title: Relating anatomical therapeutic indications by the ensemble similarity of drug sets.
abstract: The anatomical therapeutic chemical (ATC) system is a world standard to define drug indications. Despite its broad applications in pharmaceutical and biomedical research, only a few studies that examine the relationships among ATC classes have been published. Here we present a similarity-based approach, named the indication similarity ensemble approach (iSEA), that innovatively correlates ATC classes by their drug set similarity. Our study demonstrated that iSEA was capable of relating ATC classes, and these relationships could accurately assign the right indications for approved drugs and make reasonable predictions about possible clinical indications for unclassified drugs, which would provide valuable information for drug repositioning. Additionally, on the basis of iSEA, we constructed the first ATC relationship network to reflect correlations among ATCs from a network view, which would further render novel insight to understand the intrinsic relationships in the ATC system.
authors.affiliation: Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
authors.forename: Leihong
authors.initials: L
authors.lastname: Wu
authors.forename: Ni
authors.initials: N
authors.lastname: Ai
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Liu
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Fan
mesh_terms: D062313:Databases, Pharmaceutical; D017277:Drug Approval; D055808:Drug Discovery; D058492:Drug Repositioning; D004358:Drug Therapy; D048088:Informatics / Q000379:methods*; D014481:United States; D014486:United States Food and Drug Administration