Dual-channel hypergraph convolutional network for predicting herb-disease associations
文献类型：期刊论文
| 作者 | Hu, Lun3; Zhang, Menglong3; Hu, Pengwei3; Zhang, Jun2; Niu, Chao3; Lu, Xueying3; Jiang, Xiangrui1 ; Ma, Yupeng3
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| 刊名 | BRIEFINGS IN BIOINFORMATICS
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| 出版日期 | 2024-01-22 |
| 卷号 | 25期号:2页码:13 |
| 关键词 | herb-disease association prediction network pharmacology hypergraph convolutional network multi-target multi-component Chinese traditional medicine |
| ISSN号 | 1467-5463 |
| DOI | 10.1093/bib/bbae067 |
| 通讯作者 | Ma, Yupeng(ypma@ms.xjb.ac.cn) |
| 英文摘要 | Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature. |
| WOS关键词 | TARGET INTERACTION PREDICTION ; WEB SERVER ; DRUG ; CHUANXIONG ; LIVER ; PHARMACOLOGY ; MECHANISMS ; INJURY ; SAN |
| 资助项目 | Natural Science Foundation of Xinjiang Uygur Autonomous Region[2021D01D05] ; National Natural Science Foundation of China[62373348] ; Xinjiang Tianchi Talents Program[E33B9401] ; CAS Light of the West Multidisciplinary Team project[xbzg-zdsys202114] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:001253138400034 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/312134]  |
| 专题 | 中国科学院上海药物研究所 |
| 通讯作者 | Ma, Yupeng |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, Shanghai, Peoples R China 2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Comp Sci, Urumqi, Peoples R China 3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Lun,Zhang, Menglong,Hu, Pengwei,et al. Dual-channel hypergraph convolutional network for predicting herb-disease associations[J]. BRIEFINGS IN BIOINFORMATICS,2024,25(2):13. |
| APA | Hu, Lun.,Zhang, Menglong.,Hu, Pengwei.,Zhang, Jun.,Niu, Chao.,...&Ma, Yupeng.(2024).Dual-channel hypergraph convolutional network for predicting herb-disease associations.BRIEFINGS IN BIOINFORMATICS,25(2),13. |
| MLA | Hu, Lun,et al."Dual-channel hypergraph convolutional network for predicting herb-disease associations".BRIEFINGS IN BIOINFORMATICS 25.2(2024):13. |
入库方式： OAI收割
来源：上海药物研究所
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