Representation learning from similarity networks improves the prediction of disease-associated microRNAs
- 1School of Information and Communications Technology, Hanoi University of Science and Technology
Abstract
Background MicroRNAs (miRNAs) play critical roles in disease mechanisms and represent promising biomarkers and therapeutic targets, yet experimentally identifying disease–miRNA associations remains costly and incomplete. Existing computational methods often rely on known disease–miRNA associations during similarity construction or representation learning, potentially leading to information reuse between training and evaluation and limiting generalization to unseen diseases or miRNAs. To address these limitations, we propose SimNetRLMDA, an association-independent representation learning framework that predicts disease–miRNA associations by learning embeddings exclusively from association-independent similarity networks. Multiple miRNA functional similarity networks derived from miRNA–target interactions are integrated with a MeSH-based disease similarity network, while disease and miRNA representations are learned independently using graph attention networks. Known disease–miRNA associations are incorporated only at the final supervised prediction stage through a Multi-Layer Perceptron. Results Extensive five-fold cross-validation experiments demonstrate that SimNetRLMDA consistently outperforms state-of-the-art network-based and deep learning methods (RWRHMDA, MHMDA, MHXGMDA, and MAMFGAT), achieving AUROC and AUPRC values of up to 0.983. Ablation and sensitivity analyses confirm the robustness of the framework and the contribution of each model component. Independent validation using HMDD v3/v4, dbDEMC, and published literature further verifies 516 novel disease–miRNA associations, including predictions involving diseases and miRNAs without previously known associations. Conclusions SimNetRLMDA provides a robust and generalizable framework for disease–miRNA association prediction by learning representations independently of known disease–miRNA associations. Its strong predictive performance and ability to identify associations for previously unseen diseases and miRNAs highlight its potential to support biomarker discovery, disease mechanism studies, and precision medicine.
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BibTeX
@article{le2026representation,
title = {{Representation learning from similarity networks improves the prediction of disease-associated microRNAs}},
author = {Duc-Hau Le},
journal = {BMC Bioinformatics},
year = {2026},
month = jul,
doi = {10.1186/s12859-026-06554-5},
url = {https://doi.org/10.1186/s12859-026-06554-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - Representation learning from similarity networks improves the prediction of disease-associated microRNAs
AU - Duc-Hau Le
PY - 2026
DA - 2026/07/01
JO - BMC Bioinformatics
DO - 10.1186/s12859-026-06554-5
UR - https://doi.org/10.1186/s12859-026-06554-5
AB - Background MicroRNAs (miRNAs) play critical roles in disease mechanisms and represent promising biomarkers and therapeutic targets, yet experimentally identifying disease–miRNA associations remains costly and incomplete. Existing computational methods often rely on known disease–miRNA associations during similarity construction or representation learning, potentially leading to information reuse between training and evaluation and limiting generalization to unseen diseases or miRNAs. To address these limitations, we propose SimNetRLMDA, an association-independent representation learning framework that predicts disease–miRNA associations by learning embeddings exclusively from association-independent similarity networks. Multiple miRNA functional similarity networks derived from miRNA–target interactions are integrated with a MeSH-based disease similarity network, while disease and miRNA representations are learned independently using graph attention networks. Known disease–miRNA associations are incorporated only at the final supervised prediction stage through a Multi-Layer Perceptron. Results Extensive five-fold cross-validation experiments demonstrate that SimNetRLMDA consistently outperforms state-of-the-art network-based and deep learning methods (RWRHMDA, MHMDA, MHXGMDA, and MAMFGAT), achieving AUROC and AUPRC values of up to 0.983. Ablation and sensitivity analyses confirm the robustness of the framework and the contribution of each model component. Independent validation using HMDD v3/v4, dbDEMC, and published literature further verifies 516 novel disease–miRNA associations, including predictions involving diseases and miRNAs without previously known associations. Conclusions SimNetRLMDA provides a robust and generalizable framework for disease–miRNA association prediction by learning representations independently of known disease–miRNA associations. Its strong predictive performance and ability to identify associations for previously unseen diseases and miRNAs highlight its potential to support biomarker discovery, disease mechanism studies, and precision medicine.
SN - 1471-2105
ER -