GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs

• Published in: Briefings in bioinformatics Vol. 23; no. 5
• Main Authors: Dai, Qiguo, Liu, Ziqiang, Wang, Zhaowei, Duan, Xiaodong, Guo, Maozu
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 20.09.2022; Oxford Publishing Limited (England)
• Subjects: Biomarkers; Computer applications; Disease; Diseases; Embedding; Graph representations; Graphical representations; Learning; Pathogenesis; Prediction models; Predictions; Similarity; Therapeutic targets; graph convolutional network; graph attention network; circRNA–disease association; representation learning
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbac379

Abstract Motivation: CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. Results: We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA–disease associations. Firstly, the circRNA–circRNA similarity network and disease–disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA–disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA–disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.