Performance comparison of different methods.

RNA modification is a post transcriptional modification that occurs in all organisms and plays a crucial role in the stages of RNA life, closely related to many life processes. As one of the newly discovered modifications, N1-methyladenosine (m1A) plays an important role in gene expression regulation, closely related to the occurrence and development of diseases. However, due to the low abundance of m1A, verifying the associations between m1As and diseases through wet experiments requires a great quantity of manpower and resources. In this study, we proposed a computational method for predicting the associations of RNA methylation and disease based on graph convolutional network (RMDGCN) with attention mechanism. We build an adjacency matrix through the collected m1As and diseases associations, and use positive-unlabeled learning to increase the number of positive samples. By extracting the features of m1As and diseases, a heterogeneous network is constructed, and a GCN with attention mechanism is adopted to predict the associations between m1As and diseases. The experimental results indicate that under a 5-fold cross validation, RMDGCN is superior to other methods (AUC = 0.9892 and AUPR = 0.8682). In addition, case studies indicate that RMDGCN can predict the relationships between unknown m1As and diseases. In summary, RMDGCN is an effective method for predicting the associations between m1As and diseases.

RNA修饰是一类广泛存在于所有生物体内的转录后修饰，在RNA生命周期的各个阶段发挥关键调控作用，与诸多生命过程密切相关。作为新发现的修饰类型之一，N1-甲基腺嘌呤（N1-methyladenosine，m1A）在基因表达调控中扮演重要角色，且与疾病的发生发展紧密相关。然而由于m1A的天然丰度较低，通过湿实验验证m1A与疾病的关联需要耗费大量人力与资源。本研究提出了一种基于带注意力机制的图卷积网络（graph convolutional network，GCN）的RNA甲基化与疾病关联预测计算方法，命名为RMDGCN。研究团队通过收集的m1A与疾病关联数据构建邻接矩阵，并采用正样本-未标注样本学习（positive-unlabeled learning）策略扩充正样本数量。通过提取m1A与疾病的特征信息，构建异质网络，并采用带注意力机制的图卷积网络预测m1A与疾病之间的关联。实验结果表明，在5折交叉验证下，RMDGCN的性能优于其他对比方法（受试者工作特征曲线下面积（Area Under Curve，AUC）= 0.9892，精确率-召回率曲线下面积（Area Under Precision-Recall Curve，AUPR）= 0.8682）。此外，案例研究显示，RMDGCN能够预测未知m1A与疾病之间的关联。综上，RMDGCN是一种用于预测m1A与疾病关联的有效方法。