Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks.
BACKGROUND
Since traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.


RESULTS
In this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer's disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.


CONCLUSIONS
We proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer's disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.
Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China.|Changzhou University, Jiangsu, 213164, China.
Hui
H
Liu
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433, China.
Yinglong
Y
Song
Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China.
Jihong
J
Guan
Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. llb213001@163.com.
Libo
L
Luo
Changzhou NO. 7 People's Hospital, Changzhou, Jiangsu, 213011, China. cczuzzh@163.com.|Changzhou University, Jiangsu, 213164, China. cczuzzh@163.com.
Ziheng
Z
Zhuang
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D006801:Humans; D053858:Metabolic Networks and Pathways; D057285:Precision Medicine
Drug positioning; Heterogenous network; Random walk