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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.
Abstract:
In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity. Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA, and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.
Insights
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.
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.
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