title: Exploring the associations between drug side-effects and therapeutic indications.
abstract: Drug therapeutic indications and side-effects are both measurable patient phenotype changes in response to the treatment. Inferring potential drug therapeutic indications and identifying clinically interesting drug side-effects are both important and challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared drug therapeutic indication prediction using various information including chemical structures, protein targets and side-effects. We also compared drug side-effect prediction with various information sources including chemical structures, protein targets and therapeutic indication. Prediction performance based on 10-fold cross-validation demonstrates that drug side-effects and therapeutic indications are the most predictive information source for each other. In addition, we extracted 6706 statistically significant indication-side-effect associations from all known drug-disease and drug-side-effect relationships. We further developed a novel user interface that allows the user to interactively explore these associations in the form of a dynamic bipartitie graph. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/∼tua87106/druganalysis.html.
authors.affiliation: IBM T.J. Watson Research Center, Yorktown Heights, NY, USA. Electronic address: feiwang03@gmail.com.
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.affiliation: IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhang
authors.affiliation: IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Nan
authors.initials: N
authors.lastname: Cao
authors.affiliation: IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Jianying
authors.initials: J
authors.lastname: Hu
authors.affiliation: IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Sorrentino
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods; D062313:Databases, Pharmaceutical; D058492:Drug Repositioning / Q000379:methods; D004358:Drug Therapy / Q000145:classification*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000737:chemistry*
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
keywords: Associations; Side-effects; Theraputic indications