基于图神经网络的环状RNA生物标志物筛选预测算法

Emerging evidence has revealed that circular RNA (circRNA) plays an indispensable role in the pathogenesis of complex human diseases and various biological processes. Identifying the associations between circRNAs and diseases is crucial for diagnosing and treating these diseases. However, traditional wet-lab methods are often inefficient, time-consuming, and expensive, with high false-positive rates. Therefore, there is an urgent need for efficient and feasible computational methods to predict potential circRNA-disease associations on a large scale. In this paper, we propose a novel approach to predict the association between circRNA and disease by combining the high-order graph convolutional network algorithm of graph neural network with a random ferns classifier. This approach can effectively extract high-level features with high-order mixed neighborhood information from the multi-source similarity network constructed by multiple attribute information of circRNAs and diseases and accurately classify them. In a 5-fold cross-validation experiment, our method achieved an average AUC score of 93.75% on the CircR2Disease dataset. Furthermore, in case studies, the prediction results of the model were supported by biological wet experiments, and 13 of the top 15 predicted circRNA-disease associations were confirmed by recently published literature. These excellent results indicate that the proposed model is an effective tool for predicting circRNA-disease associations, and can provide a theoretical basis and highly reliable candidate biomarkers of circRNAs for biological wet experiments.