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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.

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



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.


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.


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


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

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