Dataset Information
Gene-disease relationship discovery based on model-driven data integration and database view definition.
Ontology highlight
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.
SUBMITTER: Yilmaz S
PROVIDER: S-EPMC2639000 | biostudies-literature | 2009 Jan
REPOSITORIES: biostudies-literature
Publications
Gene-disease relationship discovery based on model-driven data integration and database view definition.
Bioinformatics (Oxford, England) 20081127 2
<h4>Motivation</h4>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.<h4>Results</h4>Explicit relationships b ...[more]