GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning. (August 2022)

• Record Type: Journal Article
• Authors: Li, Guanghui; Lin, Yawei; Luo, Jiawei; Xiao, Qiu; Liang, Cheng
• Is Part Of: Computational biology and chemistry. Volume 99(2022)
• Publication Date: 2022-08
• Subjects: CircRNA-disease associations -- Graph attention network -- Random walk with restart -- Graph autoencoder; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107722
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Numerous studies have shown that circular RNAs (circRNAs) can serve as ideal disease markers as they are involved in most cellular activities of organisms and are key regulators in various pathological processes. Therefore, the association analysis of circRNAs and diseases can explore the role of circRNAs in diseases and provide help for practical medical research. However, the traditional biotechnology are not convenient for identifying unconfirmed interactions between circRNAs and diseases, which need too many resources and long experimental period. In this work, a new deep learning model is advanced, which is based on graph autoencoder (GAE) constructed with graph attention network (GAT) and random walk with restart (RWR) for predicting circRNA-disease associations (GGAECDA). In detail, GAT is designed to learn the hidden representations of circRNAs and diseases through using low-order neighbor information from circRNA similarity network and disease similarity network respectively, while RWR is employed to learn the latent features of circRNAs and diseases via using high-order neighbor information from the same two networks respectively. After that, these two parts of features of circRNAs and diseases are combined to form new feature representations of circRNAs and diseases respectively. Finally, two GAEs are constructed for co-training to fully integrate information from circRNA space and disease space and calculate potential association prediction scores. Unlike previous models, GGAECDA deeply mines low-dimensional representations from node similarity network through using GAT and RWR. The average AUC value obtained from GGAECDA with a five-fold cross-validation result is 0.9359. Furthermore, case studies demonstrate the ability of GGAECDA to detect potential candidate circRNAs for human diseases. The above results show that the GGAECDA model can be used as a reliable tool to guide subsequent studies on the regulatory functions of circRNAs. Graphical Abstract: ga1 Highlights: The hidden features are learned from low-order and high-order neighbor information. We train two graph autoencoders to integrate information from circRNA and disease. A deep learning model using graph autoencoder is proposed to infer disease circRNAs. Experimental results show the proposed model is effective to infer disease circRNAs.