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Project Title

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


Adverse drug-drug interaction (ADDI) is referred to as a situation where the unpleasant or adverse effects caused by the co-administration of two drugs, which becomes a significant problem for public health. With the increasing availability of healthcare data, many methods are proposed for ADDI prediction. However, these methods usually work in a "nondiscriminatory" manner, i.e., they treat each feature without discrimination and equally incorporate all features into the predictive models. In practice, only a few features are essentially discriminative and relevant to ADDIs. In this paper, we propose a Dependence Guided Discriminative Feature Selection (DGDFS) model for ADDI prediction. In DGDFS, two drug attributes, molecular structure and side effect are adopted to model the adverse interaction among drugs and l2,0 -norm equality constraints are introduced to select discriminative molecular substructures and side effects for ADDI prediction. Besides, three dependence guided terms, i.e., the dependence between molecular structure and ADDI, the dependence between side effect and ADDI, and the dependence between molecular structure and side effect, are designed to guide feature selection. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experimental results indicate the effectiveness of DGDFS compared with fourteen baselines and its three variants.

Branch: CSE     Domain: Data Mining

Developed In: Java