Drug Repositioning Based on a Multiplex Network by Integrating Disease, Gene, and Drug Information

Background: The research of new drugs is very expensive and the cycle is relatively long, so it has broad development prospects and good economic benefits to use validated drugs in the treatment of other diseases. Objective: The purpose of drug repositioning is to identify other indications for existing drugs. In addition to using disease and drug information for drug repositioning, other biomolecular information can also be integrated for drug repositioning. Integrating multiple biomolecular data of different types can improve the predictive performance of drug repositioning models. Methods: This paper proposes a drug repositioning algorithm based on a multiplex network (DRMN algorithm) by integrating disease, gene, and drug information. DRMN algorithm utilizes known diseasegene and gene-drug associations to connect disease phenotype similarity network, gene expression similarity network, and drug response similarity network. Then they are constructed into a multiplex network, and the importance score of each node is calculated by PageRank (PR) algorithm. Finally, disease- drug association scores are sorted to achieve drug repositioning. Results: DRMN algorithm is applied to two sets of sample data. Disease-drug association scores are calculated separately from disease PR values and drug PR values in both datasets. In top 50% of association scores, lots of disease-drug association prediction results have been verified by existing results. Compared with other algorithms, DRMN algorithm also shows better performance. Conclusion: DRMN algorithm can effectively integrate multi-omics data for drug repositioning and obtain better prediction results.