title: Data-driven methods to discover molecular determinants of serious adverse drug events.
abstract: The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher-density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs.
authors.affiliation: Department of Medicine, Stanford Center for Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: A P
authors.initials: AP
authors.lastname: Chiang
authors.forename: A J
authors.initials: AJ
authors.lastname: Butte
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods / Q000639:trends; D030541:Databases, Genetic / Q000639:trends*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis* / Q000235:genetics* / Q000517:prevention & control; D006801:Humans; D011237:Predictive Value of Tests; D055553:Prescription Drugs / Q000009:adverse effects; D012307:Risk Factors
chemical_list: D055553:Prescription Drugs