InforLorV4, PascalFrancis, Corpus, bibRecord, 000283

Gene-disease relationship discovery based on model-driven data integration and database view definition

Identifieur interne : 000283 ( PascalFrancis/Corpus ); précédent : 000282; suivant : 000284

Gene-disease relationship discovery based on model-driven data integration and database view definition

Auteurs : S. Yilmaz ; P. Jonveaux ; C. Bicep ; L. Pierron ; M. Small-Tabbone ; M. D. Devignes

Source :

Bioinformatics : (Oxford. Print) [ 1367-4803 ] ; 2009.

RBID : Pascal:09-0081159

Descripteurs français

Pascal (Inist)
- Gène, Maladie, Donnée, Base de données, Définition, Modèle de données, Intégration de données.

English descriptors

KwdEn :
- Data, Data integration, Database, Definition, Disease, Gene.

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/. See especially the file 'disease_description' and the folders 'Xcollect_scenarios' and 'ACGR_views'. Contact: devignes@loria.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A11	`02`	`1`		`@1 JONVEAUX (P.)`
A11	`03`	`1`		`@1 BICEP (C.)`
A11	`04`	`1`		`@1 PIERRON (L.)`
A11	`05`	`1`		`@1 SMALL-TABBONE (M.)`
A11	`06`	`1`		`@1 DEVIGNES (M. D.)`
A14	`01`			`@1 Laboratory for Human Genetics, Nancy Medical Faculty, rue du Morvan @2 54500 Vandoeuvre-les-Nancy @3 FRA @Z 1 aut. @Z 2 aut.`
A14	`02`			`@1 LORIA UMR7503, CNRS, INRIA, Nancy-Université, BP239 @2 54506 Vandoeuvre-les-Nancy @3 FRA @Z 3 aut. @Z 4 aut. @Z 5 aut. @Z 6 aut.`
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Format Inist (serveur)

NO :	PASCAL 09-0081159 INIST
ET :	Gene-disease relationship discovery based on model-driven data integration and database view definition
AU :	YILMAZ (S.); JONVEAUX (P.); BICEP (C.); PIERRON (L.); SMALL-TABBONE (M.); DEVIGNES (M. D.)
AF :	Laboratory for Human Genetics, Nancy Medical Faculty, rue du Morvan/54500 Vandoeuvre-les-Nancy/France (1 aut., 2 aut.); LORIA UMR7503, CNRS, INRIA, Nancy-Université, BP239/54506 Vandoeuvre-les-Nancy/France (3 aut., 4 aut., 5 aut., 6 aut.)
DT :	Publication en série; Niveau analytique
SO :	Bioinformatics : (Oxford. Print); ISSN 1367-4803; Royaume-Uni; Da. 2009; Vol. 25; No. 2; Pp. 230-236; Bibl. 3/4 p.
LA :	Anglais
EA :	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/. See especially the file 'disease_description' and the folders 'Xcollect_scenarios' and 'ACGR_views'. Contact: devignes@loria.fr Supplementary information: Supplementary data are available at Bioinformatics online.
CC :	002A01B
FD :	Gène; Maladie; Donnée; Base de données; Définition; Modèle de données; Intégration de données
ED :	Gene; Disease; Data; Database; Definition; Data integration
SD :	Gen; Enfermedad; Dato; Base dato; Definición
LO :	INIST-21331.354000186420830120
ID :	09-0081159

Links to Exploration step

Pascal:09-0081159

Le document en format XML

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<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/. See especially the file 'disease_description' and the folders 'Xcollect_scenarios' and 'ACGR_views'. Contact: devignes@loria.fr Supplementary information: Supplementary data are available at Bioinformatics online.</div>
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<ET>Gene-disease relationship discovery based on model-driven data integration and database view definition</ET>
<AU>YILMAZ (S.); JONVEAUX (P.); BICEP (C.); PIERRON (L.); SMALL-TABBONE (M.); DEVIGNES (M. D.)</AU>
<AF>Laboratory for Human Genetics, Nancy Medical Faculty, rue du Morvan/54500 Vandoeuvre-les-Nancy/France (1 aut., 2 aut.); LORIA UMR7503, CNRS, INRIA, Nancy-Université, BP239/54506 Vandoeuvre-les-Nancy/France (3 aut., 4 aut., 5 aut., 6 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Bioinformatics : (Oxford. Print); ISSN 1367-4803; Royaume-Uni; Da. 2009; Vol. 25; No. 2; Pp. 230-236; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>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/. See especially the file 'disease_description' and the folders 'Xcollect_scenarios' and 'ACGR_views'. Contact: devignes@loria.fr Supplementary information: Supplementary data are available at Bioinformatics online.</EA>
<CC>002A01B</CC>
<FD>Gène; Maladie; Donnée; Base de données; Définition; Modèle de données; Intégration de données</FD>
<ED>Gene; Disease; Data; Database; Definition; Data integration</ED>
<SD>Gen; Enfermedad; Dato; Base dato; Definición</SD>
<LO>INIST-21331.354000186420830120</LO>
<ID>09-0081159</ID>
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Gene-disease relationship discovery based on model-driven data integration and database view definition

Gene-disease relationship discovery based on model-driven data integration and database view definition

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