HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks | |
Zhao, BW (Zhao, Bo-Wei) [1] , [2]; Hu, L (Hu, Lun) [2]; You, ZH (You, Zhu-Hong) [3] , [4]; Wang, L (Wang, Lei) [5]; Su, XR (Su, Xiao-Rui) [1] , [2] | |
刊名 | BRIEFINGS IN BIOINFORMATICS |
2022 | |
卷号 | 23期号:1页码:1-15 |
关键词 | drug-disease associations prediction heterogeneous information network graph representation learning drug repositioning |
ISSN号 | 1467-5463 |
DOI | 10.1093/bib/bbab515 |
英文摘要 | Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases. |
WOS记录号 | WOS:000763000800028 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/8364] |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
通讯作者 | Hu, L (Hu, Lun) [2] |
作者单位 | 1.Guangxi Acad Sci, Nanning, Peoples R China 2.Northwestern Polytech Univ, Xian, Peoples R China 3.Cornell Univ, Ctr Biotechnol & Informat, Ithaca, NY 14853 USA 4.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhao, BW ,Hu, L ,You, ZH ,et al. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks[J]. BRIEFINGS IN BIOINFORMATICS,2022,23(1):1-15. |
APA | Zhao, BW ,Hu, L ,You, ZH ,Wang, L ,&Su, XR .(2022).HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.BRIEFINGS IN BIOINFORMATICS,23(1),1-15. |
MLA | Zhao, BW ,et al."HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks".BRIEFINGS IN BIOINFORMATICS 23.1(2022):1-15. |
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