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

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.

Methods

In this paper, we propose a novel method 'feature selection-based multi-label k-nearest neighbor method' (FS-MLKNN), which can simultaneously determine critical feature dimensions and construct high-a

SUBMITTER: Zhang W 

PROVIDER: S-EPMC4634905 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature