Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

作     者：WANG Yueyue LEI Xiujuan PAN Yi WANG Yueyue;LEI Xiujuan;PAN Yi

作者机构：School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2022年第31卷第2期

页      面：345-353页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1001[医学-基础医学(可授医学、理学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China (61672334, 61972451, 61902230) the Fundamental Research Funds for the Central Universities,Shaanxi Normal University(GK201901010) 

主　　题：diseases global structural information disease-symptom similarity patient diagnosis support vector machine classifier Gaussian processes heterogeneous network microorganisms microbe Gaussian interaction profile kernel similarity global graph feature learning microbe-disease association prediction GIP kernel similarity learning (artificial intelligence) GraRep pattern classification disease semantic similarity bioinformatics HNGFL support vector machines graph theory 

摘      要：Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.