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用于miRNA-疾病关联预测的异构图卷积网络和矩阵填充

Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction.

作者信息

Zhu Rongxiang, Ji Chaojie, Wang Yingying, Cai Yunpeng, Wu Hongyan

机构信息

Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.

出版信息

Front Bioeng Biotechnol. 2020 Aug 20;8:901. doi: 10.3389/fbioe.2020.00901. eCollection 2020.

Abstract

Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.

摘要

由于生物实验的成本和复杂性,人们提出了许多计算方法,通过利用已知的miRNA-疾病关联和其他相关信息来预测潜在的miRNA-疾病关联。然而,这些计算方法面临一些挑战。首先,miRNA与疾病之间的关系很复杂。计算网络应考虑来自网络的邻域的局部和全局影响。此外,预测没有任何已知关联的疾病相关miRNA也非常重要。本研究提出了一种新的计算方法,该方法构建了一个由miRNA相似性网络、疾病相似性网络和已知的miRNA-疾病关联网络组成的异质网络。miRNA相似性考虑了miRNA及其可能的家族和簇。异质网络中每个节点的信息是通过用图卷积网络(GCN)聚合邻域信息获得的,图卷积网络可以将一个节点的信息传递给其近邻和远邻。可以用重建的异质矩阵预测没有已知关联的疾病相关miRNA。我们应用5折交叉验证、留一病交叉验证以及全局和局部留一交叉验证来评估我们的方法。相应的曲线下面积(AUC)分别为0.9616、0.9946、0.9656和0.9532,证实我们的方法显著优于现有方法。案例研究表明,这种方法可以有效地预测没有任何已知miRNA的新疾病。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce53/7468400/98f2b3deae1e/fbioe-08-00901-g0001.jpg

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