title: Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.
abstract: BACKGROUND
  Every 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  Our 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria. matthias.samwald@meduniwien.ac.at.
authors.forename: Matthias
authors.initials: M
authors.lastname: Samwald
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.|Institute of Medical Informatics, Statistics, and Documentation; Medical University of Graz, Auenbruggerplatz 2, 8036, Graz, Austria.
authors.forename: Jose Antonio
authors.initials: JA
authors.lastname: Miñarro Giménez
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Suite 419, Pittsburgh, PA, 15206-3701, USA.
authors.forename: Richard D
authors.initials: RD
authors.lastname: Boyce
authors.affiliation: Department of Health Sciences Research; Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
authors.forename: Robert R
authors.initials: RR
authors.lastname: Freimuth
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.|Medexter Healthcare GmbH, Borschkegasse 7/5, 1090, Vienna, Austria.
authors.forename: Klaus-Peter
authors.initials: KP
authors.lastname: Adlassnig
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, 1265 Welch Road, Stanford, CA, 94305-5479, USA.
authors.forename: Michel
authors.initials: M
authors.lastname: Dumontier
mesh_terms: D001185:Artificial Intelligence; D064229:Biological Ontologies; D000066491:Clinical Decision-Making; D020000:Decision Support Systems, Clinical; D064420:Drug-Related Side Effects and Adverse Reactions / Q000517:prevention & control*; D006801:Humans; D010597:Pharmacogenetics / Q000379:methods*; D017410:Practice Guidelines as Topic; D057285:Precision Medicine / Q000379:methods*