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

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Fast graph convolutional models incorporating matrix factorization for predicting microbe-disease associations.

Qingwen Wu¹, Sujuan Tang²

¹Department of Data Center, Affiliated Hospital of Jining Medical University, Jining, China.

Scientific Reports

|December 10, 2025

Summary

This summary is machine-generated.

A new computational method, FGCNMF, improves microbe-disease association prediction. This approach uses Fast Graph Convolutional and Matrix Factorization for more accurate, cost-effective disease diagnosis and prevention insights.

Keywords:

Graph embedding Machine learning Matrix factorization Microbe-disease association Spatial convolution

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Area of Science:

Microbiology
Computational Biology
Bioinformatics

Background:

Understanding microbe-disease relationships is crucial for disease diagnosis, treatment, and prevention.
Current in-silico methods for predicting microbe-disease associations have limitations in predictive performance.
Expensive laboratory experiments and trial-and-error approaches hinder efficient discovery.

Purpose of the Study:

To develop a novel computational method for accurate microbe-disease association prediction.
To overcome the limitations of existing in-silico prediction tools.
To provide a more efficient and cost-effective approach for identifying microbe-disease links.

Main Methods:

Proposing FGCNMF (Fast Graph Convolutional and Matrix Factorization) for microbe-disease association prediction.
Formulating the problem as a binary classification task using node embedding representations on a microbe-disease network.
Integrating microbe and disease background information into a global network framework.
Employing randomized Singular Value Decomposition for initial node embedding.
Utilizing Fast Spatial Convolution to enhance embedding representations.
Applying an Extra-Trees classifier for final label prediction based on enhanced node pair representations.

Main Results:

FGCNMF demonstrates improved performance compared to existing state-of-the-art computational methods.
The method achieves higher accuracy on benchmark datasets for microbe-disease association prediction.
The integration of network information and enhanced embeddings contributes to superior predictive power.

Conclusions:

FGCNMF offers a significant advancement in computational methods for microbe-disease association prediction.
The proposed approach provides a more accurate and efficient tool for researchers.
This method has the potential to aid in disease diagnosis, treatment, and prevention strategies.