ArticleDatabase : the journal of biological databases and curation2012
MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database.
Article in Database : the journal of biological databases and curation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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91 citing papers in PubMed.
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- An analysis of entity normalization evaluation biases in specialized domains.BMC bioinformatics · 2023Article
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- Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.BMC bioinformatics · 2022Article
- Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia's Protein-Protein Interaction Network.Biomolecules · 2022Article
- How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?IEEE access : practical innovations, open solutions · 2022Article
- Medical concept normalization in clinical trials with drug and disease representation learning.Bioinformatics (Oxford, England) · 2021Article
- Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems.Evolutionary applications · 2021Article
- Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.Journal of biomedical informatics · 2021Article
- PhenoTagger: a hybrid method for phenotype concept recognition using human phenotype ontology.Bioinformatics (Oxford, England) · 2021Article
31 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators manually curate a triad of chemical-gene, chemical-disease and gene-disease relationships from the scientific literature. The CTD curation paradigm uses controlled vocabularies for chemicals, genes and diseases. To curate disease information, CTD first had to identify a source of controlled terms. Two resources seemed to be good candidates: the Online Mendelian Inheritance in Man (OMIM) and the 'Diseases' branch of the National Library of Medicine's Medical Subject Headers (MeSH). To maximize the advantages of both, CTD biocurators undertook a novel initiative to map the flat list of OMIM disease terms into the hierarchical nature of the MeSH vocabulary. The result is CTD's 'merged disease vocabulary' (MEDIC), a unique resource that integrates OMIM terms, synonyms and identifiers with MeSH terms, synonyms, definitions, identifiers and hierarchical relationships. MEDIC is both a deep and broad vocabulary, composed of 9700 unique diseases described by more than 67 000 terms (including synonyms). It is freely available to download in various formats from CTD. While neither a true ontology nor a perfect solution, this vocabulary has nonetheless proved to be extremely successful and practical for our biocurators in generating over 2.5 million disease-associated toxicogenomic relationships in CTD. Other external databases have also begun to adopt MEDIC for their disease vocabulary. Here, we describe the construction, implementation, maintenance and use of MEDIC to raise awareness of this resource and to offer it as a putative scaffold in the formal construction of an official disease ontology. DATABASE URL: http://ctd.mdibl.org/voc.go?type=disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.