Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction
Abstract Background Circular RNAs (circRNAs) and microRNAs (miRNAs) interactions have essential implications in various biological processes and diseases. Computational science approaches have emerged as powerful tools for studying and predicting these intricate molecular interactions, garnering con...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
|
| Series: | BMC Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12915-025-02227-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Background Circular RNAs (circRNAs) and microRNAs (miRNAs) interactions have essential implications in various biological processes and diseases. Computational science approaches have emerged as powerful tools for studying and predicting these intricate molecular interactions, garnering considerable attention. Current methods face two significant limitations: the lack of precise interpretable models and insufficient representation of homogeneous and heterogeneous molecules. Results We propose a novel method, MFERL, that addresses both limitations through multi-scale representation learning and an explainable fine-grained model for predicting circRNA-miRNA interactions (CMI). MFERL learns multi-scale representations by aggregating homogeneous node features and interacting with heterogeneous node features, as well as through novel dual-convolution attention mechanisms and contrastive learning to enhance features. Conclusions We utilize a manifold-based method to examine model performance in detail, revealing that MFERL exhibits robust generalization, robustness, and interpretability. Extensive experiments show that MFERL outperforms state-of-the-art models and offers a promising direction for understanding CMI intrinsic mechanisms. |
|---|---|
| ISSN: | 1741-7007 |