An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network

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成果类型：

期刊论文

作者：

Zhao, Weizhong;Yao, Wenjie;Jiang, Xingpeng;He, Tingting;Shi, Chuan;...

作者机构：

[Shi, Chuan] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

[Yao, Wenjie; Jiang, Xingpeng; He, Tingting] School of Computer, Central China Normal University, Wuhan, Hubei, China

[Hu, Xiaohua] College of Computing &

Informatics, Drexel University, Philadelphia, PA, USA

[Zhao, Weizhong] Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, School of Computer, and National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China

语种：

英文

期刊：

IEEE/ACM Transactions on Computational Biology and Bioinformatics

ISSN：

1545-5963

年：

2023

卷：

期：

页码：

3635-3647

DOI：

10.1109/TCBB.2023.3308094

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61932008) Natural Science Foundation of Hubei Province of China (Grant Number: 2022CFB289) Fundamental Research Funds for Central Universities (Grant Number: KJ02502022-0450) National Language Commission (Grant Number: ZDI135-135)

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本校为其他机构

院系归属：

计算机学院

摘要：

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have ...

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An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network

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