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ORIGINAL RESEARCH article

Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1511521

Dual Graph-Embedded Fusion Network for Predicting Potential Microbe-Disease Associations with Sequence Learning

Provisionally accepted
Junlong Wu Junlong Wu 1Liqi Xiao Liqi Xiao 1Liu Fan Liu Fan 1*Lei Wang Lei Wang 2*Xianyou Zhu Xianyou Zhu 1*
  • 1 Hengyang Normal University, Hengyang, China
  • 2 Changsha University, Changsha, Hunan, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: ： long and short-term memory networks, Graph attention networks, microbe-disease associations, Graph convolutional neural networks, full connectivity

    Received: 15 Oct 2024; Accepted: 15 Jan 2025.

    Copyright: © 2025 Wu, Xiao, Fan, Wang and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Liu Fan, Hengyang Normal University, Hengyang, China
    Lei Wang, Changsha University, Changsha, 130012, Hunan, China
    Xianyou Zhu, Hengyang Normal University, Hengyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.