RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.
RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.
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RMDGCN：基于具有注意力机制的图卷积网络预测RNA甲基化和疾病关联。
DOI：
10.1371/journal.pcbi.1011677
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发表时间：
2023-12
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
RNA修饰是一种存在于所有生物体中的转录后修饰，在RNA生命的各个阶段起着至关重要的作用，与许多生命过程密切相关。作为新发现的修饰之一，N1-甲基腺苷(M1a)在基因表达调控中发挥着重要作用，与疾病的发生发展密切相关。然而，由于M1a的丰度较低，通过湿法实验来验证M1As与疾病的相关性需要大量的人力和物力。在这项研究中，我们提出了一种基于具有注意机制的图卷积网络(RMDGCN)的计算方法来预测RNA甲基化与疾病的关联。我们通过收集的MA和疾病关联来构建邻接矩阵，并使用正-无标记学习来增加正样本的数量。通过提取m1as和疾病的特征，构建了一个异质网络，并采用具有注意力机制的GCN来预测m1a和疾病之间的关联。实验结果表明，在5倍交叉验证下，RMDGCN方法优于其他方法(AUC=0.9892，AUPR=0.8682)。此外，案例研究表明，RMDGCN可以预测未知MA与疾病之间的关系。综上所述，RMDGCN是预测m1as与疾病之间关联的有效方法。作为一种新的表位转录修饰，M1a在基因表达调控中发挥着重要作用，与疾病的发生发展密切相关。然而，由于M1a的丰度较低，通过湿法实验来验证M1As与疾病的相关性需要大量的人力和物力。尤其重要的是开发计算方法来预测M1a基因修饰和疾病之间的关联。我们开发了一个深度学习模型来预测m1a与疾病的关联，即RMDGCN。RMDGCN通过PU学习来增加m1a与疾病之间的已知关系数，并将M1a相似网络和疾病相似网络相结合来构建异质网络。它采用具有分层注意机制的GCN来预测甲基化与疾病之间的关联。5倍交叉验证结果表明，RMDGCN的性能优于其他比较算法。通过对乳腺癌的案例分析，RMDGCN可以有效地预测未知肿瘤与疾病的关系。
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. As a new epitranscriptomic modification, m1A plays an important role in the 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. It is especially important to develop computational methods for predicting the associations between m1A modifications and diseases. We developed a deep learning model to predict the associations of m1As and diseases, namely RMDGCN. RMDGCN increases the number of known relationships between m1As and diseases through PU learning, and combines m1A similarity network and disease similarity network to construct heterogeneous networks. It adopts GCN with layered attention mechanism to predict the associations between methylations and diseases. The results of the 5-fold cross validation show that the performance of RMDGCN is superior to other comparison algorithms. Through case study analysis of breast cancer, RMDGCN can effectively predict the relationships between unknown m1As and diseases.
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