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A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction
Si-Zhe Liang1, Lei Wang2,3, Zhu-Hong You4
1School of Electronic Information, Xijing Univerity, Xi'an 710123, China.
ACS Omega
|September 29, 2025
Summary
This study introduces MTGCDA, a novel computational model for predicting circular RNA-disease associations. MTGCDA leverages a multisource heterogeneous graph transformer to achieve high accuracy, aiding in early disease diagnosis and targeted treatments.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Circular RNAs (circRNAs) exhibit stability and tissue-specific expression, making them promising disease biomarkers.
- Predicting circRNA-disease associations is vital for early diagnosis and treatment but challenged by complex data and traditional methods' limitations.
Purpose of the Study:
- To develop a high-accuracy computational model, MTGCDA, for predicting circRNA-disease associations.
- To address limitations in information integration, semantic expression, and information loss in existing prediction methods.
Main Methods:
- MTGCDA integrates multisource biological information into a heterogeneous graph with multiple node and edge types.
- Representation learning is performed using a heterogeneous graph neural network to capture latent semantic features.
- A multilayer heterogeneous graph convolutional network fuses node embeddings, followed by a CatBoost classifier for association scoring.
Main Results:
- MTGCDA achieved an Area Under the Curve (AUC) of 0.9756 on the CircR2Disease dataset, outperforming existing methods.
- 17 out of 20 predicted circRNA-disease associations were validated by literature reports, confirming the model's accuracy and practicality.
Conclusions:
- The MTGCDA model demonstrates superior performance in predicting circRNA-disease associations.
- MTGCDA offers a practical and accurate computational approach for identifying potential circRNA-disease links, supporting biomarker discovery and therapeutic strategies.
