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

计算机科学 异构网络 相似性(几何) 矩阵完成 图形 交叉验证 节点(物理) 疾病 生物网络 小RNA 数据挖掘 计算生物学 人工智能 理论计算机科学 生物 医学 遗传学 物理 工程类 图像(数学) 病理 无线网络 基因 结构工程 高斯分布 无线 量子力学 电信
作者
Rongxiang Zhu,Chaojie Ji,Yingying Wang,Yunpeng Cai,Hongyan Wu
出处
期刊:Frontiers in Bioengineering and Biotechnology [Frontiers Media]
卷期号:8 被引量:12
标识
DOI:10.3389/fbioe.2020.00901
摘要

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.
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