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Recently, developing computational methods to explore drug-pathway interaction relationships has attracted attention for their potentiality in discovering unknown targets and mechanisms of drug actions. However, mining suitable features of drugs and pathways is challenging for available prediction methods. This paper performed an ensemble learning-based method to predict potential drug-pathway interactions by integrating different drug-based and pathway-based features. The main characteristic of our method lies in using the Relief algorithm for feature selection and regarding three ensemble methods (AdaBoost, Bagging and Random Subspace) for classifiers. Cross validation results showed the AdaBoost algorithm that based on the Decision Tree classifier can obtain a higher prediction accuracy, which indicated the effectiveness of ensemble learning. Moreover, some new predicted interactions were validated by database searching, which demonstrated its potentiality for further biological experiment investigation.

Ensemble Learning-based Prediction of Drug-pathway Interactions based on Features Integration