J|Li, Xuan-wen|Liang, Song-ping', 'Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.|Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.|Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.|Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.|Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.|Key Laboratory of Protein Chemistry and Developmental Biology of Education Committee, College of Life Sciences, Hunan Normal University Changsha, Peoples Republic of China.', 'China|China|China|China|China|China', 0, 'Amino Acid Sequence|Animals|Conserved Sequence|Evolution, Molecular|Hearing Loss, Sensorineural|Membrane Proteins|Molecular Sequence Data|Polycystic Kidney Diseases|Protein Structure, Tertiary|Proteins|Receptors, Cell Surface|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl123', '16632497', 'We report a novel protein domain-G8-which contains five repeated beta-strand pairs and is present in some disease-related proteins such as PKHD1, KIAA1199, TMEM2 as well as other uncharacterized proteins. Most G8-containing proteins are predicted to be membrane-integral or secreted. The G8 domain may be involved in extracellular ligand binding and catalysis. It has been reported that mis-sense mutations in the two G8 domains of human PKHD1 protein resulted in a less stable protein and are associated with autosomal-recessive polycystic kidney disease, indicating the importance of the domain structure. G8 is also present in the N-terminus of some non-syndromic hearing loss disease-related proteins such as KIAA1109 and TMEM2. Discovery of G8 domain will be important for the research of the structure/function of related proteins and beneficial for the development of novel therapeutics.'),
(72765, 2006, 'bio', 'Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques', 'Sundaresh, Suman|Doolan, Denise L|Hirst, Siddiqua|Mu, Yunxiang|Unal, Berkay|Davies, D Huw|Felgner, Philip L|Baldi, Pierre', 'School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.|School of Information and Computer Sciences, University of California, Irvine, CA, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Antibody Formation|Antigen-Antibody Complex|Antigens|Gene Expression Profiling|Immunoassay|Oligonucleotide Array Sequence Analysis|Protein Array Analysis', 'Journal Article', '10.1093/bioinformatics/btl162', '16644788', 'We present a study of antigen expression signals from a newly developed high-throughput protein microarray technique. These signals are a measure of antibody-antigen binding activity and provide a basis for understanding humoral immune responses to various infectious agents and supporting vaccine and diagnostic development.'),
(72766, 2006, 'bio', 'Method for identifying transcription factor binding sites in yeast', 'Tsai, Huai-Kuang|Huang, Grace Tzu-Wei|Chou, Meng-Yuan|Lu, Henry Horng-Shing|Li, Wen-Hsiung', 'Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.|Genomics Research Center, Academia Sinica, Taipei, 115 Taiwan.', '-|-|-|-|-', 0, 'Algorithms|Base Sequence|Binding Sites|Gene Targeting|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|Protein Binding|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl160', '16644789', 'Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit.'),
(72767, 2006, 'bio', 'An online literature mining tool for protein phosphorylation', 'Yuan, X|Hu, Z Z|Wu, H T|Torii, M|Narayanaswamy, M|Ravikumar, K E|Vijay-Shanker, K|Wu, C H', 'Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.|Protein Information Resource, Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center Washington, DC 20007, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 23, 'Animals|Computational Biology|Databases, Bibliographic|Databases, Factual|Databases, Protein|Humans|Information Storage and Retrieval|Phosphorylation|Proteins|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl159', '16644790', 'A web-based version of the RLIMS-P literature mining system was developed for online mining of protein phosphorylation information from MEDLINE abstracts. The online tool presents extracted phosphorylation objects (phosphorylated proteins, phosphorylation sites and protein kinases) in summary tables and full reports with evidence-tagged abstracts. The tool further allows mapping of phosphorylated proteins to protein entries in the UniProt Knowledgebase based on PubMed ID and/or protein name. The literature mining, coupled with database association, allows retrieval of rich biological information for the phosphorylated proteins and facilitates database annotation of phosphorylation features.'),
(72768, 2006, 'bio', 'How accurately can we control the FDR in analyzing microarray data?', 'Jung, Sin-Ho|Jang, Woncheol', 'Department of Biostatistics and Bioinformatics, Duke University, NC 27710, USA. jung0005@mc.duke.edu|Department of Biostatistics and Bioinformatics, Duke University, NC 27710, USA. jung0005@mc.duke.edu', 'United States|United States', 18, 'Algorithms|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl161', '16644791', 'We want to evaluate the performance of two FDR-based multiple testing procedures by Benjamini and Hochberg (1995, J. R. Stat. Soc. Ser. B, 57, 289-300) and Storey (2002, J. R. Stat. Soc. Ser. B, 64, 479-498) in analyzing real microarray data. These procedures commonly require independence or weak dependence of the test statistics. However, expression levels of different genes from each array are usually correlated due to coexpressing genes and various sources of errors from experiment-specific and subject-specific conditions that are not adjusted for in data analysis. Because of high dimensionality of microarray data, it is usually impossible to check whether the weak dependence condition is met for a given dataset or not. We propose to generate a large number of test statistics from a simulation model which has asymptotically (in terms of the number of arrays) the same correlation structure as the test statistics that will be calculated from the given data and to investigate how accurately the FDR-based testing procedures control the FDR on the simulated data. Our approach is to directly check the performance of these procedures for a given dataset, rather than to check the weak dependency requirement. We illustrate the proposed method with real microarray datasets, one where the clinical endpoint is disease group and another where it is survival.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72769, 2006, 'bio', 'Evolutionary trace report_maker: a new type of service for comparative analysis of proteins', 'Mihalek, I|Res, I|Lichtarge, O', 'Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. {imihalek,ires,lichtarge}@bcm.tmc.edu|Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. {imihalek,ires,lichtarge}@bcm.tmc.edu|Department of Molecular and Human Genetics, Baylor College of Medicine One Baylor Plaza, Houston, TX 77030, USA. {imihalek,ires,lichtarge}@bcm.tmc.edu', 'United States|United States|United States', 34, 'Algorithms|Animals|Biological Evolution|Computational Biology|Databases, Protein|Evolution, Molecular|Humans|Internet|Models, Statistical|Mutation|Proteins|Software', 'Journal Article', '10.1093/bioinformatics/btl157', '16644792', ': Evolutionary trace report_maker offers a new type of service for researchers investigating the function of novel proteins. It pools, from different sources, information about protein sequence, structure and elementary annotation, and to that background superimposes inference about the evolutionary behavior of individual residues, using real-valued evolutionary trace method. As its only input it takes a Protein Data Bank identifier or UniProt accession number, and returns a human-readable document in PDF format, supplemented by the original data needed to reproduce the results quoted in the report.'),
(72770, 2006, 'bio', 'Map2mod--a server for evaluation of crystallographic models and their agreement with electron density maps', 'Kirillova, Olga|Minor, Wladek', 'Department of Molecular Physiology and Biological Physics, University of Virginia Charlottesville, USA. olga,wladek@iwonka.med.virginia.edu|Department of Molecular Physiology and Biological Physics, University of Virginia Charlottesville, USA. olga,wladek@iwonka.med.virginia.edu', 'United States|United States', 0, 'Algorithms|Computational Biology|Crystallography, X-Ray|Data Interpretation, Statistical|Electrons|Internet|Models, Theoretical|Protein Conformation|Software', 'Journal Article', '10.1093/bioinformatics/btl152', '16644793', 'Here we report on recent developments of the map2mod server. It has been designed for validation of protein models created by X-ray data interpretation. It can also be used during the refinement process since it is able to indicate problem regions in the model. Apart from evaluation of model quality, it has an option to remove atoms of side chains, which are not consistent with the maps as well as improperly placed water molecules. There are two additional options: checking the B-factors of atoms in the provided model and comparison of R and R(free) values obtained as the result of refinement with the averages characteristic for the data resolution shell.'),
(72771, 2006, 'bio', 'BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge', 'Calzone, Laurence|Fages, François|Soliman, Sylvain', 'Projet Contraintes, INRIA Rocquencourt, BP105, 78153 Le Chesnay Cedex, France.|Projet Contraintes, INRIA Rocquencourt, BP105, 78153 Le Chesnay Cedex, France.|Projet Contraintes, INRIA Rocquencourt, BP105, 78153 Le Chesnay Cedex, France.', 'France|France|France', 154, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Databases, Factual|Models, Biological|Proteome|Research Design|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl172', '16672256', 'BIOCHAM (the BIOCHemical Abstract Machine) is a software environment for modeling biochemical systems. It is based on two aspects: (1) the analysis and simulation of boolean, kinetic and stochastic models and (2) the formalization of biological properties in temporal logic. BIOCHAM provides tools and languages for describing protein networks with a simple and straightforward syntax, and for integrating biological properties into the model. It then becomes possible to analyze, query, verify and maintain the model with respect to those properties. For kinetic models, BIOCHAM can search for appropriate parameter values in order to reproduce a specific behavior observed in experiments and formalized in temporal logic. Coupled with other methods such as bifurcation diagrams, this search assists the modeler/biologist in the modeling process.'),
(72772, 2006, 'bio', 'SHARP2: protein-protein interaction predictions using patch analysis', 'Murakami, Yoichi|Jones, Susan', 'Department of Biochemistry, School of Life Sciences, John Maynard Smith Building, University of Sussex, Falmer, Brighton, BN1 9QG, UK.|Department of Biochemistry, School of Life Sciences, John Maynard Smith Building, University of Sussex, Falmer, Brighton, BN1 9QG, UK.', 'United Kingdom|United Kingdom', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl171', '16672257', 'SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures.'),
(72773, 2006, 'bio', 'Ensemble classifier for protein fold pattern recognition', 'Shen, Hong-Bin|Chou, Kuo-Chen', 'Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China. lifesci-sjtu@san.rr.com|Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China. lifesci-sjtu@san.rr.com', 'China|China', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Pattern Recognition, Automated|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl170', '16672258', 'Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, as well as different dimensions of pseudo-amino acid composition, which were extracted from a training dataset. The operation engine for the constituent individual classifiers was OET-KNN (optimized evidence-theoretic k-nearest neighbors) rule. Their outcomes were combined through a weighted voting to give a final determination for classifying a query protein. The recognition was to find the true fold among the 27 possible patterns.'),
(72774, 2006, 'bio', 'TopoICE-R: 3D visualization modeling the topology of DNA recombination', 'Darcy, Isabel K|Scharein, Robert G', 'Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA. idarcy@math.uiowa.edu|Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA. idarcy@math.uiowa.edu', 'United States|United States', 0, 'Algorithms|Binding Sites|Computer Graphics|Computer Simulation|DNA|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Nucleic Acid Conformation|Protein Binding|Protein Conformation|Recombinases|Recombination, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl164', '16672259', 'TopoICE-R is a three-dimensional visualization and manipulation software for solving 2-string tangle equations and can be used to model the topology of DNA bound by proteins such as recombinases and topoisomerases.'),
(72775, 2006, 'bio', 'Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data', 'Pan, Fei|Kamath, Kiran|Zhang, Kangyu|Pulapura, Sudip|Achar, Avinash|Nunez-Iglesias, Juan|Huang, Yu|Yan, Xifeng|Han, Jiawei|Hu, Haiyan|Xu, Min|Hu, Jianjun|Zhou, Xianghong Jasmine', 'Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.|Program in Molecular and Computational Biology, University of Southern California, Los Angeles, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 12, 'Algorithms|Animals|Computational Biology|Computer Graphics|Data Interpretation, Statistical|Gene Expression Profiling|Gene Expression Regulation|Humans|Internet|Oligonucleotide Array Sequence Analysis|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl163', '16672260', 'The rapid accumulation of microarray data translates into an urgent need for tools to perform integrative microarray analysis. Integrative Array Analyzer is a comprehensive analysis and visualization software toolkit, which aims to facilitate the reuse of the large amount of cross-platform and cross-species microarray data. It is composed of the data preprocess module, the co-expression analysis module, the differential expression analysis module, the functional and transcriptional annotation module and the graph visualization module.'),
(72776, 2006, 'bio', 'Query Chem: a Google-powered web search combining text and chemical structures', 'Klekota, Justin|Roth, Frederick P|Schreiber, Stuart L', 'Howard Hughes Medical Institute 12 Oxford Street, Cambridge, MA 02138, USA. Klekota@gmail.com|Howard Hughes Medical Institute 12 Oxford Street, Cambridge, MA 02138, USA. Klekota@gmail.com|Howard Hughes Medical Institute 12 Oxford Street, Cambridge, MA 02138, USA. Klekota@gmail.com', 'United States|United States|United States', 0, 'Algorithms|Benzene|Computational Biology|Database Management Systems|Databases, Factual|Environmental Pollutants|Indoles|Information Storage and Retrieval|Internet|Models, Chemical|Programming Languages|Sirolimus|Software', 'Journal Article', '10.1093/bioinformatics/btl155', '16672261', 'Query Chem (www.QueryChem.com) is a Web program that integrates chemical structure and text-based searching using publicly available chemical databases and Googles Web Application Program Interface (API). Query Chem makes it possible to search the Web for information about chemical structures without knowing their common names or identifiers. Furthermore, a structure can be combined with textual query terms to further restrict searches. Query Chems search results can retrieve many interesting structure-property relationships of biomolecules on the Web.'),
(72777, 2006, 'bio', 'Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins', 'Nie, Lei|Wu, Gang|Brockman, Fred J|Zhang, Weiwen', 'Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Washington DC 20057, USA.|Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Washington DC 20057, USA.|Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Washington DC 20057, USA.|Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Washington DC 20057, USA.', 'United States|United States|United States|United States', 0, 'Adenosine Triphosphate|Calibration|Chromatography, Liquid|Computational Biology|Desulfovibrio vulgaris|Mass Spectrometry|Models, Statistical|Oligonucleotide Array Sequence Analysis|Poisson Distribution|Probability|Proteins|Proteomics|RNA, Messenger|Regression Analysis', 'Journal Article', '10.1093/bioinformatics/btl134', '16675466', 'Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship.'),
(72778, 2006, 'bio', 'A mixture model with random-effects components for clustering correlated gene-expression profiles', 'Ng, S K|McLachlan, G J|Wang, K|Ben-Tovim Jones, L|Ng, S-W', 'Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.|Department of Mathematics, University of Queensland, Brisbane, QLD 4072, Australia.', 'Australia|Australia|Australia|Australia|Australia', 113, 'Algorithms|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Computer Simulation|Data Interpretation, Statistical|Databases, Factual|Gene Expression Profiling|Models, Biological|Models, Statistical|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Random Allocation|Statistics as Topic', 'Journal Article', '10.1093/bioinformatics/btl165', '16675467', 'The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes.'),
(72779, 2006, 'bio', 'PROXIMO--a new docking algorithm to model protein complexes using data from radical probe mass spectrometry (RP-MS)', 'Gerega, Sebastien K|Downard, Kevin M', 'School of Molecular and Microbial Biosciences, The University of Sydney, Sydney, NSW 2006, Australia.|School of Molecular and Microbial Biosciences, The University of Sydney, Sydney, NSW 2006, Australia.', 'Australia|Australia', 28, 'Amino Acid Sequence|Computer Simulation|Mass Spectrometry|Models, Chemical|Models, Molecular|Molecular Probe Techniques|Molecular Sequence Data|Multiprotein Complexes|Oxidation-Reduction|Peptide Mapping|Protein Conformation|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl178', '16679333', 'The design and implementation of a new algorithm, known as PROXIMO for protein oxidation interface modeller, is described to predict the structure of protein complexes using data generated in radical probe mass spectrometry (RP-MS) experiments. Photochemical radiolysis and discharge sources can be used to effect RP-MS in which hydroxyl radicals are formed directly from the bulk solvent on millisecond timescales and react with surface accessible residues in footprinting-like experiments. The algorithm utilizes a geometric surface fitting routine to predict likely structures for protein complexes. These structures are scored based on a correlation between the measured solvent accessibility of oxidizable residue side chains and oxidation shielding data obtained by RP-MS. The algorithm has been implemented to predict structures for the ribonuclease S-protein-peptide and calmodulin-melittin complexes using RP-MS data generated in this laboratory. The former is in close agreement with the high-resolution experimental structure available.'),
(72780, 2006, 'bio', 'BAli-Phy: simultaneous Bayesian inference of alignment and phylogeny', 'Suchard, Marc A|Redelings, Benjamin D', 'Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA. msuchard@ucla.edu|Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA. msuchard@ucla.edu', 'United States|United States', 164, 'Algorithms|Amino Acid Sequence|Bayes Theorem|Computational Biology|Internet|Markov Chains|Molecular Sequence Data|Monte Carlo Method|Phylogeny|Programming Languages|Sequence Alignment|Software', 'Journal Article', '10.1093/bioinformatics/btl175', '16679334', 'BAli-Phy is a Bayesian posterior sampler that employs Markov chain Monte Carlo to explore the joint space of alignment and phylogeny given molecular sequence data. Simultaneous estimation eliminates bias toward inaccurate alignment guide-trees, employs more sophisticated substitution models during alignment and automatically utilizes information in shared insertion/deletions to help infer phylogenies.'),
(72781, 2006, 'bio', 'Observing local and global properties of metabolic pathways: load points and choke points in the metabolic networks', 'Rahman, Syed Asad|Schomburg, Dietmar', 'Cologne University Bioinformatics Center, CUBIC, Zülpicher Strasse 47, 50674 Koeln, Germany.|Cologne University Bioinformatics Center, CUBIC, Zülpicher Strasse 47, 50674 Koeln, Germany.', 'Germany|Germany', 0, 'Bacillus|Bacterial Proteins|Computer Simulation|Metabolism|Models, Biological|Multienzyme Complexes|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl181', '16682421', 'The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets.'),
(72782, 2006, 'bio', 'HoSeqI: automated homologous sequence identification in gene family databases', 'Arigon, Anne-Muriel|Perrière, Guy|Gouy, Manolo', 'Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude-Bernard, Lyon 1, 43 boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. arigon@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude-Bernard, Lyon 1, 43 boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. arigon@biomserv.univ-lyon1.fr|Laboratoire de Biométrie et Biologie Evolutive, UMR CNRS 5558, Université Claude-Bernard, Lyon 1, 43 boulevard du 11 Novembre 1918, 69622 Villeurbanne Cedex, France. arigon@biomserv.univ-lyon1.fr', 'France|France|France', 8, 'Algorithms|Database Management Systems|Databases, Genetic|Evolution, Molecular|Information Storage and Retrieval|Internet|Multigene Family|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl179', '16682422', 'We present a web service allowing to automatically assign sequences to homologous gene families from a set of databases. After identification of the most similar gene family to the query sequence, this sequence is added to the whole alignment and the phylogenetic tree of the family is rebuilt. Thus, the phylogenetic position of the query sequence in its gene family can be easily identified.'),
(72783, 2006, 'bio', 'Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses', 'Kwasigroch, Jean Marc|Rooman, Marianne', 'Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, Avenue Roosevelt 50, 1050 Bruxelles, Belgium. Jean.Marc.Kwasigroch@ulb.ac.be|Unité de Bioinformatique génomique et structurale, Université Libre de Bruxelles, CP 165/61, Avenue Roosevelt 50, 1050 Bruxelles, Belgium. Jean.Marc.Kwasigroch@ulb.ac.be', 'Belgium|Belgium', 2, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Protein Folding|Proteins|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl176', '16682423', 'Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold.'),
(72784, 2006, 'bio', 'PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer', 'Teschendorff, Andrew E|Naderi, Ali|Barbosa-Morais, Nuno L|Caldas, Carlos', 'Cancer Genomics Program, Department of Oncology University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Cancer Genomics Program, Department of Oncology University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Cancer Genomics Program, Department of Oncology University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk|Cancer Genomics Program, Department of Oncology University of Cambridge, Hutchison-MRC Research Centre, Hills Road, Cambridge CB2 2XZ, UK. aet21@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 34, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Breast Neoplasms|Cluster Analysis|Diagnosis, Computer-Assisted|Female|Gene Expression Profiling|Humans|Male|Neoplasm Proteins|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Prostatic Neoplasms|Software', 'Journal Article', '10.1093/bioinformatics/btl174', '16682424', 'Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable.'),
(72785, 2006, 'bio', 'Synchronizing a multicellular system by external input: an artificial control strategy', 'Wang, Ruiqi|Chen, Luonan|Aihara, Kazuyuki', 'Aihara Complexity Modelling Project, ERATO, JST, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan.|Aihara Complexity Modelling Project, ERATO, JST, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan.|Aihara Complexity Modelling Project, ERATO, JST, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan.', 'Japan|Japan|Japan', 20, 'Animals|Artificial Intelligence|Biological Clocks|Cell Communication|Computer Simulation|Connexins|Feedback|Humans|Intercellular Junctions|Models, Biological', 'Journal Article', '10.1093/bioinformatics/btl182', '16690633', 'Although there are significant advances on elucidating the collective behaviors on biological organisms in recent years, the essential mechanisms by which the collective rhythms arise remain to be fully understood, and further how to synchronize multicellular networks by artificial control strategy has not yet been well explored.'),
(72786, 2006, 'bio', 'SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments', 'Tabei, Yasuo|Tsuda, Koji|Kin, Taishin|Asai, Kiyoshi', 'Department of Computational Biology, Graduate School of Frontier Science, University of Tokyo, CB04 Kiban-tou 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. scarna@m.aist.go.jp|Department of Computational Biology, Graduate School of Frontier Science, University of Tokyo, CB04 Kiban-tou 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. scarna@m.aist.go.jp|Department of Computational Biology, Graduate School of Frontier Science, University of Tokyo, CB04 Kiban-tou 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. scarna@m.aist.go.jp|Department of Computational Biology, Graduate School of Frontier Science, University of Tokyo, CB04 Kiban-tou 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. scarna@m.aist.go.jp', 'Japan|Japan|Japan|Japan', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Molecular Sequence Data|Pattern Recognition, Automated|RNA|Sequence Alignment|Sequence Analysis, RNA|Sequence Homology, Nucleic Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl177', '16690634', 'The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but also their potential secondary structures. Sankoffs algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required.'),
(72787, 2006, 'bio', 'Developing a move-set for protein model refinement', 'Offman, Marc N|Fitzjohn, Paul W|Bates, Paul A', 'Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincolns Inn Fields Laboratories London, WC2A 3PX, UK.|Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincolns Inn Fields Laboratories London, WC2A 3PX, UK.|Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincolns Inn Fields Laboratories London, WC2A 3PX, UK.', 'United Kingdom|United Kingdom|United Kingdom', 15, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Motion|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|Software Design', 'Comparative Study', '10.1093/bioinformatics/btl192', '16705011', 'A wide variety of methods for the construction of an atomic model for a given amino acid sequence are known, the more accurate being those that use experimentally determined structures as templates. However, far fewer methods are aimed at refining these models. The approach presented here carefully blends models created by several different means, in an attempt to combine the good quality regions from each into a final, more refined, model.'),
(72788, 2006, 'bio', 'FCP: functional coverage of the proteome by structures', 'García-Serna, Ricard|Opatowski, Lulla|Mestres, Jordi', 'Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Catalonia, Spain.|Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Catalonia, Spain.|Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal dInvestigació Mèdica and Universitat Pompeu Fabra, Dr Aiguader 88, 08003 Barcelona, Catalonia, Spain.', 'Spain|Spain|Spain', 0, 'Algorithms|Amino Acid Sequence|Database Management Systems|Databases, Protein|Information Storage and Retrieval|Internet|Molecular Sequence Data|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software|Structure-Activity Relationship|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl188', '16705012', 'Tools and resources for translating the remarkable growth witnessed in recent years in the number of protein structures determined experimentally into actual gain in the functional coverage of the proteome are becoming increasingly necessary. We introduce FCP, a publicly accessible web tool dedicated to analyzing the current state and trends of the population of structures within protein families. FCP offers both graphical and quantitative data on the degree of functional coverage of enzymes and nuclear receptors by existing structures, as well as on the bias observed in the distribution of structures along their respective functional classification schemes.'),
(72789, 2006, 'bio', 'Protein classification using probabilistic chain graphs and the Gene Ontology structure', 'Carroll, Steven|Pavlovic, Vladimir', 'Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.|Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.', 'United States|United States', 29, 'Algorithms|Artificial Intelligence|Computer Simulation|Databases, Protein|Models, Statistical|Pattern Recognition, Automated|Proteins|Structure-Activity Relationship|Terminology as Topic', 'Journal Article', '10.1093/bioinformatics/btl187', '16705013', 'Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own annotation space. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms.'),
(72790, 2006, 'bio', 'Distance-based clustering of CGH data', 'Liu, Jun|Mohammed, Jaaved|Carter, James|Ranka, Sanjay|Kahveci, Tamer|Baudis, Michael', '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|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|United States|United States|United States', 58, 'Algorithms|Animals|Chromosome Aberrations|Cluster Analysis|Computational Biology|DNA, Neoplasm|Gene Expression Profiling|Gene Expression Regulation, Neoplastic|Genomics|Humans|Models, Statistical|Neoplasms|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl185', '16705014', 'We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar.'),
(72791, 2006, 'bio', 'Combining multiple microarrays in the presence of controlling variables', 'Park, Taesung|Yi, Sung-Gon|Shin, Young Kee|Lee, SeungYeoun', 'Department of Statistics, College of Pharmacy, Seoul National University Seoul, Korea. tspark@stats.snu.ac.kr|Department of Statistics, College of Pharmacy, Seoul National University Seoul, Korea. tspark@stats.snu.ac.kr|Department of Statistics, College of Pharmacy, Seoul National University Seoul, Korea. tspark@stats.snu.ac.kr|Department of Statistics, College of Pharmacy, Seoul National University Seoul, Korea. tspark@stats.snu.ac.kr', '-|-|-|-', 33, 'Algorithms|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl183', '16705015', 'Microarray technology enables the monitoring of expression levels for thousands of genes simultaneously. When the magnitude of the experiment increases, it becomes common to use the same type of microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for the differences. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. The analysis of variance (ANOVA) model has been commonly used to detect differentially expressed genes after accounting for the sources of variation commonly observed in the microarray experiment.'),
(72792, 2006, 'bio', 'Comparative gene marker selection suite', 'Gould, Joshua|Getz, Gad|Monti, Stefano|Reich, Michael|Mesirov, Jill P', 'Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. jgould@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. jgould@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. jgould@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. jgould@broad.mit.edu|Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. jgould@broad.mit.edu', 'United States|United States|United States|United States|United States', 0, 'Algorithms|Computer Simulation|Gene Expression Profiling|Genetic Markers|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl196', '16709585', 'An important step in analyzing expression profiles from microarray data is to identify genes that can discriminate between distinct classes of samples. Many statistical approaches for assigning significance values to genes have been developed. The Comparative Marker Selection suite consists of three modules that allow users to apply and compare different methods of computing significance for each marker gene, a viewer to assess the results, and a tool to create derivative datasets and marker lists based on user-defined significance criteria.'),
(72793, 2006, 'bio', 'A model diagram layout extension for SBML', 'Gauges, Ralph|Rost, Ursula|Sahle, Sven|Wegner, Katja', 'Bioinformatics and Computational Biochemistry, EML Research Schloss-Wolfsbrunnen Weg 33, D-69118 Heidelberg, Germany. Ralph.Gauges@eml-r.villa-bosch.de|Bioinformatics and Computational Biochemistry, EML Research Schloss-Wolfsbrunnen Weg 33, D-69118 Heidelberg, Germany. Ralph.Gauges@eml-r.villa-bosch.de|Bioinformatics and Computational Biochemistry, EML Research Schloss-Wolfsbrunnen Weg 33, D-69118 Heidelberg, Germany. Ralph.Gauges@eml-r.villa-bosch.de|Bioinformatics and Computational Biochemistry, EML Research Schloss-Wolfsbrunnen Weg 33, D-69118 Heidelberg, Germany. Ralph.Gauges@eml-r.villa-bosch.de', 'Germany|Germany|Germany|Germany', 0, 'Cell Physiological Phenomena|Computer Graphics|Computer Simulation|Database Management Systems|Databases, Factual|Information Storage and Retrieval|Models, Biological|Signal Transduction|Systems Biology|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl195', '16709586', 'Since the knowledge about processes in living cells is increasing, modelling and simulation techniques are used to get new insights into these complex processes. During the last few years, the SBML file format has gained in popularity and support as a means of exchanging model data between the different modelling and simulation tools. In addition to specifying the model as a set of equations, many modern modelling tools allow the user to create and to interact with the model in the form of a reaction graph. Unfortunately, the SBML file format does not provide for the storage of this graph data along with the mathematical description of the model.'),
(72794, 2006, 'bio', 'INFO-RNA--a fast approach to inverse RNA folding', 'Busch, Anke|Backofen, Rolf', 'Albert-Ludwigs-University Freiburg, Institute of Computer Science, Chair of Bioinformatics Georges-Koehler-Allee 106, 79110 Freiburg, Germany.|Albert-Ludwigs-University Freiburg, Institute of Computer Science, Chair of Bioinformatics Georges-Koehler-Allee 106, 79110 Freiburg, Germany.', 'Germany|Germany', 83, 'Algorithms|Base Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software', 'Journal Article', '10.1093/bioinformatics/btl194', '16709587', 'The structure of RNA molecules is often crucial for their function. Therefore, secondary structure prediction has gained much interest. Here, we consider the inverse RNA folding problem, which means designing RNA sequences that fold into a given structure.'),
(72795, 2006, 'bio', 'Combo: a whole genome comparative browser', 'Engels, Reinhard|Yu, Tamara|Burge, Chris|Mesirov, Jill P|DeCaprio, David|Galagan, James E', 'The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu|The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu|The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu|The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu|The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu|The Broad Institute of MIT and Harvard, 320 Charles Street, Cambridge, MA 02142, USA. reinhard@broad.mit.edu', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl193', '16709588', 'Combo is a comparative genome browser that provides a dynamic view of whole genome alignments along with their associated annotations. Combo provides two different visualization perspectives. The perpendicular (dot plot) view provides a dot plot of genome alignments synchronized with a display of genome annotations along each axis. The parallel view displays two genome annotations horizontally, synchronized through a panel displaying local alignments as trapezoids. Users can zoom to any resolution, from whole chromosomes to individual bases. They can select, highlight and view detailed information from specific alignments and annotations. Combo is an organism agnostic and can import data from a variety of file formats.'),
(72796, 2006, 'bio', 'Independent component analysis-based penalized discriminant method for tumor classification using gene expression data', 'Huang, De-Shuang|Zheng, Chun-Hou', 'Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences PO Box 1130, Hefei, Anhui 230031, China. dshuang@iim.ac.cn|Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences PO Box 1130, Hefei, Anhui 230031, China. dshuang@iim.ac.cn', 'China|China', 227, 'Algorithms|Artificial Intelligence|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Discriminant Analysis|Feasibility Studies|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Principal Component Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl190', '16709589', 'Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data.'),
(72797, 2006, 'bio', 'TRFMA: a web-based tool for terminal restriction fragment length polymorphism analysis based on molecular weight', 'Nakano, Yoshio|Takeshita, Toru|Yamashita, Yoshihisa', 'Department of Preventive Dentistry, Faculty of Dental Science, Kyushu University, Fukuoka-shi, Japan. yosh@dent.kyushu-u.ac.jp|Department of Preventive Dentistry, Faculty of Dental Science, Kyushu University, Fukuoka-shi, Japan. yosh@dent.kyushu-u.ac.jp|Department of Preventive Dentistry, Faculty of Dental Science, Kyushu University, Fukuoka-shi, Japan. yosh@dent.kyushu-u.ac.jp', 'Japan|Japan|Japan', 15, 'Algorithms|Chromosome Mapping|DNA Fingerprinting|DNA, Bacterial|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Internet|Molecular Weight|Polymorphism, Restriction Fragment Length|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl186', '16709590', 'TRFMA provides a Web environment for analyzing T-RFLP results based on molecular weights of the fragments, rather than the numbers of nucleotides, to increase accuracy. The 16S rRNA data are saved as an XML file containing around 650 sequences (light version) and a MySQL database containing around 50 000 sequences (full version), which are connected to Web server via PHP5 and manipulated on an Internet browser.'),
(72798, 2006, 'bio', 'Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset', 'Liu, X|Sivaganesan, S|Yeung, K Y|Guo, J|Bumgarner, R E|Medvedovic, Mario', 'Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.|Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.|Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.|Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.|Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.|Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue ML 56, Cincinnati, OH 45267, USA.', 'United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Artificial Intelligence|Bayes Theorem|Cluster Analysis|Computer Simulation|Data Interpretation, Statistical|Databases, Factual|Gene Expression Profiling|Models, Biological|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btl184', '16709591', 'Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional noise introduced by non-informative measurements.'),
(72799, 2006, 'bio', 'NETGEN: generating phylogenetic networks with diploid hybrids', 'Morin, M M|Moret, B M E', 'Department of Computer Science, University of New Mexico, Albuquerque, NM, USA. morin@cs.unm.edu|Department of Computer Science, University of New Mexico, Albuquerque, NM, USA. morin@cs.unm.edu', 'Mexico|Mexico', 0, 'Algorithms|Biological Evolution|Cell Lineage|Computer Simulation|Diploidy|Evolution, Molecular|Hybrid Cells|Models, Genetic|Phylogeny|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl191', '16717070', 'NetGen is an event-driven simulator that creates phylogenetic networks by extending the birth-death model to include diploid hybridizations. DNA sequences are evolved in conjunction with the topology, enabling hybridization decisions to be based on contemporary evolutionary distances. NetGen supports variable rate lineages, root sequence specification, outgroup generation and many other options. This note describes the NetGen application and proposes an extension of the Newick format to accommodate phylogenetic networks.'),
(72800, 2006, 'bio', 'SNPStats: a web tool for the analysis of association studies', 'Solé, Xavier|Guinó, Elisabet|Valls, Joan|Iniesta, Raquel|Moreno, Víctor', 'Catalan Institute of Oncology, IDIBELL, Epidemiology and Cancer Registry LHospitalet, Barcelona, Spain.|Catalan Institute of Oncology, IDIBELL, Epidemiology and Cancer Registry LHospitalet, Barcelona, Spain.|Catalan Institute of Oncology, IDIBELL, Epidemiology and Cancer Registry LHospitalet, Barcelona, Spain.|Catalan Institute of Oncology, IDIBELL, Epidemiology and Cancer Registry LHospitalet, Barcelona, Spain.|Catalan Institute of Oncology, IDIBELL, Epidemiology and Cancer Registry LHospitalet, Barcelona, Spain.', 'Spain|Spain|Spain|Spain|Spain', 0, 'Chromosome Mapping|Computer Simulation|Genetic Markers|Genetic Predisposition to Disease|Genetics, Population|Internet|Models, Genetic|Models, Statistical|Polymorphism, Single Nucleotide|Software', 'Journal Article', '10.1093/bioinformatics/btl268', '16720584', 'A web-based application has been designed from a genetic epidemiology point of view to analyze association studies. Main capabilities include descriptive analysis, test for Hardy-Weinberg equilibrium and linkage disequilibrium. Analysis of association is based on linear or logistic regression according to the response variable (quantitative or binary disease status, respectively). Analysis of single SNPs: multiple inheritance models (co-dominant, dominant, recessive, over-dominant and log-additive), and analysis of interactions (gene-gene or gene-environment). Analysis of multiple SNPs: haplotype frequency estimation, analysis of association of haplotypes with the response, including analysis of interactions.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72801, 2006, 'bio', 'Imprint of evolutionary conservation and protein structure variation on the binding function of protein tyrosine kinases', 'Verkhivker, Gennady M', 'Department of Pharmacology, University of California San Diego 9500 Gilman Drive, La Jolla, CA 92093-0392, USA. gverkhiv@ucsd.edu', 'United States', 13, 'Amino Acid Sequence|Binding Sites|Computer Simulation|Conserved Sequence|Evolution, Molecular|Genetic Variation|Models, Chemical|Models, Genetic|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Conformation|Protein-Tyrosine Kinases|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl199', '16720585', 'According to the models of divergent molecular evolution, the evolvability of new protein function may depend on the induction of new phenotypic traits by a small number of mutations of the binding site residues. Evolutionary relationships between protein kinases are often employed to infer inhibitor binding profiles from sequence analysis. However, protein kinases binding profiles may display inhibitor selectivity within a given kinase subfamily, while exhibiting cross-activity between kinases that are phylogenetically remote from the prime target. The emerging insights into kinase function and evolution combined with a rapidly growing number of publically available crystal structures of protein kinases complexes have motivated structural bioinformatics analysis of sequence-structure relationships in determining the binding function of protein tyrosine kinases.'),
(72802, 2006, 'bio', 'Identifying sequence regions undergoing conformational change via predicted continuum secondary structure', 'Bodén, Mikael|Bailey, Timothy L', 'School of Information Technology and Electrical Engineering, QLD 4072, The University of Queensland Australia. mikael@itee.uq.edu.au|School of Information Technology and Electrical Engineering, QLD 4072, The University of Queensland Australia. mikael@itee.uq.edu.au', 'Australia|Australia', 18, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Conformation|Protein Structure, Secondary|Proteins|Sequence Alignment|Sequence Analysis, Protein|Structure-Activity Relationship', 'Journal Article', '10.1093/bioinformatics/btl198', '16720586', 'Conformational flexibility is essential to the function of many proteins, e.g. catalytic activity. To assist efforts in determining and exploring the functional properties of a protein, it is desirable to automatically identify regions that are prone to undergo conformational changes. It was recently shown that a probabilistic predictor of continuum secondary structure is more accurate than categorical predictors for structurally ambivalent sequence regions, suggesting that such models are suited to characterize protein flexibility.'),
(72803, 2006, 'bio', 'BDT: an easy-to-use front-end application for automation of massive docking tasks and complex docking strategies with AutoDock', 'Vaqué, Montserrat|Arola, Anna|Aliagas, Carles|Pujadas, Gerard', 'Departament de Bioquímica i Biotecnologia, C/Marcel.lí Domingo, s/n Av. Països Catalans, 26 Campus de Sant Pere Sescelades, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain.|Departament de Bioquímica i Biotecnologia, C/Marcel.lí Domingo, s/n Av. Països Catalans, 26 Campus de Sant Pere Sescelades, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain.|Departament de Bioquímica i Biotecnologia, C/Marcel.lí Domingo, s/n Av. Països Catalans, 26 Campus de Sant Pere Sescelades, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain.|Departament de Bioquímica i Biotecnologia, C/Marcel.lí Domingo, s/n Av. Països Catalans, 26 Campus de Sant Pere Sescelades, Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain.', 'Spain|Spain|Spain|Spain', 0, 'Algorithms|Binding Sites|Computational Biology|Computer Simulation|Ligands|Models, Chemical|Models, Molecular|Multiprotein Complexes|Protein Binding|Protein Conformation|Protein Interaction Mapping|Proteins|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl197', '16720587', 'AutoGrid/AutoDock is one of the most popular software packages for docking, but its automation is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors, (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. This is an obstacle for research teams in the fields of Chemistry and the Life Sciences who are interested in conducting this kind of experiment but do not have enough programming skills. To overcome these limitations, we have designed BDT, an easy-to-use graphic interface for AutoGrid/AutoDock.'),
(72804, 2006, 'bio', 'Spatial stochastic modelling of the phosphoenolpyruvate-dependent phosphotransferase (PTS) pathway in Escherichia coli', 'Rodrxc3xadguez, J Vidal|Kaandorp, Jaap A|Dobrzyxc5x84ski, Maciej|Blom, Joke G', 'Section Computational Science, Faculty of Science, University of Amsterdam Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. jrodrigu@science.uva.nl|Section Computational Science, Faculty of Science, University of Amsterdam Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. jrodrigu@science.uva.nl|Section Computational Science, Faculty of Science, University of Amsterdam Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. jrodrigu@science.uva.nl|Section Computational Science, Faculty of Science, University of Amsterdam Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. jrodrigu@science.uva.nl', 'Netherlands|Netherlands|Netherlands|Netherlands', 70, 'Computer Simulation|Escherichia coli|Escherichia coli Proteins|Models, Biological|Models, Statistical|Phosphoenolpyruvate|Phosphotransferases|Signal Transduction|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btl271', '16731694', 'Many biochemical networks involve reactions localized on the cell membrane. This can give rise to spatial gradients of the concentration of cytosolic species. Moreover, the number of membrane molecules can be small and stochastic effects can become relevant. Pathways usually consist of a complex interaction network and are characterized by a large set of parameters. The inclusion of spatial and stochastic effects is a major challenge in developing quantitative and dynamic models of pathways.'),
(72805, 2006, 'bio', 'Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes', 'Skibbe, David S|Wang, Xiujuan|Zhao, Xuefeng|Borsuk, Lisa A|Nettleton, Dan|Schnable, Patrick S', 'Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.|Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.|Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.|Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.|Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.|Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011, USA.', 'United States|United States|United States|United States|United States|United States', 23, 'Gene Expression Profiling|Image Enhancement|Image Interpretation, Computer-Assisted|In Situ Hybridization, Fluorescence|Microscopy, Confocal|Microscopy, Fluorescence|Oligonucleotide Array Sequence Analysis|Plant Proteins|Zea mays', 'Evaluation Studies', '10.1093/bioinformatics/btl270', '16731695', 'Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed.'),
(72806, 2006, 'bio', 'ACE-it: a tool for genome-wide integration of gene dosage and RNA expression data', 'van Wieringen, Wessel N|Belien, Jeroen A M|Vosse, Sjoerd J|Achame, Eskeatnaf M|Ylstra, Bauke', 'Department of Mathematics, Vrije Universiteit De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.|Department of Mathematics, Vrije Universiteit De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.|Department of Mathematics, Vrije Universiteit De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.|Department of Mathematics, Vrije Universiteit De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.|Department of Mathematics, Vrije Universiteit De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands.', 'Netherlands|Netherlands|Netherlands|Netherlands|Netherlands', 28, 'Algorithms|Base Sequence|Chromosome Mapping|Computer Simulation|Gene Dosage|Models, Genetic|Models, Statistical|Molecular Sequence Data|Oligonucleotide Array Sequence Analysis|RNA|Sequence Analysis, RNA|Software|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl269', '16731696', 'We describe a tool, called ACE-it (Array CGH Expression integration tool). ACE-it links the chromosomal position of the gene dosage measured by array CGH to the genes measured by the expression array. ACE-it uses this link to statistically test whether gene dosage affects RNA expression.'),
(72807, 2006, 'bio', 'Metatool 50: fast and flexible elementary modes analysis', 'von Kamp, Axel|Schuster, Stefan', 'Department of Bioinformatics, Friedrich-Schiller-University Jena 07743 Jena, Germany. kamp@minet.uni-jena.de|Department of Bioinformatics, Friedrich-Schiller-University Jena 07743 Jena, Germany. kamp@minet.uni-jena.de', 'Germany|Germany', 182, 'Computer Simulation|Gene Expression|Models, Biological|Programming Languages|Proteome|Signal Transduction|Software|Systems Biology', 'Journal Article', '10.1093/bioinformatics/btl267', '16731697', 'Elementary modes analysis is a powerful tool in the constraint-based modeling of metabolic networks. In recent years, new approaches to calculating elementary modes in biochemical reaction networks have been developed. As a consequence, the program Metatool, which is one of the first programs dedicated to this purpose, has been reimplemented in order to make use of these new approaches. The performance of Metatool has been significantly increased and the new version 5.0 can now be run inside the GNU octave or Matlab environments to allow more flexible usage and integration with other tools.'),
(72808, 2006, 'bio', 'Comments on Bayesian hierarchical error model for analysis of gene expression data', 'Wu, Xiao-Lin|Forney, Larry J|Joyce, Paul', 'Department of Bioinformatics, Friedrich-Schiller-University Jena 07743 Jena, Germany. kamp@minet.uni-jena.de|Department of Bioinformatics, Friedrich-Schiller-University Jena 07743 Jena, Germany. kamp@minet.uni-jena.de|Department of Bioinformatics, Friedrich-Schiller-University Jena 07743 Jena, Germany. kamp@minet.uni-jena.de', 'Germany|Germany|Germany', 1, 'Algorithms|Animals|Bayes Theorem|Brain|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Mice|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pan troglodytes|Sequence Alignment|Sequence Analysis, DNA|Sequence Analysis, Protein', 'Comment', '10.1093/bioinformatics/btl173', '16731698', '-'),
(72809, 2006, 'bio', 'Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences', 'Li, Weizhong|Godzik, Adam', 'Burnham Institute for Medical Research La Jolla, CA 92037, USA. liwz@sdsc.edu|Burnham Institute for Medical Research La Jolla, CA 92037, USA. liwz@sdsc.edu', 'United States|United States', 4297, 'Algorithms|Animals|Cluster Analysis|Computational Biology|DNA|Databases, Nucleic Acid|Databases, Protein|Expressed Sequence Tags|Humans|Programming Languages|RNA|Sequence Analysis, DNA|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl158', '16731699', 'In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them, cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.'),
(72810, 2006, 'bio', 'PROBER: oligonucleotide FISH probe design software', 'Navin, Nicholas|Grubor, Vladimir|Hicks, Jim|Leibu, Evan|Thomas, Elizabeth|Troge, Jennifer|Riggs, Michael|Lundin, Pär|Månér, Susanne|Sebat, Jonathan|Zetterberg, Anders|Wigler, Michael', 'Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu|Watson School of Biological Sciences, Cold Spring Harbor, NY 11724, USA. navin@cshl.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 21, 'Algorithms|Base Sequence|Chromosome Mapping|Computer-Aided Design|DNA Probes|Equipment Design|In Situ Hybridization, Fluorescence|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl273', '16740623', 'PROBER is an oligonucleotide primer design software application that designs multiple primer pairs for generating PCR probes useful for fluorescence in situ hybridization (FISH). PROBER generates Tiling Oligonucleotide Probes (TOPs) by masking repetitive genomic sequences and delineating essentially unique regions that can be amplified to yield small (100-2000 bp) DNA probes that in aggregate will generate a single, strong fluorescent signal for regions as small as a single gene. TOPs are an alternative to bacterial artificial chromosomes (BACs) that are commonly used for FISH but may be unstable, unavailable, chimeric, or non-specific to small (10-100 kb) genomic regions. PROBER can be applied to any genomic locus, with the limitation that the locus must contain at least 10 kb of essentially unique blocks. To test the software, we designed a number of probes for genomic amplifications and hemizygous deletions that were initially detected by Representational Oligonucleotide Microarray Analysis of breast cancer tumors.'),
(72811, 2006, 'bio', 'Evolutionary simulations to detect functional lineage-specific genes', 'Dupanloup, Isabelle|Kaessmann, Henrik', 'Center for Integrative Genomics, University of Lausanne CH-1015 Lausanne, Switzerland.|Center for Integrative Genomics, University of Lausanne CH-1015 Lausanne, Switzerland.', 'Switzerland|Switzerland', 11, 'Animals|Base Sequence|Cell Lineage|Chromosome Mapping|Conserved Sequence|DNA Mutational Analysis|Evolution, Molecular|Hominidae|Humans|Linkage Disequilibrium|Molecular Sequence Data|Open Reading Frames|Phylogeny|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid', 'Journal Article', '10.1093/bioinformatics/btl280', '16766551', 'Supporting the functionality of recent duplicate gene copies is usually difficult, owing to high sequence similarity between duplicate counterparts and shallow phylogenies, which hamper both the statistical and experimental inference.'),
(72812, 2006, 'bio', 'caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid', 'Saltz, Joel|Oster, Scott|Hastings, Shannon|Langella, Stephen|Kurc, Tahsin|Sanchez, William|Kher, Manav|Manisundaram, Arumani|Shanbhag, Krishnakant|Covitz, Peter', 'Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu|Department of Biomedical Informatics, Ohio State University 3184 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. joel.saltz@osumc.edu', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 118, 'Biomarkers, Tumor|Computational Biology|Database Management Systems|Databases, Protein|Humans|Information Storage and Retrieval|Internet|Neoplasm Proteins|Neoplasms|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl272', '16766552', 'The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems.'),
(72813, 2006, 'bio', 'tuple_plot: fast pairwise nucleotide sequence comparison with noise suppression', 'Szafranski, Karol|Jahn, Niels|Platzer, Matthias', 'Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute Beutenbergstr. 11, 07745 Jena, Germany. szafrans@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute Beutenbergstr. 11, 07745 Jena, Germany. szafrans@fli-leibniz.de|Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute Beutenbergstr. 11, 07745 Jena, Germany. szafrans@fli-leibniz.de', 'Germany|Germany|Germany', 7, 'Algorithms|Artifacts|Base Sequence|Chromosome Mapping|Models, Genetic|Models, Statistical|Molecular Sequence Data|Nucleotides|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|Stochastic Processes', 'Journal Article', '10.1093/bioinformatics/btl277', '16766553', 'The program tuple_plot identifies and visualizes local similarities between two genomic sequences, typically 100 kb or longer, by applying the well-known dotplot principle. A dictionary of sequence words built from the input sequences serves to construct a task-specific expectancy model that is used to attribute significance values to pairwise word hits. The dictionary-based approach allows fast computation, the computation time scaling to O(N log N), depending on the size of the input sequences. The proposed scoring scheme appreciably increases the signal-to-noise ratio and may help to improve other word-based sequence comparison approaches.'),
(72814, 2006, 'bio', 'Intramolecular surface contacts contain information about protein-protein interface regions', 'de Vries, Sjoerd J|Bonvin, Alexandre M J J', 'Faculty of Sciences, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands.|Faculty of Sciences, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584CH, Utrecht, The Netherlands.', 'Netherlands|Netherlands', 0, 'Algorithms|Amino Acid Sequence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Binding|Protein Interaction Mapping|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl275', '16766554', 'Some amino acids clearly show preferences over others in protein-protein interfaces. These preferences, or so-called interface propensities can be used for a priori interface prediction. We investigated whether the prediction accuracy could be improved by considering not single but pairs of residues in an interface. Here we present the first systematic analysis of intramolecular surface contacts in interface prediction.'),
(72815, 2006, 'bio', 'Application of the multiensemble sampling to the equilibrium folding of proteins', 'Son, Hyeon S|Kim, Seung-Yeon|Lee, Jooyoung|Han, Kyu-Kwang', 'Laboratory of Computational Biology and Bioinformatics, Institute of Health and Environment, Graduate School of Public Health, Seoul National University Seoul 110-799, Korea.|Laboratory of Computational Biology and Bioinformatics, Institute of Health and Environment, Graduate School of Public Health, Seoul National University Seoul 110-799, Korea.|Laboratory of Computational Biology and Bioinformatics, Institute of Health and Environment, Graduate School of Public Health, Seoul National University Seoul 110-799, Korea.|Laboratory of Computational Biology and Bioinformatics, Institute of Health and Environment, Graduate School of Public Health, Seoul National University Seoul 110-799, Korea.', '-|-|-|-', 5, 'Algorithms|Amino Acid Sequence|Computer Simulation|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Monte Carlo Method|Motion|Protein Conformation|Proteins|Sample Size|Sequence Alignment|Sequence Analysis, Protein|Stress, Mechanical', 'Journal Article', '10.1093/bioinformatics/btl282', '16766555', 'Conventional Monte Carlo and molecular dynamics simulations of proteins in the canonical ensemble are of little use, because they tend to get trapped in states of energy local minima at low temperatures. One way to surmount this difficulty is to use a non-Boltzmann sampling method in which conformations are sampled upon a general weighting function instead of the conventional Boltzmann weighting function. The multiensemble sampling (MES) method is a non-Boltzmann sampling method that was originally developed to estimate free energy differences between systems with different potential energies and/or at different thermodynamic states. The method has not yet been applied to studies of complex molecular systems such as proteins.'),
(72816, 2006, 'bio', 'Transcriptome network component analysis with limited microarray data', 'Galbraith, Simon J|Tran, Linh M|Liao, James C', 'Department of Computer Science, University of California Los Angeles, CA, USA.|Department of Computer Science, University of California Los Angeles, CA, USA.|Department of Computer Science, University of California Los Angeles, CA, USA.', 'United States|United States|United States', 44, 'Algorithms|Cell Cycle|Computer Simulation|Gene Expression|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Sample Size|Signal Transduction|Transcription Factors|Transcriptional Activation', 'Journal Article', '10.1093/bioinformatics/btl279', '16766556', 'Network component analysis (NCA) is a method to deduce transcription factor (TF) activities and TF-gene regulation control strengths from gene expression data and a TF-gene binding connectivity network. Previously, this method could analyze a maximum number of regulators equal to the total sample size because of the identifiability limit in data decomposition. As such, the total number of source signal components was limited to the total number of experiments rather than the total number of biological regulators. However, networks that have less transcriptome data points than the number of regulators are of interest. Thus it is imperative to develop a theoretical basis that allows realistic source signal extraction based on relatively few data points. On the other hand, such methods would inherently increase numerical challenges leading to multiple solutions. Therefore, solutions to both the problems are needed.'),
(72817, 2006, 'bio', 'SeqVis: visualization of compositional heterogeneity in large alignments of nucleotides', 'Ho, Joshua W K|Adams, Cameron E|Lew, Jie Bin|Matthews, Timothy J|Ng, Chiu Chin|Shahabi-Sirjani, Arash|Tan, Leng Hong|Zhao, Yu|Easteal, Simon|Wilson, Susan R|Jermiin, Lars S', 'School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.|School of Biological Sciences Sydney, Australia.', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|Australia|', 43, 'Algorithms|Base Sequence|Computer Graphics|Conserved Sequence|Molecular Sequence Data|Sequence Alignment|Sequence Analysis, DNA|Sequence Homology, Nucleic Acid|Software|User-Computer Interface', 'Evaluation Studies', '10.1093/bioinformatics/btl283', '16766557', 'Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis.'),
(72818, 2006, 'bio', 'Combination of text-mining algorithms increases the performance', 'Malik, Rainer|Franke, Lude|Siebes, Arno', 'Universiteit Utrecht, Department of Information and Computing Sciences, Padualaan 14, 3584CH Utrecht, The Netherlands. rainer@cs.uu.nl|Universiteit Utrecht, Department of Information and Computing Sciences, Padualaan 14, 3584CH Utrecht, The Netherlands. rainer@cs.uu.nl|Universiteit Utrecht, Department of Information and Computing Sciences, Padualaan 14, 3584CH Utrecht, The Netherlands. rainer@cs.uu.nl', 'Netherlands|Netherlands|Netherlands', 16, 'Abstracting and Indexing as Topic|Database Management Systems|Databases, Protein|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Proteome|Systems Integration', 'Comparative Study', '10.1093/bioinformatics/btl281', '16766558', 'Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms.'),
(72819, 2006, 'bio', 'A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS', 'Bellew, Matthew|Coram, Marc|Fitzgibbon, Matthew|Igra, Mark|Randolph, Tim|Wang, Pei|May, Damon|Eng, Jimmy|Fang, Ruihua|Lin, Chenwei|Chen, Jinzhi|Goodlett, David|Whiteaker, Jeffrey|Paulovich, Amanda|McIntosh, Martin', 'LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.|LabKey Software, Seattle, WA 98109, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 212, 'Algorithms|Chromatography, Liquid|Complex Mixtures|Computer Graphics|Mass Spectrometry|Peptide Mapping|Proteins|Software|Systems Integration|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl276', '16766559', 'Comparing two or more complex protein mixtures using liquid chromatography mass spectrometry (LC-MS) requires multiple analysis steps to locate and quantitate natural peptides within a single experiment and to align and normalize findings across multiple experiments.'),
(72820, 2006, 'bio', 'JADE: a distributed Java application for deleterious genomic mutation (DGM) estimation', 'Li, J-L|Li, M-X|Guo, Y-F|Deng, H-Y|Deng, H-W', 'Seattle Biomedical Research Institute, Seattle, WA 98109, USA. Jinlong.Li@sbri.org|Seattle Biomedical Research Institute, Seattle, WA 98109, USA. Jinlong.Li@sbri.org|Seattle Biomedical Research Institute, Seattle, WA 98109, USA. Jinlong.Li@sbri.org|Seattle Biomedical Research Institute, Seattle, WA 98109, USA. Jinlong.Li@sbri.org|Seattle Biomedical Research Institute, Seattle, WA 98109, USA. Jinlong.Li@sbri.org', 'United States|United States|United States|United States|United States', 1, 'Biological Evolution|Chromosome Mapping|DNA Mutational Analysis|Evolution, Molecular|Genetics, Population|Models, Genetic|Mutation|Programming Languages|Selection, Genetic|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl278', '16766560', 'The characterization of deleterious genomic mutation (DGM) is of central significance for evolutionary biology and genetic studies. Fitness moment method has been developed to efficiently characterize DGM from natural population directly. In order to enable researchers to employ this method for theoretical and empirical research on characterizing DGM, we here present a distributed Java Application for DGM Estimation (JADE).'),
(72821, 2006, 'bio', 'Clustering microarray gene expression data using weighted Chinese restaurant process', 'Qin, Zhaohui S', 'Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. qin@umich.edu', 'United States', 95, 'Algorithms|Bayes Theorem|Cluster Analysis|Computational Biology|Computer Simulation|Computers|Data Interpretation, Statistical|Databases, Genetic|Gene Expression Profiling|Models, Statistical|Normal Distribution|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btl284', '16766561', 'Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging.'),
(72822, 2006, 'bio', 'NetAlign: a web-based tool for comparison of protein interaction networks', 'Liang, Zhi|Xu, Meng|Teng, Maikun|Niu, Liwen', 'Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.', 'China|China|China|China', 49, 'Algorithms|Computing Methodologies|Gene Expression Profiling|Internet|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl287', '16766562', 'NetAlign is a web-based tool designed to enable comparative analysis of protein interaction networks (PINs). NetAlign compares a query PIN with a target PIN by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs), which may derive from a common ancestor and disclose conserved topological organization of interactions in evolution. To exemplify the application of NetAlign, we perform two genome-scale comparisons with (1) the Escherichia coli PIN against the Helicobacter pylori PIN and (2) the Saccharomyces cerevisiae PIN against the Caenorrhabditis elegans PIN. Many of the identified CoNSs correspond to known complexes, therefore, cross-species PIN comparison provides a way for discovery of conserved modules. In addition, based on the species-to-species differences in CoNSs, we reformulate the problems of protein-protein interaction (PPI) prediction and species divergence from a network perspective.'),
(72823, 2006, 'bio', 'It is time to end the patenting of software', 'Quackenbush, John|Salzberg, Steven L', 'Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.|Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences, University of Science & Technology of China, 96 Jinzhai Road, Hefei, Anhui 230027, China.', 'China|China', 1, 'Computational Biology|Data Interpretation, Statistical|Patents as Topic|Programming Languages|Software', 'Editorial', '10.1093/bioinformatics/btl167', '16766564', '-'),
(72824, 2006, 'bio', 'Unbiased pattern detection in microarray data series', 'Ahnert, S E|Willbrand, K|Brown, F C S|Fink, T M A', 'Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK. sea31@cam.ac.uk|Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK. sea31@cam.ac.uk|Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK. sea31@cam.ac.uk|Theory of Condensed Matter, Cavendish Laboratory Cambridge CB3 0HE, UK. sea31@cam.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 19, 'Algorithms|Cell Cycle|Cluster Analysis|Computational Biology|Data Interpretation, Statistical|Fungal Proteins|Gene Expression Profiling|Genes, Fungal|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btl121', '16766565', 'Following the advent of microarray technology in recent years, the challenge for biologists is to identify genes of interest from the thousands of genetic expression levels measured in each microarray experiment. In many cases the aim is to identify pattern in the data series generated by successive microarray measurements.'),
(72825, 2006, 'bio', 'Robust estimation of the false discovery rate', 'Pounds, Stan|Cheng, Cheng', 'Department of Biostatistics, St. Jude Childrens Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA.|Department of Biostatistics, St. Jude Childrens Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA.', 'United States|United States', 95, 'Algorithms|Computational Biology|Computer Simulation|Data Interpretation, Statistical|False Positive Reactions|Gene Expression Profiling|Microarray Analysis|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btl328', '16777905', 'Presently available methods that use p-values to estimate or control the false discovery rate (FDR) implicitly assume that p-values are continuously distributed and based on two-sided tests. Therefore, it is difficult to reliably estimate the FDR when p-values are discrete or based on one-sided tests.'),
(72826, 2006, 'bio', 'Roundup: a multi-genome repository of orthologs and evolutionary distances', 'Deluca, Todd F|Wu, I-Hsien|Pu, Jian|Monaghan, Thomas|Peshkin, Leonid|Singh, Saurav|Wall, Dennis P', 'The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.|The Center for Biomedical Informatics & Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.', 'United States|United States|United States|United States|United States|United States|United States', 84, 'Algorithms|Computational Biology|Data Interpretation, Statistical|Databases, Genetic|Evolution, Molecular|Genome|Genome, Bacterial|Internet|Pattern Recognition, Automated|Phylogeny|Software|Species Specificity', 'Journal Article', '10.1093/bioinformatics/btl286', '16777906', 'We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data.'),
(72827, 2006, 'bio', 'THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures', 'Theobald, Douglas L|Wuttke, Deborah S', 'Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO 80309-0215, USA. douglas.theobald@colorado.edu|Department of Chemistry and Biochemistry, University of Colorado at Boulder, Boulder, CO 80309-0215, USA. douglas.theobald@colorado.edu', 'United States|United States', 138, 'Algorithms|Amino Acid Sequence|Computer Graphics|Computer Simulation|Imaging, Three-Dimensional|Internet|Likelihood Functions|Macromolecular Substances|Models, Chemical|Models, Molecular|Models, Statistical|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl332', '16777907', 'THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble.'),
(72828, 2006, 'bio', 'JCell--a Java-based framework for inferring regulatory networks from time series data', 'Spieth, C|Supper, J|Streichert, F|Speer, N|Zell, A', 'Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen Sand 1, 72076 Tübingen, Germany. christian.spieth@uni-tuebingen.de|Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen Sand 1, 72076 Tübingen, Germany. christian.spieth@uni-tuebingen.de|Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen Sand 1, 72076 Tübingen, Germany. christian.spieth@uni-tuebingen.de|Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen Sand 1, 72076 Tübingen, Germany. christian.spieth@uni-tuebingen.de|Centre for Bioinformatics Tübingen (ZBIT), University of Tübingen Sand 1, 72076 Tübingen, Germany. christian.spieth@uni-tuebingen.de', 'Germany|Germany|Germany|Germany|Germany', 0, 'Algorithms|Cluster Analysis|Computational Biology|Computer Graphics|Computer Simulation|Computers|Gene Expression Profiling|Internet|Models, Theoretical|Oligonucleotide Array Sequence Analysis|Programming Languages|Software|Time Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl322', '16782725', 'JCell is a Java-based application for reconstructing gene regulatory networks from experimental data. The framework provides several algorithms to identify genetic and metabolic dependencies based on experimental data conjoint with mathematical models to describe and simulate regulatory systems. Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers.'),
(72829, 2006, 'bio', 'Building chromosome-wide LD maps', 'Abad-Grau, María M|Montes, Rosana|Sebastiani, Paola', 'Software Engineering Department, University of Granada Granada 18071, Spain.|Software Engineering Department, University of Granada Granada 18071, Spain.|Software Engineering Department, University of Granada Granada 18071, Spain.', 'Spain|Spain|Spain', 0, 'Chromosome Mapping|Computational Biology|Humans|Linkage Disequilibrium|Polymorphism, Single Nucleotide|Programming Languages|Software', 'Journal Article', '10.1093/bioinformatics/btl288', '16782726', 'BMapBuilder builds maps of pairwise linkage disequilibrium (LD) in either two or three dimensions. The optimized resolution allows for graphical display of LD for single nucleotide polymorphisms (SNPs) in a whole chromosome.'),
(72830, 2006, 'bio', 'Additional gene ontology structure for improved biological reasoning', 'Myhre, Simen|Tveit, Henrik|Mollestad, Torulf|Laegreid, Astrid', 'Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology NO-7489 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology NO-7489 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology NO-7489 Trondheim, Norway.|Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology NO-7489 Trondheim, Norway.', 'Norway|Norway|Norway|Norway', 162, 'Computational Biology|Computer Simulation|Database Management Systems|Genes|Genome|Genomics|Humans|Models, Biological|Models, Genetic|Models, Theoretical|Software|Terminology as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl334', '16787968', 'The Gene Ontology (GO) is a widely used terminology for gene product characterization in, for example, interpretation of biology underlying microarray experiments. The current GO defines term relationships within each of the independent subontologies: molecular function, biological process and cellular component. However, it is evident that there also exist biological relationships between terms of different subontologies. Our aim was to connect the three subontologies to enable GO to cover more biological knowledge, enable a more consistent use of GO and provide new opportunities for biological reasoning.'),
(72831, 2006, 'bio', 'Transcript mapping with high-density oligonucleotide tiling arrays', 'Huber, Wolfgang|Toedling, Joern|Steinmetz, Lars M', 'European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, UK. huber@ebi.ac.uk|European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge CB10 1SD, UK. huber@ebi.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 117, 'Algorithms|Computational Biology|DNA|Gene Expression Profiling|Genome|Models, Statistical|Nucleic Acid Hybridization|Oligonucleotide Array Sequence Analysis|Oligonucleotide Probes|Oligonucleotides|RNA, Messenger|Saccharomyces cerevisiae|Transcription, Genetic', 'Journal Article', '10.1093/bioinformatics/btl289', '16787969', 'High-density DNA tiling microarrays are a powerful tool for the characterization of complete transcriptomes. The two major analytical challenges are the segmentation of the hybridization signal along genomic coordinates to accurately determine transcript boundaries and the adjustment of the sequence-dependent response of the oligonucleotide probes to achieve quantitative comparability of the signal between different probes.'),
(72832, 2006, 'bio', 'A reference database for circular dichroism spectroscopy covering fold and secondary structure space', 'Lees, Jonathan G|Miles, Andrew J|Wien, Frank|Wallace, B A', 'Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.|Department of Crystallography, Birkbeck College, University of London, London WC1E 7HX, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Biophysics|Calibration|Circular Dichroism|Cluster Analysis|Computational Biology|Databases, Genetic|Databases, Protein|Protein Folding|Protein Structure, Secondary|Ultraviolet Rays', 'Journal Article', '10.1093/bioinformatics/btl327', '16787970', 'Circular Dichroism (CD) spectroscopy is a long-established technique for studying protein secondary structures in solution. Empirical analyses of CD data rely on the availability of reference datasets comprised of far-UV CD spectra of proteins whose crystal structures have been determined. This article reports on the creation of a new reference dataset which effectively covers both secondary structure and fold space, and uses the higher information content available in synchrotron radiation circular dichroism (SRCD) spectra to more accurately predict secondary structure than has been possible with existing reference datasets. It also examines the effects of wavelength range, structural redundancy and different means of categorizing secondary structures on the accuracy of the analyses. In addition, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based on spectral properties alone. The databases are shown to be applicable in both conventional CD and SRCD spectroscopic analyses of proteins. Hence, by combining new bioinformatics and biophysical methods, a database has been produced that should have wide applicability as a tool for structural molecular biology.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72833, 2006, 'bio', 'Increasing confidence of protein interactomes using network topological metrics', 'Chen, Jin|Hsu, Wynne|Lee, Mong Li|Ng, See-Kiong', 'School of Computing, National University of Singapore, Singapore 119260.|School of Computing, National University of Singapore, Singapore 119260.|School of Computing, National University of Singapore, Singapore 119260.|School of Computing, National University of Singapore, Singapore 119260.', 'Singapore|Singapore|Singapore|Singapore', 0, 'Animals|Caenorhabditis elegans|Computational Biology|Computer Simulation|Databases, Protein|Drosophila|False Positive Reactions|Protein Binding|Protein Interaction Mapping|Proteomics|Saccharomyces cerevisiae|Sequence Analysis, Protein|Software|Two-Hybrid System Techniques', 'Journal Article', '10.1093/bioinformatics/btl335', '16787971', 'Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.'),
(72834, 2006, 'bio', 'DomainSieve: a protein domain-based screen that led to the identification of dam-associated genes with potential link to DNA maintenance', 'Brézellec, Pierre|Hoebeke, Mark|Hiet, Marie-Sophie|Pasek, Sophie|Ferat, Jean-Luc', 'Laboratoire Statistique et Génome du CNRS, Tour Evry 2, 523, Place des Terrasses de lAgora, 91034 Evry Cedex, France.|Laboratoire Statistique et Génome du CNRS, Tour Evry 2, 523, Place des Terrasses de lAgora, 91034 Evry Cedex, France.|Laboratoire Statistique et Génome du CNRS, Tour Evry 2, 523, Place des Terrasses de lAgora, 91034 Evry Cedex, France.|Laboratoire Statistique et Génome du CNRS, Tour Evry 2, 523, Place des Terrasses de lAgora, 91034 Evry Cedex, France.|Laboratoire Statistique et Génome du CNRS, Tour Evry 2, 523, Place des Terrasses de lAgora, 91034 Evry Cedex, France.', 'France|France|France|France|France', 35, 'Algorithms|Bacterial Outer Membrane Proteins|Base Pair Mismatch|Computational Biology|DNA|DNA Methylation|DNA Repair|DNA Restriction Enzymes|DNA-Binding Proteins|Escherichia coli|Escherichia coli Proteins|Genome, Bacterial|Models, Genetic|Phylogeny|Protein Structure, Tertiary|Site-Specific DNA-Methyltransferase (Adenine-Specific)', 'Journal Article', '10.1093/bioinformatics/btl336', '16787973', 'The Dam methyltransferase (DamMT) activity, broadly distributed in association with restriction endonucleases, as part of the restriction-modification defense systems, has evolved to become intimately associated with essential biological functions in a few organisms. In Escherichia coli, DamMT is involved in multiple aspects of DNA maintenance, replication initiation, daughter chromosome segregation, DNA mismatch repair, gene expression control, etc. The participation of DamMT in such a diverse set of functions required that other genes adapted, or emerged through evolution, in response to the DamMT-induced modification of the genomic environment. One example is SeqA, a protein that senses the methylation status of the origin of replication of the chromosome to control the timing of replication initiation. Interestingly, seqA is only present in a few DamMT-specifying proteobacteria. This observation led us to hypothesize that other genes, specifying related functions, might also be found in these organisms. To test this hypothesis, we implemented a large-scale comparative genomic screen meant to identify genes specifying DNA methylation sensing domains, probably involved in DNA maintenance functions.'),
(72835, 2006, 'bio', 'HCNet: a database of heart and calcium functional network', 'Hong, Seong-Eui|Rho, Seong-Hwan|Yeom, Young Il|Kim, Do Han', 'Department of Life Science, Gwangju Institute of Science and Technology 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea.|Department of Life Science, Gwangju Institute of Science and Technology 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea.|Department of Life Science, Gwangju Institute of Science and Technology 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea.|Department of Life Science, Gwangju Institute of Science and Technology 1 Oryong-dong, Buk-gu, Gwangju 500-712, Korea.', '-|-|-|-', 9, 'Algorithms|Calcium|Cluster Analysis|Computational Biology|Databases, Protein|Gene Expression Profiling|Heart|Models, Genetic|Myocardium|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Programming Languages|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl331', '16787974', 'The Heart and Calcium functional Network (HCNet) database is a collection of functional gene modules calculated from the microarray data compendium available from the GEO database. It is a specialized database designed to assist experimentalists for cardiac calcium signaling research by providing the pre-calculated gene clusters and their potential correlation network in heart. In the current release of HCNet, 57 functional modules from 786 target genes obtained by a bi-clustering analysis of 381 microarray datasets are available. Detailed information of the clusters such as expression profiles, network diagrams is provided in two categories, heart-specific genes and heart-specific genes along with calcium toolkit genes. Overrepresented gene ontological categories and transcription factors in each cluster are also provided to infer the biological implications of the detected functional modules.'),
(72836, 2006, 'bio', 'TSSub: eukaryotic protein subcellular localization by extracting features from profiles', 'Guo, Jian|Lin, Yuanlie', 'Laboratory of Statistical Computation & Bioinformatics, Department of Mathematical Sciences, Tsinghua University Beijing 100084, China. guojian99@tsinghua.org.cn|Laboratory of Statistical Computation & Bioinformatics, Department of Mathematical Sciences, Tsinghua University Beijing 100084, China. guojian99@tsinghua.org.cn', 'China|China', 24, 'Algorithms|Animals|Artificial Intelligence|Cluster Analysis|Eukaryotic Cells|Gene Expression Profiling|Humans|Proteome|Sequence Alignment|Sequence Analysis, Protein|Software|Subcellular Fractions', 'Journal Article', '10.1093/bioinformatics/btl180', '16787975', 'This paper introduces a new subcellular localization system (TSSub) for eukaryotic proteins. This system extracts features from both profiles and amino acid sequences. Four different features are extracted from profiles by four probabilistic neural network (PNN) classifiers, respectively (the amino acid composition from whole profiles, the amino acid composition from the N-terminus of profiles, the dipeptide composition from whole profiles and the amino acid composition from fragments of profiles). In addition, a support vector machine (SVM) classifier is added to implement the residue-couple feature extracted from amino acid sequences. The results from the five classifiers are fused by an additional SVM classifier. The overall accuracies of this TSSub reach 93.0 and 77.4% on Reinhardt and Hubbards eukaryotic protein dataset and Huang and Lis eukaryotic protein dataset, respectively. The comparison with existing methods results shows TSSub provides better prediction performance than existing methods.'),
(72837, 2006, 'bio', 'Cleaver: software for identifying taxon specific restriction endonuclease recognition sites', 'Jarman, Simon N', 'Department of the Environment and Heritage, Australian Antarctic Division, 203 Channel Highway, Kingston, TAS, 7050, Australia. simon.jarman@aad.gov.au', 'Australia', 0, 'Algorithms|Binding Sites|Computer Graphics|DNA|DNA Restriction Enzymes|Protein Binding|Restriction Mapping|Sequence Analysis, DNA|Sequence Analysis, Protein|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl330', '16787976', 'Cleaver is an application for identifying restriction endonuclease recognition sites that occur in some taxa but not in others. Differences in DNA fragment restriction patterns among taxa are the basis for many diagnostic assays for taxonomic identification and are used in procedures for removing the DNA of some taxa from pools of DNA from mixed sources. Cleaver analyses restriction digestion of groups of orthologous DNA sequences simultaneously to allow identification of differences in restriction pattern among the fragments derived from different taxa.'),
(72838, 2006, 'bio', 'A lock-and-key model for protein-protein interactions', 'Morrison, Julie L|Breitling, Rainer|Higham, Desmond J|Gilbert, David R', 'Bioinformatics Research Centre, Department of Computing Science, University of Glasgow G12 8QQ, UK. jmorriso@dcs.gla.ac.uk|Bioinformatics Research Centre, Department of Computing Science, University of Glasgow G12 8QQ, UK. jmorriso@dcs.gla.ac.uk|Bioinformatics Research Centre, Department of Computing Science, University of Glasgow G12 8QQ, UK. jmorriso@dcs.gla.ac.uk|Bioinformatics Research Centre, Department of Computing Science, University of Glasgow G12 8QQ, UK. jmorriso@dcs.gla.ac.uk', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 75, 'Algorithms|Computational Biology|Computer Simulation|Genome|Models, Biological|Models, Theoretical|Protein Interaction Mapping|Protein Structure, Tertiary|Proteins|Proteomics|Saccharomyces cerevisiae|Transcription Factors|Two-Hybrid System Techniques|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btl338', '16787977', 'Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organisms proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions.'),
(72839, 2006, 'bio', 'Theory and reality for software patents: good in concept, not so good in practice', 'Wren, Jonathan D', 'Bioinformatics Research Centre, Department of Computing Science, University of Glasgow G12 8QQ, UK. jmorriso@dcs.gla.ac.uk', 'United Kingdom', 0, 'Algorithms|Computational Biology|Internet|Patents as Topic|Software', 'Editorial', '10.1093/bioinformatics/btl168', '16801635', '-'),
(72840, 2006, 'bio', 'Pathway analysis using random forests classification and regression', 'Pang, Herbert|Lin, Aiping|Holford, Matthew|Enerson, Bradley E|Lu, Bin|Lawton, Michael P|Floyd, Eugenia|Zhao, Hongyu', 'Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.|Division of Biostatistics, Department of Epidemiology and Public Health, Yale University School of Medicine New Haven, CT 06520, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Computational Biology|Computer Simulation|Data Interpretation, Statistical|Gene Expression Profiling|Humans|Mice|Models, Biological|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Rats|Regression Analysis|Software', 'Journal Article', '10.1093/bioinformatics/btl344', '16809386', 'Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers.'),
(72841, 2006, 'bio', 'A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-alpha', 'Li, Lang|Cheng, Alfred S L|Jin, Victor X|Paik, Henry H|Fan, Meiyun|Li, Xiaoman|Zhang, Wei|Robarge, Jason|Balch, Curtis|Davuluri, Ramana V|Kim, Sun|Huang, Tim H-M|Nephew, Kenneth P', 'Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu|Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA. lali@iupui.edu', 'India|India|India|India|India|India|India|India|India|India|India|India|India', 11, 'Algorithms|Base Pairing|Base Sequence|Binding Sites|Computer Simulation|Discriminant Analysis|Estrogen Receptor alpha|Gene Expression Regulation|Models, Chemical|Models, Genetic|Models, Molecular|Models, Statistical|Molecular Sequence Data|Promoter Regions, Genetic|Protein Binding|Sequence Alignment|Sequence Analysis, DNA|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl329', '16809387', 'To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-alpha (ERalpha), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs.'),
(72842, 2006, 'bio', 'ET viewer: an application for predicting and visualizing functional sites in protein structures', 'Morgan, Daniel H|Kristensen, David M|Mittelman, David|Lichtarge, Olivier', 'Department of Molecular and Human Genetics, One Baylor Plaza, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, One Baylor Plaza, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, One Baylor Plaza, Houston, TX 77030, USA.|Department of Molecular and Human Genetics, One Baylor Plaza, Houston, TX 77030, USA.', 'United States|United States|United States|United States', 0, 'Amino Acid Sequence|Computational Biology|Computer Graphics|Databases, Protein|Evolution, Molecular|Molecular Sequence Data|Phylogeny|Programming Languages|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl285', '16809388', 'The Evolutionary Trace Viewer (ETV) provides a one-stop environment in which to run, visualize and interpret Evolutionary Trace (ET) predictions of functional sites in protein structures. ETV is implemented using Java to run across different operating systems using Java Web Start technology.'),
(72843, 2006, 'bio', 'Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules', 'Wang, Dong|Lv, Yingli|Guo, Zheng|Li, Xia|Li, Yanhui|Zhu, Jing|Yang, Da|Xu, Jianzhen|Wang, Chenguang|Rao, Shaoqi|Yang, Baofeng', 'Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.|Department of Bioinformatics and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University Harbin 150086, China.', 'China|China|China|China|China|China|China|China|China|China|China', 34, 'Algorithms|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl339', '16809389', 'Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis.'),
(72844, 2006, 'bio', 'Eicosapentapeptide repeats (EPRs): novel repeat proteins specific to flowering plants', 'Archak, Sunil|Nagaraju, Javaregowda', 'Laboratory of Molecular Genetics, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India.|Laboratory of Molecular Genetics, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, India.', 'India|India', 0, 'Amino Acid Sequence|Conserved Sequence|Flowers|Molecular Sequence Data|Oryza|Peptides|Plant Proteins|Sequence Alignment|Sequence Analysis, Protein|Species Specificity|Tandem Repeat Sequences', 'Journal Article', '10.1093/bioinformatics/btl340', '16809390', 'In this report, we describe a novel tandem peptide repeat protein, Eicosapentapeptide repeat (EPR), which occurs notably only in flowering plants. The EPRs are characterized by a 25 amino acid repeat unit, X(2)CX(4)CX(10)CX(2)HGGG, repeated 10 times tandemly. Sequence search revealed that the repeat motif is highly conserved across its occurrence. EPRs are predicted to exist as quasi-globular stable structures owing to highly conserved amino acid positions and potential disulfide bridges. Proteins containing EPRs are predicted to be located in chloroplasts, non-enzymatic and peptide or DNA-binding in molecular function, and they are possibly involved in transcription regulation.'),
(72845, 2006, 'bio', 'Domain organization within repeated DNA sequences: application to the study of a family of transposable elements', 'Tempel, Sébastien|Giraud, Mathieu|Lavenier, Dominique|Lerman, Israël-César|Valin, Anne-Sophie|Couée, Ivan|Amrani, Abdelhak El|Nicolas, Jacques', 'IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.|IRISA-INRIA, Campus de Beaulieu Bât 12 35042 Rennes cedex, France.', 'France|France|France|France|France|France|France|France', 9, 'Algorithms|Amino Acid Sequence|Arabidopsis|Computational Biology|DNA Transposable Elements|Genes, Plant|Markov Chains|Models, Biological|Models, Statistical|Molecular Sequence Data|Plant Proteins|Protein Structure, Tertiary|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl337', '16809391', 'The analysis of repeated elements in genomes is a fascinating domain of research that is lacking relevant tools for transposable elements (TEs), the most complex ones. The dynamics of TEs, which provides the main mechanism of mutation in some genomes, is an essential component of genome evolution. In this study we introduce a new concept of domain, a segmentation unit useful for describing the architecture of different copies of TEs. Our method extracts occurrences of a terminus-defined family of TEs, aligns the sequences, finds the domains in the alignment and searches the distribution of each domain in sequences. After a classification step relative to the presence or the absence of domains, the method results in a graphical view of sequences segmented into domains.'),
(72846, 2006, 'bio', 'PDQ Wizard: automated prioritization and characterization of gene and protein lists using biomedical literature', 'Grimes, G R|Wen, T Q|Mewissen, M|Baxter, R M|Moodie, S|Beattie, J S|Ghazal, P', 'The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk|The Scottish Centre for Genomic Technology and Informatics, University of Edinburgh 49 Little France Crescent, Edinburgh EH16 4SB, UK. Graeme.Grimes@ed.ac.uk', 'France|France|France|France|France|France|France', 14, 'Abstracting and Indexing as Topic|Animals|Computational Biology|Databases, Bibliographic|Databases, Genetic|Databases, Protein|Humans|Information Storage and Retrieval|Natural Language Processing|Pattern Recognition, Automated|Programming Languages|PubMed|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl342', '16809392', 'PDQ Wizard automates the process of interrogating biomedical references using large lists of genes, proteins or free text. Using the principle of linkage through co-citation biologists can mine PubMed with these proteins or genes to identify relationships within a biological field of interest. In addition, PDQ Wizard provides novel features to define more specific relationships, highlight key publications describing those activities and relationships, and enhance protein queries. PDQ Wizard also outputs a metric that can be used for prioritization of genes and proteins for further research.'),
(72847, 2006, 'bio', 'An iterative refinement algorithm for consistency based multiple structural alignment methods', 'Chen, Yu|Crippen, Gordon M', 'Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA.|Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA.', 'United States|United States', 4, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software', 'Journal Article', '10.1093/bioinformatics/btl351', '16809393', 'Multiple STructural Alignment (MSTA) provides valuable information for solving problems such as fold recognition. The consistency-based approach tries to find conflict-free subsets of alignments from a pre-computed all-to-all Pairwise Alignment Library (PAL). If large proportions of conflicts exist in the library, consistency can be hard to get. On the other hand, multiple structural superposition has been used in many MSTA methods to refine alignments. However, multiple structural superposition is dependent on alignments, and a superposition generated based on erroneous alignments is not guaranteed to be the optimal superposition. Correcting errors after making errors is not as good as avoiding errors from the beginning. Hence it is important to refine the pairwise library to reduce the number of conflicts before any consistency-based assembly.'),
(72848, 2006, 'bio', 'SNPeffect v20: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs', 'Reumers, Joke|Maurer-Stroh, Sebastian|Schymkowitz, Joost|Rousseau, Frederic', 'Switch Laboratory, Flanders Interuniversity, Institute of Biotechnology, Vrije Universiteit, Brussel Pleinlaan 2, Brussels, Belgium.|Switch Laboratory, Flanders Interuniversity, Institute of Biotechnology, Vrije Universiteit, Brussel Pleinlaan 2, Brussels, Belgium.|Switch Laboratory, Flanders Interuniversity, Institute of Biotechnology, Vrije Universiteit, Brussel Pleinlaan 2, Brussels, Belgium.|Switch Laboratory, Flanders Interuniversity, Institute of Biotechnology, Vrije Universiteit, Brussel Pleinlaan 2, Brussels, Belgium.', 'Belgium|Belgium|Belgium|Belgium', 72, 'Algorithms|Base Sequence|Chromosome Mapping|DNA Mutational Analysis|Genome, Human|Humans|Molecular Sequence Data|Polymorphism, Single Nucleotide|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl348', '16809394', 'Single nucleotide polymorphisms (SNPs) constitute the most fundamental type of genetic variation in human populations. About 75 000 of these reported variations cause an amino acid change in the translated protein. An important goal in genomic research is to understand how this variability affects protein function, and whether or not particular SNPs are associated to disease susceptibility. Accordingly, the SNPeffect database uses sequence- and structure-based bioinformatics tools to predict the effect of non-synonymous SNPs on the molecular phenotype of proteins. SNPeffect analyses the effect of SNPs on three categories of functional properties: (1) structural and thermodynamic properties affecting protein dynamics and stability (2) the integrity of functional and binding sites and (3) changes in posttranslational processing and cellular localization of proteins. The search interface of the database can be used to search specifically for polymorphisms that are predicted to cause a change in one of these properties. Now based on the Ensembl human databases, the SNPeffect database has been remodeled to better fit an automatically updatable structure. The current edition holds the molecular phenotype of 74 567 nsSNPs in 23 426 proteins.'),
(72849, 2006, 'bio', 'Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions', 'Bush, William S|Dudek, Scott M|Ritchie, Marylyn D', 'Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA.|Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA.|Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA.', 'United States|United States|United States', 58, 'Algorithms|Computing Methodologies|Gene Expression Profiling|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Journal Article', '10.1093/bioinformatics/btl347', '16809395', 'Parallel multifactor dimensionality reduction is a tool for large-scale analysis of gene-gene and gene-environment interactions. The MDR algorithm was redesigned to allow an unlimited number of study subjects, total variables and variable states, and to remove restrictions on the order of interactions being analyzed. In addition, the algorithm is markedly more efficient, with approximately 150-fold decrease in runtime for equivalent analyses. To facilitate the processing of large datasets, the algorithm was made parallel.'),
(72850, 2006, 'bio', 'GEL: a novel genotype calling algorithm using empirical likelihood', 'Nicolae, Dan L|Wu, Xiaolin|Miyake, Kazuaki|Cox, Nancy J', 'Department of Statistics, The University of Chicago. nicolae@galton.uchicago.edu|Department of Statistics, The University of Chicago. nicolae@galton.uchicago.edu|Department of Statistics, The University of Chicago. nicolae@galton.uchicago.edu|Department of Statistics, The University of Chicago. nicolae@galton.uchicago.edu', '-|-|-|-', 13, 'Algorithms|Computational Biology|Genotype|Likelihood Functions|Oligonucleotide Array Sequence Analysis|Programming Languages|Reproducibility of Results|Software', 'Journal Article', '10.1093/bioinformatics/btl341', '16809396', 'Preliminary results on the data produced using the Affymetrix large-scale genotyping platforms show that it is necessary to construct improved genotype calling algorithms. There is evidence that some of the existing algorithms lead to an increased error rate in heterozygous genotypes, and a disproportionately large rate of heterozygotes with missing genotypes. Non-random errors and missing data can lead to an increase in the number of false discoveries in genetic association studies. Therefore, the factors that need to be evaluated in assessing the performance of an algorithm are the missing data (call) and error rates, but also the heterozygous proportions in missing data and errors.'),
(72851, 2006, 'bio', 'Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach', 'Li, Shao|Wu, Lijiang|Zhang, Zhongqi', 'Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China. shaoli@mail.tsinghua.edu.cn|Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China. shaoli@mail.tsinghua.edu.cn|Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China. shaoli@mail.tsinghua.edu.cn', 'China|China|China', 75, 'Abstracting and Indexing as Topic|Database Management Systems|Databases, Protein|Gene Expression Profiling|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Oligonucleotide Array Sequence Analysis|Periodicals as Topic|Proteome|Signal Transduction|Systems Integration', 'Journal Article', '10.1093/bioinformatics/btl363', '16820422', 'Network reconstruction of biological entities is very important for understanding biological processes and the organizational principles of biological systems. This work focuses on integrating both the literatures and microarray gene-expression data, and a combined literature mining and microarray analysis (LMMA) approach is developed to construct gene networks of a specific biological system.'),
(72852, 2006, 'bio', 'ZooDDD: a cross-species database for digital differential display analysis', 'Chen, Yun-Ching|Hsiao, Chung-Der|Lin, Wen-Dar|Hu, Chung-Ming|Hwang, Pung-Pung|Ho, Jan-Ming', 'Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.|Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.|Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.|Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.|Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.|Institute of Information Science, Academia Sinica, NanKang 115, Taipei, Taiwan, Republic of China.', 'China|China|China|China|China|China', 10, 'Base Sequence|Chromosome Mapping|Computer Graphics|Conserved Sequence|Databases, Genetic|Evolution, Molecular|Expressed Sequence Tags|Genome|Information Storage and Retrieval|Molecular Sequence Data|Sequence Homology, Nucleic Acid|Signal Processing, Computer-Assisted|Software|Species Specificity|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl358', '16820423', 'In this article, we combined EST information from the UniGene database and orthologous relationships from the Ensembl database to construct a ZooDDD database. The primary function of ZooDDD is to mine evolutionary conserved, highly expressed, tissue-specific orthologues in model animals. The candidate genes of interest derived from the ZooDDD database will provide biologists with a good step for comparing the expression, functions and evolution of animal genomes.'),
(72853, 2006, 'bio', 'Detecting potential labeling errors in microarrays by data perturbation', 'Malossini, Andrea|Blanzieri, Enrico|Ng, Raymond T', 'Department of Information and Communication Technology, University of Trento, 38050 Povo, Italy. malossin@dit.unitn.it|Department of Information and Communication Technology, University of Trento, 38050 Povo, Italy. malossin@dit.unitn.it|Department of Information and Communication Technology, University of Trento, 38050 Povo, Italy. malossin@dit.unitn.it', 'Italy|Italy|Italy', 52, 'Artifacts|Artificial Intelligence|Computer Simulation|Data Interpretation, Statistical|Databases, Genetic|Documentation|Gene Expression Profiling|Information Storage and Retrieval|Models, Genetic|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Quality Control|Reproducibility of Results|Sample Size|Sensitivity and Specificity|Staining and Labeling', 'Journal Article', '10.1093/bioinformatics/btl346', '16820424', 'Classification is widely used in medical applications. However, the quality of the classifier depends critically on the accurate labeling of the training data. But for many medical applications, labeling a sample or grading a biopsy can be subjective. Existing studies confirm this phenomenon and show that even a very small number of mislabeled samples could deeply degrade the performance of the obtained classifier, particularly when the sample size is small. The problem we address in this paper is to develop a method for automatically detecting samples that are possibly mislabeled.'),
(72854, 2006, 'bio', 'MAGOS: multiple alignment and modelling server', 'Garnier, N|Friedrich, A|Bolze, R|Bettler, E|Moulinier, L|Geourjon, C|Thompson, J D|Deléage, G|Poch, O', 'Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.|Institut de Biologie et Chimie des Protéines (IBCP UMR 5086),CNRS, Univ. Lyon1, IFR128 BioSciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France.', 'France|France|France|France|France|France|France|France|France', 5, 'Algorithms|Amino Acid Sequence|Computer Graphics|Computer Simulation|Evolution, Molecular|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl349', '16820425', 'MAGOS is a web server allowing automated protein modelling coupled to the creation of a hierarchical and annotated multiple alignment of complete sequences. MAGOS is designed for an interactive approach of structural information within the framework of the evolutionary relevance of mined and predicted sequence information.'),
(72855, 2006, 'bio', 'Searching RNA motifs and their intermolecular contacts with constraint networks', 'Thébault, P|de Givry, S|Schiex, T|Gaspin, C', 'Unité de Biométrie & Intelligence Artificielle, INRA, Chemin de Borde Rouge Auzeville, BP 52627, 31326 Castanet-Tolosan, France.|Unité de Biométrie & Intelligence Artificielle, INRA, Chemin de Borde Rouge Auzeville, BP 52627, 31326 Castanet-Tolosan, France.|Unité de Biométrie & Intelligence Artificielle, INRA, Chemin de Borde Rouge Auzeville, BP 52627, 31326 Castanet-Tolosan, France.|Unité de Biométrie & Intelligence Artificielle, INRA, Chemin de Borde Rouge Auzeville, BP 52627, 31326 Castanet-Tolosan, France.', 'France|France|France|France', 18, 'Algorithms|Amino Acid Motifs|Artificial Intelligence|Base Sequence|Binding Sites|Chromosome Mapping|Molecular Sequence Data|Pattern Recognition, Automated|Protein Binding|RNA|RNA-Binding Proteins|Sequence Alignment|Sequence Analysis, RNA|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl354', '16820426', 'Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules.'),
(72856, 2006, 'bio', 'A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana', 'Li, Jiexun|Li, Xin|Su, Hua|Chen, Hsinchun|Galbraith, David W', 'Department of Management Information Systems, University of Arizona Tucson, AZ 85721, USA. jiexun@eller.arizona.edu|Department of Management Information Systems, University of Arizona Tucson, AZ 85721, USA. jiexun@eller.arizona.edu|Department of Management Information Systems, University of Arizona Tucson, AZ 85721, USA. jiexun@eller.arizona.edu|Department of Management Information Systems, University of Arizona Tucson, AZ 85721, USA. jiexun@eller.arizona.edu|Department of Management Information Systems, University of Arizona Tucson, AZ 85721, USA. jiexun@eller.arizona.edu', 'United States|United States|United States|United States|United States', 22, 'Arabidopsis|Bayes Theorem|Cluster Analysis|Computational Biology|Databases, Genetic|Gene Expression Profiling|Gene Expression Regulation, Plant|Genes, Plant|Genome, Plant|Models, Statistical|Pattern Recognition, Automated|Protein Interaction Mapping|Software', 'Journal Article', '10.1093/bioinformatics/btl345', '16820427', 'One of the most important goals of biological investigation is to uncover gene functional relations. In this study we propose a framework for extraction and integration of gene functional relations from diverse biological data sources, including gene expression data, biological literature and genomic sequence information. We introduce a two-layered Bayesian network approach to integrate relations from multiple sources into a genome-wide functional network. An experimental study was conducted on a test-bed of Arabidopsis thaliana. Evaluation of the integrated network demonstrated that relation integration could improve the reliability of relations by combining evidence from different data sources. Domain expert judgments on the gene functional clusters in the network confirmed the validity of our approach for relation integration and network inference.'),
(72857, 2006, 'bio', 'Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching', 'Du, Pan|Kibbe, Warren A|Lin, Simon M', 'Robert H. Lurie Comprehensive Cancer Center, Northwestern University Chicago, IL 60611, USA.|Robert H. Lurie Comprehensive Cancer Center, Northwestern University Chicago, IL 60611, USA.|Robert H. Lurie Comprehensive Cancer Center, Northwestern University Chicago, IL 60611, USA.', 'United States|United States|United States', 426, 'Algorithms|Artificial Intelligence|Pattern Recognition, Automated|Peptide Mapping|Proteome|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Signal Processing, Computer-Assisted|Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization', 'Evaluation Studies', '10.1093/bioinformatics/btl355', '16820428', 'A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods.'),
(72858, 2006, 'bio', 'Probe-level measurement error improves accuracy in detecting differential gene expression', 'Liu, Xuejun|Milo, Marta|Lawrence, Neil D|Rattray, Magnus', 'School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.|School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK.', 'United Kingdom|United Kingdom|United Kingdom|United Kingdom', 0, 'Algorithms|Animals|DNA Probes|Gene Expression|Gene Expression Profiling|Mice|Oligonucleotide Array Sequence Analysis|Proteome|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Sequence Analysis, DNA|Software', 'Comparative Study', '10.1093/bioinformatics/btl361', '16820429', 'Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes.'),
(72859, 2006, 'bio', 'Ribostral: an RNA 3D alignment analyzer and viewer based on basepair isostericities', 'Mokdad, Ali|Leontis, Neocles B', 'Department of Chemistry and Center for Biomolecular Sciences, Bowling Green State University, OH 43403, USA. mali@bgsu.edu|Department of Chemistry and Center for Biomolecular Sciences, Bowling Green State University, OH 43403, USA. mali@bgsu.edu', 'United States|United States', 13, 'Algorithms|Base Pair Mismatch|Base Pairing|Base Sequence|Computer Graphics|Computer Simulation|Evolution, Molecular|Imaging, Three-Dimensional|Internet|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nucleic Acid Conformation|RNA|Sequence Alignment|Sequence Analysis, RNA|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl360', '16820430', 'RNA atomic resolution structures have revealed the existance of different families of basepair interactions, each of which with its own isosteric sub-families. Ribostral (Ribonucleic Structural Aligner) is a user-friendly framework for analyzing, evaluating and viewing RNA sequence alignments with at least one available atomic resolution structure. It is the first of its kind that makes direct and easy- to-understand superposition of the isostericity matrices of basepairs observed in the structure onto sequence alignments, easily indicating allowed and unallowed substitutions at each BP position. Potential mistakes in the alignments can then be corrected using other sequence editing software. Ribostral has been developed and tested under Windows XP, and is capable of running on any PC or MAC platform with MATLAB 7.1 (SP3) or higher installed version. A stand-alone version is also available for the PC platform.');
INSERT INTO `papers` (`id`, `ano`, `journal`, `titulo`, `autores`, `afiliacao`, `pais`, `citacoes`, `palavra_chave`, `categorias`, `doi`, `pubmed`, `abstract`) VALUES
(72860, 2006, 'bio', 'VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles', 'La Rosa, Philippe|Viara, Eric|Hupé, Philippe|Pierron, Gaëlle|Liva, Stéphane|Neuvial, Pierre|Brito, Isabel|Lair, Séverine|Servant, Nicolas|Robine, Nicolas|Manié, Elodie|Brennetot, Caroline|Janoueix-Lerosey, Isabelle|Raynal, Virginie|Gruel, Nadège|Rouveirol, Céline|Stransky, Nicolas|Stern, Marc-Henri|Delattre, Olivier|Aurias, Alain|Radvanyi, François|Barillot, Emmanuel', 'Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.|Institut Curie, Service Bioinformatique, 26 rue dUlm, Paris 75248 cedex 05, France.', 'France|France|France|France|France|France|France|France|France|France|France|France|France|France|Fr', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Database Management Systems|Databases, Protein|Gene Dosage|Information Storage and Retrieval|Proteome|Sequence Analysis, DNA|Software|Transcription Factors|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl359', '16820431', 'Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses.'),
(72861, 2006, 'bio', 'Remote homology detection based on oligomer distances', 'Lingner, Thomas|Meinicke, Peter', 'Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstr. 1, 37077 Göttingen, Germany. thomas@gobics.de|Abteilung Bioinformatik, Institut für Mikrobiologie und Genetik, Georg-August-Universität Göttingen Goldschmidtstr. 1, 37077 Göttingen, Germany. thomas@gobics.de', 'Germany|Germany', 56, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Dimerization|Molecular Sequence Data|Pattern Recognition, Automated|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid', 'Journal Article', '10.1093/bioinformatics/btl376', '16837522', 'Remote homology detection is among the most intensively researched problems in bioinformatics. Currently discriminative approaches, especially kernel-based methods, provide the most accurate results. However, kernel methods also show several drawbacks: in many cases prediction of new sequences is computationally expensive, often kernels lack an interpretable model for analysis of characteristic sequence features, and finally most approaches make use of so-called hyperparameters which complicate the application of methods across different datasets.'),
(72862, 2006, 'bio', 'Predicting methylation status of CpG islands in the human brain', 'Fang, Fang|Fan, Shicai|Zhang, Xuegong|Zhang, Michael Q', 'Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.|Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.|Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.|Bioinformatics Division, TNLIST, Department of Automation, Tsinghua University 100084 China.', 'China|China|China|China', 0, 'Algorithms|Artificial Intelligence|Base Sequence|Brain|Chromosome Mapping|CpG Islands|DNA Methylation|Molecular Sequence Data|Nerve Tissue Proteins|Pattern Recognition, Automated|Sequence Alignment|Sequence Analysis, DNA', 'Journal Article', '10.1093/bioinformatics/btl377', '16837523', 'Over 50% of human genes contain CpG islands in their 5-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.'),
(72863, 2006, 'bio', 'ADGO: analysis of differentially expressed gene sets using composite GO annotation', 'Nam, Dougu|Kim, Sang-Bae|Kim, Seon-Kyu|Yang, Sungjin|Kim, Seon-Young|Chu, In-Sun', 'Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.|Korean BioInformation Center, Korea Research Institute of Bioscience and Biotechnology, 52 Eoun-dong Yuseong-gu, Daejeon 305-333, Korea.', '-|-|-|-|-|-', 27, 'Algorithms|Computer Simulation|Databases, Protein|Documentation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Proteins|Software', 'Evaluation Studies', '10.1093/bioinformatics/btl378', '16837524', 'Genes are typically expressed in modular manners in biological processes. Recent studies reflect such features in analyzing gene expression patterns by directly scoring gene sets. Gene annotations have been used to define the gene sets, which have served to reveal specific biological themes from expression data. However, current annotations have limited analytical power, because they are classified by single categories providing only unary information for the gene sets.'),
(72864, 2006, 'bio', 'VISTAL--a new 2D visualization tool of protein 3D structural alignments', 'Kolodny, Rachel|Honig, Barry', 'Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, 1130 St Nicholas Avenue, Room 815, New York, NY 10032, USA.|Howard Hughes Medical Institute, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, 1130 St Nicholas Avenue, Room 815, New York, NY 10032, USA.', 'United States|United States', 0, 'Algorithms|Amino Acid Sequence|Computer Graphics|Computer Simulation|Evolution, Molecular|Imaging, Three-Dimensional|Models, Chemical|Models, Molecular|Molecular Sequence Data|Protein Conformation|Proteins|Sequence Alignment|Sequence Analysis, Protein|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl353', '16837525', 'We offer a tool, denoted VISTAL, for two-dimensional visualization of protein structural alignments. VISTAL describes aligned structures as a series of matched secondary structure elements, colored according to the three-dimensional distance of their Calpha atoms.'),
(72865, 2006, 'bio', 'Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine', 'Zheng, Mingyue|Liu, Zhiguo|Xue, Chunxia|Zhu, Weiliang|Chen, Kaixian|Luo, Xiaomin|Jiang, Hualiang', 'Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.|Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.', 'China|China|China|China|China|China|China', 25, 'Algorithms|Artificial Intelligence|Hydrophobic and Hydrophilic Interactions|Models, Biological|Models, Chemical|Models, Statistical|Mutagenicity Tests|Mutagens|Pattern Recognition, Automated|Quantitative Structure-Activity Relationship|Software', 'Journal Article', '10.1093/bioinformatics/btl352', '16837526', 'Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction.'),
(72866, 2006, 'bio', 'RBR: library-less repeat detection for ESTs', 'Malde, Ketil|Schneeberger, Korbinian|Coward, Eivind|Jonassen, Inge', 'Computational Biology Unit, Bergen Centre for Computational Sciences, University of Bergen, Norway. ketil.malde@bccs.uib.no|Computational Biology Unit, Bergen Centre for Computational Sciences, University of Bergen, Norway. ketil.malde@bccs.uib.no|Computational Biology Unit, Bergen Centre for Computational Sciences, University of Bergen, Norway. ketil.malde@bccs.uib.no|Computational Biology Unit, Bergen Centre for Computational Sciences, University of Bergen, Norway. ketil.malde@bccs.uib.no', 'Norway|Norway|Norway|Norway', 0, 'Algorithms|Base Sequence|Chromosome Mapping|Cluster Analysis|Expressed Sequence Tags|Gene Library|Molecular Sequence Data|Repetitive Sequences, Nucleic Acid|Sequence Alignment|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl368', '16837527', 'Repeat sequences in ESTs are a source of problems, in particular for clustering. ESTs are therefore commonly masked against a library of known repeats. High quality repeat libraries are available for the widely studied organisms, but for most other organisms the lack of such libraries is likely to compromise the quality of EST analysis.'),
(72867, 2006, 'bio', 'Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands', 'Vernikos, Georgios S|Parkhill, Julian', 'The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA, UK. gsv@sanger.ac.uk|The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SA, UK. gsv@sanger.ac.uk', 'United Kingdom|United Kingdom', 259, 'Algorithms|Biological Evolution|DNA, Bacterial|Gene Transfer, Horizontal|Genome, Bacterial|Genomic Islands|Salmonella|Virulence|Virulence Factors', 'Journal Article', '10.1093/bioinformatics/btl369', '16837528', 'There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone, the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.'),
(72868, 2006, 'bio', 'Detecting functional modules in the yeast protein-protein interaction network', 'Chen, Jingchun|Yuan, Bo', 'Integrated Biomedical Science Graduate Program, Department of Biomedical Informatics and Department of Pharmacology, The Ohio State University 333 W. 10th Avenue, Columbus, OH 43210, USA.|Integrated Biomedical Science Graduate Program, Department of Biomedical Informatics and Department of Pharmacology, The Ohio State University 333 W. 10th Avenue, Columbus, OH 43210, USA.', 'United States|United States', 277, 'Algorithms|Computer Simulation|Gene Expression Profiling|Models, Biological|Oligonucleotide Array Sequence Analysis|Protein Interaction Mapping|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl370', '16837529', 'Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated.'),
(72869, 2006, 'bio', 'Substring selection for biomedical document classification', 'Han, Bo|Obradovic, Zoran|Hu, Zhang-Zhi|Wu, Cathy H|Vucetic, Slobodan', 'Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.|Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA.', 'United States|United States|United States|United States|United States', 0, 'Abstracting and Indexing as Topic|Algorithms|Artificial Intelligence|Database Management Systems|Documentation|Information Storage and Retrieval|MEDLINE|Natural Language Processing|Periodicals as Topic|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl350', '16837530', 'Attribute selection is a critical step in development of document classification systems. As a standard practice, words are stemmed and the most informative ones are used as attributes in classification. Owing to high complexity of biomedical terminology, general-purpose stemming algorithms are often conservative and could also remove informative stems. This can lead to accuracy reduction, especially when the number of labeled documents is small. To address this issue, we propose an algorithm that omits stemming and, instead, uses the most discriminative substrings as attributes.'),
(72870, 2006, 'bio', 'An initial strategy for comparing proteins at the domain architecture level', 'Lin, Kui|Zhu, Lei|Zhang, Da-Yong', 'MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing 100875, China. linkui@bnu.edu.cn|MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing 100875, China. linkui@bnu.edu.cn|MOE Key Laboratory for Biodiversity Science and Ecological Engineering and College of Life Sciences, Beijing Normal University, Beijing 100875, China. linkui@bnu.edu.cn', 'China|China|China', 34, 'Algorithms|Amino Acid Sequence|Molecular Sequence Data|Protein Structure, Tertiary|Proteins|Sequence Alignment|Sequence Analysis, Protein', 'Journal Article', '10.1093/bioinformatics/btl366', '16837531', 'Ideally, only proteins that exhibit highly similar domain architectures should be compared with one another as homologues or be classified into a single family. By combining three different indices, the Jaccard index, the Goodman-Kruskal gamma function and the domain duplicate index, into a single similarity measure, we propose a method for comparing proteins based on their domain architectures.'),
(72871, 2006, 'bio', 'Gene selection in cancer classification using sparse logistic regression with Bayesian regularization', 'Cawley, Gavin C|Talbot, Nicola L C', 'School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK. gcc@cmp.uea.ac.uk|School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK. gcc@cmp.uea.ac.uk', 'United Kingdom|United Kingdom', 165, 'Algorithms|Bayes Theorem|Biomarkers, Tumor|Diagnosis, Computer-Assisted|Gene Expression Profiling|Humans|Logistic Models|Models, Biological|Neoplasm Proteins|Neoplasms|Oligonucleotide Array Sequence Analysis|Regression Analysis|Reproducibility of Results|Sensitivity and Specificity', 'Journal Article', '10.1093/bioinformatics/btl386', '16844704', 'Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffreys prior. The improved algorithm (BLogReg) is then typically two or three orders of magnitude faster than the original algorithm, as there is no longer a need for a model selection step. The BLogReg algorithm is also free from selection bias in performance estimation, a common pitfall in the application of machine learning algorithms in cancer classification.'),
(72872, 2006, 'bio', 'Scoring of predicted GRK2 phosphorylation sites in Nedd4-2', 'Arthur, Jonathan W|Sanchez-Perez, Angeles|Cook, David I', 'Department of Medicine, University of Sydney Sydney, Australia. jarthur@med.usyd.edu.au|Department of Medicine, University of Sydney Sydney, Australia. jarthur@med.usyd.edu.au|Department of Medicine, University of Sydney Sydney, Australia. jarthur@med.usyd.edu.au', 'Australia|Australia|Australia', 0, 'Algorithms|Amino Acid Sequence|Artificial Intelligence|Binding Sites|Computer Simulation|Endosomal Sorting Complexes Required for Transport|G-Protein-Coupled Receptor Kinase 2|Models, Chemical|Models, Molecular|Molecular Sequence Data|Nedd4 Ubiquitin Protein Ligases|Phosphorylation|Protein Binding|Protein Interaction Mapping|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Ubiquitin-Protein Ligases|beta-Adrenergic Receptor Kinases', 'Journal Article', '10.1093/bioinformatics/btl381', '16844705', 'Epithelial Na(+) channels (ENaC) mediate the transport of sodium (Na) across epithelia in the kidney, gut and lungs and are required for blood pressure regulation. They are inhibited by ubiquitin protein ligases, such as Nedd4-2. These ligases bind to proline-rich motifs (PY motifs) present in the C-termini of ENaC subunits. Loss of this inhibition leads to hypertension. We have previously reported that ENaC channels are maintained in the active state by the G protein coupled receptor kinase, GRK2. The enzyme has been implicated in the development of essential hypertension [R. D. Feldman (2002) Mol. Pharmacol., 61, 707-709]. Additional findings in our lab pointed towards a possible role for GRK2 in the phosphorylation and inactivation of Nedd4-2.'),
(72873, 2006, 'bio', 'Global topological features of cancer proteins in the human interactome', 'Jonsson, Pall F|Bates, Paul A', 'Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute 44 Lincolns Inn Fields, London, WC2A 3PX, UK.|Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute 44 Lincolns Inn Fields, London, WC2A 3PX, UK.', 'United Kingdom|United Kingdom', 347, 'Algorithms|Biomarkers, Tumor|Computer Simulation|Humans|Models, Biological|Neoplasm Proteins|Neoplasms|Protein Interaction Mapping|Proteome|Signal Transduction', 'Journal Article', '10.1093/bioinformatics/btl390', '16844706', 'The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods.'),
(72874, 2006, 'bio', 'CART variance stabilization and regularization for high-throughput genomic data', 'Papana, Ariadni|Ishwaran, Hemant', 'Department of Statistics, Case University, 10900 Euclid Avenue Cleveland OH 44106, USA.|Department of Statistics, Case University, 10900 Euclid Avenue Cleveland OH 44106, USA.', 'United States|United States', 7, 'Artificial Intelligence|Cluster Analysis|Computer Simulation|Databases, Genetic|Gene Expression Profiling|Genetic Variation|Genomics|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|RNA, Messenger|Regression Analysis|Sequence Analysis, RNA', 'Journal Article', '10.1093/bioinformatics/btl384', '16844707', 'mRNA expression data obtained from high-throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics.'),
(72875, 2006, 'bio', 'Serial NetEvolve: a flexible utility for generating serially-sampled sequences along a tree or recombinant network', 'Buendia, Patricia|Narasimhan, Giri', 'Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University Miami, FL 33199, USA.|Bioinformatics Research Group (BioRG), School of Computing and Information Science, Florida International University Miami, FL 33199, USA.', 'United States|United States', 8, 'Algorithms|Computer Graphics|Computer Simulation|Evolution, Molecular|Models, Genetic|Recombination, Genetic|Sequence Analysis, DNA|Signal Transduction|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl387', '16844708', 'Serial NetEvolve is a flexible simulation program that generates DNA sequences evolved along a tree or recombinant network. It offers a user-friendly Windows graphical interface and a Windows or Linux simulator with a diverse selection of parameters to control the evolutionary model. Serial NetEvolve is a modification of the Treevolve program with the following additional features: simulation of serially-sampled data, the choice of either a clock-like or a variable rate model of sequence evolution, sampling from the internal nodes and the output of the randomly generated tree or network in our newly proposed NeTwick format.'),
(72876, 2006, 'bio', 'SynView: a GBrowse-compatible approach to visualizing comparative genome data', 'Wang, Haiming|Su, Yanqi|Mackey, Aaron J|Kraemer, Eileen T|Kissinger, Jessica C', 'Center for Tropical and Emerging Global Diseases, University of Georgia Athens, GA 30602-2606, USA.|Center for Tropical and Emerging Global Diseases, University of Georgia Athens, GA 30602-2606, USA.|Center for Tropical and Emerging Global Diseases, University of Georgia Athens, GA 30602-2606, USA.|Center for Tropical and Emerging Global Diseases, University of Georgia Athens, GA 30602-2606, USA.|Center for Tropical and Emerging Global Diseases, University of Georgia Athens, GA 30602-2606, USA.', 'Georgia|Georgia|Georgia|Georgia|Georgia', 0, 'Algorithms|Chromosome Mapping|Computer Graphics|Genome|Sequence Alignment|Sequence Analysis, DNA|Software|Species Specificity|User-Computer Interface', 'Comparative Study', '10.1093/bioinformatics/btl389', '16844709', 'We present SynView, a simple and generic approach to dynamically visualize multi-species comparative genome data. It is a light-weight application based on the popular and configurable web-based GBrowse framework. It can be used with a variety of databases and provides the user with a high degree of interactivity. The tool is written in Perl and runs on top of the GBrowse framework. It is in use in the PlasmoDB (http://www.PlasmoDB.org) and the CryptoDB (http://www.CryptoDB.org) projects and can be easily integrated into other cross-species comparative genome projects.'),
(72877, 2006, 'bio', 'Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks', 'Werhli, Adriano V|Grzegorczyk, Marco|Husmeier, Dirk', 'Biomathematics and Statistics Scotland, Edinburgh, UK. adriano@bioss.ac.uk|Biomathematics and Statistics Scotland, Edinburgh, UK. adriano@bioss.ac.uk|Biomathematics and Statistics Scotland, Edinburgh, UK. adriano@bioss.ac.uk', 'United Kingdom|United Kingdom|United Kingdom', 238, 'Algorithms|Artificial Intelligence|Bayes Theorem|Computer Graphics|Computer Simulation|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Models, Statistical|Normal Distribution|Protein Interaction Mapping|Proteome|Signal Transduction|Software', 'Comparative Study', '10.1093/bioinformatics/btl391', '16844710', 'An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network, (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions.'),
(72878, 2006, 'bio', 'Identification of transcription factor cooperativity via stochastic system model', 'Chang, Yu-Hsiang|Wang, Yu-Chao|Chen, Bor-Sen', 'Laboratory of Control and Systems Biology, Department of Electrical Engineering National Tsing Hua University, Hsinchu 300, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering National Tsing Hua University, Hsinchu 300, Taiwan.|Laboratory of Control and Systems Biology, Department of Electrical Engineering National Tsing Hua University, Hsinchu 300, Taiwan.', '-|-|-', 41, 'Binding Sites|Cell Cycle Proteins|Computer Simulation|Gene Expression Regulation|Models, Genetic|Models, Statistical|Protein Binding|Regulatory Elements, Transcriptional|Saccharomyces cerevisiae|Saccharomyces cerevisiae Proteins|Signal Transduction|Transcription Factors', 'Journal Article', '10.1093/bioinformatics/btl380', '16844711', 'Transcription factor binding sites are known to co-occur in the same gene owing to cooperativity of the transcription factors (TFs) that bind to them. Genome-wide location data can help us understand how an individual TF regulates its target gene. Nevertheless, how TFs cooperate to regulate their target genes still needs further study. In this study, genome-wide location data and expression profiles are integrated to reveal how TFs cooperate to regulate their target genes from the stochastic system perspective.'),
(72879, 2006, 'bio', 'OrderedList--a bioconductor package for detecting similarity in ordered gene lists', 'Lottaz, Claudio|Yang, Xinan|Scheid, Stefanie|Spang, Rainer', 'Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, D-14195 Berlin, Germany.|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, D-14195 Berlin, Germany.|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, D-14195 Berlin, Germany.|Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics Ihnestrasse 63-73, D-14195 Berlin, Germany.', 'Germany|Germany|Germany|Germany', 0, 'Algorithms|Artificial Intelligence|Computer Simulation|Gene Expression Profiling|Models, Biological|Multigene Family|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated|Software', 'Journal Article', '10.1093/bioinformatics/btl385', '16844712', 'OrderedList is a Bioconductor compliant package for meta-analysis based on ordered gene lists like those resulting from differential gene expression analysis. Our package quantifies the similarity between gene lists. The significance of the similarity score is estimated from random scores computed on perturbed data. OrderedList illustrates list similarity in intuitive plots and determines the score-driving genes for further analysis.'),
(72880, 2006, 'bio', 'A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations', 'Zhang, Chun|Bailey, Dione K|Awad, Tarif|Liu, Guoying|Xing, Guoliang|Cao, Manqiu|Valmeekam, Venu|Retief, Jacques|Matsuzaki, Hajime|Taub, Margaret|Seielstad, Mark|Kennedy, Giulia C', 'Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.|Affymetrix Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Animals|Biological Evolution|Chromosome Mapping|Evolution, Molecular|Genetic Variation|Genetics, Population|Genome, Human|Genomic Imprinting|Humans|Polymorphism, Single Nucleotide|Selection, Genetic|Sequence Analysis, DNA|Software', 'Journal Article', '10.1093/bioinformatics/btl365', '16845142', 'The identification of signatures of positive selection can provide important insights into recent evolutionary history in human populations. Current methods mostly rely on allele frequency determination or focus on one or a small number of candidate chromosomal regions per study. With the availability of large-scale genotype data, efficient approaches for an unbiased whole genome scan are becoming necessary.'),
(72881, 2006, 'bio', 'Inferring gene regulatory networks from time series data using the minimum description length principle', 'Zhao, Wentao|Serpedin, Erchin|Dougherty, Edward R', 'Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. wtzhao@ece.tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. wtzhao@ece.tamu.edu|Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA. wtzhao@ece.tamu.edu', 'United States|United States|United States', 120, 'Algorithms|Animals|Computer Simulation|Drosophila Proteins|Drosophila melanogaster|Gene Expression Profiling|Gene Expression Regulation|Models, Biological|Oligonucleotide Array Sequence Analysis|Signal Transduction|Time Factors', 'Journal Article', '10.1093/bioinformatics/btl364', '16845143', 'A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations.'),
(72882, 2006, 'bio', 'Bayesian variable selection for the analysis of microarray data with censored outcomes', 'Sha, Naijun|Tadesse, Mahlet G|Vannucci, Marina', 'Department of Mathematical Sciences, University of Texas at El Paso El Paso, TX 79968, USA.|Department of Mathematical Sciences, University of Texas at El Paso El Paso, TX 79968, USA.|Department of Mathematical Sciences, University of Texas at El Paso El Paso, TX 79968, USA.', 'United States|United States|United States', 61, 'Algorithms|Artifacts|Artificial Intelligence|Bayes Theorem|Computer Simulation|Gene Expression Profiling|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Pattern Recognition, Automated', 'Journal Article', '10.1093/bioinformatics/btl362', '16845144', 'A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions.'),
(72883, 2006, 'bio', 'Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer', 'Reverter, Antonio|Ingham, Aaron|Lehnert, Sigrid A|Tan, Siok-Hwee|Wang, Yonghong|Ratnakumar, Abhirami|Dalrymple, Brian P', 'CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au|CSIRO Livestock Industries, Queensland Bioscience Precinct 306 Carmody Road, Brisbane, Queensland 4067, Australia. Tony.Reverter-Gomez@csiro.au', 'Australia|Australia|Australia|Australia|Australia|Australia|Australia', 0, 'Adipogenesis|Animals|Biomarkers|Cattle|Computer Simulation|Gene Expression Profiling|Humans|Inflammation|Models, Biological|Models, Statistical|Multigene Family|Neoplasms|Oligonucleotide Array Sequence Analysis|Proteins', 'Journal Article', '10.1093/bioinformatics/btl392', '16864591', 'Biological differences between classes are reflected in transcriptional changes which in turn affect the levels by which essential genes are individually expressed and collectively connected. The purpose of this communication is to introduce an analytical procedure to simultaneously identify genes that are differentially expressed (DE) as well as differentially connected (DC) in two or more classes of interest.'),
(72884, 2006, 'bio', 'arrayQCplot: software for checking the quality of microarray data', 'Lee, Eun-Kyung|Yi, Sung-Gon|Park, Taesung', 'Department of Statistics, Seoul National University Seoul, Korea.|Department of Statistics, Seoul National University Seoul, Korea.|Department of Statistics, Seoul National University Seoul, Korea.', '-|-|-', 9, 'Algorithms|Computer Graphics|Data Interpretation, Statistical|Oligonucleotide Array Sequence Analysis|Quality Control|Reproducibility of Results|Sensitivity and Specificity|Software|User-Computer Interface', 'Journal Article', '10.1093/bioinformatics/btl367', '16864592', 'arrayQCplot is a software for the exploratory analysis of microarray data. This software focuses on quality control and generates newly developed plots for quality and reproducibility checks. It is developed using R and provides a user-friendly graphical interface for graphics and statistical analysis. Therefore, novice users will find arrayQCplot as an easy-to-use software for checking the quality of their data by a simple mouse click.'),
(72885, 2006, 'bio', 'Inferring gene regulatory networks from multiple microarray datasets', 'Wang, Yong|Joshi, Trupti|Zhang, Xiang-Sun|Xu, Dong|Chen, Luonan', 'Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan.|Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan.|Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan.|Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan.|Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka 574-8530, Japan.', 'Japan|Japan|Japan|Japan|Japan', 241, 'Algorithms|Cell Physiological Phenomena|Computer Simulation|Databases, Protein|Gene Expression Profiling|Gene Expression Regulation|Information Storage and Retrieval|Models, Genetic|Models, Statistical|Oligonucleotide Array Sequence Analysis|Proteome|Signal Transduction', 'Evaluation Studies', '10.1093/bioinformatics/btl396', '16864593', 'Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points.'),
(72886, 2006, 'bio', 'Bio-Ontology and text: bridging the modeling gap', 'Friedman, Carol|Borlawsky, Tara|Shagina, Lyudmila|Xing, H Rosie|Lussier, Yves A', 'Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Friedman@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Friedman@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Friedman@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Friedman@dbmi.columbia.edu|Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Friedman@dbmi.columbia.edu', 'United States|United States|United States|United States|United States', 25, 'Abstracting and Indexing as Topic|Artificial Intelligence|Database Management Systems|Databases, Genetic|Information Storage and Retrieval|Models, Genetic|Natural Language Processing|Periodicals as Topic|Phylogeny|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl405', '16870928', 'Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text.'),
(72887, 2006, 'bio', 'A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity', 'Ferraro, E|Via, A|Ausiello, G|Helmer-Citterich, M', 'Centre of Molecular Bioinformatics, Department of Biology, University of Tor Vergata Rome, Italy. enrico@cbm.bio.uniroma2.it|Centre of Molecular Bioinformatics, Department of Biology, University of Tor Vergata Rome, Italy. enrico@cbm.bio.uniroma2.it|Centre of Molecular Bioinformatics, Department of Biology, University of Tor Vergata Rome, Italy. enrico@cbm.bio.uniroma2.it|Centre of Molecular Bioinformatics, Department of Biology, University of Tor Vergata Rome, Italy. enrico@cbm.bio.uniroma2.it', 'Italy|Italy|Italy|Italy', 0, 'Algorithms|Artificial Intelligence|Binding Sites|Computer Simulation|Models, Chemical|Models, Molecular|Pattern Recognition, Automated|Protein Binding|Protein Interaction Mapping|Protein Structure, Tertiary|Proteome|Sequence Analysis, Protein|Structure-Activity Relationship|src Homology Domains', 'Journal Article', '10.1093/bioinformatics/btl403', '16870929', 'Unravelling the rules underlying protein-protein and protein-ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein-protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain-peptide interactions.'),
(72888, 2006, 'bio', 'ARCS: an aggregated related column scoring scheme for aligned sequences', 'Song, Bin|Choi, Jeong-Hyeon|Chen, Guangyu|Szymanski, Jacek|Zhang, Guo-Qiang|Tung, Anthony K H|Kang, Jaewoo|Kim, Sun|Yang, Jiong', 'Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.|Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.', 'United States|United States|United States|United States|United States|United States|United States|United States|United States', 0, 'Algorithms|Amino Acid Sequence|Conserved Sequence|Molecular Sequence Data|Proteins|Sequence Alignment|Sequence Analysis, Protein|Sequence Homology, Amino Acid|Software', 'Journal Article', '10.1093/bioinformatics/btl398', '16870930', 'Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired.'),
(72889, 2006, 'bio', 'AliBaba: PubMed as a graph', 'Plake, Conrad|Schiemann, Torsten|Pankalla, Marcus|Hakenberg, Jörg|Leser, Ulf', 'Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany.|Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany.|Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany.|Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany.|Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin, Germany.', 'Germany|Germany|Germany|Germany|Germany', 90, 'Abstracting and Indexing as Topic|Algorithms|Computer Graphics|Database Management Systems|Information Storage and Retrieval|Natural Language Processing|Periodicals as Topic|Software|User-Computer Interface|Vocabulary, Controlled', 'Journal Article', '10.1093/bioinformatics/btl408', '16870931', 'The biomedical literature contains a wealth of information on associations between many different types of objects, such as protein-protein interactions, gene-disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and hidden in natural language text. AliBaba is an interactive tool for graphical summarization of search results. It parses the set of abstracts that fit a PubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. AliBaba extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance.');
