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MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database
Allan Peter Davis1, Thomas C Wiegers, Michael C Rosenstein
1Department of Bioinformatics, The Mount Desert Island Biological Laboratory, Salisbury Cove, ME 04672, USA. apd@mdibl.org
Database : the Journal of Biological Databases and Curation
|March 22, 2012
Summary
The Comparative Toxicogenomics Database created MEDIC, a merged disease vocabulary integrating OMIM and MeSH terms. This resource enhances understanding of environmental chemical impacts on human health by organizing disease information.
Area of Science:
- Toxicogenomics
- Bioinformatics
- Controlled Vocabularies
Background:
- The Comparative Toxicogenomics Database (CTD) curates chemical-gene, chemical-disease, and gene-disease relationships from literature.
- CTD utilizes controlled vocabularies for chemicals, genes, and diseases to standardize data.
- Accurate disease terminology is crucial for understanding chemical impacts on human health.
Purpose of the Study:
- To develop a comprehensive and hierarchical disease vocabulary for CTD curation.
- To integrate the Online Mendelian Inheritance in Man (OMIM) and Medical Subject Headings (MeSH) disease terms.
- To create a practical resource for curating toxicogenomic relationships.
Main Methods:
- Manual curation of scientific literature by CTD biocurators.
- Mapping OMIM disease terms into the hierarchical MeSH vocabulary.
- Development of the merged disease vocabulary (MEDIC) integrating OMIM and MeSH data.
Main Results:
- MEDIC integrates 9700 unique diseases with over 67,000 terms and synonyms.
- MEDIC facilitates the curation of over 2.5 million disease-associated toxicogenomic relationships in CTD.
- MEDIC is available for download and has been adopted by other databases.
Conclusions:
- MEDIC is a successful and practical disease vocabulary for toxicogenomic research.
- MEDIC serves as a valuable resource for understanding chemical-disease relationships.
- MEDIC provides a potential scaffold for developing formal disease ontologies.
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