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Updated: Jan 19, 2026
Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
Published on: November 30, 2016
EPMDA: Edge Perturbation Based Method for miRNA-Disease Association Prediction
Abstract:
In the recent few years, plenty of research has shown that microRNA (miRNA) is likely to be involved in the formation of many human diseases. So effectively predicting potential associations between miRNAs and diseases helps to understand the development and treatment of diseases. In this study, an edge perturbation based method is proposed for predicting potential miRNA-disease association (EPMDA). Different from the previous studies, we design an feature vector to describe each edge of a graph by structural Hamiltonian information. Moreover, the extracted features are used to train a multi-layer perception model to predict the candidate disease-miRNA associations. The experimental results on the HMDD dataset show that EPMDA achieves the AUC value of 0.9818 through 5-fold cross-validation, which improves the AUC values by approximately 3.5 percent compared to the latest method DeepMDA. For the leave-one-disease-out cross-validation, EPMDA achieves the AUC value of 0.9371, which improves the AUC values by approximately 7.4 percent compared to DeepMDA. In the case study, we verify the prediction performance of EPMDA on three human diseases. As a result, there are 42, 46, and 41 of the top 50 predicted miRNAs for these three diseases which are confirmed by the published experimental discoveries, respectively.
Insights
This study introduces EPMDA, a novel method for predicting microRNA-disease associations. EPMDA significantly improves prediction accuracy, aiding disease understanding and treatment strategies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- MicroRNAs (miRNAs) are implicated in numerous human diseases.
- Accurate prediction of miRNA-disease associations is crucial for understanding disease mechanisms and developing treatments.
Purpose of the Study:
- To propose a novel edge perturbation-based method (EPMDA) for predicting potential miRNA-disease associations.
- To enhance the accuracy of identifying relationships between miRNAs and diseases.
Main Methods:
- Developed a feature vector using structural Hamiltonian information to describe graph edges.
- Employed a multi-layer perception model trained on extracted features for association prediction.
- Utilized an edge perturbation approach for enhanced prediction.
Main Results:
- EPMDA achieved an AUC of 0.9818 in 5-fold cross-validation on the HMDD dataset, outperforming DeepMDA by 3.5%.
- Achieved an AUC of 0.9371 in leave-one-disease-out cross-validation, surpassing DeepMDA by 7.4%.
- Case studies confirmed 42, 46, and 41 top-50 predicted miRNAs for three diseases, validating EPMDA's performance.
Conclusions:
- EPMDA demonstrates superior performance in predicting miRNA-disease associations compared to existing methods.
- The proposed method offers a valuable tool for advancing research in miRNA-related diseases.
- Findings support the utility of structural graph information and machine learning in bioinformatics.
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