Predicting drug side effects by multi-label learning and ensemble learning.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Predicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is a multi-label learning task, and we can adopt the multi-label learning techniques for it. Secondly, drug-related features are associated with side effects, and feature dimensions have specific biological meanings. Recognizing critical dimensions and reducing irrelevant dimensions may help to reveal the causes of side effects.

Authors

  • Wen Zhang
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences Wuhan 430062 China peiwuli@oilcrops.cn zhangqi521x@126.com +86-27-8681-2943 +86-27-8671-1839.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • Longqiang Luo
    School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China. lqluo@whu.edu.cn.
  • Jingxia Zhang
    School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China. jxz_job@163.com.