Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning

Abstract

Using existing drugs for diseases which are not developed for their treating (drug repositioning) provides a new approach to developing drugs at a lower cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our primary approach is to analyze graphs and complex networks in the study of drugs and their therapeutic effects. Given the nature of existing data, the use of semi-supervised learning methods is crucial. So, in our research, we have developed a label propagation method to predict drug-target, drug-disease, and disease-target interactions (Heter-LP), which integrates various data sources at different levels. The predicted interactions are the most prominent relationships among the millions of relationships suggested to the related researchers for further investigation. The main advantages of Heter-LP are the effective integration of input data, eliminating the need for negative samples, and the use of local and global features together. The main steps of this research are as follows. The first step is the construction of a heterogeneous network as a data modeling task, in which data are collected and prepared. The second step is predicting potential interactions. We present a new label propagation algorithm for heterogeneous networks, which consists of two parts, one mapping and the other an iterative method for determining the final labels of the entire network vertices. Finally, for evaluation, we calculated the AUC and AUPR with tenfold cross-validation and compared the results with the best available methods for label propagation in heterogeneous networks and drug repositioning. Also, a series of experimental evaluations and some specific case studies have been presented. The result of the AUC and AUPR for Heter-LP was much higher than the average of the best available methods.