Li Zheng-Wei, Wang Qian-Kun, Yuan Chang-An, Han Peng-Yong, You Zhu-Hong, Wang Lei
IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):2629-2638. doi: 10.1109/TCBB.2022.3196394. Epub 2023 Oct 9.
Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.
越来越多的研究表明，微小RNA（miRNA）与许多人类疾病有着千丝万缕的联系，并且已经花费了大量精力来确定它们之间的潜在关联。与传统实验方法相比，计算方法已经取得了有前景的成果。在本文中，我们提出了一种图表示学习方法来预测miRNA-疾病关联。具体而言，我们首先将已验证的miRNA-疾病关联与miRNA和疾病的相似性信息整合起来，构建一个miRNA-疾病异构图。然后，我们应用图注意力网络来聚合每一层中节点的邻居信息，接着将隐藏层的表示输入到结构感知跳跃知识网络中以获得节点的全局特征。随后，将miRNA和疾病的输出特征连接起来并输入到全连接层中对潜在关联进行评分。通过五折交叉验证，我们模型的平均AUC、准确率和精确率值分别为93.30%、85.18%和88.90%。此外，对于食管癌、淋巴瘤和前列腺癌的三个案例研究，我们模型预测的前50个miRNA中分别有46个、45个和45个被相关数据库证实。总体而言，我们的方法可为miRNA-疾病关联预测提供一种可靠的替代方法。
