In Bioinformatics (Oxford, England)
MOTIVATION : Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability, and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships.
METHODS : In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for "Genes and Gene Products" and "Treatments".
RESULTS : Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18,653 sentences that contained both "Genes and Gene Products" and "Treatments", 3,011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829), and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments.
CONCLUSION : In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Zhao Yiqing, Brush Matthew, Wang Chen, Wagner Alex H, Liu Hongfang, Freimuth Robert R
2022-Oct-12
