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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | BMC Biology |
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| Online Access: | https://doi.org/10.1186/s12915-025-02227-6 |
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| _version_ | 1849729000450031616 |
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| author | Li Peng Wang Wang Zongyi Yang Xiangzheng Fu Wei Liang Dongsheng Cao |
| author_facet | Li Peng Wang Wang Zongyi Yang Xiangzheng Fu Wei Liang Dongsheng Cao |
| author_sort | Li Peng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df060f4e0f4045b69864fcf044d958f1 |
| institution | DOAJ |
| issn | 1741-7007 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-df060f4e0f4045b69864fcf044d958f12025-08-20T03:09:21ZengBMCBMC Biology1741-70072025-05-0123111510.1186/s12915-025-02227-6Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction predictionLi Peng0Wang Wang1Zongyi Yang2Xiangzheng Fu3Wei Liang4Dongsheng Cao5School of Computer Science and Engineering, Hunan University of Science and TechnologySchool of Computer Science and Engineering, Hunan University of Science and TechnologySchool of Computer Science and Engineering, Hunan University of Science and TechnologyCollege of Computer Science and Electronic Engineering, Hunan UniversitySchool of Computer Science and Engineering, Hunan University of Science and TechnologyXiangya School of Pharmaceutical Sciences, Central South UniversityAbstract 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.https://doi.org/10.1186/s12915-025-02227-6circRNAmiRNAExplainabilityMulti-scale featureRepresentation learning |
| spellingShingle | Li Peng Wang Wang Zongyi Yang Xiangzheng Fu Wei Liang Dongsheng Cao Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction BMC Biology circRNA miRNA Explainability Multi-scale feature Representation learning |
| title | Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction |
| title_full | Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction |
| title_fullStr | Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction |
| title_full_unstemmed | Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction |
| title_short | Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction |
| title_sort | leveraging explainable multi scale features for fine grained circrna mirna interaction prediction |
| topic | circRNA miRNA Explainability Multi-scale feature Representation learning |
| url | https://doi.org/10.1186/s12915-025-02227-6 |
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