Predicting circRNA-disease associations based on autoencoder and graph embedding

Abstract Circular RNAs (circRNAs) are a special kind of non-coding RNA. They play important regulatory role in diseases through interactions of miRNAs associated with the diseases. Due to their insensitivity to nucleases, they are more stable than linear RNAs. It is thus imperative to integrate available information for predicting circRNA-disease associations in humans. Here, we propose a computational model to predict circRNA-disease associations based on accelerated attributed network embedding (AANE) algorithm and autoencoder(AE). First, we use AANE algorithm to extract low-dimensional features of circRNAs and diseases and then stacked autoencoder (SAE) to automatically extract in-depth features. The features obtained by AANE and the SAE are integrated and XGBoost is used as a binary classifier to get the predicted results. The proposed model has an average area under the receiver operating characteristic curve value of 0.8800 in 5-fold cross validation and 0.8988 in 10-fold cross validation. The factors that can affect the performance of the model are discussed and some common diseases are used as case studies. Results indicated that the model has great performance in predicting circRNA-disease associations.