Predicting circRNAdisease associations using deep generative adversarial network based on multisource fusion information Circular RNA (circRNA) is a kind of novel discovered noncoding RNA molecule with a closed loop structure, which plays a critical regulatory role in human diseases. Identifying the association between circRNAs and diseases has important potential value for the diagnosis and treatment of complex human diseases. Although biological experiments can more accurately identify the association between circRNAs and diseases, they are usually blind and limited by small scale and high cost. Therefore, there is an urgent need for efficient and feasible computational methods to predict the potential circRNAdisease associations on a large scale, so as to provide the most promising candidate for biological experiments. In this paper, we propose a novel computational method based on the deep Generative Adversarial Network (GAN) algorithm combined with the multisource similarity information to predict the circRNAdisease associations. Firstly, we fuse the multisource information of disease semantic similarity, disease and circRNA Gaussian interaction profile kernel similarity, and then use GAN to extract the hidden features of fusion information objectively and effectively in the way of confrontation learning, and finally send them to Logistic Model Tree (LMT) classifier for accurate prediction. The 5fold crossvalidation experiment of the proposed model achieved 89.2% accuracy with 89.4% precision at the AUC of 90.6% on the CIRCR2Disease dataset. Compared with the stateoftheart SVM classifier and other feature extraction methods, the proposed model shows strong competitiveness. In addition, the predicted results of this model are supported by the biological experiments, and 9 of the top 15 circRNAdisease associations with the highest scores were confirmed by recently published literature. These promising results indicate that the proposed model is an effective tool for predicting circRNAdisease associations and can provide reliable candidates for biological experiments.

Predicting LncRNAdisease Association by Autoencoder and Rotation Forest In the past few years, most diseaserelated lncRNAs have been identified, but the experimental identification is costconsuming and timeconsuming. It is therefore very important to develop a reliable computational model to predict lncRNAdisease association. In this paper, we propose a method based on similarity, combining autoencoder and rotation forest to predict lncRNAdisease association (SARLDA). This method not only makes use of disease and lncRNA similarities, but also extracts latent lowdimension features and expand the gap between samples to make it easier to predict the associations. To evaluate our method, we conducted several experiments. Sufficient validations show that this method has significantly improved the prediction performance.