Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning
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 s...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1511521/full |
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| author | Junlong Wu Liqi Xiao Liu Fan Lei Wang Xianyou Zhu Xianyou Zhu |
| author_facet | Junlong Wu Liqi Xiao Liu Fan Lei Wang Xianyou Zhu Xianyou Zhu |
| author_sort | Junlong Wu |
| collection | DOAJ |
| description | 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 understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations. |
| format | Article |
| id | doaj-art-b9a482eb3eac4ddbba699860226d1302 |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-b9a482eb3eac4ddbba699860226d13022025-02-11T06:59:43ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-02-011610.3389/fgene.2025.15115211511521Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learningJunlong Wu0Liqi Xiao1Liu Fan2Lei Wang3Xianyou Zhu4Xianyou Zhu5College of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaTechnology Innovation Center of Changsha, Changsha University, Changsha, ChinaCollege of Computer Science and Technology, Hengyang Normal University, Hengyang, ChinaHunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, ChinaRecent 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 understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations.https://www.frontiersin.org/articles/10.3389/fgene.2025.1511521/fulllong and short-term memory networksgraph attention networksmicrobe-disease associationsgraph convolutional neural networksfull connectivity |
| spellingShingle | Junlong Wu Liqi Xiao Liu Fan Lei Wang Xianyou Zhu Xianyou Zhu Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning Frontiers in Genetics long and short-term memory networks graph attention networks microbe-disease associations graph convolutional neural networks full connectivity |
| title | Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning |
| title_full | Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning |
| title_fullStr | Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning |
| title_full_unstemmed | Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning |
| title_short | Dual graph-embedded fusion network for predicting potential microbe-disease associations with sequence learning |
| title_sort | dual graph embedded fusion network for predicting potential microbe disease associations with sequence learning |
| topic | long and short-term memory networks graph attention networks microbe-disease associations graph convolutional neural networks full connectivity |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1511521/full |
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