A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2
文献类型:期刊论文
作者 | Su, XR (Su, Xiaorui) [1]; Hu, L (Hu, Lun) [1]; You, ZH (You, Zhuhong) [2]; Hu, PW (Hu, Pengwei) [1]; Wang, L (Wang, Lei) [3]; Zhao, BW (Zhao, Bowei) [1] |
刊名 | BRIEFINGS IN BIOINFORMATICS |
出版日期 | 2022 |
卷号 | 23期号:1页码:1-6 |
ISSN号 | 1467-5463 |
关键词 | SARS-CoV-2 drugrepositioning constrained multi-view nonnegative matrix factorization deeplearning graphconvolutionalnetwork |
DOI | 10.1093/bib/bbab526 |
英文摘要 | The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections. |
WOS记录号 | WOS:000763000800072 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/8363] |
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) [1] |
作者单位 | 1.Guangxi Acad Sci, Big Data & Intelligent Comp Res Ctr, Nanning, Peoples R China 2.Northwestern Polytech Univ, Sch Comp Sci, Xian, 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 | Su, XR ,Hu, L ,You, ZH ,et al. A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2[J]. BRIEFINGS IN BIOINFORMATICS,2022,23(1):1-6. |
APA | Su, XR ,Hu, L ,You, ZH ,Hu, PW ,Wang, L ,&Zhao, BW .(2022).A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2.BRIEFINGS IN BIOINFORMATICS,23(1),1-6. |
MLA | Su, XR ,et al."A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2".BRIEFINGS IN BIOINFORMATICS 23.1(2022):1-6. |
入库方式: OAI收割
来源:新疆理化技术研究所
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