A Novel Drug Repositioning Model Based on Heterogeneous Graph Convolutional Network via Multi-task Learning

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成果类型：

会议论文

作者：

Shengwei Ye;Weizhong Zhao;Xianjun Shen;Xingpeng Jiang;Tingting He

作者机构：

Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China

School of Computer, Central China Normal University, Wuhan, Hubei, PR China

National Language Resources Monitoring & Research Center for Network Media Central China Normal University, Wuhan, Hubei, PR China

[Shengwei Ye; Weizhong Zhao; Xianjun Shen; Xingpeng Jiang; Tingting He] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>School of Computer, Central China Normal University, Wuhan, Hubei, PR China<&wdkj&>National Language Resources Monitoring & Research Center for Network Media Central China Normal University, Wuhan, Hubei, PR China

语种：

英文

关键词：

drug repositioning;drug-disease associations prediction;multi-task learning;graph convolutional network;heterogeneous information network

年：

2022

页码：

633-638

会议名称：

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

会议论文集名称：

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

会议时间：

06 December 2022

会议地点：

Las Vegas, NV, USA

出版者：

IEEE

ISBN：

978-1-6654-6820-6

DOI：

10.1109/BIBM55620.2022.9994923

基金类别：

10.13039/501100001809-National Natural Science Foundation of China 10.13039/100006190-Research and Development

机构署名：

本校为第一机构

院系归属：

计算机学院

摘要：

Compared with traditional methods, drug repositioning is a viable solution to drug discovery. Drug repositioning usually applies the procedure of drug-disease associations (DDAs) prediction, which can reduce the cost and time of drug development and improve the success rate of drug discovery. In this paper, we develop a new multi-task learning framework based on heterogeneous graph convolutional network (MTHGCN) to recognize potential DDAs. In MTHGCN, a heterogeneous information network is constructed by combining multiple biological datasets. And then, a module based on graph convolutional ne...

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A Novel Drug Repositioning Model Based on Heterogeneous Graph Convolutional Network via Multi-task Learning

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