Accurately Predicting circRNA-disease Associations Using Variational Graph Auto-encoders and LightGBM

Abstract

Many studies have shown that circRNAs play essential roles in various biological processes. With the development of technology, the associations between circRNA and diseases have been discovered, and these associations will help diagnose and treat diseases. However, it is time-consuming and costly to detect the associations between circRNAs and diseases with the experimental methods. Therefore, it is necessary to develop a feasible and effective computational method for predicting circRNA-disease associations. In this paper, we propose a new computational framework called VLCDA to identify the potential circRNA-disease associations. Initially, we construct features by fusing circRNA expression profile features and circRNA protein-coding ability features, disease semantic features, circRNA and disease GIP Kernel features, and use VGAE to mine its deep latent features. Finally, we use the fusion features to train the LightGBM classifier and the trained LightGBM to identify the circRNA-disease associations. The main contribution of VLCDA is that we firstly add circRNA protein-coding ability feature to the circRNA-disease association prediction model. In addition, VLCDA uses variational graph auto-encoders to extract the latent features of circRNA-disease associations to improve the prediction model’s accuracy further. VLCDA obtained the area under the ROC curve (AUC) scores of 0.9783 in 5-fold cross-validation. In addition, in the case studies, 16 of the top 20 circRNA-disease associations predicted by VLCDA have been confirmed by relevant literature.