BiRWDDA: A Novel Drug Repositioning Method Based on Multisimilarity Fusion

Abstract

Abstract

The explosive growth of large-scale biological data enables network-based drug repositioning to be an important way of drug discovery, which can reduce the time and cost of drug discovery efficiently. Many existing approaches always construct drug–disease association network only based on some similarity measuring data for drug or disease, which ignore the impacts of different similarity measuring on predicting performance. In this study, we develop a new computational approach named BiRWDDA, which fused multiple similarity measures and bi-random walk to discover potential associations between drugs and diseases. First, multiple drug–drug similarity and disease–disease similarity are measured. Next, the information entropy of similarities measured based on different data are calculated to select proper similarities of drugs and diseases. Subsequently, improved drug–drug similarity and disease–disease similarity can be obtained by fusing similarities selected. Then, a logistic function is adopted to adjust the improved drug similarity and disease similarity. What is more, a heterogeneous network can be conducted by connecting the drug similarity network and the disease similarity network through known drug–disease associations. Finally, a bi-random walk algorithm is implemented on the heterogeneous network to predict potential drug–disease associations. Experimental results demonstrate that BiRWDDA outperforms the other state-of-the-art methods with average AUC of 0.930. Case studies for five selected drugs further verify the favorable prediction performance.