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Related Experiment Video

Updated: May 25, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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

Junlong Wu¹, Liqi Xiao¹, Liu Fan¹

¹College of Computer Science and Technology, Hengyang Normal University, Hengyang, China.

Frontiers in Genetics

|February 26, 2025

Summary

The DuGEL model accurately predicts microbe-disease links using advanced AI. This helps understand microbial roles in health and disease, aiding new therapeutic target discovery.

Keywords:

full connectivity graph attention networks graph convolutional neural networks long and short-term memory networks microbe-disease associations

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Area of Science:

Microbiology
Computational Biology
Bioinformatics

Background:

Microorganisms are vital for human health.
Microbial imbalances (dysbiosis) are linked to numerous diseases.
Understanding microbe-disease associations is critical for biomedical research.

Purpose of the Study:

To develop an advanced computational model for predicting microbe-disease associations.
To improve the accuracy and robustness of microbe-disease association prediction.
To identify potential therapeutic targets by understanding microbial roles in disease.

Main Methods:

The DuGEL model integrates Graph Convolutional Neural Networks (GCN) and Graph Attention Networks (GAT) to capture network relationships.
Long Short-Term Memory Network (LSTM) is incorporated to analyze sequential feature dependencies.
Model performance is evaluated using comparative experiments on HMDAD and Disbiome databases.

Main Results:

DuGEL demonstrates high accuracy in predicting potential microbe-disease associations.
The model effectively captures both local and global relationships in microbe-disease networks.
Case studies confirm DuGEL's capability in identifying significant microbe-disease links.

Conclusions:

DuGEL offers a robust framework for predicting microbe-disease associations, outperforming existing methods.
The model's ability to integrate graph-based and sequence-based learning enhances prediction accuracy.
DuGEL serves as a valuable tool for advancing biomedical research and discovering novel therapeutic strategies.