Title:Drug Repositioning Based on a Multiplex Network by Integrating Disease,
Gene, and Drug Information
Volume: 18
Issue: 3
Author(s): Gang Zhou, Chenxu Xuan, Yan Wang, Bai Zhang, Hanwen Wu and Jie Gao*
Affiliation:
- School of Science, Jiangnan University, Wuxi, China
Keywords:
Drug repositioning, multiplex network, PageRank, association prediction, similar network, multi-omics data.
Abstract:
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.
