Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network
文献类型：期刊论文
| 作者 | Cui, Chen2,3; Ding, Xiaoyu2,3; Wang, Dingyan2,3; Chen, Lifan2,3; Xiao, Fu2,3; Xu, Tingyang1; Zheng, Mingyue2,3 ; Luo, Xiaomin2,3; Jiang, Hualiang2,3,4; Chen, Kaixian2,3,4
|
| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2021-09-15 |
| 卷号 | 37期号:18页码:2930-2937 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btab191 |
| 通讯作者 | Zheng, Mingyue(myzheng@simm.ac.cn) ; Luo, Xiaomin(xmluo@simm.ac.cn) |
| 英文摘要 | Motivation: Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results: In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. |
| WOS关键词 | CONNECTIVITY MAP ; PREDICTION ; APOPTOSIS ; MEDICINE ; PATHWAY ; GENES ; CELLS |
| 资助项目 | State Key Program of Basic Research of China[2015CB910304] ; National Science & Technology Major Project `Key New Drug Creation and Manufacturing Program' of China[2018ZX09711002-001-003] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12020372] ; Tencent AI Lab Rhino-Bird Focused Research Program[JR202002] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000732709000017 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/300794]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Zheng, Mingyue; Luo, Xiaomin |
| 作者单位 | 1.Tencent AI Lab, Shenzhen 518057, Peoples R China 2.Chinese Acad Sci, Drug Discovery & Design Ctr, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Chen,Ding, Xiaoyu,Wang, Dingyan,et al. Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network[J]. BIOINFORMATICS,2021,37(18):2930-2937. |
| APA | Cui, Chen.,Ding, Xiaoyu.,Wang, Dingyan.,Chen, Lifan.,Xiao, Fu.,...&Chen, Kaixian.(2021).Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network.BIOINFORMATICS,37(18),2930-2937. |
| MLA | Cui, Chen,et al."Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug-drug links based on graph neural network".BIOINFORMATICS 37.18(2021):2930-2937. |
入库方式： OAI收割
来源：上海药物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。