ArticleBioinformatics (Oxford, England)2009
Gene-disease relationship discovery based on model-driven data integration and database view definition.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Emerging Bioinformatics Methods and Resources in Drug Toxicology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- National information system for rare diseases with an approach to data architecture: A systematic review.Intractable & rare diseases research · 2018Review
- Integrative gene ontology and network analysis of coronary artery disease associated genes suggests potential role of ErbB pathway gene EGFR.Molecular medicine reports · 2018Article
- Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity.BMC genomics · 2014Article
- Has discovery-based cancer research been a bust?Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2013Article
- Article
- Improving disease gene prioritization using the semantic similarity of Gene Ontology terms.Bioinformatics (Oxford, England) · 2010Article
- FunSimMat update: new features for exploring functional similarity.Nucleic acids research · 2010Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationComputational 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.
resultsExplicit 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.
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Registered trials
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