In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations. In this study, a novel drug-disease association prediction model called DAHNGC is proposed, which is based on a graph convolutional neural network. This model includes two feature extraction methods that are specifically designed to extract the attribute characteristics of drugs and diseases from both homogeneous and heterogeneous networks. First, the DropEdge technique is added to the graph convolutional neural network to alleviate the oversmoothing problem and obtain the characteristics of the same nodes of drugs or diseases in the homogeneous network. Then, an automatic feature extraction method in the heterogeneous network is designed to obtain the features of drugs or diseases at different nodes. Finally, the obtained features are put into the fully connected network for nonlinear transformation, and the potential drug-disease pairs are obtained by bilinear decoding. Experimental results demonstrate that the DAHNGC model exhibits good predictive performance for drug-disease associations.

中文翻译：

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DAHNGC：使用异构网络进行药物-疾病关联预测的图卷积模型。

在药物开发和重新定位领域，药物与疾病关联的预测是一项关键任务。最近提出的一种基于图卷积的预测药物与疾病关联的方法在很大程度上依赖于同质网络内相邻节点的特征来表征信息。然而，该方法缺乏来自异构网络的节点属性信息，这很难为预测药物与疾病的关联提供有价值的见解。在这项研究中，提出了一种名为 DAHNGC 的新型药物与疾病关联预测模型，该模型基于图卷积神经网络。该模型包括两种特征提取方法，专门用于从同质和异构网络中提取药物和疾病的属性特征。首先，在图卷积神经网络中加入DropEdge技术，缓解过平滑问题，获得同质网络中药物或疾病相同节点的特征。然后，设计了异构网络中的自动特征提取方法，以获得不同节点上的药物或疾病的特征。最后将得到的特征放入全连接网络进行非线性变换，通过双线性解码得到潜在的药物-疾病对。实验结果表明，DAHNGC 模型对药物与疾病的关联表现出良好的预测性能。