title: Prediction of drug's Anatomical Therapeutic Chemical (ATC) code by integrating drug-domain network.
abstract: Predicting Anatomical Therapeutic Chemical (ATC) code of drugs is of vital importance for drug classification and repositioning. Discovering new association information related to drugs and ATC codes is still difficult for this topic. We propose a novel method named drug-domain hybrid (dD-Hybrid) incorporating drug-domain interaction network information into prediction models to predict drug's ATC codes. It is based on the assumption that drugs interacting with the same domain tend to share therapeutic effects. The results demonstrated dD-Hybrid has comparable performance to other methods on the gold standard dataset. Further, several new predicted drug-ATC pairs have been verified by experiments, which offer a novel way to utilize drugs for new purposes effectively.
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China; Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China.
authors.forename: Fan-Shu
authors.initials: FS
authors.lastname: Chen
authors.affiliation: Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China; Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China. Electronic address: zrjiang@cs.ecnu.edu.cn.
authors.forename: Zhen-Ran
authors.initials: ZR
authors.lastname: Jiang
mesh_terms: D004358:Drug Therapy; D060388:Support Vector Machine
keywords: ATC code; Cross-validation; Drug classification; Drug–domain interaction; SVM