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

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Predicting disease-gene associations through self-supervised mutual infomax graph convolution network.

Jiancong Xie¹, Jiahua Rao¹, Junjie Xie¹

¹School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China.

Computers in Biology and Medicine

|February 4, 2024

Summary

A new computational method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), enhances the prediction of disease-gene associations. This approach improves upon existing methods by reducing noise and strengthening node interactions for better accuracy.

Keywords:

Disease-gene associations prediction Graph convolution network Mutual information

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Area of Science:

Computational biology
Bioinformatics
Genetics

Background:

Identifying disease-gene associations is crucial for understanding disease mechanisms and developing treatments.
Existing computational methods for predicting these associations have limitations due to noisy prior knowledge and limited external data.

Purpose of the Study:

To develop a novel computational method for accurately predicting disease-gene associations.
To improve the performance of existing prediction models by addressing limitations in data noise and interaction learning.

Main Methods:

Developed a Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN).
Utilized external disease-disease and gene-gene collaborative graphs.
Incorporated a graphical mutual infomax layer to eliminate noise.
Implemented an informative message passing layer to strengthen node interactions.

Main Results:

The MiGCN model demonstrated improved performance in predicting disease-gene associations.
Achieved over 8% improvement in Area Under the Curve (AUC) compared to state-of-the-art methods.
Validated through extensive experiments.

Conclusions:

MiGCN offers a more effective approach for identifying novel disease-gene associations.
The method's noise reduction and enhanced interaction learning contribute to its superior performance.
The developed model and associated resources are publicly available for further research.