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MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning.

Xinru Ruan¹, Changzhi Jiang¹, Peixuan Lin¹

¹Department of Computer Science and Technology, Xiamen University, Xiamen 361005, China.

Briefings in Bioinformatics

|February 15, 2023

Summary

This study introduces a novel multi-view self-supervised contrastive learning (MSGCL) model for predicting microRNA-disease associations (MDA). The MSGCL model enhances prediction accuracy by optimizing graph topology, outperforming existing methods.

Keywords:

contrastive learning miRNA–disease associations multi-view self-supervised

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Area of Science:

Biomedical machine learning
Computational biology
Genomics

Background:

MicroRNA-disease associations (MDA) are crucial for understanding complex human diseases.
Predicting MDA is a key area in biomedical machine learning.
Existing models struggle with noisy graph structures and over-reliance on network information.

Purpose of the Study:

To introduce the first model utilizing self-supervised graph structure learning for MDA prediction.
To develop a multi-view self-supervised contrastive learning (MSGCL) framework for enhanced MDA prediction.
To improve the accuracy and robustness of MDA prediction models.

Main Methods:

Developed a multi-view self-supervised contrastive learning (MSGCL) model.
Generated a 'learner view' without association labels and an 'anchor view' from known associations.
Optimized graph structure using contrastive loss to maximize view consistency.
Treated the model as a pre-trained network optimizing upstream tasks for graph topology.

Main Results:

The proposed MSGCL method achieved superior performance compared to state-of-the-art approaches.
Achieved a 2.79% increase in Area Under the Receiver Operating Characteristic Curve (AUC).
Achieved a 3.20% increase in Area Under the Precision/Recall Curve (AUPR).

Conclusions:

The MSGCL model effectively enhances latent representations for MDA prediction.
Self-supervised graph structure learning is a promising approach for MDA prediction.
The proposed method offers a significant advancement over existing MDA prediction techniques.