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Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

Junlin Xu¹, Lijun Cai¹, Bo Liao²

¹College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.

Frontiers in Genetics

|January 11, 2020

Summary

This summary is machine-generated.

This study introduces Probability Matrix Factorization (PMFMDA), a computational model for identifying disease-related microRNAs (miRNAs). PMFMDA demonstrates high accuracy in predicting miRNA-disease associations, aiding in understanding disease mechanisms.

Keywords:

association prediction diseases miRNAs probabilistic matrix factorization receiver operating characteristic curve (ROC)

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Published on: January 16, 2019

Area of Science:

Biomedical Informatics
Genomics
Computational Biology

Background:

MicroRNAs (miRNAs) are crucial regulators in biological processes and human diseases.
Identifying disease-associated miRNAs is key to understanding molecular pathogenesis.
Experimental methods for miRNA discovery are costly and time-consuming.

Purpose of the Study:

To develop a computational model for predicting potential disease-related miRNAs.
To enhance the accuracy and efficiency of miRNA-disease association discovery.

Main Methods:

Integrated miRNA and disease similarity information.
Constructed a probability matrix factorization algorithm (PMFMDA).
Utilized known association and integrated similarity matrices.

Main Results:

PMFMDA achieved high performance in cross-validation (AUCs of 0.9237 and 0.9187).
Outperformed several state-of-the-art computational methods (CMFMDA, IMCMDA, NCPMDA, RLSMDA, RWRMDA).
Case studies confirmed PMFMDA's predictive power for novel associations.

Conclusions:

PMFMDA is a reliable and effective computational tool for identifying disease-associated miRNAs.
The model aids in elucidating disease mechanisms at the molecular level.
Facilitates efficient discovery of potential miRNA biomarkers for diseases.