Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data - Li, Guanghui; Wang, Diancheng; Zhang, Yuejin; Liang, Cheng; Xiao, Qiu; Luo, Jiawei (Frontiers in Genetics, 2022)
Cumulative researches have verified that multiple circRNAs are closely associated with 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 multi-source 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 centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt graph attention network to learn latent representation of diseases and circRNAs respectively. Third, 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.