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Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on
Guanghui Li1, Diancheng Wang1, Yuejin Zhang1
1School of Information Engineering, East China Jiaotong University, Nanchang, China.
Insights
This study introduces GATGCN, a novel computational method for identifying circular RNA (circRNA)-disease associations. GATGCN effectively integrates diverse data sources, significantly improving the prediction of these crucial biological relationships.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Circular RNAs (circRNAs) play a role in disease pathogenesis.
- Understanding circRNA-disease links is vital for developing new treatments.
- Existing computational models struggle with multisource data and sparse networks.
Purpose of the Study:
- To develop an advanced computational method for detecting circRNA-disease relationships.
- To effectively utilize multisource biomedical data for improved prediction accuracy.
Main Methods:
- Developed GATGCN, integrating Graph Attention Network (GAT) and Graph Convolutional Network (GCN).
- Fused multiple biomedical data sources using Centered Kernel Alignment (CKA) for data weighting.
- Employed GAT for latent representation learning and GCN for feature extraction via neighbor aggregation.
Main Results:
- GATGCN achieved high performance with an AUC of 0.951 (leave-one-out) and 0.932 (5-fold cross-validation).
- Case studies on lung cancer, diabetes retinopathy, and prostate cancer confirmed GATGCN's reliability.
- The method effectively handles sparse networks and integrates heterogeneous data.
Conclusions:
- GATGCN is a robust and effective computational tool for predicting circRNA-disease associations.
- The approach offers a significant advancement in leveraging complex biomedical data for disease mechanism exploration.
- This method holds promise for identifying novel therapeutic targets and understanding disease pathways.
Abstract:
Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA-disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA-disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA-disease pairs.
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