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GRMDA: Graph Regression for MiRNA-Disease Association Prediction.
Xing Chen1, Jing-Ru Yang2, Na-Na Guan3
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Frontiers in Physiology
|March 9, 2018
Summary
A new computational method, Graph Regression for MiRNA-Disease Association prediction (GRMDA), effectively predicts links between microRNAs (miRNAs) and diseases. GRMDA shows high accuracy, aiding experimental research by identifying promising miRNA-disease associations.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are increasingly linked to various diseases, making them a significant research focus.
- Experimental validation of miRNA-disease associations is time-consuming and costly.
- Computational methods are needed to prioritize potential associations for experimental study.
Purpose of the Study:
- To develop an effective computational method for predicting miRNA-disease associations.
- To improve the accuracy and efficiency of identifying novel miRNA-disease links.
Main Methods:
- Proposed Graph Regression for MiRNA-Disease Association prediction (GRMDA).
- Integrated known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity.
- Employed graph regression in latent spaces (association, miRNA similarity, disease similarity) using Singular Value Decomposition and Partial Least-Squares.
Main Results:
- Achieved high performance in cross-validation (AUCs of 0.8272 and 0.8080 ± 0.0024).
- Demonstrated superior performance compared to previous models.
- Case studies showed high validation rates: 88% for Lymphoma, 100% for Breast Neoplasms, and 84% for Esophageal Neoplasms.
Conclusions:
- GRMDA is a robust and practical computational tool for predicting miRNA-disease associations.
- The method effectively prioritizes potential associations, reducing experimental costs.
- GRMDA shows promise for identifying novel disease-related miRNAs, even for diseases with limited prior data.
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