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AlzPharm: Integration of neurodegeneration data using RDF

BMC bioinformatics 2007年第SUPPL. 3期8卷 S4-S4页

作者： Lam, Hugo Y.K. Marenco, Luis Clark, Tim Gao, Yong Kinoshita, June Shepherd, Gordon Miller, Perry Wu, Elizabeth Wong, Gwendolyn T. Liu, Nian Crasto, Chiquito Morse, Thomas Stephens, Susie Cheung, Kei-Hoi Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Anesthesiology Yale University New Haven CT United States Department of Neurobiology Yale University New Haven CT United States Department of Molecular Cellular and Developmental Biology Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Initiative in Innovative Computing Harvard University Cambridge MA United States Massachusetts General Hospital Boston MA United States Alzheimer Research Forum Burlington MA United States Oracle Burlington MA United States

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results su

关键词： Data integration

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Epileptic seizure detection

Epileptic seizure detection

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作者： Schuyler, Ronald White, Andrew Staley, Kevin Cios, Krzysztof J. Computational Bioscience Ph.D. Program UCDHSC United States University of Colorado United States American Epilepsy Society Society for Neuroscience United States Department of Neurology Massachusetts General Hospital Harvard Medical School United States Staley Lab. United States University of Colorado at Denver Health Sciences Center United States University of Colorado Bioenergetics Institute United States Data Mining and Bioinformatics Laboratory Turkey Polish Academy of Arts and Sciences Poland Dept. of Computer Science and Engineering University of Colorado at Denver Health Sciences Center P.O. Box 173364 Denver CO 80217-3364 United States

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Statistical Dependence in Biological Sequences

Statistical Dependence in Biological Sequences

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IEEE International Symposium on Information Theory

作者： Hasan Metin Aktulga Ioannis Kontoyiannis L. Alex Lyznik Lukasz Szpankowski Ananth Y. Grama Wojciech Szpankowski Department of Computer Scienc Purdue University West Lafayette IN U.S.A. Department of Informatics Athens University of Econ Business Athens Greece Department of Informatics Athens Univ of Econ & Business Athens Greece Pioneer Hi-Bred International Johnston IA USA Bioinformatics Program UCSD San Diego CA U.S.A. Department of Computer Science Purdue University West Lafayette IN U.S.A.

ISBN: (纸本)9781424413973;1424413974

We demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural ependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from CODIS, we demonstrate that our approach is well suited for the problem of discovering short tandem repeats (STRs).

关键词： RNA Mutual information Sequences DNA Splicing Protein engineering Molecular biophysics Computer science bioinformatics Information analysis

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Reverse engineering for a dynamic model of the drosophila circadian oscillator

Reverse engineering for a dynamic model of the drosophila ci...

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International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce

作者： Tanaka, Shin Kurata, Hiroyuki Ohashi, Takeshi Program of Creation Informatics Kyushu Institute of Technology Japan Department of Bioscience and Bioinformatics Kyushu Institute of Technology Japan

ISBN: (纸本)0769525040

In the field of bioinformatics, biochemical networks have been reconstructed in computer to understand their dynamic features. A problem is how to estimate the values of many kinetic parameters that are hard to measure experimentally. In this study, we have proposed a novel parameter estimation method based on genetic algorithms (GAs) and applied it to the Drosophila circadian oscillator. We defined two evaluation functions that represent two features of the dynamic model. To optimize the two functions simultaneously, we proposed the survival ratio GA, which has a gene's lifetime as a new concept, that is, the population holds previous search histories. By alternating between two evaluation functions every generation, the population holds both previously evaluated genes and children inherit both properties. In the two-evaluation problem of the Drosophila circadian oscillation system, the survival ratio GA exhibited a considerably better optimizing performance. than traditional GA methods.

关键词： Reverse engineering

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Finding phylogenetically informative genes by estimating multispecies gene entropy

Finding phylogenetically informative genes by estimating mul...

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International Joint Conference on Neural Networks 2006, IJCNN '06

作者： Xiaoxu, Han Department of Mathematics Bioinformatics Program Eastern Michigan University Ypsilanti MI 48197 United States

ISBN: (纸本)0780394909

Although entropy and relative entropy (K-L distance) are widely applied in many bioinformatics areas, there is no method given to compute the multispecies gene entropy yet. This paper presents the first multispecies gene entropy estimation method from the data mining point of view. In this study, a self-organizing map (SOM) is employed to mine a multispecies gene set to obtain the probability distribution of each gene in the feature space, which is the approximation of its corresponding probability distribution in the original sequence space. The multispecies gene entropy is computed by the probability distribution of a gene in the feature space. The phylogenetic applications of the multispecies gene entropy are investigated in an example of resolving incongruence between gene trees and species trees. It is found that genes with nearest K-L distances to the minimum entropy gene are more likely to be phylogenetically informative. A K-L distance based gene concatenation approach under gene clustering is proposed to resolve the gene tree and species tree problem. Under the same testing dataset, the K-L distance based approach not only avoids the ad-hoc mechanism of the original gene concatenation method but also is easy to extend to other dataset and free from prohibitive phylogenetic computing from large number of taxa. © 2006 IEEE.

关键词： Gene expression

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Modeling and analyzing three-dimensional structures of human disease proteins

Modeling and analyzing three-dimensional structures of human...

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11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Ye, Yuzhen Li, Zhanwen Godzik, Adam Bioinformatics and Systems Biology Program Burnham Institute San Diego CA 92037 United States

ISBN: (纸本)9812564632

Three-dimensional structures of proteins, experimental or predicted, show us how these molecular machines actually work. With the help of information on disease-related mutations, they can also show us how they malfunction in diseases. Such understanding, currently lacking for most human diseases, is an important first step before designing drugs or therapies to cure specific diseases. Here we used homology modeling to model human disease-related proteins, and studied structural characteristics of disease related' mutations and compared them with non synonymous SNPs. 1484 domains from 874 proteins were modeled, and together with experimentally determined structures of 369 domains they provided the structural coverage of 48% of total residues in 1237 human disease proteins. We found that disease-related mutations have statistically significantly preference to form clusters on protein surfaces. In contrast, the non-synonymous SNPs appear to be randomly distributed on the surface. We interpret these results as an indication that disease mutations affect protein-protein interaction interfaces. This interpretation is supported by the analysis of 8 experimentally determined complexes between disease proteins, where disease-related mutations are clearly located in the binding interface of proteins, while SNPs are not. The non-uniform distribution of disease mutations indicates that we can use this feature as guidance in modeling and evaluating human disease proteins and their complexes. We set up a resource for Disease Protein Models (DPM at http://***/DPM>. which can be used for studying the relation between disease and mutation / polymorphism sites in the context of protein 3D structures and complexes.

关键词： Proteins

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NIDDK data repository: A central collection of clinical trial data

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BMC Medical Informatics and Decision Making 2006年第1期6卷 1-6页

作者： Cuticchia, A. Jamie Cooley, Philip C. Hall, R. David Qin, Ying Duke Institute for Genome Sciences and Policy Duke University Durham NC United States Bioinformatics Program RTI International Research Triangle Park NC United States

Background: The National Institute of Diabetes and Digestive and Kidney Diseases have established central repositories for the collection of DNA, biological samples, and clinical data to be catalogued at a single site. Here we present an overview of the site which stores the clinical data and links to biospecimens. Description: The NIDDK Data repository is a web-enabled resource cataloguing clinical trial data and supporting information from NIDDK supported studies. The Data Repository allows for the co-location of multiple electronic datasets that were created as part of clinical investigations. The Data Repository does not serve the role of a Data Coordinating Center, but rather as a warehouse for the clinical findings once the trials have been completed. Because both biological and genetic samples are collected from many of the studies, a data management system for the cataloguing and retrieval of samples was developed. Conclusion: The Data Repository provides a unique resource for researchers in the clinical areas supported by NIDDK. In addition to providing a warehouse of data, Data Repository staff work with the users to educate them on the datasets as well as assist them in the acquisition of multiple data sets for cross-study analysis. Unlike the majority of biological databases, the Data Repository acts both as a catalogue for data, biosamples, and genetic materials and as a central processing point for the requests for all biospecimens. Due to regulations on the use of clinical data, the ultimate release of that data is governed under NIDDK data release policies. The Data Repository serves as the conduit for such requests. © 2006 Cuticchia et al;licensee BioMed Central Ltd.

关键词： Interstitial Cystitis Data Repository Liver Transplant Recipient Central Repository Sample Request

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Combinatorial model for sequence and spatial motif discovery in short sequence fragments: Examples from β-barrel membrane proteins

Combinatorial model for sequence and spatial motif discovery...

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28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06

作者： Jackups Jr., Ronald Liang, Jie Program in Bioinformatics Department of Bioengineering University of Illinois at Chicago 851 S. Morgan Street Chicago IL 60607-7052 United States

ISBN: (纸本)1424400325

Motifs are over-represented sequence or spatial patterns appearing in proteins. They often play important roles in maintaining protein stability and in facilitating protein functions. When motifs are located in short sequence fragments, as in transmembrane domains that are only 10-20 residues in length, and when there is only very limited data, it is difficult to identify motifs. In this study, we develop combinatorial models for assessing statistically significant sequence and spatial patterns. We show our method can uncover previously unknown sequence and spatial motifs in β-barrel membrane proteins. © 2006 IEEE.

关键词： Proteins

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The generation challenge programme platform: Semantic standards and workbench for crop science

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International Journal of Plant Genomics 2008年第unknown期2008卷 369601页

作者： Bruskiewich, Richard Senger, Martin Davenport, Guy Ruiz, Manuel Rouard, Mathieu Hazekamp, Tom Takeya, Masaru Doi, Koji Satoh, Kouji Costa, Marcos Simon, Reinhard Balaji, Jayashree Akintunde, Akinnola Mauleon, Ramil Wanchana, Samart Shah, Trushar Anacleto, Mylah Portugal, Arllet Ulat, Victor Jun Thongjuea, Supat Braak, Kyle Ritter, Sebastian Dereeper, Alexis Skofic, Milko Rojas, Edwin Martins, Natalia Pappas, Georgios Alamban, Ryan Almodiel, Roque Barboza, Lord Hendrix Detras, Jeffrey Manansala, Kevin Mendoza, Michael Jonathan Morales, Jeffrey Peralta, Barry Valerio, Rowena Zhang, Yi Gregorio, Sergio Hermocilla, Joseph Echavez, Michael Yap, Jan Michael Farmer, Andrew Schiltz, Gary Lee, Jennifer Casstevens, Terry Jaiswal, Pankaj Meintjes, Ayton Wilkinson, Mark Good, Benjamin Wagner, James Morris, Jane Marshall, David Collins, Anthony Kikuchi, Shoshi Metz, Thomas McLaren, Graham Van Hintum, Theo Crop Research Informatics Laboratory International Rice Research Institute (IRRI) DAPO Box 7777 Metro Manila Philippines Crop Research Informatics Laboratory International Maize and Wheat Improvement Center (CIMMYT) DF 06600 Mexico Apdo. Postal 6-641 Mexico Centre International de Recherche Agronomique Pour le Développement (CIRAD) 34398 Montpellier Cedex 5 Avenue Agropolis France Bioversity International 00057 Maccarese (Fiumicino) Rome Via dei Tre Denari 472/a Italy National Institute for Agrobiological Sciences (NIAS) Tsukuba Ibaraki 305-8602 Kannondai 2-1-2 Japan Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Brasilia DF 70770-900 Parque Estaçao Biologia Final W5 Norte Brazil Centro Internacional de la Papa (CIP) Lima 12 Avenida La Molina 1895 La Molina Peru International Crops Research Institute for the Semi-Arid Tropics Patancheru Andhra Pradesh 502324 India International Center for Agricultural Research in the Dry Areas Aleppo P.O. Box 5466 Syrian Arab Republic National Center for Genetic Engineering and Biotechnology 113 Thailand Science Park Klong Luang Pathumthani 12120 Phahonyothin Road Klong 1 Thailand Institute of Computer Science College of Arts and Sciences University of the Philippines Los Banos Laguna 4031 Philippines Department of Computer Science University of the Philippines Melchor Hall Diliman Quezon City 1101 Philippines National Center for Genome Resources Santa Fe NM 87505 2935 Rodeo Park Drive East United States Scottish Crop Research Institute Invergowrie Dundee DD2 5DA United Kingdom Department of Plant Breeding Cornell University Ithaca NY 14853 United States African Centre for Gene Technologies Lynnwood Ridge 0040 P.O. Box 75011 South Africa Department of Medical Genetics Faculty of Medicine University of British Columbia Vancouver BC V6T 1Z3 Canada School of Computing Science Simon Fraser Universtiy Burnaby BC V5A 1S6 8888 University Drive Canada Bioinformatics Graduate Program Genome Sciences Centre BC

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

关键词： GENOMICS GERMPLASM resources PLANT breeding PLANT genetics COMPUTERS in biology bioinformatics -- Software

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Nonpolypoid neoplastic lesions of the colorectal mucosa

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GASTROINTESTINAL ENDOSCOPY 2008年第4期68卷 S3-S47页

作者： Kudo, Shin ei Lambert, Rene Allen, John I. Fujii, Hiroaki Fujii, Takahiro Kashida, Hiroshi Matsuda, Takahisa Mori, Masaki Saito, Hiroshi Shimoda, Tadakazu Tanaka, Shinji Watanabe, Hidenobu Sung, Joseph J. Feld, Andrew D. Inadomi, John M. O'Brien, Michael J. Lieberman, David A. Ransohoff, David F. Soetikno, Roy M. Triadafilopoulos, George Zauber, Ann Teixeira, Claudio Rolim Rey, Jean Franigois Jaramillo, Edgar Rubio, Carlos A. Van Gossum, Andre Jung, Michael Vieth, Michael Jass, Jeremy R. Hurlstone, Paul D. [a]Current affiliations: Hiroaki Fujii MD Department of Pathology (II) Juntendo University School of Medicine Tokyo Japan. Takahiro Fujii MD TF Clinic Ginza Tokyo Japan. Hiroshi Kashida MD Shin ei Kudo MD Digestive Disease Center Northern Yokohama Hospital Showa University Yokohama Japan. Takahisa Matsuda MD Division of Endoscopy Tadakazu Shimoda MD Clinical Laboratory Division National Cancer Centre Hospital Tokyo Japan. Masaki Mori MD PhD FACS Department of Surgery Medical Institute of Bioregulation Kyushu University Hospital Beppu Japan. Hiroshi Saito MD Cancer Screening Technology Division Research Center for Cancer Prevention and Screening National Cancer Center Tokyo Japan. Shinji Tanaka MD Department of Endoscopy Hiroshima University Hospital Hiroshima Japan. Hidenobu Watanabe MD Division of Molecular and Diagnostic Pathology Graduate School of Medical and Dental Sciences Niigata University Niigata Japan. Joseph J. Sung MD Institute of Digestive Diseases Chinese University of Hong Kong Prince of Wales Hospital Shatin Hong Kong. John I. Allen MD Medical Director Minnesota Gastroenterology University of Minnesota School of Medicine Minneapolis Minnesota USA. Andrew D. Feld MD Clinical Associate Professor of Medicine Central Division of Gastroenterology Group Health Cooperative University of Washington Seattle Washington USA. John M. Inadomi MD Director GI Health Outcomes Research Program University of California San Francisco Division of Gastroenterology San Francisco General Hospital San Francisco California USA. Michael J. O'Brien MD Department of Pathology Boston University Medical Center Boston Massachusetts USA. David A. Lieberman MD Division of Gastroenterology Portland VA Hospital Portland Oregon USA. David F. Ransohoff MD Bioinformatics Department of Medicine University of North Carolina Chapel Hill North Carolina USA. Roy M. Soetikno MD Gastroenterology Section Veterans Affairs Palo Alto Health

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