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MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.

Wei Liu¹, Ting Tang¹, Xu Lu^2,3

¹School of Computer Science, Xiangtan University, Xiangtan, 411105, China.

Briefings in Bioinformatics

|June 16, 2023

Summary

This study introduces MPCLCDA, a novel computational model for predicting circular RNA-disease associations (CDAs). The model effectively identifies potential CDAs, aiding disease understanding and treatment by overcoming data limitations.

Keywords:

association prediction circRNA–disease association contrastive learning meta-path

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNAs (circRNAs) are implicated in human diseases, making their association identification crucial for medical advancements.
Traditional methods for identifying circRNA-disease associations (CDAs) are inefficient and labor-intensive.
Existing computational models face challenges due to limited data, high dimensionality, and data imbalance.

Purpose of the Study:

To develop an advanced computational model for accurately predicting circRNA-disease associations (CDAs).
To address data limitations and improve the efficiency of CDA prediction.
To enhance disease prevention, diagnosis, and treatment strategies through reliable circRNA-disease linkage.

Main Methods:

Constructed a heterogeneous network integrating circRNA similarity, disease similarity, and known associations.
Employed automatically selected meta-paths and graph convolutional networks to derive low-dimensional node features.
Utilized contrastive learning to optimize node features for clearer distinction between positive and negative samples.
Predicted circRNA-disease scores using a multilayer perceptron.

Main Results:

The MPCLCDA model achieved high predictive performance across four datasets.
Average performance metrics included Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9752, Area Under the Precision-Recall Curve (AUPRC) of 0.9831, and F1 score of 0.9745.
Case studies validated the model's predictive capability and practical application in understanding human diseases.

Conclusions:

The proposed MPCLCDA model offers a powerful and efficient approach for predicting circRNA-disease associations.
This method effectively overcomes data limitations inherent in previous computational models.
The findings highlight the potential of MPCLCDA in advancing circRNA-related disease research and clinical applications.