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BMC Syst Biol. 2018 Dec 31;12(Suppl 9):134. doi: 10.1186/s12918-018-0658-7.

Computational drug repositioning using meta-path-based semantic network analysis.

Author information

1
School of Information Engineering, Zhengzhou University, Zhengzhou, 450001, People's Republic of China.
2
School of information and computer engineering, Northeast Forestry, Harbin, 150001, People's Republic of China.
3
Institute of Materials, China Academy of Engineering Physics, Jiang You, 621907, Sichuan, China.
4
School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China. guomaozu@bucea.edu.cn.
5
Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, 100044, China. guomaozu@bucea.edu.cn.

Abstract

BACKGROUND:

Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.

RESULTS:

In this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.

CONCLUSIONS:

HSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.

KEYWORDS:

Drug repositioning; HSDD; HeteSim; Meta-path-based; Semantic network analysis

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