DGDFS: Dependence Guided Discriminative Feature Selection for Predicting Adverse Drug-Drug Interaction : Extended Abstract

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue for public health. The current methods for ADDI prediction usually work in a "nondiscriminatory" manner by treating each feature without discrimination and equally employing all features into ADDI modeling. Driven by this issue, we propose a Dependence Guided Discriminative Feature Selection (DGDFS) model for ADDI prediction, in which molecular structure and side effect are adopted with the incorporation of l2,0-norm equality constraints to select discriminative molecular substructures and side effects and three dependence based terms among molecular structure, side effect, and ADDIs to guide feature selection. Extensive experiments demonstrate the superior performance of DGDFS compared with fourteen state-of-the-art ADDI prediction and feature selection models.