Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance  

作　　者：YUE Kejuan ZOU Beiji WANG Lei LI Xiao ZENG Min WEI Faran 

机构地区：[1]School of Information Science and Engineering, Central South University, Changsha 410083, China [2]Center for Ophthalmic Imaging Research, Central South university, Changsha 410083, China [3]School of Information Science and Engineering, Hunan First Normal University, Changsha 410205, China

出　　处：《Chinese Journal of Electronics》2017年第3期585-590,共6页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61573380,No.61672542);the Scientific Research Fund of Hunan Provincial Education Department(No.13C143)

摘　　要：Drug-drug interactions(DDIs)occur when two drugs react with each other,which may cause unexpected side effects and even death of the patient.Methods that use adverse event reports to predict unexpected DDIs are limited by two critical yet challenging issues.One is the difficulty of selecting discriminative features from numerous redundant and irrelevant adverse events for modeling.The other is the data imbalance,i.e.,the drug pairs causing adverse effects are far less than those not causing adverse effects,which leads to poor accuracy of DDIs detection.We propose a multi-layer feature selection method to select discriminative adverse events and apply an over-sampling technique to make the data balanced.The experimental results show that the validation accuracy of positive DDIs on the Canada Vigilance Adverse Reaction Online Database increases to two times,and 110 DDIs are identified on the drug interactions checker of Drugs.com in USA.Drug-drug interactions （DDIs） occur when two drugs react with each others which may cause unexpected side effects and even death of the patient. Methods that use adverse event reports to predict unexpected DDIs are limited by two critical yet challenging issues. One is the difficulty of selecting discriminative features from numerous redundant and irrelevant adverse events for modeling. The other is the data imbalance, i.e., the drug pairs causing adverse effects are far less than those not causing adverse effects, which leads to poor accuracy of DDIs detection. We propose a multi-layer feature selection method to select discriminative adverse events and apply an over-sampling technique to make the data balanced. The experimental results show that the validation accuracy of positive DDIs on the Canada Vigilance Adverse Reaction Online Database increases to two times, and 110 DDIs are identified on the drug interactions checker of Drugs.corn in USA.

关 键 词：Adverse event reports Drug-drug interactions(DDIs) Feature selection Data balance 

分 类 号：R96[医药卫生—药理学]