ArticleJournal of biomedical semantics2017
Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Evaluating NLP Approaches to Extract Drug Indications.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- LF-DBS of the ventral striatum shortens persistence for morphine place preference and modulates BDNF expression in the hippocampus.Behavioural brain research · 2025Article
- Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information.NAR genomics and bioinformatics · 2024Article
- preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology.Bioinformatics (Oxford, England) · 2024Article
- Hierarchical Concept Relations Improve Detection of Off-Label Drug Use in Electronic Health Records Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network.Computational and structural biotechnology journal · 2023Article
- Identification of disease treatment mechanisms through the multiscale interactome.Nature communications · 2021Article
- A data-driven methodology towards evaluating the potential of drug repurposing hypotheses.Computational and structural biotechnology journal · 2021Article
- PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.Genome medicine · 2018Article
- Article
- Knowledge-based biomedical Data Science.EPJ data science · 2017Article
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Abstract
backgroundDrug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologies. There is a wealth of available drug-indication information, but structuring and integrating it is challenging.
resultsWe created a drug-indication database (DID) of data from 12 openly available, commercially available, and proprietary information sources, integrated by terminological normalization to UMLS and other authorities. Across sources, there are 29,964 unique raw drug/chemical names, 10,938 unique raw indication "target" terms, and 192,008 unique raw drug-indication pairs. Drug/chemical name normalization to CAS numbers or UMLS concepts reduced the unique name count to 91 or 85% of the raw count, respectively, 84% if combined. Indication "target" normalization to UMLS "phenotypic-type" concepts reduced the unique term count to 57% of the raw count. The 12 sources of raw data varied widely in coverage (numbers of unique drug/chemical and indication concepts and relations) generally consistent with the idiosyncrasies of each source, but had strikingly little overlap, suggesting that we successfully achieved source/raw data diversity.
conclusionsThe DID is a database of structured drug-indication relations intended to facilitate building practical, comprehensive, integrated drug ontologies. The DID itself is not an ontology, but could be converted to one more easily than the contributing raw data. Our methodology could be adapted to the creation of other structured drug-disease databases such as for contraindications, precautions, warnings, and side effects.
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