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A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures

Author(s):

Chaokun Yan, Luping Feng, Wenxiu Wang, Jianlin Wang, Ge Zhang and Junwei Luo*   Pages 1 - 10 ( 10 )

Abstract:


Background: Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development.

Methods: In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process are further conducted to obtain more comprehensive drug and disease similarity measure,respectively.

Results: On this basis, an Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

Keywords:

Drug repositioning, Heterogeneous network, Similarity measure, Logistic function, DeepWalk, Bi-random walk

Affiliation:

School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, School of Computer and Information Engineering, Henan University, Kaifeng, College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo



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