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SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA-miRNA interactions.

Mei-Neng Wang¹, Li-Lan Lei¹, Wei He¹

¹School of Mathematics and Computer Science, Yichun University, Yichun, China.

Frontiers in Genetics

|December 2, 2022

Summary

This summary is machine-generated.

This study introduces SPCMLMI, a computational method for predicting long non-coding RNA (lncRNA) and microRNA (miRNA) interactions. The approach accurately identifies these crucial gene regulators, aiding in complex disease diagnosis and treatment.

Keywords:

bilayer network lncRNA–miRNA interactions matrix completion structural consistency structural perturbation

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Published on: December 1, 2023

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNA (lncRNA) and microRNA (miRNA) interactions are vital for gene regulation and complex disease development.
Experimental identification of lncRNA-miRNA interactions is costly and time-consuming.
Accurate computational methods are needed to infer these interactions efficiently.

Purpose of the Study:

To develop a novel computational approach, SPCMLMI, for predicting lncRNA-miRNA interactions.
To improve the accuracy and efficiency of identifying potential lncRNA-miRNA interactions.

Main Methods:

Calculated lncRNA and miRNA similarities (expression profiles, sequences).
Constructed a bilayer network integrating known interactions and similarity networks.
Applied a structural perturbation-based matrix completion method for prediction.

Main Results:

SPCMLMI achieved high prediction performance with AUCs of 0.8984 and 0.9891 on two datasets.
Outperformed existing methods in prediction accuracy.
Case studies on lncRNA XIST and miRNA hsa-mir-195-5-p validated the method's effectiveness.

Conclusions:

SPCMLMI is an effective computational tool for predicting lncRNA-miRNA interactions.
The method offers a valuable resource for understanding gene regulation in complex diseases.
The structural consistency of the bilayer network contributes to prediction accuracy.