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

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Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.

Van Tinh Nguyen^1,2, Thi Tu Kien Le², Tran Quoc Vinh Nguyen³

¹Faculty of Information Technology, Hanoi University of Industry, Hanoi, Vietnam.

BMC Medical Genomics

|November 18, 2021

Summary

This study introduces a novel computational method for identifying microRNA (miRNA)-disease associations, crucial for biological research. The approach effectively predicts new associations and aids in discovering links for previously unassociated diseases or miRNAs.

Keywords:

Collaborative filtering algorithm Infer miRNA-disease associations Recommender systems Resource allocation algorithm miRNA-disease-lncRNA tripartite graph

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

MicroRNAs (miRNAs) play vital roles in biological processes.
Experimental verification of miRNA-disease associations is costly and time-consuming.
Efficient computational methods are urgently needed to predict these associations.

Purpose of the Study:

To develop a novel computational method for inferring miRNA-disease associations.
To address challenges of imbalanced data and predict associations among multiple entities.
To provide a powerful tool for miRNA-disease association discovery.

Main Methods:

Utilized a miRNA-disease-lncRNA tripartite graph.
Employed collaborative filtering (CFNBC model) for imbalanced data.
Applied resource allocation algorithms and TPGLDA model for association prediction.

Main Results:

Achieved high performance with AUC of 0.9788 and AUPR of 0.9373.
Outperformed existing methods like DCSMDA and TPGLDA.
Identified numerous novel, literature-confirmed miRNA-disease associations and discovered associations for new diseases/miRNAs.

Conclusions:

The proposed method demonstrates reliable performance in inferring miRNA-disease associations.
It effectively discovers novel associations, including for entities with no prior known links.
The method serves as a valuable tool for advancing miRNA-disease association research.