title: Mining drug-disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies.
abstract: A novel recommendation-based drug repositioning strategy is presented to simultaneously determine novel drug indications and side effects in one integrated framework. This strategy provides a complementary method to medical genetics-based drug repositioning, which reduces the occurrence of false positives in medical genetics-based drug repositioning, resulting in a ranked list of new candidate indications and/or side effects with different confidence levels. Several new drug indications and side effects are reported with high prediction confidences.
authors: Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China; Department of Computer Science, Hefei University of Technology, Hefei, China.
authors: H
authors: H
authors: Wang
authors: Q
authors: Q
authors: Gu
authors: J
authors: J
authors: Wei
authors: Z
authors: Z
authors: Cao
authors: Q
authors: Q
authors: Liu
mesh_terms: D057225:Data Mining; D030541:Databases, Genetic; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000187:drug effects; D020022:Genetic Predisposition to Disease; D005826:Genetics, Medical / Q000379:methods*; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D018511:Systems Integration