Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition

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 .
Type de document :
Article dans une revue
Bioinformatics, Oxford University Press (OUP), 2009, 25 (2), pp.230 - 236. 〈10.1093/bioinformatics/btn612〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00359111
Contributeur : Malika Smail-Tabbone <>
Soumis le : jeudi 5 février 2009 - 16:39:06
Dernière modification le : jeudi 8 février 2018 - 16:54:03

Lien texte intégral

Identifiants

Collections

Citation

Saliha Yilmaz, Philippe Jonveaux, Cedric Bicep, Laurent Pierron, Malika Smaïl-Tabbone, et al.. Gene-Disease Relationship Discovery based on Model-driven Data Integration and Database View Definition. Bioinformatics, Oxford University Press (OUP), 2009, 25 (2), pp.230 - 236. 〈10.1093/bioinformatics/btn612〉. 〈inria-00359111〉

Partager

Métriques

Consultations de la notice

247