ArticleBMC medical informatics and decision making2015
Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.
Article in BMC medical informatics and decision making, 2015. 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.
- A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation.BMC medical informatics and decision making · 2025Article
- Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.Bioinformatics (Oxford, England) · 2022Article
- Pharmacogenomics of Cognitive Dysfunction and Neuropsychiatric Disorders in Dementia.International journal of molecular sciences · 2020Review
- A graph-based algorithm for estimating clonal haplotypes of tumor sample from sequencing data.BMC medical genomics · 2019Article
- Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach.JMIR medical informatics · 2018Article
- Improving data workflow systems with cloud services and use of open data for bioinformatics research.Briefings in bioinformatics · 2018Review
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Implementing Genomic Clinical Decision Support for Drug-Based Precision Medicine.CPT: pharmacometrics & systems pharmacology · 2017Review
- Design Recommendations for Pharmacogenomics Clinical Decision Support Systems.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- User Interface Requirements for Web-Based Integrated Care Pathways: Evidence from the Evaluation of an Online Care Pathway Investigation Tool.Journal of medical systems · 2015Article
- Creating Shareable Clinical Decision Support Rules for a Pharmacogenomics Clinical Guideline Using Structured Knowledge Representation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
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6 authors.
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Abstract
backgroundEvery year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.
methodsWe developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles.
resultsOur methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners.
conclusionsThe ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.
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