Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.
Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.
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DOI：
10.3389/fmicb.2022.919380
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发表时间：
2022
影响因子：
5.2
通讯作者：
Lei, Xiujuan
中科院分区：
文献类型：
--
作者：
Chen, Yali;Lei, Xiujuan
关键词：
越来越多的研究表明，了解微生物与疾病的关系不仅可以揭示疾病的发病机制，而且可以促进疾病的诊断和预后。由于传统的医学实验耗时且昂贵，近年来提出了许多计算方法来确定潜在的微生物与疾病的关联。在这项研究中，我们提出了一种基于异构网络和元路径聚合图神经网络（MAGNN）的方法来预测微生物与疾病的关联，称为MATHNMDA。首先，我们引入微生物-药物相互作用、药物-疾病关联和微生物-疾病关联，构建微生物-药物-疾病异质网络。然后将异构网络作为MAGNN的输入。其次，对于每一层MAGNN，我们采用多头注意机制进行元路径内聚合，通过元路径定义模式下的元路径实例编码，学习嵌入在目标节点上下文、基于元路径的邻居节点以及它们之间的上下文中的结构和语义信息。然后，我们使用元路径间聚合和注意机制来组合所有不同元路径的语义信息。第三，我们可以根据MAGNN中最后一层的输出得到微生物节点和疾病节点的最终嵌入。最后，我们通过重建微生物-疾病关联矩阵来预测潜在的微生物-疾病关联。此外，我们通过将MATHNMDA与其变体、一些最先进的方法和不同的数据集进行比较，评估了MATHNMDA的性能。结果表明，MATHNMDA是一种有效的预测方法。哮喘、炎症性肠病（IBD）和2019冠状病毒病（COVID-19）的病例研究进一步验证了MATHNMDA的有效性。
More and more studies have shown that understanding microbe-disease associations cannot only reveal the pathogenesis of diseases, but also promote the diagnosis and prognosis of diseases. Because traditional medical experiments are time-consuming and expensive, many computational methods have been proposed in recent years to identify potential microbe-disease associations. In this study, we propose a method based on heterogeneous network and metapath aggregated graph neural network (MAGNN) to predict microbe-disease associations, called MATHNMDA. First, we introduce microbe-drug interactions, drug-disease associations, and microbe-disease associations to construct a microbe-drug-disease heterogeneous network. Then we take the heterogeneous network as input to MAGNN. Second, for each layer of MAGNN, we carry out intra-metapath aggregation with a multi-head attention mechanism to learn the structural and semantic information embedded in the target node context, the metapath-based neighbor nodes, and the context between them, by encoding the metapath instances under the metapath definition mode. We then use inter-metapath aggregation with an attention mechanism to combine the semantic information of all different metapaths. Third, we can get the final embedding of microbe nodes and disease nodes based on the output of the last layer in the MAGNN. Finally, we predict potential microbe-disease associations by reconstructing the microbe-disease association matrix. In addition, we evaluated the performance of MATHNMDA by comparing it with that of its variants, some state-of-the-art methods, and different datasets. The results suggest that MATHNMDA is an effective prediction method. The case studies on asthma, inflammatory bowel disease (IBD), and coronavirus disease 2019 (COVID-19) further validate the effectiveness of MATHNMDA.
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