Identification of potential associations between circRNAs and diseases based on meta relation aware

Abstract

Aims: Circular RNAs (circRNAs) have been shown to be closely associated with the occurrence and progression of various diseases. However, most existing circRNA-disease association prediction methods are limited to homogeneous networks and are unable to effectively capture deep semantic associations through high-order meta-paths. This study aims to develop an efficient computational method for accurately predicting potential circRNA-disease associations.

Methods: We propose a meta-relation-aware heterogeneous graph learning framework for circRNA-disease association prediction. Specifically, known circRNA-disease associations are first used to compute Gaussian interaction profile kernel similarity and extract node attribute features, based on which a heterogeneous graph network is constructed. A graph neural network is then employed to perform multi-layer message passing on the heterogeneous graph, aggregating neighborhood information to achieve deep fusion of multi-source features and generate node embeddings that encode both local and global structural information. Finally, the learned embeddings are fed into a gradient boosting decision tree classifier, and an ensemble strategy is adopted to improve prediction accuracy. Five-fold cross-validation is used for performance evaluation.

Results: Experimental results on three benchmark datasets, CircR2Disease V2.0, circAtlas 3.0, and circRNADisease V2.0, show that the proposed model achieves area under the receiver operating characteristic curve (AUC) values of 92.17%, 91.83%, and 91.73%, respectively. The model outperforms traditional methods in terms of accuracy, precision, and recall. Furthermore, ablation studies validate the effectiveness of the meta-relation-aware strategy.

Conclusions: Overall, this work provides an efficient and reliable computational framework for molecular association prediction and biomarker discovery in the biomedical domain.

Keywords

circRNA-disease association prediction / meta-relation awareness / graph neural network / heterogeneous graph transformer network

Acknowledgements

The authors declare that Gemini 3.1 was used solely for language polishing during the manuscript preparation process. All research content, including study design, data analysis, interpretations, figures, and tables, is original and was not generated using AI tools. The authors take full responsibility for the integrity, originality, and accuracy of the work.

Authors contribution

Tan X: Conceptualization, methodology, software, formal analysis, investigation, data curation, writing-original draft. Wei M: Conceptualization, methodology, validation, formal analysis, writing-original draft, writing-review & editing. Xia Z: Data curation, validation, investigation. Wang X: Software, data curation, visualization. Li Y: Formal analysis, validation, investigation. Wang L: Conceptualization, supervision, funding acquisition, project administration, writing-review & editing. You Z: Supervision, funding acquisition, project administration, writing-review & editing.

Conflicts of interest

Lei Wang is an Executive Editor of Computational Biomedicine. The other authors declare no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Funding

This work was supported by the Guangxi Science and Technology Program (Grant No. 2024-102-3); the Natural Science Foundation of Guangxi (Grant Nos. 2024GXNSFAA010283 and 2023GXNSFDA026031); Natural Science Foundation of Shandong (Grant No. ZR2024MF042); National Natural Science Foundation of China (Grant Nos. 62573419 and 62172355); National Science Foundation for Distinguished Young Scholars of China (Grant No. 62325308).

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