iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations. Author summary CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. In this paper, we proposed a novel deep learning-based method called iCircDA-NEAE to discover new potential circRNA-disease associations. Experimental results demonstrated that iCircDA-NEAE outperforms other state-of-the-art prediction methods, and can accurately predict potential circRNA-disease associations. Furthermore, according to the relevant literature, we observed that novel circRNA-disease associations predicted by iCircDA-NEAE are potential associations. The performance of iCircDA-NEAE mainly depends on three factors: (i) iCircDA-NEAE incorporates multi-source biometric information to measure complex associations between circRNAs and diseases. (ii) iCircDA-NEAE uses disease semantic similarity, Gaussian interaction kernel (GIP), circRNA expression profile similarity, and Jaccard similarity to make the most of biometric information in the data. (iii) iCircDA-NEAE incorporates the advantages of ANNE and DCAE, which not only effectively integrates multi-source information, but also effectively captures hidden high-level information of data.