Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition
Identifieur interne : 002F51 ( Hal/Checkpoint ); précédent : 002F50; suivant : 002F52Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition
Auteurs : Saliha Yilmaz [France] ; Philippe Jonveaux [France] ; Cedric Bicep ; Laurent Pierron [France] ; Malika Smaïl-Tabbone [France] ; Marie-Dominique Devignes [France]Source :
- Bioinformatics [ 1367-4803 ] ; 2009.
Abstract
Motivation: Computational methods are widely used to discover gene-disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene-disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases. Results: Explicit relationships between genes and diseases are formulated as candidate gene definitions that may include intermediary genes, e.g., orthologous or interacting genes. These definitions guide data modelling in our database approach for gene-disease relationship discovery and are expressed as views which ultimately lead to the retrieval of documented sets of candidate genes. A system called ACGR (Approach for Candidate Gene Retrieval) has been implemented and tested with three case-studies including a rare orphan gene disease. Availability: The ACGR sources are freely available at http://bioinfo.loria.fr/projects/acgr/acgr-software/. Contact: devignes@loria.fr Supplementary information: See the file "disease\_description" and the folders "Xcollect\_scenarios" and "ACGR\_views" at http://bioinfo.loria.fr/projects/acgr .
Url:
DOI: 10.1093/bioinformatics/btn612
Links toward previous steps (curation, corpus...)
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Hal:inria-00359111Le document en format XML
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type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1093/bioinformatics/btn612</idno><series><title level="j">Bioinformatics</title><idno type="ISSN">1367-4803</idno><imprint><date type="datePub">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Motivation: Computational methods are widely used to discover gene-disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene-disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases. Results: Explicit relationships between genes and diseases are formulated as candidate gene definitions that may include intermediary genes, e.g., orthologous or interacting genes. These definitions guide data modelling in our database approach for gene-disease relationship discovery and are expressed as views which ultimately lead to the retrieval of documented sets of candidate genes. A system called ACGR (Approach for Candidate Gene Retrieval) has been implemented and tested with three case-studies including a rare orphan gene disease. Availability: The ACGR sources are freely available at http://bioinfo.loria.fr/projects/acgr/acgr-software/. Contact: devignes@loria.fr Supplementary information: See the file "disease\_description" and the folders "Xcollect\_scenarios" and "ACGR\_views" at http://bioinfo.loria.fr/projects/acgr .</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition</title><author role="aut"><persName><forename type="first">Saliha</forename><surname>Yilmaz</surname></persName><email></email><idno type="halauthor">384649</idno><affiliation ref="#struct-84922"></affiliation></author><author role="aut"><persName><forename type="first">Philippe</forename><surname>Jonveaux</surname></persName><email></email><idno type="halauthor">308150</idno><affiliation ref="#struct-84922"></affiliation></author><author role="aut"><persName><forename type="first">Cedric</forename><surname>Bicep</surname></persName><email></email><idno type="halauthor">384650</idno></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Pierron</surname></persName><email></email><idno type="halauthor">130330</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2496"></affiliation></author><author role="aut"><persName><forename type="first">Malika</forename><surname>Smaïl-Tabbone</surname></persName><email></email><idno type="halauthor">52675</idno><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Marie-Dominique</forename><surname>Devignes</surname></persName><email>devignes@loria.fr</email><idno type="halauthor">130177</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2358"></affiliation></author><editor role="depositor"><persName><forename>Malika</forename><surname>Smail-Tabbone</surname></persName><email>Malika.Smail@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2009-02-05 16:39:06</date><date type="whenModified">2016-05-18 09:01:01</date><date type="whenReleased">2009-02-05 16:39:06</date><date type="whenProduced">2009</date></edition><respStmt><resp>contributor</resp><name key="103146"><persName><forename>Malika</forename><surname>Smail-Tabbone</surname></persName><email>Malika.Smail@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00359111</idno><idno type="halUri">https://hal.inria.fr/inria-00359111</idno><idno type="halBibtex">yilmaz:inria-00359111</idno><idno type="halRefHtml">Bioinformatics, Oxford University Press (OUP), 2009, 25 (2), pp.230 - 236. <10.1093/bioinformatics/btn612></idno><idno type="halRef">Bioinformatics, Oxford University Press (OUP), 2009, 25 (2), pp.230 - 236. <10.1093/bioinformatics/btn612></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition</title><author role="aut"><persName><forename type="first">Saliha</forename><surname>Yilmaz</surname></persName><idno type="halAuthorId">384649</idno><affiliation ref="#struct-84922"></affiliation></author><author role="aut"><persName><forename type="first">Philippe</forename><surname>Jonveaux</surname></persName><idno type="halAuthorId">308150</idno><affiliation ref="#struct-84922"></affiliation></author><author role="aut"><persName><forename type="first">Cedric</forename><surname>Bicep</surname></persName><idno type="halAuthorId">384650</idno></author><author role="aut"><persName><forename type="first">Laurent</forename><surname>Pierron</surname></persName><idno type="halAuthorId">130330</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2496"></affiliation></author><author role="aut"><persName><forename type="first">Malika</forename><surname>Smaïl-Tabbone</surname></persName><idno type="halAuthorId">52675</idno><affiliation ref="#struct-2358"></affiliation></author><author role="aut"><persName><forename type="first">Marie-Dominique</forename><surname>Devignes</surname></persName><email>devignes@loria.fr</email><idno type="halAuthorId">130177</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2358"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">3436</idno><idno type="issn">1367-4803</idno><idno type="eissn">1460-2059</idno><title level="j">Bioinformatics</title><imprint><publisher>Oxford University Press (OUP)</publisher><biblScope unit="volume">25</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="pp">230 - 236</biblScope><date type="datePub">2009</date></imprint></monogr><idno type="doi">10.1093/bioinformatics/btn612</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info.info-bi">Computer Science [cs]/Bioinformatics [q-bio.QM]</classCode><classCode scheme="halDomain" n="sdv.bibs">Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">Motivation: Computational methods are widely used to discover gene-disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene-disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases. Results: Explicit relationships between genes and diseases are formulated as candidate gene definitions that may include intermediary genes, e.g., orthologous or interacting genes. These definitions guide data modelling in our database approach for gene-disease relationship discovery and are expressed as views which ultimately lead to the retrieval of documented sets of candidate genes. A system called ACGR (Approach for Candidate Gene Retrieval) has been implemented and tested with three case-studies including a rare orphan gene disease. Availability: The ACGR sources are freely available at http://bioinfo.loria.fr/projects/acgr/acgr-software/. Contact: devignes@loria.fr Supplementary information: See the file "disease\_description" and the folders "Xcollect\_scenarios" and "ACGR\_views" at http://bioinfo.loria.fr/projects/acgr .</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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