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.#R##N##R##N#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.#R##N##R##N#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’.#R##N##R##N#Contact: devignes@loria.fr#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online.
['Saliha Yilmaz', 'Philippe Jonveaux', 'Cédric Bicep', 'Laurent Pierron', 'Malika Smaïl-Tabbone', 'Marie-Dominique Devignes']
Gene–disease relationship discovery based on model-driven data integration and database view definition