基于全局图注意力元路径异构网络的药物-疾病关联预测

Drug-Disease Association Prediction Based on Meta-Path Heterogeneous Network with Global Graph Attention

提出了一个基于全局图注意力元路径异构网络模型(MHNGA)来进行药物-疾病关联预测。首先,收集整理药物和疾病数据,将已知的药物-疾病关联、药物相似性、疾病相似性构建为一个异构网络;其次,引入多个基于元路径的子图,使用图注意力神经网络提取这些子图的邻居节点的特征,并且通过通道注意力和空间注意力机制来增强特征;最后,通过十折交叉验证的评估,MHNGA取得了93.5%的精确召回曲线下的面积和99.4%的准确率。

In this paper,a heterogeneous network model based on global graph attention meta-path,named MHNGA,is proposed for drug-disease association prediction.Firstly,the data of drugs and diseases are collected,and the known drug-disease association,drug similarity and disease similarity are constructed as a heterogeneous network.Secondly,multiple meta-path-based subgraphs are introduced,and the graph attention neural network is used to extract the features of the neighbor nodes of these subgraphs,and the features are enhanced by channel attention and spatial attention mechanisms.Finally,through the evaluation of ten-fold cross-validation,MHNGA achieves 93.5%of the area under the accurate recall curve and 99.4%of the accuracy.