title: Computational drug repositioning: from data to therapeutics.
abstract: Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.
authors: Computational Biology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, USA.
authors: M R
authors: MR
authors: Hurle
authors: L
authors: L
authors: Yang
authors: Q
authors: Q
authors: Xie
authors: D K
authors: DK
authors: Rajpal
authors: P
authors: P
authors: Sanseau
authors: P
authors: P
authors: Agarwal
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning; D057286:Electronic Health Records; D006801:Humans; D059467:Transcriptome / Q000187:drug effects*