北京航空航天大学学报2026，Vol.52Issue(1)：61-72,12.DOI:10.13700/j.bh.1001-5965.2023.0730
基于图注意力堆叠自编码器微生物-药物关联预测
Prediction of microbe-drug association based on graph attention stacked autoencoder
摘要
Abstract
A graph attention stacking autoencoder approach for predicting the association between microorganisms and drugs,known as GATSAE,is proposed in response to the conventional method of finding new associations between microorganisms and drugs,which is primarily accomplished through biological experiments,which is highly costly and time-consuming.Firstly,establish a heterogeneous network of microorganisms and drugs to enrich the associated information.Secondly,the convolutional fusion matrix of microorganisms and drugs is obtained by extracting multi-layer latent features through graph convolutional network(GCN).Once again,an improved stacked autoencoder is used to learn unsupervised low dimensional representations of meaningful high-order similar features.Graph convolution and attention mechanisms are added to the stacked autoencoder to further optimize the extraction of high-order feature information.Finally,the low-dimensional features are concatenated with associated features,and a multi-layer perceptron(MLP)is used to score and predict the final microbial drug.According to performance evaluation,GATSAE subjects'area under the receiver operating characteristic curve(AUROC)and area under the precision-recall curve(AUPR)were 0.961 9 and 0.957 7,respectively.These results are better than those of popular deep learning techniques and traditional machine learning techniques.Case studies have shown that GATSAE can accurately predict candidate drugs related to SARS-CoV-2 and Escherichia coli,as well as candidate microorganisms related to aspirin.关键词
微生物与药物/关联预测/堆叠自编码器/注意力机制/图卷积网络/多层感知机Key words
microbes and drugs/correlation prediction/stacked autoencoder/attention mechanism/graph convolutional network/multi-layer perceptron分类
信息技术与安全科学引用本文复制引用
王波,何洋,杜晓昕,张剑飞,徐靖然,贾娜..基于图注意力堆叠自编码器微生物-药物关联预测[J].北京航空航天大学学报,2026,52(1):61-72,12.基金项目
黑龙江省省属高等学校基本科研业务费国自然培育一般项目(145409324) General Program for National Natural Science Foundation Cultivation under the Basic Research Fund for Provincial Universities in Heilongjiang(145409324) （145409324）
