MGRCDA: Metagraph Recommendation Method for Predicting CircRNA–Disease Association
提出MGRCDA模型，将环状RNA-疾病关联预测转化为推荐问题，利用元图从异质生物网络中提取特征，在circR2Disease数据集上达到92.49%的预测准确率，为湿实验提供可靠候选。
Clinical evidence began to accumulate, suggesting that circRNAs can be novel therapeutic targets for various diseases and play a critical role in human health. However, limited by the complex mechanism of circRNA, it is difficult to quickly and large-scale explore the relationship between disease and circRNA in the wet-lab experiment. In this work, we design a new computational model MGRCDA on account of the metagraph recommendation theory to predict the potential circRNA-disease associations. Specifically, we first regard the circRNA-disease association prediction problem as the system recommendation problem, and design a series of metagraphs according to the heterogeneous biological networks; then extract the semantic information of the disease and the Gaussian interaction profile kernel (GIPK) similarity of circRNA and disease as network attributes; finally, the iterative search of the metagraph recommendation algorithm is used to calculate the scores of the circRNA-disease pair. On the gold standard dataset circR2Disease, MGRCDA achieved a prediction accuracy of 92.49% with an area under the ROC curve of 0.9298, which is significantly higher than other state-of-the-art models. Furthermore, among the top 30 disease-related circRNAs recommended by the model, 25 have been verified by the latest published literature. The experimental results prove that MGRCDA is feasible and efficient, and it can recommend reliable candidates to further wet-lab experiment and reduce the scope of the experiment.