ArticleJournal of biomedical informatics2013
Disambiguation of PharmGKB drug-disease relations with NDF-RT and SPL.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer.Frontiers in neurorobotics · 2021Article
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.Frontiers in chemistry · 2019Article
- The relative resistance of children to sepsis mortality: from pathways to drug candidates.Molecular systems biology · 2018Article
- Cancer based pharmacogenomics network supported with scientific evidences: from the view of drug repurposing.BioData mining · 2015Article
- Evaluation of RxNorm for Medication Clinical Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
PharmGKB is a leading resource of high quality pharmacogenomics data that provides information about how genetic variations modulate an individual's response to drugs. PharmGKB contains information about genetic variations, pharmacokinetic and pharmacodynamic pathways, and the effect of variations on drug-related phenotypes. These relationships are represented using very general terms, however, and the precise semantic relationships among drugs, and diseases are not often captured. In this paper we develop a protocol to detect and disambiguate general clinical associations between drugs and diseases using more precise annotation terms from other data sources. PharmGKB provides very detailed clinical associations between genetic variants and drug response, including genotype-specific drug dosing guidelines, and this procedure will armGKB. The availability of more detailed data will help investigators to conduct more precise queries, such as finding particular diseases caused or treated by a specific drug. We first mapped drugs extracted from PharmGKB drug-disease relationships to those in the National Drug File Reference Terminology (NDF-RT) and to Structured Product Labels (SPLs). Specifically, we retrieved drug and disease role relationships describing and defining concepts according to their relationships with other concepts from NDF-RT. We also used the NCBO (National Center for Biomedical Ontology) annotator to annotate disease terms from the free text extracted from five SPL sections (indication, contraindication, ADE, precaution, and warning). Finally, we used the detailed drug and disease relationship information from NDF-RT and the SPLs to annotate and disambiguate the more general PharmGKB drug and disease associations.
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