School of Information Science and Engineering, Hunan University of Chinese Medicine, Changsha 410208, China.
School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Bioinformatics. 2022 Apr 12;38(8):2226-2234. doi: 10.1093/bioinformatics/btac077.
Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types.
In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations.
Data and source codes are available at https://github.com/27167199/PDMDA.
许多研究表明 microRNAs（miRNAs）在人类疾病中发挥着关键作用。同时，miRNA-疾病关联识别的传统实验方法极其昂贵、耗时且具有挑战性。因此，已经开发了许多计算方法来预测 miRNA 和疾病之间的潜在关联。然而，这些方法主要预测 miRNA-疾病关联的存在，而不能预测 miRNA-疾病关联的深层次类型。
在这项研究中，我们提出了一种新的端到端深度学习方法（称为 PDMDA），该方法使用图神经网络（GNN）和 miRNA 序列特征来预测深层次的 miRNA-疾病关联。基于 miRNA 的序列和结构特征，PDMDA 通过全连接网络（FCN）提取 miRNA 特征表示。疾病特征表示是从疾病-基因网络和基因-基因相互作用网络中通过 GNN 模型提取的。最后，设计了一个具有三个全连接层和一个 softmax 层的多层结构，基于 miRNA 和疾病的拼接特征表示来预测最终的 miRNA-疾病关联分数。请注意，PDMDA 不采用 miRNA-疾病关联矩阵作为输入来计算高斯相互作用谱相似性。我们基于六个关联类型样本（包括循环、表观遗传学、靶标、遗传学、其类型未知的已知关联和未知关联样本）进行了三个实验。我们采用五重交叉验证来评估 PDMDA 的预测性能。使用接收者操作特征曲线下的面积作为度量。实验结果表明，PDMDA 可以准确地预测深层次的 miRNA-疾病关联。
数据和源代码可在 https://github.com/27167199/PDMDA 上获取。
