Prediction of Drug-Target Interactions Based on Interactive Multi-Feature Fusion Algorithm

Abstract

Purposes The prediction of drug-target interactions plays a crucial role in drug relocation and drug development. Methods A multi-feature fusion algorithm is proposed based on the combination of Redundancy-Correlation and Interaction (RCI), and the prediction model is built by combining the stacked ensemble classifier. First, high-dimensional features of the drug and target are extracted for multi-feature fusion, and RCI is used to build a non-redundant and relevant interactive feature subset. Then, the feature subset is input into a stacked ensemble classifier composed of multiple base learners for training. Finally, the prediction is carried out in two benchmark drug target networks. Findings The experimental results show that the accuracies of ACC and AUC values of the model in this paper are better than those of the existing benchmark methods, indicating the effectiveness of the proposed algorithm.

Keywords

• drug-target interactions
• multi-feature fusion
• feature selection
• stacked ensemble classifier
• machine learning