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MicroRNA-disease association prediction by matrix tri-factorization.

Huiran Li1, Yin Guo1, Menglan Cai1

  • 1School of Mathematics and Statistics, Xi'an Jiaotong University, Xianning West 28, Xi'an, China.

BMC Genomics
|November 19, 2020
PubMed
Summary

This study introduces MTFMDA, a computational model for predicting microRNA-disease associations (MDAs). MTFMDA utilizes matrix tri-factorization, achieving superior accuracy in identifying potential disease biomarkers.

Keywords:
Matrix tri-factorizationmicoRNA-disease association prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) play crucial roles in human diseases.
  • Identifying miRNA-disease associations (MDAs) aids disease diagnosis and treatment.
  • Experimental identification of MDAs is costly, necessitating computational prediction methods.

Purpose of the Study:

  • To develop a novel computational model, MTFMDA, for predicting miRNA-disease associations.
  • To leverage matrix tri-factorization for enhanced MDA prediction accuracy.

Main Methods:

  • Proposed a matrix tri-factorization model (MTFMDA).
  • Incorporated known miRNA-disease associations, miRNA similarities, and disease similarities.
  • Utilized Laplacian regularizers to preserve similarity information in feature matrices.
  • Developed a novel algorithm to solve the optimization problem.

Main Results:

  • MTFMDA achieved significantly higher AUCs compared to state-of-the-art methods in 5-fold cross-validation.
  • The model demonstrated strong performance on known miRNA-disease associations from HMDD V2.0.
  • Validated predictions on colon and breast neoplasms.

Conclusions:

  • MTFMDA is a powerful computational approach for predicting miRNA-disease associations.
  • Newly identified associations were corroborated by external databases (dbDEMC, HMDD V3.0).
  • The model shows promise for advancing disease diagnosis and treatment strategies.