BGMSDDA: a bipartite graph diffusion algorithm with multiple similarity integration for drug–disease association prediction

Abstract

Drug repositioning, a method that relies on the information from the original drug–disease association matrix, aims to identify new indications for existing drugs and is expected to greatly reduce the cost and time of drug development. However, most current drug repositioning methods make use of the original drug–disease association matrix directly without preconditioning. As relatively only a few associations between drugs and diseases have been determined from actual observations, the original drug–disease association matrix used in the prediction is sparse, which affects the performance of the prediction method. A method for mining similar features of drugs and diseases is still lacking. To solve these problems, we developed a bipartite graph diffusion algorithm with multiple similarity integration for drug–disease association prediction (BGMSDDA). First, the weight K nearest known neighbors (WKNKN) algorithm was used to reconstruct the drug–disease association matrix. Secondly, an effective method was designed to extract similar characteristics of drugs and diseases based on integrating linear neighborhood similarity and Gaussian kernel similarity. Finally, bipartite graph diffusion was used to infer undiscovered drug–disease associations. After carrying out 10-fold cross-validation experiments, BGMSDDA showed excellent performance on two datasets, specifically with AUC values of 0.939 (Fdataset) and 0.954 (Cdataset), and AUPR values of 0.466 (Fdataset) and 0.565 (Cdataset). Furthermore, to evaluate the accuracy of the results of BGMSDDA, we conducted case studies on three medically used drugs selected from Fdataset and Cdataset and validated the predictive associated diseases of each drug with some databases. Based on the results obtained, BGMSDDA was demonstrated to be useful for predicting drug–disease associations.