As is well known, biological experiments are time-consuming and laborious, so there is absolutely no doubt that developing an effective computational model will help solve these problems. Most of computational models rely on the biological similarity and network-based methods that cannot consider the topological structures of metabolite-disease association graphs. We proposed a novel method based on graph convolutional networks to infer potential metabolite-disease association, named MDAGCN. We first calculated three kinds of metabolite similarities and three kinds of disease similarities. The final similarity of disease and metabolite will be obtained by integrating three kinds’ similarities of each and filtering out the noise similarity values. Then metabolite similarity network, disease similarity network and known metabolite-disease association network were used to construct a heterogenous network. Finally, heterogeneous network with rich information is fed into the graph convolutional networks to obtain new features of a node through aggregation of node information so as to infer the potential associations between metabolites and diseases. Experimental results show that MDAGCN achieves more reliable results in cross validation and case studies when compared with other existing methods.

中文翻译：

---

使用图卷积网络推断代谢物-疾病关联

众所周知，生物实验既费时又费力，因此毫无疑问，开发一个有效的计算模型将有助于解决这些问题。大多数计算模型依赖于生物相似性和基于网络的方法，不能考虑代谢物-疾病关联图的拓扑结构。我们提出了一种基于图卷积网络的新方法来推断潜在的代谢物与疾病的关联，称为 MDAGCN。我们首先计算了三种代谢物相似性和三种疾病相似性。将三类相似度进行综合，滤除噪声相似度值，得到最终的疾病与代谢物相似度。然后是代谢物相似网络，疾病相似性网络和已知代谢物-疾病关联网络用于构建异质网络。最后，将信息丰富的异构网络输入到图卷积网络中，通过节点信息的聚合获得节点的新特征，从而推断代谢物与疾病之间的潜在关联。实验结果表明，与其他现有方法相比，MDAGCN 在交叉验证和案例研究中取得了更可靠的结果。