Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease...

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• MicroRNAs (miRNAs) are critical in disease development, making miRNA-disease association (MDA) identification vital for understanding disease etiology.
• Existing computational models for MDA prediction face challenges with multi-source similarity interactions and "false-negative" associations.

Purpose of the Study:

• To propose ISFNMDA, a novel computational model for inferring potential miRNA-disease associations (MDAs).
• To address limitations in current MDA prediction models, specifically regarding multi-source similarity integration and "false-negative" data.

Main Methods:

• Leveraging multi-view collaborative learning for feature extraction from multi-source miRNA and disease similarities.
• Optimizing association topology using graph structure momentum contrastive learning to reduce spurious correlations.
• Employing Pearson correlation coefficient for pairwise constraints and constructing interval-shared information constraints.

Main Results:

• ISFNMDA effectively integrates multi-source similarities into a unified feature space.
• The model optimizes graph topology by reducing spurious correlations through momentum contrastive learning.
• Experimental results show ISFNMDA outperforms existing methods in predicting potential MDAs, with case studies validating its efficacy.

Conclusions:

• ISFNMDA provides an effective computational framework for accurate miRNA-disease association prediction.
• The model's ability to handle complex data interactions and optimize graph structures offers significant advancements in disease etiology research.
• This approach enhances the understanding of disease mechanisms and facilitates the identification of novel therapeutic targets.