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Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.

Shu-Hao Wang^1,2, Chun-Chun Wang^1,2, Li Huang^3,4

¹School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.

Briefings in Bioinformatics

|December 5, 2021

Summary

This summary is machine-generated.

A new computational model, Dual-network Collaborative Matrix Factorization (DCMF), effectively predicts small molecule-microRNA associations. This method enhances accuracy by integrating diverse similarity information and handling missing data, accelerating therapeutic target identification.

Keywords:

association prediction collaborative matrix factorization dual network microRNA small molecule

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Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

Published on: June 12, 2018

Area of Science:

Biomedical informatics
Computational biology
Pharmacogenomics

Background:

MicroRNAs (miRNAs) are key regulators in biological processes and diseases.
Small molecules (SMs) targeting miRNAs offer therapeutic potential.
Experimental validation of SM-miRNA associations is costly and time-consuming.

Purpose of the Study:

To develop a novel computational method for predicting potential small molecule-microRNA associations.
To address the limitations of experimental validation through an efficient predictive model.

Main Methods:

Proposed Dual-network Collaborative Matrix Factorization (DCMF) model.
Utilized Weighted K Nearest Known Neighbors (WKNKN) for data preprocessing.
Employed matrix factorization to derive latent features of SMs and miRNAs.
Integrated dual network information for enhanced similarity analysis.

Main Results:

DCMF achieved high prediction accuracy across four cross-validation methods on two datasets.
Area Under the Curves (AUC) ranged from 0.8377 to 0.9868.
Case studies confirmed numerous predicted SM-miRNA associations with published literature.

Conclusions:

DCMF is an effective computational tool for predicting small molecule-microRNA associations.
The method offers a cost-efficient and rapid approach compared to experimental validation.
DCMF facilitates the identification of novel therapeutic strategies targeting miRNAs.