Evidence map›Paper›PMID 36222570›Full record

ArticleBioinformatics (Oxford, England)2022

Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.

Yiqing Zhao, Matthew Brush, Chen Wang, Alex H Wagner, Hongfang Liu, Robert R Freimuth

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yiqing ZhaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-2874-8136
Matthew BrushDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Chen WangDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-2638-3081
Alex H WagnerThe Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH 43205, USA.ORCID 0000-0002-2502-8961
Hongfang LiuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA.
Robert R FreimuthDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0002-9673-5612

Funding

Integrated Knowledge Management Framework for Clinical GenomicsR35HG011899 · NHGRI · MAYO CLINIC ROCHESTER · PI FREIMUTH, ROBERT RICHARD · 2021 to 2025
$2.4M
NHGRI NIH HHS R35 HG011899
6 · The paper itself

Abstract

motivationDespite 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. 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'.

resultsResults 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', 3011 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. 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.

Indexed as

Genomic MedicinePharmacogeneticsKnowledge BasesPhenotype

Identifiers

PMID36222570
PMCPMC9710557

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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.