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Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning

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成果类型:
期刊论文
作者:
Wu, Junlong;Xiao, Liqi;Fan, Liu;Wang, Lei;Zhu, Xianyou
通讯作者:
Zhu, XY;Wang, L
作者机构:
[Zhu, Xianyou; Wu, Junlong; Xiao, Liqi; Fan, Liu] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
[Wang, Lei] Changsha Univ, Technol Innovat Ctr Changsha, Changsha, Peoples R China.
[Zhu, Xianyou] Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang, Peoples R China.
通讯机构:
[Zhu, XY ] H
[Wang, L ] C
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
Changsha Univ, Technol Innovat Ctr Changsha, Changsha, Peoples R China.
Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang, Peoples R China.
语种:
英文
关键词:
full connectivity;graph attention networks;graph convolutional neural networks;long and short-term memory networks;microbe-disease associations
期刊:
Frontiers in Genetics
ISSN:
1664-8021
年:
2025
卷:
16
页码:
1511521
基金类别:
National Natural Science Foundation of China [62272064]; Scientific Research Program of Education Department of Hunan Province [23A0514]; Natural Science Foundation of Hunan Province [2023JJ60185]; Natural Science Foundation of Hunan Province Program [2022JJ50138]; Application-oriented Special Disciplines, Double First-Class University Project of Hunan Province [(2018) 469]; Hunan Provincial Education Department Scientific Research Project [20B080]
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
摘要:
Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases. Therefore, understanding the impact of microbes on disease is essential. The DuGEL model leverages the strengths of graph convolutional neural network (GCN) and graph attention network (GAT), ensuring that both local and global relationships within the microbe-disease association network are captured. The integration of the Long Short-Term Memory Network (LSTM) further enhances the model's ability to ...

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