Prediction of drug-pathway interaction pairs with a disease-combined LSA-PU-KNN method

Prediction of new associations between drugs and targeting pathways can provide valuable clues for drug discovery & development. However, information integration and a class-imbalance problem are important challenges for available prediction methods. This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method. Firstly, we built a drug-disease-pathway network and combined the drug-disease and pathway-disease features obtained by different types of feature profiles. Then we applied a latent semantic analysis (LSA) method to perform dimension reduction by combining positive-unlabeled (PU) learning and k nearest neighbors (KNN) method. The experimental results showed that our method can achieve a higher AUC (the area under the ROC curve) and AUPR (the area under the PR curve) than other typical methods. Furthermore, some interesting drug-pathway interaction pairs were identified and validated. This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method.